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Rorden C, Hanayik T, Glen DR, Newman-Norlund R, Drake C, Fridriksson J, Taylor PA. Improving 3D edge detection for visual inspection of MRI coregistration and alignment. J Neurosci Methods 2024; 406:110112. [PMID: 38508496 PMCID: PMC11060928 DOI: 10.1016/j.jneumeth.2024.110112] [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: 09/12/2023] [Revised: 03/05/2024] [Accepted: 03/18/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Visualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another. NEW METHOD We suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative). RESULTS We demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity. COMPARISON WITH EXISTING METHODS Current methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. CONCLUSION The second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online.
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Affiliation(s)
- Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC 29016, USA; McCausland Center for Brain Imaging, University of South Carolina, Columbia, SC 29016, USA.
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Daniel R Glen
- Department of Communication Science & Disorders, University of South Carolina, Columbia, SC 29016, USA
| | - Roger Newman-Norlund
- Department of Psychology, University of South Carolina, Columbia, SC 29016, USA; McCausland Center for Brain Imaging, University of South Carolina, Columbia, SC 29016, USA
| | - Chris Drake
- Department of Psychology, University of South Carolina, Columbia, SC 29016, USA; McCausland Center for Brain Imaging, University of South Carolina, Columbia, SC 29016, USA
| | - Julius Fridriksson
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Paul A Taylor
- Department of Communication Science & Disorders, University of South Carolina, Columbia, SC 29016, USA
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2
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Batta I, Abrol A, Calhoun VD. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. J Neurosci Methods 2024; 406:110109. [PMID: 38494061 PMCID: PMC11100582 DOI: 10.1016/j.jneumeth.2024.110109] [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: 03/09/2023] [Revised: 02/12/2024] [Accepted: 03/12/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. NEW METHOD We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. RESULTS Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. COMPARISON WITH EXISTING METHOD(S) Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. CONCLUSIONS As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.
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Affiliation(s)
- Ishaan Batta
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.
| | - Anees Abrol
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA
| | - Vince D Calhoun
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
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3
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Sun W, Liu SH, Wei XJ, Sun H, Ma ZW, Yu XF. Potential of neuroimaging as a biomarker in amyotrophic lateral sclerosis: from structure to metabolism. J Neurol 2024; 271:2238-2257. [PMID: 38367047 DOI: 10.1007/s00415-024-12201-x] [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: 11/18/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 02/19/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by motor neuron degeneration. The development of ALS involves metabolite alterations leading to tissue lesions in the nervous system. Recent advances in neuroimaging have significantly improved our understanding of the underlying pathophysiology of ALS, with findings supporting the corticoefferent axonal disease progression theory. Current studies on neuroimaging in ALS have demonstrated inconsistencies, which may be due to small sample sizes, insufficient statistical power, overinterpretation of findings, and the inherent heterogeneity of ALS. Deriving meaningful conclusions solely from individual imaging metrics in ALS studies remains challenging, and integrating multimodal imaging techniques shows promise for detecting valuable ALS biomarkers. In addition to giving an overview of the principles and techniques of different neuroimaging modalities, this review describes the potential of neuroimaging biomarkers in the diagnosis and prognostication of ALS. We provide an insight into the underlying pathology, highlighting the need for standardized protocols and multicenter collaborations to advance ALS research.
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Affiliation(s)
- Wei Sun
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China
| | - Si-Han Liu
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xiao-Jing Wei
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China
| | - Hui Sun
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China
| | - Zhen-Wei Ma
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China
| | - Xue-Fan Yu
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China.
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Quek Y, Fung YL, Bourgeat P, Vogrin SJ, Collins SJ, Bowden SC. Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort. Brain Behav 2024; 14:e3505. [PMID: 38688879 PMCID: PMC11061200 DOI: 10.1002/brb3.3505] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 05/02/2024] Open
Abstract
INTRODUCTION The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. METHODS Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. RESULTS Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. CONCLUSION Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.
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Affiliation(s)
- Yi‐En Quek
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Yi Leng Fung
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Pierrick Bourgeat
- The Australian e‐Health Research CentreCSIRO Health and BiosecurityHerstonQueenslandAustralia
| | - Simon J. Vogrin
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
| | - Steven J. Collins
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
- Department of MedicineThe Royal Melbourne HospitalThe University of MelbourneParkvilleVictoriaAustralia
| | - Stephen C. Bowden
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
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Zhang R, Chen L, Oliver LD, Voineskos AN, Park JY. SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites. Hum Brain Mapp 2024; 45:e26692. [PMID: 38712767 PMCID: PMC11075170 DOI: 10.1002/hbm.26692] [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: 12/13/2023] [Revised: 03/27/2024] [Accepted: 04/08/2024] [Indexed: 05/08/2024] Open
Abstract
In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.
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Affiliation(s)
- Rongqian Zhang
- Department of Statistical SciencesUniversity of TorontoTorontoOntarioCanada
| | - Linxi Chen
- Department of Statistical SciencesUniversity of TorontoTorontoOntarioCanada
| | | | - Aristotle N. Voineskos
- Centre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Jun Young Park
- Department of Statistical SciencesUniversity of TorontoTorontoOntarioCanada
- Department of PsychologyUniversity of TorontoTorontoOntarioCanada
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Fu Z, Batta I, Wu L, Abrol A, Agcaoglu O, Salman MS, Du Y, Iraji A, Shultz S, Sui J, Calhoun VD. Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. Neuroimage 2024; 292:120617. [PMID: 38636639 DOI: 10.1016/j.neuroimage.2024.120617] [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: 01/08/2024] [Revised: 04/03/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States.
| | - Ishaan Batta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mustafa S Salman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Sarah Shultz
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
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Wood DA, Kafiabadi S, Busaidi AA, Guilhem E, Montvila A, Lynch J, Townend M, Agarwal S, Mazumder A, Barker GJ, Ourselin S, Cole JH, Booth TC. Accurate brain-age models for routine clinical MRI examinations. Neuroimage 2022; 249:118871. [PMID: 34995797 DOI: 10.1016/j.neuroimage.2022.118871] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [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: 09/15/2021] [Revised: 11/26/2021] [Accepted: 01/03/2022] [Indexed: 01/08/2023] Open
Abstract
Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.
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Affiliation(s)
- David A Wood
- School of Biomedical Engineering and Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, London SE17 7EH, United Kingdom
| | - Sina Kafiabadi
- King's College Hospital NHS Foundation Trust, United Kingdom
| | | | - Emily Guilhem
- King's College Hospital NHS Foundation Trust, United Kingdom
| | | | - Jeremy Lynch
- King's College Hospital NHS Foundation Trust, United Kingdom
| | | | - Siddharth Agarwal
- School of Biomedical Engineering and Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, London SE17 7EH, United Kingdom
| | - Asif Mazumder
- Guy's and St Thomas' NHS Foundation Trust, United Kingdom
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, London SE17 7EH, United Kingdom
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, United Kingdom; Dementia Research Centre, Institute of Neurology, University College London, United Kingdom; Centre for Medical Image Computing, Department of Computer Science, University College London, United Kingdom
| | - Thomas C Booth
- School of Biomedical Engineering and Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, London SE17 7EH, United Kingdom; King's College Hospital NHS Foundation Trust, United Kingdom.
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Dutt RK, Hannon K, Easley TO, Griffis JC, Zhang W, Bijsterbosch JD. Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates. Hum Brain Mapp 2022; 43:816-832. [PMID: 34708477 PMCID: PMC8720192 DOI: 10.1002/hbm.25690] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 04/02/2021] [Revised: 09/10/2021] [Accepted: 10/11/2021] [Indexed: 11/09/2022] Open
Abstract
The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging-derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.
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Affiliation(s)
- Rosie K Dutt
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kayla Hannon
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Ty O Easley
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Joseph C Griffis
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Wei Zhang
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Janine D Bijsterbosch
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
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Hedges EP, Dimitrov M, Zahid U, Brito Vega B, Si S, Dickson H, McGuire P, Williams S, Barker GJ, Kempton MJ. Reliability of structural MRI measurements: The effects of scan session, head tilt, inter-scan interval, acquisition sequence, FreeSurfer version and processing stream. Neuroimage 2022; 246:118751. [PMID: 34848299 PMCID: PMC8784825 DOI: 10.1016/j.neuroimage.2021.118751] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [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: 08/18/2021] [Revised: 11/18/2021] [Accepted: 11/20/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Large-scale longitudinal and multi-centre studies are used to explore neuroimaging markers of normal ageing, and neurodegenerative and mental health disorders. Longitudinal changes in brain structure are typically small, therefore the reliability of automated techniques is crucial. Determining the effects of different factors on reliability allows investigators to control those adversely affecting reliability, calculate statistical power, or even avoid particular brain measures with low reliability. This study examined the impact of several image acquisition and processing factors and documented the test-retest reliability of structural MRI measurements. METHODS In Phase I, 20 healthy adults (11 females; aged 20-30 years) were scanned on two occasions three weeks apart on the same scanner using the ADNI-3 protocol. On each occasion, individuals were scanned twice (repetition), after re-entering the scanner (reposition) and after tilting their head forward. At one year follow-up, nine returning individuals and 11 new volunteers were recruited for Phase II (11 females; aged 22-31 years). Scans were acquired on two different scanners using the ADNI-2 and ADNI-3 protocols. Structural images were processed using FreeSurfer (v5.3.0, 6.0.0 and 7.1.0) to provide subcortical and cortical volume, cortical surface area and thickness measurements. Intra-class correlation coefficients (ICC) were calculated to estimate test-retest reliability. We examined the effect of repetition, reposition, head tilt, time between scans, MRI sequence and scanner on reliability of structural brain measurements. Mean percentage differences were also calculated in supplementary analyses. RESULTS Using the FreeSurfer v7.1.0 longitudinal pipeline, we observed high reliability for subcortical and cortical volumes, and cortical surface areas at repetition, reposition, three weeks and one year (mean ICCs>0.97). Cortical thickness reliability was lower (mean ICCs>0.82). Head tilt had the greatest adverse impact on ICC estimates, for example reducing mean right cortical thickness to ICC=0.74. In contrast, changes in ADNI sequence or MRI scanner had a minimal effect. We observed an increase in reliability for updated FreeSurfer versions, with the longitudinal pipeline consistently having a higher reliability than the cross-sectional pipeline. DISCUSSION Longitudinal studies should monitor or control head tilt to maximise reliability. We provided the ICC estimates and mean percentage differences for all FreeSurfer brain regions, which may inform power analyses for clinical studies and have implications for the design of future longitudinal studies.
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Affiliation(s)
- Emily P Hedges
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom.
| | - Mihail Dimitrov
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Uzma Zahid
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
| | - Barbara Brito Vega
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
| | - Shuqing Si
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
| | - Hannah Dickson
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
| | - Steven Williams
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Gareth J Barker
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
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10
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Zavaliangos‐Petropulu A, Tubi MA, Haddad E, Zhu A, Braskie MN, Jahanshad N, Thompson PM, Liew S. Testing a convolutional neural network-based hippocampal segmentation method in a stroke population. Hum Brain Mapp 2022; 43:234-243. [PMID: 33067842 PMCID: PMC8675423 DOI: 10.1002/hbm.25210] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [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: 05/21/2020] [Revised: 09/03/2020] [Accepted: 09/05/2020] [Indexed: 12/22/2022] Open
Abstract
As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.
