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Eichin KN, Georgii C, Schnepper R, Voderholzer U, Blechert J. Emotional food-cue-reactivity in anorexia nervosa and bulimia nervosa: An electroencephalography study. Int J Eat Disord 2023; 56:2096-2106. [PMID: 37565581 PMCID: PMC10946739 DOI: 10.1002/eat.24028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 07/07/2023] [Accepted: 07/07/2023] [Indexed: 08/12/2023]
Abstract
OBJECTIVE Food-cue-reactivity entails neural and experiential responses to the sight and smell of attractive foods. Negative emotions can modulate such cue-reactivity and this might be central to the balance between restrictive versus bulimic symptomatology in Anorexia Nervosa (AN) and Bulimia Nervosa (BN). METHOD Pleasantness ratings and electrocortical responses to food images were measured in patients with AN (n = 35), BN (n = 32) and matched healthy controls (HC, n = 35) in a neutral state and after idiosyncratic negative emotion induction while electroencephalography (EEG) was recorded. The EEG data were analyzed using a mass testing approach. RESULTS Individuals with AN showed reduced pleasantness for foods compared to objects alongside elevated widespread occipito-central food-object discrimination between 170 and 535 ms, indicative of strong neural cue-reactivity. Food-object discrimination was further increased in the negative emotional condition between 690 and 1200 ms over centroparietal regions. Neither of these effects was seen in individuals with BN. DISCUSSION Emotion modulated food-cue-reactivity in AN might reflect a decreased appetitive response in negative mood. Such specific (emotion-)regulatory strategies require more theoretical work and clinical attention. The absence of any marked effects in BN suggests that emotional cue-reactivity might be less prominent in this group or quite specific to certain emotional contexts or food types. PUBLIC SIGNIFICANCE Negative affectivity is a risk factor for the development of eating disorders and individuals with eating disorders experience problems with emotion regulation. To better understand the effects of negative emotions, the present study investigated how they affected neural correlates of food perception in anorexia nervosa and bulimia nervosa.
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Affiliation(s)
- Katharina Naomi Eichin
- Department of Psychology and Centre for Cognitive NeuroscienceUniversity of SalzburgSalzburgAustria
- Department of PsychologyJohannes Kepler UniversityLinzAustria
| | - Claudio Georgii
- Department of Psychology and Centre for Cognitive NeuroscienceUniversity of SalzburgSalzburgAustria
| | - Rebekka Schnepper
- Department of Psychology and Centre for Cognitive NeuroscienceUniversity of SalzburgSalzburgAustria
- Department of Psychosomatic MedicineUniversity Hospital BaselBaselSwitzerland
| | | | - Jens Blechert
- Department of Psychology and Centre for Cognitive NeuroscienceUniversity of SalzburgSalzburgAustria
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2
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Al Zoubi O, Misaki M, Tsuchiyagaito A, Zotev V, White E, Paulus M, Bodurka J. Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets. Brain Connect 2022; 12:348-361. [PMID: 34269609 PMCID: PMC9131354 DOI: 10.1089/brain.2020.0878] [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] [Indexed: 11/12/2022] Open
Abstract
Background/Introduction: Sex classification using functional connectivity from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent properties such as the amplitude of low-frequency fluctuation (ALFF) and the fraction of ALFF (fALFF). This study comprehensively investigates sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of rs-fMRI with over than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions. Methods: Five rs-fMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and then were fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across-sample validation. In addition, the parcellation effect, classifier selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated thoroughly in the study. Results: The results demonstrated high sex classification accuracies from healthy adults (area under the curve >0.89), while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min). The spatial distribution of important features overlaps with previous results from studies. Discussion: Sex differences are consistent in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in rs-fMRI. Impact statement The presented study unraveled that sex differences are embedded in the blood-oxygen-level dependent (BOLD) and can be predicted using unbiased and explainable machine learning pipeline. The study revealed that psychiatric disorders and demographics might influence the BOLD signal and interact with the classification of sex. The spatial distribution of the important features presented here supports the notion that the brain is a mosaic of male and female features. The findings emphasize the importance of controlling for sex when conducting brain imaging analysis. In addition, the presented framework can be adapted to classify other variables from resting-state BOLD signals.
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Affiliation(s)
- Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Department of Psychiatry, Harvard Medical School/McLean Hospital, Boston, Massachusetts, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | | | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Evan White
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA
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3
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Wu CL. Brain Network Associated with Three Types of Remote Associations: Graph Theory Analysis. CREATIVITY RESEARCH JOURNAL 2022. [DOI: 10.1080/10400419.2022.2048229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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4
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He M, Cheng Y, Chu Z, Wang X, Xu J, Lu Y, Shen Z, Xu X. White Matter Network Disruption Is Associated With Melancholic Features in Major Depressive Disorder. Front Psychiatry 2022; 13:816191. [PMID: 35492691 PMCID: PMC9046786 DOI: 10.3389/fpsyt.2022.816191] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/22/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The efficacy and prognosis of major depressive disorder (MDD) are limited by its heterogeneity. MDD with melancholic features is an important subtype of MDD. The present study aimed to reveal the white matter (WM) network changes in melancholic depression. MATERIALS AND METHODS Twenty-three first-onset, untreated melancholic MDD, 59 non-melancholic MDD patients and 63 health controls underwent diffusion tensor imaging (DTI) scans. WM network analysis based on graph theory and support vector machine (SVM) were used for image data analysis. RESULTS Compared with HC, small-worldness was reduced and abnormal node attributes were in the right orbital inferior frontal gyrus, left orbital superior frontal gyrus, right caudate nucleus, right orbital superior frontal gyrus, right orbital middle frontal gyrus, left rectus gyrus, and left median cingulate and paracingulate gyrus of MDD patients. Compared with non-melancholic MDD, small-worldness was reduced and abnormal node attributes were in right orbital inferior frontal gyrus, left orbital superior frontal gyrus and right caudate nucleus of melancholic MDD. For correlation analysis, the 7th item score of the HRSD-17 (work and interest) was positively associated with increased node betweenness centrality (aBC) values in right orbital inferior frontal gyrus, while negatively associated with the decreased aBC in left orbital superior frontal gyrus. SVM analysis results showed that abnormal aBC in right orbital inferior frontal gyrus and left orbital superior frontal gyrus showed the highest accuracy of 81.0% (69/83), the sensitivity of 66.3%, and specificity of 85.2% for discriminating MDD patients with or without melancholic features. CONCLUSION There is a significant difference in WM network changes between MDD patients with and without melancholic features.
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Affiliation(s)
- Mengxin He
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan Clinical Research Center for Mental Disorders, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan Clinical Research Center for Mental Disorders, Kunming, China
| | - Zhaosong Chu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan Clinical Research Center for Mental Disorders, Kunming, China
| | - Xin Wang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jinlei Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yi Lu
- Department of Medical Imaging, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zonglin Shen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan Clinical Research Center for Mental Disorders, Kunming, China.,Mental Health Institute of Yunnan, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Yunnan Clinical Research Center for Mental Disorders, Kunming, China
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5
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Zhou C, Ping L, Chen W, He M, Xu J, Shen Z, Lu Y, Shang B, Xu X, Cheng Y. Altered white matter structural networks in drug-naïve patients with obsessive-compulsive disorder. Brain Imaging Behav 2021; 15:700-710. [PMID: 32314200 DOI: 10.1007/s11682-020-00278-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
White matter (WM) alteration is considered to be a vital neurological mechanism of obsessive-compulsive disorder (OCD). However, little is known regarding the changes in topological organization of WM structural network in OCD. We acquired diffusion tensor imaging (DTI) datasets from 28 drug-naïve OCD patients and 28 well-matched healthy controls (HC). A deterministic fiber tracking approach was used to construct the whole-brain structural connectome. Group differences in global and nodal topological properties as well as rich-club organizations were compared by using graph theory analysis. The relationship between the altered network metrics and the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) was calculated. Compared with controls, OCD patients exhibited a significantly decreased small-worldness (σ), normalized clustering coefficient (γ) and shortest path length (Lp), as well as an increased global efficiency (Eglob). The nodal efficiency (Enodal) was found to be reduced in the left middle frontal gyrus, and increased in the right parahippocampal gyrus and bilateral putamen in OCD patients. Besides, OCD patients showed increased rich-club, feeder and local connection strength, and the connection strength of the rich-club was positively correlated with the total Y-BOCS score. Our findings emphasized a central role for the complicatedly changed topological architecture of brain structural networks in the pathological mechanism underlying OCD.
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Affiliation(s)
- Cong Zhou
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, 361000, China
| | - Wei Chen
- Department of Medical Imaging, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Mengxin He
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Jian Xu
- Department of Internal Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Zonglin Shen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Yi Lu
- Department of Medical Imaging, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Binli Shang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China. .,The NHC Key Laboratory of Drug Addiction Medicine, Kunming, 650032, China.
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6
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Daneault V, Orban P, Martin N, Dansereau C, Godbout J, Pouliot P, Dickinson P, Gosselin N, Vandewalle G, Maquet P, Lina JM, Doyon J, Bellec P, Carrier J. Cerebral functional networks during sleep in young and older individuals. Sci Rep 2021; 11:4905. [PMID: 33649377 PMCID: PMC7921592 DOI: 10.1038/s41598-021-84417-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 02/15/2021] [Indexed: 11/09/2022] Open
Abstract
Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.
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Affiliation(s)
- Véronique Daneault
- Functional Neuroimaging Unit, University of Montreal Geriatric Institute, 4565, Queen-Mary Road, Montreal, QC, H3W 1W5, Canada.,Center for Advanced Research in Sleep Medicine (CARSM), Hôpital du Sacré-Cœur de Montréal, 5400 Gouin Boulevard West, Montreal, QC, H4J 1C5, Canada.,Department of Psychology, University of Montreal, Downtown Station, P.O. Box 6128, Montreal, QC, H3C 3J7, Canada
| | - Pierre Orban
- Functional Neuroimaging Unit, University of Montreal Geriatric Institute, 4565, Queen-Mary Road, Montreal, QC, H3W 1W5, Canada.,Department of Psychiatry, University of Montreal, Downtown Station, P.O. Box 6128, Montreal, QC, H3C 3J7, Canada.,Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, 7331 Hochelaga, Montreal, QC, H1N 3V2, Canada
| | - Nicolas Martin
- Functional Neuroimaging Unit, University of Montreal Geriatric Institute, 4565, Queen-Mary Road, Montreal, QC, H3W 1W5, Canada.,Center for Advanced Research in Sleep Medicine (CARSM), Hôpital du Sacré-Cœur de Montréal, 5400 Gouin Boulevard West, Montreal, QC, H4J 1C5, Canada.,Department of Psychology, University of Montreal, Downtown Station, P.O. Box 6128, Montreal, QC, H3C 3J7, Canada
| | - Christian Dansereau
- Functional Neuroimaging Unit, University of Montreal Geriatric Institute, 4565, Queen-Mary Road, Montreal, QC, H3W 1W5, Canada
| | - Jonathan Godbout
- Center for Advanced Research in Sleep Medicine (CARSM), Hôpital du Sacré-Cœur de Montréal, 5400 Gouin Boulevard West, Montreal, QC, H4J 1C5, Canada.,Génie Électrique, École de technologie supérieure, 1100, rue Notre-Dame Ouest, Montreal, QC, H3C 1K3, Canada
| | - Philippe Pouliot
- École Polytechnique de Montréal, Succursale Centre-Ville, C.P. 6079, Montreal, QC, H3C 3A7, Canada
| | - Philip Dickinson
- Functional Neuroimaging Unit, University of Montreal Geriatric Institute, 4565, Queen-Mary Road, Montreal, QC, H3W 1W5, Canada
| | - Nadia Gosselin
- Center for Advanced Research in Sleep Medicine (CARSM), Hôpital du Sacré-Cœur de Montréal, 5400 Gouin Boulevard West, Montreal, QC, H4J 1C5, Canada.,Department of Psychology, University of Montreal, Downtown Station, P.O. Box 6128, Montreal, QC, H3C 3J7, Canada
| | - Gilles Vandewalle
- GIGA-Cyclotron Research Centre-In Vivo Imaging, Université de Liège, Allée du 6 Août, Bâtiment B30, Sart Tilman, 4000, Liège, Belgium
| | - Pierre Maquet
- GIGA-Cyclotron Research Centre-In Vivo Imaging, Université de Liège, Allée du 6 Août, Bâtiment B30, Sart Tilman, 4000, Liège, Belgium
| | - Jean-Marc Lina
- Génie Électrique, École de technologie supérieure, 1100, rue Notre-Dame Ouest, Montreal, QC, H3C 1K3, Canada.,Centre de Recherches Mathématiques (CRM), Université de Montréal, Succursale Centre-Ville, Case postale 6128, Montreal, QC, H3C 3J7, Canada.,Biomedical Engineering Department, McGill University, 3775 University Street, Montreal, QC, H3A 2B4, Canada.,U678 INSERM, Paris, France
| | - Julien Doyon
- Functional Neuroimaging Unit, University of Montreal Geriatric Institute, 4565, Queen-Mary Road, Montreal, QC, H3W 1W5, Canada
| | - Pierre Bellec
- Functional Neuroimaging Unit, University of Montreal Geriatric Institute, 4565, Queen-Mary Road, Montreal, QC, H3W 1W5, Canada
| | - Julie Carrier
- Functional Neuroimaging Unit, University of Montreal Geriatric Institute, 4565, Queen-Mary Road, Montreal, QC, H3W 1W5, Canada. .,Center for Advanced Research in Sleep Medicine (CARSM), Hôpital du Sacré-Cœur de Montréal, 5400 Gouin Boulevard West, Montreal, QC, H4J 1C5, Canada. .,Department of Psychology, University of Montreal, Downtown Station, P.O. Box 6128, Montreal, QC, H3C 3J7, Canada.
