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Holland N, Savulich G, Jones PS, Whiteside DJ, Street D, Swann P, Naessens M, Malpetti M, Hong YT, Fryer TD, Rittman T, Mulroy E, Aigbirhio FI, Bhatia KP, O'Brien JT, Rowe JB. Differential Synaptic Loss in β-Amyloid Positive Versus β-Amyloid Negative Corticobasal Syndrome. Mov Disord 2024. [PMID: 38671545 DOI: 10.1002/mds.29814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND/OBJECTIVE The corticobasal syndrome (CBS) is a complex asymmetric movement disorder, with cognitive impairment. Although commonly associated with the primary 4-repeat-tauopathy of corticobasal degeneration, clinicopathological correlation is poor, and a significant proportion is due to Alzheimer's disease (AD). Synaptic loss is a pathological feature of many clinical and preclinical tauopathies. We therefore measured the degree of synaptic loss in patients with CBS and tested whether synaptic loss differed according to β-amyloid status. METHODS Twenty-five people with CBS, and 32 age-/sex-/education-matched healthy controls participated. Regional synaptic density was estimated by [11C]UCB-J non-displaceable binding potential (BPND), AD-tau pathology by [18F]AV-1451 BPND, and gray matter volume by T1-weighted magnetic resonance imaging. Participants with CBS had β-amyloid imaging with 11C-labeled Pittsburgh Compound-B ([11C]PiB) positron emission tomography. Symptom severity was assessed with the progressive supranuclear palsy-rating-scale, the cortical basal ganglia functional scale, and the revised Addenbrooke's Cognitive Examination. Regional differences in BPND and gray matter volume between groups were assessed by ANOVA. RESULTS Compared to controls, patients with CBS had higher [18F]AV-1451 uptake, gray matter volume loss, and reduced synaptic density. Synaptic loss was more severe and widespread in the β-amyloid negative group. Asymmetry of synaptic loss was in line with the clinically most affected side. DISCUSSION Distinct patterns of [11C]UCB-J and [18F]AV-1451 binding and gray matter volume loss, indicate differences in the pathogenic mechanisms of CBS according to whether it is associated with the presence of Alzheimer's disease or not. This highlights the potential for different therapeutic strategies in CBSs. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Negin Holland
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - George Savulich
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - P Simon Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - David J Whiteside
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Duncan Street
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Peter Swann
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Michelle Naessens
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Young T Hong
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | - Tim D Fryer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Eoin Mulroy
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Franklin I Aigbirhio
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Kailash P Bhatia
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - John T O'Brien
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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Olsson H, Millward JM, Starke L, Gladytz T, Klein T, Fehr J, Lai WC, Lippert C, Niendorf T, Waiczies S. Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex. PLoS One 2024; 19:e0301132. [PMID: 38626138 PMCID: PMC11020361 DOI: 10.1371/journal.pone.0301132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 03/11/2024] [Indexed: 04/18/2024] Open
Abstract
Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.
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Affiliation(s)
- Hampus Olsson
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.), Berlin, Germany
| | - Jason Michael Millward
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.), Berlin, Germany
- Experimental and Clinical Research Center, A Joint Cooperation Between the Charité Medical Faculty and the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Ludger Starke
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.), Berlin, Germany
| | - Thomas Gladytz
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.), Berlin, Germany
| | - Tobias Klein
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.), Berlin, Germany
| | - Jana Fehr
- Digital Health & Machine Learning Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
| | - Wei-Chang Lai
- Digital Health & Machine Learning Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
| | - Christoph Lippert
- Digital Health & Machine Learning Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Thoralf Niendorf
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.), Berlin, Germany
- Experimental and Clinical Research Center, A Joint Cooperation Between the Charité Medical Faculty and the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Sonia Waiczies
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.), Berlin, Germany
- Experimental and Clinical Research Center, A Joint Cooperation Between the Charité Medical Faculty and the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
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Zhang M, Ding B, Dragonu I, Liebig P, Rodgers CT. Dynamic parallel transmit diffusion MRI at 7T. Magn Reson Imaging 2024; 111:35-46. [PMID: 38547935 DOI: 10.1016/j.mri.2024.03.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/14/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024]
Abstract
Diffusion MRI (dMRI) is inherently limited by SNR. Scanning at 7 T increases intrinsic SNR but 7 T MRI scans suffer from regions of signal dropout, especially in the temporal lobes and cerebellum. We applied dynamic parallel transmit (pTx) to allow whole-brain 7 T dMRI and compared with circularly polarized (CP) pulses in 6 subjects. Subject-specific 2-spoke dynamic pTx pulses were designed offline for 8 slabs covering the brain. We used vendor-provided B0 and B1+ mapping. Spokes positions were set using the Fourier difference approach, and RF coefficients optimized with a Jacobi-matrix high-flip-angle optimizer. Diffusion data were analyzed with FSL. Comparing whole-brain averages for pTx against CP scans: mean flip angle error improved by 15% for excitation (2-spoke-VERSE 15.7° vs CP 18.4°, P = 0.012) and improved by 14% for refocusing (2-spoke-VERSE 39.7° vs CP 46.2°, P = 0.008). Computed spin-echo signal standard deviation improved by 14% (2-spoke-VERSE 0.185 vs 0.214 CP, P = 0.025). Temporal SNR increased by 5.4% (2-spoke-VERSE 8.47 vs CP 8.04, P = 0.004) especially in the inferior temporal lobes. Diffusion fitting uncertainty decreased by 6.2% for first fibers (2-spoke VERSE 0.0655 vs CP 0.0703, P < 0.001) and 1.3% for second fibers (2-spoke VERSE 0.139 vs CP 0.141, P = 0.01). In conclusion, dynamic parallel transmit improves the uniformity of 7 T diffusion-weighted imaging. In future, less restrictive SAR limits for parallel transmit scans are expected to allow further improvements.
