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Bridgen P, Tomi-Tricot R, Uus A, Cromb D, Quirke M, Almalbis J, Bonse B, De la Fuente Botella M, Maggioni A, Cio PD, Cawley P, Casella C, Dokumaci AS, Thomson AR, Willers Moore J, Bridglal D, Saravia J, Finck T, Price AN, Pickles E, Cordero-Grande L, Egloff A, O’Muircheartaigh J, Counsell SJ, Giles SL, Deprez M, De Vita E, Rutherford MA, Edwards AD, Hajnal JV, Malik SJ, Arichi T. High resolution and contrast 7 tesla MR brain imaging of the neonate. Front Radiol 2024; 3:1327075. [PMID: 38304343 PMCID: PMC10830693 DOI: 10.3389/fradi.2023.1327075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/29/2023] [Indexed: 02/03/2024]
Abstract
Introduction Ultra-high field MR imaging offers marked gains in signal-to-noise ratio, spatial resolution, and contrast which translate to improved pathological and anatomical sensitivity. These benefits are particularly relevant for the neonatal brain which is rapidly developing and sensitive to injury. However, experience of imaging neonates at 7T has been limited due to regulatory, safety, and practical considerations. We aimed to establish a program for safely acquiring high resolution and contrast brain images from neonates on a 7T system. Methods Images were acquired from 35 neonates on 44 occasions (median age 39 + 6 postmenstrual weeks, range 33 + 4 to 52 + 6; median body weight 2.93 kg, range 1.57 to 5.3 kg) over a median time of 49 mins 30 s. Peripheral body temperature and physiological measures were recorded throughout scanning. Acquired sequences included T2 weighted (TSE), Actual Flip angle Imaging (AFI), functional MRI (BOLD EPI), susceptibility weighted imaging (SWI), and MR spectroscopy (STEAM). Results There was no significant difference between temperature before and after scanning (p = 0.76) and image quality assessment compared favorably to state-of-the-art 3T acquisitions. Anatomical imaging demonstrated excellent sensitivity to structures which are typically hard to visualize at lower field strengths including the hippocampus, cerebellum, and vasculature. Images were also acquired with contrast mechanisms which are enhanced at ultra-high field including susceptibility weighted imaging, functional MRI, and MR spectroscopy. Discussion We demonstrate safety and feasibility of imaging vulnerable neonates at ultra-high field and highlight the untapped potential for providing important new insights into brain development and pathological processes during this critical phase of early life.
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Affiliation(s)
- Philippa Bridgen
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Raphael Tomi-Tricot
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Megan Quirke
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jennifer Almalbis
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Beya Bonse
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Miguel De la Fuente Botella
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alessandra Maggioni
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Pierluigi Di Cio
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Paul Cawley
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Chiara Casella
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Ayse Sila Dokumaci
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alice R. Thomson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Jucha Willers Moore
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Devi Bridglal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joao Saravia
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Thomas Finck
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Anthony N. Price
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Elisabeth Pickles
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, ISCIII, Madrid, Spain
| | - Alexia Egloff
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sharon L. Giles
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Enrico De Vita
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Physics, Radiology Department, Great Ormond Street Hospital for Children, London, United Kingdom
| | - Mary A. Rutherford
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - A. David Edwards
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Shaihan J. Malik
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Tomoki Arichi
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
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Neves Silva S, Aviles Verdera J, Tomi‐Tricot R, Neji R, Uus A, Grigorescu I, Wilkinson T, Ozenne V, Lewin A, Story L, De Vita E, Rutherford M, Pushparajah K, Hajnal J, Hutter J. Real-time fetal brain tracking for functional fetal MRI. Magn Reson Med 2023; 90:2306-2320. [PMID: 37465882 PMCID: PMC10952752 DOI: 10.1002/mrm.29803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI. METHODS A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets. RESULTS Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age. CONCLUSIONS Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease.
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Affiliation(s)
- Sara Neves Silva
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Raphael Tomi‐Tricot
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUK
| | - Radhouene Neji
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Irina Grigorescu
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Thomas Wilkinson
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Valery Ozenne
- CNRS, CRMSB, UMR 5536, IHU LirycUniversité de BordeauxBordeauxFrance
| | - Alexander Lewin
- Institute of Neuroscience and Medicine 11, INM‐11Forschungszentrum JülichJülichGermany
- RWTHAachen UniversityAachenGermany
| | - Lisa Story
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Department of Women & Children's HealthKing's College LondonLondonUK
| | - Enrico De Vita
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MRI Physics GroupGreat Ormond Street HospitalLondonUK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Kuberan Pushparajah
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jo Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
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Lombardi S, Tortora D, Picariello S, Sudhakar S, De Vita E, Mankad K, Varadkar S, Consales A, Nobili L, Cooper J, Tisdall MM, D'Arco F. Intraoperative MRI Assessment of the Tissue Damage during Laser Ablation of Hypothalamic Hamartoma. Diagnostics (Basel) 2023; 13:2331. [PMID: 37510075 PMCID: PMC10378573 DOI: 10.3390/diagnostics13142331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/29/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Laser ablation for treatment of hypothalamic hamartoma (HH) is a minimally invasive and effective technique used to destroy hamartomatous tissue and disconnect it from the functioning brain. Currently, the gold standard to evaluate the amount of tissue being "burned" is the use of heat maps during the ablation procedure. However, these maps have low spatial resolution and can be misleading in terms of extension of the tissue damage. The aim of this study is to use different MRI sequences immediately after each laser ablation and correlate the extension of signal changes with the volume of malacic changes in a long-term follow-up scan. During the laser ablation procedure, we imaged the hypothalamic region with high-resolution axial diffusion-weighted images (DWI) and T2-weighted images (T2WI) after each ablation. At the end of the procedure, we also added a post-contrast T1-weighted image (T1WI) of the same region. We then correlated the product of the maximum diameters on axial showing signal changes (acute oedema on T2WI, DWI restriction rim, DWI hypointense core and post-contrast T1WI rim) with the product of the maximum diameters on axial T2WI of the malacic changes in the follow-up scan, both as a fraction of the total area of the hamartoma. The area of the hypointense core on DWI acquired immediately after the laser ablation statistically correlated better with the final area of encephalomalacia, while the T2WI, hyperintense oedema, DWI rim and T1WI rim of enhancement tended to overestimate the encephalomalacic damage. In conclusion, the use of intraoperative sequences (in particular DWI) during laser ablation can give surgeons valuable information in real time about the effective heating damage on the hamartomatous tissue, with better spatial resolution in comparison to the thermal maps.
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Affiliation(s)
- Sophie Lombardi
- Radiodiagnostic Department, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Domenico Tortora
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Stefania Picariello
- Neuro-Oncology Unit, Department of Paediatric Oncology, Santobono-Pausilipon Children's Hospital, 80123 Naples, Italy
| | - Sniya Sudhakar
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Enrico De Vita
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Kshitij Mankad
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Sophia Varadkar
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Alessandro Consales
- Department of Surgical Sciences, Division of Neurosurgery, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Jessica Cooper
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Martin M Tisdall
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Felice D'Arco
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
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4
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Shuaib H, Barker GJ, Sasieni P, De Vita E, Chelliah A, Andrei R, Ashkan K, Beaumont E, Brazil L, Rowland-Hill C, Lau YH, Luis A, Powell J, Swampillai A, Tenant S, Thust SC, Wastling S, Young T, Booth TC. Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium. Br J Radiol 2023; 96:20220206. [PMID: 35616700 PMCID: PMC10997010 DOI: 10.1259/bjr.20220206] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 04/25/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. METHODS MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. RESULTS All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. CONCLUSION The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. ADVANCES IN KNOWLEDGE Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.
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Affiliation(s)
- Haris Shuaib
- Guy’s & St Thomas’ NHS Foundation Trust,
King’s College, London, United Kingdom
| | | | | | | | | | - Roman Andrei
- The Oncology Institute "Prof. Dr. Ion Chiricuţă"
Cluj-Napoca, Cluj-Napoca, Romania
| | - Keyoumars Ashkan
- King’s College Hospital NHS Foundation
Trust, London, United Kingdom
| | - Erica Beaumont
- Lancashire Teaching Hospitals NHS Foundation
Trust, Lancashire, United Kingdom
| | - Lucy Brazil
- Guy’s & St Thomas’ NHS Foundation Trust,
King’s College, London, United Kingdom
| | | | - Yue Hui Lau
- King’s College Hospital NHS Foundation
Trust, London, United Kingdom
| | - Aysha Luis
- King's College London, London, United
Kingdom
| | - James Powell
- Velindre University NHS Trust, Wales, United
Kingdom
| | - Angela Swampillai
- Guy’s & St Thomas’ NHS Foundation Trust,
King’s College, London, United Kingdom
| | - Sean Tenant
- The Christie NHS Foundation Trust, Manchester,
United Kingdom
| | - Stefanie C Thust
- National Hospital for Neurology and Neurosurgery, UCL
Institute of Neurology, London, United Kingdom
| | - Stephen Wastling
- National Hospital for Neurology and Neurosurgery, UCL
Institute of Neurology, London, United Kingdom
| | - Tom Young
- Guy’s & St Thomas’ NHS Foundation Trust,
King’s College, London, United Kingdom
| | - Thomas C Booth
- Guy’s & St Thomas’ NHS Foundation Trust,
King’s College, London, United Kingdom
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5
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Lowe AJ, Rodrigues FB, Arridge M, De Vita E, Johnson EB, Scahill RI, Byrne LM, Tortelli R, Heslegrave A, Zetterberg H, Wild EJ. Longitudinal evaluation of proton magnetic resonance spectroscopy metabolites as biomarkers in Huntington’s disease. Brain Commun 2022; 4:fcac258. [PMID: 36382217 PMCID: PMC9665272 DOI: 10.1093/braincomms/fcac258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 07/08/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022] Open
Abstract
Proton magnetic resonance spectroscopy is a non-invasive method of exploring cerebral metabolism. In Huntington’s disease, altered proton magnetic resonance spectroscopy-determined concentrations of several metabolites have been described; however, findings are often discrepant and longitudinal studies are lacking. Proton magnetic resonance spectroscopy metabolites may represent a source of biomarkers, thus their relationship with established markers of disease progression require further exploration to assess prognostic value and elucidate pathways associated with neurodegeneration. In a prospective single-site controlled cohort study with standardized collection of CSF, blood, phenotypic and volumetric imaging data, we used 3 T proton magnetic resonance spectroscopy in conjunction with the linear combination of model spectra method to quantify seven metabolites (total n-acetylaspartate, total creatine, total choline, myo-inositol, GABA, glutamate and glutathione) in the putamen of 59 participants at baseline (15 healthy controls, 15 premanifest and 29 manifest Huntington’s disease gene expansion carriers) and 48 participants at 2-year follow-up (12 healthy controls, 13 premanifest and 23 manifest Huntington’s disease gene expansion carriers). Intergroup differences in concentration and associations with CSF and plasma biomarkers; including neurofilament light chain and mutant Huntingtin, volumetric imaging markers; namely whole brain, caudate, grey matter and white matter volume, measures of disease progression and cognitive decline, were assessed cross-sectionally using generalized linear models and partial correlation. We report no significant groupwise differences in metabolite concentration at baseline but found total creatine and total n-acetylaspartate to be significantly reduced in manifest compared with premanifest participants at follow-up. Additionally, total creatine and myo-inositol displayed significant associations with reduced caudate volume across both time points in gene expansion carriers. Although relationships were observed between proton magnetic resonance spectroscopy metabolites and biofluid measures, these were not consistent across time points. To further assess prognostic value, we examined whether baseline proton magnetic resonance spectroscopy values, or rate of change, predicted subsequent change in established measures of disease progression. Several associations were found but were inconsistent across known indicators of disease progression. Finally, longitudinal mixed-effects models revealed glutamine + glutamate to display a slow linear decrease over time in gene expansion carriers. Altogether, our findings show some evidence of reduced total n-acetylaspartate and total creatine as the disease progresses and cross-sectional associations between select metabolites, namely total creatine and myo-inositol, and markers of disease progression, potentially highlighting the proposed roles of neuroinflammation and metabolic dysfunction in disease pathogenesis. However, the absence of consistent group differences, inconsistency between baseline and follow-up, and lack of clear longitudinal change suggests that proton magnetic resonance spectroscopy metabolites have limited potential as Huntington’s disease biomarkers.
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Affiliation(s)
- Alexander J Lowe
- UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London , London , UK
| | - Filipe B Rodrigues
- UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London , London , UK
| | - Marzena Arridge
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery , London , UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery , London , UK
- Department of Radiology, Great Ormond Street Hospital , London , UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, King’s Health Partners, St Thomas’ Hospital , London , UK
| | - Eileanoir B Johnson
- UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London , London , UK
| | - Rachael I Scahill
- UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London , London , UK
| | - Lauren M Byrne
- UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London , London , UK
| | - Rosanna Tortelli
- UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London , London , UK
| | - Amanda Heslegrave
- UK Dementia Research Institute at University College London , London , UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , UK
| | - Henrik Zetterberg
- UK Dementia Research Institute at University College London , London , UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg , Mölndal , Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital , Mölndal , Sweden
- Hong Kong Center for Neurodegenerative Diseases , Hong Kong , China
| | - Edward J Wild
- UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London , London , UK
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6
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Brumer I, De Vita E, Ashmore J, Jarosz J, Borri M. Reproducibility of MRI-based white matter tract estimation using multi-fiber probabilistic tractography: effect of user-defined parameters and regions. Magn Reson Mater Phy 2022; 35:365-373. [PMID: 34661789 PMCID: PMC9188621 DOI: 10.1007/s10334-021-00965-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/31/2021] [Accepted: 10/01/2021] [Indexed: 11/21/2022]
Abstract
Objective There is a pressing need to assess user-dependent reproducibility of multi-fibre probabilistic tractography in order to encourage clinical implementation of these advanced and relevant approaches. The goal of this study was to evaluate both intrinsic and inter-user reproducibility of corticospinal tract estimation. Materials and methods Six clinical datasets including motor functional and diffusion MRI were used. Three users performed an independent tractography analysis following identical instructions. Dice indices were calculated to quantify the reproducibility of seed region, fMRI-based end region, and streamline maps. Results The inter-user reproducibility ranged 41–93%, 29–94%, and 50–92%, for seed regions, end regions, and streamline maps, respectively. Differences in streamline maps correlated with differences in seed and end regions. Good inter-user agreement in seed and end regions, yielded inter-user reproducibility close to the intrinsic reproducibility (92–97%) and in most cases higher than 80%. Discussion Uncertainties related to user-dependent decisions and the probabilistic nature of the analysis should be considered when interpreting probabilistic tractography data. The standardization of the methods used to define seed and end regions is a necessary step to improve the accuracy and robustness of multi-fiber probabilistic tractography in a clinical setting. Clinical users should choose a feasible compromise between reproducibility and analysis duration.
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Brackenier Y, Cordero‐Grande L, Tomi‐Tricot R, Wilkinson T, Bridgen P, Price A, Malik SJ, De Vita E, Hajnal JV. Data‐driven motion‐corrected brain
MRI
incorporating pose‐dependent
B
0
fields. Magn Reson Med 2022; 88:817-831. [PMID: 35526212 PMCID: PMC9324873 DOI: 10.1002/mrm.29255] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/15/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022]
Abstract
Purpose To develop a fully data‐driven retrospective intrascan motion‐correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose‐dependent changes in polarizing magnetic (B0) fields. Theory and Methods Tissue susceptibility induces spatially varying B0 distributions in the head, which change with pose. A physics‐inspired B0 model has been deployed to model the B0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B0 variations from appropriately acquired data without requiring navigators. The effect on data‐driven motion correction is validated in simulations and in vivo. Results The applicability of the physics‐inspired B0 model was confirmed in vivo. Simulations show the need to include the pose‐dependent B0 fields in the reconstruction to improve motion‐correction performance and the feasibility of estimating B0 evolution from the acquired data. The proposed motion and B0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. Conclusion We have developed a motion‐correction framework that accounts for and estimates pose‐dependent B0 fields. The method improves current state‐of‐the‐art data‐driven motion‐correction techniques when B0 dependencies cannot be neglected. The use of a compact physics‐inspired B0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data‐driven approach.
