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van der Schaaf ME, Geerligs L, Toni I, Knoop H, Oosterman JM. Disentangling pain and fatigue in chronic fatigue syndrome: a resting state connectivity study before and after cognitive behavioral therapy. Psychol Med 2024; 54:1735-1748. [PMID: 38193344 DOI: 10.1017/s0033291723003690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
BACKGROUND Fatigue is a central feature of myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), but many ME/CFS patients also report comorbid pain symptoms. It remains unclear whether these symptoms are related to similar or dissociable brain networks. This study used resting-state fMRI to disentangle networks associated with fatigue and pain symptoms in ME/CFS patients, and to link changes in those networks to clinical improvements following cognitive behavioral therapy (CBT). METHODS Relationships between pain and fatigue symptoms and cortico-cortical connectivity were assessed within ME/CFS patients at baseline (N = 72) and after CBT (N = 33) and waiting list (WL, N = 18) and compared to healthy controls (HC, N = 29). The analyses focused on four networks previously associated with pain and/or fatigue, i.e. the fronto-parietal network (FPN), premotor network (PMN), somatomotor network (SMN), and default mode network (DMN). RESULTS At baseline, variation in pain and fatigue symptoms related to partially dissociable brain networks. Fatigue was associated with higher SMN-PMN connectivity and lower SMN-DMN connectivity. Pain was associated with lower PMN-DMN connectivity. CBT improved SMN-DMN connectivity, compared to WL. Larger clinical improvements were associated with larger increases in frontal SMN-DMN connectivity. No CBT effects were observed for PMN-DMN or SMN-PMN connectivity. CONCLUSIONS These results provide insight into the dissociable neural mechanisms underlying fatigue and pain symptoms in ME/CFS and how they are affected by CBT in successfully treated patients. Further investigation of how and in whom behavioral and biomedical treatments affect these networks is warranted to improve and individualize existing or new treatments for ME/CFS.
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Affiliation(s)
- Marieke E van der Schaaf
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, the Netherlands
- Radboud University, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of cognitive neuropsychology Tilburg University, Tilburg, The Netherlands
| | - Linda Geerligs
- Radboud University, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
| | - Ivan Toni
- Radboud University, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
| | - Hans Knoop
- Department of Medical Psychology and Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Joukje M Oosterman
- Radboud University, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
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2
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Moreau CA, Kumar K, Harvey A, Huguet G, Urchs SGW, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S. Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions. Brain 2023; 146:1686-1696. [PMID: 36059063 PMCID: PMC10319760 DOI: 10.1093/brain/awac315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 02/03/2023] Open
Abstract
Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic-rFunctional connectivity = 0.71 [0.40-0.87] and rTranscriptomic-rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms-amenable to intervention-across psychiatric conditions and genetic risks.
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Affiliation(s)
- Clara A Moreau
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université Paris Cité, Paris, France
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Kuldeep Kumar
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Annabelle Harvey
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Guillaume Huguet
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Sebastian G W Urchs
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Laura M Schultz
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Hanad Sharmarke
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Khadije Jizi
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | | | - Nadine Younis
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Petra Tamer
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Jean-Louis Martineau
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Pierre Orban
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, UdeM, Montréal, QC H1N 3V2, Canada
- Département de Psychiatrie et d’Addictologie, Université de Montréal, Pavillon Roger-Gaudry, C.P. 6128, Succursale Centre-ville, Montréal, QC H3C 3J7, Canada
| | - Ana Isabel Silva
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Sarah Lippé
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Carrie E Bearden
- Integrative Center for Neurogenetics, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA 90095, USA
- Department of Psychiatry, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Biobehavioral Sciences and Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David C Glahn
- Department of Psychiatry, Harvard Medical School, Cambridge, MA 02115, USA
- Boston Children’s Hospital, Tommy Fuss Center for Neuropsychiatric Disease Research, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, CA, USA
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université Paris Cité, Paris, France
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Sebastien Jacquemont
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
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3
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Moreau CA, Harvey A, Kumar K, Huguet G, Urchs SGW, Douard EA, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Rolland T, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Labbe A, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S. Genetic Heterogeneity Shapes Brain Connectivity in Psychiatry. Biol Psychiatry 2023; 93:45-58. [PMID: 36372570 PMCID: PMC10936195 DOI: 10.1016/j.biopsych.2022.08.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits. METHODS Resting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects). RESULTS Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2-0.65 z score), followed by psychiatric conditions (0.15-0.42), neuroticism and fluid intelligence (0.02-0.03), and PRSs (0.01-0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10-6). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = -0.88, p = 8.78 × 10-6). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease. CONCLUSIONS Heterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.
