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Tomescu MI, Van der Donck S, Perisanu EM, Berceanu AI, Alaerts K, Boets B, Carcea I. Social functioning predicts individual changes in EEG microstates following intranasal oxytocin administration: A double-blind, cross-over randomized clinical trial. Psychophysiology 2024; 61:e14581. [PMID: 38594888 DOI: 10.1111/psyp.14581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/11/2024]
Abstract
Oxytocin (OXT) modulates social behaviors. However, the administration of exogenous OXT in humans produces inconsistent behavioral changes, affecting future consideration of OXT as a treatment for autism and other disorders with social symptoms. Inter-individual variability in social functioning traits might play a key role in how OXT changes brain activity and, therefore, behavior. Here, we investigated if inter-individual variability might dictate how single-dose intranasal OXT administration (IN-OXT) changes spontaneous neural activity during the eyes-open resting state. We used a double-blinded, randomized, placebo-controlled, cross-over design on 30 typically developing young adult men to investigate the dynamics of EEG microstates corresponding to activity in defined neural networks. We confirmed previous reports that, at the group level, IN-OXT increases the representation of the attention and salience microstates. Furthermore, we identified a decreased representation of microstates associated with the default mode network. Using multivariate partial least square statistical analysis, we found that social functioning traits associated with IN-OXT-induced changes in microstate dynamics in specific spectral bands. Correlation analysis further revealed that the higher the social functioning, the more IN-OXT increased the appearance of the visual network-associated microstate, and suppressed the appearance of a default mode network-related microstate. The lower the social functioning, the more IN-OXT increases the appearance of the salience microstate. The effects we report on the salience microstate support the hypothesis that OXT regulates behavior by enhancing social salience. Moreover, our findings indicate that social functioning traits modulate responses to IN-OXT and could partially explain the inconsistent reports on IN-OXT effects.
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Affiliation(s)
- Miralena I Tomescu
- Faculty of Educational Sciences, Department of Psychology, University "Stefan cel Mare" of Suceava, Bucharest, Romania
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
- Faculty of Psychology and Educational Sciences, Department of Psychology, University of Bucharest, Bucharest, Romania
| | - Stephanie Van der Donck
- Center for Developmental Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Emanuela M Perisanu
- Institute of Cardiovascular Diseases, Timisoara, Romania
- Faculty of Medicine, University of Sibiu, Sibiu, Romania
| | - Alexandru I Berceanu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
| | - Kaat Alaerts
- Neuromodulation Laboratory, Research Group for Neurorehabilitation, KU Leuven, Leuven, Belgium
| | - Bart Boets
- Center for Developmental Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Ioana Carcea
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers Brain Health Institute, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
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Ward HB, Beermann A, Xie J, Yildiz G, Felix KM, Addington J, Bearden CE, Cadenhead K, Cannon TD, Cornblatt B, Keshavan M, Mathalon D, Perkins DO, Seidman L, Stone WS, Tsuang MT, Walker EF, Woods S, Coleman MJ, Bouix S, Holt DJ, Öngür D, Breier A, Shenton ME, Heckers S, Halko MA, Lewandowski KE, Brady RO. Robust Brain Correlates of Cognitive Performance in Psychosis and its Prodrome. Biol Psychiatry 2024:S0006-3223(24)01460-4. [PMID: 39032726 DOI: 10.1016/j.biopsych.2024.07.012] [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: 03/22/2024] [Revised: 07/03/2024] [Accepted: 07/13/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND Neurocognitive impairment is a well-known phenomenon in schizophrenia that begins prior to psychosis onset. Connectome-wide association studies have inconsistently linked cognitive performance to resting-state fMRI. We hypothesized a carefully selected cognitive instrument and refined population would allow identification of reliable brain-behavior associations with connectome-wide association studies. To test this hypothesis, we first identified brain-cognition correlations via a connectome-wide association study in early psychosis. We then asked, in an independent dataset, if these brain-cognition relationships would generalize to individuals who develop psychosis in the future. METHODS The Seidman Auditory Continuous Performance Task (ACPT) effectively differentiates healthy participants from those with psychosis. Our connectome-wide association study used the Human Connectome Project for Early Psychosis (n=183) to identify links between connectivity and ACPT performance. We then analyzed the North American Prodrome Longitudinal Study 2 (n=345), a multi-site prospective study of individuals at risk for psychosis. We tested the connectome-wide association study-identified cognition-connectivity relationship in both individuals at risk for psychosis and controls. RESULTS Our connectome-wide association study in early-course psychosis identified robust associations between better ACPT performance and higher prefrontal-somatomotor connectivity (p<.005). Prefrontal-somatomotor connectivity was also related to ACPT performance in at-risk individuals who would develop psychosis (n=17). This finding was not observed in nonconverters (n=196) or controls (n=132). CONCLUSIONS This connectome-wide association study identified reproducible links between connectivity and cognition in separate samples of psychosis and at-risk individuals who would later develop psychosis. A carefully selected task and population improves the ability of connectome-wide association studies to identify reliable brain-phenotype relationships.
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Affiliation(s)
- Heather Burrell Ward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center.
| | - Adam Beermann
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jing Xie
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gulcan Yildiz
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Jean Addington
- Department of Psychiatry, University of Calgary, Alberta, Canada
| | - Carrie E Bearden
- Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Behavioral Sciences and Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Tyrone D Cannon
- Department of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Barbara Cornblatt
- Department of Psychiatry, Zucker Hillside Hospital and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Glen Oaks, NY, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA
| | - Daniel Mathalon
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Larry Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA
| | - Ming T Tsuang
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Scott Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Michael J Coleman
- Department of Psychiatry, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sylvain Bouix
- Department of Software Engineering and Information Technology, École de technologie supérieure, Montréal, QC, Canada
| | - Daphne J Holt
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School Boston, MA
| | - Dost Öngür
- McLean Hospital and Harvard Medical School, Boston, MA
| | - Alan Breier
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center
| | - Mark A Halko
- McLean Hospital and Harvard Medical School, Boston, MA
| | | | - Roscoe O Brady
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA; McLean Hospital and Harvard Medical School, Boston, MA.
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3
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Zhao K, Xie H, Fonzo GA, Carlisle NB, Osorio RS, Zhang Y. Dementia Subtypes Defined Through Neuropsychiatric Symptom-Associated Brain Connectivity Patterns. JAMA Netw Open 2024; 7:e2420479. [PMID: 38976268 PMCID: PMC11231801 DOI: 10.1001/jamanetworkopen.2024.20479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 05/06/2024] [Indexed: 07/09/2024] Open
Abstract
Importance Understanding the heterogeneity of neuropsychiatric symptoms (NPSs) and associated brain abnormalities is essential for effective management and treatment of dementia. Objective To identify dementia subtypes with distinct functional connectivity associated with neuropsychiatric subsyndromes. Design, Setting, and Participants Using data from the Open Access Series of Imaging Studies-3 (OASIS-3; recruitment began in 2005) and Alzheimer Disease Neuroimaging Initiative (ADNI; recruitment began in 2004) databases, this cross-sectional study analyzed resting-state functional magnetic resonance imaging (fMRI) scans, clinical assessments, and neuropsychological measures of participants aged 42 to 95 years. The fMRI data were processed from July 2022 to February 2024, with secondary analysis conducted from August 2022 to March 2024. Participants without medical conditions or medical contraindications for MRI were recruited. Main Outcomes and Measures A multivariate sparse canonical correlation analysis was conducted to identify functional connectivity-informed NPS subsyndromes, including behavioral and anxiety subsyndromes. Subsequently, a clustering analysis was performed on obtained latent connectivity profiles to reveal neurophysiological subtypes, and differences in abnormal connectivity and phenotypic profiles between subtypes were examined. Results Among 1098 participants in OASIS-3, 177 individuals who had fMRI and at least 1 NPS at baseline were included (78 female [44.1%]; median [IQR] age, 72 [67-78] years) as a discovery dataset. There were 2 neuropsychiatric subsyndromes identified: behavioral (r = 0.22; P = .002; P for permutation = .007) and anxiety (r = 0.19; P = .01; P for permutation = .006) subsyndromes from connectivity NPS-associated latent features. The behavioral subsyndrome was characterized by connections predominantly involving the default mode (within-network contribution by summed correlation coefficients = 54) and somatomotor (within-network contribution = 58) networks and NPSs involving nighttime behavior disturbance (R = -0.29; P < .001), agitation (R = -0.28; P = .001), and apathy (R = -0.23; P = .007). The anxiety subsyndrome mainly consisted of connections involving the visual network (within-network contribution = 53) and anxiety-related NPSs (R = 0.36; P < .001). By clustering individuals along these 2 subsyndrome-associated connectivity latent features, 3 subtypes were found (subtype 1: 45 participants; subtype 2: 43 participants; subtype 3: 66 participants). Patients with dementia of subtype 3 exhibited similar brain connectivity and cognitive behavior patterns to those of healthy individuals. However, patients with dementia of subtypes 1 and 2 had different dysfunctional connectivity profiles involving the frontoparietal control network (FPC) and somatomotor network (the difference by summed z values was 230 within the SMN and 173 between the SMN and FPC for subtype 1 and 473 between the SMN and visual network for subtype 2) compared with those of healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity (eg, the median [IQR] of the total score of NPSs was 2 [2-7] for subtype 3 vs 6 [3-8] for subtype 1; P = .04 and 5.5 [3-11] for subtype 2; P = .03) and longitudinal progression of cognitive impairment and behavioral dysfunction (eg, the overall interaction association between time and subtypes to orientation was F = 4.88; P = .008; using the time × subtype 3 interaction item as the reference level: β = 0.05; t = 2.6 for time × subtype 2; P = .01). These findings were further validated using a replication dataset of 193 participants (127 female [65.8%]; median [IQR] age, 74 [69-77] years) consisting of 154 newly released participants from OASIS-3 and 39 participants from ADNI. Conclusions and Relevance These findings may provide a novel framework to disentangle the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the timely development of targeted interventions for patients with dementia.
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Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, District of Columbia
- George Washington University School of Medicine, Washington, District of Columbia
| | - Gregory A. Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin
| | - Nancy B. Carlisle
- Department of Psychology, Lehigh University, Bethlehem, Pennsylvania
| | - Ricardo S. Osorio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
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4
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Wang X, Chen H, Wen R, Ou P, Huang Y, Deng L, Shi L, Chen W, Chen H, Wang J, He C, Liu C. Exploring functional and structural connectivity disruptions in spinocerebellar ataxia type 3: Insights from gradient analysis. CNS Neurosci Ther 2024; 30:e14842. [PMID: 39014518 PMCID: PMC11251871 DOI: 10.1111/cns.14842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/30/2024] [Accepted: 06/23/2024] [Indexed: 07/18/2024] Open
Abstract
AIMS Spinocerebellar Ataxia Type 3 (SCA3) is a rare genetic ataxia that impacts the entire brain and is characterized as a neurodegenerative disorder affecting the neural network. This study explores how alterations in the functional hierarchy, connectivity, and structural changes within specific brain regions significantly contribute to the heterogeneity of symptom manifestations in patients with SCA3. METHODS We prospectively recruited 51 patients with SCA3 and 59 age-and sex-matched healthy controls. All participants underwent comprehensive multimodal neuroimaging and clinical assessments. In SCA3 patients, an innovative approach utilizing gradients in resting-state functional connectivity (FC) was employed to examine atypical patterns of hierarchical processing topology from sensorimotor to supramodal regions in the cerebellum and cerebrum. Coupling analyses of abnormal FC and structural connectivity among regions of interest (ROIs) in the brain were also performed to characterize connectivity alterations. Additionally, relationships between quantitative ROI values and clinical variables were explored. RESULTS Patients with SCA3 exhibited either compression or expansion within the primary sensorimotor-to-supramodal gradient through four distinct calculation methods, along with disruptions in FC and structural connectivity coupling. A comprehensive correlation was identified between the altered gradients and the clinical manifestations observed in patients. Notably, altered fractional anisotropy values were not significantly correlated with clinical variables. CONCLUSION Abnormal gradients and connectivity in the cerebellar and cerebral cortices in SCA3 patients may contribute to disrupted motor-to-supramodal functions. Moreover, these findings support the potential utility of FCG analysis as a biomarker for diagnosing SCA3 and assessing treatment efficacy.