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Affiliation(s)
- Yannick Brackenier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Lucilio Cordero‐Grande
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación Universidad Politécnica de Madrid and CIBER‐BNN Madrid Spain
| | - Raphael Tomi‐Tricot
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- MR Research Collaborations Siemens Healthcare Limited Frimley United Kingdom
| | - Thomas Wilkinson
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Philippa Bridgen
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Anthony Price
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Shaihan J. Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
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8
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Rojas-Villabona A, Sokolska M, Solbach T, Grieve J, Rega M, Torrealdea F, Pizzini FB, De Vita E, Suzuki Y, Van Osch MJP, Biondetti E, Shmueli K, Atkinson D, Murphy M, Paddick I, Golay X, Kitchen N, Jäger HR. Planning of gamma knife radiosurgery (GKR) for brain arteriovenous malformations using triple magnetic resonance angiography (triple-MRA). Br J Neurosurg 2022; 36:217-227. [PMID: 33645357 DOI: 10.1080/02688697.2021.1884649] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
PURPOSE Intra-arterial Digital Subtraction Angiography (DSA) is the gold standard technique for radiosurgery target delineation in brain Arterio-Venous Malformations (AVMs). This study aims to evaluate whether a combination of three Magnetic Resonance Angiography sequences (triple-MRA) could be used for delineation of brain AVMs for Gamma Knife Radiosurgery (GKR). METHODS Fifteen patients undergoing DSA for GKR targeting of brain AVMs also underwent triple-MRA: 4D Arterial Spin Labelling based angiography (ASL-MRA), Contrast-Enhanced Time-Resolved MRA (CE-MRA) and High Definition post-contrast Time-Of-Flight angiography (HD-TOF). The arterial phase of the AVM nidus was delineated on triple-MRA by an interventional neuroradiologist and a consultant neurosurgeon (triple-MRA volume). Triple-MRA volumes were compared to AVM targets delineated by the clinical team for delivery of GKR using the current planning paradigm, i.e., stereotactic DSA and volumetric MRI (DSA volume). Difference in size, degree of inclusion (DI) and concordance index (CcI) between DSA and triple-MRA volumes are reported. RESULTS AVM target volumes delineated on triple-MRA were on average 9.8% smaller than DSA volumes (95%CI:5.6-13.9%; SD:7.14%; p = .003). DI of DSA volume in triple-MRA volume was on average 73.5% (95%CI:71.2-76; range: 65-80%). The mean percentage of triple-MRA volume not included on DSA volume was 18% (95%CI:14.7-21.3; range: 7-30%). CONCLUSION The technical feasibility of using triple-MRA for visualisation and delineation of brain AVMs for GKR planning has been demonstrated. Tighter and more precise delineation of AVM target volumes could be achieved by using triple-MRA for radiosurgery targeting. However, further research is required to ascertain the impact this may have in obliteration rates and side effects.
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Affiliation(s)
- Alvaro Rojas-Villabona
- The Gamma Knife Centre at Queen Square, National Hospital for Neurology and Neurosurgery, London, UK
- Department of Neurosurgery, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Magdalena Sokolska
- Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Thomas Solbach
- The Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Joan Grieve
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Marilena Rega
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | | | | | - Enrico De Vita
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Yuriko Suzuki
- C. J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Matthias J P Van Osch
- C. J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Emma Biondetti
- MRI Group, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Karin Shmueli
- MRI Group, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Mary Murphy
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Ian Paddick
- The Gamma Knife Centre at Queen Square, National Hospital for Neurology and Neurosurgery, London, UK
| | - Xavier Golay
- Academic Neuroradiological Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Neil Kitchen
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Hans Rolf Jäger
- The Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
- Academic Neuroradiological Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
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9
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Alsaedi AF, Thomas DL, De Vita E, Panovska-Griffiths J, Bisdas S, Golay X. Repeatability of perfusion measurements in adult gliomas using pulsed and pseudo-continuous arterial spin labelling MRI. MAGMA 2022; 35:113-125. [PMID: 34817780 DOI: 10.1007/s10334-021-00975-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/30/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES To investigate the repeatability of perfusion measures in gliomas using pulsed- and pseudo-continuous-arterial spin labelling (PASL, PCASL) techniques, and evaluate different regions-of-interest (ROIs) for relative tumour blood flow (rTBF) normalisation. MATERIALS AND METHODS Repeatability of cerebral blood flow (CBF) was measured in the Contralateral Normal Appearing Hemisphere (CNAH) and in brain tumours (aTBF). rTBF was normalised using both large/small ROIs from the CNAH. Repeatability was evaluated with intra-class-correlation-coefficient (ICC), Within-Coefficient-of-Variation (WCoV) and Coefficient-of-Repeatability (CR). RESULTS PASL and PCASL demonstrated high reliability (ICC > 0.9) for CNAH-CBF, aTBF and rTBF. PCASL demonstrated a more stable signal-to-noise ratio (SNR) with a lower WCoV of the SNR than that of PASL (10.9-42.5% vs. 12.3-29.2%). PASL and PCASL showed higher WCoV in aTBF and rTBF than in CNAH CBF in WM and GM but not in the caudate, and higher WCoV for rTBF than for aTBF when normalised using a small ROI (PASL 8.1% vs. 4.7%, PCASL 10.9% vs. 7.9%, respectively). The lowest CR was observed for rTBF normalised with a large ROI. DISCUSSION PASL and PCASL showed similar repeatability for the assessment of perfusion parameters in patients with primary brain tumours as previous studies based on volunteers. Both methods displayed reasonable WCoV in the tumour area and CNAH. PCASL's more stable SNR in small areas (caudate) is likely to be due to the longer post-labelling delays.
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Affiliation(s)
- Amirah Faisal Alsaedi
- Department of Radiology Technology, Taibah University, Medina, Kingdom of Saudi Arabia.
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - David Lee Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Leonard Wolfson Experimental Neurology Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
| | - Jasmina Panovska-Griffiths
- Nuffield Department of Medicine, The Big Data Institute, University of Oxford, Oxford, UK
- The Queen's College, University of Oxford, Oxford, UK
| | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
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10
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Song Y, Lally PJ, Yanez Lopez M, Oeltzschner G, Nebel MB, Gagoski B, Kecskemeti S, Hui SCN, Zöllner HJ, Shukla D, Arichi T, De Vita E, Yedavalli V, Thayyil S, Fallin D, Dean DC, Grant PE, Wisnowski JL, Edden RAE. Edited magnetic resonance spectroscopy in the neonatal brain. Neuroradiology 2022; 64:217-232. [PMID: 34654960 PMCID: PMC8887832 DOI: 10.1007/s00234-021-02821-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 10/20/2022]
Abstract
J-difference-edited spectroscopy is a valuable approach for the detection of low-concentration metabolites with magnetic resonance spectroscopy (MRS). Currently, few edited MRS studies are performed in neonates due to suboptimal signal-to-noise ratio, relatively long acquisition times, and vulnerability to motion artifacts. Nonetheless, the technique presents an exciting opportunity in pediatric imaging research to study rapid maturational changes of neurotransmitter systems and other metabolic systems in early postnatal life. Studying these metabolic processes is vital to understanding the widespread and rapid structural and functional changes that occur in the first years of life. The overarching goal of this review is to provide an introduction to edited MRS for neonates, including the current state-of-the-art in editing methods and editable metabolites, as well as to review the current literature applying edited MRS to the neonatal brain. Existing challenges and future opportunities, including the lack of age-specific reference data, are also discussed.
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Affiliation(s)
- Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Peter J Lally
- Department of Brain Sciences, Imperial College London, London, UK
| | - Maria Yanez Lopez
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Borjan Gagoski
- Department of Radiology, Division of Neuroradiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | | | - Steve C N Hui
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Deepika Shukla
- Centre for Perinatal Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - Tomoki Arichi
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK
| | - Enrico De Vita
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, St Thomas's Hospital, Westminster Bridge Road, Lambeth Wing, 3rd Floor, London, SE1 7EH, UK
| | - Vivek Yedavalli
- Division of Neuroradiology, Park 367G, The Johns Hopkins University School of Medicine, 600 N. Wolfe St. B-112 D, Baltimore, MD, 21287, USA
| | - Sudhin Thayyil
- Centre for Perinatal Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - Daniele Fallin
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins University, Baltimore, USA.,Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Douglas C Dean
- Waisman Center, University of WI-Madison, Madison, WI, 53705, USA.,Department of Pediatrics, Division of Neonatology and Newborn Nursery, University of WI-Madison, School of Medicine and Public Health, Madison, WI, 53705, USA.,Department of Medical Physics, University of WI-Madison, School of Medicine and Public Health, Madison, WI, 53705, USA
| | - P Ellen Grant
- Department of Radiology, Division of Neuroradiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA.,Department of Medicine, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jessica L Wisnowski
- Children's Hospital Los Angeles, Los Angeles, CA, 90027, USA.,Department of Radiology and Fetal and Neonatal Institute, CHLA Division of Neonatology, Department of Pediatrics, Children's Hospital of Los Angeles, University of Southern California, Los Angeles, CA, 90033, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA. .,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA. .,Division of Neuroradiology, Park 367G, The Johns Hopkins University School of Medicine, 600 N. Wolfe St. B-112 D, Baltimore, MD, 21287, USA.
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11
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Mancini L, Casagranda S, Gautier G, Peter P, Lopez B, Thorne L, McEvoy A, Miserocchi A, Samandouras G, Kitchen N, Brandner S, De Vita E, Torrealdea F, Rega M, Schmitt B, Liebig P, Sanverdi E, Golay X, Bisdas S. CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing. Eur J Nucl Med Mol Imaging 2022; 49:2377-2391. [PMID: 35029738 PMCID: PMC9165287 DOI: 10.1007/s00259-022-05676-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/31/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE Accurate glioma classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different chemical exchange saturation transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in glioma subpopulations. METHODS Forty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI (B1rms = 2μT, Tsat = 3.5 s) at 3 T. Magnetization transfer ratio asymmetry and CEST metrics (amides: offset range 3-4 ppm, amines: 1.5-2.5 ppm, amide/amine ratio) were calculated with two models: 'asymmetry-based' (AB) and 'fluid-suppressed' (FS). The presence of T2/FLAIR mismatch was noted. RESULTS IDH-wild type had higher amide/amine ratio than IDH-mutant_1p/19qcodel (p < 0.022). Amide/amine ratio and amine levels differentiated IDH-wild type from IDH-mutant (p < 0.0045) and from IDH-mutant_1p/19qret (p < 0.021). IDH-mutant_1p/19qret had higher amides and amines than IDH-mutant_1p/19qcodel (p < 0.035). IDH-mutant_1p/19qret with AB/FS mismatch had higher amines than IDH-mutant_1p/19qret without AB/FS mismatch ( < 0.016). In IDH-mutant_1p/19qret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p = 0.014). CONCLUSIONS CEST-derived biomarkers for amides, amines, and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma subgroups that may have prognostic and clinical relevance.
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Affiliation(s)
- Laura Mancini
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK.
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK.
| | | | | | | | | | - Lewis Thorne
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Andrew McEvoy
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anna Miserocchi
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - George Samandouras
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Neil Kitchen
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Sebastian Brandner
- Division of Neuropathology, UCL Queen Square Institute of Neurology, London, UK
- The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Enrico De Vita
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Francisco Torrealdea
- University College Hospital, University College of London Hospitals NHS Foundation Trust, London, UK
| | - Marilena Rega
- University College Hospital, University College of London Hospitals NHS Foundation Trust, London, UK
| | | | | | - Eser Sanverdi
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Xavier Golay
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Sotirios Bisdas
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
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12
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Rodrigues FB, Byrne LM, Lowe AJ, Tortelli R, Heins M, Flik G, Johnson EB, De Vita E, Scahill RI, Giorgini F, Wild EJ. Kynurenine pathway metabolites in cerebrospinal fluid and blood as potential biomarkers in Huntington's disease. J Neurochem 2021; 158:539-553. [PMID: 33797782 PMCID: PMC8375100 DOI: 10.1111/jnc.15360] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/18/2021] [Accepted: 03/26/2021] [Indexed: 01/31/2023]
Abstract
Converging lines of evidence from several models, and post-mortem human brain tissue studies, support the involvement of the kynurenine pathway (KP) in Huntington's disease (HD) pathogenesis. Quantifying KP metabolites in HD biofluids is desirable, both to study pathobiology and as a potential source of biomarkers to quantify pathway dysfunction and evaluate the biochemical impact of therapeutic interventions targeting its components. In a prospective single-site controlled cohort study with standardised collection of cerebrospinal fluid (CSF), blood, phenotypic and imaging data, we used high-performance liquid-chromatography to measure the levels of KP metabolites-tryptophan, kynurenine, kynurenic acid, 3-hydroxykynurenine, anthranilic acid and quinolinic acid-in CSF and plasma of 80 participants (20 healthy controls, 20 premanifest HD and 40 manifest HD). We investigated short-term stability, intergroup differences, associations with clinical and imaging measures and derived sample-size calculation for future studies. Overall, KP metabolites in CSF and plasma were stable over 6 weeks, displayed no significant group differences and were not associated with clinical or imaging measures. We conclude that the studied metabolites are readily and reliably quantifiable in both biofluids in controls and HD gene expansion carriers. However, we found little evidence to support a substantial derangement of the KP in HD, at least to the extent that it is reflected by the levels of the metabolites in patient-derived biofluids.
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Affiliation(s)
- Filipe B. Rodrigues
- UCL Huntington's Disease CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Lauren M. Byrne
- UCL Huntington's Disease CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Alexander J. Lowe
- UCL Huntington's Disease CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Rosanna Tortelli
- UCL Huntington's Disease CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | | | - Gunnar Flik
- Charles River LaboratoriesGroningenThe Netherlands
| | - Eileanoir B. Johnson
- UCL Huntington's Disease CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Enrico De Vita
- Lysholm Department of NeuroradiologyNational Hospital for Neurology & NeurosurgeryLondonUK
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Rachael I. Scahill
- UCL Huntington's Disease CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Flaviano Giorgini
- Department of Genetics and Genome BiologyUniversity of LeicesterLeicesterUK
| | - Edward J. Wild
- UCL Huntington's Disease CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
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13
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Maria YL, Price AN, Puts NAJ, Hughes EJ, Edden RAE, McAlonan GM, Arichi T, De Vita E. Simultaneous quantification of GABA, Glx and GSH in the neonatal human brain using magnetic resonance spectroscopy. Neuroimage 2021; 233:117930. [PMID: 33711485 PMCID: PMC8204265 DOI: 10.1016/j.neuroimage.2021.117930] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 02/25/2021] [Accepted: 02/28/2021] [Indexed: 11/23/2022] Open
Abstract
Balance between inhibitory and excitatory neurotransmitter systems and the protective role of the major antioxidant glutathione (GSH) are central to early healthy brain development. Disruption has been implicated in the early life pathophysiology of psychiatric disorders and neurodevelopmental conditions including Autism Spectrum Disorder. Edited magnetic resonance spectroscopy (MRS) methods such as HERMES have great potential for providing important new non-invasive insights into these crucial processes in human infancy. In this work, we describe a systematic approach to minimise the impact of specific technical challenges inherent to acquiring MRS data in a neonatal population, including automatic segmentation, full tissue-correction and optimised GABA+ fitting and consider the minimum requirements for a robust edited-MRS acquisition. With this approach we report for the first time simultaneous GABA+, Glx (glutamate + glutamine) and GSH concentrations in the neonatal brain (n = 18) in two distinct regions (thalamus and anterior cingulate cortex (ACC)) using edited MRS at 3T. The improved sensitivity provided by our method allows specific regional neurochemical differences to be identified including: significantly lower Glx and GSH ratios to total creatine in the thalamus compared to the ACC (p < 0.001 for both), and significantly higher GSH levels in the ACC following tissue-correction (p < 0.01). Furthermore, in contrast to adult GABA+ which can typically be accurately fitted with a single peak, all neonate spectra displayed a characteristic doublet GABA+ peak at 3 ppm, indicating a lower macromolecule (MM) contribution to the 3 ppm signal in neonates. Relatively high group-level variance shows the need to maximise voxel size/acquisition time in edited neonatal MRS acquisitions for robust estimation of metabolites. Application of this method to study how these levels and balance are altered by early-life brain injury or genetic risk can provide important new knowledge about the pathophysiology underlying neurodevelopmental disorders.
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Affiliation(s)
- Yanez Lopez Maria
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Nicolaas A J Puts
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
| | - Grainne M McAlonan
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom; NIHR-Maudsley Biomedical Research, King's College London, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Bioengineering, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Enrico De Vita
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, Westminster Bridge Road, Lambeth Wing, 3rd Floor, London SE1 7EH, United Kingdom.
