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Wen X, Yang W, Du Z, Zhao J, Li Y, Yu D, Zhang J, Liu J, Yuan K. Multimodal frontal neuroimaging markers predict longitudinal craving reduction in abstinent individuals with heroin use disorder. J Psychiatr Res 2024; 177:1-10. [PMID: 38964089 DOI: 10.1016/j.jpsychires.2024.06.035] [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: 04/18/2024] [Revised: 06/02/2024] [Accepted: 06/24/2024] [Indexed: 07/06/2024]
Abstract
The variation in improvement among individuals with addiction after abstinence is a critical issue. Here, we aimed to identify robust multimodal markers associated with high response to 8-month abstinence in the individuals with heroin use disorder (HUD) and explore whether the identified markers could be generalized to the individuals with methamphetamine use disorder (MUD). According to the median of craving changes, 53 individuals with HUD with 8-month abstinence were divided into two groups: higher craving reduction and lower craving reduction. At baseline, clinical variables, cortical thickness and subcortical volume, fractional anisotropy (FA) of fibers and resting-state functional connectivity (RSFC) were extracted. Different strategies (single metric, multimodal neuroimaging fusion and multimodal neuroimaging-clinical data fusion) were used to identify reliable features for discriminating the individuals with HUD with higher craving reduction from those with lower reduction. The generalization ability of the identified features was validated in the 21 individuals with MUD. Multimodal neuroimaging-clinical fusion features with best performance was achieved an 87.1 ± 3.89% average accuracy in individuals with HUD, with a moderate accuracy of 66.7% when generalizing to individuals with MUD. The multimodal neuroimaging features, primarily converging in frontal regions (e.g., the left superior frontal (LSF) thickness, FA of the LSF-occipital tract, and RSFC of left middle frontal-right superior temporal lobe), collectively contributed to prediction alongside dosage and attention impulsiveness. In this study, we identified the validated multimodal frontal neuroimaging markers associated with higher response to long-term abstinence and revealed insights for the neural mechanisms of addiction abstinence, contributing to clinical strategies and treatment for addiction.
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Affiliation(s)
- Xinwen Wen
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Wenhan Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.
| | - Zhe Du
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Jiahao Zhao
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Yangding Li
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China
| | - Jun Zhang
- Hunan Judicial Police Academy, Changsha, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, Shaanxi, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.
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Li Y, Yang L, Hao D, Chen Y, Ye-Lin Y, Li CSR, Li G. Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking. Brain Sci 2024; 14:610. [PMID: 38928610 PMCID: PMC11201596 DOI: 10.3390/brainsci14060610] [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: 05/02/2024] [Revised: 06/15/2024] [Accepted: 06/16/2024] [Indexed: 06/28/2024] Open
Abstract
Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These "neural fingerprints" elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.
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Affiliation(s)
- Yashuang Li
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 100 Pingleyuan, Beijing 100124, China; (Y.L.)
| | - Lin Yang
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 100 Pingleyuan, Beijing 100124, China; (Y.L.)
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, 46022 Valencia, Spain
| | - Dongmei Hao
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 100 Pingleyuan, Beijing 100124, China; (Y.L.)
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, 46022 Valencia, Spain
| | - Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA (C.-S.R.L.)
| | - Yiyao Ye-Lin
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, 46022 Valencia, Spain
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Chiang-Shan Ray Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA (C.-S.R.L.)
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06511, USA
| | - Guangfei Li
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 100 Pingleyuan, Beijing 100124, China; (Y.L.)
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, 46022 Valencia, Spain
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3
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Weng Y, Kruschwitz J, Rueda-Delgado LM, Ruddy K, Boyle R, Franzen L, Serin E, Nweze T, Hanson J, Smyth A, Farnan T, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, McGrath J, Nees F, Orfanos DP, Paus T, Poustka L, Holz N, Fröhner JH, Smolka MN, Vaidya N, Schumann G, Walter H, Whelan R. A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.03.587900. [PMID: 38617224 PMCID: PMC11014614 DOI: 10.1101/2024.04.03.587900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance-use or a marker of the inclination to engage in such behaviour. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1,000 participants. Behaviours and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.
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Affiliation(s)
- Yihe Weng
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Johann Kruschwitz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Collaborative Research Centre (SFB 940) "Volition and Cognitive Control", Technische Universität Dresden, 01069, Dresden, Germany
| | - Laura M Rueda-Delgado
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Kathy Ruddy
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
- School of Psychology, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Rory Boyle
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Luisa Franzen
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Emin Serin
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Charité -Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany
| | | | - Jamie Hanson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA; Learning Research & Development Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alannah Smyth
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Tom Farnan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, United Kingdom
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli; Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli; Gif-sur-Yvette; and AP-HP. Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli; Gif-sur-Yvette; and Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Jane McGrath
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hosptalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
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Gaudreault PO, King SG, Huang Y, Ceceli AO, Kronberg G, Alia-Klein N, Goldstein RZ. FRONTAL WHITE MATTER CHANGES INDICATE RECOVERY WITH INPATIENT TREATMENT IN HEROIN ADDICTION. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.10.24308719. [PMID: 38946983 PMCID: PMC11213111 DOI: 10.1101/2024.06.10.24308719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Importance Amidst an unprecedented opioid epidemic, identifying neurobiological correlates of change with medication-assisted treatment of heroin use disorder is imperative. Distributed white matter (WM) impairments in individuals with heroin use disorder (iHUD) have been associated with increased drug craving, a reliable predictor of treatment outcomes. However, little is known about the extent of whole-brain structural connectivity changes with inpatient treatment and abstinence in iHUD. Objective To assess WM microstructure and associations with drug craving changes with inpatient treatment in iHUD (effects of time/re-scan compared to controls; CTL). Design Longitudinal cohort study (12/2020-09/2022) where iHUD and CTL underwent baseline magnetic resonance imaging (MRI#1) and follow-up (MRI#2) scans, (mean interval of 13.9 weeks in all participants combined). Setting The iHUD and CTL were recruited from urban inpatient treatment facilities and surrounding communities, respectively. Participants Thirty-four iHUD (42.1yo; 7 women), 25 age-/sex-matched CTL (40.5yo; 9 women). Intervention Between scans, inpatient iHUD continued their medically-assisted treatment and related clinical interventions. CTL participants were scanned at similar time intervals. Main Outcomes and Measures Changes in white matter diffusion metrics [fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivities (RD)] in addition to baseline and cue-induced drug craving, and other clinical outcome variables (mood, sleep, affect, perceived stress, and therapy attendance). Results Main findings showed HUD-specific WM microstructure changes encompassing mostly frontal major callosal, projection, and association tracts, characterized by increased FA (.949<1-p<.986) and decreased MD (.949<1-p<.997) and RD (.949<1-p<.999). The increased FA (r=-0.72, p<.00001) and decreased MD (r=0.69, p<.00001) and RD (r=0.67, p<.0001) in the genu and body of the corpus callosum and the left anterior corona radiata in iHUD were correlated with a reduction in baseline craving (.949<1-p<.999). No other WM correlations with outcome variables reached significance. Conclusions and Relevance Our findings suggest whole-brain normalization of structural connectivity with inpatient medically-assisted treatment in iHUD encompassing recovery in frontal WM pathways implicated in emotional regulation and top-down executive control. The association with decreases in baseline craving further supports the relevance of these WM markers to a major symptom in drug addiction, with implications for monitoring clinical outcomes.
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Affiliation(s)
- Pierre-Olivier Gaudreault
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Sarah G King
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Yuefeng Huang
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ahmet O Ceceli
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Greg Kronberg
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Nelly Alia-Klein
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Rita Z Goldstein
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
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Dan R, Whitton AE, Treadway MT, Rutherford AV, Kumar P, Ironside ML, Kaiser RH, Ren B, Pizzagalli DA. Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome. Neuropsychopharmacology 2024; 49:1162-1170. [PMID: 38480910 PMCID: PMC11109096 DOI: 10.1038/s41386-024-01842-1] [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: 10/20/2023] [Revised: 01/27/2024] [Accepted: 03/01/2024] [Indexed: 03/26/2024]
Abstract
Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.
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Affiliation(s)
- Rotem Dan
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Alexis E Whitton
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Black Dog Institute, University of New South Wales, Sydney, Australia
| | - Michael T Treadway
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Ashleigh V Rutherford
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Manon L Ironside
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Roselinde H Kaiser
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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McCurdy LY, DeVito EE, Loya JM, Nich C, Zhai ZW, Kiluk BD, Potenza MN. Structural brain changes associated with cocaine use and digital cognitive behavioral therapy in cocaine use disorder treatment. DRUG AND ALCOHOL DEPENDENCE REPORTS 2024; 11:100246. [PMID: 38966567 PMCID: PMC11222934 DOI: 10.1016/j.dadr.2024.100246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 07/06/2024]
Abstract
Background Few studies have investigated changes in brain structure and function associated with recovery from cocaine use disorder (CUD), and fewer still have identified brain changes associated with specific CUD treatments, which could inform treatment development and optimization. Methods In this longitudinal study, T1-weighted magnetic resonance imaging scans were acquired from 41 methadone-maintained individuals with CUD (15 women) at the beginning of and after 12 weeks of outpatient treatment. As part of a larger randomized controlled trial, these participants were randomly assigned to receive (or not) computer-based training for cognitive behavioral therapy (CBT4CBT), and galantamine (or placebo). Results Irrespective of treatment condition, whole-brain voxel-based morphometry analyses revealed a significant decrease in right caudate body, bilateral cerebellum, and right middle temporal gyrus gray matter volume (GMV) at post-treatment relative to the start of treatment. Subsequent region of interest analyses found that greater reductions in right caudate and bilateral cerebellar GMV were associated with higher relative and absolute levels of cocaine use during treatment, respectively. Participants who completed more CBT4CBT modules had a greater reduction in right middle temporal gyrus GMV. Conclusions These results extend previous findings regarding changes in caudate and cerebellar GMV as a function of cocaine use and provide the first evidence of a change in brain structure as a function of engagement in digital CBT for addiction. These data suggest a novel potential mechanism underlying how CBT4CBT and CBT more broadly may exert therapeutic effects on substance-use-related behaviors through brain regions implicated in semantic knowledge.