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7
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Boukhdhir A, Zhang Y, Mignotte M, Bellec P. Unraveling reproducible dynamic states of individual brain functional parcellation. Netw Neurosci 2021; 5:28-55. [PMID: 33688605 PMCID: PMC7935036 DOI: 10.1162/netn_a_00168] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 09/08/2020] [Indexed: 01/04/2023] Open
Abstract
Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, that is, they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 min) time windows into "states" with highly similar seed parcels. We split individual time series of the Midnight scan club sample into two independent sets of 2.5 hr (test and retest). We found that average within-state parcellations, called stability maps, were highly reproducible (over 0.9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well-defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may, thus, impact how researchers model the rich interactions between brain networks in health and disease.
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Affiliation(s)
- Amal Boukhdhir
- Centre de recherche de l’institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada
- Département d’informatique et de recherche opérationnelle, Université de Montréal, Montréal, Québec, Canada
| | - Yu Zhang
- Centre de recherche de l’institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada
- Département de psychologie, Université de Montréal, Montréal, Québec, Canada
| | - Max Mignotte
- Département de psychologie, Université de Montréal, Montréal, Québec, Canada
| | - Pierre Bellec
- Centre de recherche de l’institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada
- Département de psychologie, Université de Montréal, Montréal, Québec, Canada
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8
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Wang X, Zhao M, Lin L, Han Y. Plasma β-Amyloid Levels Associated With Structural Integrity Based on Diffusion Tensor Imaging in Subjective Cognitive Decline: The SILCODE Study. Front Aging Neurosci 2021; 12:592024. [PMID: 33510631 PMCID: PMC7835390 DOI: 10.3389/fnagi.2020.592024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 12/11/2020] [Indexed: 11/26/2022] Open
Abstract
Background: Accumulating evidence has demonstrated that plasma β-amyloid (Aβ) levels are useful biomarkers to reflect brain amyloidosis and gray matter structure, but little is known about their correlation with subclinical white matter (WM) integrity in individuals at risk of Alzheimer's disease (AD). Here, we investigated the microstructural changes in WM between subjects with low and high plasma Aβ levels among individuals with subjective cognitive decline (SCD). Methods: This study included 142 cognitively normal individuals with SCD who underwent a battery of neuropsychological tests, plasma Aβ measurements, and diffusion tensor imaging (DTI) based on the Sino Longitudinal Study on Cognitive Decline (SILCODE). Using tract-based spatial statistics (TBSS), we compared fractional anisotropy (FA), and mean diffusivity (MD) in WM between subjects with low (N = 71) and high (N = 71) plasma Aβ levels (cut-off: 761.45 pg/ml for Aβ40 and 10.74 pg/ml for Aβ42). Results: We observed significantly decreased FA and increased MD in the high Aβ40 group compared to the low Aβ40 group in various regions, including the body, the genu, and the splenium of the corpus callosum; the superior longitudinal fasciculus; the corona radiata; the thalamic radiation; the external and internal capsules; the inferior fronto-occipital fasciculus; and the sagittal stratum [p < 0.05, familywise error (FWE) corrected]. Average FA values were associated with poor performance on executive and memory assessments. No significant differences were found in either MD or FA between the low and high Aβ42 groups. Conclusion: Our results suggest that a correlation exists between WM integrity and plasma Aβ40 levels in individuals with SCD.
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Affiliation(s)
- Xiaoni Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Mingyan Zhao
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Li Lin
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
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9
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Brossard C, Montigon O, Boux F, Delphin A, Christen T, Barbier EL, Lemasson B. MP3: Medical Software for Processing Multi-Parametric Images Pipelines. Front Neuroinform 2020; 14:594799. [PMID: 33304261 PMCID: PMC7701116 DOI: 10.3389/fninf.2020.594799] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 10/14/2020] [Indexed: 01/28/2023] Open
Abstract
This article presents an open source software able to convert, display, and process medical images. It differentiates itself from the existing software by its ability to design complex processing pipelines and to wisely execute them on a large databases. An MP3 pipeline can contain unlimited homemade or ready-made processes and can be carried out with a parallel execution system. As a viewer, MP3 allows display of up to four images together and to draw Regions Of Interest (ROI). Two applications showing the strengths of the software are presented as examples: a preclinical study involving Magnetic Resonance Imaging (MRI) data and a clinical one involving Computed Tomography (CT) images. MP3 is downloadable at https://github.com/nifm-gin/MP3.
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Affiliation(s)
- Clément Brossard
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France.,MoGlimaging Network, HTE Program of the French Cancer Plan, Toulouse, France
| | - Olivier Montigon
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France
| | - Fabien Boux
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France.,University of Grenoble Alpes, Inria, CNRS, G-INP, Grenoble, France
| | - Aurélien Delphin
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France
| | - Thomas Christen
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France
| | - Emmanuel L Barbier
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France
| | - Benjamin Lemasson
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France.,MoGlimaging Network, HTE Program of the French Cancer Plan, Toulouse, France
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10
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Prajapati R, Emerson IA. Construction and analysis of brain networks from different neuroimaging techniques. Int J Neurosci 2020; 132:745-766. [DOI: 10.1080/00207454.2020.1837802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Rutvi Prajapati
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Isaac Arnold Emerson
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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11
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Shafiei G, Zeighami Y, Clark CA, Coull JT, Nagano-Saito A, Leyton M, Dagher A, Mišic B. Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks. Cereb Cortex 2020; 29:397-409. [PMID: 30357316 PMCID: PMC6294404 DOI: 10.1093/cercor/bhy264] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Indexed: 11/24/2022] Open
Abstract
Dopaminergic projections are hypothesized to stabilize neural signaling and neural representations, but how they shape regional information processing and large-scale network interactions remains unclear. Here we investigated effects of lowered dopamine levels on within-region temporal signal variability (measured by sample entropy) and between-region functional connectivity (measured by pairwise temporal correlations) in the healthy brain at rest. The acute phenylalanine and tyrosine depletion (APTD) method was used to decrease dopamine synthesis in 51 healthy participants who underwent resting-state functional MRI (fMRI) scanning. Functional connectivity and regional signal variability were estimated for each participant. Multivariate partial least squares (PLS) analysis was used to statistically assess changes in signal variability following APTD as compared with the balanced control treatment. The analysis captured a pattern of increased regional signal variability following dopamine depletion. Changes in hemodynamic signal variability were concomitant with changes in functional connectivity, such that nodes with greatest increase in signal variability following dopamine depletion also experienced greatest decrease in functional connectivity. Our results suggest that dopamine may act to stabilize neural signaling, particularly in networks related to motor function and orienting attention towards behaviorally-relevant stimuli. Moreover, dopamine-dependent signal variability is critically associated with functional embedding of individual areas in large-scale networks.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Yashar Zeighami
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Crystal A Clark
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jennifer T Coull
- Laboratoire des Neurosciences Cognitives UMR 7291, Federation 3C, Aix-Marseille University, France.,Centre National de la Recherche Scientifique (CNRS), Paris, France
| | - Atsuko Nagano-Saito
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, QC, Canada.,Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, Canada.,Department of Psychiatry, McGill University, Montréal, Canada
| | - Marco Leyton
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montréal, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Bratislav Mišic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, QC, Canada
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12
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Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging. Schizophr Res 2020; 216:262-271. [PMID: 31826827 DOI: 10.1016/j.schres.2019.11.046] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/04/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022]
Abstract
Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.
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13
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Ferré P, Benhajali Y, Steffener J, Stern Y, Joanette Y, Bellec P. Resting-state and Vocabulary Tasks Distinctively Inform On Age-Related Differences in the Functional Brain Connectome. LANGUAGE, COGNITION AND NEUROSCIENCE 2019; 34:949-972. [PMID: 31457069 PMCID: PMC6711486 DOI: 10.1080/23273798.2019.1608072] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 03/05/2019] [Indexed: 05/23/2023]
Abstract
Most of the current knowledge about age-related differences in brain neurofunctional organization stems from neuroimaging studies using either a "resting state" paradigm, or cognitive tasks for which performance decreases with age. However, it remains to be known if comparable age-related differences are found when participants engage in cognitive activities for which performance is maintained with age, such as vocabulary knowledge tasks. A functional connectivity analysis was performed on 286 adults ranging from 18 to 80 years old, based either on a resting state paradigm or when engaged in vocabulary tasks. Notable increases in connectivity of regions of the language network were observed during task completion. Conversely, only age-related decreases were observed across the whole connectome during resting-state. While vocabulary accuracy increased with age, no interaction was found between functional connectivity, age and task accuracy or proxies of cognitive reserve, suggesting that older individuals typically benefits from semantic knowledge accumulated throughout one's life trajectory, without the need for compensatory mechanisms.
