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Ludwig M, Yi YJ, Lüsebrink F, Callaghan MF, Betts MJ, Yakupov R, Weiskopf N, Dolan RJ, Düzel E, Hämmerer D. Functional locus coeruleus imaging to investigate an ageing noradrenergic system. Commun Biol 2024; 7:777. [PMID: 38937535 PMCID: PMC11211439 DOI: 10.1038/s42003-024-06446-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 06/12/2024] [Indexed: 06/29/2024] Open
Abstract
The locus coeruleus (LC), our main source of norepinephrine (NE) in the brain, declines with age and is a potential epicentre of protein pathologies in neurodegenerative diseases (ND). In vivo measurements of LC integrity and function are potentially important biomarkers for healthy ageing and early ND onset. In the present study, high-resolution functional MRI (fMRI), a reversal reinforcement learning task, and dedicated post-processing approaches were used to visualise age differences in LC function (N = 50). Increased LC responses were observed during emotionally and task-related salient events, with subsequent accelerations and decelerations in reaction times, respectively, indicating context-specific adaptive engagement of the LC. Moreover, older adults exhibited increased LC activation compared to younger adults, indicating possible compensatory overactivation of a structurally declining LC in ageing. Our study shows that assessment of LC function is a promising biomarker of cognitive aging.
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Affiliation(s)
- Mareike Ludwig
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
- CBBS Center for Behavioral Brain Sciences, Magdeburg, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
| | - Yeo-Jin Yi
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Falk Lüsebrink
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Biomedical Magnetic Resonance, Faculty of Natural Sciences, Otto-von-Guericke University, Magdeburg, Germany
- NMR Methods Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square, Institute of Neurology, University College London, London, UK
| | - Matthew J Betts
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- CBBS Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Nikolaus Weiskopf
- Wellcome Centre for Human Neuroimaging, UCL Queen Square, Institute of Neurology, University College London, London, UK
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square, Institute of Neurology, University College London, London, UK
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, UK
| | - Emrah Düzel
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Dorothea Hämmerer
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- CBBS Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Wellcome Centre for Human Neuroimaging, UCL Queen Square, Institute of Neurology, University College London, London, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
- Department of Psychology, University of Innsbruck, Innsbruck, Austria
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Ohki T, Chao ZC, Takei Y, Kato Y, Sunaga M, Suto T, Tagawa M, Fukuda M. Multivariate sharp-wave ripples in schizophrenia during awake state. Psychiatry Clin Neurosci 2024. [PMID: 38923051 DOI: 10.1111/pcn.13702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 04/03/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024]
Abstract
AIMS Schizophrenia (SZ) is a brain disorder characterized by psychotic symptoms and cognitive dysfunction. Recently, irregularities in sharp-wave ripples (SPW-Rs) have been reported in SZ. As SPW-Rs play a critical role in memory, their irregularities can cause psychotic symptoms and cognitive dysfunction in patients with SZ. In this study, we investigated the SPW-Rs in human SZ. METHODS We measured whole-brain activity using magnetoencephalography (MEG) in patients with SZ (n = 20) and sex- and age-matched healthy participants (n = 20) during open-eye rest. We identified SPW-Rs and analyzed their occurrence and time-frequency traits. Furthermore, we developed a novel multivariate analysis method, termed "ripple-gedMEG" to extract the global features of SPW-Rs. We also examined the association between SPW-Rs and brain state transitions. The outcomes of these analyses were modeled to predict the positive and negative syndrome scale (PANSS) scores of SZ. RESULTS We found that SPW-Rs in the SZ (1) occurred more frequently, (2) the delay of the coupling phase (3) appeared in different brain areas, (4) consisted of a less organized spatiotemporal pattern, and (5) were less involved in brain state transitions. Finally, some of the neural features associated with the SPW-Rs were found to be PANSS-positive, a pathological indicator of SZ. These results suggest that widespread but disorganized SPW-Rs underlies the symptoms of SZ. CONCLUSION We identified irregularities in SPW-Rs in SZ and confirmed that their alternations were strongly associated with SZ neuropathology. These results suggest a new direction for human SZ research.
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Affiliation(s)
- Takefumi Ohki
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan
| | - Yuichi Takei
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yutaka Kato
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
- Tsutsuji Mental Hospital, Tatebayashi, Japan
| | - Masakazu Sunaga
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Tomohiro Suto
- Gunma Prefectural Psychiatric Medical Center, Isesaki, Japan
| | - Minami Tagawa
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
- Gunma Prefectural Psychiatric Medical Center, Isesaki, Japan
| | - Masato Fukuda
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
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Zhao LS, Raithel CU, Tisdall MD, Detre JA, Gottfried JA. Leveraging Multi-echo EPI to Enhance BOLD Sensitivity in Task-based Olfactory fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575530. [PMID: 38293143 PMCID: PMC10827088 DOI: 10.1101/2024.01.15.575530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) contrast relies on gradient echo echo-planar imaging (GE-EPI) to quantify dynamic susceptibility changes associated with the hemodynamic response to neural activity. However, acquiring BOLD fMRI in human olfactory regions is particularly challenging due to their proximity to the sinuses where large susceptibility gradients induce magnetic field distortions. BOLD fMRI of the human olfactory system is further complicated by respiratory artifacts that are highly correlated with event onsets in olfactory tasks. Multi-Echo EPI (ME-EPI) acquires gradient echo data at multiple echo times (TEs) during a single acquisition and can leverage signal evolution over the multiple echo times to enhance BOLD sensitivity and reduce artifactual signal contributions. In the current study, we developed a ME-EPI acquisition protocol for olfactory task-based fMRI and demonstrated significant improvement in BOLD signal sensitivity over conventional single-echo EPI (1E-EPI). The observed improvement arose from both an increase in BOLD signal changes through a T 2 * -weighted echo combination and a reduction in non-BOLD artifacts through the application of the Multi-Echo Independent Components Analysis (ME-ICA) denoising method. This study represents one of the first direct comparisons between 1E-EPI and ME-EPI in high-susceptibility regions and provides compelling evidence in favor of using ME-EPI for future task-based fMRI studies.
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Burman Ingeberg M, Van Houten E, Zwanenburg JJM. Estimating the viscoelastic properties of the human brain at 7 T MRI using intrinsic MRE and nonlinear inversion. Hum Brain Mapp 2023; 44:6575-6591. [PMID: 37909395 PMCID: PMC10681656 DOI: 10.1002/hbm.26524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/04/2023] [Accepted: 10/09/2023] [Indexed: 11/03/2023] Open
Abstract
Intrinsic actuation magnetic resonance elastography (MRE) is a phase-contrast MRI technique that allows for in vivo quantification of mechanical properties of the brain by exploiting brain motion that arise naturally due to the cardiac pulse. The mechanical properties of the brain reflect its tissue microstructure, making it a potentially valuable parameter in studying brain disease. The main purpose of this study was to assess the feasibility of reconstructing the viscoelastic properties of the brain using high-quality 7 T MRI displacement measurements, obtained using displacement encoding with stimulated echoes (DENSE) and intrinsic actuation. The repeatability and sensitivity of the method for detecting normal regional variation in brain tissue properties was assessed as secondary goal. The displacement measurements used in this analysis were previously acquired for a separate study, where eight healthy subjects (27 ± 7 years) were imaged with repeated scans (spatial resolution approx. 2 mm isotropic, temporal resolution 75 ms, motion sensitivity 0.35 mm/2π for displacements in anterior-posterior and left-right directions, and 0.7 mm/2π for feet-head displacements). The viscoelastic properties of the brain were estimated using a subzone based non-linear inversion scheme. The results show comparable consistency to that of extrinsic MRE between the viscoelastic property maps obtained from repeated displacement measurements. The shear stiffness maps showed fairly consistent spatial patterns. The whole-brain repeatability coefficient (RC) for shear stiffness was (mean ± standard deviation) 8 ± 8% relative to the mean whole-brain stiffness, and the damping ratio RC was 28 ± 17% relative to the whole-brain damping ratio. The shear stiffness maps showed similar statistically significant regional trends as demonstrated in a publicly available atlas of viscoelastic properties obtained with extrinsic actuation MRE at 50 Hz. The damping ratio maps showed less consistency, likely due to data-model mismatch of describing the brain as a viscoelastic material under low frequencies. While artifacts induced by fluid flow within the brain remain a limitation of the technique in its current state, intrinsic actuation based MRE allow for consistent and repeatable estimation of the mechanical properties of the brain. The method provides enough sensitivity to investigate regional variation in such properties in the normal brain, which is likely sufficient to also investigate pathological changes.