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Affiliation(s)
- Li Yan McCurdy
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - Elise E. DeVito
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Jennifer M. Loya
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Charla Nich
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Zu Wei Zhai
- Program in Neuroscience, Middlebury College, Middlebury, VT 05753, USA
| | - Brian D. Kiluk
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Marc N. Potenza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA
- The Connecticut Mental Health Center, New Haven, CT 06519, USA
- The Connecticut Council on Problem Gambling, Wethersfield, CT 06109, USA
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7
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Xie A, Sun Y, Chen H, Li L, Liu P, Liao Y, Li Y. Altered dynamic functional connectivity of insular subdivisions among male cigarette smokers. Front Psychiatry 2024; 15:1353103. [PMID: 38827448 PMCID: PMC11140567 DOI: 10.3389/fpsyt.2024.1353103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
Background Insular subdivisions show distinct patterns of resting state functional connectivity with specific brain regions, each with different functional significance in chronic cigarette smokers. This study aimed to explore the altered dynamic functional connectivity (dFC) of distinct insular subdivisions in smokers. Methods Resting-state BOLD data of 31 smokers with nicotine dependence and 27 age-matched non-smokers were collected. Three bilateral insular regions of interest (dorsal, ventral, and posterior) were set as seeds for analyses. Sliding windows method was used to acquire the dFC metrics of different insular seeds. Support vector machine based on abnormal insular dFC was applied to classify smokers from non-smokers. Results We found that smokers showed lower dFC variance between the left ventral anterior insula and both the right superior parietal cortex and the left inferior parietal cortex, as well as greater dFC variance the right ventral anterior insula with the right middle cingulum cortex relative to non-smokers. Moreover, compared to non-smokers, it is found that smokers demonstrated altered dFC variance of the right dorsal insula and the right middle temporal gyrus. Correlation analysis showed the higher dFC between the right dorsal insula and the right middle temporal gyrus was associated with longer years of smoking. The altered insular subdivision dFC can classify smokers from non-smokers with an accuracy of 89.66%, a sensitivity of 96.30% and a specify of 83.87%. Conclusions Our findings highlighted the abnormal patterns of fluctuating connectivity of insular subdivision circuits in smokers and suggested that these abnormalities may play a significant role in the mechanisms underlying nicotine addiction and could potentially serve as a neural biomarker for addiction treatment.
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Affiliation(s)
- An Xie
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Yunkai Sun
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haobo Chen
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Ling Li
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peng Liu
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Yanhui Liao
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yonggang Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Harp NR, Wager TD, Kober H. Neuromarkers in addiction: definitions, development strategies, and recent advances. J Neural Transm (Vienna) 2024; 131:509-523. [PMID: 38630190 DOI: 10.1007/s00702-024-02766-2] [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: 11/21/2023] [Accepted: 03/12/2024] [Indexed: 04/28/2024]
Abstract
Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.
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Affiliation(s)
- Nicholas R Harp
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tor D Wager
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Hedy Kober
- Department of Psychiatry, Yale University, New Haven, CT, USA.
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Wu G, Cui Z, Wang X, Du Y. Unveiling the Core Functional Networks of Cognition: An Ontology-Guided Machine Learning Approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587855. [PMID: 38617291 PMCID: PMC11014632 DOI: 10.1101/2024.04.02.587855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.
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Affiliation(s)
- Guowei Wu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang, Beijing 100101, China
| | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Institute for Brain Research, Beijing 102206, China
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10
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Abuwarda H, Trainer A, Horien C, Shen X, Ju S, Constable RT, Fredericks C. Whole-brain functional connectivity predicts groupwise and sex-specific tau PET in preclincal Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587791. [PMID: 38617320 PMCID: PMC11014551 DOI: 10.1101/2024.04.02.587791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Preclinical Alzheimer's disease, characterized by the initial accumulation of amyloid and tau pathologies without symptoms, presents a critical opportunity for early intervention. Yet, the interplay between these pathological markers and the functional connectome during this window remains understudied. We therefore set out to elucidate the relationship between the functional connectome and amyloid and tau, as assessed by PET imaging, in individuals with preclinical AD using connectome-based predictive modeling (CPM). We found that functional connectivity predicts tau PET, outperforming amyloid PET models. These models were predominantly governed by linear relationships between functional connectivity and tau. Tau models demonstrated a stronger correlation to global connectivity than underlying tau PET. Furthermore, we identify sex-based differences in the ability to predict regional tau, without any underlying differences in tau PET or global connectivity. Taken together, these results suggest tau is more closely coupled to functional connectivity than amyloid in preclinical disease, and that multimodal predictive modeling approaches stand to identify unique relationships that any one modality may be insufficient to discern.
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Kohler RJ, Zhornitsky S, Potenza MN, Yip SW, Worhunsky P, Angarita GA. Cocaine self-administration behavior is associated with subcortical and cortical morphometry measures in individuals with cocaine use disorder. THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 2024:1-12. [PMID: 38551365 DOI: 10.1080/00952990.2024.2318585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 02/10/2024] [Indexed: 05/24/2024]
Abstract
Background: Individual differences in gray-matter morphometry in the limbic system and frontal cortex have been linked to clinical features of cocaine use disorder (CUD). Self-administration paradigms can provide more direct measurements of the relationship between the regulation of cocaine use and gray-matter morphometry when compared to self-report assessments.Objectives: Our goal was to investigate associations with self-administration behavior in subcortical and cortical brain regions. We hypothesized the number of cocaine infusions self-administered would be correlated with gray-matter volumes (GMVs) in the striatum, amygdala, and hippocampus. Due to scarcity in human studies, we did not hypothesize subcortical directionality. In the frontal cortex, we hypothesized thickness would be negatively correlated with self-administered cocaine.Methods: We conducted an analysis of cocaine self-administration and structural MRI data from 33 (nFemales = 10) individuals with moderate-to-severe CUD. Self-administration lasted 60-minutes and cocaine (8, 16, or 32 mg/70 kg) was delivered on an FR1 schedule (5-minute lockout). Subcortical and cortical regression analyses were performed that included combined bilateral regions and age, experimental variables and use history as confounders.Results: Self-administered cocaine infusions were positively associated with caudal GMV (b = 0.18, p = 0.030) and negatively with putamenal GMV (b = -0.10, p = 0.041). In the cortical model, infusions were positively associated with insular thickness (b = 0.39, p = 0.008) and women appeared to self-administer cocaine more frequently (b = 0.23, p = 0.019).Conclusions: Brain morphometry features in the striatum and insula may contribute to cocaine consumption in CUD. These differences in morphometry may reflect consequences of prolonged use, predisposed vulnerability, or other possibilities.Clinical Trial Numbers: NCT01978431; NCT03471182.
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Affiliation(s)
- Robert J Kohler
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Simon Zhornitsky
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Marc N Potenza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Sarah W Yip
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Patrick Worhunsky
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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12
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Yang W, Han J, Luo J, Tang F, Fan L, Du Y, Yang L, Zhang J, Zhang H, Liu J. Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders. Gen Psychiatr 2023; 36:e101304. [PMID: 38169807 PMCID: PMC10759048 DOI: 10.1136/gpsych-2023-101304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Background Individual differences have been detected in individuals with opioid use disorders (OUD) in rehabilitation following protracted abstinence. Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders (SUD). Aims This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling (CPM). Methods One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging (fMRI) scans at baseline. The Heroin Craving Questionnaire (HCQ) was used to assess craving levels at baseline and at the 8-month follow-up of abstinence. CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ (HCQfollow-up-HCQbaseline). Then, the predictive ability of identified networks was tested in a separate, heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD. Results CPM could predict craving changes induced by long-term abstinence, as shown by a significant correlation between predicted and actual HCQfollow-up (r=0.417, p<0.001) and changes in HCQ (negative: r=0.334, p=0.002;positive: r=0.233, p=0.038). Identified craving-related prediction networks included the somato-motor network (SMN), salience network (SALN), default mode network (DMN), medial frontal network, visual network and auditory network. In addition, decreased connectivity of frontal-parietal network (FPN)-SMN, FPN-DMN and FPN-SALN and increased connectivity of subcortical network (SCN)-DMN, SCN-SALN and SCN-SMN were positively correlated with craving levels. Conclusions These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence, as well as the generalisation ability; the identified brain networks might be the focus of innovative therapies in the future.