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Affiliation(s)
- Minghao Zhang
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, United Kingdom.
| | - Belinda Ding
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, United Kingdom; Siemens Healthcare Ltd, Frimley, United Kingdom
| | | | | | - Christopher T Rodgers
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
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Swaddiwudhipong N, Whiteside DJ, Hezemans FH, Street D, Rowe JB, Rittman T. Pre-diagnostic cognitive and functional impairment in multiple sporadic neurodegenerative diseases. Alzheimers Dement 2022; 19:1752-1763. [PMID: 36223793 DOI: 10.1002/alz.12802] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
INTRODUCTION The pathophysiological processes of neurodegenerative diseases begin years before diagnosis. However, pre-diagnostic changes in cognition and physical function are poorly understood, especially in sporadic neurodegenerative disease. METHODS UK Biobank data were extracted. Cognitive and functional measures in individuals who subsequently developed Alzheimer's disease (AD), Parkinson disease, frontotemporal dementia, progressive supranuclear palsy, dementia with Lewy bodies, or multiple system atrophy were compared against individuals without neurodegenerative diagnoses. The same measures were regressed against time to diagnosis, after adjusting for the effects of age. RESULTS There was evidence for pre-diagnostic cognitive impairment and decline with time, particularly in AD. Pre-diagnostic functional impairment and decline were observed in multiple diseases. DISCUSSION The scale and longitudinal follow-up of UK Biobank participants provides evidence for cognitive and functional decline years before symptoms become obvious in multiple neurodegenerative diseases. Identifying pre-diagnostic functional and cognitive changes could improve selection for preventive and early disease-modifying treatment trials.
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Affiliation(s)
- Nol Swaddiwudhipong
- Department of Clinical Neurosciences, Cambridge, UK.,Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - David J Whiteside
- Department of Clinical Neurosciences, Cambridge, UK.,Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Frank H Hezemans
- Department of Clinical Neurosciences, Cambridge, UK.,MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - James B Rowe
- Department of Clinical Neurosciences, Cambridge, UK.,Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK.,MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, Cambridge, UK.,Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
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5
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Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients. Artif Intell Med 2022; 125:102251. [DOI: 10.1016/j.artmed.2022.102251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 11/26/2021] [Accepted: 01/29/2022] [Indexed: 11/17/2022]
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Saito Y, Kamagata K, Wijeratne PA, Andica C, Uchida W, Takabayashi K, Fujita S, Akashi T, Wada A, Shimoji K, Hori M, Masutani Y, Alexander DC, Aoki S. Temporal Progression Patterns of Brain Atrophy in Corticobasal Syndrome and Progressive Supranuclear Palsy Revealed by Subtype and Stage Inference (SuStaIn). Front Neurol 2022; 13:814768. [PMID: 35280291 PMCID: PMC8914081 DOI: 10.3389/fneur.2022.814768] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Differentiating corticobasal degeneration presenting with corticobasal syndrome (CBD-CBS) from progressive supranuclear palsy with Richardson's syndrome (PSP-RS), particularly in early stages, is often challenging because the neurodegenerative conditions closely overlap in terms of clinical presentation and pathology. Although volumetry using brain magnetic resonance imaging (MRI) has been studied in patients with CBS and PSP-RS, studies assessing the progression of brain atrophy are limited. Therefore, we aimed to reveal the difference in the temporal progression patterns of brain atrophy between patients with CBS and those with PSP-RS purely based on cross-sectional data using Subtype and Stage Inference (SuStaIn)—a novel, unsupervised machine learning technique that integrates clustering and disease progression modeling. We applied SuStaIn to the cross-sectional regional brain volumes of 25 patients with CBS, 39 patients with typical PSP-RS, and 50 healthy controls to estimate the two disease subtypes and trajectories of CBS and PSP-RS, which have distinct atrophy patterns. The progression model and classification accuracy of CBS and PSP-RS were compared with those of previous studies to evaluate the performance of SuStaIn. SuStaIn identified distinct temporal progression patterns of brain atrophy for CBS and PSP-RS, which were largely consistent with previous evidence, with high reproducibility (99.7%) under cross-validation. We classified these diseases with high accuracy (0.875) and sensitivity (0.680 and 1.000, respectively) based on cross-sectional structural brain MRI data; the accuracy was higher than that reported in previous studies. Moreover, SuStaIn stage correctly reflected disease severity without the label of disease stage, such as disease duration. Furthermore, SuStaIn also showed the genialized performance of differentiation and reflection for CBS and PSP-RS. Thus, SuStaIn has potential for improving our understanding of disease mechanisms, accurately stratifying patients, and providing prognoses for patients with CBS and PSP-RS.
