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Lahnakoski JM, Nolte T, Solway A, Vilares I, Hula A, Feigenbaum J, Lohrenz T, King-Casas B, Fonagy P, Montague PR, Schilbach L. A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity. J Affect Disord 2024; 360:345-353. [PMID: 38806064 DOI: 10.1016/j.jad.2024.05.125] [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] [Received: 01/22/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. AIMS Evaluate discriminative performance and generalizability of functional connectivity markers for BPD. METHOD Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity-and evaluated the generalizability of the classification in the left-out portion of non-matched data. RESULTS Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61-70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70-75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. LIMITATIONS The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. CONCLUSIONS Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.
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Affiliation(s)
- Juha M Lahnakoski
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225 Düsseldorf, Germany.
| | - Tobias Nolte
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Anna Freud National Centre for Children and Families, London, United Kingdom
| | - Alec Solway
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Iris Vilares
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Andreas Hula
- Austrian Institute of Technology, Vienna, Austria
| | - Janet Feigenbaum
- Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
| | - Terry Lohrenz
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Brooks King-Casas
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Psychology, Virginia Tech, Blacksburg, VA, USA
| | - Peter Fonagy
- Anna Freud National Centre for Children and Families, London, United Kingdom; Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
| | - P Read Montague
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Physics, Virginia Tech, Blacksburg, VA, USA; Department of Psychiatry and Behavioral Medicine, Virginia Tech Carilion School of Medicine, Virginia Tech, Roanoke, VA, USA
| | - Leonhard Schilbach
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany; Department of Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
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Briley PM, Webster L, Boutry C, Oh H, Auer DP, Liddle PF, Morriss R. Magnetic resonance imaging connectivity features associated with response to transcranial magnetic stimulation in major depressive disorder. Psychiatry Res Neuroimaging 2024; 342:111846. [PMID: 38908353 DOI: 10.1016/j.pscychresns.2024.111846] [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] [Received: 07/24/2023] [Revised: 03/23/2024] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Abstract
Transcranial magnetic stimulation (TMS) is an FDA-approved neuromodulation treatment for major depressive disorder (MDD), thought to work by altering dysfunctional brain connectivity pathways, or by indirectly modulating the activity of subcortical brain regions. Clinical response to TMS remains highly variable, highlighting the need for baseline predictors of response and for understanding brain changes associated with response. This systematic review examined brain connectivity features, and changes in connectivity features, associated with clinical improvement following TMS in MDD. Forty-one studies met inclusion criteria, including 1097 people with MDD. Most studies delivered one of two types of TMS to left dorsolateral prefrontal cortex and measured connectivity using resting-state functional MRI. The subgenual anterior cingulate cortex was the most well-studied brain region, particularly its connectivity with the TMS target or with the "executive control network" of brain regions. There was marked heterogeneity in findings. There is a need for greater understanding of how cortical TMS modulates connectivity with, and the activity of, subcortical regions, and how these effects change within and across treatment sessions.
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Affiliation(s)
- P M Briley
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom.
| | - L Webster
- Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom
| | - C Boutry
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom; NIHR Applied Research Collaboration East Midlands, University of Nottingham, Nottingham, United Kingdom
| | - H Oh
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - D P Auer
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - P F Liddle
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom
| | - R Morriss
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom; NIHR Applied Research Collaboration East Midlands, University of Nottingham, Nottingham, United Kingdom; NIHR Mental Health (MindTech) Health Technology Collaboration, University of Nottingham, Nottingham, United Kingdom
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard J, Carhart-Harris RL, Williams GB, Craig MM, Finoia P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. A synergistic workspace for human consciousness revealed by Integrated Information Decomposition. eLife 2024; 12:RP88173. [PMID: 39022924 PMCID: PMC11257694 DOI: 10.7554/elife.88173] [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: 07/20/2024] Open
Abstract
How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Pedro AM Mediano
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Center for Complexity Science, Imperial College LondonLondonUnited Kingdom
- Data Science Institute, Imperial College LondonLondonUnited Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - John Pickard
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Psychedelics Division - Neuroscape, Department of Neurology, University of CaliforniaSan FranciscoUnited States
| | - Guy B Williams
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Michael M Craig
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Paola Finoia
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
| | - Adrian M Owen
- Department of Psychology and Department of Physiology and Pharmacology, The Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity CollegeDublinIreland
| | - David K Menon
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Daniel Bor
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Emmanuel A Stamatakis
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
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Klug S, Murgaš M, Godbersen GM, Hacker M, Lanzenberger R, Hahn A. Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain. Neuroimage 2024; 295:120658. [PMID: 38810891 DOI: 10.1016/j.neuroimage.2024.120658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/22/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.
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Affiliation(s)
- Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria.
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5
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Jin L, Lu P, Kang J, Liu F, Liu X, Song Y, Wu W, Cai K, Ru S, Cao J, Zuo Z, Gui S. Abnormal hypothalamic functional connectivity associated with cognitive impairment in craniopharyngiomas. Cortex 2024; 178:190-200. [PMID: 39018955 DOI: 10.1016/j.cortex.2024.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 03/28/2024] [Accepted: 06/10/2024] [Indexed: 07/19/2024]
Abstract
OBJECTIVE This study sought to characterize resting-state functional connectivity (rsFC) patterns of the hypothalamic and extrahypothalamic nuclei in craniopharyngioma (CP) patients, and to investigate potential correlations between hypothalamic and extrahypothalamic rsFC maps and neurocognitive performance. METHODS Ninety-two CP patients and 40 demographically-matched healthy controls were included. Whole-brain seed-to-voxel analyses were used to test for between-group rsFC differences, and regression analyses were used to correlate neurocognitive performance with voxel-wise hypothalamic and extrahypothalamic rsFC maps for CP patients. Finally, spectral DCM analysis was used to explore the hypothalamus circuit associated with neurocognitive performance. RESULTS The seed-to-voxel analyses demonstrated that the hypothalamic nuclei showed mainly significant rsFC reduction in brain areas overlayed with the cortical regions of default mode network (DMN), notably in the bilateral anterior cingulate cortices and posterior cingulate cortices. The extrahypothalamic nuclei showed significant rsFC reduction in the limbic system of bilateral caudate nuclei, corpus callosum, fornix, and thalamus. Regression analyses revealed that worse cognitive performance was correlated with abnormal hypothalamic rsFC with brain areas in DMN, and DCM analysis revealed a hypothalamus-DMN circuit responsible for functional modulation of cognitive impairment in CP patients. CONCLUSIONS Our study demonstrated that CPs invading into hypothalamus impacted hypothalamic and extrahypothalamic rsFC with brain areas of DMN and limbic system, the severity of which was parallel with the grading system of hypothalamus involvement. In addition to the CP-induced structural damage to the hypothalamus alone, abnormal functional connectivity within the hypothalamus-DMN circuit might be a functional mechanism leading to the cognitive impairment in CP patients.
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Affiliation(s)
- Lu Jin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China
| | - Pengwei Lu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China
| | - Jie Kang
- Department of Otolaryngology, Head and Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, PR China
| | - Fangzheng Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China
| | - Xin Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China
| | - Yifan Song
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China
| | - Wentao Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China
| | - Kefan Cai
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China
| | - Siming Ru
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China
| | - Jingtao Cao
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, PR China
| | - Zentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, PR China.
| | - Songbai Gui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China.
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Northoff G, Hirjak D. Is depression a global brain disorder with topographic dynamic reorganization? Transl Psychiatry 2024; 14:278. [PMID: 38969642 PMCID: PMC11226458 DOI: 10.1038/s41398-024-02995-9] [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: 02/16/2024] [Revised: 06/20/2024] [Accepted: 06/27/2024] [Indexed: 07/07/2024] Open
Abstract
Major depressive disorder (MDD) is characterized by a multitude of psychopathological symptoms including affective, cognitive, perceptual, sensorimotor, and social. The neuronal mechanisms underlying such co-occurrence of psychopathological symptoms remain yet unclear. Rather than linking and localizing single psychopathological symptoms to specific regions or networks, this perspective proposes a more global and dynamic topographic approach. We first review recent findings on global brain activity changes during both rest and task states in MDD showing topographic reorganization with a shift from unimodal to transmodal regions. Next, we single out two candidate mechanisms that may underlie and mediate such abnormal uni-/transmodal topography, namely dynamic shifts from shorter to longer timescales and abnormalities in the excitation-inhibition balance. Finally, we show how such topographic shift from unimodal to transmodal regions relates to the various psychopathological symptoms in MDD including their co-occurrence. This amounts to what we describe as 'Topographic dynamic reorganization' which extends our earlier 'Resting state hypothesis of depression' and complements other models of MDD.
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Affiliation(s)
- Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- German Centre for Mental Health (DZPG), Partner Site Mannheim, Mannheim, Germany.
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7
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Sun S, Yan C, Qu S, Luo G, Liu X, Tian F, Dong Q, Li X, Hu B. Resting-state dynamic functional connectivity in major depressive disorder: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2024:111076. [PMID: 38972502 DOI: 10.1016/j.pnpbp.2024.111076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/02/2024] [Accepted: 06/26/2024] [Indexed: 07/09/2024]
Abstract
As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.