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Affiliation(s)
- Artemis Zavaliangos‐Petropulu
- Neural Plasticity and Neurorehabilitation LaboratoryUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Meral A. Tubi
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Elizabeth Haddad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Alyssa Zhu
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Meredith N. Braskie
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Sook‐Lei Liew
- Neural Plasticity and Neurorehabilitation LaboratoryUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
- Chan Division of Occupational Science and Occupational TherapyOstrow School of Dentistry, University of Southern CaliforniaLos AngelesCaliforniaUSA
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11
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Kong X, Francks C. Reproducibility in the absence of selective reporting: An illustration from large-scale brain asymmetry research. Hum Brain Mapp 2022; 43:244-254. [PMID: 32841457 PMCID: PMC8675427 DOI: 10.1002/hbm.25154] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [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: 04/21/2020] [Revised: 07/13/2020] [Accepted: 07/19/2020] [Indexed: 12/27/2022] Open
Abstract
The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes.
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Affiliation(s)
- Xiang‐Zhen Kong
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
| | - Clyde Francks
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
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12
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Zugman A, Harrewijn A, Cardinale EM, Zwiebel H, Freitag GF, Werwath KE, Bas‐Hoogendam JM, Groenewold NA, Aghajani M, Hilbert K, Cardoner N, Porta‐Casteràs D, Gosnell S, Salas R, Blair KS, Blair JR, Hammoud MZ, Milad M, Burkhouse K, Phan KL, Schroeder HK, Strawn JR, Beesdo‐Baum K, Thomopoulos SI, Grabe HJ, Van der Auwera S, Wittfeld K, Nielsen JA, Buckner R, Smoller JW, Mwangi B, Soares JC, Wu M, Zunta‐Soares GB, Jackowski AP, Pan PM, Salum GA, Assaf M, Diefenbach GJ, Brambilla P, Maggioni E, Hofmann D, Straube T, Andreescu C, Berta R, Tamburo E, Price R, Manfro GG, Critchley HD, Makovac E, Mancini M, Meeten F, Ottaviani C, Agosta F, Canu E, Cividini C, Filippi M, Kostić M, Munjiza A, Filippi CA, Leibenluft E, Alberton BAV, Balderston NL, Ernst M, Grillon C, Mujica‐Parodi LR, van Nieuwenhuizen H, Fonzo GA, Paulus MP, Stein MB, Gur RE, Gur RC, Kaczkurkin AN, Larsen B, Satterthwaite TD, Harper J, Myers M, Perino MT, Yu Q, Sylvester CM, Veltman DJ, Lueken U, Van der Wee NJA, Stein DJ, Jahanshad N, Thompson PM, Pine DS, Winkler AM. Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group. Hum Brain Mapp 2022; 43:255-277. [PMID: 32596977 PMCID: PMC8675407 DOI: 10.1002/hbm.25096] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [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: 03/02/2020] [Revised: 05/26/2020] [Accepted: 05/31/2020] [Indexed: 12/15/2022] Open
Abstract
The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.
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Affiliation(s)
- André Zugman
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Anita Harrewijn
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Elise M. Cardinale
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Hannah Zwiebel
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Gabrielle F. Freitag
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Katy E. Werwath
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Janna M. Bas‐Hoogendam
- Leiden University Medical Center, Department of PsychiatryLeidenThe Netherlands
- Leiden Institute for Brain and Cognition (LIBC)LeidenThe Netherlands
- Leiden University, Institute of Psychology, Developmental and Educational PsychologyLeidenThe Netherlands
| | - Nynke A. Groenewold
- Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Moji Aghajani
- Department. of PsychiatryAmsterdam UMC/VUMCAmsterdamThe Netherlands
- GGZ InGeestDepartment of Research & InnovationAmsterdamThe Netherlands
| | - Kevin Hilbert
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Narcis Cardoner
- Department of Mental HealthUniversity Hospital Parc Taulí‐I3PTBarcelonaSpain
- Department of Psychiatry and Forensic MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud MentalCarlos III Health InstituteMadridSpain
| | - Daniel Porta‐Casteràs
- Department of Mental HealthUniversity Hospital Parc Taulí‐I3PTBarcelonaSpain
- Department of Psychiatry and Forensic MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud MentalCarlos III Health InstituteMadridSpain
| | - Savannah Gosnell
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | - Ramiro Salas
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | - Karina S. Blair
- Center for Neurobehavioral ResearchBoys Town National Research HospitalBoys TownNebraskaUSA
| | - James R. Blair
- Center for Neurobehavioral ResearchBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Mira Z. Hammoud
- Department of PsychiatryNew York UniversityNew YorkNew YorkUSA
| | - Mohammed Milad
- Department of PsychiatryNew York UniversityNew YorkNew YorkUSA
| | - Katie Burkhouse
- Department of PsychiatryUniversity of Illinois at ChicagoChicagoIllinoisUSA
| | - K. Luan Phan
- Department of Psychiatry and Behavioral HealthThe Ohio State UniversityColumbusOhioUSA
| | - Heidi K. Schroeder
- Department of Psychiatry & Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Jeffrey R. Strawn
- Department of Psychiatry & Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Katja Beesdo‐Baum
- Behavioral EpidemiologyInstitute of Clinical Psychology and Psychotherapy, Technische Universität DresdenDresdenGermany
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Hans J. Grabe
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Sandra Van der Auwera
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Katharina Wittfeld
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Jared A. Nielsen
- Department of PsychologyHarvard UniversityCambridgeMassachusettsUSA
- Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
| | - Randy Buckner
- Department of PsychologyHarvard UniversityCambridgeMassachusettsUSA
- Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Jordan W. Smoller
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Benson Mwangi
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Jair C. Soares
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Mon‐Ju Wu
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Giovana B. Zunta‐Soares
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Andrea P. Jackowski
- LiNC, Department of PsychiatryFederal University of São PauloSão PauloSão PauloBrazil
| | - Pedro M. Pan
- LiNC, Department of PsychiatryFederal University of São PauloSão PauloSão PauloBrazil
| | - Giovanni A. Salum
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do SulPorto AlegreRio Grande do SulBrazil
| | - Michal Assaf
- Olin Neuropsychiatry Research CenterInstitute of Living, Hartford HospitalHartfordConnecticutUSA
- Department of PsychiatryYale School of MedicineNew HavenConnecticutUSA
| | - Gretchen J. Diefenbach
- Anxiety Disorders CenterInstitute of Living, Hartford HospitalHartfordConnecticutUSA
- Yale School of MedicineNew HavenConnecticutUSA
| | - Paolo Brambilla
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Eleonora Maggioni
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - David Hofmann
- Institute of Medical Psychology and Systems Neuroscience, University of MuensterMuensterGermany
| | - Thomas Straube
- Institute of Medical Psychology and Systems Neuroscience, University of MuensterMuensterGermany
| | - Carmen Andreescu
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Rachel Berta
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Erica Tamburo
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Rebecca Price
- Department of Psychiatry & PsychologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Gisele G. Manfro
- Anxiety Disorder ProgramHospital de Clínicas de Porto AlegrePorto AlegreRio Grande do SulBrazil
- Department of PsychiatryFederal University of Rio Grande do SulPorto AlegreRio Grande do SulBrazil
| | - Hugo D. Critchley
- Department of NeuroscienceBrighton and Sussex Medical School, University of SussexBrightonUK
| | - Elena Makovac
- Centre for Neuroimaging ScienceKings College LondonLondonUK
| | - Matteo Mancini
- Department of NeuroscienceBrighton and Sussex Medical School, University of SussexBrightonUK
| | | | | | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
| | - Elisa Canu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Camilla Cividini
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
- Neurology and Neurophysiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Milutin Kostić
- Institute of Mental Health, University of BelgradeBelgradeSerbia
- Department of Psychiatry, School of MedicineUniversity of BelgradeBelgradeSerbia
| | - Ana Munjiza
- Institute of Mental Health, University of BelgradeBelgradeSerbia
| | - Courtney A. Filippi
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Ellen Leibenluft
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Bianca A. V. Alberton
- Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do ParanáCuritibaPuerto RicoBrazil
| | - Nicholas L. Balderston
- Center for Neuromodulation in Depression and StressUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Monique Ernst
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Christian Grillon
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | | | | | - Gregory A. Fonzo
- Department of PsychiatryThe University of Texas at Austin Dell Medical SchoolAustinTexasUSA
| | | | - Murray B. Stein
- Department of Psychiatry & Family Medicine and Public HealthUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Raquel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Bart Larsen
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jennifer Harper
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | - Michael Myers
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | | | - Qiongru Yu
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | | | - Dick J. Veltman
- Department. of PsychiatryAmsterdam UMC/VUMCAmsterdamThe Netherlands
| | - Ulrike Lueken
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Nic J. A. Van der Wee
- Leiden University Medical Center, Department of PsychiatryLeidenThe Netherlands
- Leiden Institute for Brain and Cognition (LIBC)LeidenThe Netherlands
| | - Dan J. Stein
- Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- SAMRC Unite on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Daniel S. Pine
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Anderson M. Winkler
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
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13
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Abstract
Quantitative susceptibility mapping (QSM) is an MRI-based, computational method for anatomically localizing and measuring concentrations of specific biomarkers in tissue such as iron. Growing research suggests QSM is a viable method for evaluating the impact of iron overload in neurological disorders and on cognitive performance in aging. Several software toolboxes are currently available to reconstruct QSM maps from 3D GRE MR Images. However, few if any software packages currently exist that offer fully automated pipelines for QSM-based data analyses: from DICOM images to region-of-interest (ROI) based QSM values. Even less QSM-based software exist that offer quality control measures for evaluating the QSM output. Here, we address these gaps in the field by introducing and demonstrating the reliability and external validity of Ironsmith; an open-source, fully automated pipeline for creating and processing QSM maps, extracting QSM values from subcortical and cortical brain regions (89 ROIs) and evaluating the quality of QSM data using SNR measures and assessment of outlier regions on phase images. Ironsmith also features automatic filtering of QSM outlier values and precise CSF-only QSM reference masks that minimize partial volume effects. Testing of Ironsmith revealed excellent intra- and inter-rater reliability. Finally, external validity of Ironsmith was demonstrated via an anatomically selective relationship between motor performance and Ironsmith-derived QSM values in motor cortex. In sum, Ironsmith provides a freely-available, reliable, turn-key pipeline for QSM-based data analyses to support research on the impact of brain iron in aging and neurodegenerative disease.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States.
| | - Christopher E Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States
| | - David K Powell
- Department of Neuroscience, Magnetic Resonance Imaging and Spectroscopy Center, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States
| | - Brian T Gold
- Department of Neuroscience, Sanders-Brown Center on Aging, Magnetic Resonance Imaging and Spectroscopy Center, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States.
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14
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Verdi S, Marquand AF, Schott JM, Cole JH. Beyond the average patient: how neuroimaging models can address heterogeneity in dementia. Brain 2021; 144:2946-2953. [PMID: 33892488 PMCID: PMC8634113 DOI: 10.1093/brain/awab165] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [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: 10/23/2020] [Revised: 02/24/2021] [Accepted: 04/08/2021] [Indexed: 11/25/2022] Open
Abstract
Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological 'fingerprints'. Such methods have the potential to detect clinically relevant subtypes, track an individual's disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.