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Figley CR, Figley TD, Wong K, Uddin MN, Dalvit Carvalho da Silva R, Kornelsen J. Periventricular and juxtacortical characterization of UManitoba-JHU functionally defined human white matter atlas networks. Front Hum Neurosci 2023; 17:1196624. [PMID: 37484918 PMCID: PMC10357038 DOI: 10.3389/fnhum.2023.1196624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/22/2023] [Indexed: 07/25/2023] Open
Abstract
Background The open-access UManitoba-JHU functionally defined human white matter (WM) atlas contains specific WM pathways and general WM regions underlying 12 functional brain networks in ICBM152 template space. However, it is not known whether any of these WM networks are disproportionately co-localized with periventricular and/or juxtacortical WM (PVWM and JCWM), which could potentially impact their ability to infer network-specific effects in future studies-particularly in patient populations expected to have disproportionate PVWM and/or JCWM damage. Methods The current study therefore identified intersecting regions of PVWM and JCWM (defined as WM within 5 mm of the ventricular and cortical boundaries) and: (1) the ICBM152 global WM mask, and (2) all 12 UManitoba-JHU WM networks. Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), and proportion of volume (POV) values between PVWM (and JCWM) and each functionally defined WM network were then compared to corresponding values between PVWM (and JCWM) and global WM. Results Between the 12 WM networks and PVWM, 8 had lower DSC, JSC, and POV; 1 had lower DSC and JSC, but higher POV; and 3 had higher DSC, JSC, and POV compared to global WM. For JCWM, all 12 WM networks had lower DSC, JSC, and POV compared to global WM. Conclusion The majority of UManitoba-JHU functionally defined WM networks exhibited lower than average spatial similarity with PVWM, and all exhibited lower than average spatial similarity with JCWM. This suggests that they can be used to explore network-specific WM changes, even in patient populations with known predispositions toward PVWM and/or JCWM damage.
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Affiliation(s)
- Chase R. Figley
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
| | - Teresa D. Figley
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Kaihim Wong
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Md Nasir Uddin
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Department of Neurology, University of Rochester, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
| | - Rodrigo Dalvit Carvalho da Silva
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Jennifer Kornelsen
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
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Poiret C, Bouyeure A, Patil S, Grigis A, Duchesnay E, Faillot M, Bottlaender M, Lemaitre F, Noulhiane M. A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics. Front Neuroinform 2023; 17:1130845. [PMID: 37396459 PMCID: PMC10308024 DOI: 10.3389/fninf.2023.1130845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 05/22/2023] [Indexed: 07/04/2023] Open
Abstract
The hippocampal subfields, pivotal to episodic memory, are distinct both in terms of cyto- and myeloarchitectony. Studying the structure of hippocampal subfields in vivo is crucial to understand volumetric trajectories across the lifespan, from the emergence of episodic memory during early childhood to memory impairments found in older adults. However, segmenting hippocampal subfields on conventional MRI sequences is challenging because of their small size. Furthermore, there is to date no unified segmentation protocol for the hippocampal subfields, which limits comparisons between studies. Therefore, we introduced a novel segmentation tool called HSF short for hippocampal segmentation factory, which leverages an end-to-end deep learning pipeline. First, we validated HSF against currently used tools (ASHS, HIPS, and HippUnfold). Then, we used HSF on 3,750 subjects from the HCP development, young adults, and aging datasets to study the effect of age and sex on hippocampal subfields volumes. Firstly, we showed HSF to be closer to manual segmentation than other currently used tools (p < 0.001), regarding the Dice Coefficient, Hausdorff Distance, and Volumetric Similarity. Then, we showed differential maturation and aging across subfields, with the dentate gyrus being the most affected by age. We also found faster growth and decay in men than in women for most hippocampal subfields. Thus, while we introduced a new, fast and robust end-to-end segmentation tool, our neuroanatomical results concerning the lifespan trajectories of the hippocampal subfields reconcile previous conflicting results.
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Affiliation(s)
- Clement Poiret
- UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Antoine Bouyeure
- UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Sandesh Patil
- UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Antoine Grigis
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Edouard Duchesnay
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Matthieu Faillot
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- BioMaps, Service Hospitalier Frédéric Joliot, CNRS, Inserm, Université Paris-Saclay, Orsay, France
| | - Michel Bottlaender
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- BioMaps, Service Hospitalier Frédéric Joliot, CNRS, Inserm, Université Paris-Saclay, Orsay, France
| | - Frederic Lemaitre
- CETAPS EA 3832, Université de Rouen, Rouen, France
- CRIOBE, UAR 3278, CNRS-EPHE-UPVD, Mooréa, France
| | - Marion Noulhiane
- UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
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Fletcher E, Farias S, DeCarli C, Gavett B, Widaman K, De Leon F, Mungas D. Toward a statistical validation of brain signatures as robust measures of behavioral substrates. Hum Brain Mapp 2023; 44:3094-3111. [PMID: 36939069 PMCID: PMC10171525 DOI: 10.1002/hbm.26265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 02/10/2023] [Accepted: 02/21/2023] [Indexed: 03/21/2023] Open
Abstract
The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.
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Affiliation(s)
- Evan Fletcher
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Sarah Farias
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Brandon Gavett
- School of Psychological ScienceUniversity of Western AustraliaPerthAustralia
| | - Keith Widaman
- School of EducationUniversity of California, RiversideRiversideCaliforniaUSA
| | - Fransia De Leon
- School of MedicineUniversity of California, DavisDavisCaliforniaUSA
| | - Dan Mungas
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
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Li H, Cui L, Wang M, Liao M, Li JB, Ouyang F, Mei T, Zen H, Fan Y. Apathy is associated with striatal atrophy and cognitive impairment in cerebral small vessel disease. J Affect Disord 2023; 328:39-46. [PMID: 36775253 DOI: 10.1016/j.jad.2023.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 01/20/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Apathy has been considered a common neuropsychiatric symptom and an important contributor to cognitive impairment in cerebral small vessel disease (SVD). However, the mechanism leading to apathy in SVD and the process whereby apathy promotes cognitive impairments remain largely unknown. We aimed to explore the relationship between apathy, cognition, and structural changes of deep grey matter (DGM) in SVD patients. METHODS Participants were screened for SVD, completed assessments of apathy cognition, underwent magnetic resonance imaging (MRI) scanning, and then stratified into apathy and non-apathy groups. We used region of interest (ROI)-based, voxel-based volume, and vertex-based shape analyses to compare DGM structures between study groups. Using linear regression analysis, we examined the association between apathy, structural changes, and cognition, followed by a mediation analysis of these factors. RESULTS A total of sixty-four SVD participants were included, with thirty in the apathy group and thirty-four in the non-apathy group. Intergroup comparison showed significantly lower volumes in bilateral caudate, right putamen, and pallidum and smaller vertex-based shapes in the right caudate and pallidum in participants with apathy compared to those without apathy. Apathy was associated with the striatal atrophy (i.e., lower volumes and smaller shape) and independently contributed to cognitive impairments in SVD. However, the above structural differences did not mediate the association between apathy and cognitive impairments. CONCLUSION These results highlight the important role of striatal atrophy in apathy in SVD and call for additional studies to explore the relationship between apathy, cognition, and DGM.
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Affiliation(s)
- Hao Li
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou 510080, China; Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
| | - Liqian Cui
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou 510080, China.
| | - Meng Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Road 2, Guangzhou 510080, China
| | - Mengshi Liao
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou 510080, China
| | - Jin Biao Li
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou 510080, China
| | - Fubing Ouyang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou 510080, China
| | - Ting Mei
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Huixing Zen
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou 510080, China
| | - Yuhua Fan
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou 510080, China.
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9
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Bedini M, Olivetti E, Avesani P, Baldauf D. Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis. Brain Struct Funct 2023; 228:997-1017. [PMID: 37093304 PMCID: PMC10147761 DOI: 10.1007/s00429-023-02641-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 04/08/2023] [Indexed: 04/25/2023]
Abstract
The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.
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Affiliation(s)
- Marco Bedini
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy.