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14
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Patkee PA, Baburamani AA, Long KR, Dimitrova R, Ciarrusta J, Allsop J, Hughes E, Kangas J, McAlonan GM, Rutherford MA, De Vita E. Neurometabolite mapping highlights elevated myo-inositol profiles within the developing brain in down syndrome. Neurobiol Dis 2021; 153:105316. [PMID: 33711492 PMCID: PMC8039898 DOI: 10.1016/j.nbd.2021.105316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 02/06/2021] [Accepted: 02/22/2021] [Indexed: 01/02/2023] Open
Abstract
The neurodevelopmental phenotype in Down Syndrome (DS), or Trisomy 21, is variable including a wide spectrum of cognitive impairment and a high risk of early-onset Alzheimer's disease (AD). A key metabolite of interest within the brain in DS is Myo-inositol (mIns). The NA+/mIns co-transporter is located on human chromosome 21 and is overexpressed in DS. In adults with DS, elevated brain mIns was previously associated with cognitive impairment and proposed as a risk marker for progression to AD. However, it is unknown if brain mIns is increased earlier in development. The aim of this study was to estimate mIns concentration levels and key brain metabolites [N-acetylaspartate (NAA), Choline (Cho) and Creatine (Cr)] in the developing brain in DS and aged-matched controls. We used in vivo magnetic resonance spectroscopy (MRS) in neonates with DS (n = 12) and age-matched controls (n = 26) scanned just after birth (36-45 weeks postmenstrual age). Moreover, we used Mass Spectrometry in early (10-20 weeks post conception) ex vivo fetal brain tissue samples from DS (n = 14) and control (n = 30) cases. Relative to [Cho] and [Cr], we report elevated ratios of [mIns] in vivo in the basal ganglia/thalamus, in neonates with DS, when compared to age-matched typically developing controls. Glycine concentration ratios [Gly]/[Cr] and [Cho]/[Cr] also appear elevated. We observed elevated [mIns] in the ex vivo fetal cortical brain tissue in DS compared with controls. In conclusion, a higher level of brain mIns was evident as early as 10 weeks post conception and was measurable in vivo from 36 weeks post-menstrual age. Future work will determine if this early difference in metabolites is linked to cognitive outcomes in childhood or has utility as a potential treatment biomarker for early intervention.
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Affiliation(s)
- Prachi A Patkee
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - Ana A Baburamani
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - Katherine R Long
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE1 1UL, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, SE1 1UL, UK
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK; Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, UK
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK; Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, UK
| | - Joanna Allsop
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - Johanna Kangas
- Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, UK
| | - Grainne M McAlonan
- Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK
| | - Enrico De Vita
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK; Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, UK.
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15
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Markiewicz PJ, Matthews JC, Ashburner J, Cash DM, Thomas DL, De Vita E, Barnes A, Cardoso MJ, Modat M, Brown R, Thielemans K, da Costa-Luis C, Lopes Alves I, Gispert JD, Schmidt ME, Marsden P, Hammers A, Ourselin S, Barkhof F. Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging. Neuroimage 2021; 232:117821. [PMID: 33588030 PMCID: PMC8204268 DOI: 10.1016/j.neuroimage.2021.117821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/25/2020] [Accepted: 01/21/2021] [Indexed: 10/29/2022] Open
Abstract
Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.
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Affiliation(s)
- Pawel J Markiewicz
- Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK. http://www.nmi.cs.ucl.ac.uk
| | - Julian C Matthews
- Division of Neuroscience & Experimental Psychology, University of Manchester, UK
| | - John Ashburner
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, UK
| | - David M Cash
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - David L Thomas
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, UK; Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - Enrico De Vita
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Richard Brown
- Institute of Nuclear Medicine, University College London, London, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
| | - Casper da Costa-Luis
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK
| | - Isadora Lopes Alves
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, Netherlands
| | - Juan Domingo Gispert
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | | | - Paul Marsden
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, Netherlands
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16
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Ou ZYA, Byrne LM, Rodrigues FB, Tortelli R, Johnson EB, Foiani MS, Arridge M, De Vita E, Scahill RI, Heslegrave A, Zetterberg H, Wild EJ. Brain-derived neurotrophic factor in cerebrospinal fluid and plasma is not a biomarker for Huntington's disease. Sci Rep 2021; 11:3481. [PMID: 33568689 PMCID: PMC7876124 DOI: 10.1038/s41598-021-83000-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 01/18/2021] [Indexed: 11/08/2022] Open
Abstract
Brain-derived neurotrophic factor (BDNF) is implicated in the survival of striatal neurons. BDNF function is reduced in Huntington's disease (HD), possibly because mutant huntingtin impairs its cortico-striatal transport, contributing to striatal neurodegeneration. The BDNF trophic pathway is a therapeutic target, and blood BDNF has been suggested as a potential biomarker for HD, but BDNF has not been quantified in cerebrospinal fluid (CSF) in HD. We quantified BDNF in CSF and plasma in the HD-CSF cohort (20 pre-manifest and 40 manifest HD mutation carriers and 20 age and gender-matched controls) using conventional ELISAs and an ultra-sensitive immunoassay. BDNF concentration was below the limit of detection of the conventional ELISAs, raising doubt about previous CSF reports in neurodegeneration. Using the ultra-sensitive method, BDNF concentration was quantifiable in all samples but did not differ between controls and HD mutation carriers in CSF or plasma, was not associated with clinical scores or MRI brain volumetric measures, and had poor ability to discriminate controls from HD mutation carriers, and premanifest from manifest HD. We conclude that BDNF in CSF and plasma is unlikely to be a biomarker of HD progression and urge caution in interpreting studies where conventional ELISA was used to quantify CSF BDNF.
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Affiliation(s)
- Zhen-Yi Andy Ou
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Lauren M Byrne
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Filipe B Rodrigues
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Rosanna Tortelli
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Eileanoir B Johnson
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Martha S Foiani
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, WC1N 3BG, UK
| | - Marzena Arridge
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Rachael I Scahill
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Amanda Heslegrave
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, WC1N 3BG, UK
| | - Henrik Zetterberg
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, WC1N 3BG, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, 431 80, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 80, Mölndal, Sweden
| | - Edward J Wild
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK.
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17
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Rodrigues FB, Byrne LM, Tortelli R, Johnson EB, Wijeratne PA, Arridge M, De Vita E, Ghazaleh N, Houghton R, Furby H, Alexander DC, Tabrizi SJ, Schobel S, Scahill RI, Heslegrave A, Zetterberg H, Wild EJ. Mutant huntingtin and neurofilament light have distinct longitudinal dynamics in Huntington's disease. Sci Transl Med 2020; 12:eabc2888. [PMID: 33328328 PMCID: PMC7611886 DOI: 10.1126/scitranslmed.abc2888] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 09/04/2020] [Indexed: 07/26/2023]
Abstract
The longitudinal dynamics of the most promising biofluid biomarker candidates for Huntington's disease (HD)-mutant huntingtin (mHTT) and neurofilament light (NfL)-are incompletely defined. Characterizing changes in these candidates during disease progression could increase our understanding of disease pathophysiology and help the identification of effective therapies. In an 80-participant cohort over 24 months, mHTT in cerebrospinal fluid (CSF), as well as NfL in CSF and blood, had distinct longitudinal trajectories in HD mutation carriers compared with controls. Baseline analyte values predicted clinical disease status, subsequent clinical progression, and brain atrophy, better than did the rate of change in analytes. Overall, NfL was a stronger monitoring and prognostic biomarker for HD than mHTT. Nonetheless, mHTT has prognostic value and might be a valuable pharmacodynamic marker for huntingtin-lowering trials.
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Affiliation(s)
- Filipe B Rodrigues
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
| | - Lauren M Byrne
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
| | - Rosanna Tortelli
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
| | - Eileanoir B Johnson
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Marzena Arridge
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Naghmeh Ghazaleh
- PD Personalised Healthcare, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Richard Houghton
- PD Personalised Healthcare, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Hannah Furby
- PD Personalised Healthcare, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Sarah J Tabrizi
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
- UK Dementia Research Institute at UCL, London WC1E 6BT, UK
| | - Scott Schobel
- Product Development Neuroscience, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Rachael I Scahill
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
| | - Amanda Heslegrave
- UK Dementia Research Institute at UCL, London WC1E 6BT, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Henrik Zetterberg
- UK Dementia Research Institute at UCL, London WC1E 6BT, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, 431 80 Mölndal, Sweden
| | - Edward J Wild
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK.
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18
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Goh V, De Vita E. Arterial Spin Labeled Perfusion MRI for Assessing Antiangiogenic Therapy: A Step Forward or Just More Spin? Radiology 2020; 298:341-342. [PMID: 33263501 DOI: 10.1148/radiol.2020204199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Vicky Goh
- From the School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners (V.G., E.D.V.); and Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, Level 1, Lambeth Wing, St Thomas' Hospital, Westminster Bridge Rd, London SE1 7EH, England (V.G.)
| | - Enrico De Vita
- From the School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners (V.G., E.D.V.); and Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, Level 1, Lambeth Wing, St Thomas' Hospital, Westminster Bridge Rd, London SE1 7EH, England (V.G.)
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19
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Hyare H, De Vita E, Porter MC, Simpson I, Ridgway G, Lowe J, Thompson A, Carswell C, Ourselin S, Modat M, Dos Santos Canas L, Caine D, Fox Z, Rudge P, Collinge J, Mead S, Thornton JS. Putaminal diffusion tensor imaging measures predict disease severity across human prion diseases. Brain Commun 2020; 2:fcaa032. [PMID: 32954290 PMCID: PMC7425333 DOI: 10.1093/braincomms/fcaa032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 12/23/2019] [Accepted: 12/29/2019] [Indexed: 11/13/2022] Open
Abstract
Therapeutic trials of disease-modifying agents in neurodegenerative disease typically require several hundred participants and long durations for clinical endpoints. Trials of this size are not feasible for prion diseases, rare dementia disorders associated with misfolding of prion protein. In this situation, biomarkers are particularly helpful. On diagnostic imaging, prion diseases demonstrate characteristic brain signal abnormalities on diffusion-weighted MRI. The aim of this study was to determine whether cerebral water diffusivity could be a quantitative imaging biomarker of disease severity. We hypothesized that the basal ganglia were most likely to demonstrate functionally relevant changes in diffusivity. Seventy-one subjects (37 patients and 34 controls) of whom 47 underwent serial scanning (23 patients and 24 controls) were recruited as part of the UK National Prion Monitoring Cohort. All patients underwent neurological assessment with the Medical Research Council Scale, a functionally orientated measure of prion disease severity, and diffusion tensor imaging. Voxel-based morphometry, voxel-based analysis of diffusion tensor imaging and regions of interest analyses were performed. A significant voxel-wise correlation of decreased Medical Research Council Scale score and decreased mean, radial and axial diffusivities in the putamen bilaterally was observed (P < 0.01). Significant decrease in putamen mean, radial and axial diffusivities over time was observed for patients compared with controls (P = 0.01), and there was a significant correlation between monthly decrease in putamen mean, radial and axial diffusivities and monthly decrease in Medical Research Council Scale (P < 0.001). Step-wise linear regression analysis, with dependent variable decline in Medical Research Council Scale, and covariates age and disease duration, showed the rate of decrease in putamen radial diffusivity to be the strongest predictor of rate of decrease in Medical Research Council Scale (P < 0.001). Sample size calculations estimated that, for an intervention study, 83 randomized patients would be required to provide 80% power to detect a 75% amelioration of decline in putamen radial diffusivity. Putamen radial diffusivity has potential as a secondary outcome measure biomarker in future therapeutic trials in human prion diseases.
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Affiliation(s)
- Harpreet Hyare
- MRC Prion Unit at UCL, Institute of Prion Diseases, London SE1 7EH, UK
| | - Enrico De Vita
- Department of Biomedical Engineering, Centre for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, UK
| | | | | | | | - Jessica Lowe
- MRC Prion Unit at UCL, Institute of Prion Diseases, London SE1 7EH, UK
| | - Andrew Thompson
- MRC Prion Unit at UCL, Institute of Prion Diseases, London SE1 7EH, UK
| | - Chris Carswell
- MRC Prion Unit at UCL, Institute of Prion Diseases, London SE1 7EH, UK
| | - Sebastien Ourselin
- Department of Biomedical Engineering, Centre for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, UK
| | - Marc Modat
- Department of Biomedical Engineering, Centre for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, UK
| | | | - Diana Caine
- MRC Prion Unit at UCL, Institute of Prion Diseases, London SE1 7EH, UK
| | - Zoe Fox
- Education Unit, UCL Institute of Neurology, London, UK.,UCL/UCLH Joint Research Office, London, UK
| | - Peter Rudge
- MRC Prion Unit at UCL, Institute of Prion Diseases, London SE1 7EH, UK
| | - John Collinge
- MRC Prion Unit at UCL, Institute of Prion Diseases, London SE1 7EH, UK
| | - Simon Mead
- MRC Prion Unit at UCL, Institute of Prion Diseases, London SE1 7EH, UK
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20
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Zhao H, Brigadoi S, Chitnis D, Vita ED, Castellaro M, Powell S, Everdell NL, Cooper RJ. A wide field-of-view, modular, high-density diffuse optical tomography system for minimally constrained three-dimensional functional neuroimaging. Biomed Opt Express 2020; 11:4110-4129. [PMID: 32923032 PMCID: PMC7449732 DOI: 10.1364/boe.394914] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 06/03/2020] [Accepted: 06/09/2020] [Indexed: 05/09/2023]
Abstract
The ability to produce high-quality images of human brain function in any environment and during unconstrained movement of the subject has long been a goal of neuroimaging research. Diffuse optical tomography, which uses the intensity of back-scattered near-infrared light from multiple source-detector pairs to image changes in haemoglobin concentrations in the brain, is uniquely placed to achieve this goal. Here, we describe a new generation of modular, fibre-less, high-density diffuse optical tomography technology that provides exceptional sensitivity, a large dynamic range, a field-of-view sufficient to cover approximately one-third of the adult scalp, and also incorporates dedicated motion sensing into each module. Using in-vivo measures, we demonstrate a noise-equivalent power of 318 fW, and an effective dynamic range of 142 dB. We describe the application of this system to a novel somatomotor neuroimaging paradigm that involves subjects walking and texting on a smartphone. Our results demonstrate that wearable high-density diffuse optical tomography permits three-dimensional imaging of the human brain function during overt movement of the subject; images of somatomotor cortical activation can be obtained while subjects move in a relatively unconstrained manner, and these images are in good agreement with those obtained while the subjects remain stationary. The scalable nature of the technology we described here paves the way for the routine acquisition of high-quality, three-dimensional, whole-cortex diffuse optical tomography images of cerebral haemodynamics, both inside and outside of the laboratory environment, which has profound implications for neuroscience.
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Affiliation(s)
- Hubin Zhao
- DOT-HUB, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - Sabrina Brigadoi
- Department of Developmental and Social Psychology, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Danial Chitnis
- School of Engineering, Institute for Integrated Micro and Nano Systems, University of Edinburgh, Edinburgh, EH9 3FF, UK
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Marco Castellaro
- Department of Information Engineering, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Samuel Powell
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Nicholas L. Everdell
- DOT-HUB, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - Robert J. Cooper
- DOT-HUB, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
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21
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Mutsaerts HJMM, Petr J, Groot P, Vandemaele P, Ingala S, Robertson AD, Václavů L, Groote I, Kuijf H, Zelaya F, O'Daly O, Hilal S, Wink AM, Kant I, Caan MWA, Morgan C, de Bresser J, Lysvik E, Schrantee A, Bjørnebekk A, Clement P, Shirzadi Z, Kuijer JPA, Wottschel V, Anazodo UC, Pajkrt D, Richard E, Bokkers RPH, Reneman L, Masellis M, Günther M, MacIntosh BJ, Achten E, Chappell MA, van Osch MJP, Golay X, Thomas DL, De Vita E, Bjørnerud A, Nederveen A, Hendrikse J, Asllani I, Barkhof F. ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies. Neuroimage 2020; 219:117031. [PMID: 32526385 DOI: 10.1016/j.neuroimage.2020.117031] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/29/2020] [Accepted: 06/04/2020] [Indexed: 01/01/2023] Open
Abstract
Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.