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Affiliation(s)
- Serena Verdi
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - James H Cole
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
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15
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Kerscher SR, Zipfel J, Groeschel S, Bevot A, Haas-Lude K, Schuhmann MU. Comparison of B-Scan Ultrasound and MRI-Based Optic Nerve Sheath Diameter (ONSD) Measurements in Children. Pediatr Neurol 2021; 124:15-20. [PMID: 34508997 DOI: 10.1016/j.pediatrneurol.2021.08.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/09/2021] [Accepted: 08/08/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Qualitative, noninvasive assessment of intracranial pressure is of eminent importance in pediatric patients in many clinical situations and can reliably be performed using transorbital ultrasonographic measurement of the optic nerve sheath diameter (ONSD). MRI-based determination of ONSD can serve as an alternative if ultrasound (US) is not possible or available for various reasons, for example, in small, incompliant children. This study investigates repeatability and observer reliability of US ONSD and correlation and bias of US- versus MRI-based ONSD assessment in pediatric patients. METHODS One hundred fifty children diagnosed with tumor (n = 40), hydrocephalus (n = 42), and other cranial pathologies (n = 68) were included. Bilateral ONSD was quantified by US using a 12-MHz linear array transducer. This was compared with ONSD measured in simultaneously acquired (≤24 h) T2-weighted MRI scans of the orbit. RESULTS Repeatability of individual US values and intraobserver ONSD was outstanding (Cronbach's α = 0.984 and 0.996, respectively). Overall mean values for ONSD were 5.8 ± 0.88 mm and 5.7 ± 0.89 mm for US and MRI, respectively. Correlation between US and MRI-based ONSD was strong (r = 0.976, P < 0.01). Bland and Altman analysis showed a mean bias of 0.078 mm. A repeated-measures correlation (rrm) in 9 patients showed an excellent value (rrm = 0.94, P < 0.01). CONCLUSIONS Repeatability and reliability of US ONSD determination is excellent. In case US ONSD assessment is not possible or available, MRI scans can serve as an excellent alternative. The difference of US and MRI ONSD is minimal and insignificant, and thus, both techniques can complement each other.
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Affiliation(s)
- Susanne R Kerscher
- Division of Pediatric Neurosurgery, Department of Neurosurgery, University Hospital of Tuebingen, Tübingen, Germany.
| | - Julian Zipfel
- Division of Pediatric Neurosurgery, Department of Neurosurgery, University Hospital of Tuebingen, Tübingen, Germany
| | - Samuel Groeschel
- Department of Pediatric Neurology and Developmental Medicine, University Children's Hospital of Tuebingen, Tübingen, Germany
| | - Andrea Bevot
- Department of Pediatric Neurology and Developmental Medicine, University Children's Hospital of Tuebingen, Tübingen, Germany
| | - Karin Haas-Lude
- Department of Pediatric Neurology and Developmental Medicine, University Children's Hospital of Tuebingen, Tübingen, Germany
| | - Martin U Schuhmann
- Division of Pediatric Neurosurgery, Department of Neurosurgery, University Hospital of Tuebingen, Tübingen, Germany
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16
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Weinstein SM, Vandekar SN, Adebimpe A, Tapera TM, Robert‐Fitzgerald T, Gur RC, Gur RE, Raznahan A, Satterthwaite TD, Alexander‐Bloch AF, Shinohara RT. A simple permutation-based test of intermodal correspondence. Hum Brain Mapp 2021; 42:5175-5187. [PMID: 34519385 PMCID: PMC8519855 DOI: 10.1002/hbm.25577] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 12/15/2020] [Revised: 05/25/2021] [Accepted: 06/10/2021] [Indexed: 12/14/2022] Open
Abstract
Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state-of-the-art methods involve comparing observed group-level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group-level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject-level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p-value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n-back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference.
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Affiliation(s)
- Sarah M. Weinstein
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | | | - Azeez Adebimpe
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Tinashe M. Tapera
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Timothy Robert‐Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Ruben C. Gur
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Raquel E. Gur
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Armin Raznahan
- Section on Developmental NeurogenomicsNational Institute of Mental Health Intramural Research ProgramBethesdaMaryland
| | - Theodore D. Satterthwaite
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Aaron F. Alexander‐Bloch
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
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Hallett M, DelRosso LM, Elble R, Ferri R, Horak FB, Lehericy S, Mancini M, Matsuhashi M, Matsumoto R, Muthuraman M, Raethjen J, Shibasaki H. Evaluation of movement and brain activity. Clin Neurophysiol 2021; 132:2608-2638. [PMID: 34488012 PMCID: PMC8478902 DOI: 10.1016/j.clinph.2021.04.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 01/23/2021] [Revised: 04/07/2021] [Accepted: 04/25/2021] [Indexed: 11/25/2022]
Abstract
Clinical neurophysiology studies can contribute important information about the physiology of human movement and the pathophysiology and diagnosis of different movement disorders. Some techniques can be accomplished in a routine clinical neurophysiology laboratory and others require some special equipment. This review, initiating a series of articles on this topic, focuses on the methods and techniques. The methods reviewed include EMG, EEG, MEG, evoked potentials, coherence, accelerometry, posturography (balance), gait, and sleep studies. Functional MRI (fMRI) is also reviewed as a physiological method that can be used independently or together with other methods. A few applications to patients with movement disorders are discussed as examples, but the detailed applications will be the subject of other articles.
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Affiliation(s)
- Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA.
| | | | - Rodger Elble
- Department of Neurology, Southern Illinois University School of Medicine, Springfield, IL, USA
| | | | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Stephan Lehericy
- Paris Brain Institute (ICM), Centre de NeuroImagerie de Recherche (CENIR), Team "Movement, Investigations and Therapeutics" (MOV'IT), INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate, School of Medicine, Japan
| | - Riki Matsumoto
- Division of Neurology, Kobe University Graduate School of Medicine, Japan
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Jan Raethjen
- Neurology Outpatient Clinic, Preusserstr. 1-9, 24105 Kiel, Germany
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18
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Kindalova P, Kosmidis I, Nichols TE. Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework. Neuroimage 2021; 236:118090. [PMID: 33895308 PMCID: PMC8752964 DOI: 10.1016/j.neuroimage.2021.118090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 01/11/2021] [Revised: 03/08/2021] [Accepted: 04/14/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES White matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence. METHODS We review three regression approaches for modelling binary lesion masks including mass-univariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we propose matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting. RESULTS Our findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates. CONCLUSIONS Bias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available.
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Affiliation(s)
- Petya Kindalova
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Ioannis Kosmidis
- Department of Statistics, University of Warwick, Coventry CV4 7AL, UK; The Alan Turing Institute, London NW1 2DB, UK
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, OX3 7LF, UK.
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19
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Autio JA, Zhu Q, Li X, Glasser MF, Schwiedrzik CM, Fair DA, Zimmermann J, Yacoub E, Menon RS, Van Essen DC, Hayashi T, Russ B, Vanduffel W. Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection. Neuroimage 2021; 236:118082. [PMID: 33882349 PMCID: PMC8594288 DOI: 10.1016/j.neuroimage.2021.118082] [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: 09/01/2020] [Revised: 02/16/2021] [Accepted: 04/07/2021] [Indexed: 02/07/2023] Open
Abstract
Recent methodological advances in MRI have enabled substantial growth in neuroimaging studies of non-human primates (NHPs), while open data-sharing through the PRIME-DE initiative has increased the availability of NHP MRI data and the need for robust multi-subject multi-center analyses. Streamlined acquisition and analysis protocols would accelerate and improve these efforts. However, consensus on minimal standards for data acquisition protocols and analysis pipelines for NHP imaging remains to be established, particularly for multi-center studies. Here, we draw parallels between NHP and human neuroimaging and provide minimal guidelines for harmonizing and standardizing data acquisition. We advocate robust translation of widely used open-access toolkits that are well established for analyzing human data. We also encourage the use of validated, automated pre-processing tools for analyzing NHP data sets. These guidelines aim to refine methodological and analytical strategies for small and large-scale NHP neuroimaging data. This will improve reproducibility of results, and accelerate the convergence between NHP and human neuroimaging strategies which will ultimately benefit fundamental and translational brain science.
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Affiliation(s)
- Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Qi Zhu
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
| | - Xiaolian Li
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium
| | - Matthew F Glasser
- Departments of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Departments of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstraße 5, 37077 Göttingen, Germany; Perception and Plasticity Group, German Primate Center - Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Damien A Fair
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Jan Zimmermann
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, Western University, London, ON, Canada
| | - David C Van Essen
- Departments of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Brian Russ
- Department of Psychiatry, New York University Langone, New York City, New York, USA; Center for the Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York, USA; Department of Neuroscience, Icahn School of Medicine, Mount Sinai, New York City, New York, USA
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
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20
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Song X, García-Saldivar P, Kindred N, Wang Y, Merchant H, Meguerditchian A, Yang Y, Stein EA, Bradberry CW, Ben Hamed S, Jedema HP, Poirier C. Strengths and challenges of longitudinal non-human primate neuroimaging. Neuroimage 2021; 236:118009. [PMID: 33794361 PMCID: PMC8270888 DOI: 10.1016/j.neuroimage.2021.118009] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 07/21/2020] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 01/20/2023] Open
Abstract
Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.
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Affiliation(s)
- Xiaowei Song
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Pamela García-Saldivar
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Nathan Kindred
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, United Kingdom
| | - Hugo Merchant
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Adrien Meguerditchian
- Laboratoire de Psychologie Cognitive, UMR7290, Université Aix-Marseille/CNRS, Institut Language, Communication and the Brain 13331 Marseille, France
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Elliot A Stein
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Charles W Bradberry
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, Université de Lyon - CNRS, France
| | - Hank P Jedema
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA.
| | - Colline Poirier
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom.
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21
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Dadar M, Potvin O, Camicioli R, Duchesne S. Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations! Hum Brain Mapp 2021; 42:2734-2745. [PMID: 33783933 PMCID: PMC8127151 DOI: 10.1002/hbm.25398] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [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: 07/10/2020] [Revised: 02/19/2021] [Accepted: 02/19/2021] [Indexed: 12/11/2022] Open
Abstract
Volumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer gray matter (GM) structure volumes and its possible bias on functional relationships. T1-weighted images from 1,077 participants (4,321 timepoints) from the Alzheimer's Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery images. Mixed-effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overall WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. Participants with higher WMH volumes had higher overlaps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (p < .0001). When not corrected for WMHs, caudate volumes increased with age (p < .0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimer's disease patients. After correcting for WMHs, caudate volumes decreased with age (p < .0001), and Alzheimer's disease patients had lower caudate volumes than cognitively healthy individuals (p < .01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (p < .0001). Presence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.