- Department of Psychology, University of California, San Diego, McGill Hall 9500 Gilman Dr, La Jolla, CA, 92093-0109, USA.
| | - Emanuele Olivetti
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Paolo Avesani
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Daniel Baldauf
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
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10
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Li M, Xu X, Cao Z, Chen R, Zhao R, Zhao Z, Dang X, Oishi K, Wu D. Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity. Neuroimage 2023; 272:120071. [PMID: 37003446 DOI: 10.1016/j.neuroimage.2023.120071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population. Using 348 multi-modal MRI datasets from the developing human connectome project, we found that the information fused from the structural, diffusion, and functional MRI was relatively stable across MRI features and showed high reproducibility at the group level. Therefore, we generated automated multi-resolution parcellations (300 - 500 parcels) based on the similarity across multi-modal features using a gradient-based parcellation algorithm. In addition, to acquire a parcellation with high interpretability, we provided a manually delineated parcellation (210 parcels), which was approximately symmetric, and the adjacent areas around each boundary were statistically different in terms of the integrated similarity metric and at least one kind of original features. Overall, the present study provided multi-resolution and neonate-specific parcellations of the cerebral cortex based on multi-modal MRI properties, which may facilitate future studies of the human connectome in the early development period.
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Affiliation(s)
- Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Zuozhen Cao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Ruoke Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Xixi Dang
- Department of Psychology, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore 21205, United States
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China.
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11
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Cohen AL, Kroeck MR, Wall J, McManus P, Ovchinnikova A, Sahin M, Krueger DA, Bebin EM, Northrup H, Wu JY, Warfield SK, Peters JM, Fox MD. Tubers Affecting the Fusiform Face Area Are Associated with Autism Diagnosis. Ann Neurol 2023; 93:577-590. [PMID: 36394118 PMCID: PMC9974824 DOI: 10.1002/ana.26551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 11/11/2022] [Accepted: 11/13/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Tuberous sclerosis complex (TSC) is associated with focal brain "tubers" and a high incidence of autism spectrum disorder (ASD). The location of brain tubers associated with autism may provide insight into the neuroanatomical substrate of ASD symptoms. METHODS We delineated tuber locations for 115 TSC participants with ASD (n = 31) and without ASD (n = 84) from the Tuberous Sclerosis Complex Autism Center of Excellence Research Network. We tested for associations between ASD diagnosis and tuber burden within the whole brain, specific lobes, and at 8 regions of interest derived from the ASD neuroimaging literature, including the anterior cingulate, orbitofrontal and posterior parietal cortices, inferior frontal and fusiform gyri, superior temporal sulcus, amygdala, and supplemental motor area. Next, we performed an unbiased data-driven voxelwise lesion symptom mapping (VLSM) analysis. Finally, we calculated the risk of ASD associated with positive findings from the above analyses. RESULTS There were no significant ASD-related differences in tuber burden across the whole brain, within specific lobes, or within a priori regions derived from the ASD literature. However, using VLSM analysis, we found that tubers involving the right fusiform face area (FFA) were associated with a 3.7-fold increased risk of developing ASD. INTERPRETATION Although TSC is a rare cause of ASD, there is a strong association between tuber involvement of the right FFA and ASD diagnosis. This highlights a potentially causative mechanism for developing autism in TSC that may guide research into ASD symptoms more generally. ANN NEUROL 2023;93:577-590.
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Affiliation(s)
- Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mallory R Kroeck
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Juliana Wall
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter McManus
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Arina Ovchinnikova
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School at University of Texas Health Science Center at Houston and Children's Memorial Hermann Hospital, Houston, TX, USA
| | - Joyce Y Wu
- Division of Neurology & Epilepsy, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
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12
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Martinez CS, Cuadra MB, Jorge J. BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development. Neuroimage 2023; 273:120074. [PMID: 37004826 DOI: 10.1016/j.neuroimage.2023.120074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/16/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
The benefits, opportunities and growing availability of ultra-high field magnetic resonance imaging (MRI) for humans have prompted an expansion in research and development efforts towards increasingly more advanced high-resolution imaging techniques. To maximize their effectiveness, these efforts need to be supported by powerful computational simulation platforms that can adequately reproduce the biophysical characteristics of MRI, with high spatial resolution. In this work, we have sought to address this need by developing a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI properties that affect image generation. This phantom, termed BigBrain-MR, was generated from the publicly available BigBrain histological dataset and lower-resolution in-vivo 7T-MRI data, using a newly-developed image processing framework that allows mapping the general properties of the latter into the fine anatomical scale of the former. Overall, the mapping framework was found to be effective and robust, yielding a diverse range of realistic "in-vivo-like" MRI contrasts and maps at 100-µm resolution. BigBrain-MR was then tested in three imaging applications (motion effects and interpolation, super-resolution imaging, and parallel imaging reconstruction) to investigate its properties, value and validity as a simulation platform. The results consistently showed that BigBrain-MR can closely approximate the behavior of real in-vivo data, more realistically and with more extensive features than a more classic option such as the Shepp-Logan phantom. Its flexibility in simulating different contrast mechanisms and artifacts may also prove valuable for educational applications. BigBrain-MR is therefore deemed a favorable choice to support methodological development and demonstration in brain MRI, and has been made freely available to the community.
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13
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Carcagnì P, Leo M, Del Coco M, Distante C, De Salve A. Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI. SENSORS (BASEL, SWITZERLAND) 2023; 23:1694. [PMID: 36772733 PMCID: PMC9919436 DOI: 10.3390/s23031694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/28/2022] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive impairment. The aim of this work is to improve the automatic detection of dementia in MRI brain data. For this purpose, we used an established pipeline that includes the registration, slicing, and classification steps. The contribution of this research was to investigate for the first time, to our knowledge, three current and promising deep convolutional models (ResNet, DenseNet, and EfficientNet) and two transformer-based architectures (MAE and DeiT) for mapping input images to clinical diagnosis. To allow a fair comparison, the experiments were performed on two publicly available datasets (ADNI and OASIS) using multiple benchmarks obtained by changing the number of slices per subject extracted from the available 3D voxels. The experiments showed that very deep ResNet and DenseNet models performed better than the shallow ResNet and VGG versions tested in the literature. It was also found that transformer architectures, and DeiT in particular, produced the best classification results and were more robust to the noise added by increasing the number of slices. A significant improvement in accuracy (up to 7%) was achieved compared to the leading state-of-the-art approaches, paving the way for the use of CAD approaches in real-world applications.
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14
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Stanley EAM, Wilms M, Mouches P, Forkert ND. Fairness-related performance and explainability effects in deep learning models for brain image analysis. J Med Imaging (Bellingham) 2022; 9:061102. [PMID: 36046104 PMCID: PMC9412191 DOI: 10.1117/1.jmi.9.6.061102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/18/2022] [Indexed: 08/28/2023] Open
Abstract
Purpose: Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. However, there has been limited work on investigating how unfair performance manifests in explainable artificial intelligence (XAI) methods, and how XAI can be used to investigate potential reasons for unfairness. Thus, the aim of this work was to analyze the effects of previously established sociodemographic-related confounders on classifier performance and explainability methods. Approach: A convolutional neural network (CNN) was trained to predict biological sex from T1-weighted brain MRI datasets of 4547 9- to 10-year-old adolescents from the Adolescent Brain Cognitive Development study. Performance disparities of the trained CNN between White and Black subjects were analyzed and saliency maps were generated for each subgroup at the intersection of sex and race. Results: The classification model demonstrated a significant difference in the percentage of correctly classified White male ( 90.3 % ± 1.7 % ) and Black male ( 81.1 % ± 4.5 % ) children. Conversely, slightly higher performance was found for Black female ( 89.3 % ± 4.8 % ) compared with White female ( 86.5 % ± 2.0 % ) children. Saliency maps showed subgroup-specific differences, corresponding to brain regions previously associated with pubertal development. In line with this finding, average pubertal development scores of subjects used in this study were significantly different between Black and White females ( p < 0.001 ) and males ( p < 0.001 ). Conclusions: We demonstrate that a CNN with significantly different sex classification performance between Black and White adolescents can identify different important brain regions when comparing subgroup saliency maps. Importance scores vary substantially between subgroups within brain structures associated with pubertal development, a race-associated confounder for predicting sex. We illustrate that unfair models can produce different XAI results between subgroups and that these results may explain potential reasons for biased performance.