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Affiliation(s)
- Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- *Correspondence: Koji Kamagata
| | - Peter A. Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keigo Shimoji
- Department of Radiology, Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Yoshitaka Masutani
- Department of Biomedical Information Sciences, Hiroshima City University Graduate School of Information Sciences, Hiroshima, Japan
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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Carlos AF, Tosakulwong N, Weigand SD, Buciuc M, Ali F, Clark HM, Botha H, Utianski RL, Machulda MM, Schwarz CG, Reid RI, Senjem ML, Jack CR, Ahlskog JE, Dickson DW, Josephs KA, Whitwell JL. OUP accepted manuscript. Brain Commun 2022; 4:fcac108. [PMID: 35663380 PMCID: PMC9155234 DOI: 10.1093/braincomms/fcac108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/22/2022] [Accepted: 04/26/2022] [Indexed: 11/29/2022] Open
Abstract
Primary four-repeat tauopathies are characterized by depositions of the four-repeat isoform of the microtubule binding protein, tau. The two most common sporadic four-repeat tauopathies are progressive supranuclear palsy and corticobasal degeneration. Because tau PET tracers exhibit poor binding affinity to four-repeat pathology, determining how well in vivo MRI findings relate to underlying pathology is critical to evaluating their utility as surrogate markers to aid in diagnosis and as outcome measures for clinical trials. We studied the relationship of cross-sectional imaging findings, such as MRI volume loss and diffusion tensor imaging white matter tract abnormalities, to tau histopathology in four-repeat tauopathies. Forty-seven patients with antemortem 3 T MRI volumetric and diffusion tensor imaging scans plus post-mortem pathological diagnosis of a four-repeat tauopathy (28 progressive supranuclear palsy; 19 corticobasal degeneration) were included in the study. Tau lesion types (pretangles/neurofibrillary tangles, neuropil threads, coiled bodies, astrocytic lesions) were semiquantitatively graded in disease-specific cortical, subcortical and brainstem regions. Antemortem regional volumes, fractional anisotropy and mean diffusivity were modelled using linear regression with post-mortem tau lesion scores considered separately, based on cellular type (neuronal versus glial), or summed (total tau). Results showed that greater total tau burden was associated with volume loss in the subthalamic nucleus (P = 0.001), midbrain (P < 0.001), substantia nigra (P = 0.03) and red nucleus (P = 0.004), with glial lesions substantially driving the associations. Decreased fractional anisotropy and increased mean diffusivity in the superior cerebellar peduncle correlated with glial tau in the cerebellar dentate (P = 0.04 and P = 0.02, respectively) and red nucleus (P < 0.001 for both). Total tau and glial pathology also correlated with increased mean diffusivity in the midbrain (P = 0.02 and P < 0.001, respectively). Finally, increased subcortical white matter mean diffusivity was associated with total tau in superior frontal and precentral cortices (each, P = 0.02). Overall, results showed clear relationships between antemortem MRI changes and pathology in four-repeat tauopathies. Our findings show that brain volume could be a useful surrogate marker of tau pathology in subcortical and brainstem regions, whereas white matter integrity could be a useful marker of tau pathology in cortical regions. Our findings also suggested an important role of glial tau lesions in the pathogenesis of neurodegeneration in four-repeat tauopathies. Thus, development of tau PET tracers selectively binding to glial tau lesions could potentially uncover mechanisms of disease progression.