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Affiliation(s)
- Henk J M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands; Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium.
| | - Jan Petr
- Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Paul Groot
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Pieter Vandemaele
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Andrew D Robertson
- Schlegel-UW Research Institute for Aging, University of Waterloo, Waterloo, Ontario, Canada
| | - Lena Václavů
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Inge Groote
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Hugo Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore; Memory Aging and Cognition Center, National University Health System, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Ilse Kant
- Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Intensive Care, University Medical Centre, Utrecht, the Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, the Netherlands
| | - Catherine Morgan
- School of Psychology and Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Jeroen de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Elisabeth Lysvik
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Anouk Schrantee
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Astrid Bjørnebekk
- The Anabolic Androgenic Steroid Research Group, National Advisory Unit on Substance Use Disorder Treatment, Oslo University Hospital, Oslo, Norway
| | - Patricia Clement
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Zahra Shirzadi
- Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Joost P A Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Udunna C Anazodo
- Department of Medical Biophysics, University of Western Ontario, London, Canada; Imaging Division, Lawson Health Research Institute, London, Canada
| | - Dasja Pajkrt
- Department of Pediatric Infectious Diseases, Emma Children's Hospital, Amsterdam University Medical Centre, Location Academic Medical Center, Amsterdam, the Netherlands
| | - Edo Richard
- Department of Neurology, Donders Institute for Brain, Behavior and Cognition, Radboud University Medical Centre, Nijmegen, the Netherlands; Neurology, Amsterdam University Medical Center, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Reinoud P H Bokkers
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Liesbeth Reneman
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Mario Masellis
- Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Matthias Günther
- Fraunhofer MEVIS, Bremen, Germany; University of Bremen, Bremen, Germany; Mediri GmbH, Heidelberg, Germany
| | | | - Eric Achten
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Michael A Chappell
- Institute of Biomedical Engineering, Department of Engineering Science & Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Matthias J P van Osch
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Xavier Golay
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David L Thomas
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Atle Bjørnerud
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Aart Nederveen
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Jeroen Hendrikse
- Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Iris Asllani
- Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Clinical Imaging Sciences Centre, Department of Neuroscience, Brighton and Sussex Medical School, Brighton, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands; UCL Queen Square Institute of Neurology, University College London, London, UK; Centre for Medical Image Computing (CMIC), Faculty of Engineering Science, University College London, London, UK
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22
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Brumer I, De Vita E, Ashmore J, Jarosz J, Borri M. Implementation of clinically relevant and robust fMRI-based language lateralization: Choosing the laterality index calculation method. PLoS One 2020; 15:e0230129. [PMID: 32163517 PMCID: PMC7067428 DOI: 10.1371/journal.pone.0230129] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 02/23/2020] [Indexed: 11/19/2022] Open
Abstract
The assessment of language lateralization has become widely used when planning neurosurgery close to language areas, due to individual specificities and potential influence of brain pathology. Functional magnetic resonance imaging (fMRI) allows non-invasive and quantitative assessment of language lateralization for presurgical planning using a laterality index (LI). However, the conventional method is limited by the dependence of the LI on the chosen activation threshold. To overcome this limitation, different threshold-independent LI calculations have been reported. The purpose of this study was to propose a simplified approach to threshold-independent LI calculation and compare it with three previously reported methods on the same cohort of subjects. Fifteen healthy subjects, who performed picture naming, verb generation, and word fluency tasks, were scanned. LI values were calculated for all subjects using four methods, and considering either the whole hemisphere or an atlas-defined language area. For each method, the subjects were ranked according to the calculated LI values, and the obtained rankings were compared. All LI calculation methods agreed in differentiating strong from weak lateralization on both hemispheric and regional scales (Spearman's correlation coefficients 0.59-1.00). In general, a more lateralized activation was found in the language area than in the whole hemisphere. The new method is well suited for application in the clinical practice as it is simple to implement, fast, and robust. The good agreement between LI calculation methods suggests that the choice of method is not key. Nevertheless, it should be consistent to allow a relative comparison of language lateralization between subjects.
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Affiliation(s)
- Irène Brumer
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital, London, United Kingdom
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan Ashmore
- Department of Neuroradiology, King’s College Hospital, London, United Kingdom
- Department of Medical Physics and Bioengineering, NHS Highland, Inverness, United Kingdom
| | - Jozef Jarosz
- Department of Neuroradiology, King’s College Hospital, London, United Kingdom
| | - Marco Borri
- Department of Neuroradiology, King’s College Hospital, London, United Kingdom
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23
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Harteveld AA, Hutter J, Franklin SL, Jackson LH, Rutherford M, Hajnal JV, van Osch MJP, Bos C, De Vita E. Systematic evaluation of velocity-selective arterial spin labeling settings for placental perfusion measurement. Magn Reson Med 2020; 84:1828-1843. [PMID: 32141655 PMCID: PMC7384055 DOI: 10.1002/mrm.28240] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 02/04/2020] [Accepted: 02/10/2020] [Indexed: 01/15/2023]
Abstract
Purpose Placental function is key for successful human pregnancies. Perfusion may be a sensitive marker for the in vivo assessment of placental function. Arterial spin labeling (ASL) MRI enables noninvasive measurement of tissue perfusion and it was recently suggested that ASL with velocity‐selective (VS) labeling could be advantageous in the placenta. We systematically evaluated essential VS‐ASL sequence parameters to determine optimal settings for efficient placental perfusion measurements. Methods Eleven pregnant women were scanned at 3T using VS‐ASL with 2D multislice echo planar imaging (EPI)‐readout. One reference VS‐ASL scan was acquired in all subjects; within subgroups the following parameters were systematically varied: cutoff velocity, velocity encoding direction, and inflow time. Visual evaluation and region of interest analyses were performed to compare perfusion signal differences between acquisitions. Results In all subjects, a perfusion pattern with clear hyperintense focal regions was observed. Perfusion signal decreased with inflow time and cutoff velocity. Subject‐specific dependence on velocity encoding direction was observed. High temporal signal‐to‐noise ratios with high contrast on the perfusion images between the hyperintense regions and placental tissue were seen at ~1.6 cm/s cutoff velocity and ~1000 ms inflow time. Evaluation of measurements at multiple inflow times revealed differences in blood flow dynamics between placental regions. Conclusion Placental perfusion measurements are feasible at 3T using VS‐ASL with 2D multislice EPI‐readout. A clear dependence of perfusion signal on VS labeling parameters and inflow time was demonstrated. Whereas multiple parameter combinations may advance the interpretation of placental circulation dynamics, this study provides a basis to select an effective set of parameters for the observation of placenta perfusion natural history and its potential pathological changes.
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Affiliation(s)
- Anita A Harteveld
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jana Hutter
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Suzanne L Franklin
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,C.J. Gorter Center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Laurence H Jackson
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Mary Rutherford
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Matthias J P van Osch
- C.J. Gorter Center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Clemens Bos
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Enrico De Vita
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
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24
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Mehranian A, McGinnity CJ, Neji R, Prieto C, Hammers A, De Vita E, Reader AJ. Motion‐corrected and high‐resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling MRI. Magn Reson Med 2020; 84:1306-1320. [PMID: 32125015 PMCID: PMC8614125 DOI: 10.1002/mrm.28205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 01/12/2020] [Accepted: 01/18/2020] [Indexed: 11/30/2022]
Abstract
Purpose A model‐based reconstruction framework is proposed for motion‐corrected and high‐resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling (ASL) data. In this framework, all low‐resolution ASL control‐label pairs are used to reconstruct a single high‐resolution cerebral blood flow (CBF) map, corrected for rigid‐motion, point‐spread‐function blurring and partial volume effect. Methods Six volunteers were recruited for CBF imaging using pseudo‐continuous ASL labeling, two‐shot 3D gradient and spin‐echo sequences and high‐resolution T1‐weighted MRI. For 2 volunteers, high‐resolution scans with double and triple resolution in the partition direction were additionally collected. Simulations were designed for evaluations against a high‐resolution ground‐truth CBF map, including a simulated hyperperfused lesion and hyperperfusion/hypoperfusion abnormalities. The MOCHA technique was compared with standard reconstruction and a 3D linear regression partial‐volume effect correction method and was further evaluated for acquisitions with reduced control‐label pairs and k‐space undersampling. Results The MOCHA reconstructions of low‐resolution ASL data showed enhanced image quality, particularly in the partition direction. In simulations, both MOCHA and 3D linear regression provided more accurate CBF maps than the standard reconstruction; however, MOCHA resulted in the lowest errors and well delineated the abnormalities. The MOCHA reconstruction of standard‐resolution in vivo data showed good agreement with higher‐resolution scans requiring 4‐times and 9‐times longer acquisitions. The MOCHA reconstruction was found to be robust for 4‐times‐accelerated ASL acquisitions, achieved by reduced control‐label pairs or k‐space undersampling. Conclusion The MOCHA reconstruction reduces partial‐volume effect by direct reconstruction of CBF maps in the high‐resolution space of the corresponding anatomical image, incorporating motion correction and point spread function modeling. Following further evaluation, MOCHA should promote the clinical application of ASL.
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Affiliation(s)
- Abolfazl Mehranian
- Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
| | - Colm J. McGinnity
- School of Biomedical Engineering and Imaging Sciences, King’s College London and King’s College London & Guy’s and St. Thomas’ PET Centre, St. Thomas’ Hospital London United Kingdom
| | - Radhouene Neji
- Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
- MR Research Collaborations Siemens Healthcare Frimley United Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King’s College London and King’s College London & Guy’s and St. Thomas’ PET Centre, St. Thomas’ Hospital London United Kingdom
| | - Enrico De Vita
- Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
| | - Andrew J. Reader
- Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
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25
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Gregory S, Johnson E, Byrne LM, Rodrigues FB, Henderson A, Moss J, Thomas D, Zhang H, De Vita E, Tabrizi SJ, Rees G, Scahill RI, Wild EJ. Characterizing White Matter in Huntington's Disease. Mov Disord Clin Pract 2020; 7:52-60. [PMID: 31970212 PMCID: PMC6962665 DOI: 10.1002/mdc3.12866] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 09/27/2019] [Accepted: 10/28/2019] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Investigating early white matter (WM) change in Huntington's disease (HD) can improve our understanding of the way in which disease spreads from the striatum. OBJECTIVES We provide a detailed characterization of pathology-related WM change in HD. We first examined WM microstructure using diffusion-weighted imaging and then investigated both underlying biological properties of WM and products of WM damage including iron, myelin plus neurofilament light, a biofluid marker of axonal degeneration-in parallel with the mutant huntingtin protein. METHODS We examined WM change in HD gene carriers from the HD-CSFcohort, baseline visit. We used standard-diffusion magnetic resonance imaging to measure metrics including fractional anisotropy, a marker of WM integrity, and diffusivity; a novel diffusion model (neurite orientation dispersion and density imaging) to measure axonal density and organization; T1-weighted and T2-weighted structural magnetic resonance imaging images to derive proxy iron content and myelin-contrast measures; and biofluid concentrations of neurofilament light (in cerebrospinal fluid (CSF) and plasma) and mutant huntingtin protein (in CSF). RESULTS HD gene carriers displayed reduced fractional anisotropy and increased diffusivity when compared with controls, both of which were also associated with disease progression, CSF, and mutant huntingtin protein levels. HD gene carriers also displayed proxy measures of reduced myelin contrast and iron in the striatum. CONCLUSION Collectively, these findings present a more complete characterization of HD-related microstructural brain changes. The correlation between reduced fractional anisotropy, increased axonal orientation, and biofluid markers suggest that axonal breakdown is associated with increased WM degeneration, whereas higher quantitative T2 signal and lower myelin-contrast may indicate a process of demyelination limited to the striatum.
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Affiliation(s)
- Sarah Gregory
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Eileanoir Johnson
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Lauren M. Byrne
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Filipe B. Rodrigues
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Alexandra Henderson
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - John Moss
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - David Thomas
- Department of Brain Repair and Rehabilitation, University College London Queen Square Institute of NeurologyUniversity College LondonUnited Kingdom
- Leonard Wolfson Experimental Neurology Centre, University College London Queen Square Institute of NeurologyUniversity College LondonUnited Kingdom
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image ComputingUniversity College LondonLondonUnited Kingdom
| | - Enrico De Vita
- Lysholm Department of NeuroradiologyNational Hospital for Neurology and NeurosurgeryLondonUnited Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Sarah J. Tabrizi
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Geraint Rees
- Wellcome Trust Centre for Neuroimaging, Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Rachael I. Scahill
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Edward J. Wild
- University College London Huntington's Disease Centre, Department of Neurodegenerative DiseaseUniversity College London Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
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26
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Scott CJ, Jiao J, Melbourne A, Burgos N, Cash DM, De Vita E, Markiewicz PJ, O'Connor A, Thomas DL, Weston PS, Schott JM, Hutton BF, Ourselin S. Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL-MRI: proof of concept. J Cereb Blood Flow Metab 2019; 39:2419-2432. [PMID: 30182792 PMCID: PMC6891000 DOI: 10.1177/0271678x18797343] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Pharmacokinetic modelling on dynamic positron emission tomography (PET) data is a quantitative technique. However, the long acquisition time is prohibitive for routine clinical use. Instead, the semi-quantitative standardised uptake value ratio (SUVR) from a shorter static acquisition is used, despite its sensitivity to blood flow confounding longitudinal analysis. A method has been proposed to reduce the dynamic acquisition time for quantification by incorporating cerebral blood flow (CBF) information from arterial spin labelling (ASL) magnetic resonance imaging (MRI) into the pharmacokinetic modelling. In this work, we optimise and validate this framework for a study of ageing and preclinical Alzheimer's disease. This methodology adapts the simplified reference tissue model (SRTM) for a reduced acquisition time (RT-SRTM) and is applied to [18F]-florbetapir PET data for amyloid-β quantification. Evaluation shows that the optimised RT-SRTM can achieve amyloid burden estimation from a 30-min PET/MR acquisition which is comparable with the gold standard SRTM applied to 60 min of PET data. Conversely, SUVR showed a significantly higher error and bias, and a statistically significant correlation with tracer delivery due to the influence of blood flow. The optimised RT-SRTM produced amyloid burden estimates which were uncorrelated with tracer delivery indicating its suitability for longitudinal studies.
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Affiliation(s)
- Catherine J Scott
- Translational Imaging Group, CMIC, University College London, London, UK
| | - Jieqing Jiao
- Translational Imaging Group, CMIC, University College London, London, UK
| | - Andrew Melbourne
- Translational Imaging Group, CMIC, University College London, London, UK
| | - Ninon Burgos
- Translational Imaging Group, CMIC, University College London, London, UK.,Inria, Aramis project-team, Institut du Cerveau et de la Moelle épinière, Inserm, CNRS, Sorbonne Université, Paris, France
| | - David M Cash
- Translational Imaging Group, CMIC, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCL Hospitals Foundation Trust, London, UK.,Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, UK
| | - Pawel J Markiewicz
- Translational Imaging Group, CMIC, University College London, London, UK
| | - Antoinette O'Connor
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - David L Thomas
- Translational Imaging Group, CMIC, University College London, London, UK.,Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK.,Leonard Wolfson Experimental Neurology Centre, UCL Institute of Neurology London, UK
| | - Philip Sj Weston
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London, London, UK.,Centre for Medical Radiation Physics, University of Wollongong, NSW, Australia
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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Canas LS, Sudre CH, De Vita E, Nihat A, Mok TH, Slattery CF, Paterson RW, Foulkes AJM, Hyare H, Cardoso MJ, Thornton J, Schott JM, Barkhof F, Collinge J, Ourselin S, Mead S, Modat M. Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process. Neuroimage Clin 2019; 24:102051. [PMID: 31734530 PMCID: PMC6978211 DOI: 10.1016/j.nicl.2019.102051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 09/25/2019] [Accepted: 10/21/2019] [Indexed: 02/01/2023]
Abstract
Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by prion protein gene mutations, or exposure to prions in the diet or by medical procedures, such us surgeries. To date, there are no accurate quantitative imaging biomarkers that can be used to predict the future clinical diagnosis of a healthy subject, or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the heterogeneity of phenotypes and the lack of a consistent geometrical pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of human form of prion disease. In this paper, using a tailored framework, we aim to classify and stratify patients with prion disease, according to the severity of their illness. The framework is initialised with the extraction of subject-specific imaging biomarkers. The extracted biomakers are then combined with genetic and demographic information within a Gaussian Process classifier, used to calculate the probability of a subject to be diagnosed with prion disease in the next year. We evaluate the effectiveness of the proposed method in a cohort of patients with inherited and sporadic forms of prion disease. The model has shown to be effective in the prediction of both inherited CJD (92% of accuracy) and sporadic CJD (95% of accuracy). However the model has shown to be less effective when used to stratify the different stages of the disease, in which the average accuracy is 85%, whilst the recall is 59%. Finally, our framework was extended as a differential diagnosis tool to identify both forms of CJD among another neurodegenerative disease. In summary we have developed a novel method for prion disease diagnosis and prediction of clinical onset using multiple sources of features, which may have use in other disorders with heterogeneous imaging features.
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Affiliation(s)
- Liane S Canas
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom.