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Affiliation(s)
- Emma A. M. Stanley
- University of Calgary, Department of Biomedical Engineering, Calgary, Alberta, Canada
- University of Calgary, Department of Radiology, Calgary, Alberta, Canada
- University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Matthias Wilms
- University of Calgary, Department of Radiology, Calgary, Alberta, Canada
- University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada
- University of Calgary, Alberta Children’s Hospital Research Institute, Calgary, Alberta, Canada
| | - Pauline Mouches
- University of Calgary, Department of Biomedical Engineering, Calgary, Alberta, Canada
- University of Calgary, Department of Radiology, Calgary, Alberta, Canada
- University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Nils D. Forkert
- University of Calgary, Department of Biomedical Engineering, Calgary, Alberta, Canada
- University of Calgary, Department of Radiology, Calgary, Alberta, Canada
- University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada
- University of Calgary, Alberta Children’s Hospital Research Institute, Calgary, Alberta, Canada
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15
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White matter hyperintensity distribution differences in aging and neurodegenerative disease cohorts. Neuroimage Clin 2022; 36:103204. [PMID: 36155321 PMCID: PMC9668605 DOI: 10.1016/j.nicl.2022.103204] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 01/18/2023]
Abstract
INTRODUCTION White matter hyperintensities (WMHs) are common magnetic resonance imaging (MRI) findings in the aging population in general, as well as in patients with neurodegenerative diseases. They are known to exacerbate the cognitive deficits and worsen the clinical outcomes in the patients. However, it is not well-understood whether there are disease-specific differences in prevalence and distribution of WMHs in different neurodegenerative disorders. METHODS Data included 976 participants with cross-sectional T1-weighted and fluid attenuated inversion recovery (FLAIR) MRIs from the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) cohort of the Canadian Consortium on Neurodegeneration in Aging (CCNA) with eleven distinct diagnostic groups: cognitively intact elderly (CIE), subjective cognitive impairment (SCI), mild cognitive impairment (MCI), vascular MCI (V-MCI), Alzheimer's dementia (AD), vascular AD (V-AD), frontotemporal dementia (FTD), Lewy body dementia (LBD), cognitively intact elderly with Parkinson's disease (PD-CIE), cognitively impaired Parkinson's disease (PD-CI), and mixed dementias. WMHs were segmented using a previously validated automated technique. WMH volumes in each lobe and hemisphere were compared against matched CIE individuals, as well as each other, and between men and women. RESULTS All cognitively impaired diagnostic groups had significantly greater overall WMH volumes than the CIE group. Vascular groups (i.e. V-MCI, V-AD, and mixed dementia) had significantly greater WMH volumes than all other groups, except for FTD, which also had significantly greater WMH volumes than all non-vascular groups. Women tended to have lower WMH burden than men in most groups and regions, controlling for age. The left frontal lobe tended to have a lower WMH burden than the right in all groups. In contrast, the right occipital lobe tended to have greater WMH volumes than the left. CONCLUSIONS There were distinct differences in WMH prevalence and distribution across diagnostic groups, sexes, and in terms of asymmetry. WMH burden was significantly greater in all neurodegenerative dementia groups, likely encompassing areas exclusively impacted by neurodegeneration as well as areas related to cerebrovascular disease pathology.
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16
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Dadar M, Zhernovaia M, Mahmoud S, Camicioli R, Maranzano J, Duchesne S. Using transfer learning for automated microbleed segmentation. FRONTIERS IN NEUROIMAGING 2022; 1:940849. [PMID: 37555147 PMCID: PMC10406212 DOI: 10.3389/fnimg.2022.940849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/18/2022] [Indexed: 08/10/2023]
Abstract
INTRODUCTION Cerebral microbleeds are small perivascular hemorrhages that can occur in both gray and white matter brain regions. Microbleeds are a marker of cerebrovascular pathology and are associated with an increased risk of cognitive decline and dementia. Microbleeds can be identified and manually segmented by expert radiologists and neurologists, usually from susceptibility-contrast MRI. The latter is hard to harmonize across scanners, while manual segmentation is laborious, time-consuming, and subject to interrater and intrarater variability. Automated techniques so far have shown high accuracy at a neighborhood ("patch") level at the expense of a high number of false positive voxel-wise lesions. We aimed to develop an automated, more precise microbleed segmentation tool that can use standardizable MRI contrasts. METHODS We first trained a ResNet50 network on another MRI segmentation task (cerebrospinal fluid vs. background segmentation) using T1-weighted, T2-weighted, and T2* MRIs. We then used transfer learning to train the network for the detection of microbleeds with the same contrasts. As a final step, we employed a combination of morphological operators and rules at the local lesion level to remove false positives. Manual segmentation of microbleeds from 78 participants was used to train and validate the system. We assessed the impact of patch size, freezing weights of the initial layers, mini-batch size, learning rate, and data augmentation on the performance of the Microbleed ResNet50 network. RESULTS The proposed method achieved high performance, with a patch-level sensitivity, specificity, and accuracy of 99.57, 99.16, and 99.93%, respectively. At a per lesion level, sensitivity, precision, and Dice similarity index values were 89.1, 20.1, and 0.28% for cortical GM; 100, 100, and 1.0% for deep GM; and 91.1, 44.3, and 0.58% for WM, respectively. DISCUSSION The proposed microbleed segmentation method is more suitable for the automated detection of microbleeds with high sensitivity.
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Affiliation(s)
- Mahsa Dadar
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Maryna Zhernovaia
- Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| | - Sawsan Mahmoud
- Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| | - Richard Camicioli
- Department of Medicine, Division of Neurology, University of Alberta, Edmonton, AB, Canada
| | - Josefina Maranzano
- Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| | - Simon Duchesne
- CERVO Brain Research Center, Quebec City, QC, Canada
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Université Laval, Quebec City, QC, Canada
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17
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Mofrad MH, Gilmore G, Koller D, Mirsattari SM, Burneo JG, Steven DA, Khan AR, Suller Marti A, Muller L. Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load. eLife 2022; 11:75769. [PMID: 35766286 PMCID: PMC9242645 DOI: 10.7554/elife.75769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 05/27/2022] [Indexed: 11/22/2022] Open
Abstract
Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex. The brain processes memories as we sleep, generating rhythms of electrical activity called ‘sleep spindles’. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas. Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys. The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes. In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer’s disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings.
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Affiliation(s)
- Maryam H Mofrad
- Department of Mathematics, Western University, London, Canada.,Brain and Mind Institute, Western University, London, Canada
| | - Greydon Gilmore
- Brain and Mind Institute, Western University, London, Canada.,Department of Biomedical Engineering, Western University, London, Canada
| | - Dominik Koller
- Advanced Concepts Team, European Space Agency, Noordwijk, Netherlands
| | - Seyed M Mirsattari
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Psychology, Western University, London, Canada
| | - Jorge G Burneo
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - David A Steven
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Ali R Khan
- Brain and Mind Institute, Western University, London, Canada.,Department of Biomedical Engineering, Western University, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Ana Suller Marti
- Brain and Mind Institute, Western University, London, Canada.,Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Lyle Muller
- Department of Mathematics, Western University, London, Canada.,Brain and Mind Institute, Western University, London, Canada
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18
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Zeighami Y, Dadar M, Daoust J, Pelletier M, Biertho L, Bouvet-Bouchard L, Fulton S, Tchernof A, Dagher A, Richard D, Evans A, Michaud A. Impact of Weight Loss on Brain Age: Improved Brain Health Following Bariatric Surgery. Neuroimage 2022; 259:119415. [PMID: 35760293 DOI: 10.1016/j.neuroimage.2022.119415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 06/17/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022] Open
Abstract
Individuals living with obesity tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM) measurements. We used three distinct datasets to perform this study: 1) CamCAN dataset to train the brain age prediction model, 2) Human Connectome Project (HCP) dataset to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n=87, age: 44.0±9.2 years, BMI: 43.9±4.2 kg/m2) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. As expected, our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (T-value = 7.08, p-value < 0.0001). We also found significant improvement in brain health, indicated by a decrease of 2.9 and 5.6 years in adjusted delta age at 12 and 24 months following bariatric surgery compared to baseline (p-value < 0.0005 for both). While the overall effect seemed to be driven by a global change across all brain regions and not from a specific region, our exploratory analysis showed lower delta age in certain brain regions (mainly in somatomotor, visual, and ventral attention networks) at 24 months. This reduced age was also associated with post-surgery improvements in BMI, systolic/diastolic blood pressure, and HOMA-IR (T-valueBMI=4.29, T-valueSBP=4.67, T-valueDBP=4.12, T-valueHOMA-IR=3.16, all p-values < 0.05). In conclusion, these results suggest that obesity-related brain health abnormalities (as measured by delta age) might be reversed by bariatric surgery-induced weight loss and widespread improvements in cardiometabolic alterations.