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Affiliation(s)
- Wenhan Yang
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jungong Han
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - Jing Luo
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Fei Tang
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Li Fan
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yanyao Du
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Longtao Yang
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jun Zhang
- Hunan Judicial Police Academy, Changsha, Hunan, China
| | | | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
- Department of Radiology Quality Control Center in Hunan Province, Changsha, Hunan, China
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Keller AS, Pines AR, Shanmugan S, Sydnor VJ, Cui Z, Bertolero MA, Barzilay R, Alexander-Bloch AF, Byington N, Chen A, Conan GM, Davatzikos C, Feczko E, Hendrickson TJ, Houghton A, Larsen B, Li H, Miranda-Dominguez O, Roalf DR, Perrone A, Shetty A, Shinohara RT, Fan Y, Fair DA, Satterthwaite TD. Personalized functional brain network topography is associated with individual differences in youth cognition. Nat Commun 2023; 14:8411. [PMID: 38110396 PMCID: PMC10728159 DOI: 10.1038/s41467-023-44087-0] [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: 11/02/2022] [Accepted: 11/29/2023] [Indexed: 12/20/2023] Open
Abstract
Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.
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Affiliation(s)
- Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam R Pines
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sheila Shanmugan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Maxwell A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ran Barzilay
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Andrew Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory M Conan
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hongming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Alisha Shetty
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
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Zhang AR, Bell RP, An C, Tang R, Hall SA, Chan C, Al-Khalil K, Meade CS. Cocaine Use Prediction With Tensor-Based Machine Learning on Multimodal MRI Connectome Data. Neural Comput 2023; 36:107-127. [PMID: 38052079 PMCID: PMC11075092 DOI: 10.1162/neco_a_01623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/08/2023] [Indexed: 12/07/2023]
Abstract
This letter considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study used functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the data sets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from 275 (individuals) × 2 (fMRI and dMRI) × 246 (ROIs) × 246 (ROIs) to 275 (individuals) × 2 (fMRI and dMRI) × 6 (clusters) × 6 (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into six clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting data set was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented. Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse.
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Affiliation(s)
- Anru R Zhang
- Department of Biostatistics and Bioinformatics and Department of Computer Science, Duke University, Durham, NC 27710, U.S.A.
| | - Ryan P Bell
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, U.S.A.
| | - Chen An
- Department of Mathematics, Duke University, Durham, NC 27708, U.S.A.
| | - Runshi Tang
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, U.S.A.
| | - Shana A Hall
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, U.S.A.
| | - Cliburn Chan
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, U.S.A.
| | - Kareem Al-Khalil
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, U.S.A.
| | - Christina S Meade
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, U.S.A.
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Mestre-Bach G, Potenza MN. Neural mechanisms linked to treatment outcomes and recovery in substance-related and addictive disorders. DIALOGUES IN CLINICAL NEUROSCIENCE 2023; 25:75-91. [PMID: 37594217 PMCID: PMC10444012 DOI: 10.1080/19585969.2023.2242359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 07/24/2023] [Accepted: 07/24/2023] [Indexed: 08/19/2023]
Abstract
The present review focuses on potential neural mechanisms underlying recovery from psychiatric conditions characterised by impaired impulse control, specifically substance use disorders, gambling disorder, and internet gaming disorder. Existing treatments (both pharmacological and psychological) for these addictions may impact brain processes, and these have been evaluated in neuroimaging studies. Medication challenge and short-term intervention administration will be considered with respect to treatment utility. Main models of addiction (e.g., dual process, reward deficiency syndrome) will be considered in the context of extant data. Additionally, advanced analytic approaches (e.g., machine-learning approaches) will be considered with respect to guiding treatment development efforts. Thus, this narrative review aims to provide directions for treatment development for addictive disorders.
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Affiliation(s)
- Gemma Mestre-Bach
- Centro de Investigación, Transferencia e Innovación (CITEI), Universidad Internacional de La Rioja, La Rioja, Spain
| | - Marc N. Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
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16
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Antons S, Yip SW, Lacadie CM, Dadashkarimi J, Scheinost D, Brand M, Potenza MN. Connectome-based prediction of craving in gambling disorder and cocaine use disorder. DIALOGUES IN CLINICAL NEUROSCIENCE 2023; 25:33-42. [PMID: 37190759 PMCID: PMC10190201 DOI: 10.1080/19585969.2023.2208586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 04/24/2023] [Indexed: 05/17/2023]
Abstract
INTRODUCTION Craving, involving intense and urgent desires to engage in specific behaviours, is a feature of addictions. Multiple studies implicate regions of salience/limbic networks and basal ganglia, fronto-parietal, medial frontal regions in craving in addictions. However, prior studies have not identified common neural networks that reliably predict craving across substance and behavioural addictions. METHODS Functional magnetic resonance imaging during an audiovisual cue-reactivity task and connectome-based predictive modelling (CPM), a data-driven method for generating brain-behavioural models, were used to study individuals with cocaine-use disorder and gambling disorder. Functions of nodes and networks relevant to craving were identified and interpreted based on meta-analytic data. RESULTS Craving was predicted by neural connectivity across disorders. The highest degree nodes were mostly located in the prefrontal cortex. Overall, the prediction model included complex networks including motor/sensory, fronto-parietal, and default-mode networks. The decoding revealed high functional associations with components of memory, valence ratings, physiological responses, and finger movement/motor imagery. CONCLUSIONS Craving could be predicted across substance and behavioural addictions. The model may reflect general neural mechanisms of craving despite specificities of individual disorders. Prefrontal regions associated with working memory and autobiographical memory seem important in predicting craving. For further validation, the model should be tested in diverse samples and contexts.
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Affiliation(s)
- Stephanie Antons
- General Psychology: Cognition and Center for Behavioral Addiction Research (CeBAR), University of Duisburg-Essen, Duisburg, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany
| | - Sarah W. Yip
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Cheryl M. Lacadie
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Matthias Brand
- General Psychology: Cognition and Center for Behavioral Addiction Research (CeBAR), University of Duisburg-Essen, Duisburg, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany
| | - Marc N. Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
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17
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Zhou Y, Xue T, Cheng Y, Wang J, Dong F, Jia S, Zhang F, Wang X, Lv X, Wang H, Yuan K, Yu D. The changes of intrinsic connectivity contrast in young smokers. Addict Biol 2023; 28:e13347. [PMID: 38017637 DOI: 10.1111/adb.13347] [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: 04/02/2023] [Revised: 07/07/2023] [Accepted: 09/26/2023] [Indexed: 11/30/2023]
Abstract
Previous studies demonstrated that reward circuit plays an important role in smoking. The differences of functional and structural connectivity were found among several brain regions such as thalamus and frontal lobe. However, few studies focused on functional connectivity (FC) in whole-brain voxel level of young smokers. In this study, intrinsic connectivity contrast (ICC) was used to perform voxel-based whole-brain analyses in 55 young smokers and 55 matched non-smokers to identify brain regions with significant group differences. ICC results showed that the connectivity of young smokers in medial frontal cortex (MedFC), supramarginal gyrus anterior division left (L_aSMG), central opercular cortex left (L_CO) and middle frontal gyrus left (L_MidFG) showed a significantly lower trend compared with the non-smokers. The seed-based FC analysis about MedFC indicated that young smokers showed reduced connectivity between the MedFC and left hippocampus, left amygdala compared to non-smokers. Correlation analysis showed that the ICC of MedFC in young smokers was significantly negatively correlated with Fagerstrom test for nicotine dependence (FTND) and Questionnaire on Smoking Urges (QSU). The FC between the MedFC and left hippocampus, left amygdala was significantly negatively correlated with Pack_years. The mediation analysis indicated that ICC of MedFC completely mediated FTND and QSU of young smokers. The results suggest that nicotine accumulation may affect the communication of the frontal lobe with the whole brain to some extent, leading to changes in smoking cravings. The above research also provides in-depth insights into the mechanism of adolescent smoking addiction and related intervention treatment.
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Affiliation(s)
- Yang Zhou
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Ting Xue
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Yongxin Cheng
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Juan Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Fang Dong
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Shaodi Jia
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Fan Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Xiaoqing Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Xiaoqi Lv
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot, Inner Mongolia, China
| | - Hongde Wang
- Xilinguole Meng Mongolian General Hospital, Xilinhaote, Inner Mongolia, China
| | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
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18
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Ye J, Garrison KA, Lacadie C, Potenza MN, Sinha R, Goldfarb EV, Scheinost D. Network state dynamics underpin craving in a transdiagnostic population. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.03.23296454. [PMID: 37873309 PMCID: PMC10593000 DOI: 10.1101/2023.10.03.23296454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Emerging fMRI brain dynamic methods present a unique opportunity to capture how brain region interactions across time give rise to evolving affective and motivational states. As the unfolding experience and regulation of affective states affect psychopathology and well-being, it is important to elucidate their underlying time-varying brain responses. Here, we developed a novel framework to identify network states specific to an affective state of interest and examine how their instantaneous engagement contributed to its experience. This framework investigated network state dynamics underlying craving, a clinically meaningful and changeable state. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (N=252), we utilized connectome-based predictive modeling (CPM) to identify craving-predictive edges. An edge-centric timeseries approach was leveraged to quantify the instantaneous engagement of the craving-positive and craving-negative networks during independent scan runs. Individuals with higher craving persisted longer in a craving-positive network state while dwelling less in a craving-negative network state. We replicated the latter results externally in an independent group of healthy controls and individuals with alcohol use disorder exposed to different stimuli during the scan (N=173). The associations between craving and network state dynamics can still be consistently observed even when craving-predictive edges were instead identified in the replication dataset. These robust findings suggest that variations in craving-specific network state recruitment underpin individual differences in craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our changing affective experiences.