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Affiliation(s)
- Shuting Sun
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Chang Yan
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Shanshan Qu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Gang Luo
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xuesong Liu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Fuze Tian
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Qunxi Dong
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Bin Hu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
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8
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Wu X, Xu K, Li T, Wang L, Fu Y, Ma Z, Wu X, Wang Y, Chen F, Song J, Song Y, Lv Y. Abnormal intrinsic functional hubs and connectivity in patients with post-stroke depression. Ann Clin Transl Neurol 2024; 11:1852-1867. [PMID: 38775214 PMCID: PMC11251479 DOI: 10.1002/acn3.52091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 04/08/2024] [Accepted: 05/06/2024] [Indexed: 07/17/2024] Open
Abstract
OBJECTIVE The present study aimed to investigate the specific alterations of brain networks in patients with post-stroke depression (PSD), and further assist in elucidating the brain mechanisms underlying the PSD which would provide supporting evidence for early diagnosis and interventions for the disease. METHODS Resting-state functional magnetic resonace imaging data were acquired from 82 nondepressed stroke patients (Stroke), 39 PSD patients, and 74 healthy controls (HC). Voxel-wise degree centrality (DC) conjoined with seed-based functional connectivity (FC) analyses were performed to investigate the PSD-related connectivity alterations. The relationship between these alterations and depression severity was further examined in PSD patients. RESULTS Relative to both Stroke and HC groups, (1) PSD showed increased centrality in regions within the default mode network (DMN), including contralesional angular gyrus (ANG), posterior cingulate cortex (PCC), and hippocampus (HIP). DC values in contralesional ANG positively correlated with the Patient Health Questionnaire-9 (PHQ-9) scores in PSD group. (2) PSD exhibited increased connectivity between these three seeds showing altered DC and regions within the DMN: bilateral medial prefrontal cortex and middle temporal gyrus and ipsilesional superior parietal gyrus, and regions outside the DMN: bilateral calcarine, ipsilesional inferior occipital gyrus and contralesional lingual gyrus, while decreased connectivity between contralesional ANG and contralesional supramarginal gyrus. Moreover, these FC alterations could predict PHQ-9 scores in PSD group. INTERPRETATION These findings highlight that PSD was related with increased functional connectivity strength in some areas within the DMN, which might be attribute to the specific alterations of connectivity between within DMN and outside DMN regions in PSD.
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Affiliation(s)
- Xiumei Wu
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiangChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiangChina
| | - Kang Xu
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiangChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiangChina
| | - Tongyue Li
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiangChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiangChina
| | - Luoyu Wang
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
| | - Yanhui Fu
- Department of NeurologyAnshan Changda HospitalAnshanLiaoningChina
| | - Zhenqiang Ma
- Department of NeurologyAnshan Changda HospitalAnshanLiaoningChina
| | - Xiaoyan Wu
- Department of ImageAnshan Changda HospitalAnshanLiaoningChina
| | - Yiying Wang
- Department of UltrasonicsAnshan Changda HospitalAnshanLiaoningChina
| | - Fenyang Chen
- The Fourth Clinical Medical CollegeZhejiang Chinese Medical UniversityHangzhouZhejiangChina
| | - Jinyi Song
- III Department of Clinic MedicineZhejiang UniversityHangzhouZhejiangChina
| | - Yulin Song
- Department of NeurologyAnshan Changda HospitalAnshanLiaoningChina
| | - Yating Lv
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiangChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiangChina
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9
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Mortaheb S, Fort LD, Mason NL, Mallaroni P, Ramaekers JG, Demertzi A. Dynamic Functional Hyperconnectivity After Psilocybin Intake Is Primarily Associated With Oceanic Boundlessness. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:681-692. [PMID: 38588855 DOI: 10.1016/j.bpsc.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Psilocybin is a widely studied psychedelic substance that leads to the psychedelic state, a specific altered state of consciousness. To date, the relationship between the psychedelic state's neurobiological and experiential patterns remains undercharacterized because they are often analyzed separately. We investigated the relationship between neurobiological and experiential patterns after psilocybin by focusing on the link between dynamic cerebral connectivity and retrospective questionnaire assessment. METHODS Healthy participants were randomized to receive either psilocybin (n = 22) or placebo (n = 27) and scanned for 6 minutes in an eyes-open resting state during the peak subjective drug effect (102 minutes posttreatment) in ultrahigh field 7T magnetic resonance imaging. The 5-Dimensional Altered States of Consciousness Rating Scale was administered 360 minutes after drug intake. RESULTS Under psilocybin, there were alterations across all dimensions of the 5-Dimensional Altered States of Consciousness Rating Scale and widespread increases in averaged brain functional connectivity. Time-varying functional connectivity analysis unveiled a recurrent hyperconnected pattern characterized by low blood oxygen level-dependent signal amplitude, suggesting heightened cortical arousal. In terms of neuroexperiential links, canonical correlation analysis showed higher transition probabilities to the hyperconnected pattern with feelings of oceanic boundlessness and secondly with visionary restructuralization. CONCLUSIONS Psilocybin generates profound alterations at both the brain and the experiential levels. We suggest that the brain's tendency to enter a hyperconnected-hyperarousal pattern under psilocybin represents the potential to entertain variant mental associations. These findings illuminate the intricate interplay between brain dynamics and subjective experience under psilocybin, thereby providing insights into the neurophysiology and neuroexperiential qualities of the psychedelic state.
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Affiliation(s)
- Sepehr Mortaheb
- Physiology of Cognition, GIGA Research, CRC Human Imaging Unit, University of Liège, Liège, Belgium; Fund for Scientific Research FNRS, Brussels, Belgium
| | - Larry D Fort
- Physiology of Cognition, GIGA Research, CRC Human Imaging Unit, University of Liège, Liège, Belgium; Fund for Scientific Research FNRS, Brussels, Belgium
| | - Natasha L Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Johannes G Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | - Athena Demertzi
- Physiology of Cognition, GIGA Research, CRC Human Imaging Unit, University of Liège, Liège, Belgium; Fund for Scientific Research FNRS, Brussels, Belgium; Psychology & Neuroscience of Cognition, University of Liège, Liège, Belgium.
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10
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Yamaya N, Hashimoto T, Ikeda S, Brilliant T D, Tsujimoto M, Nakagawa S, Kawashima R. Preventive effect of one-session brief focused attention meditation on state fatigue: Resting state functional magnetic resonance imaging study. Neuroimage 2024; 297:120709. [PMID: 38936650 DOI: 10.1016/j.neuroimage.2024.120709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/16/2024] [Accepted: 06/24/2024] [Indexed: 06/29/2024] Open
Abstract
INTRODUCTION The extended practice of meditation may reduce the influence of state fatigue by changing neurocognitive processing. However, little is known about the preventive effects of one-session brief focused attention meditation (FAM) on state fatigue in healthy participants or its potential neural mechanisms. This study examined the preventive effects of one-session brief FAM on state fatigue and its neural correlates using resting-state functional MRI (rsfMRI) measurements. METHODS We randomly divided 56 meditation-naïve participants into FAM and control groups. After the first rsfMRI scan, each group performed a 10-minute each condition while wearing a functional near-infrared spectroscopy (fNIRS) device for assessing brain activity. Subsequently, following a second rsfMRI scan, the participants completed a fatigue-inducing task (a Go/NoGo task) for 60 min. We evaluated the temporal changes in the Go/NoGo task performance of participants as an indicator of state fatigue. We then calculated changes in the resting-state functional connectivity (rsFC) of the rsfMRI from before to after each condition and compared them between groups. We also evaluated neural correlates between the changes in rsFC and state fatigue. RESULTS AND DISCUSSION The fNIRS measurements indicated differences in brain activity during each condition between the FAM and control groups, showing decreased medial prefrontal cortex activity and decreased functional connectivity between the medial prefrontal cortex and middle frontal gyrus. The control group exhibited a decrement in Go/NoGo task performance over time, whereas the FAM group did not. These results, thus, suggested that FAM could prevent state fatigue. Compared with the control group, the rsFC analysis revealed a significant increase in the connectivity between the left dorsomedial prefrontal cortex and right superior parietal lobule in the FAM group, suggesting a modification of attention regulation by cognitive effort. In the control group, increased connectivity was observed between the bilateral posterior cingulate cortex and left inferior occipital gyrus, which might be associated with poor attention regulation and reduced higher-order cognitive function. Additionally, the change in the rsFC of the control group was related to state fatigue. CONCLUSION Our findings suggested that one session of 10-minute FAM could prevent behavioral state fatigue by employing cognitive effort to modify attention regulation as well as suppressing poor attention regulation and reduced higher-order cognitive function.