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Affiliation(s)
- Yashar Zeighami
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, Canada; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada.
| | - Mahsa Dadar
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, Canada
| | - Justine Daoust
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Mélissa Pelletier
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Laurent Biertho
- Département de chirurgie générale, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Léonie Bouvet-Bouchard
- Département de chirurgie générale, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Stephanie Fulton
- Centre de Recherche du CHUM, Department of Nutrition, Université de Montréal, Montreal Diabetes Research Center, Montreal, QC, Canada
| | - André Tchernof
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Alain Dagher
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Denis Richard
- Département de chirurgie générale, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Alan Evans
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Andréanne Michaud
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada.
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19
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Multi sequence average templates for aging and neurodegenerative disease populations. Sci Data 2022; 9:238. [PMID: 35624290 PMCID: PMC9142602 DOI: 10.1038/s41597-022-01341-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 05/03/2022] [Indexed: 11/20/2022] Open
Abstract
Magnetic resonance image (MRI) processing pipelines use average templates to enable standardization of individual MRIs in a common space. MNI-ICBM152 is currently used as the standard template by most MRI processing tools. However, MNI-ICBM152 represents an average of 152 healthy young adult brains and is vastly different from brains of patients with neurodegenerative diseases. In those populations, extensive atrophy might cause inevitable registration errors when using an average template of young healthy individuals for standardization. Disease-specific templates that represent the anatomical characteristics of the populations can reduce such errors and improve downstream driven estimates. We present multi-sequence average templates for Alzheimer’s Dementia (AD), Fronto-temporal Dementia (FTD), Lewy Body Dementia (LBD), Mild Cognitive Impairment (MCI), cognitively intact and impaired Parkinson’s Disease patients (PD-CIE and PD-CI, respectively), individuals with Subjective Cognitive Impairment (SCI), AD with vascular contribution (V-AD), Vascular Mild Cognitive Impairment (V-MCI), Cognitively Intact Elderly (CIE) individuals, and a human phantom. We also provide separate templates for males and females to allow better representation of the diseases in each sex group. Measurement(s) | Human Brain | Technology Type(s) | Magnetic resonance imaging | Sample Characteristic - Organism | Homo Sapiens | Sample Characteristic - Location | Canada |
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20
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Fonov VS, Dadar M, Adni TPARG, Collins DL. DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template. Neuroimage 2022; 257:119266. [PMID: 35500807 DOI: 10.1016/j.neuroimage.2022.119266] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 12/21/2022] Open
Abstract
Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans and 64476 registrations from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.
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Affiliation(s)
- Vladimir S Fonov
- Image Processing Laboratory, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, Quebec H3A2B4, Canada.
| | - Mahsa Dadar
- Image Processing Laboratory, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, Quebec H3A2B4, Canada; Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada
| | | | - D Louis Collins
- Image Processing Laboratory, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, Quebec H3A2B4, Canada
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21
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Manera AL, Dadar M, Collins DL, Ducharme S. Ventricular features as reliable differentiators between bvFTD and other dementias. Neuroimage Clin 2022; 33:102947. [PMID: 35134704 PMCID: PMC8856914 DOI: 10.1016/j.nicl.2022.102947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 11/24/2021] [Accepted: 01/19/2022] [Indexed: 11/28/2022]
Abstract
Our results showed a consistent pattern of ventricle enlargement in the bvFTD patients, particularly in the anterior parts of the frontal and temporal horns of the lateral ventricles. The estimation of the proposed ventricular anteroposterior ratio (APR) resulted in statistically significant difference compared to all other groups. Our study proposes an easy to obtain and generalizable ventricle-based feature (APR) from T1-weighted structural MRI (routinely acquired and available in the clinic) that can be used not only to differentiate bvFTD from normal subjects, but also from other FTD variants (SV and PNFA), MCI, and AD patients. We have made our ventricle feature estimation and bvFTD diagnosis tool (VentRa) publicly available, allowing application of our model in other studies. If validated in a prospective study, VentRa has the potential to aid bvFTD diagnosis, particularly in settings where access to specialized FTD care is limited.
Introduction Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases. Methods Using 678 subjects −69 bvFTD, 38 semantic variant, 37 primary non-fluent aphasia, 218 amyloid + mild cognitive impairment, 74 amyloid + Alzheimer’s Dementia and 242 normal controls- with a total of 2750 timepoints, lateral ventricles were segmented and differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts. Results Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with other ventricular features (i.e., total ventricular volume and left–right lateral ventricle ratios), and 76% accuracy using only the single APR feature. Discussion Ventricular features, particularly the APR, might be reliable and easy-to-implement markers for bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.
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Affiliation(s)
- Ana L Manera
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada.
| | - Mahsa Dadar
- Department of Psychiatry, Douglas Mental Health University Health Centre, McGill University, Montreal, Quebec (QC), Canada; Douglas Mental Health University Institute, Verdun, QC, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada; Department of Psychiatry, Douglas Mental Health University Health Centre, McGill University, Montreal, Quebec (QC), Canada
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22
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Mouches P, Wilms M, Rajashekar D, Langner S, Forkert ND. Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions. Hum Brain Mapp 2022; 43:2554-2566. [PMID: 35138012 PMCID: PMC9057090 DOI: 10.1002/hbm.25805] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.
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Affiliation(s)
- Pauline Mouches
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, Alberta, Canada
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Deepthi Rajashekar
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, Alberta, Canada
| | - Sönke Langner
- Institute for Diagnostic Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
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23
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Morrison C, Dadar M, Shafiee N, Villeneuve S, Louis Collins D. Regional brain atrophy and cognitive decline depend on definition of subjective cognitive decline. Neuroimage Clin 2021; 33:102923. [PMID: 34959049 PMCID: PMC8718726 DOI: 10.1016/j.nicl.2021.102923] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/26/2021] [Accepted: 12/20/2021] [Indexed: 01/20/2023]
Abstract
Background People with subjective cognitive decline (SCD) may be at increased risk for Alzheimer’s disease (AD). However, not all studies have observed this increased risk. This project examined whether four common methods of defining SCD yields different patterns of atrophy and future cognitive decline between cognitively normal older adults with (SCD+ ) and without SCD (SCD−). Methods Data from 273 Alzheimer’s Disease Neuroimaging Initiative cognitively normal older adults were examined. To operationalize SCD we used four common methods: Cognitive Change Index (CCI), Everyday Cognition Scale (ECog), ECog + Worry, and Worry. Voxel-based logistic regressions were applied to deformation-based morphology results to determine if regional atrophy between SCD− and SCD+ differed by SCD definition. Linear mixed-effects models were used to evaluate differences in future cognitive decline. Results Results varied between the four methods of defining SCD. Left hippocampal grading was more similar to AD in SCD+ than SCD− when using the CCI (p = .041) and Worry (p = .021) definitions. The right (p=.008) and left (p=.003) superior temporal regions had smaller volumes in SCD+ than SCD−, but only with the ECog. SCD+ was associated with greater future cognitive decline measured by Alzheimer’s Disease Assessment Scale, but only with the CCI definition. In contrast, only the ECog definition of SCD was associated with future decline on the Montreal Cognitive Assessment. Conclusion These findings suggest that the various methods used to differentiate between SCD− and SCD+ influence whether volume differences and findings of cognitive decline are observed between groups in this retrospective analysis.