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Affiliation(s)
- Jean Ye
- Interdepartmental Neuroscience Program, Yale School of Medicine
| | | | - Cheryl Lacadie
- Department of Radiology & Biomedical Imaging, Yale School of Medicine
| | - Marc N. Potenza
- Interdepartmental Neuroscience Program, Yale School of Medicine
- Department of Psychiatry, Yale School of Medicine
- Child Study Center, Yale School of Medicine
- Department of Neuroscience, Yale School of Medicine
- Connecticut Mental Health Center
- Connecticut Council on Problem Gambling
- Wu Tsai Institute, Yale University
| | - Rajita Sinha
- Department of Psychiatry, Yale School of Medicine
- Child Study Center, Yale School of Medicine
- Department of Neuroscience, Yale School of Medicine
| | - Elizabeth V. Goldfarb
- Interdepartmental Neuroscience Program, Yale School of Medicine
- Department of Psychiatry, Yale School of Medicine
- Wu Tsai Institute, Yale University
- Department of Psychology, Yale University
- National Center for PTSD
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine
- Department of Radiology & Biomedical Imaging, Yale School of Medicine
- Child Study Center, Yale School of Medicine
- Wu Tsai Institute, Yale University
- Department of Biomedical Engineering, Yale University
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19
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Pizzagalli D, Whitton A, Treadway M, Rutherford A, Kumar P, Ironside M, Kaiser R, Ren B, Dan R. Brain-based graph-theoretical predictive modeling to map the trajectory of transdiagnostic symptoms of anhedonia, impulsivity, and hypomania from the human functional connectome. RESEARCH SQUARE 2023:rs.3.rs-3168186. [PMID: 37841877 PMCID: PMC10571608 DOI: 10.21203/rs.3.rs-3168186/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM's mean square error (MSE) to that of a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region for information spread) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders, highlighting transdiagnostic generalization. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level. ClinicalTrials.gov identifier: NCT01976975.
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Affiliation(s)
| | - Alexis Whitton
- Black Dog Institute, University of New South Wales, Sydney
| | | | | | | | | | | | - Boyu Ren
- McLean Hospital / Harvard Medical School
| | - Rotem Dan
- McLean Hospital / Harvard Medical School
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20
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Dhamala E, Rong Ooi LQ, Chen J, Ricard JA, Berkeley E, Chopra S, Qu Y, Zhang XH, Lawhead C, Yeo BTT, Holmes AJ. Brain-Based Predictions of Psychiatric Illness-Linked Behaviors Across the Sexes. Biol Psychiatry 2023; 94:479-491. [PMID: 37031778 PMCID: PMC10524434 DOI: 10.1016/j.biopsych.2023.03.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023]
Abstract
BACKGROUND Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined. METHODS In this work, we used predictive modeling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development Study (N = 5260; 2571 females). RESULTS We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females but predict internalizing behaviors only in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks, including default, limbic, control, and dorsal attention networks, are associated with internalizing and externalizing behaviors. CONCLUSIONS Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. Based on these results, we recommend that both clinicians and researchers carefully consider how sex may influence the presentation of psychiatric illnesses, especially those along the internalizing-externalizing spectrum.
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Affiliation(s)
- Elvisha Dhamala
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York; Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore
| | - Jocelyn A Ricard
- Department of Psychology, Yale University, New Haven, Connecticut
| | | | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Yueyue Qu
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Xi-Han Zhang
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Connor Lawhead
- Department of Psychology, Yale University, New Haven, Connecticut
| | - B T Thomas Yeo
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut; Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, New Jersey.
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21
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Mestre-Bach G, Potenza MN. Neuroimaging correlates of internet gaming disorder: Can we achieve the promise of translating understandings of brain functioning into clinical advances? CANADIAN JOURNAL OF ADDICTION 2023; 14:7-17. [PMID: 38371925 PMCID: PMC10871672 DOI: 10.1097/cxa.0000000000000178] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Objectives Here we aimed to consider the neural factors associated with internet gaming disorder (IGD), as well as the associations between these factors and existing treatments for the disorder. Methods A narrative review was conducted. Results Pharmacological as well as psychological treatments for IGD may be associated with specific changes in multiple brain areas and circuits. In particular, fronto-striatal and subcortical regions and pathways appear relevant to IGD and its treatment. Conclusions Neuroimaging holds promise for identifying specific mechanisms underlying IGD interventions. However, to date, firm conclusions are difficult to draw and more research examining neural mechanisms of empirically supported treatments for IGD is needed.
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Affiliation(s)
| | - Marc N. Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
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22
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Loya JM, Benitez B, Kiluk BD. The Effect of Cognitive Behavioral Therapy on Impulsivity in Addictive Disorders: a Narrative Review. CURRENT ADDICTION REPORTS 2023; 10:485-493. [PMID: 38269068 PMCID: PMC10805411 DOI: 10.1007/s40429-023-00491-6] [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] [Accepted: 04/30/2023] [Indexed: 01/26/2024]
Abstract
Purpose of Review Impulsivity is considered an important construct in the cycle of addiction, yet the effect of evidence-based treatments on impulsivity is unclear. The goal of this paper was to review the evidence regarding the effect of cognitive behavioral therapy (CBT), one of the most studied psychotherapies for addiction, on measures of impulsivity in addictive disorders. Recent Findings There is a robust literature implicating impulsivity as risk factor for development of a range of addictions and poorer treatment outcomes. However, this review identified only four randomized controlled trials evaluating CBT for an addictive behavior that included repeated assessment of impulsivity. All four were studies targeting substance use. Summary There is limited evidence that CBT has a direct effect on change in measures of impulsivity among individuals being treated for substance use. Future clinical trials should include repeated measurement of impulsivity to examine CBT's effect on the underlying characteristics of addiction.
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Affiliation(s)
- Jennifer M. Loya
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA
| | - Bryan Benitez
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA
| | - Brian D. Kiluk
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA
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23
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Chen X, Dong D, Zhou F, Gao X, Liu Y, Wang J, Qin J, Tian Y, Xiao M, Xu X, Li W, Qiu J, Feng T, He Q, Lei X, Chen H. Connectome-based prediction of eating disorder-associated symptomatology. Psychol Med 2023; 53:5786-5799. [PMID: 36177890 DOI: 10.1017/s0033291722003026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM). METHODS CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants. RESULTS The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect. CONCLUSIONS These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.
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Affiliation(s)
- Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Feng Zhou
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Junjie Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jingmin Qin
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yun Tian
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaofei Xu
- School of Computing Technologies, RMIT University, Melbourne, Australia
| | - Wei Li
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
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24
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Lichenstein SD, Kohler R, Ye F, Potenza MN, Kiluk B, Yip SW. Distinct neural networks predict cocaine versus cannabis treatment outcomes. Mol Psychiatry 2023; 28:3365-3372. [PMID: 37308679 PMCID: PMC10713861 DOI: 10.1038/s41380-023-02120-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/11/2023] [Accepted: 06/01/2023] [Indexed: 06/14/2023]
Abstract
Treatment outcomes for individuals with substance use disorders (SUDs) are variable and more individualized approaches may be needed. Cross-validated, machine-learning methods are well-suited for probing neural mechanisms of treatment outcomes. Our prior work applied one such approach, connectome-based predictive modeling (CPM), to identify dissociable and substance-specific neural networks of cocaine and opioid abstinence. In Study 1, we aimed to replicate and extend prior work by testing the predictive ability of the cocaine network in an independent sample of 43 participants from a trial of cognitive-behavioral therapy for SUD, and evaluating its ability to predict cannabis abstinence. In Study 2, CPM was applied to identify an independent cannabis abstinence network. Additional participants were identified for a combined sample of 33 with cannabis-use disorder. Participants underwent fMRI scanning before and after treatment. Additional samples of 53 individuals with co-occurring cocaine and opioid-use disorders and 38 comparison subjects were used to assess substance specificity and network strength relative to participants without SUDs. Results demonstrated a second external replication of the cocaine network predicting future cocaine abstinence, however it did not generalize to cannabis abstinence. An independent CPM identified a novel cannabis abstinence network, which was (i) anatomically distinct from the cocaine network, (ii) specific for predicting cannabis abstinence, and for which (iii) network strength was significantly stronger in treatment responders relative to control particpants. Results provide further evidence for substance specificity of neural predictors of abstinence and provide insight into neural mechanisms of successful cannabis treatment, thereby identifying novel treatment targets. Clinical trials registation: "Computer-based training in cognitive-behavioral therapy web-based (Man VS Machine)", registration number: NCT01442597 . "Maximizing the Efficacy of Cognitive Behavior Therapy and Contingency Management", registration number: NCT00350649 . "Computer-Based Training in Cognitive Behavior Therapy (CBT4CBT)", registration number: NCT01406899 .