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Affiliation(s)
- Perrine Ferré
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
| | - Yassine Benhajali
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
| | - Jason Steffener
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
- PERFORM Center, Concordia University
- Interdisciplinary School of Health Sciences, University of Ottawa, 200 Lees, Lees Campus, Office # E-250C, Ottawa, Ontario. K1S 5S9, CANADA
| | - Yaakov Stern
- Cognitive Neuroscience Division, Columbia University, 710 W 168th St, New York, NY 10032, USA
| | - Yves Joanette
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
| | - Pierre Bellec
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
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14
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Tam A, Dansereau C, Iturria-Medina Y, Urchs S, Orban P, Sharmarke H, Breitner J, Bellec P. A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia. Gigascience 2019; 8:giz055. [PMID: 31077314 PMCID: PMC6511068 DOI: 10.1093/gigascience/giz055] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 03/07/2019] [Accepted: 04/21/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Clinical trials in Alzheimer's disease need to enroll patients whose cognition will decline over time, if left untreated, in order to demonstrate the efficacy of an intervention. Machine learning models used to screen for patients at risk of progression to dementia should therefore favor specificity (detecting only progressors) over sensitivity (detecting all progressors), especially when the prevalence of progressors is low. Here, we explore whether such high-risk patients can be identified using cognitive assessments and structural neuroimaging by training machine learning tools in a high-specificity regime. RESULTS A multimodal signature of Alzheimer's dementia was first extracted from the ADNI1 dataset. We then validated the predictive value of this signature on ADNI1 patients with mild cognitive impairment (N = 235). The signature was optimized to predict progression to dementia over 3 years with low sensitivity (55.1%) but high specificity (95.6%), resulting in only moderate accuracy (69.3%) but high positive predictive value (80.4%, adjusted for a "typical" 33% prevalence rate of true progressors). These results were replicated in ADNI2 (N = 235), with 87.8% adjusted positive predictive value (96.7% specificity, 47.3% sensitivity, 85.1% accuracy). CONCLUSIONS We found that cognitive measures alone could identify high-risk individuals, with structural measurements providing a slight improvement. The signature had comparable receiver operating characteristics to standard machine learning tools, yet a marked improvement in positive predictive value was achieved over the literature by selecting a high-specificity operating point. The multimodal signature can be readily applied for the enrichment of clinical trials.
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Affiliation(s)
- Angela Tam
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, 6875 Lasalle Boulevard, Montréal, QC, H4H 1R3, Canada
| | - Christian Dansereau
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Département d'informatique et de recherche opérationnelle, Université de Montréal, 2920 chemin de la Tour, Montréal, QC, H3T 1J4, Canada
| | - Yasser Iturria-Medina
- Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, QC, H3A 2B4, Canada
| | - Sebastian Urchs
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, QC, H3A 2B4, Canada
| | - Pierre Orban
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, 7331 rue Hochelaga, Montréal, QC, H1N 3V2, Canada
- Département de psychiatrie, Université de Montréal, 2900 boulevard Édouard-Montpetit, Montréal, QC, H3T 1J4, Canada
| | - Hanad Sharmarke
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
| | - John Breitner
- Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, 6875 Lasalle Boulevard, Montréal, QC, H4H 1R3, Canada
- Department of Psychiatry, McGill University, 1033 Pine Avenue West, Montréal, QC, H3A 1A1, Canada
| | - Pierre Bellec
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Département de psychologie, Université de Montréal, 90 avenue Vincent d'Indy, Montréal, QC, H3C 3J7, Canada
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15
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Nagano-Saito A, Bellec P, Hanganu A, Jobert S, Mejia-Constain B, Degroot C, Lafontaine AL, Lissemore JI, Smart K, Benkelfat C, Monchi O. Why Is Aging a Risk Factor for Cognitive Impairment in Parkinson's Disease?-A Resting State fMRI Study. Front Neurol 2019; 10:267. [PMID: 30967835 PMCID: PMC6438889 DOI: 10.3389/fneur.2019.00267] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 02/27/2019] [Indexed: 01/12/2023] Open
Abstract
Using resting-state functional MRI (rsfMRI) data of younger and older healthy volunteers and patients with Parkinson's disease (PD) with and without mild cognitive impairment (MCI) and applying two different analytic approaches, we investigated the effects of age, pathology, and cognition on brain connectivity. When comparing rsfMRI connectivity strength of PD patients and older healthy volunteers, reduction between multiple brain regions in PD patients with MCI (PD-MCI) compared with PD patients without MCI (PD-non-MCI) was observed. This group difference was not affected by the number and location of clusters but was reduced when age was included as a covariate. Next, we applied a graph-theory method with a cost-threshold approach to the rsfMRI data from patients with PD with and without MCI as well as groups of younger and older healthy volunteers. We observed decreased hub function (measured by degree and betweenness centrality) mainly in the medial prefrontal cortex (mPFC) in older healthy volunteers compared with younger healthy volunteers. We also found increased hub function in the posterior medial structure (precuneus and the cingulate cortex) in PD-non-MCI patients compared with older healthy volunteers and PD-MCI patients. Hub function in these posterior medial structures was positively correlated with cognitive function in all PD patients. Together these data suggest that overlapping patterns of hub modifications could mediate the effect of age as a risk factor for cognitive decline in PD, including age-related reduction of hub function in the mPFC, and recruitment availability of the posterior medial structure, possibly to compensate for impaired basal ganglia function.
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Affiliation(s)
- Atsuko Nagano-Saito
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada.,Department of Neurology & Neurosurgery, and Psychiatry, McGill University, Montreal, QC, Canada
| | - Pierre Bellec
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada.,Université de Montréal, Montreal, QC, Canada
| | - Alexandru Hanganu
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada.,Université de Montréal, Montreal, QC, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, Calgary, AB, Canada.,Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Stevan Jobert
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Béatriz Mejia-Constain
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Clotilde Degroot
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada.,Department of Neurology & Neurosurgery, and Psychiatry, McGill University, Montreal, QC, Canada
| | - Anne-Louise Lafontaine
- Department of Neurology & Neurosurgery, and Psychiatry, McGill University, Montreal, QC, Canada.,Movement Disorders Unit, McGill University Health Center, Montreal, QC, Canada.,Department of Neurology, Montreal Neurological Hospital, Montreal, QC, Canada.,Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Jennifer I Lissemore
- Department of Neurology & Neurosurgery, and Psychiatry, McGill University, Montreal, QC, Canada
| | - Kelly Smart
- Department of Neurology & Neurosurgery, and Psychiatry, McGill University, Montreal, QC, Canada
| | - Chawki Benkelfat
- Department of Neurology & Neurosurgery, and Psychiatry, McGill University, Montreal, QC, Canada
| | - Oury Monchi
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada.,Department of Neurology & Neurosurgery, and Psychiatry, McGill University, Montreal, QC, Canada.,Université de Montréal, Montreal, QC, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, Calgary, AB, Canada.,Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, AB, Canada.,Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
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16
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Kiar G, Brown ST, Glatard T, Evans AC. A Serverless Tool for Platform Agnostic Computational Experiment Management. Front Neuroinform 2019; 13:12. [PMID: 30890927 PMCID: PMC6411646 DOI: 10.3389/fninf.2019.00012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 02/15/2019] [Indexed: 01/22/2023] Open
Abstract
Neuroscience has been carried into the domain of big data and high performance computing (HPC) on the backs of initiatives in data collection and an increasingly compute-intensive tools. While managing HPC experiments requires considerable technical acumen, platforms, and standards have been developed to ease this burden on scientists. While web-portals make resources widely accessible, data organizations such as the Brain Imaging Data Structure and tool description languages such as Boutiques provide researchers with a foothold to tackle these problems using their own datasets, pipelines, and environments. While these standards lower the barrier to adoption of HPC and cloud systems for neuroscience applications, they still require the consolidation of disparate domain-specific knowledge. We present Clowdr, a lightweight tool to launch experiments on HPC systems and clouds, record rich execution records, and enable the accessible sharing and re-launch of experimental summaries and results. Clowdr uniquely sits between web platforms and bare-metal applications for experiment management by preserving the flexibility of do-it-yourself solutions while providing a low barrier for developing, deploying and disseminating neuroscientific analysis.
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Affiliation(s)
- Gregory Kiar
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Shawn T. Brown
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tristan Glatard
- Department of Computer Science, Concordia University, Montreal, QC, Canada
| | - Alan C. Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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17
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Glatard T, Kiar G, Aumentado-Armstrong T, Beck N, Bellec P, Bernard R, Bonnet A, Brown ST, Camarasu-Pop S, Cervenansky F, Das S, Ferreira da Silva R, Flandin G, Girard P, Gorgolewski KJ, Guttmann CRG, Hayot-Sasson V, Quirion PO, Rioux P, Rousseau MÉ, Evans AC. Boutiques: a flexible framework to integrate command-line applications in computing platforms. Gigascience 2018; 7:4951979. [PMID: 29718199 PMCID: PMC6007562 DOI: 10.1093/gigascience/giy016] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 02/20/2018] [Indexed: 11/14/2022] Open
Abstract
We present Boutiques, a system to automatically publish, integrate, and execute command-line applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitates the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.
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Affiliation(s)
- Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
| | - Gregory Kiar
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | | | - Natacha Beck
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | - Pierre Bellec
- Centre de Recherche de l'Institut de Gériatrie de Montréal CRIUGM, Montréal, QC, Canada
| | - Rémi Bernard
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | - Axel Bonnet
- University of Lyon, CNRS, INSERM, CREATIS, Villeurbanne, France
| | - Shawn T Brown
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | | | | | - Samir Das
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | | | | | - Pascal Girard
- University of Lyon, CNRS, INSERM, CREATIS, Villeurbanne, France
| | | | - Charles R G Guttmann
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital,, Boston, Massachusetts, USA
| | - Valérie Hayot-Sasson
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
| | | | - Pierre Rioux
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | | | - Alan C Evans
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
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18
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Ribeiro MJ, Castelo-Branco M. Age-related differences in event-related potentials and pupillary responses in cued reaction time tasks. Neurobiol Aging 2018; 73:177-189. [PMID: 30366291 DOI: 10.1016/j.neurobiolaging.2018.09.028] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 09/18/2018] [Accepted: 09/22/2018] [Indexed: 10/28/2022]
Abstract
Deficits in the noradrenergic system are associated with age-related cognitive decline, yet how healthy aging influences the functional properties of this arousal system is still poorly understood. We addressed this question in humans using pupillometry, a well-established indicator of activity levels in the locus coeruleus (LC), the main noradrenergic center in the brain. We recorded the pupillogram and the electroencephalogram of 36 young and 39 older adults, while they were engaged in cued reaction time tasks known to elicit LC responses in monkeys. Event-related potentials (ERPs) revealed significant group differences. Older adults showed higher cortical activation during preparatory processes reflected in enhanced cue-evoked frontocentral ERPs and reduced parietal ERPs at the time of the motor response. In contrast, the amplitude of the task-related pupillary responses did not show a significant group effect. Our findings suggest that aging-related changes in cortical processing during motor preparation and execution, as documented by electroencephalogram, are not accompanied by changes in the amplitude of activation of the LC, as documented by pupillography.
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Affiliation(s)
- Maria J Ribeiro
- CIBIT, Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal; CNC.IBILI, University of Coimbra, Coimbra, Portugal; Visual Neuroscience Laboratory, Institute for Biomedical Imaging and Life Sciences (IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
| | - Miguel Castelo-Branco
- CIBIT, Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal; CNC.IBILI, University of Coimbra, Coimbra, Portugal; Visual Neuroscience Laboratory, Institute for Biomedical Imaging and Life Sciences (IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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19
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Xu K, Liu Y, Zhan Y, Ren J, Jiang T. BRANT: A Versatile and Extendable Resting-State fMRI Toolkit. Front Neuroinform 2018; 12:52. [PMID: 30233348 PMCID: PMC6129764 DOI: 10.3389/fninf.2018.00052] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 07/24/2018] [Indexed: 01/08/2023] Open
Abstract
Data processing toolboxes for resting-state functional MRI (rs-fMRI) have provided us with a variety of functions and user friendly graphic user interfaces (GUIs). However, many toolboxes only cover a certain range of functions, and use exclusively designed GUIs. To facilitate data processing and alleviate the burden of manually drawing GUIs, we have developed a versatile and extendable MATLAB-based toolbox, BRANT (BRAinNetome fmri Toolkit), with a wide range of rs-fMRI data processing functions and code-generated GUIs. During the implementation, we have also empowered the toolbox with parallel computing techniques, efficient file handling methods for compressed file format, and one-line scripting. In BRANT, users can find rs-fMRI batch processing functions for preprocessing, brain spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, and results visualization, while developers can quickly publish scripts with code-generated GUIs.