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Affiliation(s)
- Clara A Moreau
- Human Genetics and Cognitive Functions, Institut Pasteur, Université Paris Cité, Paris, France; Sainte-Justine Research Center, University of Montréal, Montréal, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada.
| | - Annabelle Harvey
- Sainte-Justine Research Center, University of Montréal, Montréal, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
| | - Kuldeep Kumar
- Sainte-Justine Research Center, University of Montréal, Montréal, Canada
| | - Guillaume Huguet
- Sainte-Justine Research Center, University of Montréal, Montréal, Canada
| | - Sebastian G W Urchs
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Elise A Douard
- Sainte-Justine Research Center, University of Montréal, Montréal, Canada
| | - Laura M Schultz
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hanad Sharmarke
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
| | - Khadije Jizi
- Sainte-Justine Research Center, University of Montréal, Montréal, Canada
| | | | - Nadine Younis
- Sainte-Justine Research Center, University of Montréal, Montréal, Canada
| | - Petra Tamer
- Sainte-Justine Research Center, University of Montréal, Montréal, Canada
| | - Thomas Rolland
- Human Genetics and Cognitive Functions, Institut Pasteur, Université Paris Cité, Paris, France
| | | | - Pierre Orban
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada; Département de Psychiatrie et d'Addictologie, Université de Montréal, Montréal, Canada
| | - Ana Isabel Silva
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom; School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Marianne B M van den Bree
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom; School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Aurelie Labbe
- Département des Sciences de la Décision, HEC, Québec, Montréal, Canada
| | - Sarah Lippé
- Sainte-Justine Research Center, University of Montréal, Montréal, Canada
| | - Carrie E Bearden
- Integrative Center for Neurogenetics, Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California, Los Angeles, Los Angeles, California
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Pennsylvania; Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David C Glahn
- Harvard Medical School, Department of Psychiatry, Boston, Massachusetts; Boston Children's Hospital, Tommy Fuss Center for Neuropsychiatric Disease Research, Boston, Massachusetts
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur, Université Paris Cité, Paris, France
| | - Pierre Bellec
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
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Kassinopoulos M, Mitsis GD. A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity. Magn Reson Imaging 2021; 85:228-250. [PMID: 34715292 DOI: 10.1016/j.mri.2021.10.028] [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] [Received: 03/18/2021] [Revised: 09/17/2021] [Accepted: 10/17/2021] [Indexed: 12/17/2022]
Abstract
It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing.
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Affiliation(s)
- Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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5
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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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6
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Moreau CA, Urchs SGW, Kuldeep K, Orban P, Schramm C, Dumas G, Labbe A, Huguet G, Douard E, Quirion PO, Lin A, Kushan L, Grot S, Luck D, Mendrek A, Potvin S, Stip E, Bourgeron T, Evans AC, Bearden CE, Bellec P, Jacquemont S. Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia. Nat Commun 2020; 11:5272. [PMID: 33077750 PMCID: PMC7573583 DOI: 10.1038/s41467-020-18997-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Deficit-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. Here we report an analysis of resting-state FC using magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We characterize CNV FC-signatures and use them to identify dimensions contributing to complex idiopathic conditions. CNVs have large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions play a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibit worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.
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Affiliation(s)
- Clara A Moreau
- Sainte Justine Hospital Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada.
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4565 Queen Mary Rd, Montreal, QC, H3W 1W5, Canada.
| | - Sebastian G W Urchs
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4565 Queen Mary Rd, Montreal, QC, H3W 1W5, Canada.
- Montreal Neurological Institute and Hospital, McGill University, 3801 Rue de l'Université, Montreal, QC, H3A 2B4, Canada.