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom; Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Enrico De Vita
- Institute of Neurology, University College London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Akin Nihat
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Tze How Mok
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Catherine F Slattery
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Alexander J M Foulkes
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Harpreet Hyare
- NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - M Jorge Cardoso
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - John Thornton
- Institute of Neurology, University College London, United Kingdom
| | - Jonathan M Schott
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - John Collinge
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Sébastien Ourselin
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Simon Mead
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Marc Modat
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
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28
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Byrne LM, Rodrigues FB, Johnson EB, Wijeratne PA, De Vita E, Alexander DC, Palermo G, Czech C, Schobel S, Scahill RI, Heslegrave A, Zetterberg H, Wild EJ. Evaluation of mutant huntingtin and neurofilament proteins as potential markers in Huntington's disease. Sci Transl Med 2019; 10:10/458/eaat7108. [PMID: 30209243 DOI: 10.1126/scitranslmed.aat7108] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 08/23/2018] [Indexed: 11/02/2022]
Abstract
Huntington's disease (HD) is a genetic progressive neurodegenerative disorder, caused by a mutation in the HTT gene, for which there is currently no cure. The identification of sensitive indicators of disease progression and therapeutic outcome could help the development of effective strategies for treating HD. We assessed mutant huntingtin (mHTT) and neurofilament light (NfL) protein concentrations in cerebrospinal fluid (CSF) and blood in parallel with clinical evaluation and magnetic resonance imaging in premanifest and manifest HD mutation carriers. Among HD mutation carriers, NfL concentrations in plasma and CSF correlated with all nonbiofluid measures more closely than did CSF mHTT concentration. Longitudinal analysis over 4 to 8 weeks showed that CSF mHTT, CSF NfL, and plasma NfL concentrations were highly stable within individuals. In our cohort, concentration of CSF mHTT accurately distinguished between controls and HD mutation carriers, whereas NfL concentration, in both CSF and plasma, was able to segregate premanifest from manifest HD. In silico modeling indicated that mHTT and NfL concentrations in biofluids might be among the earliest detectable alterations in HD, and sample size prediction suggested that low participant numbers would be needed to incorporate these measures into clinical trials. These findings provide evidence that biofluid concentrations of mHTT and NfL have potential for early and sensitive detection of alterations in HD and could be integrated into both clinical trials and the clinic.
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Affiliation(s)
- Lauren M Byrne
- Huntington's Disease Centre, University College London (UCL) Institute of Neurology, London WC1N 3BG, UK.
| | - Filipe B Rodrigues
- Huntington's Disease Centre, University College London (UCL) Institute of Neurology, London WC1N 3BG, UK
| | - Eileanor B Johnson
- Huntington's Disease Centre, University College London (UCL) Institute of Neurology, London WC1N 3BG, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, UCL, London WC1E 6EA, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.,Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, UCL, London WC1E 6EA, UK.,Clinical Imaging Research Centre, National University of Singapore, Singapore 117599, Singapore
| | - Giuseppe Palermo
- Neuroscience, Ophthalmology, and Rare Diseases, Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffman-La Roche Ltd., 4070 Basel, Switzerland
| | - Christian Czech
- Neuroscience, Ophthalmology, and Rare Diseases, Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffman-La Roche Ltd., 4070 Basel, Switzerland
| | - Scott Schobel
- Neuroscience, Ophthalmology, and Rare Diseases, Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffman-La Roche Ltd., 4070 Basel, Switzerland
| | - Rachael I Scahill
- Huntington's Disease Centre, University College London (UCL) Institute of Neurology, London WC1N 3BG, UK
| | - Amanda Heslegrave
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Henrik Zetterberg
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.,UK Dementia Research Institute at UCL, London WC1E 6BT, UK.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, 405 30 Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 413 45 Gothenburg, Sweden
| | - Edward J Wild
- Huntington's Disease Centre, University College London (UCL) Institute of Neurology, London WC1N 3BG, UK.
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29
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Hutter J, Harteveld AA, Jackson LH, Franklin S, Bos C, van Osch MJP, O'Muircheartaigh J, Ho A, Chappell L, Hajnal JV, Rutherford M, De Vita E. Perfusion and apparent oxygenation in the human placenta (PERFOX). Magn Reson Med 2019; 83:549-560. [PMID: 31433077 PMCID: PMC6825519 DOI: 10.1002/mrm.27950] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 07/18/2019] [Accepted: 07/25/2019] [Indexed: 02/05/2023]
Abstract
PURPOSE To study placental function-both perfusion and an oxygenation surrogate ( T 2 * )-simultaneously and quantitatively in-vivo. METHODS Fifteen pregnant women were scanned on a 3T MR scanner. For perfusion measurements, a velocity selective arterial spin labeling preparation module was placed before a multi-echo gradient echo EPI readout to integrate T 2 * and perfusion measurements in 1 joint perfusion-oxygenation (PERFOX) acquisition. Joint motion correction and quantification were performed to evaluate changes in T 2 * and perfusion over GA. RESULTS The optimized integrated PERFOX protocol and post-processing allowed successful visualization and quantification of perfusion and T 2 * in all subjects. Areas of high T 2 * and high perfusion appear to correspond to placental sub-units and show a systematic offset in location along the maternal-fetal axis. The areas of highest perfusion are consistently closer to the maternal basal plate and the areas of highest T 2 * closer to the fetal chorionic plate. Quantitative results show a strong negative correlation of gestational age with T 2 * and weak negative correlation with perfusion. CONCLUSIONS A strength of the joint sequence is that it provides truly simultaneous and co-registered estimates of local T 2 * and perfusion, however, to achieve this, the time per slice is prolonged compared to a perfusion only scan which can potentially limit coverage. The achieved interlocking can be particularly useful when quantifying transient physiological effects such as uterine contractions. PERFOX opens a new avenue to elucidate the relationship between maternal supply and oxygen uptake, both of which are central to placental function and dysfunction.
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Affiliation(s)
- Jana Hutter
- Centre for the Developing BrainKing's College LondonLondonUnited Kingdom
- School of Medical EngineeringKing's College LondonLondonUnited Kingdom
| | - Anita A. Harteveld
- Department of RadiologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Laurence H. Jackson
- Centre for the Developing BrainKing's College LondonLondonUnited Kingdom
- School of Medical EngineeringKing's College LondonLondonUnited Kingdom
| | - Suzanne Franklin
- C.J. Gorter Center for High Field MRIDepartment of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Clemens Bos
- Department of RadiologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Matthias J. P. van Osch
- C.J. Gorter Center for High Field MRIDepartment of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Jonathan O'Muircheartaigh
- Centre for the Developing BrainKing's College LondonLondonUnited Kingdom
- School of Medical EngineeringKing's College LondonLondonUnited Kingdom
| | - Alison Ho
- Academic Women's Health DepartmentKing's College LondonLondonUnited Kingdom
| | - Lucy Chappell
- Academic Women's Health DepartmentKing's College LondonLondonUnited Kingdom
| | - Joseph V. Hajnal
- Centre for the Developing BrainKing's College LondonLondonUnited Kingdom
- School of Medical EngineeringKing's College LondonLondonUnited Kingdom
| | - Mary Rutherford
- Centre for the Developing BrainKing's College LondonLondonUnited Kingdom
- School of Medical EngineeringKing's College LondonLondonUnited Kingdom
| | - Enrico De Vita
- School of Medical EngineeringKing's College LondonLondonUnited Kingdom
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30
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Boscolo Galazzo I, Storti SF, Barnes A, De Blasi B, De Vita E, Koepp M, Duncan JS, Groves A, Pizzini FB, Menegaz G, Fraioli F. Arterial Spin Labeling Reveals Disrupted Brain Networks and Functional Connectivity in Drug-Resistant Temporal Epilepsy. Front Neuroinform 2019; 12:101. [PMID: 30894811 PMCID: PMC6414423 DOI: 10.3389/fninf.2018.00101] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 12/12/2018] [Indexed: 01/08/2023] Open
Abstract
Resting-state networks (RSNs) and functional connectivity (FC) have been increasingly exploited for mapping brain activity and identifying abnormalities in pathologies, including epilepsy. The majority of studies currently available are based on blood-oxygenation-level-dependent (BOLD) contrast in combination with either independent component analysis (ICA) or pairwise region of interest (ROI) correlations. Despite its success, this approach has several shortcomings as BOLD is only an indirect and non-quantitative measure of brain activity. Conversely, promising results have recently been achieved by arterial spin labeling (ASL) MRI, primarily developed to quantify brain perfusion. However, the wide application of ASL-based FC has been hampered by its complexity and relatively low robustness to noise, leaving several aspects of this approach still largely unexplored. In this study, we firstly aimed at evaluating the effect of noise reduction on spatio-temporal ASL analyses and quantifying the impact of two ad-hoc processing pipelines (basic and advanced) on connectivity measures. Once the optimal strategy had been defined, we investigated the applicability of ASL for connectivity mapping in patients with drug-resistant temporal epilepsy vs. controls (10 per group), aiming at revealing between-group voxel-wise differences in each RSN and ROI-wise FC changes. We first found ASL was able to identify the main network (DMN) along with all the others generally detected with BOLD but never previously reported from ASL. For all RSNs, ICA-based denoising (advanced pipeline) allowed to increase their similarity with the corresponding BOLD template. ASL-based RSNs were visibly consistent with literature findings; however, group differences could be identified in the structure of some networks. Indeed, statistics revealed areas of significant FC decrease in patients within different RSNs, such as DMN and cerebellum (CER), while significant increases were found in some cases, such as the visual networks. Finally, the ROI-based analyses identified several inter-hemispheric dysfunctional links (controls > patients) mainly between areas belonging to the DMN, right-left thalamus and right-left temporal lobe. Conversely, fewer connections, predominantly intra-hemispheric, showed the opposite pattern (controls < patients). All these elements provide novel insights into the pathological modulations characterizing a "network disease" as epilepsy, shading light on the importance of perfusion-based approaches for identifying the disrupted areas and communications between brain regions.
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Affiliation(s)
| | | | - Anna Barnes
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Bianca De Blasi
- Department of Medical Physics, University College London, London, United Kingdom
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's Health Partners, King's College London, London, United Kingdom
| | - Matthias Koepp
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom
| | - John Sidney Duncan
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom
| | - Ashley Groves
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | | | - Gloria Menegaz
- Department of Computer Science, University of Verona, Verona, Italy
| | - Francesco Fraioli
- Institute of Nuclear Medicine, University College London, London, United Kingdom
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31
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Rodrigues FB, Byrne LM, De Vita E, Johnson EB, Hobbs NZ, Thornton JS, Scahill RI, Wild EJ. Cerebrospinal fluid flow dynamics in Huntington's disease evaluated by phase contrast MRI. Eur J Neurosci 2019; 49:1632-1639. [PMID: 30687961 PMCID: PMC6618296 DOI: 10.1111/ejn.14356] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/11/2019] [Accepted: 01/22/2019] [Indexed: 11/27/2022]
Abstract
Multiple targeted therapeutics for Huntington's disease are now in clinical trials, including intrathecally delivered compounds. Previous research suggests that CSF dynamics may be altered in Huntington's disease, which could be of paramount relevance to intrathecal drug delivery to the brain. To test this hypothesis, we conducted a prospective cross-sectional study comparing people with early stage Huntington's disease with age- and gender-matched healthy controls. CSF peak velocity, mean velocity and mean flow at the level of the cerebral aqueduct, and sub-arachnoid space in the upper and lower spine, were quantified using phase contrast MRI. We calculated Spearman's rank correlations, and tested inter-group differences with Wilcoxon rank-sum test. Ten people with early Huntington's disease, and 10 controls were included. None of the quantified measures was associated with potential modifiers of CSF dynamics (demographics, osmolality, and brain volumes), or by known modifiers of Huntington's disease (age and HTTCAG repeat length); and no significant differences were found between the two studied groups. While external validation is required, the attained results are sufficient to conclude tentatively that a clinically relevant alteration of CSF dynamics - that is, one that would justify dose-adjustments of intrathecal drugs - is unlikely to exist in Huntington's disease.
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Affiliation(s)
- Filipe B Rodrigues
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Lauren M Byrne
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Eileanoir B Johnson
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - John S Thornton
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rachael I Scahill
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Edward J Wild
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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Nery F, De Vita E, Clark CA, Gordon I, Thomas DL. Robust kidney perfusion mapping in pediatric chronic kidney disease using single-shot 3D-GRASE ASL with optimized retrospective motion correction. Magn Reson Med 2018; 81:2972-2984. [PMID: 30536817 DOI: 10.1002/mrm.27614] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 10/24/2018] [Accepted: 10/25/2018] [Indexed: 12/18/2022]
Abstract
PURPOSE To develop a robust renal arterial spin labeling (ASL) acquisition and processing strategy for mapping renal blood flow (RBF) in a pediatric cohort with severe kidney disease. METHODS A single-shot background-suppressed 3D gradient and spin-echo (GRASE) flow-sensitive alternating inversion recovery (FAIR) ASL acquisition method was used to perform 2 studies. First, an evaluation of the feasibility of single-shot 3D-GRASE and retrospective noise reduction methods was performed in healthy volunteers. Second, a pediatric cohort with severe chronic kidney disease underwent single-shot 3D-GRASE FAIR ASL and RBF was quantified following several retrospective motion correction pipelines, including image registration and threshold-free weighted averaging. The effect of motion correction on the fit errors of saturation recovery (SR) images (required for RBF quantification) and on the perfusion-weighted image (PWI) temporal signal-to-noise ratio (tSNR) was evaluated, as well as the intra- and inter-session repeatability of renal longitudinal relaxation time (T1 ) and RBF. RESULTS The mean cortical and/or functional renal parenchyma RBF in healthy volunteers and CKD patients was 295 ± 97 and 95 ± 47 mL/100 g/min, respectively. Motion-correction reduced image artefacts in both T1 and RBF maps, significantly reduced SR fit errors, significantly increased the PWI tSNR and improved the improved the repeatability of T1 and RBF in the pediatric patient cohort. CONCLUSION Single-shot 3D-GRASE ASL combined with retrospective motion correction enabled repeatable non-invasive RBF mapping in the first pediatric cohort with severe kidney disease undergoing ASL scans.
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Affiliation(s)
- Fabio Nery
- Developmental Imaging and Biophysics Section, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, United Kingdom
| | - Chris A Clark
- Developmental Imaging and Biophysics Section, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Isky Gordon
- Developmental Imaging and Biophysics Section, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, University College London Queen Square Institute of Neurology, Queen Square, London, United Kingdom.,Leonard Wolfson Experimental Neurology Centre, University College London Queen Square Institute of Neurology, Queen Square, London, United Kingdom
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Abstract
The hybrid PET/MR scanner represents the first implementation of the effective integration of two modalities allowing truly synchronous/simultaneous acquisition of their imaging signals. This integration, resulting from the innovation and development of specific hardware components has paved the way for new approaches in the study of neurodegenerative diseases. This chapter will describe the hardware development that has led to the availability of different clinical solutions for PET/MR imaging as well as the still-open technological challenges and opportunities related to the processing and exploitation of the simultaneous acquisition in neurological studies.
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Affiliation(s)
| | | | | | | | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, United Kingdom
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34
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Petr J, Mutsaerts HJMM, De Vita E, Steketee RME, Smits M, Nederveen AJ, Hofheinz F, van den Hoff J, Asllani I. Effects of systematic partial volume errors on the estimation of gray matter cerebral blood flow with arterial spin labeling MRI. MAGMA 2018; 31:725-734. [PMID: 29916058 DOI: 10.1007/s10334-018-0691-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 05/22/2018] [Accepted: 05/23/2018] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Partial volume (PV) correction is an important step in arterial spin labeling (ASL) MRI that is used to separate perfusion from structural effects when computing the mean gray matter (GM) perfusion. There are three main methods for performing this correction: (1) GM-threshold, which includes only voxels with GM volume above a preset threshold; (2) GM-weighted, which uses voxel-wise GM contribution combined with thresholding; and (3) PVC, which applies a spatial linear regression algorithm to estimate the flow contribution of each tissue at a given voxel. In all cases, GM volume is obtained using PV maps extracted from the segmentation of the T1-weighted (T1w) image. As such, PV maps contain errors due to the difference in readout type and spatial resolution between ASL and T1w images. Here, we estimated these errors and evaluated their effect on the performance of each PV correction method in computing GM cerebral blood flow (CBF). MATERIALS AND METHODS Twenty-two volunteers underwent scanning using 2D echo planar imaging (EPI) and 3D spiral ASL. For each PV correction method, GM CBF was computed using PV maps simulated to contain estimated errors due to spatial resolution mismatch and geometric distortions which are caused by the mismatch in readout between ASL and T1w images. Results were analyzed to assess the effect of each error on the estimation of GM CBF from ASL data. RESULTS Geometric distortion had the largest effect on the 2D EPI data, whereas the 3D spiral was most affected by the resolution mismatch. The PVC method outperformed the GM-threshold even in the presence of combined errors from resolution mismatch and geometric distortions. The quantitative advantage of PVC was 16% without and 10% with the combined errors for both 2D and 3D ASL. Consistent with theoretical expectations, for error-free PV maps, the PVC method extracted the true GM CBF. In contrast, GM-weighted overestimated GM CBF by 5%, while GM-threshold underestimated it by 16%. The presence of PV map errors decreased the calculated GM CBF for all methods. CONCLUSION The quality of PV maps presents no argument for the preferential use of the GM-threshold method over PVC in the clinical application of ASL.