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Affiliation(s)
- Xingang Wang
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Hui Chen
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Ru Wen
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Peiling Ou
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Yonghua Huang
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Lihua Deng
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Linfeng Shi
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Wei Chen
- MR Research Collaboration TeamSiemens Healthineers Ltd.WuhanChina
| | - Huafu Chen
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
- Biomedical EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jian Wang
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Changchun He
- College of Blockchain IndustryChengdu University of Information TechnologyChengduChina
| | - Chen Liu
- Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
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Siffredi V, Liverani MC, Fernandez N, Freitas LGA, Borradori Tolsa C, Van De Ville D, Hüppi PS, Ha-Vinh Leuchter R. Impact of a mindfulness-based intervention on neurobehavioral functioning and its association with large-scale brain networks in preterm young adolescents. Psychiatry Clin Neurosci 2024; 78:416-425. [PMID: 38757554 DOI: 10.1111/pcn.13675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 04/15/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
AIM Adolescents born very preterm (VPT; <32 weeks of gestation) face an elevated risk of executive, behavioral, and socioemotional difficulties. Evidence suggests beneficial effects of mindfulness-based intervention (MBI) on these abilities. This study seeks to investigate the association between the effects of MBI on executive, behavioral, and socioemotional functioning and reliable changes in large-scale brain networks dynamics during rest in VPT young adolescents who completed an 8-week MBI program. METHODS Neurobehavioral assessments and resting-state functional magnetic resonance imaging were performed before and after MBI in 32 VPT young adolescents. Neurobehavioral abilities in VPT participants were compared with full-term controls. In the VPT group, dynamic functional connectivity was extracted by using the innovation-driven coactivation patterns framework. The reliable change index was used to quantify change after MBI. A multivariate data-driven approach was used to explore associations between MBI-related changes on neurobehavioral measures and temporal brain dynamics. RESULTS Compared with term-born controls, VPT adolescents showed reduced executive and socioemotional functioning before MBI. After MBI, a significant improvement was observed for all measures that were previously reduced in the VPT group. The increase in executive functioning, only, was associated with reliable changes in the duration of activation of large-scale brain networks, including frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks. CONCLUSION The improvement in executive functioning after an MBI was associated with reliable changes in large-scale brain network dynamics during rest. These changes encompassed frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks that are related to different executive processes including self-regulation, attentional control, and attentional awareness of relevant sensory stimuli.
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Affiliation(s)
- Vanessa Siffredi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Neuro-X Institute, École polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Maria Chiara Liverani
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- SensoriMotor, Affective and Social Development Laboratory, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Natalia Fernandez
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Lorena G A Freitas
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Neuro-X Institute, École polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Cristina Borradori Tolsa
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Neuro-X Institute, École polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Petra Susan Hüppi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Russia Ha-Vinh Leuchter
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
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Tomescu MI, Papasteri C, Sofonea A, Berceanu AI, Carcea I. Personality Moderates Intra-Individual Variability in EEG Microstates and Spontaneous Thoughts. Brain Topogr 2024; 37:524-535. [PMID: 38038786 PMCID: PMC11199214 DOI: 10.1007/s10548-023-01019-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023]
Abstract
Variability in brain activity that persists after accounting for overt behavioral and physiological states is often considered noise and controlled as a covariate in research. However, studying intra-individual variability in brain function can provide valuable insights into the dynamic nature of the brain. To explore this, we conducted a study on 43 participants analyzing the EEG microstate dynamics and self-reported spontaneous mental activity during five-minute resting-state recordings on two separate days with a twenty days average delay between recordings. Our results showed that the associations between EEG microstates and spontaneous cognition significantly changed from one day to another. Moreover, microstate changes were associated with changes in spontaneous cognition. Specifically, inter-day changes in Verbal thoughts about Others and future Planning were positively related to bottom-up sensory network-related microstate changes and negatively associated with top-down, attention, and salience network-related microstates. In addition, we find that personality traits are related to inter-day changes in microstates and spontaneous thoughts. Specifically, extraversion, neuroticism, agreeableness, and openness to experience moderated the relationship between inter-day changes in EEG microstates and spontaneous thoughts. Our study provides valuable information on the dynamic changes in the EEG microstate-spontaneous cognition organization, which could be essential for developing interventions and treatments for neuropsychiatric disorders.
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Affiliation(s)
- Miralena I Tomescu
- Department of Psychology, Faculty of Educational Sciences, University "Stefan cel Mare" of Suceava, Suceava, Romania.
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania.
- Department of Cognitive Sciences, Faculty of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania.
| | - Claudiu Papasteri
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania
- Department of Cognitive Sciences, Faculty of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania
| | - Alexandra Sofonea
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania
| | - Alexandru I Berceanu
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania
| | - Ioana Carcea
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers Brain Health Institute, New Jersey, NJ, USA
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7
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Nakua H, Yu JC, Abdi H, Hawco C, Voineskos A, Hill S, Lai MC, Wheeler AL, McIntosh AR, Ameis SH. Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample. Netw Neurosci 2024; 8:576-596. [PMID: 38952810 PMCID: PMC11168718 DOI: 10.1162/netn_a_00363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/17/2024] [Indexed: 07/03/2024] Open
Abstract
Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.
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Affiliation(s)
- Hajer Nakua
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Ju-Chi Yu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Hervé Abdi
- The University of Texas at Dallas, Richardson, TX, USA
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sean Hill
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Anne L. Wheeler
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | | | - Stephanie H. Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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8
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Colombo F, Calesella F, Bravi B, Fortaner-Uyà L, Monopoli C, Tassi E, Carminati M, Zanardi R, Bollettini I, Poletti S, Lorenzi C, Spadini S, Brambilla P, Serretti A, Maggioni E, Fabbri C, Benedetti F, Vai B. Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance. Eur Neuropsychopharmacol 2024; 85:45-57. [PMID: 38936143 DOI: 10.1016/j.euroneuro.2024.05.015] [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: 11/21/2023] [Revised: 05/20/2024] [Accepted: 05/27/2024] [Indexed: 06/29/2024]
Abstract
An estimated 30 % of Major Depressive Disorder (MDD) patients exhibit resistance to conventional antidepressant treatments. Identifying reliable biomarkers of treatment-resistant depression (TRD) represents a major goal of precision psychiatry, which is hampered by the clinical and biological heterogeneity. To uncover biologically-driven subtypes of MDD, we applied an unsupervised data-driven framework to stratify 102 MDD patients on their neuroimaging signature, including extracted measures of cortical thickness, grey matter volumes, and white matter fractional anisotropy. Our novel analytical pipeline integrated different machine learning algorithms to harmonize data, perform data dimensionality reduction, and provide a stability-based relative clustering validation. The obtained clusters were characterized for immune-inflammatory peripheral biomarkers, TRD, history of childhood trauma and depressive symptoms. Our results indicated two different clusters of patients, differentiable with 67 % of accuracy: one cluster (n = 59) was associated with a higher proportion of TRD, and higher scores of energy-related depressive symptoms, history of childhood abuse and emotional neglect; this cluster showed a widespread reduction in cortical thickness (d = 0.43-1.80) and volumes (d = 0.45-1.05), along with fractional anisotropy in the fronto-occipital fasciculus, stria terminalis, and corpus callosum (d = 0.46-0.52); the second cluster (n = 43) was associated with cognitive and affective depressive symptoms, thicker cortices and wider volumes. Multivariate analyses revealed distinct brain-inflammation relationships between the two clusters, with increase in pro-inflammatory markers being associated with decreased cortical thickness and volumes. Our stratification of MDD patients based on structural neuroimaging identified clinically-relevant subgroups of MDD with specific symptomatic and immune-inflammatory profiles, which can contribute to the development of tailored personalized interventions for MDD.
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Affiliation(s)
- Federica Colombo
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy.
| | - Federico Calesella
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Beatrice Bravi
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Lidia Fortaner-Uyà
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Camilla Monopoli
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Emma Tassi
- Department of Neurosciences and Mental Health, IRCCS Fondazione Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy
| | | | - Raffaella Zanardi
- University Vita-Salute San Raffaele, Milano, Italy; Mood Disorders Unit, Scientific Institute IRCCS San Raffaele Hospital, Milan, Italy
| | - Irene Bollettini
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Sara Poletti
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Cristina Lorenzi
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Sara Spadini
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, IRCCS Fondazione Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Eleonora Maggioni
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Francesco Benedetti
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Benedetta Vai
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
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9
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Braak S, Penninx BW, Su T, Pijnenburg Y, Nijland D, Campos AV, de la Torre-Luque A, Saris IMJ, Reus LM, Beckenstrom AC, Malik A, Dawson GR, Marston H, Alvarez-Linera J, Ayuso-Mateos JLL, Arango C, van der Wee N, Kas MJ, Aghajani M. Social dysfunction relates to shifts within socioaffective brain systems among Schizophrenia and Alzheimer's disease patients. Eur Neuropsychopharmacol 2024; 86:1-10. [PMID: 38909542 DOI: 10.1016/j.euroneuro.2024.05.004] [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: 10/23/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 06/25/2024]
Abstract
Social dysfunction represents one of the most common signs of neuropsychiatric disorders, such as Schizophrenia (SZ) and Alzheimer's disease (AD). Perturbed socioaffective neural processing is crucially implicated in SZ/AD and generally linked to social dysfunction. Yet, transdiagnostic properties of social dysfunction and its neurobiological underpinnings remain unknown. As part of the European PRISM project, we examined whether social dysfunction maps onto shifts within socioaffective brain systems across SZ and AD patients. We probed coupling of social dysfunction with socioaffective neural processing, as indexed by an implicit facial emotional processing fMRI task, across SZ (N = 46), AD (N = 40) and two age-matched healthy control (HC) groups (N = 26 HC-younger and N = 27 HC-older). Behavioural (i.e., social withdrawal, interpersonal dysfunction, diminished prosocial or recreational activity) and subjective (i.e., feelings of loneliness) aspects of social dysfunction were assessed using the Social Functioning Scale and De Jong-Gierveld loneliness questionnaire, respectively. Across SZ/AD/HC participants, more severe behavioural social dysfunction related to hyperactivity within fronto-parieto-limbic brain systems in response to sad emotions (P = 0.0078), along with hypoactivity of these brain systems in response to happy emotions (P = 0.0418). Such relationships were not found for subjective experiences of social dysfunction. These effects were independent of diagnosis, and not confounded by clinical and sociodemographic factors. In conclusion, behavioural aspects of social dysfunction across SZ/AD/HC participants are associated with shifts within fronto-parieto-limbic brain systems. These findings pinpoint altered socioaffective neural processing as a putative marker for social dysfunction, and could aid personalized care initiatives grounded in social behaviour.