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Affiliation(s)
- Mahsa Dadar
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
| | - Olivier Potvin
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
| | - Richard Camicioli
- Department of Medicine, Division of NeurologyUniversity of AlbertaEdmontonAlbertaCanada
| | - Simon Duchesne
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
- Department of Radiology and Nuclear Medicine, Faculty of MedicineUniversité LavalQuébecQuebecCanada
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22
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Wolfers T, Rokicki J, Alnæs D, Berthet P, Agartz I, Kia SM, Kaufmann T, Zabihi M, Moberget T, Melle I, Beckmann CF, Andreassen OA, Marquand AF, Westlye LT. Replicating extensive brain structural heterogeneity in individuals with schizophrenia and bipolar disorder. Hum Brain Mapp 2021; 42:2546-2555. [PMID: 33638594 PMCID: PMC8090780 DOI: 10.1002/hbm.25386] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [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: 10/30/2020] [Revised: 01/22/2021] [Accepted: 02/12/2021] [Indexed: 12/17/2022] Open
Abstract
Identifying brain processes involved in the risk and development of mental disorders is a major aim. We recently reported substantial interindividual heterogeneity in brain structural aberrations among patients with schizophrenia and bipolar disorder. Estimating the normative range of voxel-based morphometry (VBM) data among healthy individuals using a Gaussian process regression (GPR) enables us to map individual deviations from the healthy range in unseen datasets. Here, we aim to replicate our previous results in two independent samples of patients with schizophrenia (n1 = 94; n2 = 105), bipolar disorder (n1 = 116; n2 = 61), and healthy individuals (n1 = 400; n2 = 312). In line with previous findings with exception of the cerebellum our results revealed robust group level differences between patients and healthy individuals, yet only a small proportion of patients with schizophrenia or bipolar disorder exhibited extreme negative deviations from normality in the same brain regions. These direct replications support that group level-differences in brain structure disguise considerable individual differences in brain aberrations, with important implications for the interpretation and generalization of group-level brain imaging findings to the individual with a mental disorder.
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Affiliation(s)
- Thomas Wolfers
- Department of PsychologyUniversity of OsloOsloNorway
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
- Donders Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Jaroslav Rokicki
- Department of PsychologyUniversity of OsloOsloNorway
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
| | - Dag Alnæs
- Department of PsychologyUniversity of OsloOsloNorway
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
| | - Pierre Berthet
- Department of PsychologyUniversity of OsloOsloNorway
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
- KG Jebsen Center for Neurodevelopmental DisordersUniversity of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- Department of Clinical NeuroscienceCenter for Psychiatric ResearchStockholmSweden
| | - Seyed Mostafa Kia
- Donders Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Tobias Kaufmann
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
| | - Mariam Zabihi
- Donders Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Torgeir Moberget
- Department of PsychologyUniversity of OsloOsloNorway
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
| | - Ingrid Melle
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
| | - Christian F. Beckmann
- Donders Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenThe Netherlands
| | - Ole A. Andreassen
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
- KG Jebsen Center for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Andre F. Marquand
- Donders Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenThe Netherlands
- Department of Neuroimaging, Center for Neuroimaging SciencesInstitute of Psychiatry, King's College LondonLondonUK
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo and Oslo University HospitalOsloNorway
- KG Jebsen Center for Neurodevelopmental DisordersUniversity of OsloOsloNorway
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23
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Chen G, Nash TA, Cole KM, Kohn PD, Wei SM, Gregory MD, Eisenberg DP, Cox RW, Berman KF, Shane Kippenhan J. Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies. Neuroimage 2021; 233:117891. [PMID: 33667672 PMCID: PMC8284193 DOI: 10.1016/j.neuroimage.2021.117891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 11/01/2020] [Revised: 02/14/2021] [Accepted: 02/15/2021] [Indexed: 12/03/2022] Open
Abstract
The ubiquitous adoption of linearity for quantitative predictors in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variable's effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any nonlinearity of a quantitative predictor for population-level neuroimaging data analysis. With no prior knowledge regarding the underlying relationship other than a parsimonious assumption about the extent of smoothness (e.g., no sharp corners), we express the unknown relationship with a sufficient number of smoothing splines and use the data to adaptively determine the specifics of the nonlinearity. In addition to introducing the theoretical framework of MSS as an efficient approach with a counterbalance between flexibility and stability, we strive to (a) lay out the specific schemes for population-level nonlinear analyses that may involve task (e.g., contrasting conditions) and subject-grouping (e.g., patients vs controls) factors; (b) provide modeling accommodations to adaptively reveal, estimate and compare any nonlinear effects of a predictor across the brain, or to more accurately account for the effects (including nonlinear effects) of a quantitative confound; (c) offer the associated program 3dMSS to the neuroimaging community for whole-brain voxel-wise analysis as part of the AFNI suite; and (d) demonstrate the modeling approach and visualization processes with a longitudinal dataset of structural MRI scans.
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Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA.
| | - Tiffany A Nash
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Katherine M Cole
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA; Section on Behavioral Endocrinology, National Institute of Mental Health, USA
| | - Philip D Kohn
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Shau-Ming Wei
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA; Section on Behavioral Endocrinology, National Institute of Mental Health, USA
| | - Michael D Gregory
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Daniel P Eisenberg
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Robert W Cox
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Karen F Berman
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - J Shane Kippenhan
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
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Veraart J, Raven EP, Edwards LJ, Weiskopf N, Jones DK. The variability of MR axon radii estimates in the human white matter. Hum Brain Mapp 2021; 42:2201-2213. [PMID: 33576105 PMCID: PMC8046139 DOI: 10.1002/hbm.25359] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [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: 09/16/2020] [Revised: 01/07/2021] [Accepted: 01/21/2021] [Indexed: 12/13/2022] Open
Abstract
The noninvasive quantification of axonal morphology is an exciting avenue for gaining understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, that is, diffusion-weighted MRI, has been bolstered by recent hardware developments, specifically MR gradient design. Here the whole brain characterization of the effective MR axon radius is presented and the inter- and intra-scanner test-retest repeatability and reproducibility are evaluated to promote the further development of the effective MR axon radius as a neuroimaging biomarker. A coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius is observed in the test-retest analysis, but it is shown that the performance can be improved fourfold using a customized along-tract analysis.
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Affiliation(s)
- Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Erika P. Raven
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University Grossman School of MedicineNew YorkNew YorkUSA
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
| | - Luke J. Edwards
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Nikolaus Weiskopf
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth SciencesLeipzig UniversityLeipzigGermany
| | - Derek K. Jones
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
- Mary MacKillop Institute for Health ResearchAustralian Catholic UniversityMelbourneVictoriaAustralia
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Abstract
Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.
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Affiliation(s)
- Adam J Schwarz
- Takeda Pharmaceuticals Ltd., 40 Landsdowne Street, Cambridge, MA, 02139, USA.
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Lu H, Kashani AH, Arfanakis K, Caprihan A, DeCarli C, Gold BT, Li Y, Maillard P, Satizabal CL, Stables L, Wang DJJ, Corriveau RA, Singh H, Smith EE, Fischl B, van der Kouwe A, Schwab K, Helmer KG, Greenberg SM. MarkVCID cerebral small vessel consortium: II. Neuroimaging protocols. Alzheimers Dement 2021; 17:716-725. [PMID: 33480157 PMCID: PMC8627001 DOI: 10.1002/alz.12216] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [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: 05/26/2020] [Accepted: 09/22/2020] [Indexed: 01/04/2023]
Abstract
The MarkVCID consortium was formed under cooperative agreements with the National Institute of Neurologic Diseases and Stroke (NINDS) and National Institute on Aging (NIA) in 2016 with the goals of developing and validating biomarkers for the cerebral small vessel diseases associated with the vascular contributions to cognitive impairment and dementia (VCID). Rigorously validated biomarkers have consistently been identified as crucial for multicenter studies to identify effective strategies to prevent and treat VCID, specifically to detect increased VCID risk, diagnose the presence of small vessel disease and its subtypes, assess prognosis for disease progression or response to treatment, demonstrate target engagement or mechanism of action for candidate interventions, and monitor disease progression during treatment. The seven project sites and central coordinating center comprising MarkVCID, working with NINDS and NIA, identified a panel of 11 candidate fluid- and neuroimaging-based biomarker kits and established harmonized multicenter study protocols (see companion paper "MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols" for full details). Here we describe the MarkVCID neuroimaging protocols with specific focus on validating their application to future multicenter trials. MarkVCID procedures for participant enrollment; clinical and cognitive evaluation; and collection, handling, and instrumental validation of fluid samples are described in detail in a companion paper. Magnetic resonance imaging (MRI) has long served as the neuroimaging modality of choice for cerebral small vessel disease and VCID because of its sensitivity to a wide range of brain properties, including small structural lesions, connectivity, and cerebrovascular physiology. Despite MRI's widespread use in the VCID field, there have been relatively scant data validating the repeatability and reproducibility of MRI-based biomarkers across raters, scanner types, and time intervals (collectively defined as instrumental validity). The MRI protocols described here address the core MRI sequences for assessing cerebral small vessel disease in future research studies, specific sequence parameters for use across various research scanner types, and rigorous procedures for determining instrumental validity. Another candidate neuroimaging modality considered by MarkVCID is optical coherence tomography angiography (OCTA), a non-invasive technique for directly visualizing retinal capillaries as a marker of the cerebral capillaries. OCTA has theoretical promise as a unique opportunity to visualize small vessels derived from the cerebral circulation, but at a considerably earlier stage of development than MRI. The additional OCTA protocols described here address procedures for determining OCTA instrumental validity, evaluating sources of variability such as pupil dilation, and handling data to maintain participant privacy. MRI protocol and instrumental validation The core sequences selected for the MarkVCID MRI protocol are three-dimensional T1-weighted multi-echo magnetization-prepared rapid-acquisition-of-gradient-echo (ME-MPRAGE), three-dimensional T2-weighted fast spin echo fluid-attenuated-inversion-recovery (FLAIR), two-dimensional diffusion-weighted spin-echo echo-planar imaging (DWI), three-dimensional T2*-weighted multi-echo gradient echo (3D-GRE), three-dimensional T2 -weighted fast spin-echo imaging (T2w), and two-dimensional T2*-weighted gradient echo echo-planar blood-oxygenation-level-dependent imaging with brief periods of CO2 inhalation (BOLD-CVR). Harmonized parameters for each of these core sequences were developed for four 3 Tesla MRI scanner models in widespread use at academic medical centers. MarkVCID project sites are trained and certified for their instantiation of the consortium MRI protocols. Sites are required to perform image quality checks every 2 months using the Alzheimer's Disease Neuroimaging Initiative phantom. Instrumental validation for MarkVCID MRI-based biomarkers is operationally defined as inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility. Assessments of these instrumental properties are performed on individuals representing a range of cerebral small vessel disease from mild to severe. Inter-rater reliability is determined by distribution of an independent dataset of MRI scans to each analysis site. Test-retest repeatability is determined by repeat MRI scans performed on individual participants on a single MRI scanner after a short (1- to 14-day) interval. Inter-scanner reproducibility is determined by repeat MRI scans performed on individuals performed across four MRI scanner models. OCTA protocol and instrumental validation The MarkVCID OCTA protocol uses a commercially available, Food and Drug Administration-approved OCTA apparatus. Imaging is performed on one dilated and one undilated eye to assess the need for dilation. Scans are performed in quadruplicate. MarkVCID project sites participating in OCTA validation are trained and certified by this biomarker's lead investigator. Inter-rater reliability for OCTA is assessed by distribution of OCTA datasets to each analysis site. Test-retest repeatability is assessed by repeat OCTA imaging on individuals on the same day as their baseline OCTA and a different-day repeat session after a short (1- to 14-day) interval. Methods were developed to allow the OCTA data to be de-identified by the sites before transmission to the central data management system. The MarkVCID neuroimaging protocols, like the other MarkVCID procedures, are designed to allow translation to multicenter trials and as a template for outside groups to generate directly comparable neuroimaging data. The MarkVCID neuroimaging protocols are available to the biomedical community and intended to be shared. In addition to the instrumental validation procedures described here, each of the neuroimaging MarkVCID kits will undergo biological validation to determine its ability to measure important aspects of VCID such as cognitive function. The analytic methods for the neuroimaging-based kits and the results of these validation studies will be published separately. The results will ultimately determine the neuroimaging kits' potential usefulness for multicenter interventional trials in small vessel disease-related VCID.