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Affiliation(s)
- Kaibin Xu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Sino-Danish Center for Education and Research, Beijing, China.,Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yafeng Zhan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jiaji Ren
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
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20
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Wang S, Zuo L, Jiang T, Peng P, Chu S, Xiao D. Abnormal white matter microstructure among early adulthood smokers: a tract-based spatial statistics study. Neurol Res 2017; 39:1094-1102. [PMID: 28934078 DOI: 10.1080/01616412.2017.1379277] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Objectives Cigarette smoking is an important risk factor of central nervous system diseases. However, the white matter (WM) integrity of early adulthood chronic smokers has not been attached enough importance to as it deserves, and the relationship between the chronic smoking effect and the WM is still unclear. The purpose of this study was to investigate whole - brain WM microstructure of early adulthood smokers and explore the structural correlates of behaviorally relevant features of the disorder. Methods We compared multiple DTI-derived indices, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD), between early adulthood smokers (n = 19) and age-, education- and gender-matched controls (n = 23) using a whole-brain tract-based spatial statistics approach. We also explored the correlations of the mean DTI index values with pack-years and Fagerström Test for Nicotine Dependence. Results The smokers showed increased FA in left superior longitudinal fasciculus (SLF), left anterior corona radiate, left superior corona radiate, left posterior corona radiate, left external capsule (EC), left inferior fronto-occipital fasciculus and sagittal stratum (SS), and decreased RD in left SLF. There were significant negative correlations among the average FA in the left external capsule and pack-years in smokers. In addition, significant positive correlation was found between RD values in the left SLF and pack-years. Discussion These findings indicate that smokers show microstructural changes in several white-matter regions. The correlation between the cumulative effect and microstructural WM alternations suggests that WM properties may become the new biomarkers in practice.
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Affiliation(s)
- Shuangkun Wang
- a Department of Radiology, Beijing Chao-Yang Hospital , Capital Medical University , Beijing , China
| | - Long Zuo
- a Department of Radiology, Beijing Chao-Yang Hospital , Capital Medical University , Beijing , China
| | - Tao Jiang
- a Department of Radiology, Beijing Chao-Yang Hospital , Capital Medical University , Beijing , China
| | - Peng Peng
- a Department of Radiology, Beijing Chao-Yang Hospital , Capital Medical University , Beijing , China
| | - Shuilian Chu
- b Clinical Research Center, Beijing Chao-Yang Hospital , Capital Medical University , Beijing , China
| | - Dan Xiao
- c Tobacco Medicine and Tobacco Cessation Center , China-Japan Friendship Hospital , Beijing , China.,d WHO Collaborating Center for Tobacco Cessation and Respiratory Diseases Prevention , China-Japan Friendship Hospital , Beijing , China
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21
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Dansereau C, Benhajali Y, Risterucci C, Pich EM, Orban P, Arnold D, Bellec P. Statistical power and prediction accuracy in multisite resting-state fMRI connectivity. Neuroimage 2017; 149:220-232. [DOI: 10.1016/j.neuroimage.2017.01.072] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 01/27/2017] [Accepted: 01/30/2017] [Indexed: 12/29/2022] Open
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22
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Passerat-Palmbach J, Reuillon R, Leclaire M, Makropoulos A, Robinson EC, Parisot S, Rueckert D. Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System. Front Neuroinform 2017; 11:21. [PMID: 28381997 PMCID: PMC5361107 DOI: 10.3389/fninf.2017.00021] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 03/01/2017] [Indexed: 11/26/2022] Open
Abstract
OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition. The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments. Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behavior of an application, or perform automatic parameter tuning. In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerization solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously. The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstration of the flexibility of our solution showcases three neuroimaging pipelines harnessing distributed computing environments as heterogeneous as local clusters or the European Grid Infrastructure (EGI).
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Affiliation(s)
| | - Romain Reuillon
- Institut des Systemes Complexes Paris Ile de FranceParis, France
| | - Mathieu Leclaire
- Institut des Systemes Complexes Paris Ile de FranceParis, France
| | | | - Emma C. Robinson
- BioMedIA Group, Department of Computing, Imperial College LondonLondon, UK
| | - Sarah Parisot
- BioMedIA Group, Department of Computing, Imperial College LondonLondon, UK
| | - Daniel Rueckert
- BioMedIA Group, Department of Computing, Imperial College LondonLondon, UK
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23
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Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, Churchill NW, Cohen AL, Craddock RC, Devenyi GA, Eklund A, Esteban O, Flandin G, Ghosh SS, Guntupalli JS, Jenkinson M, Keshavan A, Kiar G, Liem F, Raamana PR, Raffelt D, Steele CJ, Quirion PO, Smith RE, Strother SC, Varoquaux G, Wang Y, Yarkoni T, Poldrack RA. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol 2017; 13:e1005209. [PMID: 28278228 PMCID: PMC5363996 DOI: 10.1371/journal.pcbi.1005209] [Citation(s) in RCA: 159] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 03/23/2017] [Accepted: 02/23/2017] [Indexed: 12/13/2022] Open
Abstract
The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.
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Affiliation(s)
| | - Fidel Alfaro-Almagro
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford University, Oxford, United Kingdom
| | - Tibor Auer
- Department of Psychology, Royal Holloway University of London, Egham, United Kingdom
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire Gériatrique de Montréal, Montreal, Canada
- Department of computer science and operations research, Université de Montréal, Montreal, Canada
| | - Mihai Capotă
- Parallel Computing Lab, Intel Corporation, Santa Clara, CA & Hillsboro, Oregon, United States of America
| | - M. Mallar Chakravarty
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Department of Psychiatry McGill University, Montreal, Canada
| | - Nathan W. Churchill
- Keenan Research Centre of the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Ontario, Canada
| | - Alexander Li Cohen
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - R. Cameron Craddock
- Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America
| | - Gabriel A. Devenyi
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Department of Psychiatry McGill University, Montreal, Canada
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Department of Computer and Information Science, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Oscar Esteban
- Department of Psychology, Stanford University, Stanford, California, United States of America
| | | | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Otolaryngology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - J. Swaroop Guntupalli
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Mark Jenkinson
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford University, Oxford, United Kingdom
| | - Anisha Keshavan
- UC Berkeley-UCSF Graduate Program in Bioengineering, San Francisco, California, United States of America
| | - Gregory Kiar
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Franziskus Liem
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Pradeep Reddy Raamana
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - David Raffelt
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - Christopher J. Steele
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Department of Psychiatry McGill University, Montreal, Canada
| | - Pierre-Olivier Quirion
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Robert E. Smith
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Gaël Varoquaux
- Parietal team, INRIA Saclay Ile-de-France, Palaiseau, France
| | - Yida Wang
- Parallel Computing Lab, Intel Corporation, Santa Clara, CA & Hillsboro, Oregon, United States of America
| | - Tal Yarkoni
- Department of Psychology, University of Texas at Austin, Austin, Texas, United States of America
| | - Russell A. Poldrack
- Department of Psychology, Stanford University, Stanford, California, United States of America
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24
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Nguyen HD, McLachlan GJ, Orban P, Bellec P, Janke AL. Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data. Neural Comput 2017; 29:990-1020. [PMID: 28095191 DOI: 10.1162/neco_a_00938] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires evaluating products of large numbers of densities of normal random variables. In practical scenarios, these products converge to zero as the length of the time series increases, and thus the ML estimation of MoAR models becomes infeasible without the use of numerical tricks. We propose a maximum pseudolikelihood (MPL) estimation approach as an alternative to the use of numerical tricks. The MPL estimator is proved to be consistent and can be computed with an EM (expectation-maximization) algorithm. Simulations are used to assess the performance of the MPL estimator against that of the ML estimator in cases where the latter was able to be calculated. An application to the clustering of time series data arising from a resting state fMRI experiment is presented as a demonstration of the methodology.
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Affiliation(s)
- Hien D Nguyen
- Department of Mathematics and Statistics, La Trobe University, Victoria, Bundoora 3086, Australia
| | - Geoffrey J McLachlan
- School of Mathematics and Physics, University of Queensland, St. Lucia 4072, Australia
| | - Pierre Orban
- Centre de Recherche, Institut Universitaire de Geriatrie, Montreal H3W 1W5, Quebec, Canada
| | - Pierre Bellec
- Centre de Recherche, Institut Universitaire de Geriatrie, Montreal H3W 1W5, Quebec, Canada
| | - Andrew L Janke
- Centre for Advanced Imaging, University of Queensland, St. Lucia 4072, Australia
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25
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Orban P, Desseilles M, Mendrek A, Bourque J, Bellec P, Stip E. Altered brain connectivity in patients with schizophrenia is consistent across cognitive contexts. J Psychiatry Neurosci 2017; 42:17-26. [PMID: 27091719 PMCID: PMC5373708 DOI: 10.1503/jpn.150247] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Schizophrenia has been defined as a dysconnection syndrome characterized by aberrant functional brain connectivity. Using task-based fMRI, we assessed to what extent the nature of the cognitive context may further modulate abnormal functional brain connectivity. METHODS We analyzed data matched for motion in patients with schizophrenia and healthy controls who performed 3 different tasks. Tasks 1 and 2 both involved emotional processing and only slighlty differed (incidental encoding v. memory recognition), whereas task 3 was a much different mental rotation task. We conducted a connectome-wide general linear model analysis aimed at identifying context-dependent and independent functional brain connectivity alterations in patients with schizophrenia. RESULTS After matching for motion, we included 30 patients with schizophrenia and 30 healthy controls in our study. Abnormal connectivity in patients with schizophrenia followed similar patterns regardless of the degree of similarity between cognitive tasks. Decreased connectivity was most notable in the medial prefrontal cortex, the anterior and posterior cingulate, the temporal lobe, the lobule IX of the cerebellum and the premotor cortex. LIMITATIONS A more circumscribed yet significant context-dependent effect might be detected with larger sample sizes or cognitive domains other than emotional and visuomotor processing. CONCLUSION The context-independence of functional brain dysconnectivity in patients with schizophrenia provides a good justification for pooling data from multiple experiments in order to identify connectivity biomarkers of this mental illness.
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Affiliation(s)
- Pierre Orban
- Correspondence to: P. Orban, CRIUGM, Université de Montréal, 4545 Queen Mary, Montreal, QC H3W 1W5;
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26
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Lu Y, Shen Z, Cheng Y, Yang H, He B, Xie Y, Wen L, Zhang Z, Sun X, Zhao W, Xu X, Han D. Alternations of White Matter Structural Networks in First Episode Untreated Major Depressive Disorder with Short Duration. Front Psychiatry 2017; 8:205. [PMID: 29118724 PMCID: PMC5661170 DOI: 10.3389/fpsyt.2017.00205] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 10/02/2017] [Indexed: 01/20/2023] Open
Abstract
It is crucial to explore the pathogenesis of major depressive disorder (MDD) at the early stage for the better diagnostic and treatment strategies. It was suggested that MDD might be involving in functional or structural alternations at the brain network level. However, at the onset of MDD, whether the whole brain white matter (WM) alterations at network level are already evident still remains unclear. In the present study, diffusion MRI scanning was adopt to depict the unique WM structural network topology across the entire brain at the early stage of MDD. Twenty-one first episode, short duration (<1 year) and drug-naïve depression patients, and 25 healthy control (HC) subjects were recruited. To construct the WM structural network, atlas-based brain regions were used for nodes, and the value of multiplying fiber number by the mean fractional anisotropy along the fiber bundles connected a pair of brain regions were used for edges. The structural network was analyzed by graph theoretic and network-based statistic methods. Pearson partial correlation analysis was also performed to evaluate their correlation with the clinical variables. Compared with HCs, the MDD patients had a significant decrease in the small-worldness (σ). Meanwhile, the MDD patients presented a significantly decreased subnetwork, which mainly involved in the frontal-subcortical and limbic regions. Our results suggested that the abnormal structural network of the orbitofrontal cortex and thalamus, involving the imbalance with the limbic system, might be a key pathology in early stage drug-naive depression. And the structural network analysis might be potential in early detection and diagnosis of MDD.