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Affiliation(s)
- Cassandra Morrison
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, H3A 2B4 Montreal, Quebec, Canada.
| | - Mahsa Dadar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Department of Radiology and Nuclear Medicine, Faculty of Medicine, Laval University, Canada
| | - Neda Shafiee
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, H3A 2B4 Montreal, Quebec, Canada; Department of Psychiatry, McGill University, H3A 1A1 Montreal, Quebec, Canada; Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StoP-AD) Centre, H4H 1R3 Montreal, Quebec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, H3A 2B4 Montreal, Quebec, Canada
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24
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Dadar M, Manera AL, Ducharme S, Collins DL. White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia. Neurobiol Aging 2021; 111:54-63. [PMID: 34968832 DOI: 10.1016/j.neurobiolaging.2021.11.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 10/21/2021] [Accepted: 11/26/2021] [Indexed: 01/18/2023]
Abstract
White matter hyperintensities (WMHs) are commonly assumed to represent non-specific cerebrovascular disease comorbid to neurodegenerative processes, rather than playing a synergistic role. We compared the impact of WMHs on grey matter (GM) atrophy and cognition in normal aging (n = 571), mild cognitive impairment (MCI, n = 551), Alzheimer's dementia (AD, n = 212), fronto-temporal dementia (FTD, n = 125), and Parkinson's disease (PD, n = 271). Longitudinal data were obtained from ADNI, FTLDNI, and PPMI datasets. Mixed-effects models were used to compare WMHs and GM atrophy between patients and controls and assess the impact of WMHs on GM atrophy and cognition. MCI, AD, and FTD patients had significantly higher WMH loads than controls. WMHs were related to GM atrophy in insular and parieto-occipital regions in MCI/AD, and frontal regions and basal ganglia in FTD. In addition, WMHs contributed to more severe cognitive deficits in AD and FTD compared to controls, whereas their impact in MCI and PD was not significantly different from controls. These results suggest potential synergistic effects between WMHs and proteinopathies in the neurodegenerative process in MCI, AD and FTD.
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Affiliation(s)
- Mahsa Dadar
- NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | - Ana Laura Manera
- NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Psychiatry, Douglas Mental Health University Institute and Douglas Research Centre, McGill University, Montreal, QC, Canada
| | - D Louis Collins
- NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Gómez Vecchio T, Neimantaite A, Corell A, Bartek J, Jensdottir M, Reinertsen I, Solheim O, Jakola AS. Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions. Front Oncol 2021; 11:748229. [PMID: 34621684 PMCID: PMC8490663 DOI: 10.3389/fonc.2021.748229] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/27/2021] [Indexed: 01/14/2023] Open
Abstract
Background Glioma is the most common intra-axial tumor, and its location relative to critical areas of the brain is important for treatment decision-making. Studies often report tumor location based on anatomical taxonomy alone since the estimation of eloquent regions requires considerable knowledge of functional neuroanatomy and is, to some degree, a subjective measure. An unbiased and reproducible method to determine tumor location and eloquence is desirable, both for clinical use and for research purposes. Objective To report on a voxel-based method for assessing anatomical distribution and proximity to eloquent regions in diffuse lower-grade gliomas (World Health Organization grades 2 and 3). Methods A multi-institutional population-based dataset of adult patients (≥18 years) histologically diagnosed with lower-grade glioma was analyzed. Tumor segmentations were registered to a standardized space where two anatomical atlases were used to perform a voxel-based comparison of the proximity of segmentations to brain regions of traditional clinical interest. Results Exploring the differences between patients with oligodendrogliomas, isocitrate dehydrogenase (IDH) mutated astrocytomas, and patients with IDH wild-type astrocytomas, we found that the latter were older, more often had lower Karnofsky performance status, and that these tumors were more often found in the proximity of eloquent regions. Eloquent regions are found slightly more frequently in the proximity of IDH-mutated astrocytomas compared to oligodendrogliomas. The regions included in our voxel-based definition of eloquence showed a high degree of association with performing biopsy compared to resection. Conclusion We present a simple, robust, unbiased, and clinically relevant method for assessing tumor location and eloquence in lower-grade gliomas.
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Affiliation(s)
- Tomás Gómez Vecchio
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Alice Neimantaite
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Alba Corell
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden.,Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jiri Bartek
- Department of Neurosurgery, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.,Department of Neurosurgery, Rigshospitalet, Copenhagen, Denmark
| | - Margret Jensdottir
- Department of Neurosurgery, Karolinska University Hospital, Stockholm, Sweden
| | - Ingerid Reinertsen
- Department of Health Research, SINTEF Digital, Trondheim, Norway.,Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | - Ole Solheim
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway.,Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Asgeir S Jakola
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden.,Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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26
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Kuhn T, Becerra S, Duncan J, Spivak N, Dang BH, Habelhah B, Mahdavi KD, Mamoun M, Whitney M, Pereles FS, Bystritsky A, Jordan SE. Translating state-of-the-art brain magnetic resonance imaging (MRI) techniques into clinical practice: multimodal MRI differentiates dementia subtypes in a traditional clinical setting. Quant Imaging Med Surg 2021; 11:4056-4073. [PMID: 34476189 PMCID: PMC8339641 DOI: 10.21037/qims-20-1355] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 04/25/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND This study sought to validate the clinical utility of multimodal magnetic resonance imaging (MRI) techniques in the assessment of neurodegenerative disorders. We intended to demonstrate that advanced neuroimaging techniques commonly used in research can effectively be employed in clinical practice to accurately differentiate heathy aging and dementia subtypes. METHODS Twenty patients with dementia of the Alzheimer's type (DAT) and 18 patients with Parkinson's disease dementia (PDD) were identified using gold-standard techniques. Twenty-three healthy, age and sex matched control participants were also recruited. All participants underwent multimodal MRI including T1 structural, diffusion tensor imaging (DTI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS). MRI modalities were evaluated by trained neuroimaging readers and were separately assessed using cross-validated, iterative discriminant function analyses with subsequent feature reduction techniques. In this way, each modality was evaluated for its ability to differentiate patients with dementia from healthy controls as well as to differentiate dementia subtypes. RESULTS Following individual and group feature reduction, each of the multimodal MRI metrics except MRS successfully differentiated healthy aging from dementia and also demonstrated distinct dementia subtypes. Using the following ten metrics, excellent separation (95.5% accuracy, 92.3% sensitivity; 100.0% specificity) was achieved between healthy aging and neurodegenerative conditions: volume of the left frontal pole, left occipital pole, right posterior superior temporal gyrus, left posterior cingulate gyrus, right planum temporale; perfusion of the left hippocampus and left occipital lobe; fractional anisotropy (FA) of the forceps major and bilateral anterior thalamic radiation. Using volume of the left frontal pole, right posterior superior temporal gyrus, left posterior cingulate gyrus, perfusion of the left hippocampus and left occipital lobe; FA of the forceps major and bilateral anterior thalamic radiation, neurodegenerative subtypes were accurately differentiated as well (87.8% accuracy, 95.2% sensitivity; 85.0% specificity). CONCLUSIONS Regional volumetrics, DTI metrics, and ASL successfully differentiated dementia patients from controls with sufficient sensitivity to differentiate dementia subtypes. Similarly, feature reduction results suggest that advanced analyses can meaningfully identify brain regions with the most positive predictive value and discriminant validity. Together, these advanced neuroimaging techniques can contribute significantly to diagnosis and treatment planning for individual patients.
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Affiliation(s)
- Taylor Kuhn
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | - Sergio Becerra
- Neurology Management Associates, Los Angeles, California, USA
| | - John Duncan
- Neurology Management Associates, Los Angeles, California, USA
| | - Norman Spivak
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | - Bianca Huan Dang
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | | | | | | | | | | | - Alexander Bystritsky
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | - Sheldon E. Jordan
- Neurology Management Associates, Los Angeles, California, USA
- Department of Neurology, University of California, Los Angeles, Los Angeles, California, USA
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27
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Yu B, Li L, Guan X, Xu X, Liu X, Yang Q, Wei H, Zuo C, Zhang Y. HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease. Hum Brain Mapp 2021; 42:4399-4421. [PMID: 34101297 PMCID: PMC8357000 DOI: 10.1002/hbm.25556] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 05/27/2021] [Accepted: 05/30/2021] [Indexed: 12/29/2022] Open
Abstract
Human brain atlases are essential for research and surgical treatment of Parkinson's disease (PD). For example, deep brain stimulation for PD often requires human brain atlases for brain structure identification. However, few atlases can provide disease-specific subcortical structures for PD, and most of them are based on T1w and T2w images. In this work, we construct a HybraPD atlas using fused quantitative susceptibility mapping (QSM) and T1w images from 87 patients with PD. The constructed HybraPD atlas provides a series of templates, that is, T1w, GRE magnitude, QSM, R2*, and brain tissue probabilistic maps. Then, we manually delineate a parcellation map with 12 bilateral subcortical nuclei, which are highly related to PD pathology, such as sub-regions in globus pallidus and substantia nigra. Furthermore, we build a whole-brain parcellation map by combining existing cortical parcellation and white-matter segmentation with the proposed subcortical nuclei map. Considering the multimodality of the HybraPD atlas, the segmentation accuracy of each nucleus is evaluated using T1w and QSM templates, respectively. The results show that the HybraPD atlas provides more accurate segmentation than existing atlases. Moreover, we analyze the metabolic difference in subcortical nuclei between PD patients and healthy control subjects by applying the HybraPD atlas to calculate uptake values of contrast agents on positron emission tomography (PET) images. The atlas-based analysis generates accurate disease-related brain nuclei segmentation on PET images. The newly developed HybraPD atlas could serve as an efficient template to study brain pathological alterations in subcortical regions for PD research.