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Affiliation(s)
- Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany.
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA.
| | - Henri J M M Mutsaerts
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA
- Sunnybrook Research Institute, Toronto, Canada
- Department of Radiology, Academic Medical Center Amsterdam, Amsterdam, The Netherlands
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, Kings Health Partners, St Thomas Hospital, London, UK
| | - Rebecca M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Aart J Nederveen
- Department of Radiology, Academic Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Frank Hofheinz
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Jörg van den Hoff
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
- Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Iris Asllani
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA
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35
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Melbourne A, Toussaint N, Owen D, Simpson I, Anthopoulos T, De Vita E, Atkinson D, Ourselin S. NiftyFit: a Software Package for Multi-parametric Model-Fitting of 4D Magnetic Resonance Imaging Data. Neuroinformatics 2018; 14:319-37. [PMID: 26972806 PMCID: PMC4896995 DOI: 10.1007/s12021-016-9297-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Multi-modal, multi-parametric Magnetic Resonance (MR) Imaging is becoming an increasingly sophisticated tool for neuroimaging. The relationships between parameters estimated from different individual MR modalities have the potential to transform our understanding of brain function, structure, development and disease. This article describes a new software package for such multi-contrast Magnetic Resonance Imaging that provides a unified model-fitting framework. We describe model-fitting functionality for Arterial Spin Labeled MRI, T1 Relaxometry, T2 relaxometry and Diffusion Weighted imaging, providing command line documentation to generate the figures in the manuscript. Software and data (using the nifti file format) used in this article are simultaneously provided for download. We also present some extended applications of the joint model fitting framework applied to diffusion weighted imaging and T2 relaxometry, in order to both improve parameter estimation in these models and generate new parameters that link different MR modalities. NiftyFit is intended as a clear and open-source educational release so that the user may adapt and develop their own functionality as they require.
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Affiliation(s)
- Andrew Melbourne
- Centre for Medical Image Computing, University College London, London, UK.
| | - Nicolas Toussaint
- Centre for Medical Image Computing, University College London, London, UK
| | - David Owen
- Centre for Medical Image Computing, University College London, London, UK
| | - Ivor Simpson
- Centre for Medical Image Computing, University College London, London, UK
| | | | - Enrico De Vita
- Academic Neuroradiological Unit, UCL Institute of Neurology, London, UK
| | - David Atkinson
- Medical Physics, University College Hospital, London, UK
| | - Sebastien Ourselin
- Centre for Medical Image Computing, University College London, London, UK
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Akram H, Dayal V, Mahlknecht P, Georgiev D, Hyam J, Foltynie T, Limousin P, De Vita E, Jahanshahi M, Ashburner J, Behrens T, Hariz M, Zrinzo L. Connectivity derived thalamic segmentation in deep brain stimulation for tremor. Neuroimage Clin 2018; 18:130-142. [PMID: 29387530 PMCID: PMC5790021 DOI: 10.1016/j.nicl.2018.01.008] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 12/23/2017] [Accepted: 01/13/2018] [Indexed: 02/02/2023]
Abstract
The ventral intermediate nucleus (VIM) of the thalamus is an established surgical target for stereotactic ablation and deep brain stimulation (DBS) in the treatment of tremor in Parkinson's disease (PD) and essential tremor (ET). It is centrally placed on a cerebello-thalamo-cortical network connecting the primary motor cortex, to the dentate nucleus of the contralateral cerebellum through the dentato-rubro-thalamic tract (DRT). The VIM is not readily visible on conventional MR imaging, so identifying the surgical target traditionally involved indirect targeting that relies on atlas-defined coordinates. Unfortunately, this approach does not fully account for individual variability and requires surgery to be performed with the patient awake to allow for intraoperative targeting confirmation. The aim of this study is to identify the VIM and the DRT using probabilistic tractography in patients that will undergo thalamic DBS for tremor. Four male patients with tremor dominant PD and five patients (three female) with ET underwent high angular resolution diffusion imaging (HARDI) (128 diffusion directions, 1.5 mm isotropic voxels and b value = 1500) preoperatively. Patients received VIM-DBS using an MR image guided and MR image verified approach with indirect targeting. Postoperatively, using parallel Graphical Processing Unit (GPU) processing, thalamic areas with the highest diffusion connectivity to the primary motor area (M1), supplementary motor area (SMA), primary sensory area (S1) and contralateral dentate nucleus were identified. Additionally, volume of tissue activation (VTA) corresponding to active DBS contacts were modelled. Response to treatment was defined as 40% reduction in the total Fahn-Tolosa-Martin Tremor Rating Score (FTMTRS) with DBS-ON, one year from surgery. Three out of nine patients had a suboptimal, long-term response to treatment. The segmented thalamic areas corresponded well to anatomically known counterparts in the ventrolateral (VL) and ventroposterior (VP) thalamus. The dentate-thalamic area, lay within the M1-thalamic area in a ventral and lateral location. Streamlines corresponding to the DRT connected M1 to the contralateral dentate nucleus via the dentate-thalamic area, clearly crossing the midline in the mesencephalon. Good response was seen when the active contact VTA was in the thalamic area with highest connectivity to the contralateral dentate nucleus. Non-responders had active contact VTAs outside the dentate-thalamic area. We conclude that probabilistic tractography techniques can be used to segment the VL and VP thalamus based on cortical and cerebellar connectivity. The thalamic area, best representing the VIM, is connected to the contralateral dentate cerebellar nucleus. Connectivity based segmentation of the VIM can be achieved in individual patients in a clinically feasible timescale, using HARDI and high performance computing with parallel GPU processing. This same technique can map out the DRT tract with clear mesencephalic crossing.
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Key Words
- AC, anterior commissure
- BEDPOSTX, Bayesian estimation of diffusion parameters obtained using sampling techniques X
- BET, brain extraction tool
- CI, confidence interval
- CON, connectivity
- Connectivity
- DBS
- DBS, deep brain stimulation
- DF, degrees of freedom
- DICOM, digital imaging and communications in medicine
- DRT
- DWI
- DWI, diffusion weighted imaging
- Deep brain stimulation
- Dentate nucleus
- Dentato-rubro-thalamic tract
- Diffusion weighted imaging
- EV, explanatory variable
- FLIRT, FMRIB's linear image registration tool
- FMRIB, Oxford centre for functional MRI of the brain
- FNIRT, FMRIB's non-linear image registration tool
- FSL, FMRIB's software library
- FoV, field of view
- GLM, general linear model
- HARDI, high angular resolution diffusion imaging
- HFS, high frequency stimulation
- IPG, implantable pulse generator
- LC, Levodopa challenge
- LEDD, l-DOPA equivalent daily dose
- M1, primary motor cortex
- MMS, mini-mental score
- MNI, Montreal neurological institute
- MPRAGE, magnetization-prepared rapid gradient-echo
- MPTP, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine
- NHNN, National Hospital for Neurology and Neurosurgery
- NIfTI, neuroimaging informatics technology initiative
- PC, posterior commissure
- PD
- PFC, prefrontal cortex
- PMC, premotor cortex
- Parkinson's disease
- S1, primary sensory cortex
- SAR, specific absorption rate
- SD, standard deviation
- SE, standard error
- SMA, supplementary motor area
- SNR, signal-to-noise ratio
- SSEPI, single-shot echo planar imaging
- STN, subthalamic nucleus
- TFCE, threshold-free cluster enhancement
- TMS, transcranial magnetic stimulation
- Tremor
- UPDRS, unified Parkinson's disease rating scale
- VBM, voxel based morphometry
- VIM
- VL
- VL, ventral lateral
- VP, ventral posterior
- VTA, volume of tissue activated
- Ventrointermedialis
- Ventrolateral nucleus
- cZI, caudal zona incerta
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Affiliation(s)
- Harith Akram
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK.
| | - Viswas Dayal
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Philipp Mahlknecht
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Dejan Georgiev
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Jonathan Hyam
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
| | - Thomas Foltynie
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Patricia Limousin
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Enrico De Vita
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Marjan Jahanshahi
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Tim Behrens
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Marwan Hariz
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Department of Clinical Neuroscience, Umeå University, Umeå, Sweden
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
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Lawlor M, Danesh-Meyer H, Levin LA, Davagnanam I, De Vita E, Plant GT. Glaucoma and the brain: Trans-synaptic degeneration, structural change, and implications for neuroprotection. Surv Ophthalmol 2017; 63:296-306. [PMID: 28986311 DOI: 10.1016/j.survophthal.2017.09.010] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 09/11/2017] [Accepted: 09/22/2017] [Indexed: 01/20/2023]
Abstract
A recent hypothesis to enter the literature suggests that glaucoma is a neurodegenerative disease. The basis for this has been the finding of central nervous system changes in glaucoma patients on histology and neuroimaging. It is known that retinal ganglion cell pathology of any cause leads to anterograde and retrograde retinal ganglion cell degeneration, as well as trans-synaptic (transneuronal) anterograde degeneration. Trans-synaptic degeneration has been demonstrated in a range of optic neuropathies including optic nerve transection, optic neuritis, and hereditary optic neuropathies. More recently, similar changes have been confirmed in glaucoma patients using the neuroimaging techniques of voxel-based morphometry and diffusion tensor imaging. Some studies have reported brain changes in glaucoma outside the retino-geniculo-cortical pathway; however, these are preliminary and exploratory in nature. Further research is required to identify whether the degenerative brain changes in glaucoma are entirely secondary to the optic neuropathy or whether there is additional primary central nervous system pathology. This has critical implications for neuroprotective and regenerative treatment strategies and our basic understanding of glaucoma.
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Affiliation(s)
- Mitchell Lawlor
- Save Sight Institute, Discipline of Clinical Ophthalmology and Eye Health, University of Sydney, Sydney, New South Wales, Australia; Department of Neuro-Ophthalmology, Moorfields Eye Hospital, London, United Kingdom.
| | - Helen Danesh-Meyer
- Department of Ophthalmology, University of Auckland, Auckland, New Zealand; University of Melbourne, Parkville, Victoria, Australia
| | - Leonard A Levin
- Departments of Ophthalmology and Neurology & Neurosurgery, McGill University, Montreal, Quebec, Canada; Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin, USA
| | - Indran Davagnanam
- Department of Neuro-Ophthalmology, Moorfields Eye Hospital, London, United Kingdom; Academic Neuroradiological Unit, Department of Brain Repair & Rehabilitation, UCL Institute of Neurology, London, United Kingdom; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCL Hospitals Foundation Trust, London, United Kingdom
| | - Enrico De Vita
- Academic Neuroradiological Unit, Department of Brain Repair & Rehabilitation, UCL Institute of Neurology, London, United Kingdom; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCL Hospitals Foundation Trust, London, United Kingdom; Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Gordon T Plant
- Department of Neuro-Ophthalmology, Moorfields Eye Hospital, London, United Kingdom; Department of Neuro-Ophthalmology, National Hospital for Neurology and Neurosurgery, London, United Kingdom; The Medical Eye Unit, St Thomas' Hospital, London, United Kingdom
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Akram H, Sotiropoulos SN, Jbabdi S, Georgiev D, Mahlknecht P, Hyam J, Foltynie T, Limousin P, De Vita E, Jahanshahi M, Hariz M, Ashburner J, Behrens T, Zrinzo L. Subthalamic deep brain stimulation sweet spots and hyperdirect cortical connectivity in Parkinson's disease. Neuroimage 2017; 158:332-345. [PMID: 28711737 DOI: 10.1016/j.neuroimage.2017.07.012] [Citation(s) in RCA: 148] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Revised: 07/05/2017] [Accepted: 07/09/2017] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVES Firstly, to identify subthalamic region stimulation clusters that predict maximum improvement in rigidity, bradykinesia and tremor, or emergence of side-effects; and secondly, to map-out the cortical fingerprint, mediated by the hyperdirect pathways which predict maximum efficacy. METHODS High angular resolution diffusion imaging in twenty patients with advanced Parkinson's disease was acquired prior to bilateral subthalamic nucleus deep brain stimulation. All contacts were screened one-year from surgery for efficacy and side-effects at different amplitudes. Voxel-based statistical analysis of volumes of tissue activated models was used to identify significant treatment clusters. Probabilistic tractography was employed to identify cortical connectivity patterns associated with treatment efficacy. RESULTS All patients responded well to treatment (46% mean improvement off medication UPDRS-III [p < 0.0001]) without significant adverse events. Cluster corresponding to maximum improvement in tremor was in the posterior, superior and lateral portion of the nucleus. Clusters corresponding to improvement in bradykinesia and rigidity were nearer the superior border in a further medial and posterior location. The rigidity cluster extended beyond the superior border to the area of the zona incerta and Forel-H2 field. When the clusters where averaged, the coordinates of the area with maximum overall efficacy was X = -10(-9.5), Y = -13(-1) and Z = -7(-3) in MNI(AC-PC) space. Cortical connectivity to primary motor area was predictive of higher improvement in tremor; whilst that to supplementary motor area was predictive of improvement in bradykinesia and rigidity; and connectivity to prefrontal cortex was predictive of improvement in rigidity. INTERPRETATION These findings support the presence of overlapping stimulation sites within the subthalamic nucleus and its superior border, with different cortical connectivity patterns, associated with maximum improvement in tremor, rigidity and bradykinesia.
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Affiliation(s)
- Harith Akram
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3BG, UK.
| | - Stamatios N Sotiropoulos
- Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, Oxford, OX3 9DU, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
| | - Saad Jbabdi
- Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Dejan Georgiev
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Philipp Mahlknecht
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Jonathan Hyam
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3BG, UK
| | - Thomas Foltynie
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Patricia Limousin
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Enrico De Vita
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Marjan Jahanshahi
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Marwan Hariz
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK; Department of Clinical Neuroscience, Umeå University, Umeå, Sweden
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Tim Behrens
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK; Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3BG, UK
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Akram H, Wu C, Hyam J, Foltynie T, Limousin P, De Vita E, Yousry T, Jahanshahi M, Hariz M, Behrens T, Ashburner J, Zrinzo L. l-Dopa responsiveness is associated with distinctive connectivity patterns in advanced Parkinson's disease. Mov Disord 2017; 32:874-883. [PMID: 28597560 DOI: 10.1002/mds.27017] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 02/27/2017] [Accepted: 03/03/2017] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Neuronal loss and dopamine depletion alter motor signal processing between cortical motor areas, basal ganglia, and the thalamus, resulting in the motor manifestations of Parkinson's disease. Dopamine replacement therapy can reverse these manifestations with varying degrees of improvement. METHODS To evaluate functional connectivity in patients with advanced Parkinson's disease and changes in functional connectivity in relation to the degree of response to l-dopa, 19 patients with advanced Parkinson's disease underwent resting-state functional magnetic resonance imaging in the on-medication state. Scans were obtained on a 3-Tesla scanner in 3 × 3 × 2.5 mm3 voxels. Seed-based bivariate regression analyses were carried out with atlas-defined basal ganglia regions as seeds, to explore relationships between functional connectivity and improvement in the motor section of the UPDRS-III following an l-dopa challenge. False discovery rate-corrected P was set at < 0.05 for a 2-tailed t test. RESULTS A greater improvement in UPDRS-III scores following l-dopa administration was characterized by higher resting-state functional connectivity between the prefrontal cortex and the striatum (P = 0.001) and lower resting-state functional connectivity between the pallidum (P = 0.001), subthalamic nucleus (P = 0.003), and the paracentral lobule (supplementary motor area, mesial primary motor, and primary sensory areas). CONCLUSIONS Our findings show characteristic basal ganglia resting-state functional connectivity patterns associated with different degrees of l-dopa responsiveness in patients with advanced Parkinson's disease. l-Dopa exerts a graduated influence on remapping connectivity in distinct motor control networks, potentially explaining some of the variance in treatment response. © 2017 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Harith Akram
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK.,Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Chengyuan Wu
- Department of Neurosurgery, Jefferson University Hospitals, Philadelphia, Pennsylvania, USA
| | - Jonathan Hyam
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK.,Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Thomas Foltynie
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Patricia Limousin
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Enrico De Vita
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Tarek Yousry
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Marjan Jahanshahi
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Marwan Hariz
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK.,Department of Clinical Neuroscience, Umeå University, Umeå, Sweden
| | - Timothy Behrens
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK.,Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, Oxford, UK
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK.,Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
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Lane CA, Parker TD, Cash DM, Macpherson K, Donnachie E, Murray-Smith H, Barnes A, Barker S, Beasley DG, Bras J, Brown D, Burgos N, Byford M, Jorge Cardoso M, Carvalho A, Collins J, De Vita E, Dickson JC, Epie N, Espak M, Henley SMD, Hoskote C, Hutel M, Klimova J, Malone IB, Markiewicz P, Melbourne A, Modat M, Schrag A, Shah S, Sharma N, Sudre CH, Thomas DL, Wong A, Zhang H, Hardy J, Zetterberg H, Ourselin S, Crutch SJ, Kuh D, Richards M, Fox NC, Schott JM. Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development. BMC Neurol 2017; 17:75. [PMID: 28420323 PMCID: PMC5395844 DOI: 10.1186/s12883-017-0846-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 03/21/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including β-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. METHODS/DESIGN This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, β-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). DISCUSSION Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.