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Affiliation(s)
- Yi Lu
- Department of Medical Imaging, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Zonglin Shen
- Department of Psychiatry, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Hui Yang
- Biomedical Engineering Research Center, Kunming Medical University, Kunming, China
| | - Bo He
- Department of Medical Imaging, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Yue Xie
- Department of Medical Imaging, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Liang Wen
- Department of Medical Imaging, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Zhenguang Zhang
- Department of Medical Imaging, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Xuejin Sun
- Department of Medical Imaging, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Wei Zhao
- Department of Medical Imaging, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Dan Han
- Department of Medical Imaging, The First Affiliated Hospital, Kunming Medical University, Kunming, China
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27
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Pelland M, Orban P, Dansereau C, Lepore F, Bellec P, Collignon O. State-dependent modulation of functional connectivity in early blind individuals. Neuroimage 2016; 147:532-541. [PMID: 28011254 DOI: 10.1016/j.neuroimage.2016.12.053] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 10/13/2016] [Accepted: 12/18/2016] [Indexed: 12/11/2022] Open
Abstract
Resting-state functional connectivity (RSFC) studies have provided strong evidences that visual deprivation influences the brain's functional architecture. In particular, reduced RSFC coupling between occipital (visual) and temporal (auditory) regions has been reliably observed in early blind individuals (EB) at rest. In contrast, task-dependent activation studies have repeatedly demonstrated enhanced co-activation and connectivity of occipital and temporal regions during auditory processing in EB. To investigate this apparent discrepancy, the functional coupling between temporal and occipital networks at rest was directly compared to that of an auditory task in both EB and sighted controls (SC). Functional brain clusters shared across groups and cognitive states (rest and auditory task) were defined. In EBs, we observed higher occipito-temporal correlations in activity during the task than at rest. The reverse pattern was observed in SC. We also observed higher temporal variability of occipito-temporal RSFC in EB suggesting that occipital regions in this population may play the role of a multiple demand system. Our study reveals how the connectivity profile of sighted and early blind people is differentially influenced by their cognitive state, bridging the gap between previous task-dependent and RSFC studies. Our results also highlight how inferring group-differences in functional brain architecture solely based on resting-state acquisition has to be considered with caution.
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Affiliation(s)
- Maxime Pelland
- Departement of Psychology, University of Montreal, Montreal, Quebec, Canada; Centre de Recherche en Neuropsychologie et Cognition, University of Montreal, Montreal, QC, Canada.
| | - Pierre Orban
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Quebec, Canada; Department of Psychiatry, University of Montreal, Montreal, Quebec, Canada
| | - Christian Dansereau
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Quebec, Canada; Department of Computer Science and Operations Research, University of Montreal, Montreal, Quebec, Canada
| | - Franco Lepore
- Departement of Psychology, University of Montreal, Montreal, Quebec, Canada; Centre de Recherche en Neuropsychologie et Cognition, University of Montreal, Montreal, QC, Canada
| | - Pierre Bellec
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Quebec, Canada; Department of Computer Science and Operations Research, University of Montreal, Montreal, Quebec, Canada
| | - Olivier Collignon
- Institute of Psychology (IPSY) and Institute of Neuroscience (IoNS), Université catholique de Louvain, Belgium; CIMeC - Center for Mind/Brain Sciences, University of Trento, via delle Regole 101, Mattarello, TN, Italy.
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28
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Xu M, Tan X, Zhang X, Guo Y, Mei Y, Feng Q, Xu Y, Feng Y. Alterations of white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus identified by probabilistic tractography and connectivity-based analyses. Neuroimage Clin 2016; 13:349-360. [PMID: 28066709 PMCID: PMC5200918 DOI: 10.1016/j.nicl.2016.12.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/04/2016] [Accepted: 12/17/2016] [Indexed: 01/13/2023]
Abstract
PURPOSE Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. METHODS Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. RESULTS Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. CONCLUSION This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients.
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Affiliation(s)
- Man Xu
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xiangliang Tan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xinyuan Zhang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yihao Guo
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yingjie Mei
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Philips Healthcare, Guangzhou, China
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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29
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Tam A, Dansereau C, Badhwar A, Orban P, Belleville S, Chertkow H, Dagher A, Hanganu A, Monchi O, Rosa-Neto P, Shmuel A, Breitner J, Bellec P. A dataset of multiresolution functional brain parcellations in an elderly population with no or mild cognitive impairment. Data Brief 2016; 9:1122-1129. [PMID: 27924300 PMCID: PMC5128734 DOI: 10.1016/j.dib.2016.11.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 11/10/2016] [Accepted: 11/11/2016] [Indexed: 01/26/2023] Open
Abstract
We present group eight resolutions of brain parcellations for clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment, pooled from four independent datasets. This dataset was generated as part of the following study: Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies (Tam et al., 2015) [1]. The brain parcellations have been registered to both symmetric and asymmetric MNI brain templates and generated using a method called bootstrap analysis of stable clusters (BASC) (Bellec et al., 2010) [2]. We present two variants of these parcellations. One variant contains bihemisphereic parcels (4, 6, 12, 22, 33, 65, 111, and 208 total parcels across eight resolutions). The second variant contains spatially connected regions of interest (ROIs) that span only one hemisphere (10, 17, 30, 51, 77, 199, and 322 total ROIs across eight resolutions). We also present maps illustrating functional connectivity differences between patients and controls for four regions of interest (striatum, dorsal prefrontal cortex, middle temporal lobe, and medial frontal cortex). The brain parcels and associated statistical maps have been publicly released as 3D volumes, available in .mnc and .nii file formats on figshare and on Neurovault. Finally, the code used to generate this dataset is available on Github.
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Affiliation(s)
- Angela Tam
- McGill University, Montreal, QC, Canada; Douglas Mental Health University Institute, Research Centre, Montreal, QC, Canada; Centre de recherche de l'institut universitaire de gériatrie de Montréal, QC, Canada
| | - Christian Dansereau
- Centre de recherche de l'institut universitaire de gériatrie de Montréal, QC, Canada; Université de Montréal, QC, Canada
| | - AmanPreet Badhwar
- Centre de recherche de l'institut universitaire de gériatrie de Montréal, QC, Canada; Université de Montréal, QC, Canada
| | - Pierre Orban
- Douglas Mental Health University Institute, Research Centre, Montreal, QC, Canada; Centre de recherche de l'institut universitaire de gériatrie de Montréal, QC, Canada
| | - Sylvie Belleville
- Centre de recherche de l'institut universitaire de gériatrie de Montréal, QC, Canada; Université de Montréal, QC, Canada
| | | | | | - Alexandru Hanganu
- Centre de recherche de l'institut universitaire de gériatrie de Montréal, QC, Canada; University of Calgary, AB, Canada; Hotchkiss Brain Institute, Calgary, AB, Canada
| | - Oury Monchi
- Centre de recherche de l'institut universitaire de gériatrie de Montréal, QC, Canada; Université de Montréal, QC, Canada; University of Calgary, AB, Canada; Hotchkiss Brain Institute, Calgary, AB, Canada
| | - Pedro Rosa-Neto
- McGill University, Montreal, QC, Canada; Douglas Mental Health University Institute, Research Centre, Montreal, QC, Canada
| | | | - John Breitner
- McGill University, Montreal, QC, Canada; Douglas Mental Health University Institute, Research Centre, Montreal, QC, Canada
| | - Pierre Bellec
- Centre de recherche de l'institut universitaire de gériatrie de Montréal, QC, Canada; Université de Montréal, QC, Canada
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30
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Wu CL, Zhong S, Chan YC, Chen HC, Gong G, He Y, Li P. White-Matter Structural Connectivity Underlying Human Laughter-Related Traits Processing. Front Psychol 2016; 7:1637. [PMID: 27833572 PMCID: PMC5082228 DOI: 10.3389/fpsyg.2016.01637] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 10/06/2016] [Indexed: 11/24/2022] Open
Abstract
Most research into the neural mechanisms of humor has not explicitly focused on the association between emotion and humor on the brain white matter networks mediating this connection. However, this connection is especially salient in gelotophobia (the fear of being laughed at), which is regarded as the presentation of humorlessness, and two related traits, gelotophilia (the enjoyment of being laughed at) and katagelasticism (the enjoyment of laughing at others). Here, we explored whether the topological properties of white matter networks can account for the individual differences in the laughter-related traits of 31 healthy adults. We observed a significant negative correlation between gelotophobia scores and the clustering coefficient, local efficiency and global efficiency, but a positive association between gelotophobia scores and path length in the brain's white matter network. Moreover, the current study revealed that with increasing individual fear of being laughed at, the linking efficiencies in superior frontal gyrus, anterior cingulate cortex, parahippocampal gyrus, and middle temporal gyrus decreased. However, there were no significant correlations between either gelotophilia or katagelasticism scores or the topological properties of the brain white matter network. These findings suggest that the fear of being laughed at is directly related to the level of local and global information processing of the brain network, which might provide new insights into the neural mechanisms of the humor information processing.
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Affiliation(s)
- Ching-Lin Wu
- Department of Educational Psychology and Counseling, National Taiwan Normal University Taipei, Taiwan
| | - Suyu Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China; School of Systems Science, Beijing Normal UniversityBeijing, China
| | - Yu-Chen Chan
- Institute of Learning Sciences, National Tsing Hua University Hsinchu, Taiwan
| | - Hsueh-Chih Chen
- Department of Educational Psychology and Counseling, National Taiwan Normal University Taipei, Taiwan
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Ping Li
- Department of Psychology and Institute for CyberScience, Pennsylvania State University, University Park PA, USA
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31
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Wu C, Zhong S, Chen H. Discriminating the Difference between Remote and Close Association with Relation to White-Matter Structural Connectivity. PLoS One 2016; 11:e0165053. [PMID: 27760177 PMCID: PMC5070771 DOI: 10.1371/journal.pone.0165053] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 10/05/2016] [Indexed: 01/03/2023] Open
Abstract
Remote association is a core ability that influences creative output. In contrast to close association, remote association is commonly agreed to be connected with more original and unique concepts. However, although existing studies have discovered that creativity is closely related to the white-matter structure of the brain, there are no studies that examine the relevance between the connectivity efficiencies and creativity of the brain regions from the perspective of networks. Consequently, this study constructed a brain white matter network structure that consisted of cerebral tissues and nerve fibers and used graph theory to analyze the connection efficiencies among the network nodes, further illuminating the differences between remote and close association in relation to the connectivity of the brain network. Researchers analyzed correlations between the scores of 35 healthy adults with regard to remote and close associations and the connectivity efficiencies of the white-matter network of the brain. Controlling for gender, age, and verbal intelligence, the remote association positively correlated with the global efficiency and negatively correlated with the levels of small-world. A close association negatively correlated with the global efficiency. Notably, the node efficiency in the middle temporal gyrus (MTG) positively correlated with remote association and negatively correlated with close association. To summarize, remote and close associations work differently as patterns in the brain network. Remote association requires efficient and convenient mutual connections between different brain regions, while close association emphasizes the limited connections that exist in a local region. These results are consistent with previous results, which indicate that creativity is based on the efficient integration and connection between different regions of the brain and that temporal lobes are the key regions for discriminating remote and close associations.