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Affiliation(s)
- Boliang Yu
- School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
| | - Ling Li
- PET Center, Huashan HospitalFudan UniversityShanghaiChina
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xueling Liu
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
| | - Qing Yang
- Institute of Brain‐Intelligence Technology, Zhangjiang LaboratoryShanghaiChina
| | - Hongjiang Wei
- Institute for Medicine Imaging Technology, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Chuantao Zuo
- PET Center, Huashan HospitalFudan UniversityShanghaiChina
| | - Yuyao Zhang
- School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
- Shanghai Engineering Research Center of Intelligent Vision and ImagingShanghaiTech UniversityShanghaiChina
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28
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Iceta S, Dadar M, Daoust J, Scovronec A, Leblanc V, Pelletier M, Biertho L, Tchernof A, Bégin C, Michaud A. Association between Visceral Adiposity Index, Binge Eating Behavior, and Grey Matter Density in Caudal Anterior Cingulate Cortex in Severe Obesity. Brain Sci 2021; 11:brainsci11091158. [PMID: 34573180 PMCID: PMC8468041 DOI: 10.3390/brainsci11091158] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/24/2021] [Accepted: 08/25/2021] [Indexed: 01/01/2023] Open
Abstract
Visceral adipose tissue accumulation is an important determinant of metabolic risk and can be estimated by the visceral adiposity index (VAI). Visceral adiposity may impact brain regions involved in eating behavior. We aimed to examine the association between adiposity measurements, binge eating behavior, and grey matter density. In 20 men and 59 women with severe obesity, Grey matter density was measured by voxel-based morphometry for six regions of interest associated with reward, emotion, or self-regulation: insula, orbitofrontal cortex, caudal and rostral anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), and dorsolateral prefrontal cortex (DLPFC). Binge eating behavior, depression and impulsivity was assessed by the Binge Eating Scale, Beck Depression Inventory and UPPS Impulsive Behavior Scale, respectively. Men and women were distinctively divided into two subgroups (low-VAI and high-VAI) based on the mean VAI score. Women with high-VAI were characterized by metabolic alterations, higher binge eating score and lower grey matter density in the caudal ACC compared to women with low-VAI. Men with high-VAI were characterized by a higher score for the sensation-seeking subscale of the UPPS–Impulsive Behavior Scale compared to men with low-VAI. Using a moderation–mediation analysis, we found that grey matter density in the caudal ACC mediates the association between VAI and binge eating score. In conclusion, visceral adiposity is associated with higher binge eating severity in women. Decreased grey matter density in the caudal ACC, a region involved in cognition and emotion regulation, may influence this relationship.
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Affiliation(s)
- Sylvain Iceta
- Research Center of the Quebec Heart and Lung Institute, Université Laval, Quebec City, QC G1V 4G5, Canada; (J.D.); (A.S.); (M.P.); (A.T.); (C.B.)
- School of Nutrition, Université Laval, Quebec City, QC G1V 0A6, Canada
- Correspondence: (S.I.); (A.M.)
| | - Mahsa Dadar
- CERVO Brain Research Center, Centre Intégré Universitaire Santé et Services Sociaux de la Capitale Nationale, Université Laval, Quebec City, QC G1E 1T2, Canada;
| | - Justine Daoust
- Research Center of the Quebec Heart and Lung Institute, Université Laval, Quebec City, QC G1V 4G5, Canada; (J.D.); (A.S.); (M.P.); (A.T.); (C.B.)
- School of Nutrition, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Anais Scovronec
- Research Center of the Quebec Heart and Lung Institute, Université Laval, Quebec City, QC G1V 4G5, Canada; (J.D.); (A.S.); (M.P.); (A.T.); (C.B.)
- School of Nutrition, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Vicky Leblanc
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Quebec City, QC G1V 0A6, Canada;
| | - Melissa Pelletier
- Research Center of the Quebec Heart and Lung Institute, Université Laval, Quebec City, QC G1V 4G5, Canada; (J.D.); (A.S.); (M.P.); (A.T.); (C.B.)
| | - Laurent Biertho
- Département de Chirurgie Générale, Quebec Heart and Lung Institute, Université Laval, Quebec City, QC G1V 4G5, Canada;
| | - André Tchernof
- Research Center of the Quebec Heart and Lung Institute, Université Laval, Quebec City, QC G1V 4G5, Canada; (J.D.); (A.S.); (M.P.); (A.T.); (C.B.)
- School of Nutrition, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Catherine Bégin
- Research Center of the Quebec Heart and Lung Institute, Université Laval, Quebec City, QC G1V 4G5, Canada; (J.D.); (A.S.); (M.P.); (A.T.); (C.B.)
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Quebec City, QC G1V 0A6, Canada;
- School of Psychology, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Andreanne Michaud
- Research Center of the Quebec Heart and Lung Institute, Université Laval, Quebec City, QC G1V 4G5, Canada; (J.D.); (A.S.); (M.P.); (A.T.); (C.B.)
- School of Nutrition, Université Laval, Quebec City, QC G1V 0A6, Canada
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Quebec City, QC G1V 0A6, Canada;
- Correspondence: (S.I.); (A.M.)
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Paquola C, Royer J, Lewis LB, Lepage C, Glatard T, Wagstyl K, DeKraker J, Toussaint PJ, Valk SL, Collins L, Khan AR, Amunts K, Evans AC, Dickscheid T, Bernhardt B. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. eLife 2021; 10:e70119. [PMID: 34431476 PMCID: PMC8445620 DOI: 10.7554/elife.70119] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023] Open
Abstract
Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Lindsay B Lewis
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Claude Lepage
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia UniversityMontrealCanada
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College LondonLondonUnited Kingdom
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Brain and Mind Institute, University of Western OntarioOntarioCanada
| | - Paule-J Toussaint
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum JülichJülichGermany
| | - Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western OntarioLondonCanada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
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Dadar M, Manera AL, Fonov VS, Ducharme S, Collins DL. MNI-FTD templates, unbiased average templates of frontotemporal dementia variants. Sci Data 2021; 8:222. [PMID: 34429437 PMCID: PMC8385071 DOI: 10.1038/s41597-021-01007-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 07/30/2021] [Indexed: 01/18/2023] Open
Abstract
Standard templates are widely used in human neuroimaging processing pipelines to facilitate group-level analyses and comparisons across subjects/populations. MNI-ICBM152 template is the most commonly used standard template, representing an average of 152 healthy young adult brains. However, in patients with neurodegenerative diseases such as frontotemporal dementia (FTD), high atrophy levels lead to significant differences between individuals' brain shapes and MNI-ICBM152 template. Such differences might inevitably lead to registration errors or subtle biases in downstream analyses and results. Disease-specific templates are therefore desirable to reflect the anatomical characteristics of the populations of interest and reduce potential registration errors. Here, we present MNI-FTD136, MNI-bvFTD70, MNI-svFTD36, and MNI-pnfaFTD30, four unbiased average templates of 136 FTD patients, 70 behavioural variant (bv), 36 semantic variant (sv), and 30 progressive nonfluent aphasia (pnfa) variant FTD patients and a corresponding age-matched template of 133 controls (MNI-CN133), along with probabilistic tissue maps for each template. Public availability of these templates will facilitate analyses of FTD cohorts and enable comparisons between different studies in an appropriate common standardized space.
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Affiliation(s)
- Mahsa Dadar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada.