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Affiliation(s)
- Noriki Yamaya
- Graduate School of Medicine, Tohoku University, 2-1 Seiryomachi, Aobaku, Sendai 9808575, Japan; Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryomachi, Aobaku, Sendai 9808575, Japan.
| | - Teruo Hashimoto
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryomachi, Aobaku, Sendai 9808575, Japan
| | - Shigeyuki Ikeda
- Faculty of Engineering, University of Toyama, Gofuku 3190, Toyama-shi, Toyama 9308555, Japan
| | - Denilson Brilliant T
- Graduate School of Medicine, Tohoku University, 2-1 Seiryomachi, Aobaku, Sendai 9808575, Japan; Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryomachi, Aobaku, Sendai 9808575, Japan
| | - Masayuki Tsujimoto
- Graduate School of Medicine, Tohoku University, 2-1 Seiryomachi, Aobaku, Sendai 9808575, Japan; Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryomachi, Aobaku, Sendai 9808575, Japan
| | - Seishu Nakagawa
- Division of Psychiatry, Tohoku Medical and Pharmaceutical University, 1-15-1 Fukumuro, Miyaginoku, Sendai, Miyagi 983-8536, Japan; Department of Human Brain Science, Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryomachi, Aobaku, Sendai 9808575, Japan
| | - Ryuta Kawashima
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryomachi, Aobaku, Sendai 9808575, Japan
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11
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Chopra S, Cocuzza CV, Lawhead C, Ricard JA, Labache L, Patrick LM, Kumar P, Rubenstein A, Moses J, Chen L, Blankenbaker C, Gillis B, Germine LT, Harpaz-Rote I, Yeo BTT, Baker JT, Holmes AJ. The Transdiagnostic Connectome Project: a richly phenotyped open dataset for advancing the study of brain-behavior relationships in psychiatry. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.18.24309054. [PMID: 38946958 PMCID: PMC11213088 DOI: 10.1101/2024.06.18.24309054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs. Additionally, participants completed over 50 psychological and cognitive assessments. Here, we detail available behavioral data as well as raw and processed MRI derivatives. Associations between data processing and quality metrics, such as head motion, are reported. Processed data exhibit classic task activation effects and canonical functional network organization. Overall, we provide a comprehensive and analysis-ready transdiagnostic dataset, which we hope will accelerate the identification of illness-relevant features of brain functioning, enabling future discoveries in basic and clinical neuroscience.
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Affiliation(s)
- Sidhant Chopra
- 1. Department of Psychology, Yale University, New Haven, CT, USA
- 2. Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
- 3. Orygen, Center for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Carrisa V. Cocuzza
- 1. Department of Psychology, Yale University, New Haven, CT, USA
- 2. Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
| | - Connor Lawhead
- 1. Department of Psychology, Yale University, New Haven, CT, USA
- 4. Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Jocelyn A. Ricard
- 1. Department of Psychology, Yale University, New Haven, CT, USA
- 5. Stanford Neurosciences Interdepartmental Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Loïc Labache
- 1. Department of Psychology, Yale University, New Haven, CT, USA
- 2. Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
| | - Lauren M. Patrick
- 1. Department of Psychology, Yale University, New Haven, CT, USA
- 6. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
- 7. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Poornima Kumar
- 8. Department of Psychiatry, Harvard Medical School, Boston, USA
- 9. Centre for Depression, Anxiety and Stress Research, McLean Hospital, Boston, USA
| | | | - Julia Moses
- 1. Department of Psychology, Yale University, New Haven, CT, USA
| | - Lia Chen
- 10. Department of Psychology, Cornell University, Ithaca, NY, USA
| | | | - Bryce Gillis
- 11. Institute for Technology in Psychiatry, McLean Hospital, Boston, USA
- 12. Department of Psychiatry, Harvard Medical School, Boston, USA
| | - Laura T. Germine
- 11. Institute for Technology in Psychiatry, McLean Hospital, Boston, USA
- 12. Department of Psychiatry, Harvard Medical School, Boston, USA
| | - Ilan Harpaz-Rote
- 1. Department of Psychology, Yale University, New Haven, CT, USA
- 13. Department of Psychiatry, Yale University, New Haven, USA
- 14. Wu Tsai Institute, Yale University, New Haven, USA
| | - BT Thomas Yeo
- 15. Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- 16. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- 17. N.1 Institute for Health National University of Singapore, Singapore, Singapore
- 18. Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- 19. Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- 20. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, USA
| | - Justin T. Baker
- 11. Institute for Technology in Psychiatry, McLean Hospital, Boston, USA
- 12. Department of Psychiatry, Harvard Medical School, Boston, USA
| | - Avram J. Holmes
- 1. Department of Psychology, Yale University, New Haven, CT, USA
- 2. Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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12
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Korponay C, Janes AC, Frederick BB. Brain-wide functional connectivity artifactually inflates throughout functional magnetic resonance imaging scans. Nat Hum Behav 2024:10.1038/s41562-024-01908-6. [PMID: 38898230 DOI: 10.1038/s41562-024-01908-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 05/03/2024] [Indexed: 06/21/2024]
Abstract
Functional magnetic resonance imaging (fMRI) is a central tool for investigating human brain function, organization and disease. Here, we show that fMRI-based estimates of functional brain connectivity artifactually inflate at spatially heterogeneous rates during resting-state and task-based scans. This produces false positive connection strength changes and spatial distortion of brain connectivity maps. We demonstrate that this artefact is driven by temporal inflation of the non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning and is not addressed by standard denoising procedures. We provide evidence that sLFO inflation reflects perturbations in cerebral blood flow by respiration and heart rate changes that accompany diminishing arousal during scanning, although the mechanisms of this pathway are uncertain. Finally, we show that adding a specialized sLFO denoising procedure to fMRI processing pipelines mitigates the artifactual inflation of functional connectivity, enhancing the validity and within-scan reliability of fMRI findings.
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Affiliation(s)
- Cole Korponay
- Department of Psychiatry, Harvard University Medical School, Boston, MA, USA.
- McLean Hospital Brain Imaging Center, Belmont, MA, USA.
| | - Amy C Janes
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Blaise B Frederick
- Department of Psychiatry, Harvard University Medical School, Boston, MA, USA
- McLean Hospital Brain Imaging Center, Belmont, MA, USA
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13
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Zhang X, Xu R, Ma H, Qian Y, Zhu J. Brain Structural and Functional Damage Network Localization of Suicide. Biol Psychiatry 2024; 95:1091-1099. [PMID: 38215816 DOI: 10.1016/j.biopsych.2024.01.003] [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] [Received: 11/04/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Extensive neuroimaging research on brain structural and functional correlates of suicide has produced inconsistent results. Despite increasing recognition that damage in multiple different brain locations that causes the same symptom can map to a common brain network, there is still a paucity of research investigating network localization of suicide. METHODS To clarify this issue, we initially identified brain structural and functional damage locations in relation to suicide from 63 published studies with 2135 suicidal and 2606 nonsuicidal individuals. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 suicide brain damage networks corresponding to different imaging modalities. RESULTS The suicide gray matter volume damage network comprised widely distributed brain areas primarily involving the dorsal default mode, basal ganglia, and anterior salience networks. The suicide task-induced activation damage network was similar to but less extensive than the gray matter volume damage network, predominantly implicating the same canonical networks. The suicide resting-state activity damage network manifested as a localized set of brain regions encompassing the orbitofrontal cortex and middle cingulate cortex. CONCLUSIONS Our findings not only may help reconcile prior heterogeneous neuroimaging results, but also may provide insights into the neurobiological mechanisms of suicide from a network perspective, which may ultimately inform more targeted and effective strategies to prevent suicide.
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Affiliation(s)
- Xiaohan Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Ruoxuan Xu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Haining Ma
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
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14
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Madden DJ, Merenstein JL, Mullin HA, Jain S, Rudolph MD, Cohen JR. Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance. Brain Struct Funct 2024:10.1007/s00429-024-02807-2. [PMID: 38856933 DOI: 10.1007/s00429-024-02807-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.
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Affiliation(s)
- David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA.
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, 27710, USA.
- Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708, USA.
| | - Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
| | - Hollie A Mullin
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
- Department of Psychology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Shivangi Jain
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
- AdventHealth Research Institute, Neuroscience Institute, Orlando, FL, 32804, USA
| | - Marc D Rudolph
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
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15
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Schaper FLWVJ, Morton-Dutton M, Pacheco-Barrios N, Turner JI, Drew W, Khosravani S, Joutsa J, Fox MD. Brain lesions causing parkinsonism versus seizures map to opposite brain networks. Brain Commun 2024; 6:fcae196. [PMID: 38915927 PMCID: PMC11195636 DOI: 10.1093/braincomms/fcae196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/03/2024] [Accepted: 06/04/2024] [Indexed: 06/26/2024] Open
Abstract
Recent epidemiological studies propose an association between parkinsonism and seizures, but the direction of this association is unclear. Focal brain lesions causing new-onset parkinsonism versus seizures may provide a unique perspective on the causal relationship between the two symptoms and involved brain networks. We studied lesions causing parkinsonism versus lesions causing seizures and used the human connectome to identify their connected brain networks. Brain networks for parkinsonism and seizures were compared using spatial correlations on a group and individual lesion level. Lesions not associated with either symptom were used as controls. Lesion locations from 29 patients with parkinsonism were connected to a brain network with the opposite spatial topography (spatial r = -0.85) compared to 347 patients with lesions causing seizures. A similar inverse relationship was found when comparing the connections that were most specific on a group level (spatial r = -0.51) and on an individual lesion level (average spatial r = -0.042; P < 0.001). The substantia nigra was found to be most positively correlated to the parkinsonism network but most negatively correlated to the seizure network (spatial r > 0.8). Brain lesions causing parkinsonism versus seizures map to opposite brain networks, providing neuroanatomical insight into conflicting epidemiological evidence.