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Affiliation(s)
- Chinglin Wu
- Department of Educational Psychology and Counseling, Taiwan Normal University, Taipei, 10610, Taiwan
| | - Suyu Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Hsuehchih Chen
- Department of Educational Psychology and Counseling, Taiwan Normal University, Taipei, 10610, Taiwan
- * E-mail:
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Vincent RD, Neelin P, Khalili-Mahani N, Janke AL, Fonov VS, Robbins SM, Baghdadi L, Lerch J, Sled JG, Adalat R, MacDonald D, Zijdenbos AP, Collins DL, Evans AC. MINC 2.0: A Flexible Format for Multi-Modal Images. Front Neuroinform 2016; 10:35. [PMID: 27563289 PMCID: PMC4980430 DOI: 10.3389/fninf.2016.00035] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Accepted: 07/27/2016] [Indexed: 11/16/2022] Open
Abstract
It is often useful that an imaging data format can afford rich metadata, be flexible, scale to very large file sizes, support multi-modal data, and have strong inbuilt mechanisms for data provenance. Beginning in 1992, MINC was developed as a system for flexible, self-documenting representation of neuroscientific imaging data with arbitrary orientation and dimensionality. The MINC system incorporates three broad components: a file format specification, a programming library, and a growing set of tools. In the early 2000's the MINC developers created MINC 2.0, which added support for 64-bit file sizes, internal compression, and a number of other modern features. Because of its extensible design, it has been easy to incorporate details of provenance in the header metadata, including an explicit processing history, unique identifiers, and vendor-specific scanner settings. This makes MINC ideal for use in large scale imaging studies and databases. It also makes it easy to adapt to new scanning sequences and modalities.
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Affiliation(s)
- Robert D Vincent
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | | | - Najmeh Khalili-Mahani
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Andrew L Janke
- Center for Advanced Imaging, The University of Queensland Brisbane, QLD, Australia
| | - Vladimir S Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Steven M Robbins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Leila Baghdadi
- Mouse Imaging Centre, The Hospital for Sick Children Toronto, ON, Canada
| | - Jason Lerch
- Mouse Imaging Centre, The Hospital for Sick ChildrenToronto, ON, Canada; Department of Medical Biophysics, University of TorontoToronto, ON, Canada
| | - John G Sled
- Mouse Imaging Centre, The Hospital for Sick ChildrenToronto, ON, Canada; Department of Medical Biophysics, University of TorontoToronto, ON, Canada
| | - Reza Adalat
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | | | | | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada; Department of Biomedical Engineering, McGill UniversityMontreal, QC, Canada
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
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Bellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS, Craddock RC. The Neuro Bureau ADHD-200 Preprocessed repository. Neuroimage 2016; 144:275-286. [PMID: 27423255 DOI: 10.1016/j.neuroimage.2016.06.034] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2015] [Revised: 05/28/2016] [Accepted: 06/17/2016] [Indexed: 12/17/2022] Open
Abstract
In 2011, the "ADHD-200 Global Competition" was held with the aim of identifying biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (s-MRI) data collected on 973 individuals. Statisticians and computer scientists were potentially the most qualified for the machine learning aspect of the competition, but generally lacked the specialized skills to implement the necessary steps of data preparation for rs-fMRI. Realizing this barrier to entry, the Neuro Bureau prospectively collaborated with all competitors by preprocessing the data and sharing these results at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (http://www.nitrc.org/frs/?group_id=383). This "ADHD-200 Preprocessed" release included multiple analytical pipelines to cater to different philosophies of data analysis. The processed derivatives included denoised and registered 4D fMRI volumes, regional time series extracted from brain parcellations, maps of 10 intrinsic connectivity networks, fractional amplitude of low frequency fluctuation, and regional homogeneity, along with grey matter density maps. The data was used by several teams who competed in the ADHD-200 Global Competition, including the winning entry by a group of biostaticians. To the best of our knowledge, the ADHD-200 Preprocessed release was the first large public resource of preprocessed resting-state fMRI and structural MRI data, and remains to this day the only resource featuring a battery of alternative processing paths.
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Affiliation(s)
- Pierre Bellec
- The Neuro Bureau, Germany; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada; Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada.
| | - Carlton Chu
- The Neuro Bureau, Germany; Google DeepMind, London, UK.
| | - François Chouinard-Decorte
- The Neuro Bureau, Germany; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Canada.
| | - Yassine Benhajali
- The Neuro Bureau, Germany; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada; Département d'Anthropologie, Université de Montréal, Montréal, Canada.
| | - Daniel S Margulies
- The Neuro Bureau, Germany; Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - R Cameron Craddock
- The Neuro Bureau, Germany; Computational Neuroimaging Laboratory, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
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Gorgolewski KJ, Poldrack RA. A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research. PLoS Biol 2016; 14:e1002506. [PMID: 27389358 PMCID: PMC4936733 DOI: 10.1371/journal.pbio.1002506] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.
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Affiliation(s)
- Krzysztof J. Gorgolewski
- Department of Psychology, Stanford University, Stanford, California, United States of America
- * E-mail:
| | - Russell A. Poldrack
- Department of Psychology, Stanford University, Stanford, California, United States of America
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35
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Mathotaarachchi S, Wang S, Shin M, Pascoal TA, Benedet AL, Kang MS, Beaudry T, Fonov VS, Gauthier S, Labbe A, Rosa-Neto P. VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis. Front Neuroinform 2016; 10:20. [PMID: 27378902 PMCID: PMC4908129 DOI: 10.3389/fninf.2016.00020] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 06/01/2016] [Indexed: 11/15/2022] Open
Abstract
In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.
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Affiliation(s)
- Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada
| | - Seqian Wang
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Monica Shin
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Tharick A. Pascoal
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Andrea L. Benedet
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Thomas Beaudry
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Vladimir S. Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada
| | - Serge Gauthier
- McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Department of Psychiatry, McGill UniversityMontreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill UniversityMontreal, QC, Canada
| | - Aurélie Labbe
- Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Department of Psychiatry, McGill UniversityMontreal, QC, Canada
- Department of Epidemiology and Biostatistics, McGill UniversityMontreal, QC, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Department of Psychiatry, McGill UniversityMontreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill UniversityMontreal, QC, Canada
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36
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Lee K, Lina JM, Gotman J, Grova C. SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity. Neuroimage 2016; 134:434-449. [PMID: 27046111 DOI: 10.1016/j.neuroimage.2016.03.049] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Revised: 02/08/2016] [Accepted: 03/21/2016] [Indexed: 01/05/2023] Open
Abstract
Functional hubs are defined as the specific brain regions with dense connections to other regions in a functional brain network. Among them, connector hubs are of great interests, as they are assumed to promote global and hierarchical communications between functionally specialized networks. Damage to connector hubs may have a more crucial effect on the system than does damage to other hubs. Hubs in graph theory are often identified from a correlation matrix, and classified as connector hubs when the hubs are more connected to regions in other networks than within the networks to which they belong. However, the identification of hubs from functional data is more complex than that from structural data, notably because of the inherent problem of multicollinearity between temporal dynamics within a functional network. In this context, we developed and validated a method to reliably identify connectors and corresponding overlapping network structure from resting-state fMRI. This new method is actually handling the multicollinearity issue, since it does not rely on counting the number of connections from a thresholded correlation matrix. The novelty of the proposed method is that besides counting the number of networks involved in each voxel, it allows us to identify which networks are actually involved in each voxel, using a data-driven sparse general linear model in order to identify brain regions involved in more than one network. Moreover, we added a bootstrap resampling strategy to assess statistically the reproducibility of our results at the single subject level. The unified framework is called SPARK, i.e. SParsity-based Analysis of Reliable k-hubness, where k-hubness denotes the number of networks overlapping in each voxel. The accuracy and robustness of SPARK were evaluated using two dimensional box simulations and realistic simulations that examined detection of artificial hubs generated on real data. Then, test/retest reliability of the method was assessed using the 1000 Functional Connectome Project database, which includes data obtained from 25 healthy subjects at three different occasions with long and short intervals between sessions. We demonstrated that SPARK provides an accurate and reliable estimation of k-hubness, suggesting a promising tool for understanding hub organization in resting-state fMRI.
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Affiliation(s)
- Kangjoo Lee
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Duff Medical Building, 3775 Rue University, Montreal, QC H3A 2B4, Canada; Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada.
| | - Jean-Marc Lina
- École de Technologie Supérieure, 1100 Rue Notre-Dame O, Montreal, QC H3C 1K3, Canada; Centre de Recherches Mathématiques, Université de Montréal, Pavillon André-Aisenstadt 2920 Chemin de la tour, Room 5357, Montreal, QC H3T 1J4, Canada
| | - Jean Gotman
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Duff Medical Building, 3775 Rue University, Montreal, QC H3A 2B4, Canada; Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada; Centre de Recherches Mathématiques, Université de Montréal, Pavillon André-Aisenstadt 2920 Chemin de la tour, Room 5357, Montreal, QC H3T 1J4, Canada; Physics Department and PERFORM Centre, Concordia University, 7200 Rue Sherbrooke St. W, Montreal, QC H4B 1R6, Canada
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37
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Cameron Craddock R, S Margulies D, Bellec P, Nolan Nichols B, Alcauter S, A Barrios F, Burnod Y, J Cannistraci C, Cohen-Adad J, De Leener B, Dery S, Downar J, Dunlop K, R Franco A, Seligman Froehlich C, J Gerber A, S Ghosh S, J Grabowski T, Hill S, Sólon Heinsfeld A, Matthew Hutchison R, Kundu P, R Laird A, Liew SL, J Lurie D, G McLaren D, Meneguzzi F, Mennes M, Mesmoudi S, O'Connor D, H Pasaye E, Peltier S, Poline JB, Prasad G, Fraga Pereira R, Quirion PO, Rokem A, S Saad Z, Shi Y, C Strother S, Toro R, Q Uddin L, D Van Horn J, W Van Meter J, C Welsh R, Xu T. Brainhack: a collaborative workshop for the open neuroscience community. Gigascience 2016; 5:16. [PMID: 27042293 PMCID: PMC4818387 DOI: 10.1186/s13742-016-0121-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 03/15/2016] [Indexed: 11/10/2022] Open
Abstract
Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.