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Affiliation(s)
| | - Robert Kohler
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Marc N Potenza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Brian Kiluk
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Sarah W Yip
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
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25
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Zilverstand A, Parvaz MA, Moeller SJ, Kalayci S, Kundu P, Malaker P, Alia-Klein N, Gümüş ZH, Goldstein RZ. Whole-brain resting-state connectivity underlying impaired inhibitory control during early versus longer-term abstinence in cocaine addiction. Mol Psychiatry 2023; 28:3355-3364. [PMID: 37528227 PMCID: PMC10731999 DOI: 10.1038/s41380-023-02199-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 07/18/2023] [Accepted: 07/21/2023] [Indexed: 08/03/2023]
Abstract
Lapses in inhibitory control have been linked to relapse in human drug addiction. Evidence suggests differences in inhibitory control depending on abstinence duration, but the underlying neural mechanisms remain unknown. We hypothesized that early abstinence (2-5 days) would be characterized by the strongest impairments of inhibitory control and most wide-spread deviations in resting-state functional connectivity of brain networks, while longer-term abstinence (>30 days) would be characterized by weaker impairments as compared to healthy controls. In this laboratory-based cross-sectional study, we compared individuals with Cocaine Use Disorder (iCUD) during early (cocaine urine-positive: N = 19, iCUD+; 32% female; mean age: 46.8 years) and longer-term abstinence (cocaine urine-negative: N = 29, iCUD-; 15% female; mean age: 46.6 years) to healthy controls (N = 33; 24% female; mean age: 40.9 years). We compared the groups on inhibitory control performance (Stop-Signal Task) and, using a whole-brain graph theory analysis (638 region parcellation) of functional magnetic resonance imaging (fMRI) data, we tested for group differences in resting-state brain function (local/global efficiency). We characterized how resting-state brain function was associated with inhibitory control performance within iCUD. Inhibitory control performance was worst in the early abstinence group, and intermediate in the longer-term abstinence group, as compared to the healthy control group (P < 0.01). More recent use of cocaine (CUD+ > CUD- > healthy controls) was characterized by decreased efficiency in fronto-temporal and subcortical networks (primarily in the salience, semantic, and basal ganglia networks) and increased efficiency in visual networks. Importantly, a similar functional connectivity pattern characterized impaired inhibitory control performance within iCUD (all brain analyses P < 0.05, FWE-corrected). Together, we demonstrated that a similar pattern of systematic and widespread deviations in resting-state brain efficiency, extending beyond the networks commonly investigated in human drug addiction, is linked to both abstinence duration and inhibitory control deficits in iCUD.
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Affiliation(s)
- Anna Zilverstand
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Muhammad A Parvaz
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Scott J Moeller
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Selim Kalayci
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Prantik Kundu
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ceretype Neuromedicine, Cambridge, MA, USA
| | - Pias Malaker
- Tom and Anne Smith MD-PhD Program, School of Medicine, University of Missouri, Columbia, MO, USA
| | - Nelly Alia-Klein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zeynep H Gümüş
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rita Z Goldstein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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26
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Zhang Y, Wang F, Sui J. Decoding individual differences in self-prioritization from the resting-state functional connectome. Neuroimage 2023; 276:120205. [PMID: 37253415 DOI: 10.1016/j.neuroimage.2023.120205] [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: 01/13/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/01/2023] Open
Abstract
Although the self has traditionally been viewed as a higher-order mental function by most theoretical frameworks, recent research advocates a fundamental self hypothesis, viewing the self as a baseline function of the brain embedded within its spontaneous activities, which dynamically regulates cognitive processing and subsequently guides behavior. Understanding this fundamental self hypothesis can reveal where self-biased behaviors emerge and to what extent brain signals at rest can predict such biased behaviors. To test this hypothesis, we investigated the association between spontaneous neural connectivity and robust self-bias in a perceptual matching task using resting-state functional magnetic resonance imaging (fMRI) in 348 young participants. By decoding whole-brain connectivity patterns, the support vector regression model produced the best predictions of the magnitude of self-bias in behavior, which was evaluated via a nested cross-validation procedure. The out-of-sample generalizability was further authenticated using an external dataset of older adults. The functional connectivity results demonstrated that self-biased behavior was associated with distinct connections between the default mode, cognitive control, and salience networks. Consensus network and computational lesion analyses further revealed contributing regions distributed across six networks, extending to additional nodes, such as the thalamus, whose role in self-related processing remained unclear. These results provide evidence that self-biased behavior derives from spontaneous neural connectivity, supporting the fundamental self hypothesis. Thus, we propose an integrated neural network model of this fundamental self that synthesizes previous theoretical models and portrays the brain mechanisms by which the self emerges at rest internally and regulates responses to the external environment.
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Affiliation(s)
- Yongfa Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China
| | - Fei Wang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China; Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China; The Centre for Positive Psychology Research, Tsinghua University, Beijing 100084, China.
| | - Jie Sui
- School of Psychology, University of Aberdeen, Aberdeen AB24 3FX, Scotland, Great Britain
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Wang W, Kang Y, Niu X, Zhang Z, Li S, Gao X, Zhang M, Cheng J, Zhang Y. Connectome-based predictive modeling of smoking severity using individualized structural covariance network in smokers. Front Neurosci 2023; 17:1227422. [PMID: 37547147 PMCID: PMC10400777 DOI: 10.3389/fnins.2023.1227422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 07/04/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers. Methods A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing. Results As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edges mainly located between the subcortical-cerebellum network and networks including the frontoparietal default model and motor and visual networks. Discussion These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity.
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Agurto C, Cecchi G, King S, Eyigoz EK, Parvaz MA, Alia-Klein N, Goldstein RZ. Speak and you shall predict: speech at initial cocaine abstinence as a biomarker of long-term drug use behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.18.549548. [PMID: 37503140 PMCID: PMC10370100 DOI: 10.1101/2023.07.18.549548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Importance Valid biomarkers that can predict longitudinal clinical outcomes at low cost are a holy grail in psychiatric research, promising to ultimately be used to optimize and tailor intervention and prevention efforts. Objective To determine if baseline linguistic markers in natural speech, as compared to non-speech clinical and demographic measures, can predict drug use severity measures at future sessions in initially abstinent individuals with cocaine use disorder (iCUD). Design A longitudinal cohort study (August 2017 - March 2020), where baseline measures were used to predict outcomes collected at three-month intervals for up to one year of follow-up. Participants Eighty-eight initially abstinent iCUD were studied at baseline; 57 (46 male, age 50.7+/-7.9 years) came back for at least another session. Main Outcomes and Measures Outcomes were self-reported symptoms of withdrawal, craving, abstinence duration and frequency of cocaine use in the past 90 days at each study session. The predictors were derived from 5-min recordings of vocal descriptions of the positive consequences of abstinence and the negative consequences of using cocaine; the baseline cocaine and other common drug use measures, demographic and neuropsychological variables were used for comparison. Results Models using the non-speech variables showed the best predictive performance at three(r>0.45, P<2×10-3) and six months follow-up (r>0.37, P<3×10-2). At 12 months, the natural language processing-based model showed significant correlations with withdrawal (r=0.43, P=3×10-2), craving (r=0.72, P=5×10-5), days of abstinence (r=0.76, P=1×10-5), and cocaine use in the past 90 days (r=0.61, P=2×10-3), significantly outperforming the other models for abstinence prediction. Conclusions and Relevance At short time intervals, maximal predictive power was obtained with models that used baseline drug use (in addition to demographic and neuropsychological) measures, potentially reflecting a slow rate of change in these measures, which could be estimated by linear functions. In contrast, short speech samples predicted longer-term changes in drug use, implying deeper penetrance by potentially capturing non-linear dynamics over longer intervals. Results suggest that, compared to the common outcome measures used in clinical trials, speech-based measures could be leveraged as better predictors of longitudinal drug use outcomes in initially abstinent iCUD, as potentially generalizable to other substance use disorders and related comorbidity.
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Affiliation(s)
- Carla Agurto
- IBM Research, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598
| | | | - Sarah King
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York City, NY, 10029
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York City, NY, 10029
| | - Elif K. Eyigoz
- IBM Research, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598
| | - Muhammad A. Parvaz
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York City, NY, 10029
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York City, NY, 10029
- Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029
| | - Nelly Alia-Klein
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York City, NY, 10029
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York City, NY, 10029
| | - Rita Z. Goldstein
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York City, NY, 10029
- Psychiatry and Neuroscience Departments, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York City, NY, 10029
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Rosenblatt M, Rodriguez RX, Westwater ML, Dai W, Horien C, Greene AS, Constable RT, Noble S, Scheinost D. Connectome-based machine learning models are vulnerable to subtle data manipulations. PATTERNS (NEW YORK, N.Y.) 2023; 4:100756. [PMID: 37521052 PMCID: PMC10382940 DOI: 10.1016/j.patter.2023.100756] [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] [Received: 01/06/2023] [Revised: 03/10/2023] [Accepted: 04/24/2023] [Indexed: 08/01/2023]
Abstract
Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (r = 0.99) and did not affect other downstream analysis. Essentially, connectome data could be inconspicuously modified to achieve any desired prediction performance. Overall, our enhancement attacks and evaluation of existing adversarial noise attacks in connectome-based models highlight the need for counter-measures that improve the trustworthiness to preserve the integrity of academic research and any potential translational applications.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06510, USA
| | - Raimundo X. Rodriguez
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
| | - Margaret L. Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
| | - Abigail S. Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
| | - R. Todd Constable
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06510, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06510, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT 06510, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06510, USA
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30
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Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
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Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
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31
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Yip SW, Barch DM, Chase HW, Flagel S, Huys QJ, Konova AB, Montague R, Paulus M. From Computation to Clinic. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:319-328. [PMID: 37519475 PMCID: PMC10382698 DOI: 10.1016/j.bpsgos.2022.03.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 02/25/2022] [Accepted: 03/22/2022] [Indexed: 12/12/2022] Open
Abstract
Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).