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Affiliation(s)
- Simon Braak
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress and Neurodegeneration programs, Amsterdam, the Netherlands.
| | - Brenda Wjh Penninx
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress and Neurodegeneration programs, Amsterdam, the Netherlands
| | - Tanja Su
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress and Neurodegeneration programs, Amsterdam, the Netherlands
| | - Yolande Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | - Daphne Nijland
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress and Neurodegeneration programs, Amsterdam, the Netherlands
| | - Alba Vieira Campos
- Centre of Biomedical Research in Mental Health, CIBERSAM, Spain; Memory Unit, Department of Neurology, Hospital Universitario de la Princesa, Madrid, Spain
| | - Alejandro de la Torre-Luque
- Centre of Biomedical Research in Mental Health, CIBERSAM, Spain; Department of Legal Medicine, Psychiatry and Pathology. Complutense University of Madrid, Madrid, Spain
| | - Ilja M J Saris
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress and Neurodegeneration programs, Amsterdam, the Netherlands
| | - Lianne M Reus
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands; Center for Neurobehavioral Genetics, University of California, Los Angeles, Los Angeles, California, United States
| | | | - Asad Malik
- P1vital Ltd. Manor House, Howbery Park, Wallingford, United Kingdom
| | - Gerard R Dawson
- P1vital Ltd. Manor House, Howbery Park, Wallingford, United Kingdom
| | | | | | - Jose-Luis L Ayuso-Mateos
- Centre of Biomedical Research in Mental Health, CIBERSAM, Spain; Department of Psychiatry, Universidad Autonoma de Madrid, Instituto de Investigación Sanitaria Princesa, Spain
| | - Celso Arango
- Centre of Biomedical Research in Mental Health, CIBERSAM, Spain; Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Gregorio Marañon University Hospital, IiSGM, Spain; Universidad Complutense de Madrid, Spain
| | - Nic van der Wee
- Leiden University Medical Centre, Department of Psychiatry, the Netherlands
| | - Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, the Netherlands
| | - Moji Aghajani
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, Boelelaan 1117, Amsterdam, the Netherlands; Institute of Education & Child Studies, Section Forensic Family & Youth Care, Leiden University, the Netherlands
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10
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Wang HR, Liu ZQ, Nakua H, Hegarty CE, Thies MB, Patel PK, Schleifer CH, Boeck TP, McKinney RA, Currin D, Leathem L, DeRosse P, Bearden CE, Misic B, Karlsgodt KH. Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated with Psychopathology Across Independent Cohorts. Biol Psychiatry 2024:S0006-3223(24)01391-X. [PMID: 38908657 DOI: 10.1016/j.biopsych.2024.06.011] [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: 01/04/2024] [Revised: 05/14/2024] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions. METHODS A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. RESULTS In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use. CONCLUSIONS This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.
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Affiliation(s)
- Haley R Wang
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States
| | - Zhen-Qi Liu
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Hajer Nakua
- Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Catherine E Hegarty
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Melanie Blair Thies
- Department of Psychiatry & Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Pooja K Patel
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States; Desert Pacific Mental Illness Research, Education, and Clinical Center Greater Los Angeles VA Healthcare System, Los Angeles, CA, United States
| | - Charles H Schleifer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States; David Geffen School of Medicine, University of California, Los Angeles, CA, United States
| | - Thomas P Boeck
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Rachel A McKinney
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Danielle Currin
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Logan Leathem
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Pamela DeRosse
- Department of Psychology, Stony Brook University, Stony Brook, NY, United States
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States; Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Katherine H Karlsgodt
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States.
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11
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Janssen J, Gallego AG, Díaz-Caneja CM, Lois NG, Janssen N, González-Peñas J, Gordaliza PM, Buimer EE, van Haren NE, Arango C, Kahn RS, Hulshoff Pol HE, Schnack HG. Heterogeneity of morphometric similarity networks in health and schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586768. [PMID: 38948832 PMCID: PMC11212887 DOI: 10.1101/2024.03.26.586768] [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
Introduction Morphometric similarity is a recently developed neuroimaging phenotype of inter-regional connectivity by quantifying the similarity of a region to other regions based on multiple MRI parameters. Altered average morphometric similarity has been reported in psychotic disorders at the group level, with considerable heterogeneity across individuals. We used normative modeling to address cross-sectional and longitudinal inter-individual heterogeneity of morphometric similarity in health and schizophrenia. Methods Morphometric similarity for 62 cortical regions was obtained from baseline and follow-up T1-weighted scans of healthy individuals and patients with chronic schizophrenia. Cortical regions were classified into seven predefined brain functional networks. Using Bayesian Linear Regression and taking into account age, sex, image quality and scanner, we trained and validated normative models in healthy controls from eleven datasets (n = 4310). Individual deviations from the norm (z-scores) in morphometric similarity were computed for each participant for each network and region at both timepoints. A z-score ≧ than 1.96 was considered supra-normal and a z-score ≦ -1.96 infra-normal. As a longitudinal metric, we calculated the change over time of the total number of infra- or supra-normal regions per participant. Results At baseline, patients with schizophrenia had decreased morphometric similarity of the default mode network and increased morphometric similarity of the somatomotor network when compared with healthy controls. The percentage of patients with infra- or supra-normal values for any region at baseline and follow-up was low (<6%) and did not differ from healthy controls. Mean intra-group changes over time in the total number of infra- or supra-normal regions were small in schizophrenia and healthy control groups (<1) and there were no significant between-group differences. Conclusions In a case-control setting, a decrease of morphometric similarity within the default mode network may be a robust finding implicated in schizophrenia. However, normative modeling suggests that significant reductions and changes over time of regional morphometric similarity are evident only in a minority of patients.
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Affiliation(s)
- Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Ana Guil Gallego
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Covadonga M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Noemi González Lois
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Niels Janssen
- Department of Psychology, Universidad de la Laguna, Tenerife, Spain
- Institute of Biomedical Technologies, Universidad de La Laguna, Tenerife, Spain
- Institute of Neurosciences, Universidad de la Laguna, Santa Cruz de Tenerife, Spain
| | - Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Pedro M. Gordaliza
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Elizabeth E.L. Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Neeltje E.M. van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - René S. Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Hilleke E. Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hugo G. Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands
- Department of Languages, Literature, and Communication, Faculty of Humanities, Utrecht University, Utrecht, The Netherlands
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12
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Xiao X, Hammond C, Salmeron BJ, Wang D, Gu H, Zhai T, Nguyen H, Lu H, Ross TJ, Yang Y. Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents. Biol Psychiatry 2024; 95:1081-1090. [PMID: 37769982 PMCID: PMC10963340 DOI: 10.1016/j.biopsych.2023.08.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 07/27/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND Cognitive function and general psychopathology are two important classes of human behavior dimensions that are individually related to mental disorders across diagnostic categories. However, whether these two transdiagnostic dimensions are linked to common or distinct brain networks that convey resilience or risk for the development of psychiatric disorders remains unclear. METHODS The current study is a longitudinal investigation with 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study at ages 9 to 10 years at the onset of the study. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multidomain behavioral assessments including cognitive functions and psychopathological measures. For the latent resting-state functional connectivity factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using 2-year follow-up data, and its genetic association was evaluated using twin data from the same cohort. RESULTS A latent functional connectome pattern was identified that showed a strong and generalizable association with the multidomain behavioral assessments (5-fold cross-validation: ρ = 0.68-0.73 for the training set [n = 5096]; ρ = 0.56-0.58 for the test set [n = 1476]). This functional connectome pattern was highly heritable (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with the cumulative number of psychiatric disorders assessed concurrently and at 2 years post-magnetic resonance imaging scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period. CONCLUSIONS These findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in the development of psychiatric disorders during early adolescence.
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Affiliation(s)
- Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Christopher Hammond
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Betty Jo Salmeron
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Danni Wang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hong Gu
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Tianye Zhai
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hieu Nguyen
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hanbing Lu
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Thomas J Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland.
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13
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Tavakol S, Kebets V, Royer J, Li Q, Auer H, DeKraker J, Jefferies E, Bernasconi N, Bernasconi A, Helmstaedter C, Arafat T, Armony J, Nathan Spreng R, Caciagli L, Frauscher B, Smallwood J, Bernhardt B. Differential relational memory impairment in temporal lobe epilepsy. Epilepsy Behav 2024; 155:109722. [PMID: 38643660 DOI: 10.1016/j.yebeh.2024.109722] [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: 10/23/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is typically associated with pathology of the hippocampus, a key structure involved in relational memory, including episodic, semantic, and spatial memory processes. While it is widely accepted that TLE-associated hippocampal alterations underlie memory deficits, it remains unclear whether impairments relate to a specific cognitive domain or multiple ones. METHODS We administered a recently validated task paradigm to evaluate episodic, semantic, and spatial memory in 24 pharmacoresistant TLE patients and 50 age- and sex-matched healthy controls. We carried out two-way analyses of variance to identify memory deficits in individuals with TLE relative to controls across different relational memory domains, and used partial least squares correlation to identify factors contributing to variations in relational memory performance across both cohorts. RESULTS Compared to controls, TLE patients showed marked impairments in episodic and spatial memory, with mixed findings in semantic memory. Even when additionally controlling for age, sex, and overall cognitive function, between-group differences persisted along episodic and spatial domains. Moreover, age, diagnostic group, and hippocampal volume were all associated with relational memory behavioral phenotypes. SIGNIFICANCE Our behavioral findings show graded deficits across relational memory domains in people with TLE, which provides further insights into the complex pattern of cognitive impairment in the condition.
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Affiliation(s)
- Shahin Tavakol
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Valeria Kebets
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Qiongling Li
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Hans Auer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | | | - Neda Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Andrea Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | | | - Thaera Arafat
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Jorge Armony
- Department of Psychiatry, McGill University, Montreal, Canada.
| | - R Nathan Spreng
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Birgit Frauscher
- ANPHY Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | | | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
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14
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Saccaro LF, Giff A, De Rossi MM, Piguet C. Interventions targeting emotion regulation: A systematic umbrella review. J Psychiatr Res 2024; 174:263-274. [PMID: 38677089 DOI: 10.1016/j.jpsychires.2024.04.025] [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: 09/30/2023] [Revised: 03/12/2024] [Accepted: 04/15/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND Emotion dysregulation (ED), the difficulty in modulating which emotions are felt, and when and how they are expressed or experienced, has been implicated in an array of psychological disorders. Despite potentially different manifestations depending on the disorder, this symptom is emerging as a transdiagnostic construct that can and should be targeted early, given the associations with various maladaptive behaviors as early as childhood and adolescence. As such, our goal was to investigate the psychotherapeutic interventions used to address ED and gauge their effectiveness, safety, and potential mechanisms across various populations. METHODS This umbrella systematic review, pre-registered under PROSPERO (registration: CRD42023411452), consolidates evidence from systematic reviews and meta-analyses on psychotherapeutic interventions targeting ED, in accordance with PRISMA guidelines. RESULTS Our synthesis of quantitative and qualitative evidence from 21 systematic reviews (including 11 meta-analyses) points-with moderate overall risk of bias-to the effectiveness of Dialectical Behavior Therapy and Cognitive Behavioral Therapy in reducing ED in a wide range of adult transdiagnostic psychiatric patients and healthy participants. Similar results have emerged in other less extensively researched methods as well. However, results on adolescents and children are sparse, highlighting the need for additional research to tailor these interventions to the unique challenges of ED in younger populations with diverse externalizing and internalizing disorders. CONCLUSIONS These demonstrated transdiagnostic advantages of psychotherapy for ED underscore the potential for specifically designed interventions that address this issue directly, particularly for high-risk individuals. In these individuals, early interventions targeting transdiagnostic core dimensions may mitigate the emergence of full-blown disorders. Future research on the mediating factors, the durability of intervention effects, and the exploration of understudied interventions and populations may enhance prevention and treatment efficiency, enhancing the quality of life for those affected by varied manifestations of ED.