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Affiliation(s)
- Arenn F. Carlos
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Nirubol Tosakulwong
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Stephen D. Weigand
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Marina Buciuc
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Farwa Ali
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Mary M. Machulda
- Department of Psychology and Psychiatry, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Robert I. Reid
- Department of Psychology and Psychiatry, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA
| | - Matthew L. Senjem
- Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA
| | - J. Eric Ahlskog
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Dennis W. Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Jennifer L. Whitwell
- Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA
- Correspondence to: Jennifer L. Whitwell, PhD Professor of Radiology, Department of Radiology Mayo Clinic, 200 1st St SW Rochester, MN 55905, USA E-mail:
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Stamelou M, Respondek G, Giagkou N, Whitwell JL, Kovacs GG, Höglinger GU. Evolving concepts in progressive supranuclear palsy and other 4-repeat tauopathies. Nat Rev Neurol 2021; 17:601-620. [PMID: 34426686 DOI: 10.1038/s41582-021-00541-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2021] [Indexed: 02/07/2023]
Abstract
Tauopathies are classified according to whether tau deposits predominantly contain tau isoforms with three or four repeats of the microtubule-binding domain. Those in which four-repeat (4R) tau predominates are known as 4R-tauopathies, and include progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease, globular glial tauopathies and conditions associated with specific MAPT mutations. In these diseases, 4R-tau deposits are found in various cell types and anatomical regions of the brain and the conditions share pathological, pathophysiological and clinical characteristics. Despite being considered 'prototype' tauopathies and, therefore, ideal for studying neuroprotective agents, 4R-tauopathies are still severe and untreatable diseases for which no validated biomarkers exist. However, advances in research have addressed the issues of phenotypic overlap, early clinical diagnosis, pathophysiology and identification of biomarkers, setting a road map towards development of treatments. New clinical criteria have been developed and large cohorts with early disease are being followed up in prospective studies. New clinical trial readouts are emerging and biomarker research is focused on molecular pathways that have been identified. Lessons learned from failed trials of neuroprotective drugs are being used to design new trials. In this Review, we present an overview of the latest research in 4R-tauopathies, with a focus on progressive supranuclear palsy, and discuss how current evidence dictates ongoing and future research goals.
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Affiliation(s)
- Maria Stamelou
- Parkinson's Disease and Movement Disorders Dept, HYGEIA Hospital, Athens, Greece. .,European University of Cyprus, Nicosia, Cyprus. .,Philipps University, Marburg, Germany.
| | - Gesine Respondek
- Department of Neurology, Hanover Medical School, Hanover, Germany
| | - Nikolaos Giagkou
- Parkinson's Disease and Movement Disorders Dept, HYGEIA Hospital, Athens, Greece
| | | | - Gabor G Kovacs
- Department of Laboratory Medicine and Pathobiology and Tanz Centre for Research in Neurodegenerative Disease (CRND), University of Toronto, Toronto, Ontario, Canada.,Laboratory Medicine Program and Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Günter U Höglinger
- Department of Neurology, Hanover Medical School, Hanover, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
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Testini P, Sarva H, Schwalb J, Barkan S, Cabrera LY. Neurosurgeons perspective on the shift towards earlier use of deep brain stimulation for Parkinson disease. INTERDISCIPLINARY NEUROSURGERY 2021. [DOI: 10.1016/j.inat.2021.101224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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10
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Malpetti M, Passamonti L, Jones PS, Street D, Rittman T, Fryer TD, Hong YT, Vàsquez Rodriguez P, Bevan-Jones WR, Aigbirhio FI, O'Brien JT, Rowe JB. Neuroinflammation predicts disease progression in progressive supranuclear palsy. J Neurol Neurosurg Psychiatry 2021; 92:769-775. [PMID: 33731439 PMCID: PMC7611006 DOI: 10.1136/jnnp-2020-325549] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/29/2020] [Accepted: 01/26/2021] [Indexed: 12/21/2022]
Abstract
INTRODUCTION In addition to tau pathology and neuronal loss, neuroinflammation occurs in progressive supranuclear palsy (PSP). However, the prognostic value of the in vivo imaging markers for these processes in PSP remains unclear. We test the primary hypothesis that baseline in vivo imaging assessment of neuroinflammation in subcortical regions predicts clinical progression in patients with PSP. METHODS Seventeen patients with PSP-Richardson's syndrome underwent a baseline multimodal imaging assessment, including [11C]PK11195 positron emission tomography (PET) to index microglial activation, [18F]AV-1451 PET for tau pathology and structural MRI. Disease severity was measured at baseline and serially up to 4 years with the Progressive Supranuclear Palsy Rating Scale (PSPRS) (average interval of 5 months). Regional grey-matter volumes and PET ligand binding potentials were summarised by three principal component analyses (PCAs). A linear mixed-effects model was applied to the longitudinal PSPRS scores. Single-modality imaging predictors were regressed against the individuals' estimated rate of progression to identify the prognostic value of baseline imaging markers. RESULTS PCA components reflecting neuroinflammation and tau burden in the brainstem and cerebellum correlated with the subsequent annual rate of change in the PSPRS. PCA-derived PET markers of neuroinflammation and tau pathology correlated with regional brain volume in the same regions. However, MRI volumes alone did not predict the rate of clinical progression. CONCLUSIONS Molecular imaging with PET for microglial activation and tau pathology can predict clinical progression in PSP. These data encourage the evaluation of immunomodulatory approaches to disease-modifying therapies in PSP and the potential for PET to stratify patients in early phase clinical trials.