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Affiliation(s)
- Sarah W. Yip
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Deanna M. Barch
- Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University, St. Louis, Missouri
| | - Henry W. Chase
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Shelly Flagel
- Department of Psychiatry and Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan
| | - Quentin J.M. Huys
- Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Institute of Neurology, University College London, London, United Kingdom
- Camden and Islington NHS Foundation Trust, London, United Kingdom
| | - Anna B. Konova
- Department of Psychiatry and Brain Health Institute, Rutgers University, Piscataway, New Jersey
| | - Read Montague
- Fralin Biomedical Research Institute and Department of Physics, Virginia Tech, Blacksburg, Virginia
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma
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32
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Konova AB, Schweitzer EM. Decoding Craving: Insights From a Brain-Based Connectome Predictive Model of Subjective Reports. Am J Psychiatry 2023; 180:407-409. [PMID: 37259510 DOI: 10.1176/appi.ajp.20230299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Affiliation(s)
- Anna B Konova
- Department of Psychiatry, University Behavioral Health Care, and the Brain Health Institute (Konova, Schweitzer), and Graduate Program in Neuroscience, Robert Wood Johnson Medical School (Schweitzer), Rutgers University-New Brunswick, Piscataway, N.J
| | - Emma M Schweitzer
- Department of Psychiatry, University Behavioral Health Care, and the Brain Health Institute (Konova, Schweitzer), and Graduate Program in Neuroscience, Robert Wood Johnson Medical School (Schweitzer), Rutgers University-New Brunswick, Piscataway, N.J
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33
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Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, Jutla A, Kotov R, Paulus MP, Rubio JM, Sanacora G, Veenstra-VanderWeele J, Krystal JH. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023; 22:236-262. [PMID: 37159365 PMCID: PMC10168176 DOI: 10.1002/wps.21078] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.
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Affiliation(s)
- Anissa Abi-Dargham
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Scott J Moeller
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Farzana Ali
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Amandeep Jutla
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Roman Kotov
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | | | - Jose M Rubio
- Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA
- Feinstein Institute for Medical Research - Northwell, Manhasset, NY, USA
- Zucker Hillside Hospital - Northwell Health, Glen Oaks, NY, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jeremy Veenstra-VanderWeele
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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34
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Horien C, Greene AS, Shen X, Fortes D, Brennan-Wydra E, Banarjee C, Foster R, Donthireddy V, Butler M, Powell K, Vernetti A, Mandino F, O’Connor D, Lake EMR, McPartland JC, Volkmar FR, Chun M, Chawarska K, Rosenberg MD, Scheinost D, Constable RT. A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth. Cereb Cortex 2023; 33:6320-6334. [PMID: 36573438 PMCID: PMC10183743 DOI: 10.1093/cercor/bhac506] [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: 03/09/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 12/29/2022] Open
Abstract
Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- MD-PhD Program, Yale School of Medicine, New Haven, CT, United States
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- MD-PhD Program, Yale School of Medicine, New Haven, CT, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Diogo Fortes
- Yale Child Study Center, New Haven, CT, United States
| | | | | | - Rachel Foster
- Yale Child Study Center, New Haven, CT, United States
| | | | | | - Kelly Powell
- Yale Child Study Center, New Haven, CT, United States
| | | | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - James C McPartland
- Yale Child Study Center, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Fred R Volkmar
- Yale Child Study Center, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Marvin Chun
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Katarzyna Chawarska
- Yale Child Study Center, New Haven, CT, United States
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, United States
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, United States
- Neuroscience Institute, University of Chicago, Chicago, IL, United States
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Yale Child Study Center, New Haven, CT, United States
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, United States
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35
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Wen X, Yue L, Du Z, Li L, Zhu Y, Yu D, Yuan K. Implications of neuroimaging findings in addiction. PSYCHORADIOLOGY 2023; 3:kkad006. [PMID: 38666116 PMCID: PMC10917371 DOI: 10.1093/psyrad/kkad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/14/2023] [Accepted: 04/28/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Xinwen Wen
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Lirong Yue
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Zhe Du
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Linling Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518060, China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xidian University, Xi'an 710126, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an 710126, China
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36
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Dhamala E, Yeo BTT, Holmes AJ. One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry. Biol Psychiatry 2023; 93:717-728. [PMID: 36577634 DOI: 10.1016/j.biopsych.2022.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022]
Abstract
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
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37
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Sankar A, Shen X, Colic L, Goldman DA, Villa LM, Kim JA, Pittman B, Scheinost D, Constable RT, Blumberg HP. Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes. Psychol Med 2023; 53:1-10. [PMID: 36891769 PMCID: PMC10491744 DOI: 10.1017/s003329172300003x] [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: 07/14/2022] [Revised: 12/07/2022] [Accepted: 01/03/2023] [Indexed: 03/10/2023]
Abstract
BACKGROUND The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM). METHODS Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with BD. RESULTS CPM predicted the severity of depressed [concordance between actual and predicted values (r = 0.23, pperm (permutation test) = 0.031) and elevated (r = 0.27, pperm = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample (r ⩾ 0.45, p = 0.002). CONCLUSIONS This study identified distributed functional connectomes predictive of depressed and elevated mood severity in BD. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.
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Affiliation(s)
- Anjali Sankar
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lejla Colic
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- German Center for Mental Health, Halle-Jena-Magdeburg, Magdeburg, Germany
| | - Danielle A. Goldman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - Luca M. Villa
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Jihoon A. Kim
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Brian Pittman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hilary P. Blumberg
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
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38
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Peng SJ, Chen YW, Hung A, Wang KW, Tsai JZ. Connectome-based predictive modeling for functional recovery of acute ischemic stroke. Neuroimage Clin 2023; 38:103369. [PMID: 36917922 PMCID: PMC10011051 DOI: 10.1016/j.nicl.2023.103369] [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: 07/19/2022] [Revised: 02/08/2023] [Accepted: 03/03/2023] [Indexed: 03/09/2023]
Abstract
Patients of acute ischemic stroke possess considerable chance of recovery of various levels in the first several weeks after stroke onset. Prognosis of functional recovery is important for decision-making in poststroke patient care and placement. Poststroke functional recovery has conventionally been based on demographic and clinical variables such as age, gender, and severity of stroke impairment. On the other hand, the concept of connectome has become a basis of interpreting the functional impairment and recovery of stroke patients. In this research, the connectome-based predictive modeling was used to provide predictive models for prognosing poststroke functional recovery. Predictive models were developed to use the brain connectivity at stroke onset to predict functional assessment scores at one or three months later, or to use the brain connectivity one-month poststroke to predict functional assessment scores at three months after stroke onset. The brain connectivity was computed from the resting-state fMRI signals. The functional assessment scores used in this research included modified Rankin Scale (mRS) and Barthel Index (BI). This research found significant models that used the brain connectivity at onset to predict the mRS one-month poststroke and to predict the BI three-month poststroke for patients with supratentorial infarction, as well as predictive models that used the brain connectivity one-month poststroke to predict the mRS three-month poststroke for patients with supratentorial infarction in the right hemisphere. The connectome-based predictive modeling could provide clinical value in prognosis of acute ischemic stroke.
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Affiliation(s)
- Syu-Jyun Peng
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Wei Chen
- Department of Neurology, Landseed International Hospital, Taoyuan, Taiwan; Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
| | - Andrew Hung
- Department of Electrical Engineering, National Central University, Taoyuan, Taiwan
| | - Kuo-Wei Wang
- Department of General Affairs, Landseed International Hospital, Taoyuan, Taiwan
| | - Jang-Zern Tsai
- Department of Electrical Engineering, National Central University, Taoyuan, Taiwan.
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Konova AB, Zilverstand A. Deriving Generalizable and Interpretable Brain-Behavior Phenotypes of Cannabis Use. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:238-240. [PMID: 36889869 PMCID: PMC10766112 DOI: 10.1016/j.bpsc.2023.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/09/2023] [Indexed: 03/08/2023]
Affiliation(s)
- Anna B Konova
- Department of Psychiatry, University Behavioral Health Care, and the Brain Health Institute, Rutgers University-New Brunswick, Piscataway, New Jersey.
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, Medical Discovery Team on Addiction, University of Minnesota, Minneapolis, Minnesota
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40
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Wu X, Yang Q, Xu C, Huo H, Seger CA, Peng Z, Chen Q. Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder. Cereb Cortex 2023; 33:1412-1425. [PMID: 35443038 DOI: 10.1093/cercor/bhac145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
Compulsion is one of core symptoms of obsessive-compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients. We then applied a computational lesion version of CPM to examine the importance of specific brain areas. We also compared the predictive network strength in OCD with unaffected first-degree relatives (UFDR) of patients and healthy controls. CPM successfully predicted individual level of compulsion and identified networks positively (primarily subcortical areas of the striatum and limbic regions of the hippocampus) and negatively (primarily frontoparietal regions) correlated with compulsion. The prediction power of the negative model significantly decreased when simulating lesions to the prefrontal cortex and cerebellum, supporting the importance of these regions for compulsion prediction. We found a similar pattern of network strength in the negative predictive network for OCD patients and their UFDR, demonstrating the potential of CPM to identify vulnerability markers for psychopathology.