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Affiliation(s)
- Luigi F Saccaro
- Psychiatry Department, Geneva University Hospital, Switzerland; Psychiatry Department, Faculty of Medicine, University of Geneva, Switzerland.
| | | | - Maurilio Menduni De Rossi
- Interdisciplinary Center for Health Sciences, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa 56127, Italy; Faculty of Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Camille Piguet
- Psychiatry Department, Geneva University Hospital, Switzerland; Psychiatry Department, Faculty of Medicine, University of Geneva, Switzerland
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15
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Kardan O, Weigard A, Cope L, Martz M, Angstadt M, McCurry KL, Michael C, Hardee J, Hyde LW, Sripada C, Heitzeg MM. Functional brain connectivity predictors of prospective substance use initiation and their environmental correlates. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24308134. [PMID: 38853927 PMCID: PMC11160855 DOI: 10.1101/2024.05.29.24308134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Background Early substance use initiation (SUI) places youth at substantially higher risk for later substance use disorders. Furthermore, adolescence is a critical period for the maturation of brain networks, the pace and magnitude of which are susceptible to environmental influences and may shape risk for SUI. Methods We examined whether patterns of functional brain connectivity during rest (rsFC), measured longitudinally in pre-and-early adolescence, can predict future SUI. In an independent sub-sample, we also tested whether these patterns are associated with key environmental factors, specifically neighborhood pollution and socioeconomic dimensions. We utilized data from the Adolescent Brain Cognitive Development (ABCD) Study®. SUI was defined as first-time use of at least one full dose of alcohol, nicotine, cannabis, or other drugs. We created a control group (N = 228) of participants without SUI who were matched with the SUI group (N = 233) on age, sex, race/ethnicity, and parental income and education. Results Multivariate analysis showed that whole-brain rsFC prior to SUI during 9-10 and 11-12 years of age successfully differentiated the prospective SUI and control groups. This rsFC signature was expressed more at older ages in both groups, suggesting a pattern of accelerated maturation in the SUI group in the years prior to SUI. In an independent sub-sample (N = 2,854) and adjusted for family socioeconomic factors, expression of this rsFC pattern was associated with higher pollution, but not neighborhood disadvantage. Conclusion Brain functional connectivity patterns in early adolescence that are linked to accelerated maturation and environmental exposures can predict future SUI in youth.
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Affiliation(s)
- Omid Kardan
- University of Michigan, Department of Psychiatry
- University of Michigan, Department of Psychology
| | | | - Lora Cope
- University of Michigan, Department of Psychiatry
| | - Meghan Martz
- University of Michigan, Department of Psychiatry
| | | | | | | | | | - Luke W. Hyde
- University of Michigan, Department of Psychology
- University of Michigan, Survey Research Center at the Institute for Social Research
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16
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Li L, Zheng Q, Xue Y, Bai M, Mu Y. Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02474-y. [PMID: 38814465 DOI: 10.1007/s00787-024-02474-y] [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: 02/13/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.
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Affiliation(s)
- Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Qingyu Zheng
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, People's Republic of China
| | - Yang Xue
- Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, People's Republic of China
| | - Miaoshui Bai
- Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, People's Republic of China
| | - Yueming Mu
- Department of Dermatology, The First Hospital of Jilin University, Jilin University, 71 Xinmin Street, Changchun, 130021, People's Republic of China.
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17
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Pereira Camejo M, Escobar Saade L, Liverani MC, Fischi-Gomez E, Gui L, Borradori Tolsa C, Ha-Vinh Leuchter R, Hüppi PS, Siffredi V. Amygdala volumes and associations with socio-emotional competencies in preterm youth: cross-sectional and longitudinal data. Pediatr Res 2024:10.1038/s41390-024-03227-y. [PMID: 38762662 DOI: 10.1038/s41390-024-03227-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/13/2024] [Accepted: 04/11/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Socio-emotional difficulties often result from very preterm (VPT) birth. The amygdala's developmental trajectory, including its nuclei, has been recognized as a significant factor in observed difficulties. This study aims to assess the relationship between amygdala volume and socio-emotional competencies in VPT children and adolescents. METHODS Socio-emotional competencies were assessed, and amygdala volumes, including subnuclei, were extracted automatically from structural scans in a cross-sectional cohort of VPT (n = 75) and full-term (FT, n = 41) aged 6-14 years. Group differences in amygdala volumes were assessed using ANCOVA, and associations with socio-emotional competencies were studied using partial least squares correlation (PLSC). In a VPT subgroup, additional longitudinal data with amygdala volumes at term-equivalent age (TEA) were manually extracted, growth rates calculated, and associations with school-age socio-emotional competencies investigated using PLSC. RESULTS Using cross-sectional data at school-age, amygdala volumes displayed comparable developmental patterns between the VPT and the FT groups. Greater volumes were associated with more emotional regulation difficulties in VPT and lower affect recognition competencies in FT. In the longitudinal VPT subgroup, no significant associations were found between amygdala volume trajectory and socio-emotional competencies. CONCLUSION Although our findings suggest typical amygdala development after VPT birth, further research is necessary to elucidate the developmental trajectory of amygdala and the role of resilience factors. IMPACT In our cohort, amygdala volumes, including subnuclei, displayed comparable developmental trajectories between the very preterm and the full-term groups. Higher amygdala volumes at school-age were associated with higher emotional regulation difficulties in the very-preterm born group, and with lower affect recognition abilities in full-term born children and adolescents. In a subgroup of very-preterm children and adolescents followed from birth to school-age, no significant associations were found between amygdala volumes at term-equivalent age and socio-emotional competencies at school-age.
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Affiliation(s)
- Maricé Pereira Camejo
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Luciana Escobar Saade
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Maria Chiara Liverani
- SensoriMotor, Affective and Social Development Laboratory, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Elda Fischi-Gomez
- Centre for Biomedical Imaging (CIBM), SP CHUV-EPFL Section, Lausanne, Switzerland
- Signal processing laboratory 5, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology, University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Laura Gui
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Cristina Borradori Tolsa
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Russia Ha-Vinh Leuchter
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Petra Susan Hüppi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Vanessa Siffredi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.
- Neuro-X Institute, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland.
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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18
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Wang HR, Liu ZQ, Nakua H, Hegarty CE, Thies MB, Patel PK, Schleifer CH, Boeck TP, McKinney RA, Currin D, Leathem L, DeRosse P, Bearden CE, Misic B, Karlsgodt KH. Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated with Psychopathology Across Independent Cohorts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593636. [PMID: 38766080 PMCID: PMC11100779 DOI: 10.1101/2024.05.10.593636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions. Methods A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. Results In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use. Conclusions This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.
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19
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Wu YK, Zhu LL, Li JT, Li Q, Dai YR, Li K, Mitchell PB, Si TM, Su YA. Striatal Functional Alterations Link to Distinct Symptomatology Across Mood States in Bipolar Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00116-2. [PMID: 38703823 DOI: 10.1016/j.bpsc.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/07/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND As a central hub in cognitive and emotional brain circuits, the striatum is considered likely to be integrally involved in the psychopathology of bipolar disorder (BD). However, it remains unclear how alterations in striatal function contribute to distinct symptomatology of BD during different mood states. METHODS Behavioral assessment (i.e., emotional symptoms and cognitive performance) and neuroimaging data were collected from 125 participants comprising 31 (hypo)manic, 31 depressive, and 31 euthymic patients with BD, and 32 healthy control participants. We compared the functional connectivity (FC) of striatal subregions across BD mood states with healthy control participants and then used a multivariate data-driven approach to explore dimensional associations between striatal connectivity and behavioral performance. Finally, we compared the FC and behavioral composite scores, which reflect the individual weighted representation of the associations, among different mood states. RESULTS Patients in all mood states exhibited increased FC between the bilateral ventral rostral putamen and ventrolateral thalamus. Bipolar (hypo)mania uniquely exhibited increased ventral rostral putamen connectivity and superior ventral striatum connectivity. One latent component was identified, whereby increased FCs of striatal subregions were associated with distinct psychopathological symptomatology (more manic symptoms, elevated positive mood, less depressive symptoms, and worse cognitive performance). Patients with bipolar (hypo)mania had the highest FC and behavioral composite scores while bipolar patients with depression had the lowest scores. CONCLUSIONS Our data demonstrated both trait features of BD and state features specific to bipolar (hypo)mania. The findings underscored the fundamental role of the striatum in the pathophysiological processes underlying specific symptomatology across all mood states.
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Affiliation(s)
- Yan-Kun Wu
- 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), Beijing, China
| | - Lin-Lin 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), Beijing, China
| | - Ji-Tao 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), Beijing, China
| | - Qian 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), Beijing, China
| | - You-Ran Dai
- 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), Beijing, China
| | - Ke Li
- PLA Strategic support Force Characteristic Medical Center, Beijing, China
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Randwick, New South Wales, Australia; Black Dog Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Tian-Mei Si
- 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), Beijing, China
| | - Yun-Ai Su
- 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), Beijing, China.
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20
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Grot S, Smine S, Potvin S, Darcey M, Pavlov V, Genon S, Nguyen H, Orban P. Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110950. [PMID: 38266867 DOI: 10.1016/j.pnpbp.2024.110950] [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: 06/02/2023] [Revised: 11/11/2023] [Accepted: 01/17/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed patterns of functional brain dysconnectivity in psychiatric disorders such as major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ). Although these disorders have been mostly studied in isolation, there is mounting evidence of shared neurobiological alterations across them. METHODS To uncover the nature of the relatedness between these psychiatric disorders, we conducted an innovative meta-analysis of dysconnectivity findings reported separately in MDD, BD and SZ. Rather than relying on a classical voxel level coordinate-based approach, our procedure extracted relevant neuroanatomical labels from text data and examined findings at the whole brain network level. Data were drawn from 428 rsfMRI studies investigating MDD (158 studies, 7429 patients/7414 controls), BD (81 studies, 3330 patients/4096 patients) and/or SZ (223 studies, 11,168 patients/11,754 controls). Permutation testing revealed commonalities and differences in hypoconnectivity and hyperconnectivity patterns across disorders. RESULTS Hypoconnectivity and hyperconnectivity patterns of higher-order cognitive (default-mode, fronto-parietal, cingulo-opercular) networks were similarly observed across the three disorders. By contrast, dysconnectivity of lower-order (somatomotor, visual, auditory) networks in some cases differed between disorders, notably dissociating SZ from BD and MDD. CONCLUSIONS Findings suggest that functional brain dysconnectivity of higher-order cognitive networks is largely transdiagnostic in nature while that of lower-order networks may best discriminate between mood and psychotic disorders, thus emphasizing the relevance of motor and sensory networks to psychiatric neuroscience.
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Affiliation(s)
- Stéphanie Grot
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada; Department of Psychiatry and Addictology, University of Montreal, Montréal, Québec, Canada
| | - Salima Smine
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada
| | - Stéphane Potvin
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada; Department of Psychiatry and Addictology, University of Montreal, Montréal, Québec, Canada
| | - Maëliss Darcey
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada
| | - Vilena Pavlov
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Hien Nguyen
- School of Mathematics and Physics, University of Queensland, St. Lucia, Queensland, Australia; Department of Mathematics and Statistics, Latrobe University, Melbourne, Victoria, Australia
| | - Pierre Orban
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada; Department of Psychiatry and Addictology, University of Montreal, Montréal, Québec, Canada.