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Affiliation(s)
- R Cameron Craddock
- The Neuro Bureau, Leipzig, 04317 Germany ; Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, 10962 USA ; Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
| | - Daniel S Margulies
- The Neuro Bureau, Leipzig, 04317 Germany ; Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103 Germany
| | - Pierre Bellec
- The Neuro Bureau, Leipzig, 04317 Germany ; Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Québec H3W 1W5, Canada ; Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada
| | - B Nolan Nichols
- The Neuro Bureau, Leipzig, 04317 Germany ; Center for Health Sciences, SRI International, Menlo Park, California, 94025 USA ; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, 94305 USA
| | - Sarael Alcauter
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Fernando A Barrios
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Yves Burnod
- Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Université Paris 06, Paris, 75005 France ; Institut des Systèmes Complexes de Paris-Île-de-France, Paris, 75013 France
| | - Christopher J Cannistraci
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029 USA
| | - Julien Cohen-Adad
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada ; Institute of Biomedical Engineering, Ecole Polytechnique de Montréal, Montréal, Québec H3T 1J4, Canada
| | - Benjamin De Leener
- Institute of Biomedical Engineering, Ecole Polytechnique de Montréal, Montréal, Québec H3T 1J4, Canada
| | - Sebastien Dery
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Jonathan Downar
- MRI-Guided rTMS Clinic, University Health Network, Toronto, Ontario M5T 2S8, Canada ; Department of Psychiatry, University Health Network, University of Toronto, Toronto, Ontario M5T 2S8, Canada ; Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Katharine Dunlop
- MRI-Guided rTMS Clinic, University Health Network, Toronto, Ontario M5T 2S8, Canada ; Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Alexandre R Franco
- The Neuro Bureau, Leipzig, 04317 Germany ; Faculdade de Engenharia, PUCRS, Porto Alegre, 90619 Brazil ; Instituto do Cérebro do Rio Grande do Sul, PUCRS, Porto Alegre, 90610 Brazil ; Faculdade de Medicina, PUCRS, Porto Alegre, 90619 Brazil
| | - Caroline Seligman Froehlich
- The Neuro Bureau, Leipzig, 04317 Germany ; Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, 10962 USA
| | - Andrew J Gerber
- New York State Psychiatric Institute, New York, New York, 10032 USA ; Division of Child and Adolescent Psychiatry, Department of Psychiatry, Columbia University, New York, New York, 10032 USA
| | - Satrajit S Ghosh
- The Neuro Bureau, Leipzig, 04317 Germany ; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139 USA ; Department of Otology and Laryngology, Harvard Medical School, Boston, Massachusetts, 02115 USA
| | - Thomas J Grabowski
- Department of Radiology, University of Washington, Seattle, Washington, 98105 USA ; Department of Neurology, University of Washington, Seattle, Washington, 98105 USA
| | - Sean Hill
- International Neuroinformatics Coordinating Facility, Stockholm, 171 77 Sweden ; Karolinska Institutet, Stockholm, 171 77 Sweden
| | | | - R Matthew Hutchison
- The Neuro Bureau, Leipzig, 04317 Germany ; Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138 USA
| | - Prantik Kundu
- The Neuro Bureau, Leipzig, 04317 Germany ; Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029 USA
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, Florida, 33199 USA
| | - Sook-Lei Liew
- The Neuro Bureau, Leipzig, 04317 Germany ; Chan Division of Occupational Science and Occupational Therapy, Division of Physical Therapy and Biokinesiology, Department of Neurology, University of Southern California, Los Angeles, California, 90033 USA ; USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, Canada, 90033 USA
| | - Daniel J Lurie
- Department of Psychology,, University of California, Berkeley, California, 94720 USA
| | - Donald G McLaren
- The Neuro Bureau, Leipzig, 04317 Germany ; Biospective, Inc., Montréal,, Québec H4P 1K6, Canada ; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, 02114, USA
| | | | - Maarten Mennes
- The Neuro Bureau, Leipzig, 04317 Germany ; Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, 6525 EN The Netherlands
| | - Salma Mesmoudi
- Institut des Systèmes Complexes de Paris-Île-de-France, Paris, 75013 France ; Sorbonne Universités, Paris-1 Université, Equipement d'Excellence MATRICE, Paris, 75005, France
| | - David O'Connor
- Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
| | - Erick H Pasaye
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Scott Peltier
- Functional MRI Laboratory, University of Michigan, Ann Arbor, Michigan, 48109 USA
| | - Jean-Baptiste Poline
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, 94720 USA ; Henry H. Wheeler Jr. Brain Imaging Center, University of California, Berkeley, California, 94709 USA
| | - Gautam Prasad
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, 90033 USA
| | | | - Pierre-Olivier Quirion
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada
| | - Ariel Rokem
- The University of Washington eScience Institute, Seattle, Washington, 98195 USA
| | - Ziad S Saad
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, Maryland, 20892 USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, 90033 USA
| | - Stephen C Strother
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada ; Rotman Research Institute, Baycrest Hospital, Toronto, Ontario M6A 2E1, Canada ; Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Roberto Toro
- The Neuro Bureau, Leipzig, 04317 Germany ; Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, 75015 France ; Unité Mixte de Recherche 3571, Genes, Synapses and Cognition, Centre National de la Recherche Scientifique, Institut Pasteur, Paris, 75015 France
| | - Lucina Q Uddin
- The Neuro Bureau, Leipzig, 04317 Germany ; Department of Psychology, University of Miami, Coral Gables, Florida, 33124 USA ; Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida, 33136 USA
| | - John D Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, Canada, 90033 USA
| | - John W Van Meter
- Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington,, 20007 DC USA
| | - Robert C Welsh
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, 48109 USA ; Department of Radiology,, University of Michigan, Ann Arbor, Michigan, 48109 USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
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Jakobsen E, Böttger J, Bellec P, Geyer S, Rübsamen R, Petrides M, Margulies DS. Subdivision of Broca's region based on individual-level functional connectivity. Eur J Neurosci 2016; 43:561-71. [DOI: 10.1111/ejn.13140] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 11/01/2015] [Accepted: 11/17/2015] [Indexed: 12/30/2022]
Affiliation(s)
- Estrid Jakobsen
- Max Planck Institute for Human Cognitive and Brain Sciences; Stephanstrasse 1A 04103 Leipzig Germany
| | - Joachim Böttger
- Max Planck Institute for Human Cognitive and Brain Sciences; Stephanstrasse 1A 04103 Leipzig Germany
| | - Pierre Bellec
- Centre de recherche de l'institut de Gériatrie de Montréal; Montreal QC Canada
| | - Stefan Geyer
- Max Planck Institute for Human Cognitive and Brain Sciences; Stephanstrasse 1A 04103 Leipzig Germany
| | | | - Michael Petrides
- Montreal Neurological Institute and Hospital; Montreal QC Canada
| | - Daniel S. Margulies
- Max Planck Institute for Human Cognitive and Brain Sciences; Stephanstrasse 1A 04103 Leipzig Germany
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Markers of cognitive decline in PD: The case for heterogeneity. Parkinsonism Relat Disord 2016; 24:8-14. [PMID: 26774536 DOI: 10.1016/j.parkreldis.2016.01.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Revised: 01/04/2016] [Accepted: 01/05/2016] [Indexed: 01/25/2023]
Abstract
Cognitive impairment is highly prevalent and has a severe negative effect on health related and perceived quality of life in Parkinson's disease (PD). It is now established that 20-40% of persons with PD will develop cognitive deficits early in the disease. Moreover, the risk of developing dementia is six times higher in PD patients than in age-matched controls and it is estimated that 80% of patients will develop dementia after 20 years of the disease. In order to address these symptoms properly it is crucial to identify very early in the disease the patients who are most likely to develop dementia rapidly. Persons who meet criteria for mild cognitive impairment (MCI) exhibit measurable cognitive deficits but those deficits are not severe enough to interfere significantly with daily life. While the presence of MCI in PD increases the chance of developing dementia, various studies suggest that PD-MCI might consist of distinct subtypes with different pathophysiologies and prognoses. In this paper we comment on various biomarkers associated with cognitive decline in PD, specifically clinical, neuropathological, genetic and neuroimaging ones. We also discuss disrupted functional connectivity in PD-MCI and reveal preliminary results from our own group. We propose that the current studies looking at different types of biomarkers provide support for different causes being associated with cognitive decline in PD. Large-scale multi-disciplinary and multi-modal longitudinal studies are required to identify more specifically the different phenotypes associated with different cognitive profiles and evolution in PD.
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Tam A, Dansereau C, Badhwar A, Orban P, Belleville S, Chertkow H, Dagher A, Hanganu A, Monchi O, Rosa-Neto P, Shmuel A, Wang S, Breitner J, Bellec P. Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies. Front Aging Neurosci 2015; 7:242. [PMID: 26733866 PMCID: PMC4689788 DOI: 10.3389/fnagi.2015.00242] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 12/10/2015] [Indexed: 12/13/2022] Open
Abstract
Resting-state functional connectivity is a promising biomarker for Alzheimer's disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimer's disease and amnestic mild cognitive impairment (aMCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from aMCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore combined four datasets collected independently, including 112 healthy controls and 143 patients with aMCI. We systematically tested multiple brain connections for associations with aMCI using a weighted average routinely used in meta-analyses. The largest effects involved the superior medial frontal cortex (including the anterior cingulate), dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with aMCI exhibited significantly decreased connectivity between default mode network nodes and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified common aMCI-related connectivity changes with small to medium effect sizes and sample size estimates recommending a minimum of 140 to upwards of 600 total subjects to achieve adequate statistical power in the context of a multisite study with 5-10 scanning sites and about 10 subjects per group and per site. If our findings can be replicated and associated with other established biomarkers of Alzheimer's disease (e.g., amyloid and tau quantification), then these functional connections may be promising candidate biomarkers for Alzheimer's disease.
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Affiliation(s)
- Angela Tam
- McGill UniversityMontreal, QC, Canada; Douglas Mental Health University Institute, Research CentreMontreal, QC, Canada; Centre de Recherche de L'institut Universitaire de Gériatrie de MontréalMontreal, QC, Canada
| | - Christian Dansereau
- Centre de Recherche de L'institut Universitaire de Gériatrie de MontréalMontreal, QC, Canada; Université de MontréalMontreal, QC, Canada
| | - AmanPreet Badhwar
- Centre de Recherche de L'institut Universitaire de Gériatrie de MontréalMontreal, QC, Canada; Université de MontréalMontreal, QC, Canada
| | - Pierre Orban
- Douglas Mental Health University Institute, Research CentreMontreal, QC, Canada; Centre de Recherche de L'institut Universitaire de Gériatrie de MontréalMontreal, QC, Canada
| | - Sylvie Belleville
- Centre de Recherche de L'institut Universitaire de Gériatrie de MontréalMontreal, QC, Canada; Université de MontréalMontreal, QC, Canada
| | | | | | - Alexandru Hanganu
- Centre de Recherche de L'institut Universitaire de Gériatrie de MontréalMontreal, QC, Canada; University of CalgaryCalgary, AB, Canada; Hotchkiss Brain InstituteCalgary, AB, Canada
| | - Oury Monchi
- Centre de Recherche de L'institut Universitaire de Gériatrie de MontréalMontreal, QC, Canada; Université de MontréalMontreal, QC, Canada; University of CalgaryCalgary, AB, Canada; Hotchkiss Brain InstituteCalgary, AB, Canada
| | - Pedro Rosa-Neto
- McGill UniversityMontreal, QC, Canada; Douglas Mental Health University Institute, Research CentreMontreal, QC, Canada
| | | | - Seqian Wang
- McGill UniversityMontreal, QC, Canada; Douglas Mental Health University Institute, Research CentreMontreal, QC, Canada
| | - John Breitner
- McGill UniversityMontreal, QC, Canada; Douglas Mental Health University Institute, Research CentreMontreal, QC, Canada
| | - Pierre Bellec
- Centre de Recherche de L'institut Universitaire de Gériatrie de MontréalMontreal, QC, Canada; Université de MontréalMontreal, QC, Canada
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Reid AT, Lewis J, Bezgin G, Khundrakpam B, Eickhoff SB, McIntosh AR, Bellec P, Evans AC. A cross-modal, cross-species comparison of connectivity measures in the primate brain. Neuroimage 2015; 125:311-331. [PMID: 26515902 DOI: 10.1016/j.neuroimage.2015.10.057] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2015] [Revised: 10/16/2015] [Accepted: 10/22/2015] [Indexed: 12/23/2022] Open
Abstract
In systems neuroscience, the term "connectivity" has been defined in numerous ways, according to the particular empirical modality from which it is derived. Due to large differences in the phenomena measured by these modalities, the assumptions necessary to make inferences about axonal connections, and the limitations accompanying each, brain connectivity remains an elusive concept. Despite this, only a handful of studies have directly compared connectivity as inferred from multiple modalities, and there remains much ambiguity over what the term is actually referring to as a biological construct. Here, we perform a direct comparison based on the high-resolution and high-contrast Enhanced Nathan Klein Institute (NKI) Rockland Sample neuroimaging data set, and the CoCoMac database of tract tracing studies. We compare four types of commonly-used primate connectivity analyses: tract tracing experiments, compiled in CoCoMac; group-wise correlation of cortical thickness; tractographic networks computed from diffusion-weighted MRI (DWI); and correlational networks obtained from resting-state BOLD (fMRI). We find generally poor correspondence between all four modalities, in terms of correlated edge weights, binarized comparisons of thresholded networks, and clustering patterns. fMRI and DWI had the best agreement, followed by DWI and CoCoMac, while other comparisons showed striking divergence. Networks had the best correspondence for local ipsilateral and homotopic contralateral connections, and the worst correspondence for long-range and heterotopic contralateral connections. k-Means clustering highlighted the lowest cross-modal and cross-species consensus in lateral and medial temporal lobes, anterior cingulate, and the temporoparietal junction. Comparing the NKI results to those of the lower resolution/contrast International Consortium for Brain Imaging (ICBM) dataset, we find that the relative pattern of intermodal relationships is preserved, but the correspondence between human imaging connectomes is substantially better for NKI. These findings caution against using "connectivity" as an umbrella term for results derived from single empirical modalities, and suggest that any interpretation of these results should account for (and ideally help explain) the lack of multimodal correspondence.