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Affiliation(s)
- Xiangshu Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Qiong Yang
- Affiliated Brain Hospital of Guangzhou Medical University, 510370 Guangzhou, China
| | - Chuanyong Xu
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518047, China
| | - Hangfeng Huo
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Carol A Seger
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.,Department of Psychology, Colorado State University, Fort Collins, CO 80523, United States
| | - Ziwen Peng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.,Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen University School of Medicine, Shenzhen 518061, China
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
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41
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Anderson AJ, Perone S. Predicting individual differences in behavioral activation and behavioral inhibition from functional networks in the resting EEG. Biol Psychol 2023; 177:108483. [PMID: 36587892 DOI: 10.1016/j.biopsycho.2022.108483] [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: 05/11/2022] [Revised: 12/09/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022]
Abstract
The behavioral activation system (BAS) and behavioral inhibition system (BIS) are thought to underly affective dispositions and self-regulatory processes. The BAS is sensitive to reward and involved in approach behaviors, and the BIS is sensitive to punishment and involved in avoidance behaviors. Trait BAS and BIS relate to distinct behavioral profiles and neural activity, but little is known about how trait BAS and BIS relate to functional networks in EEG. We applied a data-driven method called connectome predictive modeling (CPM) to identify networks relating to trait BAS and BIS and tested whether the strength of those networks predicted trait BAS and BIS in novel subjects using a leave-one-out cross-validation procedure. Adult participants (N = 107) completed a resting state task with eyes closed and eyes open, and trait BAS and BIS were measured via Carver and White's (1994) BIS and BAS scales. We hypothesized distinct positive (more synchronization) and negative (less synchronization) networks would relate to trait BAS and BIS. For eyes closed, we identified two negative networks, one in theta and one in alpha predicted BIS. We identified three positive networks, one in theta and one in beta predicted Fun Seeking and one in theta predicted Drive. For eyes open, negative theta and alpha networks predicted BIS, a positive theta network predicted Fun Seeking, and a negative gamma network predicted mean BAS. Visualization of the networks are presented. Discussion centers on the observed networks and how to advance application of CPM to EEG, including with clinical implications.
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Affiliation(s)
- Alana J Anderson
- Department of Human Development, Washington State University, USA.
| | - Sammy Perone
- Department of Human Development, Washington State University, USA
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Lu L, Yang W, Zhao D, Wen X, Liu J, Liu J, Yuan K. Brain recovery of the NAc fibers and prediction of craving changes in person with heroin addiction: A longitudinal study. Drug Alcohol Depend 2023; 243:109749. [PMID: 36565569 DOI: 10.1016/j.drugalcdep.2022.109749] [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: 08/17/2022] [Revised: 11/14/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Progress have been made in brain function recovery after long-term abstinence in person with heroin addiction (PHA). However, less is known about whether the nucleus accumbens (NAc) white matter pathways can recover in PHA by prolonged abstinence. METHODS Forty-two PHA and Thirty-nine age- and gender- matched healthy controls (HCs) were recruited. Two MRI scans were obtained at baseline (PHA1) and 8-month follow-up (PHA2). We employed tractography atlas-based analysis (TABS) method to investigate fractional anisotropy (FA) changes in NAc fiber tracts (i.e., Insula-NAc, ventral tegmental area (VTA)-NAc, medial prefrontal cortex (MPFC)-NAc) in PHA. A partial least square regression (PLSR) analysis was carried to explore whether FA of NAc fiber tracts can predict longitudinal craving changes. RESULTS Relative to HCs, lower FA was found in the right Insula-NAc and VTA-NAc fiber tracts in PHA1, and PHA2 showed increased FA values in these tracts compared with PHA1. Furthermore, changes of FA of NAc fiber tracts can predict longitudinal craving changes (r = 0.51). Additionally, craving changes can also be predicted from FA changes in the left Insula-NAc (r = 0.601) and VTA-NAc (r = 0.384) fiber alone. CONCLUSIONS Results indicated that the right Insula-NAc and VTA-NAc fiber tracts are potential biomarkers for brain recovery. Prediction of craving changes highlighted the utility of structural markers to inform clinical decision-making of treatment for PHA.
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Affiliation(s)
- Ling Lu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, Shaanxi, 710071, China
| | - Wenhan Yang
- Department of Radiology, Second Xiangya Hospital of Central South University, Changsha, China
| | - Desheng Zhao
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, Shaanxi, 710071, China
| | - Xinwen Wen
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, Shaanxi, 710071, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital of Central South University, Changsha, China.
| | - Jixin Liu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, Shaanxi, 710071, China.
| | - Kai Yuan
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, Shaanxi, 710071, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China.
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43
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Wang ZL, Potenza MN, Song KR, Dong GH, Fang XY, Zhang JT. Subgroups of internet gaming disorder based on addiction-related resting-state functional connectivity. Addiction 2023; 118:327-339. [PMID: 36089824 DOI: 10.1111/add.16047] [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: 03/31/2022] [Accepted: 08/31/2022] [Indexed: 01/05/2023]
Abstract
AIMS To identify subgroups of people with internet gaming disorder (IGD) based on addiction-related resting-state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. DESIGN Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. SETTING Zhejiang province and Beijing, China. PARTICIPANTS One hundred and sixty-nine IGD and 147 control subjects. MEASUREMENTS k-Means algorithmic and support-vector machine-learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue-reactivity and guessing-related measures. FINDINGS Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 ('craving-related subgroup') and subgroup 2 ('mixed psychological subgroup'). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10-fold validation (n = 218) and out-of-sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue-reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non-adaptive emotion-regulation strategies and guessing-task fMRI measures. Subgroups 1 and 2 showed largely opposite functional-connectivity patterns in overlapping networks. CONCLUSIONS There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.
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Affiliation(s)
- Zi-Liang Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Marc N Potenza
- Department of Psychiatry and Child Study Center, Yale University School of Medicine, New Haven, CT, USA.,Connecticut Council on Problem Gambling, Wethersfield, CT, USA.,Connecticut Mental Health Center, New Haven, CT, USA.,Department of Neuroscience and Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Kun-Ru Song
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Xiao-Yi Fang
- Institute of Developmental Psychology, Beijing Normal University, Beijing, China
| | - Jin-Tao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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44
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Yang L, Du Y, Yang W, Liu J. Machine learning with neuroimaging biomarkers: Application in the diagnosis and prediction of drug addiction. Addict Biol 2023; 28:e13267. [PMID: 36692873 DOI: 10.1111/adb.13267] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/19/2022] [Accepted: 12/14/2022] [Indexed: 01/18/2023]
Abstract
Drug abuse is a serious problem worldwide. Owing to intermittent intake of certain substances and the early inconspicuous clinical symptoms, this brings huge challenges for timely diagnosing addiction status and preventing substance use disorders (SUDs). As a non-invasive technique, neuroimaging can capture neurobiological signatures of abnormality in multiple brain regions caused by drug consumption in each clinical stage, like parenchymal morphology alteration as well as aberrant functional activity and connectivity of cerebral areas, making it realizable to diagnosis, prediction and even preemptive therapy of addiction. Machine learning (ML) algorithms primarily used for classification have been extensively applied in analysing medical imaging datasets. Significant neurobiological characteristics employed and revealed by classifiers were used to diagnose addictive states and predict initiation and vulnerability to drug usage, treatment abstinence, relapse and resilience of addicts and the risk of SUD. In this review, we summarize application of ML methods in neuroimaging focusing on addicts' diagnosis of clinical status and risk prediction and elucidate the discriminative neurobiological features from brain electrophysiological, morphological and functional perspectives that contribute most to the classifier, finally highlighting the auxiliary role of ML in addiction treatment.
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Affiliation(s)
- Longtao Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yanyao Du
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Wenhan Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China.,Department of Radiology Quality Control Center in Hunan Province, Changsha, China
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45
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Boyle R, Connaughton M, McGlinchey E, Knight SP, De Looze C, Carey D, Stern Y, Robertson IH, Kenny RA, Whelan R. Connectome-based predictive modelling of cognitive reserve using task-based functional connectivity. Eur J Neurosci 2023; 57:490-510. [PMID: 36512321 PMCID: PMC10107737 DOI: 10.1111/ejn.15896] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 11/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022]
Abstract
Cognitive reserve supports cognitive function in the presence of pathology or atrophy. Functional neuroimaging may enable direct and accurate measurement of cognitive reserve which could have considerable clinical potential. The present study aimed to develop and validate a measure of cognitive reserve using task-based fMRI data that could then be applied to independent resting-state data. Connectome-based predictive modelling with leave-one-out cross-validation was applied to predict a residual measure of cognitive reserve using task-based functional connectivity from the Cognitive Reserve/Reference Ability Neural Network studies (n = 220, mean age = 51.91 years, SD = 17.04 years). This model generated summary measures of connectivity strength that accurately predicted a residual measure of cognitive reserve in unseen participants. The theoretical validity of these measures was established via a positive correlation with a socio-behavioural proxy of cognitive reserve (verbal intelligence) and a positive correlation with global cognition, independent of brain structure. This fitted model was then applied to external test data: resting-state functional connectivity data from The Irish Longitudinal Study on Ageing (TILDA, n = 294, mean age = 68.3 years, SD = 7.18 years). The network-strength predicted measures were not positively associated with a residual measure of cognitive reserve nor with measures of verbal intelligence and global cognition. The present study demonstrated that task-based functional connectivity data can be used to generate theoretically valid measures of cognitive reserve. Further work is needed to establish if, and how, measures of cognitive reserve derived from task-based functional connectivity can be applied to independent resting-state data.