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21
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Kushwaha A, Basera DS, Kumari S, Sutar RF, Singh V, Das S, Agrawal A. Assessment of memory deficits in psychiatric disorders: A systematic literature review. J Neurosci Rural Pract 2024; 15:182-193. [PMID: 38746499 PMCID: PMC11090569 DOI: 10.25259/jnrp_456_2023] [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: 08/17/2023] [Accepted: 12/12/2023] [Indexed: 05/16/2024] Open
Abstract
Memory deficits are observed across psychiatric disorders ranging from the prodrome of psychosis to common mental disorders such as anxiety, depression, and dissociative disorders. Memory deficits among patients recovering from psychiatric disorders could be directly related to the primary illness or secondary to the adverse effect of a treatment such as Electroconvulsive Therapy (ECT). The trouble in the meaningful integration of working-memory and episodic memory is the most commonly affected domain that requires routine assessments. An update on the recent trends of methods of assessment of memory deficits is the first step towards understanding and correcting these deficits to target optimum recovery. A systematic literature search was conducted from October 2018 to October 2022 to review the recent methods of assessment of memory deficits in psychiatric disorders. The definition of 'Memory deficit' was operationalized as 'selective processes of memory, commonly required for activities of daily living, and affected among psychiatric disorders resulting in subjective distress and dysfunction'. We included 110 studies, most of them being conducted in western countries on patients with schizophrenia. Other disorders included dementia and mild cognitive impairment. Brief Assessment of Cognition in Schizophrenia, Cambridge Automated Neuropsychological Test Battery, California Verbal Learning Test, Trail Making Test Part A and B, Rey Auditory Verbal Learning Test, Wechsler Memory Scale, Wechsler Adults Intelligence Scale-IV were the most common neuropsychological assessments used. Mini-Mental State Examination and Montreal Cognitive Assessment were the most common bedside assessment tools used while Squire Subjective Memory Questionnaire was commonly used to measure ECT-related memory deficits. The review highlights the recent developments in the field of assessment of memory deficits in psychiatric disorders. Findings recommend and emphasize routine assessment of memory deficits among psychiatric disorders in developing countries especially severe mental illnesses. It remains interesting to see the role of standardized assessments in diagnostic systems given more than a decade of research on memory deficits in psychiatric disorders.
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Affiliation(s)
- Anuradha Kushwaha
- Department of Psychiatry, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Devendra Singh Basera
- Department of Psychiatry, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Sangita Kumari
- Department of Psychiatry, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Roshan Fakirchand Sutar
- Department of Psychiatry, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Vijender Singh
- Department of Psychiatry, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Saikat Das
- Department of Radiotherapy, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Amit Agrawal
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
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Kim W, Kim MJ. Adaptive-to-maladaptive gradient of emotion regulation tendencies are embedded in the functional-structural hybrid connectome. Psychol Med 2024:1-13. [PMID: 38533787 DOI: 10.1017/s0033291724000473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
BACKGROUND Emotion regulation tendencies are well-known transdiagnostic markers of psychopathology, but their neurobiological foundations have mostly been examined within the theoretical framework of cortical-subcortical interactions. METHODS We explored the connectome-wide neural correlates of emotion regulation tendencies using functional and diffusion magnetic resonance images of healthy young adults (N = 99; age 20-30; 28 females). We first tested the importance of considering both the functional and structural connectome through intersubject representational similarity analyses. Then, we employed a canonical correlation analysis between the functional-structural hybrid connectome and 23 emotion regulation strategies. Lastly, we sought to externally validate the results on a transdiagnostic adolescent sample (N = 93; age 11-19; 34 females). RESULTS First, interindividual similarity of emotion regulation profiles was significantly correlated with interindividual similarity of the functional-structural hybrid connectome, more so than either the functional or structural connectome. Canonical correlation analysis revealed that an adaptive-to-maladaptive gradient of emotion regulation tendencies mapped onto a specific configuration of covariance within the functional-structural hybrid connectome, which primarily involved functional connections in the motor network and the visual networks as well as structural connections in the default mode network and the subcortical-cerebellar network. In the transdiagnostic adolescent dataset, stronger functional signatures of the found network were associated with higher general positive affect through more frequent use of adaptive coping strategies. CONCLUSIONS Taken together, our study illustrates a gradient of emotion regulation tendencies that is best captured when simultaneously considering the functional and structural connections across the whole brain.
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Affiliation(s)
- Wonyoung Kim
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Psychology, Sungkyunkwan University, Seoul, South Korea
| | - M Justin Kim
- Department of Psychology, Sungkyunkwan University, Seoul, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
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Huang X, Gao L, Xiao J, Li L, Shan X, Chen H, Chai X, Duan X. Family Environment Modulates Linkage of Transdiagnostic Psychiatric Phenotypes and Dissociable Brain Features in the Developing Brain. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00081-8. [PMID: 38537777 DOI: 10.1016/j.bpsc.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/26/2024] [Accepted: 03/16/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Family environment has long been known for shaping brain function and psychiatric phenotypes, especially during childhood and adolescence. Accumulating neuroimaging evidence suggests that across different psychiatric disorders, common phenotypes may share common neural bases, indicating latent brain-behavior relationships beyond diagnostic categories. However, the influence of family environment on the brain-behavior relationship from a transdiagnostic perspective remains unknown. METHODS We included a community-based sample of 699 participants (ages 5-22 years) and applied partial least squares regression analysis to determine latent brain-behavior relationships from whole-brain functional connectivity and comprehensive phenotypic measures. Comparisons were made between diagnostic and nondiagnostic groups to help interpret the latent brain-behavior relationships. A moderation model was introduced to examine the potential moderating role of family factors in the estimated brain-behavior associations. RESULTS Four significant latent brain-behavior pairs were identified that reflected the relationship of dissociable brain network and general behavioral problems, cognitive and language skills, externalizing problems, and social dysfunction, respectively. The group comparisons exhibited interpretable variations across different diagnostic groups. A warm family environment was found to moderate the brain-behavior relationship of core symptoms in internalizing disorders. However, in neurodevelopmental disorders, family factors were not found to moderate the brain-behavior relationship of core symptoms, but they were found to affect the brain-behavior relationship in other domains. CONCLUSIONS Our findings leveraged a transdiagnostic analysis to investigate the moderating effects of family factors on brain-behavior associations, emphasizing the different roles that family factors play during this developmental period across distinct diagnostic groups.
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Affiliation(s)
- Xinyue Huang
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Leying Gao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Jinming Xiao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Lei Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaolong Shan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaoqian Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Xujun Duan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
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24
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Perrault AA, Kebets V, Kuek NMY, Cross NE, Tesfaye R, Pomares FB, Li J, Chee MW, Dang-Vu TT, Thomas Yeo B. A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580583. [PMID: 38559143 PMCID: PMC10979931 DOI: 10.1101/2024.02.15.580583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.
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Affiliation(s)
- Aurore A. Perrault
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
- Sleep & Circadian Research Group, Woolcock Institute of Medical Research, Macquarie University, Sydney, NSW, Australia
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- McGill University, Montreal, QC, Canada
| | - Nicole M. Y. Kuek
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Nathan E. Cross
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
- School of Psychology, University of Sydney, NSW, Australia
| | | | - Florence B. Pomares
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
| | - Jingwei Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Michael W.L. Chee
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Thien Thanh Dang-Vu
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
| | - B.T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachussetts General Hospital, Charlestown, MA, USA
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25
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Kwak S, Kim H, Kim KY, Oh DY, Lee D, Nam G, Lee JY. Neuroanatomical and neurocognitive correlates of delusion in Alzheimer's disease and mild cognitive impairment. BMC Neurol 2024; 24:89. [PMID: 38448803 PMCID: PMC10916051 DOI: 10.1186/s12883-024-03568-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/12/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Neuropsychiatric symptoms and delusions are highly prevalent among people with dementia. However, multiple roots of neurobiological bases and shared neural basis of delusion and cognitive function remain to be characterized. By utilizing a fine-grained multivariable approach, we investigated distinct neuroanatomical correlates of delusion symptoms across a large population of dementing illnesses. METHODS In this study, 750 older adults with mild cognitive impairment and Alzheimer's disease completed brain structural imaging and neuropsychological assessment. We utilized principal component analysis followed by varimax rotation to identify the distinct multivariate correlates of cortical thinning patterns. Five of the cognitive domains were assessed whether the general cognitive abilities mediate the association between cortical thickness and delusion. RESULTS The result showed that distributed thickness patterns of temporal and ventral insular cortex (component 2), inferior and lateral prefrontal cortex (component 1), and somatosensory-visual cortex (component 5) showed negative correlations with delusions. Subsequent mediation analysis showed that component 1 and 2, which comprises inferior frontal, anterior insula, and superior temporal regional thickness accounted for delusion largely through lower cognitive functions. Specifically, executive control function assessed with the Trail Making Test mediated the relationship between two cortical thickness patterns and delusions. DISCUSSION Our findings suggest that multiple distinct subsets of brain regions underlie the delusions among older adults with cognitive impairment. Moreover, a neural loss may affect the occurrence of delusion in dementia largely due to impaired general cognitive abilities.
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Affiliation(s)
- Seyul Kwak
- Department of Psychology, Pusan National University, Busan, Republic of Korea
| | - Hairin Kim
- Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul, 07061, Republic of Korea
| | - Keun You Kim
- Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul, 07061, Republic of Korea
| | - Da Young Oh
- Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul, 07061, Republic of Korea
| | - Dasom Lee
- Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul, 07061, Republic of Korea
| | - Gieun Nam
- Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul, 07061, Republic of Korea
| | - Jun-Young Lee
- Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul, 07061, Republic of Korea.
- Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
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26
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Wu YK, Su YA, Zhu LL, Yan C, Li JT, Lin JY, Chen J, Chen L, Li K, Stein DJ, Si TM. A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder. Transl Psychiatry 2024; 14:136. [PMID: 38443354 PMCID: PMC10915152 DOI: 10.1038/s41398-024-02838-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 02/11/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
Major depressive disorder (MDD) is associated with functional disturbances in subcortical regions. In this naturalistic prospective study (NCT03294525), we aimed to investigate relationships among subcortical functional connectivity (FC), mood symptom profiles and treatment outcome in MDD using multivariate methods. Medication-free participants with MDD (n = 135) underwent a functional magnetic resonance imaging scan at baseline and completed posttreatment clinical assessment after 8 weeks of antidepressant monotherapy. We used partial least squares (PLS) correlation analysis to explore the association between subcortical FC and mood symptom profiles. FC score, reflecting the weighted representation of each individual in this association, was computed. Replication analysis was undertaken in an independent sample (n = 74). We also investigated the relationship between FC score and treatment outcome in the main sample. A distinctive subcortical connectivity pattern was found to be associated with negative affect. In general, higher FC between the caudate, putamen and thalamus was associated with greater negative affect. This association was partly replicated in the independent sample (similarity between the two samples: r = 0.66 for subcortical connectivity, r = 0.75 for mood symptom profile). Lower FC score predicted both remission and response to treatment after 8 weeks of antidepressant monotherapy. The emphasis here on the role of dorsal striatum and thalamus consolidates prior work of subcortical connectivity in MDD. The findings provide insight into the pathogenesis of MDD, linking subcortical FC with negative affect. However, while the FC score significantly predicted treatment outcome, the low odds ratio suggests that finding predictive biomarkers for depression remains an aspiration.