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Affiliation(s)
- Christopher A. Lane
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Thomas D. Parker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Dave M. Cash
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Kirsty Macpherson
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Elizabeth Donnachie
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Suzie Barker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Daniel G. Beasley
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jose Bras
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Medical Sciences and Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - David Brown
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Ninon Burgos
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - M. Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ana Carvalho
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Jessica Collins
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - John C. Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Norah Epie
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Miklos Espak
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Susie M. D. Henley
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Chandrashekar Hoskote
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Michael Hutel
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jana Klimova
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Pawel Markiewicz
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Andrew Melbourne
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Anette Schrag
- Department of Clinical Neuroscience, Institute of Neurology, University College London, London, UK
| | - Sachit Shah
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Nikhil Sharma
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - David L. Thomas
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK
| | - John Hardy
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
| | - Henrik Zetterberg
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Sebastian J. Crutch
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | | | - Nick C. Fox
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jonathan M. Schott
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
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De Vita E, Ridgway GR, White MJ, Porter MC, Caine D, Rudge P, Collinge J, Yousry TA, Jager HR, Mead S, Thornton JS, Hyare H. Neuroanatomical correlates of prion disease progression - a 3T longitudinal voxel-based morphometry study. Neuroimage Clin 2016; 13:89-96. [PMID: 27942451 PMCID: PMC5133666 DOI: 10.1016/j.nicl.2016.10.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 09/19/2016] [Accepted: 10/28/2016] [Indexed: 11/18/2022]
Abstract
PURPOSE MRI has become an essential tool for prion disease diagnosis. However there exist only a few serial MRI studies of prion patients, and these mostly used whole brain summary measures or region of interest based approaches. We present here the first longitudinal voxel-based morphometry (VBM) study in prion disease. The aim of this study was to systematically characterise progressive atrophy in patients with prion disease and identify whether atrophy in specific brain structures correlates with clinical assessment. METHODS Twenty-four prion disease patients with early stage disease (3 sporadic, 2 iatrogenic, 1 variant and 18 inherited CJD) and 25 controls were examined at 3T with a T1-weighted 3D MPRAGE sequence at multiple time-points (2-6 examinations per subject, interval range 0.1-3.2 years). Longitudinal VBM provided intra-subject and inter-subject image alignment, allowing voxel-wise comparison of progressive structural change. Clinical disease progression was assessed using the MRC Prion Disease Rating Scale. Firstly, in patients, we determined the brain regions where grey and white matter volume change between baseline and final examination correlated with the corresponding change in MRC Scale score. Secondly, in the 21/24 patients with interscan interval longer than 3 months, we identified regions where annualised rates of regional volume change in patients were different from rates in age-matched controls. Given the heterogeneity of the cohort, the regions identified reflect the common features of the different prion sub-types studied. RESULTS In the patients there were multiple regions where volume loss significantly correlated with decreased MRC scale, partially overlapping with anatomical regions where yearly rates of volume loss were significantly greater than controls. The key anatomical areas involved included: the basal ganglia and thalamus, pons and medulla, the hippocampal formation and the superior parietal lobules. There were no areas demonstrating volume loss significantly higher in controls than patients or negative correlation between volume and MRC Scale score. CONCLUSIONS Using 3T MRI and longitudinal VBM we have identified key anatomical regions of progressive volume loss which correlate with an established clinical disease severity index and are relevant to clinical deterioration. Localisation of the regions of progressive brain atrophy correlating most strongly with clinical decline may help to provide more targeted imaging endpoints for future clinical trials.
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Affiliation(s)
- Enrico De Vita
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 65, Queen Square, London WC1N 3BG, United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Gerard R Ridgway
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, United Kingdom
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom
| | - Mark J White
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 65, Queen Square, London WC1N 3BG, United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Marie-Claire Porter
- National Prion Clinic, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 98, Queen Square, London WC1N 3BG, United Kingdom
- MRC Prion Unit, Department of Neurodegenerative Diseases, UCL Institute of Neurology, Queen Square House, Queen Square, London WC1N 3BG, United Kingdom
| | - Diana Caine
- National Prion Clinic, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 98, Queen Square, London WC1N 3BG, United Kingdom
- MRC Prion Unit, Department of Neurodegenerative Diseases, UCL Institute of Neurology, Queen Square House, Queen Square, London WC1N 3BG, United Kingdom
| | - Peter Rudge
- National Prion Clinic, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 98, Queen Square, London WC1N 3BG, United Kingdom
- MRC Prion Unit, Department of Neurodegenerative Diseases, UCL Institute of Neurology, Queen Square House, Queen Square, London WC1N 3BG, United Kingdom
| | - John Collinge
- National Prion Clinic, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 98, Queen Square, London WC1N 3BG, United Kingdom
- MRC Prion Unit, Department of Neurodegenerative Diseases, UCL Institute of Neurology, Queen Square House, Queen Square, London WC1N 3BG, United Kingdom
| | - Tarek A Yousry
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 65, Queen Square, London WC1N 3BG, United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Hans Rolf Jager
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 65, Queen Square, London WC1N 3BG, United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Simon Mead
- National Prion Clinic, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 98, Queen Square, London WC1N 3BG, United Kingdom
- MRC Prion Unit, Department of Neurodegenerative Diseases, UCL Institute of Neurology, Queen Square House, Queen Square, London WC1N 3BG, United Kingdom
| | - John S Thornton
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 65, Queen Square, London WC1N 3BG, United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Harpreet Hyare
- National Prion Clinic, National Hospital for Neurology and Neurosurgery, UCLH Hospitals NHS Foundation Trust, Box 98, Queen Square, London WC1N 3BG, United Kingdom
- MRC Prion Unit, Department of Neurodegenerative Diseases, UCL Institute of Neurology, Queen Square House, Queen Square, London WC1N 3BG, United Kingdom
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Lehmann M, Melbourne A, Dickson JC, Ahmed RM, Modat M, Cardoso MJ, Thomas DL, De Vita E, Crutch SJ, Warren JD, Mahoney CJ, Bomanji J, Hutton BF, Fox NC, Golay X, Ourselin S, Schott JM. A novel use of arterial spin labelling MRI to demonstrate focal hypoperfusion in individuals with posterior cortical atrophy: a multimodal imaging study. J Neurol Neurosurg Psychiatry 2016; 87:1032-4. [PMID: 26733599 PMCID: PMC5013120 DOI: 10.1136/jnnp-2015-312782] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 11/27/2015] [Indexed: 11/20/2022]
Affiliation(s)
- Manja Lehmann
- Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Andrew Melbourne
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - John C Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Rebekah M Ahmed
- Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Marc Modat
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - M Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - David L Thomas
- Dementia Research Centre, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
- Neuroradiological Academic Unit, Brain Repair & Rehabilitation, UCL Institute of Neurology, London, UK
| | | | - Jason D Warren
- Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Colin J Mahoney
- Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Jamshed Bomanji
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Xavier Golay
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
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Magerkurth J, Mancini L, Penny W, Flandin G, Ashburner J, Micallef C, De Vita E, Daga P, White MJ, Buckley C, Yamamoto AK, Ourselin S, Yousry T, Thornton JS, Weiskopf N. Objective Bayesian fMRI analysis-a pilot study in different clinical environments. Front Neurosci 2015; 9:168. [PMID: 26029041 PMCID: PMC4428130 DOI: 10.3389/fnins.2015.00168] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 04/26/2015] [Indexed: 11/13/2022] Open
Abstract
Functional MRI (fMRI) used for neurosurgical planning delineates functionally eloquent brain areas by time-series analysis of task-induced BOLD signal changes. Commonly used frequentist statistics protect against false positive results based on a p-value threshold. In surgical planning, false negative results are equally if not more harmful, potentially masking true brain activity leading to erroneous resection of eloquent regions. Bayesian statistics provides an alternative framework, categorizing areas as activated, deactivated, non-activated or with low statistical confidence. This approach has not yet found wide clinical application partly due to the lack of a method to objectively define an effect size threshold. We implemented a Bayesian analysis framework for neurosurgical planning fMRI. It entails an automated effect-size threshold selection method for posterior probability maps accounting for inter-individual BOLD response differences, which was calibrated based on the frequentist results maps thresholded by two clinical experts. We compared Bayesian and frequentist analysis of passive-motor fMRI data from 10 healthy volunteers measured on a pre-operative 3T and an intra-operative 1.5T MRI scanner. As a clinical case study, we tested passive motor task activation in a brain tumor patient at 3T under clinical conditions. With our novel effect size threshold method, the Bayesian analysis revealed regions of all four categories in the 3T data. Activated region foci and extent were consistent with the frequentist analysis results. In the lower signal-to-noise ratio 1.5T intra-operative scanner data, Bayesian analysis provided improved brain-activation detection sensitivity compared with the frequentist analysis, albeit the spatial extents of the activations were smaller than at 3T. Bayesian analysis of fMRI data using operator-independent effect size threshold selection may improve the sensitivity and certainty of information available to guide neurosurgery.
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Affiliation(s)
- Joerg Magerkurth
- Department for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK ; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK
| | - Laura Mancini
- Department for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK
| | - William Penny
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK
| | - Guillaume Flandin
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK
| | - Caroline Micallef
- Department for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK
| | - Enrico De Vita
- Department for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK
| | - Pankaj Daga
- Centre for Medical Image Computing, University College London London, UK
| | - Mark J White
- Department for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK
| | | | - Adam K Yamamoto
- Department for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK
| | - Sebastien Ourselin
- Centre for Medical Image Computing, University College London London, UK
| | - Tarek Yousry
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK
| | - John S Thornton
- Department for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK
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Caine D, Tinelli RJ, Hyare H, De Vita E, Lowe J, Lukic A, Thompson A, Porter MC, Cipolotti L, Rudge P, Collinge J, Mead S. The cognitive profile of prion disease: a prospective clinical and imaging study. Ann Clin Transl Neurol 2015; 2:548-58. [PMID: 26000326 PMCID: PMC4435708 DOI: 10.1002/acn3.195] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 02/13/2015] [Indexed: 11/18/2022] Open
Abstract
Objectives Prion diseases are dementing illnesses with poorly defined neuropsychological features. This is probably because the most common form, sporadic Creutzfeldt-Jakob disease, is often rapidly progressive with pervasive cognitive decline making detailed neuropsychological investigation difficult. This study, which includes patients with inherited, acquired (iatrogenic and variant) and sporadic forms of the disease, is the only large-scale neuropsychological investigation of this patient group ever undertaken and aimed to define a neuropsychological profile of human prion diseases. Methods A tailored short cognitive examination of all of the patients (n = 81), with detailed neuropsychological testing in a subset with mild disease (n = 30) and correlation with demographic, clinical, genetic (PRNP mutation and polymorphic codon 129 genotype), and other variables (MRI brain signal change in cortex, basal ganglia or thalamus; quantitative research imaging, cerebrospinal fluid 14-3-3 protein). Results Comparison with healthy controls showed patients to be impaired on all tasks. Principal components analysis showed a major axis of fronto-parietal dysfunction that accounted for approximately half of the variance observed. This correlated strongly with volume reduction in frontal and parietal gray matter on MRI. Examination of individual patients' performances confirmed early impairment on this axis, suggesting characteristic cognitive features in mild disease: prominent executive impairment, parietal dysfunction, a largely expressive dysphasia, with reduced motor speed. Interpretation Taken together with typical neurological features, these results complete a profile that should improve differential diagnosis in a clinical setting. We propose a tailored neuropsychological battery for early recognition of clinical onset of symptoms with potential for use in clinical trials involving at-risk individuals.
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Affiliation(s)
- Diana Caine
- NHS National Prion Clinic, National Hospital for Neurology and Neurosurgery (NHNN), University College London Hospitals NHS Foundation Trust London, United Kingdom ; Department of Neuropsychology, NHNN, University College London Hospitals NHS Foundation Trust London, United Kingdom ; Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
| | - Renata J Tinelli
- Department of Neuropsychology, NHNN, University College London Hospitals NHS Foundation Trust London, United Kingdom
| | - Harpreet Hyare
- NHS National Prion Clinic, National Hospital for Neurology and Neurosurgery (NHNN), University College London Hospitals NHS Foundation Trust London, United Kingdom ; Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
| | - Enrico De Vita
- Academic Neuroradiological Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology London, United Kingdom ; Lysholm Department of Neuroradiology, NHNN, University College London Hospitals NHS Foundation Trust London, United Kingdom
| | - Jessica Lowe
- Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
| | - Ana Lukic
- NHS National Prion Clinic, National Hospital for Neurology and Neurosurgery (NHNN), University College London Hospitals NHS Foundation Trust London, United Kingdom ; Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
| | - Andrew Thompson
- NHS National Prion Clinic, National Hospital for Neurology and Neurosurgery (NHNN), University College London Hospitals NHS Foundation Trust London, United Kingdom ; Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
| | - Marie-Claire Porter
- NHS National Prion Clinic, National Hospital for Neurology and Neurosurgery (NHNN), University College London Hospitals NHS Foundation Trust London, United Kingdom ; Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
| | - Lisa Cipolotti
- Department of Neuropsychology, NHNN, University College London Hospitals NHS Foundation Trust London, United Kingdom
| | - Peter Rudge
- NHS National Prion Clinic, National Hospital for Neurology and Neurosurgery (NHNN), University College London Hospitals NHS Foundation Trust London, United Kingdom ; MRC Prion Unit, Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
| | - John Collinge
- NHS National Prion Clinic, National Hospital for Neurology and Neurosurgery (NHNN), University College London Hospitals NHS Foundation Trust London, United Kingdom ; MRC Prion Unit, Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
| | - Simon Mead
- NHS National Prion Clinic, National Hospital for Neurology and Neurosurgery (NHNN), University College London Hospitals NHS Foundation Trust London, United Kingdom ; MRC Prion Unit, Department of Neurodegenerative Disease, UCL Institute of Neurology London, United Kingdom
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45
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Rohrer JD, Nicholas JM, Cash DM, van Swieten J, Dopper E, Jiskoot L, van Minkelen R, Rombouts SA, Cardoso MJ, Clegg S, Espak M, Mead S, Thomas DL, De Vita E, Masellis M, Black SE, Freedman M, Keren R, MacIntosh BJ, Rogaeva E, Tang-Wai D, Tartaglia MC, Laforce R, Tagliavini F, Tiraboschi P, Redaelli V, Prioni S, Grisoli M, Borroni B, Padovani A, Galimberti D, Scarpini E, Arighi A, Fumagalli G, Rowe JB, Coyle-Gilchrist I, Graff C, Fallström M, Jelic V, Ståhlbom AK, Andersson C, Thonberg H, Lilius L, Frisoni GB, Pievani M, Bocchetta M, Benussi L, Ghidoni R, Finger E, Sorbi S, Nacmias B, Lombardi G, Polito C, Warren JD, Ourselin S, Fox NC, Rossor MN, Binetti G. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis. Lancet Neurol 2015; 14:253-62. [PMID: 25662776 PMCID: PMC6742501 DOI: 10.1016/s1474-4422(14)70324-2] [Citation(s) in RCA: 371] [Impact Index Per Article: 41.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Frontotemporal dementia is a highly heritable neurodegenerative disorder. In about a third of patients, the disease is caused by autosomal dominant genetic mutations usually in one of three genes: progranulin (GRN), microtubule-associated protein tau (MAPT), or chromosome 9 open reading frame 72 (C9orf72). Findings from studies of other genetic dementias have shown neuroimaging and cognitive changes before symptoms onset, and we aimed to identify whether such changes could be shown in frontotemporal dementia. METHODS We recruited participants to this multicentre study who either were known carriers of a pathogenic mutation in GRN, MAPT, or C9orf72, or were at risk of carrying a mutation because a first-degree relative was a known symptomatic carrier. We calculated time to expected onset as the difference between age at assessment and mean age at onset within the family. Participants underwent a standardised clinical assessment and neuropsychological battery. We did MRI and generated cortical and subcortical volumes using a parcellation of the volumetric T1-weighted scan. We used linear mixed-effects models to examine whether the association of neuropsychology and imaging measures with time to expected onset of symptoms differed between mutation carriers and non-carriers. FINDINGS Between Jan 30, 2012, and Sept 15, 2013, we recruited participants from 11 research sites in the UK, Italy, the Netherlands, Sweden, and Canada. We analysed data from 220 participants: 118 mutation carriers (40 symptomatic and 78 asymptomatic) and 102 non-carriers. For neuropsychology measures, we noted the earliest significant differences between mutation carriers and non-carriers 5 years before expected onset, when differences were significant for all measures except for tests of immediate recall and verbal fluency. We noted the largest Z score differences between carriers and non-carriers 5 years before expected onset in tests of naming (Boston Naming Test -0·7; SE 0·3) and executive function (Trail Making Test Part B, Digit Span backwards, and Digit Symbol Task, all -0·5, SE 0·2). For imaging measures, we noted differences earliest for the insula (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume was 0·80% in mutation carriers and 0·84% in non-carriers; difference -0·04, SE 0·02) followed by the temporal lobe (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume 8·1% in mutation carriers and 8·3% in non-carriers; difference -0·2, SE 0·1). INTERPRETATION Structural imaging and cognitive changes can be identified 5-10 years before expected onset of symptoms in asymptomatic adults at risk of genetic frontotemporal dementia. These findings could help to define biomarkers that can stage presymptomatic disease and track disease progression, which will be important for future therapeutic trials. FUNDING Centres of Excellence in Neurodegeneration.