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Affiliation(s)
- Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Amir H. Kashani
- Department of Ophthalmology, USC Roski Eye Institute, USC Ginsberg Institute for Biomedical Therapeutics, Los Angeles, CA 90033; Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616; Rush Alzheimer’s Disease Center, Department of Diagnostic Radiology and Nuclear Medicine, Rush University, Chicago, IL 60612, USA
| | | | - Charles DeCarli
- Department of Neurology, University of California, Davis, Davis, CA 95616, USA
| | - Brian T. Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY 40508, USA
| | - Yang Li
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, Davis, CA 95616, USA
| | - Claudia L. Satizabal
- Department of Epidemiology and Biostatistics, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Lara Stables
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Danny JJ Wang
- Departments of Neurology and Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | | | - Herpreet Singh
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Eric E. Smith
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Bruce Fischl
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129; Department of Radiology, Harvard Medical School, Boston, MA 02114, USA
- Computer Science and AI Lab, MIT, Cambridge, MA 02139, USA
| | - Andre van der Kouwe
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129; Department of Radiology, Harvard Medical School, Boston, MA 02114, USA
| | - Kristin Schwab
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Karl G. Helmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129; Department of Radiology, Harvard Medical School, Boston, MA 02114, USA
| | - Steven M. Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
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Paladini D, Malinger G, Birnbaum R, Monteagudo A, Pilu G, Salomon LJ, Timor-Tritsch IE. ISUOG Practice Guidelines (updated): sonographic examination of the fetal central nervous system. Part 2: performance of targeted neurosonography. Ultrasound Obstet Gynecol 2021; 57:661-671. [PMID: 33734522 DOI: 10.1002/uog.23616] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 02/10/2021] [Indexed: 06/12/2023]
Affiliation(s)
- D Paladini
- Fetal Medicine and Surgery Unit, Istituto G. Gaslini, Genoa, Italy
| | - G Malinger
- Division of Ultrasound in Obstetrics and Gynecology, Lis Maternity Hospital, Tel Aviv Sourasky Medical Centre, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - R Birnbaum
- Division of Ultrasound in Obstetrics and Gynecology, Lis Maternity Hospital, Tel Aviv Sourasky Medical Centre, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - A Monteagudo
- Carnegie Imaging for Women, Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - G Pilu
- Obstetric Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - L J Salomon
- Hôpital Necker Enfants Malades, AP-HP, and LUMIERE platform, EA 7328 Université de Paris, Paris, France
| | - I E Timor-Tritsch
- Division of Obstetrical and Gynecological Ultrasound, NYU School of Medicine, New York, NY, USA
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Ni H, Feng Z, Guan Y, Jia X, Chen W, Jiang T, Zhong Q, Yuan J, Ren M, Li X, Gong H, Luo Q, Li A. DeepMapi: a Fully Automatic Registration Method for Mesoscopic Optical Brain Images Using Convolutional Neural Networks. Neuroinformatics 2021; 19:267-284. [PMID: 32754778 PMCID: PMC8004526 DOI: 10.1007/s12021-020-09483-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [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] [Indexed: 11/05/2022]
Abstract
The extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. However, different brain images are normally not naturally aligned even when they are imaged with the same setup, let alone under the differing resolutions and dataset sizes used in mesoscopic imaging. As a result, it is difficult to achieve high-throughput automatic registration without manual intervention. Here, we propose a deep learning-based registration method called DeepMapi to predict a deformation field used to register mesoscopic optical images to an atlas. We use a self-feedback strategy to address the problem of imbalanced training sets (sampling at a fixed step size in nonuniform brains of structures and deformations) and use a dual-hierarchical network to capture the large and small deformations. By comparing DeepMapi with other registration methods, we demonstrate its superiority over a set of ground truth images, including both optical and MRI images. DeepMapi achieves fully automatic registration of mesoscopic micro-optical images, even macroscopic MRI datasets, in minutes, with an accuracy comparable to those of manual annotations by anatomists.
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Affiliation(s)
- Hong Ni
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Zhao Feng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yue Guan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xueyan Jia
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Wu Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Jiang
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Qiuyuan Zhong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Miao Ren
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China.
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Takata N, Sato N, Komaki Y, Okano H, Tanaka KF. Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy. Sci Rep 2021; 11:6234. [PMID: 33737651 PMCID: PMC7973786 DOI: 10.1038/s41598-021-85807-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 02/27/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
A brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher's necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.
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Affiliation(s)
- Norio Takata
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.
- Central Institute for Experimental Animals (CIEA), 3-25-12, Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan.
| | - Nobuhiko Sato
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Yuji Komaki
- Central Institute for Experimental Animals (CIEA), 3-25-12, Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Kenji F Tanaka
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
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Abstract
PURPOSE OF REVIEW This article describes how imaging can be used by physicians in diagnosing, determining prognosis, and making appropriate treatment decisions in a timely manner in patients with acute stroke. RECENT FINDINGS Advances in acute stroke treatment, including the use of endovascular thrombectomy in patients with large vessel occlusion and, more recently, of IV thrombolysis in an extended time window, have resulted in a paradigm shift in how imaging is used in patients with acute stroke. This paradigm shift, combined with the understanding that "time is brain," means that imaging must be fast, reliable, and available around the clock for physicians to make appropriate clinical decisions. CT has therefore become the primary imaging modality of choice. Recognition of a large vessel occlusion using CT angiography has become essential in identifying patients for endovascular thrombectomy, and techniques such as imaging collaterals on CT angiography or measuring blood flow to predict tissue fate using CT perfusion have become useful tools in selecting patients for acute stroke therapy. Understanding the use of these imaging modalities and techniques in dealing with an emergency such as acute stroke has therefore become more important than ever for physicians treating patients with acute stroke. SUMMARY Imaging the brain and the blood vessels supplying it using modern tools and techniques is a key step in understanding the pathophysiology of acute stroke and making appropriate and timely clinical decisions.
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Heim B, Mangesius S, Krismer F, Wenning GK, Hussl A, Scherfler C, Gizewski ER, Schocke M, Esterhammer R, Quattrone A, Poewe W, Seppi K. Diagnostic accuracy of MR planimetry in clinically unclassifiable parkinsonism. Parkinsonism Relat Disord 2020; 82:87-91. [PMID: 33271461 DOI: 10.1016/j.parkreldis.2020.11.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 11/17/2020] [Accepted: 11/20/2020] [Indexed: 01/08/2023]
Abstract
INTRODUCTION Quantitative MR planimetric measurements were reported to discriminate between progressive supranuclear palsy (PSP) and non-PSP parkinsonism, yet few data exist on the usefulness of these markers in early disease stages. METHODS The pons-to-midbrain area ratio (P/M) and the Magnetic Resonance Parkinsonism Index (MRPI) as well as new indices, termed P/M2.0 and MRPI2.0, multiplying the former by a ratio of the third ventricle (3rdV) width/frontal horns (FH) width, were calculated on T1-weighted images in 84 patients with clinically unclassifiable neurodegenerative parkinsonism (CUP) at the time of imaging. Areas under the curve (AUCs) of these markers for predicting future PSP was determined. The final clinical diagnosis was made after at least 24 months of follow-up. RESULTS Final diagnosis was Parkinson's disease in 55 patients, multiple system atrophy in 12 cases, and PSP in 17. At baseline imaging, patients with a final PSP diagnosis had significantly higher MRPI, P/M, MRPI2.0 and P/M2.0 values compared to the other groups. AUCs in discriminating between future PSP and non-PSP parkinsonism were 0.91 for both the P/M and the MRPI and 0.98 for the P/M2.0 and the MRPI2.0. CONCLUSIONS Brainstem-derived MR planimetric measures yield high diagnostic accuracy for separating PSP from non-PSP parkinsonism in early disease stages when clinical criteria are not yet fully met. Consistent with the underlying pathology in PSP, our study suggests that inclusion of 3rdV width makes P/M2.0 and MRPI2.0 more accurate in diagnosing early stage PSP patients than the P/M and MRPI.
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Affiliation(s)
- Beatrice Heim
- Department of Neurology, Medical University of Innsbruck, Austria
| | - Stephanie Mangesius
- Department of Neuroradiology, Medical University Innsbruck, Innsbruck, Austria; Neuroimaging Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Florian Krismer
- Department of Neurology, Medical University of Innsbruck, Austria
| | - Gregor K Wenning
- Department of Neurology, Medical University of Innsbruck, Austria
| | - Anna Hussl
- Department of Neurology, Medical University of Innsbruck, Austria
| | - Christoph Scherfler
- Department of Neurology, Medical University of Innsbruck, Austria; Neuroimaging Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Elke R Gizewski
- Department of Neuroradiology, Medical University Innsbruck, Innsbruck, Austria; Neuroimaging Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Michael Schocke
- Department of Neuroradiology, Medical University Innsbruck, Innsbruck, Austria; Neuroimaging Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Regina Esterhammer
- Department of Neuroradiology, Medical University Innsbruck, Innsbruck, Austria
| | - Andrea Quattrone
- Institute of Neurology, Department of Medical Sciences, Magna Graecia University of Catanzaro, Italy
| | - Werner Poewe
- Department of Neurology, Medical University of Innsbruck, Austria; Neuroimaging Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Klaus Seppi
- Department of Neurology, Medical University of Innsbruck, Austria; Neuroimaging Core Facility, Medical University Innsbruck, Innsbruck, Austria.
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St‐Jean S, Viergever MA, Leemans A. Harmonization of diffusion MRI data sets with adaptive dictionary learning. Hum Brain Mapp 2020; 41:4478-4499. [PMID: 32851729 PMCID: PMC7555079 DOI: 10.1002/hbm.25117] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [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: 04/23/2020] [Revised: 06/11/2020] [Accepted: 06/16/2020] [Indexed: 01/05/2023] Open
Abstract
Diffusion magnetic resonance imaging can indirectly infer the microstructure of tissues and provide metrics subject to normal variability in a population. Potentially abnormal values may yield essential information to support analysis of controls and patients cohorts, but subtle confounds could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability of the data. Our harmonization algorithm does not require paired training data sets, nor spatial registration or matching spatial resolution. Overcomplete dictionaries are learned iteratively from all data sets at the same time with an adaptive regularization criterion, removing variability attributable to the scanners in the process. The obtained mapping is applied directly in the native space of each subject toward a scanner-space. The method is evaluated with a public database which consists of two different protocols acquired on three different scanners. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner. Experiments with alterations using a free water compartment, which is not simulated in the training data, shows that the modifications applied to the diffusion weighted images are preserved in the diffusion metrics after harmonization, while still reducing global variability at the same time. The algorithm could help multicenter studies pooling their data by removing scanner specific confounds, and increase statistical power in the process.