| | - Kumar Kuldeep
- Sainte Justine Hospital Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada
| | - Pierre Orban
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, 7401 Rue Hochelaga, Montreal, QC, H1N 3M5, Canada
- Département de Psychiatrie et d'Addictologie, Université de Montréal, Pavillon Roger-Gaudry, C.P. 6128, succursale Centre-ville, Montreal, QC, H3C 3J7, Canada
| | - Catherine Schramm
- Sainte Justine Hospital Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1E2, Canada
| | - Guillaume Dumas
- Sainte Justine Hospital Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada
- Human Genetics and Cognitive Functions, Institut Pasteur, Université de Paris, UMR3571 CNRS, Paris, France
| | - Aurélie Labbe
- Département des Sciences de la Décision, HEC, 3000, chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 2A7, Canada
| | - Guillaume Huguet
- Sainte Justine Hospital Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada
| | - Elise Douard
- Sainte Justine Hospital Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada
| | - Pierre-Olivier Quirion
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4565 Queen Mary Rd, Montreal, QC, H3W 1W5, Canada
- Canadian Center for Computational Genomics, McGill University and Genome Quebec Innovation Center 740, Dr. Penfield Avenue, H3A 0G1, Montreal, Canada
| | - Amy Lin
- Semel Institute for Neuroscience and Human Behavior and Department of Psychology, University of California, Los Angeles, Semel Institute/NPI, 760 Westwood Plaza, Los Angeles, CA, 90024, USA
| | - Leila Kushan
- Semel Institute for Neuroscience and Human Behavior and Department of Psychology, University of California, Los Angeles, Semel Institute/NPI, 760 Westwood Plaza, Los Angeles, CA, 90024, USA
| | - Stephanie Grot
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, 7401 Rue Hochelaga, Montreal, QC, H1N 3M5, Canada
- Département de Psychiatrie et d'Addictologie, Université de Montréal, Pavillon Roger-Gaudry, C.P. 6128, succursale Centre-ville, Montreal, QC, H3C 3J7, Canada
| | - David Luck
- Sainte Justine Hospital Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada
| | - Adrianna Mendrek
- Department of Psychology, Bishop's University, 2600 College Street, Sherbrooke, QC, J1M IZ7, Canada
| | - Stephane Potvin
- Département de Psychiatrie et d'Addictologie, Université de Montréal, Pavillon Roger-Gaudry, C.P. 6128, succursale Centre-ville, Montreal, QC, H3C 3J7, Canada
| | - Emmanuel Stip
- Département de Psychiatrie et d'Addictologie, Université de Montréal, Pavillon Roger-Gaudry, C.P. 6128, succursale Centre-ville, Montreal, QC, H3C 3J7, Canada
- United Arab Emirates University, College of Medicine and health Sciences, PO 17666, Al Ain, QC, UAE
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur, Université de Paris, UMR3571 CNRS, Paris, France
| | - Alan C Evans
- Montreal Neurological Institute and Hospital, McGill University, 3801 Rue de l'Université, Montreal, QC, H3A 2B4, Canada
| | - Carrie E Bearden
- Semel Institute for Neuroscience and Human Behavior and Department of Psychology, University of California, Los Angeles, Semel Institute/NPI, 760 Westwood Plaza, Los Angeles, CA, 90024, USA
| | - Pierre Bellec
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4565 Queen Mary Rd, Montreal, QC, H3W 1W5, Canada
| | - Sebastien Jacquemont
- Sainte Justine Hospital Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada.
- Department of Pediatrics, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada.
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7
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Gomez DE, Llera A, Marques JPF, Beckmann CF, Norris DG. Single-subject Single-session Temporally-Independent Functional Modes of Brain Activity. Neuroimage 2020; 218:116783. [DOI: 10.1016/j.neuroimage.2020.116783] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 02/10/2020] [Accepted: 04/03/2020] [Indexed: 12/24/2022] Open
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8
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Badhwar A, Collin-Verreault Y, Lussier D, Sharmarke H, Orban P, Urchs S, Chouinard I, Vogel J, Potvin O, Duchesne S, Bellec P. A dataset of long-term consistency values of resting-state fMRI connectivity maps in a single individual derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol. Data Brief 2020; 31:105699. [PMID: 32518809 PMCID: PMC7270189 DOI: 10.1016/j.dib.2020.105699] [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: 11/06/2019] [Revised: 05/01/2020] [Accepted: 05/06/2020] [Indexed: 11/25/2022] Open
Abstract
The impact of multisite acquisition on resting-state functional MRI (rsfMRI) connectivity has recently gained attention. We provide consistency values (Pearson's correlation) between rsfMRI connectivity maps of an adult volunteer (Csub) scanned 25 times over 3.5 years at 13 sites using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). This dataset was generated as part of the following article: Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors [1]. Acquired on three 3T scanner vendors (GE, Siemens and Philips), the Csub dataset is part of an ongoing effort to monitor the quality and comparability of MRI data collected across the Canadian Consortium on Neurodegeneration in Aging (CCNA) imaging network. The participant was scanned 25 times in the above-mentioned article: multiple times at six sites over a period of 2.5 years, and once at the remaining seven sites. Since then the participant was scanned an additional 45 times, allowing us to extend the dataset to 70 rsfMRI scans over a period of >4 years. In addition, we provide intra- and inter-subject consistency values of rsfMRI connectivity maps derived from 26 adult participants belonging to the publicly released Hangzhou Normal University dataset (HNU1). All HNU1 participants underwent 10 rsfMRI scans over one month on a single 3T scanner (GE). Connectivity maps of seven canonical networks were generated for each scan in the two datasets (Csub and HNU1). All consistency values, along with the scripts used to preprocess the rsfMRI data and generate connectivity maps and pairwise consistency values, have been made available on two public repositories, Github and Zenodo. We have also made available four Jupyter notebooks that use the provided consistency values to (a) generate interactive graphical summaries – 1 notebook, (b) perform statistical analyses - 2 notebooks, and (c) perform data-driven cluster analysis for the recovery of subject identity (i.e. rsfMRI fingerprinting) – 1 notebook. In addition, we provide two interactive dashboards that allow visualization of individual connectivity maps from the two datasets. Finally, we also provide minimally preprocessed rsfMRI data in Brain Imaging Data Standard (BIDS) format on all 70 scans in the extended dataset.