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Affiliation(s)
- Maura Malpetti
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Luca Passamonti
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
- Institute of Molecular Bioimaging and Physiology, National Research Council, Milan, Italy
| | - Peter Simon Jones
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Duncan Street
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Timothy D Fryer
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Young T Hong
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Patricia Vàsquez Rodriguez
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | | | - Franklin I Aigbirhio
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | | | - James Benedict Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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Bai X, Zhou C, Guo T, Guan X, Wu J, Liu X, Gao T, Gu L, Xuan M, Gu Q, Huang P, Song Z, Yan Y, Pu J, Zhang B, Xu X, Zhang M. Progressive microstructural alterations in subcortical nuclei in Parkinson's disease: A diffusion magnetic resonance imaging study. Parkinsonism Relat Disord 2021; 88:82-89. [PMID: 34147950 DOI: 10.1016/j.parkreldis.2021.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 04/22/2021] [Accepted: 06/06/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To explore the microstructural alterations in subcortical nuclei in Parkinson's disease (PD) at different stages with diffusion kurtosis imaging (DKI) and tensor imaging and to test the performance of diffusion metrics in identifying PD. METHODS 108 PD patients (64 patients in early-stage PD group (EPD) and 44 patients in moderate-late-stage PD group (MLPD)) and 64 healthy controls (HC) were included. Tensor and kurtosis metrics in the subcortical nuclei were compared. Partial correlation was used to correlate the diffusion metrics and Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) score. Logistic regression and receiver operating characteristic analysis were applied to test the diagnostic performance of the diffusion metrics. RESULTS Compared with HC, both EPD and MLPD patients showed higher fractional anisotropy and axial diffusivity, lower mean kurtosis (MK) and axial kurtosis in substantia nigra, lower MK and radial kurtosis (RK) in globus pallidus (GP) and thalamus (all p < 0.05). Compared with EPD, MLPD patients showed lower MK and RK in GP and thalamus (all p < 0.05). MK and RK in GP and thalamus were negatively correlated with UPDRS-III score (all p < 0.01). The logistic regression model combining kurtosis and tensor metrics showed the best performance in diagnosing PD, EPD, and MLPD (areas under curve were 0.817, 0.769, and 0.914, respectively). CONCLUSIONS PD has progressive microstructural alterations in the subcortical nuclei. DKI is sensitive to detect microstructural alterations in GP and thalamus during PD progression. Combining kurtosis and tensor metrics can achieve a good performance in diagnosing PD.
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Affiliation(s)
- Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Zhe Song
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Yaping Yan
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China.
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Update on neuroimaging for categorization of Parkinson's disease and atypical parkinsonism. Curr Opin Neurol 2021; 34:514-524. [PMID: 34010220 DOI: 10.1097/wco.0000000000000957] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE OF REVIEW Differential diagnosis of Parkinsonism may be difficult. The objective of this review is to present the work of the last three years in the field of imaging for diagnostic categorization of parkinsonian syndromes focusing on progressive supranuclear palsy (PSP) and multiple system atrophy (MSA). RECENT FINDINGS Two main complementary approaches are being pursued. The first seeks to develop and validate manual qualitative or semi-quantitative imaging markers that can be easily used in clinical practice. The second is based on quantitative measurements of magnetic resonance imaging abnormalities integrated in a multimodal approach and in automatic categorization machine learning tools. SUMMARY These two complementary approaches obtained high diagnostic around 90% and above in the classical Richardson form of PSP and probable MSA. Future work will determine if these techniques can improve diagnosis in other PSP variants and early forms of the diseases when all clinical criteria are not fully met.
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