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Affiliation(s)
| | - Jennifer M Nicholas
- Dementia Research Centre, University College London, London, UK; Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - David M Cash
- Dementia Research Centre, University College London, London, UK; Department of Neurodegenerative Disease, University College London Institute of Neurology, and Centre for Medical Image Computing, University College London, London, UK
| | - John van Swieten
- Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Elise Dopper
- Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Lize Jiskoot
- Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Rick van Minkelen
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Serge A Rombouts
- Institute of Psychology, Leiden University, and Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - M Jorge Cardoso
- Dementia Research Centre, University College London, London, UK; Department of Neurodegenerative Disease, University College London Institute of Neurology, and Centre for Medical Image Computing, University College London, London, UK
| | - Shona Clegg
- Dementia Research Centre, University College London, London, UK
| | - Miklos Espak
- Dementia Research Centre, University College London, London, UK; Department of Neurodegenerative Disease, University College London Institute of Neurology, and Centre for Medical Image Computing, University College London, London, UK
| | - Simon Mead
- Medical Research Council Prion Unit, University College London, London, UK
| | - David L Thomas
- Dementia Research Centre, University College London, London, UK; Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, University College London, London, UK
| | - Enrico De Vita
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, University College London, London, UK; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Mario Masellis
- LC Campbell Cognitive Neurology Research Unit, Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- LC Campbell Cognitive Neurology Research Unit, Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Morris Freedman
- Department of Medicine, Division of Neurology, Baycrest, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada; Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Ron Keren
- University Health Network Memory Clinic, Toronto Western Hospital, Toronto, ON, Canada
| | - Bradley J MacIntosh
- LC Campbell Cognitive Neurology Research Unit, Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Ekaterina Rogaeva
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - David Tang-Wai
- University Health Network Memory Clinic, Toronto Western Hospital, Toronto, ON, Canada
| | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, Hôpital de l'Enfant-Jésus, and Faculté de Médecine, Université Laval, QC, Canada
| | - Fabrizio Tagliavini
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
| | - Pietro Tiraboschi
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
| | - Veronica Redaelli
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
| | - Sara Prioni
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
| | - Marina Grisoli
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Medical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Medical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Daniela Galimberti
- Neurology Unit, Department of Physiopathology and Transplantation, University of Milan, Fondazione Cà Granda, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico, Milan, Italy
| | - Elio Scarpini
- Neurology Unit, Department of Physiopathology and Transplantation, University of Milan, Fondazione Cà Granda, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico, Milan, Italy
| | - Andrea Arighi
- Neurology Unit, Department of Physiopathology and Transplantation, University of Milan, Fondazione Cà Granda, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico, Milan, Italy
| | - Giorgio Fumagalli
- Neurology Unit, Department of Physiopathology and Transplantation, University of Milan, Fondazione Cà Granda, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico, Milan, Italy
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ian Coyle-Gilchrist
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Caroline Graff
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Huddinge, Sweden; Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Marie Fallström
- Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Vesna Jelic
- Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden; Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Anne Kinhult Ståhlbom
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Huddinge, Sweden; Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Christin Andersson
- Department of Clinical Neuroscience, Karolinska Institutet, Huddinge, Sweden; Department of Psychology, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Håkan Thonberg
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Huddinge, Sweden; Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Lena Lilius
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Huddinge, Sweden
| | - Giovanni B Frisoni
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Michela Pievani
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Martina Bocchetta
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy; Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Luisa Benussi
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Roberta Ghidoni
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Gemma Lombardi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Cristina Polito
- Department of Clinical Pathophysiology, Nuclear Medicine Division, University of Florence, Florence, Italy
| | - Jason D Warren
- Dementia Research Centre, University College London, London, UK
| | - Sebastien Ourselin
- Dementia Research Centre, University College London, London, UK; Department of Neurodegenerative Disease, University College London Institute of Neurology, and Centre for Medical Image Computing, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, University College London, London, UK
| | - Martin N Rossor
- Dementia Research Centre, University College London, London, UK.
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Vidorreta M, Balteau E, Wang Z, De Vita E, Pastor MA, Thomas DL, Detre JA, Fernández-Seara MA. Evaluation of segmented 3D acquisition schemes for whole-brain high-resolution arterial spin labeling at 3 T. NMR Biomed 2014; 27:1387-96. [PMID: 25263944 PMCID: PMC4233410 DOI: 10.1002/nbm.3201] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Revised: 07/18/2014] [Accepted: 08/17/2014] [Indexed: 05/23/2023]
Abstract
Recent technical developments have significantly increased the signal-to-noise ratio (SNR) of arterial spin labeled (ASL) perfusion MRI. Despite this, typical ASL acquisitions still employ large voxel sizes. The purpose of this work was to implement and evaluate two ASL sequences optimized for whole-brain high-resolution perfusion imaging, combining pseudo-continuous ASL (pCASL), background suppression (BS) and 3D segmented readouts, with different in-plane k-space trajectories. Identical labeling and BS pulses were implemented for both sequences. Two segmented 3D readout schemes with different in-plane trajectories were compared: Cartesian (3D GRASE) and spiral (3D RARE Stack-Of-Spirals). High-resolution perfusion images (2 × 2 × 4 mm(3) ) were acquired in 15 young healthy volunteers with the two ASL sequences at 3 T. The quality of the perfusion maps was evaluated in terms of SNR and gray-to-white matter contrast. Point-spread-function simulations were carried out to assess the impact of readout differences on the effective resolution. The combination of pCASL, in-plane segmented 3D readouts and BS provided high-SNR high-resolution ASL perfusion images of the whole brain. Although both sequences produced excellent image quality, the 3D RARE Stack-Of-Spirals readout yielded higher temporal and spatial SNR than 3D GRASE (spatial SNR = 8.5 ± 2.8 and 3.7 ± 1.4; temporal SNR = 27.4 ± 12.5 and 15.6 ± 7.6, respectively) and decreased through-plane blurring due to its inherent oversampling of the central k-space region, its reduced effective TE and shorter total readout time, at the expense of a slight increase in the effective in-plane voxel size.
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Affiliation(s)
- Marta Vidorreta
- Neuroimaging Laboratory, CIMA, University of Navarra, Pamplona, Navarra, Spain
| | - Evelyne Balteau
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Ze Wang
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Enrico De Vita
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, United Kingdom
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - María A. Pastor
- Neuroimaging Laboratory, CIMA, University of Navarra, Pamplona, Navarra, Spain
| | - David L. Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, United Kingdom
| | - John A. Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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Muhlert N, Atzori M, De Vita E, Thomas DL, Samson RS, Wheeler-Kingshott CAM, Geurts JJG, Miller DH, Thompson AJ, Ciccarelli O. Memory in multiple sclerosis is linked to glutamate concentration in grey matter regions. J Neurol Neurosurg Psychiatry 2014; 85:833-9. [PMID: 24431465 PMCID: PMC4112488 DOI: 10.1136/jnnp-2013-306662] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Glutamate is the principal excitatory neurotransmitter and is involved in normal brain function. Cognitive impairment is common in multiple sclerosis (MS), and understanding its mechanisms is crucial for developing effective treatments. We used structural and metabolic brain imaging to test two hypotheses: (i) glutamate levels in grey matter regions are abnormal in MS, and (ii) patients show a relationship between glutamate concentration and memory performance. METHODS Eighteen patients with relapsing-remitting MS and 17 healthy controls were cognitively assessed and underwent (1)H-magnetic resonance spectroscopy at 3 T to assess glutamate levels in the hippocampus, thalamus, cingulate and parietal cortices. Regression models investigated the association between glutamate concentration and memory performance independently of magnetisation transfer ratio values and grey matter lesions withint he same regions, and whole-brain grey matter volume. RESULTS Patients had worse visual and verbal memory than controls. A positive relationship between glutamate levels in the hippocampal, thalamic and cingulate regions and visuospatial memory was detected in patients, but not in healthy controls. CONCLUSIONS The relationship between memory and glutamate concentration, which is unique to MS patients, suggests the reliance of memory on glutamatergic systems in MS.
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Affiliation(s)
- Nils Muhlert
- Department of Neuroinflammation, NMR Research Unit, UCL Institute of Neurology, London, UK Cognitive Neuroscience, Department of Psychology, Cardiff University, Cardiff, UK
| | - Matteo Atzori
- Department of Brain Repair and Rehabilitation, NMR Research Unit, UCL Institute of Neurology, London, UK Department of Neurology, University of Padova, Padova, Italy
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK
| | - David L Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK
| | - Rebecca S Samson
- Department of Neuroinflammation, NMR Research Unit, UCL Institute of Neurology, London, UK
| | | | - Jeroen J G Geurts
- Department of Anatomy and Neuroscience, Section of Clinical Neuroscience, VU University Medical Center, VUmc MS Center Amsterdam, Amsterdam, The Netherlands
| | - David H Miller
- Department of Neuroinflammation, NMR Research Unit, UCL Institute of Neurology, London, UK National Institute for Health and Research (NIHR) University College London Hospital (UCLH) Biomedical Research Centre, London, UK
| | - Alan J Thompson
- Department of Brain Repair and Rehabilitation, NMR Research Unit, UCL Institute of Neurology, London, UK National Institute for Health and Research (NIHR) University College London Hospital (UCLH) Biomedical Research Centre, London, UK
| | - Olga Ciccarelli
- Department of Brain Repair and Rehabilitation, NMR Research Unit, UCL Institute of Neurology, London, UK National Institute for Health and Research (NIHR) University College London Hospital (UCLH) Biomedical Research Centre, London, UK
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48
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Melbourne A, Eaton-Rosen Z, De Vita E, Bainbridge A, Cardoso MJ, Price D, Cady E, Kendall GS, Robertson NJ, Marlow N, Ourselin S. Multi-modal measurement of the myelin-to-axon diameter g-ratio in preterm-born neonates and adult controls. Med Image Comput Comput Assist Interv 2014; 17:268-75. [PMID: 25485388 DOI: 10.1007/978-3-319-10470-6_34] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Infants born prematurely are at increased risk of adverse functional outcome. The measurement of white matter tissue composition and structure can help predict functional performance and this motivates the search for new multi-modal imaging biomarkers. In this work we develop a novel combined biomarker from diffusion MRI and multi-component T2 relaxation measurements in a group of infants born very preterm and scanned between 30 and 40 weeks equivalent gestational age. We also investigate this biomarker on a group of seven adult controls, using a multi-modal joint model-fitting strategy. The proposed emergent biomarker is tentatively related to axonal energetic efficiency (in terms of axonal membrane charge storage) and conduction velocity and is thus linked to the tissue electrical properties, giving it a good theoretical justification as a predictive measurement of functional outcome.
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Ciccarelli O, Thomas DL, De Vita E, Wheeler-Kingshott CAM, Kachramanoglou C, Kapoor R, Leary S, Matthews L, Palace J, Chard D, Miller DH, Toosy AT, Thompson AJ. Low myo-inositol indicating astrocytic damage in a case series of neuromyelitis optica. Ann Neurol 2013; 74:301-5. [PMID: 23553900 DOI: 10.1002/ana.23909] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 03/22/2013] [Accepted: 03/28/2013] [Indexed: 11/10/2022]
Abstract
Astrocytic necrosis is a prominent pathological feature of neuromyelitis optica (NMO) lesions and is clinically relevant. We report 5 NMO-related cases, all with longitudinally extensive lesions in the upper cervical cord, who underwent cervical cord (1) H-magnetic resonance spectroscopy. Lower myo-inositol/creatine values, suggesting astrocytic damage, were consistently found within the NMO lesions when compared with healthy controls and patients with multiple sclerosis (MS), who showed at least 1 demyelinating lesion at the same cord level. Therefore, the in vivo quantification of myo-inositol may distinguish NMO from MS. This is an important step toward developing imaging markers for clinical trials in NMO.
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Affiliation(s)
- Olga Ciccarelli
- Department of Brain Repair and Rehabilitation, Nuclear Magnetic Resonance Research Unit, University College London Institute of Neurology, London; National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London
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50
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Thayyil S, Sebire NJ, Chitty LS, Wade A, Chong W, Olsen O, Gunny RS, Offiah AC, Owens CM, Saunders DE, Scott RJ, Jones R, Norman W, Addison S, Bainbridge A, Cady EB, Vita ED, Robertson NJ, Taylor AM. Post-mortem MRI versus conventional autopsy in fetuses and children: a prospective validation study. Lancet 2013; 382:223-33. [PMID: 23683720 DOI: 10.1016/s0140-6736(13)60134-8] [Citation(s) in RCA: 198] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Post-mortem MRI is a potential diagnostic alternative to conventional autopsy, but few large prospective studies have compared its accuracy with that of conventional autopsy. We assessed the accuracy of whole-body, post-mortem MRI for detection of major pathological lesions associated with death in a prospective cohort of fetuses and children. METHODS In this prospective validation study, we did pre-autopsy, post-mortem, whole-body MRI at 1·5 T in an unselected population of fetuses (≤24 weeks' or >24 weeks' gestation) and children (aged <16 years) at two UK centres in London between March 1, 2007 and Sept 30, 2011. With conventional autopsy as the diagnostic gold standard, we assessed MRI findings alone, or in conjunction with other minimally invasive post-mortem investigations (minimally invasive autopsy), for accuracy in detection of cause of death or major pathological abnormalities. A radiologist and pathologist who were masked to the autopsy findings indicated whether the minimally invasive autopsy would have been adequate. The primary outcome was concordance rate between minimally invasive and conventional autopsy. FINDINGS We analysed 400 cases, of which 277 (69%) were fetuses and 123 (31%) were children. Cause of death or major pathological lesion detected by minimally invasive autopsy was concordant with conventional autopsy in 357 (89·3%, 95% CI 85·8-91·9) cases: 175 (94·6%, 90·3-97·0) of 185 fetuses at 24 weeks' gestation or less, 88 (95·7%, 89·3-98·3) of 92 fetuses at more than 24 weeks' gestation, 34 (81·0%, 66·7-90·0) [corrected] of 42 newborns aged 1 month or younger, 45 (84·9%, 72·9-92·1) of 53 infants aged older than 1 month to 1 year or younger, and 15 (53·6%, 35·8-70·5) of 28 children aged older than 1 year to 16 years or younger. The dedicated radiologist or pathologist review of the minimally invasive autopsy showed that in 165 (41%) cases a full autopsy might not have been needed; in these cases, concordance between autopsy and minimally invasive autopsy was 99·4% (96·6-99·9). INTERPRETATION Minimally invasive autopsy has accuracy similar to that of conventional autopsy for detection of cause of death or major pathological abnormality after death in fetuses, newborns, and infants, but was less accurate in older children. If undertaken jointly by pathologists and radiologists, minimally invasive autopsy could be an acceptable alternative to conventional autopsy in selected cases. FUNDING Policy research Programme, Department of Health, UK.
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Affiliation(s)
- Sudhin Thayyil
- Centre for Cardiovascular Imaging, Institute of Cardiovascular Science, University College London (UCL), London, UK.
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