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Affiliation(s)
- Yan-Kun Wu
- 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), Beijing, 100191, China
| | - Yun-Ai Su
- 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), Beijing, 100191, China.
| | - Lin-Lin 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), Beijing, 100191, China
| | - ChaoGan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Ji-Tao 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), Beijing, 100191, China
| | - Jing-Yu 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), Beijing, 100191, China
| | - JingXu Chen
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Lin Chen
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Ke Li
- PLA Strategic support Force Characteristic Medical Center, Beijing, 100101, China
| | - Dan J Stein
- Neuroscience Institute, Department of Psychiatry and Mental Health, South African Medical Research Council (SAMRC), Unit on Risk and Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa
| | - Tian-Mei Si
- 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), Beijing, 100191, China.
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27
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Helmer M, Warrington S, Mohammadi-Nejad AR, Ji JL, Howell A, Rosand B, Anticevic A, Sotiropoulos SN, Murray JD. On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations. Commun Biol 2024; 7:217. [PMID: 38383808 PMCID: PMC11245620 DOI: 10.1038/s42003-024-05869-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 01/28/2024] [Indexed: 02/23/2024] Open
Abstract
Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the feature pattern underlying the association. We confirmed these trends across two neuroimaging modalities and in independent datasets with n ≈ 1000 and n = 20,000, and found that only the latter comprised sufficient observations for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses. Collectively, we characterize how to limit detrimental effects of overfitting on CCA/PLS stability, and provide recommendations for future studies.
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Affiliation(s)
- Markus Helmer
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom
| | - Ali-Reza Mohammadi-Nejad
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Ctr, Queens Medical Ctr, Nottingham, United Kingdom
| | - Jie Lisa Ji
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Amber Howell
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Benjamin Rosand
- Department of Physics, Yale University, New Haven, CT, 06511, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychology, Yale University, New Haven, CT, 06511, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom.
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Ctr, Queens Medical Ctr, Nottingham, United Kingdom.
| | - John D Murray
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA.
- Manifest Technologies, New Haven, CT, 06510, USA.
- Department of Physics, Yale University, New Haven, CT, 06511, USA.
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA.
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28
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Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [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: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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29
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Li D, Hou D, Zhang Y, Zhao Y, Cui X, Niu Y, Xiang J, Wang B. Aberrant Functional Connectivity in Core-Periphery Structure Based on WSBM in ADHD. J Atten Disord 2024; 28:415-430. [PMID: 38102929 DOI: 10.1177/10870547231214985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
OBJECTIVE Brain network studies have revealed that the community structure of ADHD is altered. However, these studies have only focused on modular community structure, ignoring the core-periphery community structure. METHOD This paper employed the weighted stochastic block model to divide the functional connectivity (FC) into 10 communities. And we adopted core score to define the core-periphery structure of FC. Finally, connectivity strength (CS) and disruption index (DI) were used to evaluate the changes of core-periphery structure in ADHD. RESULTS The core community of visual network showed reduced CS and a positive value of DI, while the CS of periphery community was enhanced. In addition, the interaction between core communities (involving the sensorimotor and visual network) and periphery community of attention network showed increased CS and a negative valve of DI. CONCLUSION Anomalies in core-periphery community structure provide a new perspective for understanding the community structure of ADHD.
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Affiliation(s)
- Dandan Li
- Taiyuan University of Technology, Shanxi, China
| | - Dianni Hou
- Taiyuan University of Technology, Shanxi, China
| | | | - Yao Zhao
- Taiyuan University of Technology, Shanxi, China
| | | | - Yan Niu
- Taiyuan University of Technology, Shanxi, China
| | - Jie Xiang
- Taiyuan University of Technology, Shanxi, China
| | - Bin Wang
- Taiyuan University of Technology, Shanxi, China
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30
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Ibrahim K, Iturmendi-Sabater I, Vasishth M, Barron DS, Guardavaccaro M, Funaro MC, Holmes A, McCarthy G, Eickhoff SB, Sukhodolsky DG. Neural circuit disruptions of eye gaze processing in autism spectrum disorder and schizophrenia: An activation likelihood estimation meta-analysis. Schizophr Res 2024; 264:298-313. [PMID: 38215566 PMCID: PMC10922721 DOI: 10.1016/j.schres.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 09/07/2023] [Accepted: 12/05/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND Impairment in social cognition, particularly eye gaze processing, is a shared feature common to autism spectrum disorder (ASD) and schizophrenia. However, it is unclear if a convergent neural mechanism also underlies gaze dysfunction in these conditions. The present study examined whether this shared eye gaze phenotype is reflected in a profile of convergent neurobiological dysfunction in ASD and schizophrenia. METHODS Activation likelihood estimation (ALE) meta-analyses were conducted on peak voxel coordinates across the whole brain to identify spatial convergence. Functional coactivation with regions emerging as significant was assessed using meta-analytic connectivity modeling. Functional decoding was also conducted. RESULTS Fifty-six experiments (n = 30 with schizophrenia and n = 26 with ASD) from 36 articles met inclusion criteria, which comprised 354 participants with ASD, 275 with schizophrenia and 613 healthy controls (1242 participants in total). In ASD, aberrant activation was found in the left amygdala relative to unaffected controls during gaze processing. In schizophrenia, aberrant activation was found in the right inferior frontal gyrus and supplementary motor area. Across ASD and schizophrenia, aberrant activation was found in the right inferior frontal gyrus and right fusiform gyrus during gaze processing. Functional decoding mapped the left amygdala to domains related to emotion processing and cognition, the right inferior frontal gyrus to cognition and perception, and the right fusiform gyrus to visual perception, spatial cognition, and emotion perception. These regions also showed meta-analytic connectivity to frontoparietal and frontotemporal circuitry. CONCLUSION Alterations in frontoparietal and frontotemporal circuitry emerged as neural markers of gaze impairments in ASD and schizophrenia. These findings have implications for advancing transdiagnostic biomarkers to inform targeted treatments for ASD and schizophrenia.
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Affiliation(s)
- Karim Ibrahim
- Yale University School of Medicine, Child Study Center, United States of America.
| | | | - Maya Vasishth
- Yale University School of Medicine, Child Study Center, United States of America
| | - Daniel S Barron
- Brigham and Women's Hospital, Department of Psychiatry, Anesthesiology and Pain Medicine, United States of America; Harvard Medical School, Department of Psychiatry, United States of America
| | | | - Melissa C Funaro
- Yale University, Harvey Cushing/John Hay Whitney Medical Library, United States of America
| | - Avram Holmes
- Yale University, Department of Psychology, United States of America; Yale University, Department of Psychiatry, United States of America; Yale University, Wu Tsai Institute, United States of America
| | - Gregory McCarthy
- Yale University, Department of Psychology, United States of America; Yale University, Wu Tsai Institute, United States of America
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Denis G Sukhodolsky
- Yale University School of Medicine, Child Study Center, United States of America
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31
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Camargos GS, Garcia MAVA, de Almeida CA, Lopes AM, Borghi FA, de Araújo Filho GM, de Mattos LC, Brandão CC. Clinical and epidemiological profile of patients with mental disorders in a specialized outpatient clinic and its role in the psychosocial care network. Front Psychiatry 2024; 15:1274192. [PMID: 38328761 PMCID: PMC10847542 DOI: 10.3389/fpsyt.2024.1274192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 01/04/2024] [Indexed: 02/09/2024] Open
Abstract
Introduction Mental health disorders (MHDs) are responsible for much impairment of quality of life in Brazil and worldwide. Early diagnosis and effective treatment strategies are required due to the heterogeneous symptoms and multifactorial etiology. Methods A descriptive retrospective observational study was performed aiming to characterize the clinical and psychiatric profiles of patients with MHD attending a Brazilian public tertiary psychiatric outpatient clinic, which is a reference health service for more than 2 million inhabitants. Predominant clinical and sociodemographic aspects of patients were evaluated between March 2019 and March 2021. Results A total of 8,384 appointments were analyzed. The majority of patients were female, and the mean age was 45 years old. Generalized anxiety disorder (GAD) was the most common MHD. The prevailing symptoms were sadness, anxiety, and irritability, with the most prescribed medications being selective serotonin reuptake inhibitors. Conclusion The epidemiological characterization of mental disorders in specialized mental health outpatient clinics provides evidence for the establishment of more specific protocols and advocates a dimensional transdiagnostic approach as an aid to public mental health services.
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Affiliation(s)
- Gláucio Silva Camargos
- Faculdade de Medicina de São José do Rio Preto – FAMERP, São José do Rio Preto, São Paulo, Brazil
| | | | | | - Angélica Marta Lopes
- Faculdade de Medicina de São José do Rio Preto – FAMERP, São José do Rio Preto, São Paulo, Brazil
| | | | - Gerardo Maria de Araújo Filho
- Faculdade de Medicina de São José do Rio Preto – FAMERP, São José do Rio Preto, São Paulo, Brazil
- Hospital de Base de São José do Rio Preto, São Paulo, Brazil
| | - Luíz Carlos de Mattos
- Faculdade de Medicina de São José do Rio Preto – FAMERP, São José do Rio Preto, São Paulo, Brazil
| | - Cinara Cássia Brandão
- Faculdade de Medicina de São José do Rio Preto – FAMERP, São José do Rio Preto, São Paulo, Brazil
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32
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Niu L, Fang K, Han S, Xu C, Sun X. Resolving heterogeneity in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder through individualized structural covariance network analysis. Cereb Cortex 2024; 34:bhad391. [PMID: 38142281 DOI: 10.1093/cercor/bhad391] [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: 09/10/2023] [Revised: 09/30/2023] [Accepted: 10/01/2023] [Indexed: 12/25/2023] Open
Abstract
Disruptions in large-scale brain connectivity are hypothesized to contribute to psychiatric disorders, including schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. However, high inter-individual variation among patients with psychiatric disorders hinders achievement of unified findings. To this end, we adopted a newly proposed method to resolve heterogeneity of differential structural covariance network in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. This method could infer individualized structural covariance aberrance by assessing the deviation from healthy controls. T1-weighted anatomical images of 114 patients with psychiatric disorders (schizophrenia: n = 37; bipolar I disorder: n = 37; attention-deficit/hyperactivity disorder: n = 37) and 110 healthy controls were analyzed to obtain individualized differential structural covariance network. Patients exhibited tremendous heterogeneity in profiles of individualized differential structural covariance network. Despite notable heterogeneity, patients with the same disorder shared altered edges at network level. Moreover, individualized differential structural covariance network uncovered two distinct psychiatric subtypes with opposite differences in structural covariance edges, that were otherwise obscured when patients were merged, compared with healthy controls. These results provide new insights into heterogeneity and have implications for the nosology in psychiatric disorders.
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Affiliation(s)
- Lianjie Niu
- Department of Breast Disease, Henan Breast Cancer Center. The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Keke Fang
- Department of Pharmacy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450008, China
| | - Chunmiao Xu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Xianfu Sun
- Department of Breast Disease, Henan Breast Cancer Center. The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
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33
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Chen P, An L, Wulan N, Zhang C, Zhang S, Ooi LQR, Kong R, Chen J, Wu J, Chopra S, Bzdok D, Eickhoff SB, Holmes AJ, Yeo BT. Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.569848. [PMID: 38106085 PMCID: PMC10723283 DOI: 10.1101/2023.12.05.569848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.