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Affiliation(s)
- Samuel St‐Jean
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Max A. Viergever
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Alexander Leemans
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
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Abstract
Antiepileptic drugs afford good seizure control for approximately 70% of individuals with epilepsy. Epilepsy surgery is extremely helpful for appropriate individuals with drug resistance. Since antiquity, trephination was a crude and invasive technique to manage epilepsy. The late 1800s saw the advent of a more evidence-based approach with attempts to define seizure foci and determine areas of function. Seizure localization initially required direct brain stimulation during surgery before resection. Fortunately, improved knowledge of seizure semiology and advancements in preoperative investigations have enabled epilepsy specialists to better analyze the benefit of seizure reduction versus risk of functional harm. This preoperative phase and the investigative techniques used to analyze surgical candidacy will be discussed in this article.
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Affiliation(s)
- Dave F Clarke
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas.
| | - Ekta G Shah
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas
| | - Freedom F Perkins
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas
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Cauda F, Nani A, Liloia D, Manuello J, Premi E, Duca S, Fox PT, Costa T. Finding specificity in structural brain alterations through Bayesian reverse inference. Hum Brain Mapp 2020; 41:4155-4172. [PMID: 32829507 PMCID: PMC7502845 DOI: 10.1002/hbm.25105] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 02/20/2020] [Revised: 05/19/2020] [Accepted: 06/10/2020] [Indexed: 12/20/2022] Open
Abstract
In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.
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Affiliation(s)
- Franco Cauda
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Andrea Nani
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Donato Liloia
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Jordi Manuello
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Enrico Premi
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali CiviliSpedali Civili HospitalBresciaItaly
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Sergio Duca
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
| | - Peter T. Fox
- Research Imaging InstituteUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- South Texas Veterans Health Care SystemSan AntonioTexasUSA
| | - Tommaso Costa
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
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Stolicyn A, Harris MA, Shen X, Barbu MC, Adams MJ, Hawkins EL, de Nooij L, Yeung HW, Murray AD, Lawrie SM, Steele JD, McIntosh AM, Whalley HC. Automated classification of depression from structural brain measures across two independent community-based cohorts. Hum Brain Mapp 2020; 41:3922-3937. [PMID: 32558996 PMCID: PMC7469862 DOI: 10.1002/hbm.25095] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [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: 12/07/2019] [Revised: 05/16/2020] [Accepted: 05/25/2020] [Indexed: 12/30/2022] Open
Abstract
Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.
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Affiliation(s)
- Aleks Stolicyn
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Mathew A. Harris
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Xueyi Shen
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Miruna C. Barbu
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Mark J. Adams
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Emma L. Hawkins
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Laura de Nooij
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Hon Wah Yeung
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Alison D. Murray
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenLilian Sutton Building, ForesterhillAberdeenUK
| | - Stephen M. Lawrie
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - J. Douglas Steele
- School of Medicine (Division of Imaging Science and Technology)University of DundeeDundeeUK
| | - Andrew M. McIntosh
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Heather C. Whalley
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
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Arabi H, Bortolin K, Ginovart N, Garibotto V, Zaidi H. Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum Brain Mapp 2020; 41:3667-3679. [PMID: 32436261 PMCID: PMC7416024 DOI: 10.1002/hbm.25039] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [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: 03/03/2020] [Revised: 04/15/2020] [Accepted: 05/08/2020] [Indexed: 12/25/2022] Open
Abstract
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized from nonattenuation corrected PET (PET-nonAC) images in an end-to-end fashion using deep learning approaches (DLAC) is evaluated for various radiotracers used in molecular neuroimaging studies. One hundred eighty brain PET scans acquired using 18 F-FDG, 18 F-DOPA, 18 F-Flortaucipir (targeting tau pathology), and 18 F-Flutemetamol (targeting amyloid pathology) radiotracers (40 + 5, training/validation + external test, subjects for each radiotracer) were included. The PET data were reconstructed using CT-based AC (CTAC) to generate reference PET-CTAC and without AC to produce PET-nonAC images. A deep convolutional neural network was trained to generate PET attenuation corrected images (PET-DLAC) from PET-nonAC. The quantitative accuracy of this approach was investigated separately for each radiotracer considering the values obtained from PET-CTAC images as reference. A segmented AC map (PET-SegAC) containing soft-tissue and background air was also included in the evaluation. Quantitative analysis of PET images demonstrated superior performance of the DLAC approach compared to SegAC technique for all tracers. Despite the relatively low quantitative bias observed when using the DLAC approach, this approach appears vulnerable to outliers, resulting in noticeable local pseudo uptake and false cold regions. Direct AC in image-space using deep learning demonstrated quantitatively acceptable performance with less than 9% absolute SUV bias for the four different investigated neuroimaging radiotracers. However, this approach is vulnerable to outliers which result in large local quantitative bias.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical ImagingGeneva University HospitalGenevaSwitzerland
| | - Karin Bortolin
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical ImagingGeneva University HospitalGenevaSwitzerland
| | - Nathalie Ginovart
- Department of PsychiatryGeneva UniversityGenevaSwitzerland
- Department of Basic NeurosciencesGeneva UniversityGenevaSwitzerland
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical ImagingGeneva University HospitalGenevaSwitzerland
- Geneva Neuroscience CenterGeneva UniversityGenevaSwitzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical ImagingGeneva University HospitalGenevaSwitzerland
- Geneva Neuroscience CenterGeneva UniversityGenevaSwitzerland
- Department of Nuclear Medicine and Molecular ImagingUniversity of Groningen, University Medical Center GroningenGroningenNetherlands
- Department of Nuclear MedicineUniversity of Southern DenmarkOdenseDenmark
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37
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Cooney TM, Cohen KJ, Guimaraes CV, Dhall G, Leach J, Massimino M, Erbetta A, Chiapparini L, Malbari F, Kramer K, Pollack IF, Baxter P, Laughlin S, Patay Z, Young Poussaint T, Warren KE. Response assessment in diffuse intrinsic pontine glioma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. Lancet Oncol 2020; 21:e330-e336. [PMID: 32502459 DOI: 10.1016/s1470-2045(20)30166-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/25/2020] [Accepted: 03/04/2020] [Indexed: 12/20/2022]
Abstract
Optimising the conduct of clinical trials for diffuse intrinsic pontine glioma involves use of consistent, objective disease assessments and standardised response criteria. The Response Assessment in Pediatric Neuro-Oncology working group, consisting of an international panel of paediatric and adult neuro-oncologists, clinicians, radiologists, radiation oncologists, and neurosurgeons, was established to address issues and unique challenges in assessing response in children with CNS tumours. A working group was formed specifically to address response assessment in children and young adults with diffuse intrinsic pontine glioma and to develop a consensus on recommendations for response assessment. Response should be assessed using MRI of brain and spine, neurological examination, and anti-inflammatory or antiangiogenic drugs. Clinical imaging standards are defined. As with previous consensus recommendations, these recommendations will need to be validated in prospective clinical trials.
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Affiliation(s)
- Tabitha M Cooney
- Department of Pediatric Oncology, Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kenneth J Cohen
- Departments of Pediatrics and Oncology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Girish Dhall
- Department of Pediatrics, Division of Hematology-Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - James Leach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Maura Massimino
- Department of Pediatric Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Alessandra Erbetta
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Luisa Chiapparini
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Fatema Malbari
- Department of Pediatrics, Section of Neurology and Developmental Neurosciences, Texas Children's Hospital, Houston, TX, USA
| | - Kim Kramer
- Department of Pediatric Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ian F Pollack
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Patricia Baxter
- Section of Pediatric Hematology-Oncology, Texas Children's Hospital, Houston, TX, USA
| | - Suzanne Laughlin
- Department of Medical Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Zoltán Patay
- Department of Radiology, St Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Katherine E Warren
- Department of Pediatric Oncology, Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Dana-Farber Cancer Institute, Boston, MA, USA.
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38
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Jelistratova I, Teipel SJ, Grothe MJ. Longitudinal validity of PET-based staging of regional amyloid deposition. Hum Brain Mapp 2020; 41:4219-4231. [PMID: 32648624 PMCID: PMC7502828 DOI: 10.1002/hbm.25121] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 12/20/2019] [Revised: 05/29/2020] [Accepted: 06/22/2020] [Indexed: 12/12/2022] Open
Abstract
Positron emission tomography (PET)-based staging of regional amyloid deposition has recently emerged as a promising tool for sensitive detection and stratification of pathology progression in Alzheimer's Disease (AD). Here we present an updated methodological framework for PET-based amyloid staging using region-specific amyloid-positivity thresholds and assess its longitudinal validity using serial PET acquisitions. We defined region-specific thresholds of amyloid-positivity based on Florbetapir-PET data of 13 young healthy individuals (age ≤ 45y), applied these thresholds to Florbetapir-PET data of 179 cognitively normal older individuals to estimate a regional amyloid staging model, and tested this model in a larger sample of patients with mild cognitive impairment (N = 403) and AD dementia (N = 85). 2-year follow-up Florbetapir-PET scans from a subset of this sample (N = 436) were used to assess the longitudinal validity of the cross-sectional model based on individual stage transitions and data-driven longitudinal trajectory modeling. Results show a remarkable congruence between cross-sectionally estimated and longitudinally modeled trajectories of amyloid accumulation, beginning in anterior temporal areas, followed by frontal and medial parietal areas, the remaining associative neocortex, and finally primary sensory-motor areas and subcortical regions. Over 98% of individual amyloid deposition profiles and longitudinal stage transitions adhered to this staging scheme of regional pathology progression, which was further supported by corresponding changes in cerebrospinal fluid biomarkers. In conclusion, we provide a methodological refinement and longitudinal validation of PET-based staging of regional amyloid accumulation, which may help improving early detection and in-vivo stratification of pathologic disease progression in AD.
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Affiliation(s)
| | - Stefan J. Teipel
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
- Department of Psychosomatic MedicineUniversity of RostockRostockGermany
| | - Michel J. Grothe
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de SevillaHospital Universitario Virgen del Rocío/CSIC/Universidad de SevillaSevilleSpain
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Zhang J, Liu Z, Zhang S, Zhang H, Spincemaille P, Nguyen TD, Sabuncu MR, Wang Y. Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction. Neuroimage 2020; 211:116579. [PMID: 31981779 PMCID: PMC7093048 DOI: 10.1016/j.neuroimage.2020.116579] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 12/20/2019] [Accepted: 01/20/2020] [Indexed: 01/19/2023] Open
Abstract
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DL-based methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DL-based image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pre-trained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and under-sampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.