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Affiliation(s)
- Rory Boyle
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Michael Connaughton
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
- Department of Psychiatry, School of MedicineTrinity College DublinDublinIreland
| | - Eimear McGlinchey
- School of Nursing and MidwiferyTrinity College DublinDublinIreland
- Global Brain Health InstituteTrinity College DublinDublinIreland
| | - Silvin P. Knight
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Céline De Looze
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Daniel Carey
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of NeurologyColumbia UniversityNew York CityNew YorkUSA
| | - Ian H. Robertson
- Global Brain Health InstituteTrinity College DublinDublinIreland
| | - Rose Anne Kenny
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
- Mercer's Institute for Successful AgeingSt. James's HospitalDublinIreland
| | - Robert Whelan
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
- Global Brain Health InstituteTrinity College DublinDublinIreland
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Ma SS, Zhang JT, Song KR, Zhao R, Fang RH, Wang LB, Yao ST, Hu YF, Jiang XY, Potenza MN, Fang XY. Connectome-based prediction of marital quality in husbands' processing of spousal interactions. Soc Cogn Affect Neurosci 2022; 17:1055-1067. [PMID: 35560211 PMCID: PMC9714425 DOI: 10.1093/scan/nsac034] [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: 08/19/2021] [Revised: 04/12/2022] [Accepted: 05/13/2022] [Indexed: 01/12/2023] Open
Abstract
Marital quality may decrease during the early years of marriage. Establishing models predicting individualized marital quality may help develop timely and effective interventions to maintain or improve marital quality. Given that marital interactions have an important impact on marital well-being cross-sectionally and prospectively, neural responses during marital interactions may provide insight into neural bases underlying marital well-being. The current study applies connectome-based predictive modeling, a recently developed machine-learning approach, to functional magnetic resonance imaging (fMRI) data from both partners of 25 early-stage Chinese couples to examine whether an individual's unique pattern of brain functional connectivity (FC) when responding to spousal interactive behaviors can reliably predict their own and their partners' marital quality after 13 months. Results revealed that husbands' FC involving multiple large networks, when responding to their spousal interactive behaviors, significantly predicted their own and their wives' marital quality, and this predictability showed gender specificity. Brain connectivity patterns responding to general emotional stimuli and during the resting state were not significantly predictive. This study demonstrates that husbands' differences in large-scale neural networks during marital interactions may contribute to their variability in marital quality and highlights gender-related differences. The findings lay a foundation for identifying reliable neuroimaging biomarkers for developing interventions for marital quality early in marriages.
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Affiliation(s)
- Shan-Shan Ma
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Jin-Tao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Kun-Ru Song
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Rui Zhao
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Ren-Hui Fang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Luo-Bin Wang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Shu-Ting Yao
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Yi-Fan Hu
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
| | - Xin-Ying Jiang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Marc N Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
- Child Study Center, Yale University School of Medicine, New Haven, CT 06519, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT 06109, USA
- Connecticut Mental Health Center, New Haven, CT 06519, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Xiao-Yi Fang
- Correspondence should be addressed to Xiao-Yi Fang, Institute of Developmental Psychology, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing 100875, China. E-mail:
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Li Y, Cheng P, Liang L, Dong H, Liu H, Shen W, Zhou W. Abnormal resting-state functional connectome in methamphetamine-dependent patients and its application in machine-learning-based classification. Front Neurosci 2022; 16:1014539. [DOI: 10.3389/fnins.2022.1014539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/04/2022] [Indexed: 11/18/2022] Open
Abstract
Brain resting-state functional connectivity (rsFC) has been widely analyzed in substance use disorders (SUDs), including methamphetamine (MA) dependence. Most of these studies utilized Pearson correlation analysis to assess rsFC, which cannot determine whether two brain regions are connected by direct or indirect pathways. Moreover, few studies have reported the application of rsFC-based graph theory in MA dependence. We evaluated alterations in Tikhonov regularization-based rsFC and rsFC-based topological attributes in 46 MA-dependent patients, as well as the correlations between topological attributes and clinical variables. Moreover, the topological attributes selected by least absolute shrinkage and selection operator (LASSO) were used to construct a support vector machine (SVM)-based classifier for MA dependence. The MA group presented a subnetwork with increased rsFC, indicating overactivation of the reward circuit that makes patients very sensitive to drug-related visual cues, and a subnetwork with decreased rsFC suggesting aberrant synchronized spontaneous activity in subregions within the orbitofrontal cortex (OFC) system. The MA group demonstrated a significantly decreased area under the curve (AUC) for the clustering coefficient (Cp) (Pperm < 0.001), shortest path length (Lp) (Pperm = 0.007), modularity (Pperm = 0.006), and small-worldness (σ, Pperm = 0.004), as well as an increased AUC for global efficiency (E.glob) (Pperm = 0.009), network strength (Sp) (Pperm = 0.009), and small-worldness (ω, Pperm < 0.001), implying a shift toward random networks. MA-related increased nodal efficiency (E.nodal) and altered betweenness centrality were also discovered in several brain regions. The AUC for ω was significantly positively associated with psychiatric symptoms. An SVM classifier trained by 36 features selected by LASSO from all topological attributes achieved excellent performance, cross-validated prediction area under the receiver operating characteristics curve, accuracy, sensitivity, specificity, and kappa of 99.03 ± 1.79, 94.00 ± 5.78, 93.46 ± 8.82, 94.52 ± 8.11, and 87.99 ± 11.57%, respectively (Pperm < 0.001), indicating that rsFC-based topological attributes can provide promising features for constructing a high-efficacy classifier for MA dependence.
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Are we compulsively chasing rainbows? Neuropsychopharmacology 2022; 47:2013-2015. [PMID: 35982236 PMCID: PMC9556748 DOI: 10.1038/s41386-022-01419-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 11/26/2022]
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Lin X, Zhu X, Zhou W, Zhang Z, Li P, Dong G, Meng S, Deng J, Lu L. Connectome-based predictive modelling of smoking severity in smokers. Addict Biol 2022; 27:e13242. [PMID: 36301219 DOI: 10.1111/adb.13242] [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: 06/06/2022] [Revised: 08/11/2022] [Accepted: 09/27/2022] [Indexed: 01/24/2023]
Abstract
The functional connectivity within and between networks could provide a framework to characterize the neurobiological mechanism of nicotine addiction. This study examined the brain regions that were functionally connected in response to smoking cues and established the brain-behaviour relationships in smokers. Sixty-seven male smokers were enrolled and scanned while performing the cue-reactivity and Stroop task. A whole-brain analysis approach, connectome-based predictive modelling (CPM), was conducted on the data from the cue-reactivity task to identify the networks that could predict the smoking severity with the Shen atlas as templates. Then, the brain-behaviour relationships were verified in a different brain state (Stroop task). CPM identified the smoking severity-related network, as indicated by a significant correlation between predicted and actual smoking severity scores (r = 0.31, p = 0.02). Identified networks mainly involved the canonical networks implicated in the reward process (motor/sensory network and salience network) and executive control (frontoparietal network). Network strength in the Stroop task marginally significantly predicted smoking severity scores (r = 0.23, p = 0.06), partially replicating the brain-behaviour relationship. The CPM results identified the whole-brain neural network related to smoking severity, which was cross-validated by the AAL and Shen atlas. These findings contribute to more profound insights into neural substrates underlying the smoking severity.
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Affiliation(s)
- Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Ximei Zhu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Weiran Zhou
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Zhibo Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Guangheng Dong
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Shiqiu Meng
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
| | - Jiahui Deng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China.,National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
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Wang Y, Dong D, Chen X, Gao X, Liu Y, Xiao M, Guo C, Chen H. Individualized morphometric similarity predicts body mass index and food approach behavior in school-age children. Cereb Cortex 2022; 33:4794-4805. [PMID: 36300597 DOI: 10.1093/cercor/bhac380] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/15/2022] Open
Abstract
Abstract
Childhood obesity is associated with alterations in brain structure. Previous studies generally used a single structural index to characterize the relationship between body mass index(BMI) and brain structure, which could not describe the alterations of structural covariance between brain regions. To cover this research gap, this study utilized two independent datasets with brain structure profiles and BMI of 155 school-aged children. Connectome-based predictive modeling(CPM) was used to explore whether children’s BMI is reliably predictable by the novel individualized morphometric similarity network(MSN). We revealed the MSN can predict the BMI in school-age children with good generalizability to unseen dataset. Moreover, these revealed significant brain structure covariant networks can further predict children’s food approach behavior. The positive predictive networks mainly incorporated connections between the frontoparietal network(FPN) and the visual network(VN), between the FPN and the limbic network(LN), between the default mode network(DMN) and the LN. The negative predictive network primarily incorporated connections between the FPN and DMN. These results suggested that the incomplete integration of the high-order brain networks and the decreased dedifferentiation of the high-order networks to the primary reward networks can be considered as a core structural basis of the imbalance between inhibitory control and reward processing in childhood obesity.
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Affiliation(s)
- Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University , Chongqing, 400715, China
- Key Laboratory of Cognition and Personality of Ministry of Education , Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality of Ministry of Education , Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) , Research Centre Jülich, Jülich, Germany
| | - Ximei Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology , Southwest University, Chongqing, 400715, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Cheng Guo
- Research Center of Mental Health Education, Faculty of Psychology, Southwest University , Chongqing, 400715, Germany
| | - Hong Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology , Southwest University, Chongqing, 400715, China
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