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Affiliation(s)
- Frederic L W V J Schaper
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Mae Morton-Dutton
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Niels Pacheco-Barrios
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Joseph I Turner
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - William Drew
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sanaz Khosravani
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Juho Joutsa
- Turku Brain and Mind Center, Clinical Neurosciences, University of Turku, 20520 Turku, Finland
- Turku PET Centre, Neurocenter, Turku University Hospital, 20520 Turku, Finland
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
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16
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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17
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Khanra P, Nakuci J, Muldoon S, Watanabe T, Masuda N. Reliability of energy landscape analysis of resting-state functional MRI data. Eur J Neurosci 2024. [PMID: 38837814 DOI: 10.1111/ejn.16390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 04/05/2024] [Accepted: 04/25/2024] [Indexed: 06/07/2024]
Abstract
Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.
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Affiliation(s)
- Pitambar Khanra
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Sarah Muldoon
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo, Tokyo, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
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18
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Wang S, Li T, He H, Li Y. Dynamical changes of interaction across functional brain communities during propofol-induced sedation. Cereb Cortex 2024; 34:bhae263. [PMID: 38918077 DOI: 10.1093/cercor/bhae263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/28/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0 μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.
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Affiliation(s)
- Shengpei Wang
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
| | - Tianzuo Li
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, No. 10 Yangfangdian Tieyi Rd, Haidian District, Beijing 100038, PR China
| | - Huiguang He
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 1 Yanqihu East Road, Huairou District, Beijing 101408, PR China
| | - Yun Li
- Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, No. 119, South Fourth Ring West Road, Fengtai District, Beijing 100070, PR China
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19
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Li L, Li Y, Li Z, Huang G, Liang Z, Zhang L, Wan F, Shen M, Han X, Zhang Z. Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback. Cogn Neurodyn 2024; 18:847-862. [PMID: 38826665 PMCID: PMC11143167 DOI: 10.1007/s11571-023-09939-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/29/2022] [Accepted: 01/31/2023] [Indexed: 02/23/2023] Open
Abstract
EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional features. However, they only focused on regional brain structure or function and overlooked possible neural correlates of the brain network. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. We designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data from healthy participants before training. We assessed the learning performance for evoked EEG modulations during training (L1) and at rest (L2), and investigated performance-related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. The main findings of this study are described below. First, both real and sham groups could increase their FAA during training, but only the real group showed a significant increase in FAA at rest. Second, the predictors during training blocks and at rests were different: L1 was correlated with the graph-theoretical metrics (clustering coefficient and local efficiency) of the right hemispheric gray matter and functional networks, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left the hemispheric functional network. Therefore, the individual differences in FAA neurofeedback learning could be explained by individual variations in structural/functional architecture, and the correlated graph-theoretical metrics of learning performance indices showed different laterality of hemispheric networks. These results provided insight into the neural correlates of inter-individual differences in neurofeedback learning. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09939-x.
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Affiliation(s)
- Linling Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Yutong Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Zhaoxun Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Gan Huang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Zhen Liang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Li Zhang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Feng Wan
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Manjun Shen
- Department of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen 518060, China
| | - Xue Han
- Department of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen 518060, China
| | - Zhiguo Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518060, China
- Peng Cheng Laboratory, Shenzhen 518060, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen 518060, China
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20
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Zhang S, Jung K, Langner R, Florin E, Eickhoff SB, Popovych OV. Impact of data processing varieties on DCM estimates of effective connectivity from task-fMRI. Hum Brain Mapp 2024; 45:e26751. [PMID: 38864293 PMCID: PMC11167406 DOI: 10.1002/hbm.26751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 01/05/2024] [Accepted: 05/22/2024] [Indexed: 06/13/2024] Open
Abstract
Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.
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Affiliation(s)
- Shufei Zhang
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Robert Langner
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Oleksandr V. Popovych
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
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21
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Smolders L, De Baene W, Rutten GJ, van der Hofstad R, Florack L. Can structure predict function at individual level in the human connectome? Brain Struct Funct 2024; 229:1209-1223. [PMID: 38656375 PMCID: PMC11147846 DOI: 10.1007/s00429-024-02796-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024]
Abstract
Several studies predicting Functional Connectivity (FC) from Structural Connectivity (SC) at individual level have been published in recent years, each promising increased performance and utility. We investigated three of these studies, analyzing whether the results truly represent a meaningful individual-level mapping from SC to FC. Using data from the Human Connectome Project shared accross the three studies, we constructed a predictor by averaging FC of training data and analyzed its performance in the same way. In each case, we found that group average FC is an equivalent or better predictor of individual FC than the predictive models in terms of raw prediction performance. Furthermore, we showed that additional analyses performed by the authors of the three studies, in which they attempt to show that their predicted FC has value beyond raw prediction performance, could also be reproduced using the group average FC predictor. This makes it unclear whether any of the three methods represent a meaningful individual-level predictive model. We conclude that either the methods are not appropriate for the data, that the sample size is too small, or that the data does not contain sufficient information to learn a mapping from SC to FC. We advise future individual-level studies to explicitly report results in comparison to the performance of the group average, and carefully demonstrate that their predictions contain meaningful individual-level information. Finally, we believe that investigating alternatives for the construction of SC and FC may improve the chances of developing a meaningful individual-level mapping from SC to FC.
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Affiliation(s)
- Lars Smolders
- Eindhoven University of Technology , Department of Mathematics and Computer Science, PO Box 513, Eindhoven, 5600 MB, Netherlands.
- Elisabeth-TweeSteden Hospital, Department of Neurosurgery, Hilvarenbeekseweg 60, Tilburg, 5022 GC, The Netherlands.
| | - Wouter De Baene
- Tilburg University, Department of Cognitive Neuropsychology, Warandelaan 2, Tilburg, 5000 LE, Netherlands
| | - Geert-Jan Rutten
- Elisabeth-TweeSteden Hospital, Department of Neurosurgery, Hilvarenbeekseweg 60, Tilburg, 5022 GC, The Netherlands
| | - Remco van der Hofstad
- Eindhoven University of Technology , Department of Mathematics and Computer Science, PO Box 513, Eindhoven, 5600 MB, Netherlands
| | - Luc Florack
- Eindhoven University of Technology , Department of Mathematics and Computer Science, PO Box 513, Eindhoven, 5600 MB, Netherlands
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22
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Anumba N, Kelberman MA, Pan W, Marriott A, Zhang X, Xu N, Weinshenker D, Keilholz S. The Effects of Locus Coeruleus Optogenetic Stimulation on Global Spatiotemporal Patterns in Rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595327. [PMID: 38826205 PMCID: PMC11142206 DOI: 10.1101/2024.05.23.595327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. These results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting.
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Affiliation(s)
- Nmachi Anumba
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Michael A Kelberman
- Department of Human Genetics, Emory University, Atlanta, GA, United States
- Molecular Cellular and Developmental Biology Department, University of Colorado Boulder, Boulder, CO, United States
| | - Wenju Pan
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Alexia Marriott
- Department of Human Genetics, Emory University, Atlanta, GA, United States
| | - Xiaodi Zhang
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Nan Xu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - David Weinshenker
- Department of Human Genetics, Emory University, Atlanta, GA, United States
| | - Shella Keilholz
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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23
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Stewart BW, Keaser ML, Lee H, Margerison SM, Cormie MA, Moayedi M, Lindquist MA, Chen S, Mathur BN, Seminowicz DA. Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain. Curr Biol 2024; 34:1953-1966.e6. [PMID: 38614082 DOI: 10.1016/j.cub.2024.03.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/08/2024] [Accepted: 03/13/2024] [Indexed: 04/15/2024]
Abstract
Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a region near the posterior inferior frontal sulcus of the right dorsolateral prefrontal cortex (piDLPFC) in migraine patients during acute pain and cognitive task performance. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this piDLPFC region, and diffusion weighted imaging verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.
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Affiliation(s)
- Brent W Stewart
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA
| | - Michael L Keaser
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA
| | - Hwiyoung Lee
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Wade Avenue, Catonsville, MD 21228, USA
| | - Sarah M Margerison
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA; Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Penn Street, Baltimore, MD 21201, USA
| | - Matthew A Cormie
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Edward Street, Toronto, ON M5G 1E2, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Edward Street, Toronto, ON M5G 1E2, Canada; Department of Dentistry, Mount Sinai Hospital, University Avenue, Toronto, ON M5G 1X5, Canada; Division of Clinical & Computational Neuroscience, Krembil Brain Institute, University Health Network, Nassau Street, Toronto, ON M5T 1M8, Canada
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, N Wolfe Street, Baltimore, MD 21205, USA
| | - Shuo Chen
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Wade Avenue, Catonsville, MD 21228, USA
| | - Brian N Mathur
- Department of Pharmacology, University of Maryland School of Medicine, W Baltimore Street, Baltimore, MD 21201, USA; Department of Psychiatry, University of Maryland School of Medicine, W Baltimore Street, Baltimore, MD 21201, USA.
| | - David A Seminowicz
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA; Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, Richmond Street, London, ON N6A 5C1, Canada.
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24
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Xu Y, Liao X, Lei T, Cao M, Zhao J, Zhang J, Zhao T, Li Q, Jeon T, Ouyang M, Chalak L, Rollins N, Huang H, He Y. Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2. Cereb Cortex 2024; 34:bhae204. [PMID: 38771241 DOI: 10.1093/cercor/bhae204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/23/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2 years of age. Our findings highlight network-level neural substrates underlying early cognitive development.