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Affiliation(s)
- Jinwei Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Zhe Liu
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Shun Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Hang Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Mert R Sabuncu
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
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Ma R, Akçakaya M, Moeller S, Auerbach E, Uğurbil K, Van de Moortele PF. A field-monitoring-based approach for correcting eddy-current-induced artifacts of up to the 2 nd spatial order in human-connectome-project-style multiband diffusion MRI experiment at 7T: A pilot study. Neuroimage 2020; 216:116861. [PMID: 32305565 DOI: 10.1016/j.neuroimage.2020.116861] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 04/09/2020] [Accepted: 04/14/2020] [Indexed: 01/30/2023] Open
Abstract
Over the recent years, significant advances in Spin-Echo (SE) Echo-Planar (EP) Diffusion MRI (dMRI) have enabled improved fiber tracking conspicuity in the human brain. At the same time, pushing the spatial resolution and using higher b-values inherently expose the acquired images to further eddy-current-induced distortion and blurring. Recently developed data-driven correction techniques, capable of significantly mitigating these defects, are included in the reconstruction pipelines developed for the Human Connectome Project (HCP) driven by the NIH BRAIN initiative. In this case, however, corrections are derived from the original diffusion-weighted (DW) magnitude images affected by distortion and blurring. Considering the complexity of k-space deviations in the presence of time varying high spatial order eddy currents, distortion and blurring may not be fully reversed when relying on magnitude DW images only. An alternative approach, consisting of iteratively reconstructing DW images based on the actual magnetic field spatiotemporal evolution measured with a magnetic field monitoring camera, has been successfully implemented at 3T in single band dMRI (Wilm et al., 2017, 2015). In this study, we aim to demonstrate the efficacy of this eddy current correction method in the challenging context of HCP-style multiband (MB = 2) dMRI protocol. The magnetic field evolution was measured during the EP-dMRI readout echo train with a field monitoring camera equipped with 16 19F NMR probes. The time variation of 0th, 1st and 2nd order spherical field harmonics were used to reconstruct DW images. Individual DW images reconstructed with and without field correction were compared. The impact of eddy current correction was evaluated by comparing the corresponding direction-averaged DW images and fractional anisotropy (FA) maps. 19F field monitoring data confirmed the existence of significant field deviations induced by the diffusion-encoding gradients, with variations depending on diffusion gradient amplitude and direction. In DW images reconstructed with the field correction, residual aliasing artifacts were reduced or eliminated, and when high b-values were applied, better gray/white matter delineation and sharper gyri contours were observed, indicating reduced signal blurring. The improvement in image quality further contributed to sharper contours and better gray/white matter delineation in mean DW images and FA maps. In conclusion, we demonstrate that up-to-2nd-order-eddy-current-induced field perturbation in multiband, in-plane accelerated HCP-style dMRI acquisition at 7T can be corrected by integrating the measured field evolution in image reconstruction.
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Affiliation(s)
- Ruoyun Ma
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Mehmet Akçakaya
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA; Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Steen Moeller
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Edward Auerbach
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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41
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Bagnato F, Franco G, Ye F, Fan R, Commiskey P, Smith SA, Xu J, Dortch R. Selective inversion recovery quantitative magnetization transfer imaging: Toward a 3 T clinical application in multiple sclerosis. Mult Scler 2020; 26:457-467. [PMID: 30907234 PMCID: PMC7528886 DOI: 10.1177/1352458519833018] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [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] [Indexed: 11/16/2022]
Abstract
BACKGROUND Assessing the degree of myelin injury in patients with multiple sclerosis (MS) is challenging due to the lack of magnetic resonance imaging (MRI) methods specific to myelin quantity. By measuring distinct tissue parameters from a two-pool model of the magnetization transfer (MT) effect, quantitative magnetization transfer (qMT) may yield these indices. However, due to long scan times, qMT has not been translated clinically. OBJECTIVES We aim to assess the clinical feasibility of a recently optimized selective inversion recovery (SIR) qMT and to test the hypothesis that SIR-qMT-derived metrics are informative of radiological and clinical disease-related changes in MS. METHODS A total of 18 MS patients and 9 age- and sex-matched healthy controls (HCs) underwent a 3.0 Tesla (3 T) brain MRI, including clinical scans and an optimized SIR-qMT protocol. Four subjects were re-scanned at a 2-week interval to determine inter-scan variability. RESULTS SIR-qMT measures differed between lesional and non-lesional tissue (p < 0.0001) and between normal-appearing white matter (NAWM) of patients with more advanced disability and normal white matter (WM) of HCs (p < 0.05). SIR-qMT measures were associated with lesion volumes, disease duration, and disability scores (p ⩽ 0.002). CONCLUSION SIR-qMT at 3 T is clinically feasible and predicts both radiological and clinical disease severity in MS.
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Affiliation(s)
- Francesca Bagnato
- Department of Neurology, Neuro-Immunology Division/Neuro-Imaging Unit, Vanderbilt University Medical Center (VUMC), Nashville, TN
| | - Giulia Franco
- Department of Neurology, Neuro-Immunology Division/Neuro-Imaging Unit, Vanderbilt University Medical Center (VUMC), Nashville, TN
- IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, Dino Ferrari Center, Neuroscience Section, Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Fei Ye
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN; USA
| | - Run Fan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN; USA
| | | | - Seth A. Smith
- Vanderbilt University Institute of Imaging Science; Nashville, TN
| | - Junzhong Xu
- Vanderbilt University Institute of Imaging Science; Nashville, TN
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Richard Dortch
- Vanderbilt University Institute of Imaging Science; Nashville, TN
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
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42
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Tognin S, van Hell HH, Merritt K, Winter-van Rossum I, Bossong MG, Kempton MJ, Modinos G, Fusar-Poli P, Mechelli A, Dazzan P, Maat A, de Haan L, Crespo-Facorro B, Glenthøj B, Lawrie SM, McDonald C, Gruber O, van Amelsvoort T, Arango C, Kircher T, Nelson B, Galderisi S, Bressan R, Kwon JS, Weiser M, Mizrahi R, Sachs G, Maatz A, Kahn R, McGuire P. Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies-PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice. Schizophr Bull 2020; 46:432-441. [PMID: 31424555 PMCID: PMC7043057 DOI: 10.1093/schbul/sbz067] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.
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Affiliation(s)
- Stefania Tognin
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK,Outreach and Support in South London (OASIS), South London and Maudsley NHS Foundation Trust, London, UK
| | - Hendrika H van Hell
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands,To whom correspondence should be addressed; Clinical Trial Center, Department of Psychiatry, University Medical Center Utrecht Brain Center, PO Box 85500, 3508 GA Utrecht, The Netherlands; tel: +31 88 755 7247, e-mail:
| | - Kate Merritt
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Inge Winter-van Rossum
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Matthijs G Bossong
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Arija Maat
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Lieuwe de Haan
- Department Early Psychosis, Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Benedicto Crespo-Facorro
- CIBERSAM, Department of Psychiatry, University Hospital Virgen del Rocío, Sevilla, Spain,IDIVAL, Marqués de Valdecilla University Hospital, Santander, Spain,School of Medicine, University of Cantabria, Santander, Spain
| | - Birte Glenthøj
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark,Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañon, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Tilo Kircher
- Department of Psychiatry, University of Marburg, Marburg, Germany
| | - Barnaby Nelson
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Rodrigo Bressan
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Department of Psychiatry, Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil
| | - Jun S Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Mark Weiser
- Department of Psychiatry, Sheba Medical Center, Tel Hashomer, Israel,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Romina Mizrahi
- Institute of Medical Science, University of Toronto, Toronto, Canada,Centre for Addiction and Mental Health, Toronto, Canada,Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Gabriele Sachs
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Anke Maatz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - René Kahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Phillip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK,Outreach and Support in South London (OASIS), South London and Maudsley NHS Foundation Trust, London, UK,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
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Huang SJ, Diao SS, Lu Y, Li T, Zhang LL, Ding YP, Fang Q, Cai XY, Xu Z, Kong Y. Value of thrombus imaging in predicting the outcomes of patients with large-vessel occlusive strokes after endovascular therapy. Neurol Sci 2020; 41:1451-1458. [PMID: 32086687 DOI: 10.1007/s10072-020-04296-7] [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] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 11/22/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Acute ischemic stroke leads to serious long-term disability and high mortality, especially in patients with large-vessel occlusive strokes. Nowadays, endovascular therapy is considered as an alternative treatment for these patients. Several studies have used thrombus characteristics based on non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) to predict prognosis in ischemic stroke. We conducted a systematic review to identify potential imaging predictive factors for successful recanalization and improved clinical outcome after endovascular therapy in patients with large-vessel occlusion (LVO) in anterior arterial circulation. METHODS The PubMed databases were searched for related studies reported between September 18, 2009, and September 18, 2019. RESULTS We selected 11 studies on revascularization and 12 studies on clinical outcome. Patients with thrombus of higher Hounsfield unit (HU), shorter length, higher clot burden score, and increased thrombus permeability may achieve higher recanalization and improved clinical outcome, but the matter is still under debate. CONCLUSION Imaging of thrombus can be used as an aseessment tool to predict the outcomes and it needs further studies in the future.
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Affiliation(s)
- Shuang-Jiao Huang
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China
| | - Shan-Shan Diao
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China
| | - Yue Lu
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China
| | - Tan Li
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China
| | - Lu-Lu Zhang
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China
| | - Yi-Ping Ding
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China
| | - Qi Fang
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China
| | - Xiu-Ying Cai
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China.
| | - Zhuan Xu
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China.
| | - Yan Kong
- Department of neurology, The First Affiliated Hospital of Soochow University, No.899, Pinghai Road, Suzhou, 215000, Jiangsu, China.
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Pizzolato M, Gilbert G, Thiran JP, Descoteaux M, Deriche R. Adaptive phase correction of diffusion-weighted images. Neuroimage 2020; 206:116274. [PMID: 31629826 PMCID: PMC7355239 DOI: 10.1016/j.neuroimage.2019.116274] [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] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 10/08/2019] [Accepted: 10/10/2019] [Indexed: 12/22/2022] Open
Abstract
Phase correction (PC) is a preprocessing technique that exploits the phase of images acquired in Magnetic Resonance Imaging (MRI) to obtain real-valued images containing tissue contrast with additive Gaussian noise, as opposed to magnitude images which follow a non-Gaussian distribution, e.g. Rician. PC finds its natural application to diffusion-weighted images (DWIs) due to their inherent low signal-to-noise ratio and consequent non-Gaussianity that induces a signal overestimation bias that propagates to the calculated diffusion indices. PC effectiveness depends upon the quality of the phase estimation, which is often performed via a regularization procedure. We show that a suboptimal regularization can produce alterations of the true image contrast in the real-valued phase-corrected images. We propose adaptive phase correction (APC), a method where the phase is estimated by using MRI noise information to perform a complex-valued image regularization that accounts for the local variance of the noise. We show, on synthetic and acquired data, that APC leads to phase-corrected real-valued DWIs that present a reduced number of alterations and a reduced bias. The substantial absence of parameters for which human input is required favors a straightforward integration of APC in MRI processing pipelines.
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Affiliation(s)
- Marco Pizzolato
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | | | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Rachid Deriche
- Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, France
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Goubran M, Ntiri EE, Akhavein H, Holmes M, Nestor S, Ramirez J, Adamo S, Ozzoude M, Scott C, Gao F, Martel A, Swardfager W, Masellis M, Swartz R, MacIntosh B, Black SE. Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks. Hum Brain Mapp 2020; 41:291-308. [PMID: 31609046 PMCID: PMC7267905 DOI: 10.1002/hbm.24811] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [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: 05/06/2019] [Revised: 09/09/2019] [Accepted: 09/19/2019] [Indexed: 11/22/2022] Open
Abstract
Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated hippocampal segmentation methods are limited because the algorithms are (a) not publicly available, (b) subject to error with significant brain atrophy, cerebrovascular disease and lesions, and/or (c) computationally expensive or require parameter tuning. In this study, we trained a 3D convolutional neural network using 259 bilateral manually delineated segmentations collected from three studies, acquired at multiple sites on different scanners with variable protocols. Our training dataset consisted of elderly cases difficult to segment due to extensive atrophy, vascular disease, and lesions. Our algorithm, (HippMapp3r), was validated against four other publicly available state-of-the-art techniques (HippoDeep, FreeSurfer, SBHV, volBrain, and FIRST). HippMapp3r outperformed the other techniques on all three metrics, generating an average Dice of 0.89 and a correlation coefficient of 0.95. It was two orders of magnitude faster than some of the tested techniques. Further validation was performed on 200 subjects from two other disease populations (frontotemporal dementia and vascular cognitive impairment), highlighting our method's low outlier rate. We finally tested the methods on real and simulated "clinical adversarial" cases to study their robustness to corrupt, low-quality scans. The pipeline and models are available at: https://hippmapp3r.readthedocs.ioto facilitate the study of the hippocampus in large multisite studies.