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Affiliation(s)
- Andrew T Reid
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
| | - John Lewis
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Gleb Bezgin
- Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, ON, Canada.
| | - Budhachandra Khundrakpam
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany.
| | - Anthony R McIntosh
- Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, ON, Canada.
| | - Pierre Bellec
- Centre de Recherche de l'Institut de Gériatrie de Montréal CRIUGM, Montreal, QC, Canada.
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
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Orban P, Madjar C, Savard M, Dansereau C, Tam A, Das S, Evans AC, Rosa-Neto P, Breitner JCS, Bellec P. Test-retest resting-state fMRI in healthy elderly persons with a family history of Alzheimer's disease. Sci Data 2015; 2:150043. [PMID: 26504522 PMCID: PMC4603392 DOI: 10.1038/sdata.2015.43] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 07/21/2015] [Indexed: 11/21/2022] Open
Abstract
We present a test-retest dataset of resting-state fMRI data obtained in 80 cognitively normal elderly volunteers enrolled in the “Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease” (PREVENT-AD) Cohort. Subjects with a family history of Alzheimer's disease in first-degree relatives were recruited as part of an on-going double blind randomized clinical trial of Naproxen or placebo. Two pairs of scans were acquired ~3 months apart, allowing the assessment of both intra- and inter-session reliability, with the possible caveat of treatment effects as a source of inter-session variation. Using the NeuroImaging Analysis Kit (NIAK), we report on the standard quality of co-registration and motion parameters of the data, and assess their validity based on the spatial distribution of seed-based connectivity maps as well as intra- and inter-session reliability metrics in the default-mode network. This resource, released publicly as sample UM1 of the Consortium for Reliability and Reproducibility (CoRR), will benefit future studies focusing on the preclinical period preceding the appearance of dementia in Alzheimer's disease.
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Affiliation(s)
- Pierre Orban
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 Queen Mary , Montreal, QC H3W 1W5, Canada ; Université de Montréal, 2900 Boulevard Edouard-Montpetit , Montreal, QC H3T 1J4, Canada
| | - Cécile Madjar
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada
| | - Mélissa Savard
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada
| | - Christian Dansereau
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 Queen Mary , Montreal, QC H3W 1W5, Canada ; Université de Montréal, 2900 Boulevard Edouard-Montpetit , Montreal, QC H3T 1J4, Canada
| | - Angela Tam
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada
| | - Samir Das
- McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada ; McConnell Brain Imaging Center, Montreal Neurological Institute, 3801 University , Montreal, QC H3A 2B4, Canada
| | - Alan C Evans
- McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada ; McConnell Brain Imaging Center, Montreal Neurological Institute, 3801 University , Montreal, QC H3A 2B4, Canada
| | - Pedro Rosa-Neto
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada ; McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada
| | - John C S Breitner
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada
| | - Pierre Bellec
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 Queen Mary , Montreal, QC H3W 1W5, Canada ; Université de Montréal, 2900 Boulevard Edouard-Montpetit , Montreal, QC H3T 1J4, Canada
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43
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Marrelec G, Messé A, Bellec P. A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables. PLoS One 2015; 10:e0137278. [PMID: 26406245 PMCID: PMC4583305 DOI: 10.1371/journal.pone.0137278] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 08/16/2015] [Indexed: 11/19/2022] Open
Abstract
The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering.
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Affiliation(s)
- Guillaume Marrelec
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’imagerie biomédicale (LIB), F-75013, Paris, France
- * E-mail:
| | - Arnaud Messé
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Pierre Bellec
- Département d’informatique et recherche opérationnelle, Centre de recherche de l’institut universitaire de gériatrie de Montréal, Université de Montréal, Montréal, Qc, Canada
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44
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Das S, Glatard T, MacIntyre LC, Madjar C, Rogers C, Rousseau ME, Rioux P, MacFarlane D, Mohades Z, Gnanasekaran R, Makowski C, Kostopoulos P, Adalat R, Khalili-Mahani N, Niso G, Moreau JT, Evans AC. The MNI data-sharing and processing ecosystem. Neuroimage 2015; 124:1188-1195. [PMID: 26364860 DOI: 10.1016/j.neuroimage.2015.08.076] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 08/22/2015] [Accepted: 08/24/2015] [Indexed: 11/29/2022] Open
Abstract
Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of "Big Data", there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.
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Affiliation(s)
- Samir Das
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
| | - Tristan Glatard
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Université de Lyon, CREATIS ; CNRS UMR5220 ; Inserm U1044 ; INSA-Lyon ; Université Claude Bernard Lyon 1, France
| | - Leigh C MacIntyre
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Cecile Madjar
- Douglas Mental Health University Institute, Montreal, Canada
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Marc-Etienne Rousseau
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Dave MacFarlane
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Zia Mohades
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Rathi Gnanasekaran
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Carolina Makowski
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Penelope Kostopoulos
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Reza Adalat
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Jeremy T Moreau
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
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45
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Zeighami Y, Ulla M, Iturria-Medina Y, Dadar M, Zhang Y, Larcher KMH, Fonov V, Evans AC, Collins DL, Dagher A. Network structure of brain atrophy in de novo Parkinson's disease. eLife 2015; 4:e08440. [PMID: 26344547 PMCID: PMC4596689 DOI: 10.7554/elife.08440] [Citation(s) in RCA: 153] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 09/05/2015] [Indexed: 01/01/2023] Open
Abstract
We mapped the distribution of atrophy in Parkinson's disease (PD) using magnetic resonance imaging (MRI) and clinical data from 232 PD patients and 117 controls from the Parkinson's Progression Markers Initiative. Deformation-based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation.
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Affiliation(s)
- Yashar Zeighami
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Miguel Ulla
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
- Service de Neurologie A, CHU Clermont-Ferrand, Clermont-Ferrand, France
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Mahsa Dadar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Yu Zhang
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Vladimir Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
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Wang J, Wang X, Xia M, Liao X, Evans A, He Y. Corrigendum: GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 2015; 9:458. [PMID: 26347640 PMCID: PMC4541037 DOI: 10.3389/fnhum.2015.00458] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 08/03/2015] [Indexed: 11/23/2022] Open
Affiliation(s)
- Jinhui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Cognition and Brain Disorders, Hangzhou Normal University Hangzhou, China ; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou, China
| | - Xindi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Xuhong Liao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Alan Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute Montreal, QC, Canada
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
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47
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Bellec P, Benhajali Y, Carbonell F, Dansereau C, Albouy G, Pelland M, Craddock C, Collignon O, Doyon J, Stip E, Orban P. Impact of the resolution of brain parcels on connectome-wide association studies in fMRI. Neuroimage 2015; 123:212-28. [PMID: 26241681 DOI: 10.1016/j.neuroimage.2015.07.071] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Revised: 07/16/2015] [Accepted: 07/23/2015] [Indexed: 10/23/2022] Open
Abstract
A recent trend in functional magnetic resonance imaging is to test for association of clinical disorders with every possible connection between selected brain parcels. We investigated the impact of the resolution of functional brain parcels, ranging from large-scale networks to local regions, on a mass univariate general linear model (GLM) of connectomes. For each resolution taken independently, the Benjamini-Hochberg procedure controlled the false-discovery rate (FDR) at nominal level on realistic simulations. However, the FDR for tests pooled across all resolutions could be inflated compared to the FDR within resolution. This inflation was severe in the presence of no or weak effects, but became negligible for strong effects. We thus developed an omnibus test to establish the overall presence of true discoveries across all resolutions. Although not a guarantee to control the FDR across resolutions, the omnibus test may be used for descriptive analysis of the impact of resolution on a GLM analysis, in complement to a primary analysis at a predefined single resolution. On three real datasets with significant omnibus test (schizophrenia, congenital blindness, motor practice), markedly higher rate of discovery were obtained at low resolutions, below 50, in line with simulations showing increase in sensitivity at such resolutions. This increase in discovery rate came at the cost of a lower ability to localize effects, as low resolution parcels merged many different brain regions together. However, with 30 or more parcels, the statistical effect maps were biologically plausible and very consistent across resolutions. These results show that resolution is a key parameter for GLM-connectome analysis with FDR control, and that a functional brain parcellation with 30 to 50 parcels may lead to an accurate summary of full connectome effects with good sensitivity in many situations.
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Affiliation(s)
- Pierre Bellec
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Canada; Department of Computer Science and Operations Research, University of Montreal, Montreal, QC, Canada.
| | - Yassine Benhajali
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Canada; Department of Anthropology, University of Montreal, Montreal, QC, Canada
| | | | - Christian Dansereau
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Canada; Department of Computer Science and Operations Research, University of Montreal, Montreal, QC, Canada
| | - Geneviève Albouy
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Canada; Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Maxime Pelland
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Cameron Craddock
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Oliver Collignon
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Julien Doyon
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Canada; Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Emmanuel Stip
- Department of Psychiatry, University of Montreal, Montreal, QC, Canada; Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Pierre Orban
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Canada; Department of Psychiatry, University of Montreal, Montreal, QC, Canada
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48
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Abstract
The ability to recognize, create, and use complex tools is a milestone in human evolution. Widely distributed brain regions in parietal, frontal, and temporal cortices have been implicated in using and understanding tools, but the roles of their anatomical connections in supporting tool use and tool conceptual behaviors are unclear. Using deterministic fiber tracking in healthy participants, we first examined how 14 cortical regions that are consistently activated by tool processing are connected by white matter (WM) tracts. The relationship between the integrity of each of the 33 obtained tracts and tool processing deficits across 86 brain-damaged patients was investigated. WM tract integrity was measured with both lesion percentage (structural imaging) and mean fractional anisotropy (FA) values (diffusion imaging). Behavioral abilities were assessed by a tool use task, a range of conceptual tasks, and control tasks. We found that three left hemisphere tracts connecting frontoparietal and intrafrontal areas overlapping with left superior longitudinal fasciculus are crucial for tool use such that larger lesion and lower mean FA values on these tracts were associated with more severe tool use deficits. These tracts and five additional left hemisphere tracts connecting frontal and temporal/parietal regions, mainly overlapping with left superior longitudinal fasciculus, inferior frontooccipital fasciculus, uncinate fasciculus, and anterior thalamic radiation, are crucial for tool concept processing. Largely consistent results were also obtained using voxel-based symptom mapping analyses. Our results revealed the WM structural networks that support the use and conceptual understanding of tools, providing evidence for the anatomical skeleton of the tool knowledge network.
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Wang J, Wang X, Xia M, Liao X, Evans A, He Y. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 2015; 9:386. [PMID: 26175682 PMCID: PMC4485071 DOI: 10.3389/fnhum.2015.00386] [Citation(s) in RCA: 512] [Impact Index Per Article: 56.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 06/16/2015] [Indexed: 01/19/2023] Open
Abstract
Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.1
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Affiliation(s)
- Jinhui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Cognition and Brain Disorders, Hangzhou Normal University Hangzhou, China ; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou, China
| | - Xindi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Xuhong Liao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Alan Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC, Canada
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Cui Z, Zhao C, Gong G. Parallel workflow tools to facilitate human brain MRI post-processing. Front Neurosci 2015; 9:171. [PMID: 26029043 PMCID: PMC4429548 DOI: 10.3389/fnins.2015.00171] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 04/27/2015] [Indexed: 02/04/2023] Open
Abstract
Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues.
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Affiliation(s)
- Zaixu Cui
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Chenxi Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
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