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Affiliation(s)
- Yuehua Xu
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Miao Cao
- Institution of Science and Technology for Brain-Inspired Intelligence, Fudan University, No. 220 Handan Road, Shanghai 200433, China
| | - Jianlong Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Chinese Institute for Brain Research, No. 26 Kexueyuan Road, Beijing 102206, China
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25
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Liang Q, Ma J, Chen X, Lin Q, Shu N, Dai Z, Lin Y. A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1895-1909. [PMID: 38194401 DOI: 10.1109/tmi.2024.3351907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.
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26
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Damiani S, La-Torraca-Vittori P, Tarchi L, Tosi E, Ricca V, Scalabrini A, Politi P, Fusar-Poli P. On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study. Neuroimage 2024; 291:120585. [PMID: 38527658 DOI: 10.1016/j.neuroimage.2024.120585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/10/2024] [Accepted: 03/22/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND The dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). METHODS As primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21-50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. RESULTS Sensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). CONCLUSION GSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.
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Affiliation(s)
- Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | | | - Livio Tarchi
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Eleonora Tosi
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Andrea Scalabrini
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy; Department of Psychosis Studies, King's College London, London, UK; Outreach and Support in South-London (OASIS) service, South London and Maudlsey (SLaM) NHS Foundation Trust, UK; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
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27
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Davies C, Martins D, Dipasquale O, McCutcheon RA, De Micheli A, Ramella-Cravaro V, Provenzani U, Rutigliano G, Cappucciati M, Oliver D, Williams S, Zelaya F, Allen P, Murguia S, Taylor D, Shergill S, Morrison P, McGuire P, Paloyelis Y, Fusar-Poli P. Connectome dysfunction in patients at clinical high risk for psychosis and modulation by oxytocin. Mol Psychiatry 2024; 29:1241-1252. [PMID: 38243074 PMCID: PMC11189815 DOI: 10.1038/s41380-024-02406-x] [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: 03/22/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/21/2024]
Abstract
Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all pFDR < 0.05) most localised to the frontoparietal network. Treatment effects were found in 7 regions (all pFDR < 0.05) predominantly within the ventral attention network. Our major finding was that many effects of oxytocin on network topology differ across CHR-P and healthy individuals, with significant interaction effects observed in numerous subcortical regions strongly implicated in psychosis onset, such as the thalamus, pallidum and nucleus accumbens, and cortical regions which localised primarily to the default mode network (12 regions, all pFDR < 0.05). Collectively, our findings provide new insights on aberrant functional brain network organisation associated with psychosis risk and demonstrate, for the first time, that oxytocin modulates network topology in brain regions implicated in the pathophysiology of psychosis in a clinical status (CHR-P vs healthy control) specific manner.
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Affiliation(s)
- Cathy Davies
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre (BRC), South London and Maudsley NHS Foundation Trust, London, UK
- Department of Psychiatry, University Hospitals of Genève, Geneva, Switzerland
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Andrea De Micheli
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Outreach And Support in South London (OASIS) Service, South London and Maudsley NHS Foundation Trust, London, UK
| | - Valentina Ramella-Cravaro
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Umberto Provenzani
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Grazia Rutigliano
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marco Cappucciati
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Dominic Oliver
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Steve Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Paul Allen
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Silvia Murguia
- Tower Hamlets Early Detection Service, East London NHS Foundation Trust, London, UK
| | - David Taylor
- Institute of Pharmaceutical Science, King's College London, London, UK
| | - Sukhi Shergill
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Kent and Medway Medical School, Canterbury, UK
| | - Paul Morrison
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK
- NIHR Oxford Health Biomedical Research Centre, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Yannis Paloyelis
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre (BRC), South London and Maudsley NHS Foundation Trust, London, UK
- Outreach And Support in South London (OASIS) Service, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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28
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Meyer-Baese L, Anumba N, Bolt T, Daley L, LaGrow TJ, Zhang X, Xu N, Pan WJ, Schumacher E, Keilholz S. Variation in the Distribution of Large-scale Spatiotemporal Patterns of Activity Across Brain States. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591295. [PMID: 38746246 PMCID: PMC11092498 DOI: 10.1101/2024.04.26.591295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.
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Affiliation(s)
- Lisa Meyer-Baese
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Nmachi Anumba
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - T Bolt
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - L Daley
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - T J LaGrow
- Electrical and Computer Engineering, Georgia Institute of Technology
| | - Xiaodi Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Wen-Ju Pan
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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29
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Watters H, Davis A, Fazili A, Daley L, LaGrow TJ, Schumacher EH, Keilholz S. Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590625. [PMID: 38712098 PMCID: PMC11071428 DOI: 10.1101/2024.04.22.590625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.
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Affiliation(s)
| | - Aleah Davis
- Agnes Scott College
- Georgia Institute of Technology School of Psychology
| | | | - Lauren Daley
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - TJ LaGrow
- Georgia Institute of Technology School of Electrical and Computer Engineering
| | | | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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30
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Tsang T, Green SA, Liu J, Lawrence K, Jeste S, Bookheimer SY, Dapretto M. Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism. Commun Biol 2024; 7:485. [PMID: 38649483 PMCID: PMC11035613 DOI: 10.1038/s42003-024-06016-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/06/2024] [Indexed: 04/25/2024] Open
Abstract
Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network - an early-emerging neural network involved in orienting attention to the most salient aspects of one's internal and external environment - may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.
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Affiliation(s)
| | - Shulamite A Green
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Katherine Lawrence
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shafali Jeste
- Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mirella Dapretto
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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31
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Jamison KW, Gu Z, Wang Q, Sabuncu MR, Kuceyeski A. Release the Krakencoder: A unified brain connectome translation and fusion tool. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589274. [PMID: 38659856 PMCID: PMC11042193 DOI: 10.1101/2024.04.12.589274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.
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Affiliation(s)
- Keith W Jamison
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Zijin Gu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Qinxin Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Mert R Sabuncu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Amy Kuceyeski
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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32
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Joliot M, Cremona S, Tzourio C, Etard O. Modulate the impact of the drowsiness on the resting state functional connectivity. Sci Rep 2024; 14:8652. [PMID: 38622265 PMCID: PMC11018752 DOI: 10.1038/s41598-024-59476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
This research explores different methodologies to modulate the effects of drowsiness on functional connectivity (FC) during resting-state functional magnetic resonance imaging (RS-fMRI). The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy, alert, and mixed/undetermined states based on observed respiratory oscillations. We analyzed the FC group difference between drowsy and alert individuals after five different processing methods: the reference method, two based on physiological and a global signal regression of the BOLD time series signal, and two based on Gaussian standardizations of the FC distribution. According to the reference method, drowsy individuals exhibit higher cortico-cortical FC than alert individuals. First, we demonstrated that each method reduced the differences between drowsy and alert states. The second result is that the global signal regression was quantitively the most effective, minimizing significant FC differences to only 3.3% of the total FCs. However, one should consider the risks of overcorrection often associated with this methodology. Therefore, choosing a less aggressive form of regression, such as the physiological method or Gaussian-based approaches, might be a more cautious approach. Third and last, using the Gaussian-based methods, cortico-subcortical and intra-default mode network (DMN) FCs were significantly greater in alert than drowsy subjects. These findings bear resemblance to the anticipated patterns during the onset of sleep, where the cortex isolates itself to assist in transitioning into deeper slow wave sleep phases, simultaneously disconnecting the DMN.
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Affiliation(s)
- Marc Joliot
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
| | - Sandrine Cremona
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France
| | | | - Olivier Etard
- Normandie Université, UNICAEN, INSERM, COMETE U1075, CYCERON, CHU Caen, 14000, Caen, France
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33
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Ma L, Braun SE, Steinberg JL, Bjork JM, Martin CE, Keen Ii LD, Moeller FG. Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis. Neuroimage 2024; 292:120604. [PMID: 38604537 DOI: 10.1016/j.neuroimage.2024.120604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/31/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024] Open
Abstract
Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA.
| | | | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - James M Bjork
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - Caitlin E Martin
- Institute for Drug and Alcohol Studies, USA; Department of Obstetrics and Gynecology, USA
| | - Larry D Keen Ii
- Department of Psychology, Virginia State University, Petersburg, VA, USA
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA; Department of Neurology, USA; Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, USA
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Watters H, Fazili A, Daley L, Belden A, LaGrow TJ, Bolt T, Loui P, Keilholz S. Creative tempo: Spatiotemporal dynamics of the default mode network in improvisational musicians. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588391. [PMID: 38645080 PMCID: PMC11030431 DOI: 10.1101/2024.04.07.588391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The intrinsic dynamics of human brain activity display a recurring pattern of anti-correlated activity between the default mode network (DMN), associated with internal processing and mentation, and task positive regions, associated with externally directed attention. In human functional magnetic resonance imaging (fMRI) data, this anti-correlated pattern is detectable on the infraslow timescale (<0.1 Hz) as a quasi-periodic pattern (QPP). While the DMN is implicated in creativity and musicality in traditional time-averaged functional connectivity studies, no one has yet explored how creative training may alter dynamic spatiotemporal patterns involving the DMN such as QPPs. In the present study, we compare the outputs of two QPP detection approaches, sliding window algorithm and complex principal components analysis (cPCA). We apply both methods to an existing dataset of musicians captured with resting state fMRI, grouped as either classical, improvisational, or minimally trained non-musicians. The original time-averaged functional connectivity (FC) analysis of this dataset used improvisation as a proxy for creative thinking and found that the DMN and visual networks (VIS) display higher connectivity in improvisational musicians. We expand upon this dataset's original study and find that QPP analysis detects convergent results at the group level with both methods. In improvisational musicians, dynamic functional correlation in the group-averaged QPP was found to be increased between the DMN-VIS and DMN-FPN for both the QPP algorithm and complex principal components analysis (cPCA) methods. Additionally, we found an unexpected increase in FC in the group-averaged QPP between the dorsal attention network and amygdala in improvisational musicians; this result was not reported in the original seed-based study of this dataset. The current study represents a novel application of two dynamic FC detection methods with results that replicate and expand upon previous seed-based FC findings. The results show the robustness of both the QPP phenomenon and its detection methods. This study also demonstrates the value of dynamic FC methods in reproducing seed-based findings and their promise in detecting group-wise or individual differences that may be missed by traditional seed-based resting state fMRI studies.