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Affiliation(s)
- Maged Goubran
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Emmanuel Edward Ntiri
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Hassan Akhavein
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Melissa Holmes
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Sean Nestor
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Sabrina Adamo
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Miracle Ozzoude
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Christopher Scott
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Fuqiang Gao
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
| | - Anne Martel
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
| | - Walter Swardfager
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
- Department of Pharmacology and ToxicologyUniversity of TorontoTorontoOntarioCanada
| | - Mario Masellis
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
- Department of Medicine (Neurology division)University of TorontoTorontoOntarioCanada
| | - Richard Swartz
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
- Department of Medicine (Neurology division)University of TorontoTorontoOntarioCanada
| | - Bradley MacIntosh
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
| | - Sandra E. Black
- LC Campbell Cognitive Neurology UnitHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Canadian Partnership for Stroke RecoveryHeart and Stroke FoundationTorontoOntarioCanada
- Department of Medical ImagingUniversity of TorontoTorontoOntarioCanada
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Cragun BN, Noorbakhsh MR, Hite Philp F, Suydam ER, Ditillo MF, Philp AS, Murdock AD. Traumatic Parafalcine Subdural Hematoma: A Clinically Benign Finding. J Surg Res 2020; 249:99-103. [PMID: 31926402 DOI: 10.1016/j.jss.2019.12.019] [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] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 05/19/2019] [Accepted: 12/06/2019] [Indexed: 12/16/2022]
Abstract
BACKGROUND Guidelines for management of intracranial hemorrhage do not account for bleed location. We hypothesize that parafalcine subdural hematoma (SDH), as compared to convexity SDH, is a distinct clinical entity and these patients do not benefit from critical care monitoring or repeat imaging. METHODS We identified patients presenting to a single level I trauma center with isolated head injuries from February 2016 to August 2017. We identified 88 patients with isolated blunt traumatic parafalcine SDH and 228 with convexity SDH. RESULTS Demographics, comorbidities, and use of antiplatelet and anticoagulant agents were similar between the groups. As compared to patients with convexity SDH, patients with parafalcine SDH had a significantly lower incidence of radiographic progression, and had no cases of neurologic deterioration, neurosurgical intervention, or mortality (all P < 0.005). Compared to patients admitted to the intensive care unit, patients with parafalcine SDH admitted to the floor had a shorter length of stay (2.0 ± 1.6 versus 3.8 ± 2.9 d, P < 0.005) with no difference in outcomes. CONCLUSIONS Patients presenting with a parafalcine SDH are a distinct and relatively benign clinical entity as compared to convexity SDH and do not benefit from repeat imaging or intensive care unit admission.
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Affiliation(s)
- Benjamin N Cragun
- Department of Surgery, Allegheny General Hospital, Pittsburgh, Pennsylvania.
| | | | - Frances Hite Philp
- Department of Surgery, Allegheny General Hospital, Pittsburgh, Pennsylvania
| | - Erin R Suydam
- Department of Surgery, Allegheny General Hospital, Pittsburgh, Pennsylvania
| | - Michael F Ditillo
- Department of Surgery, Allegheny General Hospital, Pittsburgh, Pennsylvania
| | - Allan S Philp
- Department of Surgery, Allegheny General Hospital, Pittsburgh, Pennsylvania
| | - Alan D Murdock
- Department of Surgery, Allegheny General Hospital, Pittsburgh, Pennsylvania
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Vieira S, Gong QY, Pinaya WHL, Scarpazza C, Tognin S, Crespo-Facorro B, Tordesillas-Gutierrez D, Ortiz-García V, Setien-Suero E, Scheepers FE, Van Haren NEM, Marques TR, Murray RM, David A, Dazzan P, McGuire P, Mechelli A. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull 2020; 46:17-26. [PMID: 30809667 PMCID: PMC6942152 DOI: 10.1093/schbul/sby189] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.
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Affiliation(s)
- Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Qi-yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, China
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
- Centre of Mathematics, Computation, and Cognition, Universidade Federal do ABC, São Paulo, Brazil
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
- Department of General Psychology, University of Padova, Padova, Italy
| | - Stefania Tognin
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Diana Tordesillas-Gutierrez
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Santander, Cantabria, Spain
| | - Victor Ortiz-García
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Esther Setien-Suero
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Floortje E Scheepers
- Department of Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Neeltje E M Van Haren
- Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Tiago R Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Anthony David
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
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Abstract
BACKGROUND Age-associated increases in medical complexity, frailty, and cognitive impairment may compromise reliable reporting of medical history. OBJECTIVE To evaluate the influence of increasing age and cognitive impairment on concordance between reported history of stroke and cerebral infarction, and reported history of diabetes and elevated hemoglobinA1c in community-dwelling older adults. METHODS The association between participant-specific factors and accurate reporting of stroke or diabetes was evaluated using multivariable logistic regression in 1,401 participants enrolled in longitudinal studies of memory and aging, including 425 participants with dementia (30.3%). Stroke and diabetes were selected as index variables as gold standard measures of both were obtained in all participants: magnetic resonance neuroimaging for cerebral infarcts and hemoglobinA1c (≥6.5%) for diabetes. RESULTS Concordance between reported history of stroke and imaging-confirmed cerebral infarction was low (sensitivity: 17.4%, 8/46; specificity: 97.9%, 799/816). Small infarcts were strongly associated with inaccurate reporting (OR = 265.8; 95% CI: 86.2, 819.4), suggesting that occult/silent infarcts contributed to discordant reporting. Reporting accuracy was higher concerning diabetes (sensitivity: 83.5%, 147/176; specificity: 96.2%, 1100/1143). A history of hypertension (OR = 2.3; 95% CI: 1.3, 4.2), higher hemoglobinA1c (OR = 1.9; 95% CI: 1.5, 2.4), and hemoglobinA1c compatible with impaired glucose tolerance (OR = 3.1; 95% CI 1.8, 5.3) associated with increased odds of discordant reporting. Cognitive impairment and increased age were not independently associated with reliable reporting. CONCLUSION Factors beyond advancing age and cognitive impairment appear to drive discordance in reported medical history in older participants. Objective testing for cerebral infarcts or diabetes should be performed when relevant to diagnostic or therapeutic decisions in clinical and research settings.
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Affiliation(s)
| | - Allison Long
- Hendrix College, Conway, AR, USA
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, St. Louis, MO, USA
| | - John C Morris
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, St. Louis, MO, USA
- Washington University School of Medicine, St. Louis, MO, USA
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Abstract
BACKGROUND Early identification of people at risk of imminent progression to dementia due to Alzheimer disease is crucial for timely intervention and treatment. We investigated whether the texture of MRI brain scans could predict the progression of mild cognitive impairment (MCI) to Alzheimer disease earlier than volume. METHODS We constructed a development data set (121 people who were cognitively normal and 145 who had mild Alzheimer disease) and a validation data set (113 patients with stable MCI who did not progress to Alzheimer disease for 3 years; 40 with early MCI who progressed to Alzheimer disease after 12–36 months; and 41 with late MCI who progressed to Alzheimer disease within 12 months) from the Alzheimer’s Disease Neuroimaging Initiative. We analyzed the texture of the hippocampus, precuneus and posterior cingulate cortex using a grey-level co-occurrence matrix. We constructed texture and volume indices from the development data set using logistic regression. Using area under the curve (AUC) of receiver operator characteristics, we compared the accuracy of hippocampal volume, hippocampal texture and the composite texture of the hippocampus, precuneus and posterior cingulate cortex in predicting conversion from MCI to Alzheimer disease in the validation data set. RESULTS Compared with hippocampal volume, hippocampal texture (0.790 v. 0.739, p = 0.047) and composite texture (0.811 v. 0.739, p = 0.007) showed larger AUCs for conversion to Alzheimer disease from both early and late MCI. Hippocampal texture showed a marginally larger AUC than hippocampal volume in early MCI (0.795 v. 0.726, p = 0.060). Composite texture showed a larger AUC for conversion to Alzheimer disease than hippocampal volume in both early (0.817 v. 0.726, p = 0.027) and late MCI (0.805 v. 0.753, p = 0.019). LIMITATIONS This study was limited by the absence of histological data, and the pathology reflected by the texture measures remains to be validated. CONCLUSION Textures of the hippocampus, precuneus and posterior cingulate cortex predicted conversion from MCI to Alzheimer disease at an earlier time point and with higher accuracy than hippocampal volume.
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Affiliation(s)
- Subin Lee
- From the Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea (S. Lee, Kim); the Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (H. Lee); the Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea (Kim); and the Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea (Kim)
| | - Hyunna Lee
- From the Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea (S. Lee, Kim); the Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (H. Lee); the Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea (Kim); and the Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea (Kim)
| | - Ki Woong Kim
- From the Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea (S. Lee, Kim); the Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (H. Lee); the Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea (Kim); and the Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea (Kim)
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Fernández-Corazza M, Turovets S, Muravchik CH. Unification of optimal targeting methods in transcranial electrical stimulation. Neuroimage 2019; 209:116403. [PMID: 31862525 PMCID: PMC7110419 DOI: 10.1016/j.neuroimage.2019.116403] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [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: 07/25/2019] [Revised: 11/11/2019] [Accepted: 11/24/2019] [Indexed: 12/22/2022] Open
Abstract
One of the major questions in high-density transcranial electrical stimulation (TES) is: given a region of interest (ROI) and electric current limits for safety, how much current should be delivered by each electrode for optimal targeting of the ROI? Several solutions, apparently unrelated, have been independently proposed depending on how "optimality" is defined and on how this optimization problem is stated mathematically. The least squares (LS), weighted LS (WLS), or reciprocity-based approaches are the simplest ones and have closed-form solutions. An extended optimization problem can be stated as follows: maximize the directional intensity at the ROI, limit the electric fields at the non-ROI, and constrain total injected current and current per electrode for safety. This problem requires iterative convex or linear optimization solvers. We theoretically prove in this work that the LS, WLS and reciprocity-based closed-form solutions are specific solutions to the extended directional maximization optimization problem. Moreover, the LS/WLS and reciprocity-based solutions are the two extreme cases of the intensity-focality trade-off, emerging under variation of a unique parameter of the extended directional maximization problem, the imposed constraint to the electric fields at the non-ROI. We validate and illustrate these findings with simulations on an atlas head model. The unified approach we present here allows a better understanding of the nature of the TES optimization problem and helps in the development of advanced and more effective targeting strategies.
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Affiliation(s)
- Mariano Fernández-Corazza
- LEICI Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales, Universidad Nacional de La Plata, CONICET, Argentina.
| | - Sergei Turovets
- NeuroInformatics Center, University of Oregon, Eugene, OR, USA
| | - Carlos Horacio Muravchik
- LEICI Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales, Universidad Nacional de La Plata, CONICET, Argentina; Comisión de Investigaciones Científicas, CICPBA, Provincia de Buenos Aires, Argentina
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