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Affiliation(s)
- AmanPreet Badhwar
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada.,Université de Montréal, Montréal, Canada
| | - Yannik Collin-Verreault
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Desiree Lussier
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Hanad Sharmarke
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Pierre Orban
- Université de Montréal, Montréal, Canada.,Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montréal, Canada
| | - Sebastian Urchs
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada.,McGill University, Montréal, Canada
| | | | | | - Olivier Potvin
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada
| | - Simon Duchesne
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada.,Department of Radiology, Faculty of Medicine, Université Laval, Quebec, Canada
| | - Pierre Bellec
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada.,Université de Montréal, Montréal, Canada
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9
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Spisak T, Kincses B, Schlitt F, Zunhammer M, Schmidt-Wilcke T, Kincses ZT, Bingel U. Pain-free resting-state functional brain connectivity predicts individual pain sensitivity. Nat Commun 2020; 11:187. [PMID: 31924769 PMCID: PMC6954277 DOI: 10.1038/s41467-019-13785-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 11/21/2019] [Indexed: 01/04/2023] Open
Abstract
Individual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature allows assessing the individual sensitivity to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have implications for translational research and the development and assessment of analgesic treatment strategies.
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Affiliation(s)
- Tamas Spisak
- Department of Neurology, University Hospital Essen, Hufelandstrasse, 5545147, Essen, Germany.
| | - Balint Kincses
- Department of Neurology, University of Szeged, Tisza Lajos krt. 113, 6725, Szeged, Hungary
| | - Frederik Schlitt
- Department of Neurology, University Hospital Essen, Hufelandstrasse, 5545147, Essen, Germany
| | - Matthias Zunhammer
- Department of Neurology, University Hospital Essen, Hufelandstrasse, 5545147, Essen, Germany
| | - Tobias Schmidt-Wilcke
- Institute of Clinical Neuroscience and Medical Psychology, University of Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany.,Mauritius Therapieklinik, Strümper Str. 111, 40670, Meerbusch, Meerbusch, Germany
| | - Zsigmond T Kincses
- Department of Neurology, University of Szeged, Tisza Lajos krt. 113, 6725, Szeged, Hungary
| | - Ulrike Bingel
- Department of Neurology, University Hospital Essen, Hufelandstrasse, 5545147, Essen, Germany
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10
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Badhwar A, Collin-Verreault Y, Orban P, Urchs S, Chouinard I, Vogel J, Potvin O, Duchesne S, Bellec P. Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors. Neuroimage 2019; 205:116210. [PMID: 31593793 DOI: 10.1016/j.neuroimage.2019.116210] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 09/15/2019] [Accepted: 09/17/2019] [Indexed: 11/26/2022] Open
Abstract
Studies using resting-state functional magnetic resonance imaging (rsfMRI) are increasingly collecting data at multiple sites in order to speed up recruitment or increase sample size. The main objective of this study was to assess the long-term consistency of rsfMRI connectivity maps derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). Nine to 10 min of functional BOLD images were acquired from an adult cognitively healthy volunteer scanned repeatedly at 13 Canadian sites on three scanner makes (General Electric, Philips and Siemens) over the course of 2.5 years. The consistency (spatial Pearson's correlation) of rsfMRI connectivity maps for seven canonical networks ranged from 0.3 to 0.8, with a negligible effect of time, but significant site and vendor effects. We noted systematic differences in data quality (i.e. head motion, number of useable time frames, temporal signal-to-noise ratio) across vendors, which may also confound some of these results, and could not be disentangled in this sample. We also pooled the long-term longitudinal data with a single-site, short-term (1 month) data sample acquired on 26 subjects (10 scans per subject), called HNU1. Using randomly selected pairs of scans from each subject, we quantified the ability of a data-driven unsupervised cluster analysis to match two scans of the same subjects. In this "fingerprinting" experiment, we found that scans from the Canadian subject (Csub) could be matched with high accuracy intra-site (>95% for some networks), but that the accuracy decreased substantially for scans drawn from different sites and vendors, even falling outside of the range of accuracies observed in HNU1. Overall, our results demonstrate good multivariate stability of rsfMRI measures over several years, but substantial impact of scanning site and vendors. How detrimental these effects are will depend on the application, yet our results demonstrate that new methods for harmonizing multisite analysis represent an important area for future work.