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Affiliation(s)
| | | | - Lauren Daley
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - T J LaGrow
- Georgia Institute of Technology School of Electrical and Computer Engineering
| | - Taylor Bolt
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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Perrault AA, Kebets V, Kuek NMY, Cross NE, Tesfaye R, Pomares FB, Li J, Chee MW, Dang-Vu TT, Yeo BT. A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures. RESEARCH SQUARE 2024:rs.3.rs-4078779. [PMID: 38659875 PMCID: PMC11042395 DOI: 10.21203/rs.3.rs-4078779/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.
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Affiliation(s)
- Aurore A. Perrault
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ilede-Montréal, QC, Canada
- Sleep & Circadian Research Group, Woolcock Institute of Medical Research, Macquarie University, Sydney, NSW, Australia
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- McGill University, Montreal, QC, Canada
| | - Nicole M. Y. Kuek
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Nathan E. Cross
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ilede-Montréal, QC, Canada
- School of Psychology, University of Sydney, NSW, Australia
| | | | - Florence B. Pomares
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ilede-Montréal, QC, Canada
| | - Jingwei Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Michael W.L. Chee
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Thien Thanh Dang-Vu
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ilede-Montréal, QC, Canada
| | - B.T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachussetts General Hospital, Charlestown, MA, USA
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Shi L, Fu X, Gui S, Wan T, Zhuo J, Lu J, Li P. Global spatiotemporal synchronizing structures of spontaneous neural activities in different cell types. Nat Commun 2024; 15:2884. [PMID: 38570488 PMCID: PMC10991327 DOI: 10.1038/s41467-024-46975-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Increasing evidence has revealed the large-scale nonstationary synchronizations as traveling waves in spontaneous neural activity. However, the interplay of various cell types in fine-tuning these spatiotemporal patters remains unclear. Here, we performed comprehensive exploration of spatiotemporal synchronizing structures across different cell types, states (awake, anesthesia, motion) and developmental axis in male mice. We found traveling waves in glutamatergic neurons exhibited greater variety than those in GABAergic neurons. Moreover, the synchronizing structures of GABAergic neurons converged toward those of glutamatergic neurons during development, but the evolution of waves exhibited varying timelines for different sub-type interneurons. Functional connectivity arises from both standing and traveling waves, and negative connections can be elucidated by the spatial propagation of waves. In addition, some traveling waves were correlated with the spatial distribution of gene expression. Our findings offer further insights into the neural underpinnings of traveling waves, functional connectivity, and resting-state networks, with cell-type specificity and developmental perspectives.
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Affiliation(s)
- Liang Shi
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China
| | - Xiaoxi Fu
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China
| | - Shen Gui
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China
| | - Tong Wan
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572025, China
| | - Junjie Zhuo
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572025, China
| | - Jinling Lu
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China.
| | - Pengcheng Li
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China.
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572025, China.
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Chen S, Zhang X, Lin S, Zhang Y, Xu Z, Li Y, Xu M, Hou G, Qiu Y. Connectome architecture modulates the gray matter atrophy in major depression disorder patients with diverse suicidal ideations. Brain Imaging Behav 2024; 18:378-386. [PMID: 38147272 DOI: 10.1007/s11682-023-00826-x] [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] [Accepted: 11/19/2023] [Indexed: 12/27/2023]
Abstract
Gray matter (GM) atrophy is well documented in patients with major depressive disorder (MDD), but its underlying mechanism remains unknown. This study aimed to examine the GM atrophy in MDD patients with diverse suicidal ideations (SIs) and to explore whether those alterations were driven by connections. GM volume was estimated in 163 patients with recurrent MDD (comprising 122 with SI [MDDSI] and 41 without SI [MDDNSI]) and 134 health controls (HCs). A two-sample t-test was used to identify GM volume abnormalities in MDD patients and their subgroups. Functional connectivity was computed between pairs of aberrant GM in both patients and HCs, which were further compared with the connectivity of random brain regions. A permutation test was performed to assess its significance. Propensity score matching (PSM) was further performed to validate the main results. Compared with HCs, the MDDNSI group exhibited GM atrophy in 24 regions, with the largest effect sizes found in the frontal and parietal lobes, while the MDDSI group exhibited more widespread GM atrophy involving 49 regions, with the largest effect sizes in the frontal lobe, parietal lobe, temporal lobe, and the limbic system. Furthermore, patients and HCs exhibited significantly increased functional connectivity between regions with GM atrophy compared with randomly selected regions (p < 0.05). PSM analysis presented similar results to the main analysis. MDD patients had diverse GM atrophy features according to their SI tendency. Moreover, connectome architecture modulates the GM atrophy in MDD patients, implying the possibility that connections drive these pathological changes.
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Affiliation(s)
- Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Xiaojing Zhang
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, 518060, People's Republic of China
| | - Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Yingli Zhang
- Department of Depressive Disorders, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu District, Shenzhen, 518020, People's Republic of China
| | - Ziyun Xu
- Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu District, Shenzhen, 518020, People's Republic of China
| | - Yanqing Li
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Manxi Xu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu District, Shenzhen, 518020, People's Republic of China.
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen, 518000, People's Republic of China.
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Wang X, Xia Y, Yan R, Sun H, Huang Y, Xia Q, Sheng J, You W, Hua L, Tang H, Yao Z, Lu Q. Sex differences in anhedonia in bipolar depression: a resting-state fMRI study. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01765-4. [PMID: 38558145 DOI: 10.1007/s00406-024-01765-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/13/2024] [Indexed: 04/04/2024]
Abstract
Previous studies about anhedonia symptoms in bipolar depression (BD) ignored the unique role of gender on brain function. This study aims to explore the regional brain neuroimaging features of BD with anhedonia and the sex differences in these patients. The resting-fMRI by applying fractional amplitude of low-frequency fluctuation (fALFF) method was estimated in 263 patients with BD (174 high anhedonia [HA], 89 low anhedonia [LA]) and 213 healthy controls. The effects of two different factors in patients with BD were analyzed using a 3 (group: HA, LA, HC) × 2 (sex: male, female) ANOVA. The fALFF values were higher in the HA group than in the LA group in the right medial cingulate gyrus and supplementary motor area. For the sex-by-group interaction, the fALFF values of the right hippocampus, left medial occipital gyrus, right insula, and bilateral medial cingulate gyrus were significantly higher in HA males than in LA males but not females. These results suggested that the pattern of high activation could be a marker of anhedonia symptoms in BD males, and the sex differences should be considered in future studies of BD with anhedonia symptoms.
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Affiliation(s)
- Xiaoqin Wang
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Yi Xia
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Rui Yan
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Hao Sun
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, 22 Hankou Road, Nanjing, 210093, China
| | - Yinghong Huang
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, 22 Hankou Road, Nanjing, 210093, China
| | - Qiudong Xia
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Junling Sheng
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Wei You
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Lingling Hua
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Hao Tang
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Zhijian Yao
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China.
- Nanjing Brain Hospital, Medical School of Nanjing University, 22 Hankou Road, Nanjing, 210093, China.
- School of Biological Sciences and Medical Engineering, Southeast University, 2 sipailou, Nanjing, 210096, China.
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, 2 sipailou, Nanjing, 210096, China.
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China.
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Leserri S, Segura-Amil A, Nowacki A, Debove I, Petermann K, Schäppi L, Preti MG, Van De Ville D, Pollo C, Walther S, Nguyen TAK. Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series. Eur Arch Psychiatry Clin Neurosci 2024; 274:685-696. [PMID: 37668723 PMCID: PMC10994999 DOI: 10.1007/s00406-023-01683-x] [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: 04/06/2023] [Accepted: 08/14/2023] [Indexed: 09/06/2023]
Abstract
Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities between the stimulated area and other brain regions, or to depression-associated networks. In this longitudinal, retrospective study, four female patients with treatment-resistant depression were implanted for stimulation in the nucleus accumbens area at our center. We analyzed the structural and functional connectivity of the stimulation area: the structural connectivity was investigated with probabilistic tractography; the functional connectivity was estimated by combining patient-specific stimulation volumes and a normative functional connectome. These structural and functional connectivity profiles were then related to four clinical outcome scores. At 1-year follow-up, the remission rate was 66%. We observed a consistent structural connectivity to Brodmann area 25 in the patient with the longest remission phase. The functional connectivity analysis resulted in patient-specific R-maps describing brain areas significantly correlated with symptom improvement in this patient, notably the prefrontal cortex. But the connectivity analysis was mixed across patients, calling for confirmation in a larger cohort and over longer time periods.