- CERVO Brain Research Center, Centre intégré universitaire santé et services sociaux de la Capitale Nationale, Québec, QC, Canada.
| | - Ana L Manera
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada
| | - Vladimir S Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada
- Douglas Mental Health University Institute, Department of Psychiatry, 6875 Boulevard LaSalle, Montreal, QC, H4H 1R3, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada
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Zeighami Y, Iceta S, Dadar M, Pelletier M, Nadeau M, Biertho L, Lafortune A, Tchernof A, Fulton S, Evans A, Richard D, Dagher A, Michaud A. Spontaneous neural activity changes after bariatric surgery: A resting-state fMRI study. Neuroimage 2021; 241:118419. [PMID: 34302967 DOI: 10.1016/j.neuroimage.2021.118419] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 06/24/2021] [Accepted: 07/20/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Metabolic disorders associated with obesity could lead to alterations in brain structure and function. Whether these changes can be reversed after weight loss is unclear. Bariatric surgery provides a unique opportunity to address these questions because it induces marked weight loss and metabolic improvements which in turn may impact the brain in a longitudinal fashion. Previous studies found widespread changes in grey matter (GM) and white matter (WM) after bariatric surgery. However, findings regarding changes in spontaneous neural activity following surgery, as assessed with the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity of neural activity (ReHo), are scarce and heterogenous. In this study, we used a longitudinal design to examine the changes in spontaneous neural activity after bariatric surgery (comparing pre- to post-surgery), and to determine whether these changes are related to cardiometabolic variables. METHODS The study included 57 participants with severe obesity (mean BMI=43.1 ± 4.3 kg/m2) who underwent sleeve gastrectomy (SG), biliopancreatic diversion with duodenal switch (BPD), or Roux-en-Y gastric bypass (RYGB), scanned prior to bariatric surgery and at follow-up visits of 4 months (N = 36), 12 months (N = 29), and 24 months (N = 14) after surgery. We examined fALFF and ReHo measures across 1022 cortical and subcortical regions (based on combined Schaeffer-Xiao parcellations) using a linear mixed effect model. Voxel-based morphometry (VBM) based on T1-weighted images was also used to measure GM density in the same regions. We also used an independent sample from the Human Connectome Project (HCP) to assess regional differences between individuals who had normal-weight (N = 46) or severe obesity (N = 46). RESULTS We found a global increase in the fALFF signal with greater increase within dorsolateral prefrontal cortex, precuneus, inferior temporal gyrus, and visual cortex. This effect was more significant 4 months after surgery. The increase within dorsolateral prefrontal cortex, temporal gyrus, and visual cortex was more limited after 12 months and only present in the visual cortex after 24 months. These increases in neural activity measured by fALFF were also significantly associated with the increase in GM density following surgery. Furthermore, the increase in neural activity was significantly related to post-surgery weight loss and improvement in cardiometabolic variables, such as blood pressure. In the independent HCP sample, normal-weight participants had higher global and regional fALFF signals, mainly in dorsolateral/medial frontal cortex, precuneus and middle/inferior temporal gyrus compared to the obese participants. These BMI-related differences in fALFF were associated with the increase in fALFF 4 months post-surgery especially in regions involved in control, default mode and dorsal attention networks. CONCLUSIONS Bariatric surgery-induced weight loss and improvement in metabolic factors are associated with widespread global and regional increases in neural activity, as measured by fALFF signal. These findings alongside the higher fALFF signal in normal-weight participants compared to participants with severe obesity in an independent dataset suggest an early recovery in the neural activity signal level after the surgery.
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Affiliation(s)
- Yashar Zeighami
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada
| | - Sylvain Iceta
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Mahsa Dadar
- CERVO Brain Research Center, Centre intégré universitaire santé et services sociaux de la Capitale Nationale, Université Laval, Québec, Canada
| | - Mélissa Pelletier
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Mélanie Nadeau
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Laurent Biertho
- Département de chirurgie générale, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Annie Lafortune
- Département de chirurgie générale, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - André Tchernof
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Stephanie Fulton
- Centre de Recherche du CHUM and Montreal Diabetes Research Center, Montreal, QC, Canada
| | - Alan Evans
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada
| | - Denis Richard
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Alain Dagher
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Andréanne Michaud
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada.
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Dadar M, Camicioli R, Duchesne S, Collins DL. The temporal relationships between white matter hyperintensities, neurodegeneration, amyloid beta, and cognition. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2020; 12:e12091. [PMID: 33083512 PMCID: PMC7552231 DOI: 10.1002/dad2.12091] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/15/2020] [Accepted: 07/24/2020] [Indexed: 02/03/2023]
Abstract
Introduction Cognitive decline in Alzheimer's disease is associated with amyloid beta (Aβ) accumulation, neurodegeneration, and cerebral small vessel disease, but the temporal relationships among these factors is not well established. Methods Data included white matter hyperintensity (WMH) load, gray matter (GM) atrophy and Alzheimer's Disease Assessment Scale‐Cognitive‐Plus (ADAS13) scores for 720 participants and cerebrospinal fluid amyloid (Aβ1–42) for 461 participants from the Alzheimer's Disease Neuroimaging Initiative. Linear regressions were used to assess the relationships among baseline WMH, GM, and Aβ1–42 to changes in WMH, GM, Aβ1–42, and cognition at 1‐year follow‐up. Results Baseline WMHs and Aβ1–42 predicted WMH increase and GM atrophy. Baseline WMHs and Aβ1–42 predicted worsening cognition. Only baseline Aβ1–42 predicted change in Aβ1–42. Discussion Baseline WMHs lead to greater future GM atrophy and cognitive decline, suggesting that WM damage precedes neurodegeneration and cognitive decline. Baseline Aβ1–42 predicted WMH increase, suggesting a potential role of amyloid in WM damage.
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Affiliation(s)
- Mahsa Dadar
- CERVO Brain Research Center Centre intégré universitaire santé et services sociaux de la Capitale Nationale Québec Quebec Canada
| | - Richard Camicioli
- Department of Medicine, Division of Neurology University of Alberta Edmonton Alberta Canada
| | - Simon Duchesne
- CERVO Brain Research Center Centre intégré universitaire santé et services sociaux de la Capitale Nationale Québec Quebec Canada.,Department of Radiology and Nuclear Medicine, Faculty of Medicine Université Laval Québec City Quebec Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute McGill University Montreal Quebec Canada.,Department of Neurology and Neurosurgery, Faculty of Medicine McGill University Montreal Quebec Canada.,Department of Biomedical Engineering, Faculty of Medicine McGill University Montreal Quebec Canada
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Dadar M, Gee M, Shuaib A, Duchesne S, Camicioli R. Cognitive and motor correlates of grey and white matter pathology in Parkinson's disease. Neuroimage Clin 2020; 27:102353. [PMID: 32745994 PMCID: PMC7399172 DOI: 10.1016/j.nicl.2020.102353] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/25/2020] [Accepted: 07/15/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Previous studies have found associations between grey matter atrophy and white matter hyperintensities (WMH) of vascular origin with cognitive and motor deficits in Parkinson's disease (PD). Here we investigate these relationships in a sample of PD patients and age-matched healthy controls. METHODS Data included 50 PD patients and 45 age-matched controls with T1-weighted and FLAIR scans at baseline, 18-months, and 36-months follow-up. Deformation-based morphometry was used to measure grey matter atrophy. SNIPE (Scoring by Nonlocal Image Patch Estimator) was used to measure Alzheimer's disease-like textural patterns in the hippocampi. WMHs were segmented using T1-weighted and FLAIR images. The relationship between MRI features and clinical scores was assessed using mixed-effects models. The motor subscore of the Unified Parkinson's Disease Rating Scale (UPDRSIII), number of steps in a walking trial, and Dementia Rating Scale (DRS) were used respectively as measures of motor function, gait, and cognition. RESULTS Substantia nigra atrophy was significantly associated with motor deficits, with a greater impact in PDs (p < 0.05). Hippocampal SNIPE scores were associated with cognitve decline in both PD and controls (p < 0.01). WMH burden was significantly associated with cognitive decline and increased motor deficits in the PD group, and gait deficits in both PD and controls (p < 0.03). CONCLUSION While substantia nigra atrophy and WMH burden were significantly associated with additional motor deficits, WMH burden and hippocampal atrophy were associated with cognitive deficits in PD patients. These results suggest an additive contribution of both grey and white matter damage to the motor and cognitive deficits in PD.
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Affiliation(s)
- Mahsa Dadar
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Laval University, Canada.
| | - Myrlene Gee
- Department of Medicine, Division of Neurology, University of Alberta, Canada.
| | - Ashfaq Shuaib
- Department of Medicine, Division of Neurology, University of Alberta, Canada.
| | - Simon Duchesne
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Laval University, Canada.
| | - Richard Camicioli
- Department of Medicine, Division of Neurology, University of Alberta, Canada.
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