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Affiliation(s)
- AmanPreet Badhwar
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; Université de Montréal, Montréal, Canada.
| | - Yannik Collin-Verreault
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Pierre Orban
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada; Université de Montréal, Montréal, Canada
| | - Sebastian Urchs
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; McGill University, Montréal, Canada
| | | | | | - Olivier Potvin
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada
| | - Simon Duchesne
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada; Department of Radiology, Faculty of Medicine, Université Laval, Quebec, Canada
| | - Pierre Bellec
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; Université de Montréal, Montréal, Canada
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11
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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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12
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Combrisson E, Vallat R, O'Reilly C, Jas M, Pascarella A, Saive AL, Thiery T, Meunier D, Altukhov D, Lajnef T, Ruby P, Guillot A, Jerbi K. Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization. Front Neuroinform 2019; 13:14. [PMID: 30967769 PMCID: PMC6439346 DOI: 10.3389/fninf.2019.00014] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 02/19/2019] [Indexed: 11/13/2022] Open
Abstract
We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuro-oriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal, for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org).
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Affiliation(s)
- Etienne Combrisson
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada
| | - Raphael Vallat
- Lyon Neuroscience Research Center, Brain Dynamics and Cognition team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Christian O'Reilly
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Mainak Jas
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada
| | - Annalisa Pascarella
- Institute for Applied Mathematics Mauro Picone, National Research Council, Rome, Italy
| | - Anne-Lise Saive
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada
| | - Thomas Thiery
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada
| | - David Meunier
- Aix Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France
| | - Dmitrii Altukhov
- National Research University Higher School of Economics, Moscow, Russia.,MEG Center, Moscow State University of Pedagogics and Education, Moscow, Russia
| | - Tarek Lajnef
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada.,Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, QC, Canada
| | - Perrine Ruby
- Lyon Neuroscience Research Center, Brain Dynamics and Cognition team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Aymeric Guillot
- Inter-University Laboratory of Human Movement Biology, University of Lyon, University Claude Bernard Lyon 1, Villeurbanne, France
| | - Karim Jerbi
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada.,MEG Unit, University of Montreal, Montreal, QC, Canada
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13
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Vogel JW, Vachon-Presseau E, Pichet Binette A, Tam A, Orban P, La Joie R, Savard M, Picard C, Poirier J, Bellec P, Breitner JCS, Villeneuve S. Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease. Brain 2018; 141:1871-1883. [PMID: 29688388 PMCID: PMC5972641 DOI: 10.1093/brain/awy093] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2017] [Revised: 02/20/2018] [Accepted: 02/25/2018] [Indexed: 11/16/2022] Open
Abstract
See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | | | - Alexa Pichet Binette
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Angela Tam
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
| | - Pierre Orban
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Department of Psychiatry, University of Montreal, Montreal, Quebec, Canada
| | - Renaud La Joie
- Memory and Aging Center, University of California, San Francisco, California, USA
| | - Mélissa Savard
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Cynthia Picard
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Judes Poirier
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- McGill University and Genome Quebec Innovation Centre, Quebec, Canada
| | - Pierre Bellec
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Department of Computer Science and Operations Research, University of Montreal, Montreal, QC, Canada
| | - John C S Breitner
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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