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Affiliation(s)
- Simona Leserri
- Department of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- ARTORG Center for Biomedical Engineering Research, University Bern, Bern, Switzerland
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alba Segura-Amil
- Department of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- ARTORG Center for Biomedical Engineering Research, University Bern, Bern, Switzerland
| | - Andreas Nowacki
- Department of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ines Debove
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Katrin Petermann
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Lea Schäppi
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Maria Giulia Preti
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology and Medical InformaticsFaculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology and Medical InformaticsFaculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - T A Khoa Nguyen
- Department of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- ARTORG Center for Biomedical Engineering Research, University Bern, Bern, Switzerland.
- ARTORG IGT, Murtenstrasse 50, 3008, Bern, Switzerland.
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Cheng X, Chen J, Zhang X, Wang T, Sun J, Zhou Y, Yang R, Xiao Y, Chen A, Song Z, Chen P, Yang C, QiuxiaWu, Lin T, Chen Y, Cao L, Wei X. Characterizing the temporal dynamics of intrinsic brain activities in depressed adolescents with prior suicide attempts. Eur Child Adolesc Psychiatry 2024; 33:1179-1191. [PMID: 37284850 PMCID: PMC11032277 DOI: 10.1007/s00787-023-02242-4] [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: 03/09/2023] [Accepted: 05/24/2023] [Indexed: 06/08/2023]
Abstract
Converging evidence has revealed disturbances in the corticostriatolimic system are associated with suicidal behaviors in adults with major depressive disorder. However, the neurobiological mechanism that confers suicidal vulnerability in depressed adolescents is largely unknown. A total of 86 depressed adolescents with and without prior suicide attempts (SA) and 47 healthy controls underwent resting-state functional imaging (R-fMRI) scans. The dynamic amplitude of low-frequency fluctuations (dALFF) was measured using sliding window approach. We identified SA-related alterations in dALFF variability primarily in the left middle temporal gyrus, inferior frontal gyrus, middle frontal gyrus (MFG), superior frontal gyrus (SFG), right SFG, supplementary motor area (SMA) and insula in depressed adolescents. Notably, dALFF variability in the left MFG and SMA was higher in depressed adolescents with recurrent suicide attempts than in those with a single suicide attempt. Moreover, dALFF variability was capable of generating better diagnostic and prediction models for suicidality than static ALFF. Our findings suggest that alterations in brain dynamics in regions involved in emotional processing, decision-making and response inhibition are associated with an increased risk of suicidal behaviors in depressed adolescents. Furthermore, dALFF variability could serve as a sensitive biomarker for revealing the neurobiological mechanisms underlying suicidal vulnerability.
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Affiliation(s)
- Xiaofang Cheng
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Jianshan Chen
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Xiaofei Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Ting Wang
- The Second Affiliated Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Yuexiu district, Guangzhou, 510180, Guangdong, People's Republic of China
| | - Jiaqi Sun
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Yanling Zhou
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Ruilan Yang
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Yeyu Xiao
- Guangzhou Integrated Traditional Chinese and Western Medicine, Guangzhou, 510800, Guangdong, People's Republic of China
| | - Amei Chen
- The Second Affiliated Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Yuexiu district, Guangzhou, 510180, Guangdong, People's Republic of China
| | - Ziyi Song
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Pinrui Chen
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Chanjuan Yang
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - QiuxiaWu
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Taifeng Lin
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Yingmei Chen
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China
| | - Liping Cao
- The Affiliated Brain Hospital of Guangzhou Medical University, 36 Mingxin Road, liwan district, Guangzhou, 510370, Guangdong, People's Republic of China.
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, 510370, Guangdong, People's Republic of China.
| | - Xinhua Wei
- The Second Affiliated Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Yuexiu district, Guangzhou, 510180, Guangdong, People's Republic of China.
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Kleckner IR, Manuweera T, Lin PJ, Chung KH, Kleckner AS, Gewandter JS, Culakova E, Tivarus ME, Dunne RF, Loh KP, Mohile NA, Kesler SR, Mustian KM. Pilot trial testing the effects of exercise on chemotherapy-induced peripheral neurotoxicity (CIPN) and the interoceptive brain system. RESEARCH SQUARE 2024:rs.3.rs-4022351. [PMID: 38559210 PMCID: PMC10980099 DOI: 10.21203/rs.3.rs-4022351/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Purpose Chemotherapy-induced peripheral neurotoxicity (CIPN) is a prevalent, dose-limiting, tough-to-treat toxicity involving numbness, tingling, and pain in the extremities with enigmatic pathophysiology. This randomized controlled pilot study explored the feasibility and preliminary efficacy of exercise during chemotherapy on CIPN and the role of the interoceptive brain system, which processes bodily sensations. Methods Nineteen patients (65±11 years old, 52% women; cancer type: breast, gastrointestinal, multiple myeloma) starting neurotoxic chemotherapy were randomized to 12 weeks of exercise (home-based, individually tailored, moderate intensity, progressive walking and resistance training) or active control (nutrition education). At pre-, mid-, and post-intervention, we assessed CIPN symptoms (primary clinical outcome: CIPN-20), CIPN signs (tactile sensitivity using monofilaments), and physical function (leg strength). At pre- and post-intervention, we used task-free ("resting") fMRI to assess functional connectivity in the interoceptive brain system, involving the salience and default mode networks. Results The study was feasible (74-89% complete data across measures) and acceptable (95% retention). We observed moderate/large beneficial effects of exercise on CIPN symptoms (CIPN-20, 0-100 scale: -7.9±5.7, effect size [ES]=-0.9 at mid-intervention; -4.8±7.3, -ES=0.5 at post-intervention), CIPN signs (ES=-1.0 and -0.1), and physical function (ES=0.4 and 0.3). Patients with worse CIPN after neurotoxic chemotherapy had lower functional connectivity within the default mode network (R2=40-60%) and higher functional connectivity within the salience network (R2=20-40%). Exercise tended to increase hypoconnectivity and decrease hyperconnectivity seen in CIPN (R2 = 12%). Conclusion Exercise during neurotoxic chemotherapy is feasible and may attenuate CIPN symptoms and signs, perhaps via changes in interoceptive brain circuitry. Future work should test for replication with larger samples. ClinicalTrials.gov identifier NCT03021174.
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Affiliation(s)
| | | | - Po-Ju Lin
- University of Rochester Medical Center
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Perrault AA, Kebets V, Kuek NMY, Cross NE, Tesfaye R, Pomares FB, Li J, Chee MW, Dang-Vu TT, Thomas Yeo B. A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580583. [PMID: 38559143 PMCID: PMC10979931 DOI: 10.1101/2024.02.15.580583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.
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Affiliation(s)
- Aurore A. Perrault
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
- Sleep & Circadian Research Group, Woolcock Institute of Medical Research, Macquarie University, Sydney, NSW, Australia
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- McGill University, Montreal, QC, Canada
| | - Nicole M. Y. Kuek
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Nathan E. Cross
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
- School of Psychology, University of Sydney, NSW, Australia
| | | | - Florence B. Pomares
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
| | - Jingwei Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Michael W.L. Chee
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Thien Thanh Dang-Vu
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
| | - B.T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachussetts General Hospital, Charlestown, MA, USA
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Islam S, Khanra P, Nakuci J, Muldoon SF, Watanabe T, Masuda N. State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis. BMC Neurosci 2024; 25:14. [PMID: 38438838 PMCID: PMC10913599 DOI: 10.1186/s12868-024-00854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
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Affiliation(s)
- Saiful Islam
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
| | - Pitambar Khanra
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
| | - Sarah F Muldoon
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
- Neuroscience Program, University at Buffalo, State University of New York at Buffalo, 955 Main Street, Buffalo, 14203, NY, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, 731 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Naoki Masuda
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA.
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA.
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Kurkela K, Ritchey M. Intrinsic functional connectivity among memory networks does not predict individual differences in narrative recall. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.31.555768. [PMID: 38464053 PMCID: PMC10925185 DOI: 10.1101/2023.08.31.555768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Individuals differ greatly in their ability to remember the details of past events, yet little is known about the brain processes that explain such individual differences in a healthy young population. Previous research suggests that episodic memory relies on functional communication among ventral regions of the default mode network ("DMN-C") that are strongly interconnected with the medial temporal lobes. In this study, we investigated whether the intrinsic functional connectivity of the DMN-C subnetwork is related to individual differences in memory ability, examining this relationship across 243 individuals (ages 18-50 years) from the openly available Cambridge Center for Aging and Neuroscience (Cam-CAN) dataset. We first estimated each participant's whole-brain intrinsic functional brain connectivity by combining data from resting-state, movie-watching, and sensorimotor task scans to increase statistical power. We then examined whether intrinsic functional connectivity predicted performance on a narrative recall task. We found no evidence that functional connectivity of the DMN-C, with itself, with other related DMN subnetworks, or with the rest of the brain, was related to narrative recall. Exploratory connectome-based predictive modeling (CBPM) analyses of the entire connectome revealed a whole-brain multivariate pattern that predicted performance, although these changes were largely outside of known memory networks. These results add to emerging evidence suggesting that individual differences in memory cannot be easily explained by brain differences in areas typically associated with episodic memory function.