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Affiliation(s)
- Pansheng Chen
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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
| | - Lijun An
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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
| | - Naren Wulan
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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
| | - Chen Zhang
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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
| | - Shaoshi Zhang
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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 (ISEP), National University of Singapore, Singapore
| | - Leon Qi Rong Ooi
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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 (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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
| | - Jianzhong Chen
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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
| | - Jianxiao Wu
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal QC, Canada
- Mila – Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Avram J Holmes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
| | - B.T. Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of 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 (ISEP), National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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34
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Holmes A, Levi PT, Chen YC, Chopra S, Aquino KM, Pang JC, Fornito A. Disruptions of Hierarchical Cortical Organization in Early Psychosis and Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1240-1250. [PMID: 37683727 DOI: 10.1016/j.bpsc.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/27/2023] [Accepted: 08/14/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND The cerebral cortex is organized hierarchically along an axis that spans unimodal sensorimotor to transmodal association areas. This hierarchy is often characterized using low-dimensional embeddings, termed gradients, of interregional functional coupling estimates measured with resting-state functional magnetic resonance imaging. Such analyses may offer insights into the pathophysiology of schizophrenia, which has been frequently linked to dysfunctional interactions between association and sensorimotor areas. METHODS To examine disruptions of hierarchical cortical function across distinct stages of psychosis, we applied diffusion map embedding to 2 independent functional magnetic resonance imaging datasets: one comprising 114 patients with early psychosis and 48 control participants, and the other comprising 50 patients with established schizophrenia and 121 control participants. Then, we analyzed the primary sensorimotor-to-association and secondary visual-to-sensorimotor gradients of each participant in both datasets. RESULTS There were no significant differences in regional gradient scores between patients with early psychosis and control participants. Patients with established schizophrenia showed significant differences in the secondary, but not primary, gradient compared with control participants. Gradient differences in schizophrenia were characterized by lower within-network dispersion in the dorsal attention (false discovery rate [FDR]-corrected p [pFDR] < .001), visual (pFDR = .003), frontoparietal (pFDR = .018), and limbic (pFDR = .020) networks and lower between-network dispersion between the visual network and other networks (pFDR < .001). CONCLUSIONS These findings indicate that differences in cortical hierarchical function occur along the secondary visual-to-sensorimotor axis rather than the primary sensorimotor-to-association axis as previously thought. The absence of differences in early psychosis suggests that visual-sensorimotor abnormalities may emerge as the illness progresses.
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Affiliation(s)
- Alexander Holmes
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia.
| | - Priscila T Levi
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
| | - Yu-Chi Chen
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia; Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Kevin M Aquino
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - James C Pang
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
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35
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Michael C, Taxali A, Angstadt M, Kardan O, Weigard A, Molloy MF, McCurry KL, Hyde LW, Heitzeg MM, Sripada C. Socioeconomic resources in youth are linked to divergent patterns of network integration and segregation across the brain's transmodal axis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.08.565517. [PMID: 38014302 PMCID: PMC10680554 DOI: 10.1101/2023.11.08.565517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully influence intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neurobiological meaning of these widespread alterations remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage the resources of graph theory to precisely characterize multivariate and univariate associations between household SER and the functional integration and segregation (i.e., participation coefficient, within-module degree) of brain regions across major cognitive, affective, and sensorimotor systems during the resting state in 5,821 youth (ages 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are related to greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that the effects of SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread effects of SER on brain organization, indicating that SER levels differentially configure the intrinsic functional architecture of developing unimodal and transmodal systems. This study highlights both sensorimotor and higher-order networks that may serve as neural markers of environmental stress and opportunity, and which may guide efforts to scaffold healthy neurobehavioral development among disadvantaged communities of youth.
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36
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Xu L, Choi S, Zhao Y, Li M, Rogers BP, Anderson A, Gore JC, Gao Y, Ding Z. Seasonal variations of functional connectivity of human brains. Sci Rep 2023; 13:16898. [PMID: 37803105 PMCID: PMC10558480 DOI: 10.1038/s41598-023-43152-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 09/20/2023] [Indexed: 10/08/2023] Open
Abstract
Seasonal variations have long been observed in various aspects of human life. While there is an abundance of research that has characterized seasonality effects in, for example, cognition, mood, and behavior, including studies of underlying biophysical mechanisms, direct measurements of seasonal variations of brain functional activities have not gained wide attention. We have quantified seasonal effects on functional connectivity as derived from MRI scans. A cohort of healthy human subjects was divided into four groups based on the seasons of their scanning dates as documented in the image database of the Human Connectome Project. Sinusoidal functions were used as regressors to determine whether there were significant seasonal variations in measures of brain activities. We began with the analysis of seasonal variations of the fractional amplitudes of low frequency fluctuations of regional functional signals, followed by the seasonal variations of functional connectivity in both global- and network-level. Furthermore, relevant environmental factors, including average temperature and daylength, were found to be significantly associated with brain functional activities, which may explain how the observed seasonal fluctuations arise. Finally, topological properties of the brain functional network also showed significant variations across seasons. All the observations accumulated revealed seasonality effects of human brain activities in a resting-state, which may have important practical implications for neuroimaging research.
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Affiliation(s)
- Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Soyoung Choi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
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37
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Bruin WB, Abe Y, Alonso P, Anticevic A, Backhausen LL, Balachander S, Bargallo N, Batistuzzo MC, Benedetti F, Bertolin Triquell S, Brem S, Calesella F, Couto B, Denys DAJP, Echevarria MAN, Eng GK, Ferreira S, Feusner JD, Grazioplene RG, Gruner P, Guo JY, Hagen K, Hansen B, Hirano Y, Hoexter MQ, Jahanshad N, Jaspers-Fayer F, Kasprzak S, Kim M, Koch K, Bin Kwak Y, Kwon JS, Lazaro L, Li CSR, Lochner C, Marsh R, Martínez-Zalacaín I, Menchon JM, Moreira PS, Morgado P, Nakagawa A, Nakao T, Narayanaswamy JC, Nurmi EL, Zorrilla JCP, Piacentini J, Picó-Pérez M, Piras F, Piras F, Pittenger C, Reddy JYC, Rodriguez-Manrique D, Sakai Y, Shimizu E, Shivakumar V, Simpson BH, Soriano-Mas C, Sousa N, Spalletta G, Stern ER, Evelyn Stewart S, Szeszko PR, Tang J, Thomopoulos SI, Thorsen AL, Yoshida T, Tomiyama H, Vai B, Veer IM, Venkatasubramanian G, Vetter NC, Vriend C, Walitza S, Waller L, Wang Z, Watanabe A, Wolff N, Yun JY, Zhao Q, van Leeuwen WA, van Marle HJF, van de Mortel LA, van der Straten A, van der Werf YD, Thompson PM, Stein DJ, van den Heuvel OA, van Wingen GA. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium. Mol Psychiatry 2023; 28:4307-4319. [PMID: 37131072 PMCID: PMC10827654 DOI: 10.1038/s41380-023-02077-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 05/04/2023]
Abstract
Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.
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Grants
- R01 AG058854 NIA NIH HHS
- R01 MH126213 NIMH NIH HHS
- R21 MH101441 NIMH NIH HHS
- R01 MH121520 NIMH NIH HHS
- R21 MH093889 NIMH NIH HHS
- R01 MH116147 NIMH NIH HHS
- R01 MH111794 NIMH NIH HHS
- R01 MH085900 NIMH NIH HHS
- P41 EB015922 NIBIB NIH HHS
- IA/CPHE/18/1/503956 DBT-Wellcome Trust India Alliance
- UL1 TR001863 NCATS NIH HHS
- R01 MH081864 NIMH NIH HHS
- R01 MH104648 NIMH NIH HHS
- U54 EB020403 NIBIB NIH HHS
- R01 MH117601 NIMH NIH HHS
- R01 MH116038 NIMH NIH HHS
- R01 MH126981 NIMH NIH HHS
- R01 NS107513 NINDS NIH HHS
- RF1 MH123163 NIMH NIH HHS
- R33 MH107589 NIMH NIH HHS
- K24 MH121571 NIMH NIH HHS
- R01 MH121246 NIMH NIH HHS
- Wellcome Trust
- K23 MH115206 NIMH NIH HHS
- R01 AG059874 NIA NIH HHS
- Funding from Japan Society for the Promotion of Science (KAKENHI Grant No. 18K15523)
- Carlos III Health Institute PI18/00856
- NIMH: 5R01MH116038
- Sara Bertolin was supported by Instituto de Salud Carlos III through the grant CM21/00278 (Co-funded by European Social Fund. ESF investing in your future).
- Hartmann Müller Foundation (no. 1460, principal investigator: S.Brem)
- NIHM: R01MH085900, R01MH121520
- NIH: K23 MH115206 & IOCDF Annual Research Award
- AMED Brain/MINDS Beyond program Grant No. JP22dm0307002, JSPS KAKENHI Grants No. 22H01090, 21K03084, 19K03309, 16K04344
- NIH: R01MH117601, R01AG059874, P41EB015922, R01MH126213, R01MH121246
- Michael Smith Health Research BC
- the Deutsche Forschungsgemeinschaf (KO 3744/11-1)
- This work was supported by the Medical Research Council of South Africa (SAMRC), and the National Research Foundation of South Africa (Christine Lochner), and we acknowledge the contribution of our research assistants.
- NIMH: R21MH093889, R21MH101441 and R01MH104648
- IM-Z was supported by a PFIS grant (FI17/00294) from the Carlos III Health Institute
- This work was supported by National funds, through the Foundation for Science and Technology (project UIDB/50026/2020 and UIDP/50026/2020); by the Norte Portugal Regional Operational Programme (NORTE 2020) under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) (projects NORTE-01-0145-FEDER-000013 and NORTE-01-0145-FEDER-000023), and by the FLAD Science Award Mental Health 2021.
- JSPS KAKENHI (C)21K07547, 22K07598 and 22K15766
- Government of India grants from Department of Science and Technology (DST INSPIRE faculty grant -IFA12-LSBM-26) & Department of Biotechnology (BT/06/IYBA/2012)
- NIMH: R01MH081864
- MPP was supported by the Spanish Ministry of Universities, with funds from the European Union - NextGenerationEU (MAZ/2021/11).
- Italian Ministry of Health, Ricerca Corrente 2022, 2023
- NIMH: K24MH121571
- Government of India grants to: Prof. Reddy [(SR/S0/HS/0016/2011) & (BT/PR13334/Med/30/259/2009)], Dr. Janardhanan Narayanaswamy (DST INSPIRE faculty grant -IFA12-LSBM-26) & (BT/06/IYBA/2012) and the Wellcome-DBT India Alliance grant to Dr. Ganesan Venkatasubramanian (500236/Z/11/Z)
- the Japan Agency for Medical Research and Development: JP22dm0307008
- DBT-Wellcome Trust India Alliance Early Career Fellowship grant (IA/CPHE/18/1/503956)
- NIMH: R21MH093889 and R01MH104648
- Grant #PI19/01171 from the Carlos III Health Institute, and 2017SGR 1247 from AGAUR-Generalitat de Catalunya.
- Italian Ministry of Health grant RC19-20-21-22/A
- Grants R01MH126981, R01MH111794, and R33MH107589 from the National Institute of Mental Health/National Institute of Health awarded to ERS.