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Affiliation(s)
- Kyle Kurkela
- Department of Psychology and Neuroscience, Boston College
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Wang HT, Meisler SL, Sharmarke H, Clarke N, Gensollen N, Markiewicz CJ, Paugam F, Thirion B, Bellec P. Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn. PLoS Comput Biol 2024; 20:e1011942. [PMID: 38498530 PMCID: PMC10977879 DOI: 10.1371/journal.pcbi.1011942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/28/2024] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.
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Affiliation(s)
- Hao-Ting Wang
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Steven L. Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Massachusetts, United States of America
| | - Hanad Sharmarke
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Natasha Clarke
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | | | | | - François Paugam
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada
- Mila—Institut Québécois d’Intelligence Artificielle, Montréal, Canada
| | | | - Pierre Bellec
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Psychology Department, Université de Montréal, Montréal, Québec, Canada
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Yablonski M, Karipidis II, Kubota E, Yeatman JD. The transition from vision to language: Distinct patterns of functional connectivity for subregions of the visual word form area. Hum Brain Mapp 2024; 45:e26655. [PMID: 38488471 PMCID: PMC10941549 DOI: 10.1002/hbm.26655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/22/2023] [Accepted: 02/26/2024] [Indexed: 03/18/2024] Open
Abstract
Reading entails transforming visual symbols to sound and meaning. This process depends on specialized circuitry in the visual cortex, the visual word form area (VWFA). Recent findings suggest that this text-selective cortex comprises at least two distinct subregions: the more posterior VWFA-1 is sensitive to visual features, while the more anterior VWFA-2 processes higher level language information. Here, we explore whether these two subregions also exhibit different patterns of functional connectivity. To this end, we capitalize on two complementary datasets: Using the Natural Scenes Dataset (NSD), we identify text-selective responses in high-quality 7T adult data (N = 8), and investigate functional connectivity patterns of VWFA-1 and VWFA-2 at the individual level. We then turn to the Healthy Brain Network (HBN) database to assess whether these patterns replicate in a large developmental sample (N = 224; age 6-20 years), and whether they relate to reading development. In both datasets, we find that VWFA-1 is primarily correlated with bilateral visual regions. In contrast, VWFA-2 is more strongly correlated with language regions in the frontal and lateral parietal lobes, particularly the bilateral inferior frontal gyrus. Critically, these patterns do not generalize to adjacent face-selective regions, suggesting a specific relationship between VWFA-2 and the frontal language network. No correlations were observed between functional connectivity and reading ability. Together, our findings support the distinction between subregions of the VWFA, and suggest that functional connectivity patterns in the ventral temporal cortex are consistent over a wide range of reading skills.
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Affiliation(s)
- Maya Yablonski
- Division of Developmental‐Behavioral Pediatrics, Department of PediatricsStanford University School of MedicineStanfordCaliforniaUSA
- Stanford University Graduate School of EducationStanfordCaliforniaUSA
| | - Iliana I. Karipidis
- Department of Psychiatry and Behavioral SciencesStanford School of MedicineStanfordCaliforniaUSA
- Department of Child and Adolescent Psychiatry and PsychotherapyUniversity Hospital of Psychiatry Zurich, University of ZurichZürichSwitzerland
- Neuroscience Center ZurichUniversity of Zurich and ETHZurichSwitzerland
| | - Emily Kubota
- Psychology DepartmentStanford UniversityStanfordCaliforniaUSA
| | - Jason D. Yeatman
- Division of Developmental‐Behavioral Pediatrics, Department of PediatricsStanford University School of MedicineStanfordCaliforniaUSA
- Stanford University Graduate School of EducationStanfordCaliforniaUSA
- Psychology DepartmentStanford UniversityStanfordCaliforniaUSA
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Li Y, Ran Y, Yao M, Chen Q. Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study. Neurobiol Dis 2024; 192:106425. [PMID: 38296113 DOI: 10.1016/j.nbd.2024.106425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy. METHODS Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN. RESULTS Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables. CONCLUSIONS Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
| | - Yun Ran
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Maohua Yao
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
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Mo F, Zhao H, Li Y, Cai H, Song Y, Wang R, Yu Y, Zhu J. Network Localization of State and Trait of Auditory Verbal Hallucinations in Schizophrenia. Schizophr Bull 2024:sbae020. [PMID: 38401526 DOI: 10.1093/schbul/sbae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/26/2024]
Abstract
BACKGROUND AND HYPOTHESIS Neuroimaging studies investigating the neural substrates of auditory verbal hallucinations (AVH) in schizophrenia have yielded mixed results, which may be reconciled by network localization. We sought to examine whether AVH-state and AVH-trait brain alterations in schizophrenia localize to common or distinct networks. STUDY DESIGN We initially identified AVH-state and AVH-trait brain alterations in schizophrenia reported in 48 previous studies. By integrating these affected brain locations with large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we then leveraged novel functional connectivity network mapping to construct AVH-state and AVH-trait dysfunctional networks. STUDY RESULTS The neuroanatomically heterogeneous AVH-state and AVH-trait brain alterations in schizophrenia localized to distinct and specific networks. The AVH-state dysfunctional network comprised a broadly distributed set of brain regions mainly involving the auditory, salience, basal ganglia, language, and sensorimotor networks. Contrastingly, the AVH-trait dysfunctional network manifested as a pattern of circumscribed brain regions principally implicating the caudate and inferior frontal gyrus. Additionally, the AVH-state dysfunctional network aligned with the neuromodulation targets for effective treatment of AVH, indicating possible clinical relevance. CONCLUSIONS Apart from unifying the seemingly irreproducible neuroimaging results across prior AVH studies, our findings suggest different neural mechanisms underlying AVH state and trait in schizophrenia from a network perspective and more broadly may inform future neuromodulation treatment for AVH.
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Affiliation(s)
- Fan Mo
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Han Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yifan Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yang Song
- Department of Pain, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
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Yang C, Biswal B, Cui Q, Jing X, Ao Y, Wang Y. Frequency-dependent alterations of global signal topography in patients with major depressive disorder. Psychol Med 2024:1-10. [PMID: 38362834 DOI: 10.1017/s0033291724000254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear. METHODS We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed. RESULTS The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group. CONCLUSION The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.
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Affiliation(s)
- Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiujuan Jing
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Yujia Ao
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
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Crowley SJ, Iordan AD, Rinna K, Barmada S, Hampstead BM. Comparing high definition transcranial direct current stimulation to left temporoparietal junction and left inferior frontal gyrus for logopenic primary progressive aphasia: A single-case study. Neuropsychol Rehabil 2024:1-26. [PMID: 38358112 DOI: 10.1080/09602011.2024.2314878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 11/18/2023] [Indexed: 02/16/2024]
Abstract
Logopenic variant primary progressive aphasia (lvPPA) is characterized by word-finding deficits and phonologic errors in fluent speech. Transcranial direct current stimulation (tDCS) targeting either left temporoparietal junction (TPJ) or left inferior frontal gyrus (IFG) show evidence of improving language function in lvPPA. The present case study evaluated the effects of two separate rounds of high definition tDCS (HD-tDCS) (4 mA; 30 sessions) on language and functional neuroimaging in a 57-year-old woman with lvPPA. Stimulation was centred on two different regions across rounds: (1) left TPJ, and (2) left (IFG). Results showed an improved proportion of content to floorholder words during a naturalistic speech task through both rounds as well as change in confrontation naming after TPJ (improvement) and IFG (worsened) stimulation. fMRI connectivity during task showed left lateralized positive correlations following round 1 and anti-correlations with components of the default mode network following round 2. Resting state segregation of a language-associated functional network increased following both rounds, and task-based segregation of the same network increased following IFG stimulation. These results suggest that stimulation to both regions using HD-tDCS may improve language function in lvPPA, while simultaneously eliciting widespread changes beyond the targeted area in neuronal activity and functional connectivity.
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Affiliation(s)
- Samuel J Crowley
- Research Program on Cognition and Neuromodulation Based Interventions, Department of Psychiatry, University of Michigan Medicine, Ann Arbor, MI, USA
- Mental Health Service, Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA
| | - Alexandru D Iordan
- Research Program on Cognition and Neuromodulation Based Interventions, Department of Psychiatry, University of Michigan Medicine, Ann Arbor, MI, USA
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Kayla Rinna
- Research Program on Cognition and Neuromodulation Based Interventions, Department of Psychiatry, University of Michigan Medicine, Ann Arbor, MI, USA
- Department of Psychology, Eastern Michigan University, Ypsilanti, MI, USA
| | - Sami Barmada
- Department of Neurology, University of Michigan Medicine, Ann Arbor, MI, USA
| | - Benjamin M Hampstead
- Research Program on Cognition and Neuromodulation Based Interventions, Department of Psychiatry, University of Michigan Medicine, Ann Arbor, MI, USA
- Mental Health Service, Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA
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