- National Natural Science Foundation of China (Nos. 81871057, 82171495), and Key Technologies Research and Development Program of China (Nos.2022YFE0103700)
- Helse Vest Health Authority (Grant ID 911754 and 911880)
- JSPS KAKENHI (C) JP21K07547, 22K07598 and 22K15766.
- Ganesan Venkatasubramanian acknowledges the support of Department of Biotechnology (DBT) - Wellcome Trust India Alliance CRC grant (IA/CRC/19/1/610005) & senior fellowship grant (500236/Z/11/Z)
- Supported by an grant from Amsterdam Neuroscience CIA-2019-03-A
- Swiss National Science Foundation (no. 320030_130237, principal investigator: S.Walitza)
- The National Natural Science Foundation of China (82071518)
- Else Kröner Fresenius Stiftung (2017_A101)
- ENIGMA World Aging Center, NIA Award No. R01AG058854; ENIGMA Parkinson's Initiative: A Global Initiative for Parkinson's Disease, NINDS award RO1NS107513
- the Obsessive-Compulsive Foundation to Dan J. Stein
- Dutch Organization for Scientific Research (NWO/ZonMW) VENI grant (916-86-038) and Brain & Behavior Research Foundation (NARSAD grant), Netherlands Brain Foundation (2010(1)-50)
- Netherlands Organization for Scientific Research (NWO/ZonMW Vidi Grant No. 165.610.002, 016.156.318, and 917.15.318 G.A. van Wingen)
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Affiliation(s)
- Willem B Bruin
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Yoshinari Abe
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Pino Alonso
- Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Science, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
| | - Alan Anticevic
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Lea L Backhausen
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Srinivas Balachander
- Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
| | - Nuria Bargallo
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Radiology Service, Diagnosis Image Center, Hospital Clinic de Barcelona, Barcelona, Spain
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marcelo C Batistuzzo
- Department of Psychiatry, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
- Department of Methods and Techniques in Psychology, Pontifical Catholic University, Sao Paulo, Brazil
| | - Francesco Benedetti
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Sara Bertolin Triquell
- Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Federico Calesella
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Beatriz Couto
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
| | - Damiaan A J P Denys
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Marco A N Echevarria
- Department of Psychiatry, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Goi Khia Eng
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Sónia Ferreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
| | - Jamie D Feusner
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- General Adult Psychiatry & Health Systems, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | | | - Patricia Gruner
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Joyce Y Guo
- University of California, San Diego, CA, USA
| | - Kristen Hagen
- Molde Hospital, Møre og Romsdal Hospital Trust, Molde, Norway
- Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
- Department of Mental Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjarne Hansen
- Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
- Center for Crisis Psychology, University of Bergen, Bergen, Norway
| | - Yoshiyuki Hirano
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
| | - Marcelo Q Hoexter
- Department of Psychiatry, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Fern Jaspers-Fayer
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Selina Kasprzak
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kathrin Koch
- Department of Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Yoo Bin Kwak
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Luisa Lazaro
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Hospital Clinic of Barcelona, Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
| | | | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Rachel Marsh
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Ignacio Martínez-Zalacaín
- Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Jose M Menchon
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Pedro S Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
| | - Akiko Nakagawa
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
| | - Tomohiro Nakao
- Graduate School of Medical Sciences, Kyushu University, Fukuoka-shi, Japan
| | - Janardhanan C Narayanaswamy
- National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
- GVAMHS, Goulburn Valley Health, Shepparton, VIC, Australia
| | - Erika L Nurmi
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA
| | - Jose C Pariente Zorrilla
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - John Piacentini
- Division of Child and Adolescent Psychiatry, UCLA Semel Institute for Neuroscience, Los Angeles, CA, USA
| | - Maria Picó-Pérez
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Castelló de la Plana, Spain
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | | | - Janardhan Y C Reddy
- Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
| | - Daniela Rodriguez-Manrique
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center (TUM-NIC) of Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Graduate School of Systemic Neurosciences (GSN), Ludwig-Maximilians-Universität, Munich, Germany
| | - Yuki Sakai
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
| | - Eiji Shimizu
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Japan
- Department of Cognitive Behavioral Physiology Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Venkataram Shivakumar
- Department of Integrative Medicine, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
| | - Blair H Simpson
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Carles Soriano-Mas
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona-UB, Barcelona, Spain
| | - Nuno Sousa
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Emily R Stern
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - S Evelyn Stewart
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- British Columbia Children's Hospital Research Institute, Vancouver, BC, Canada
- British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - Philip R Szeszko
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Jinsong Tang
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anders L Thorsen
- Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
- Center for Crisis Psychology, University of Bergen, Bergen, Norway
| | - Tokiko Yoshida
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
| | - Hirofumi Tomiyama
- Graduate School of Medical Sciences, Kyushu University, Fukuoka-shi, Japan
| | - Benedetta Vai
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Ilya M Veer
- Department of Developmental Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Ganesan Venkatasubramanian
- Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
| | - Nora C Vetter
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- Department of Psychology, Faculty of Natural Sciences, MSB Medical School Berlin, Berlin, Germany
| | - Chris Vriend
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention program, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging program, Amsterdam, The Netherlands
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
| | - Lea Waller
- Department of Psychiatry and Neurosciences CCM, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Zhen Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao, China
| | - Anri Watanabe
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Nicole Wolff
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Qing Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao, China
| | - Wieke A van Leeuwen
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Hein J F van Marle
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood Anxiety Psychosis Stress Sleep, Amsterdam, The Netherlands
| | - Laurens A van de Mortel
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Anouk van der Straten
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Ysbrand D van der Werf
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention program, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging program, Amsterdam, The Netherlands
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dan J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Odile A van den Heuvel
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention program, Amsterdam, The Netherlands
| | - Guido A van Wingen
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
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Sun Y, Zhang M, Saggar M. Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders. Neuroimage 2023; 279:120302. [PMID: 37579998 PMCID: PMC10515743 DOI: 10.1016/j.neuroimage.2023.120302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 07/26/2023] [Accepted: 07/29/2023] [Indexed: 08/16/2023] Open
Abstract
Resting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Brain network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). An alternative approach is to examine multi(or cross)-attractor dynamics, which can be used to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multi-attractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the global coupling between regions was significantly decreased in schizophrenia patients relative to healthy controls. At the same time, the ratio between local excitation and inhibition was significantly higher in the schizophrenia group than the ADHD group. In line with these results, the schizophrenia group had the lowest switching costs (energy gaps) across groups for several networks including the default mode network. Paired comparison also showed that schizophrenia patients had significantly lower energy gaps than healthy controls for the somatomotor and visual networks. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders' pathophysiology.
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Affiliation(s)
- Yinming Sun
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Mengsen Zhang
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA.
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Han Z, Liu T, Shi Z, Zhang J, Suo D, Wang L, Chen D, Wu J, Yan T. Investigating the heterogeneity within the somatosensory-motor network and its relationship with the attention and default systems. PNAS NEXUS 2023; 2:pgad276. [PMID: 37693210 PMCID: PMC10485902 DOI: 10.1093/pnasnexus/pgad276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 06/23/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023]
Abstract
The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.
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Affiliation(s)
- Ziteng Han
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Zhongyan Shi
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Jian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Li Wang
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
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40
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Durham EL, Ghanem K, Stier AJ, Cardenas-Iniguez C, Reimann GE, Jeong HJ, Dupont RM, Dong X, Moore TM, Berman MG, Lahey BB, Bzdok D, Kaczkurkin AN. Multivariate analytical approaches for investigating brain-behavior relationships. Front Neurosci 2023; 17:1175690. [PMID: 37583413 PMCID: PMC10423877 DOI: 10.3389/fnins.2023.1175690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 07/13/2023] [Indexed: 08/17/2023] Open
Abstract
Background Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations. Method We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes. Results The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods. Conclusion Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method's underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.
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Affiliation(s)
- E. Leighton Durham
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Karam Ghanem
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Andrew J. Stier
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | | | - Hee Jung Jeong
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Randolph M. Dupont
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Xiaoyu Dong
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Marc G. Berman
- Department of Psychology, University of Chicago, Chicago, IL, United States
- The University of Chicago Neuroscience Institute, University of Chicago, Chicago, IL, United States
| | - Benjamin B. Lahey
- Department of Health Studies, University of Chicago, Chicago, IL, United States
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States
| | - Danilo Bzdok
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
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41
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Zhao K, Xie H, Fonzo GA, Carlisle N, Osorio RS, Zhang Y. Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.02.547427. [PMID: 37461451 PMCID: PMC10349933 DOI: 10.1101/2023.07.02.547427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
BACKGROUND Dementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia. METHODS Using large-scale neuroimaging data from the Open Access Series of Imaging Studies (OASIS-3), we conducted a multivariate sparse canonical correlation analysis to identify functional connectivity-informed symptom dimensions. Subsequently, we performed a clustering analysis on the obtained latent connectivity profiles to reveal neurophysiological subtypes and examined differences in abnormal connectivity and phenotypic profiles between subtypes. RESULTS We identified two reliable neuropsychiatric subsyndromes - behavioral and anxiety in the connectivity-NPS linked latent space. The behavioral subsyndrome was characterized by the connections predominantly involving the default mode and somatomotor networks and neuropsychiatric symptoms involving nighttime behavior disturbance, agitation, and apathy. The anxiety subsyndrome was mainly contributed by connections involving the visual network and the anxiety neuropsychiatric symptom. By clustering individuals along these two subsyndromes-linked connectivity latent features, we uncovered three subtypes encompassing both dementia patients and healthy controls. Dementia in one subtype exhibited similar brain connectivity and cognitive-behavior patterns to healthy individuals. However, dementia in the other two subtypes showed different dysfunctional connectivity profiles involving the default mode, frontoparietal control, somatomotor, and ventral attention networks, compared to healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity and longitudinal progression of cognitive impairment and behavioral dysfunction. CONCLUSIONS Our findings shed valuable insights into disentangling the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the development of timely and targeted interventions for dementia patients.
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Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
- George Washington University School of Medicine, Washington, DC, USA
| | - Gregory A. Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Ricardo S. Osorio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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42
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Mwilambwe-Tshilobo L, Setton R, Bzdok D, Turner GR, Spreng RN. Age differences in functional brain networks associated with loneliness and empathy. Netw Neurosci 2023; 7:496-521. [PMID: 37397888 PMCID: PMC10312262 DOI: 10.1162/netn_a_00293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 11/18/2022] [Indexed: 03/14/2024] Open
Abstract
Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality-loneliness and empathic responding-and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.
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Affiliation(s)
- Laetitia Mwilambwe-Tshilobo
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Roni Setton
- Department of Psychology, Harvard University, Boston, MA, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- School of Computer Science, McGill University, Montreal, QC, Canada
- Mila–Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Gary R. Turner
- Department of Psychology, York University, Toronto, ON, Canada
| | - R. Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Verdun, QC, Canada
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43
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Lydia Qu Y, Chen J, Tam A, Ooi LQR, Dhamala E, Cocuzza C, Lawhead C, Yeo BTT, Holmes AJ. Distinct brain network features predict internalizing and externalizing traits in children and adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.20.541490. [PMID: 37292775 PMCID: PMC10245695 DOI: 10.1101/2023.05.20.541490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Internalizing and externalizing traits are two distinct classes of behaviors in psychiatry. However, whether shared or unique brain network features predict internalizing and externalizing behaviors in children and adults remain poorly understood. Using a sample of 2262 children from the Adolescent Brain Cognitive Development (ABCD) study and 752 adults from the Human Connectome Project (HCP), we show that network features predicting internalizing and externalizing behavior are, at least in part, dissociable in children, but not in adults. In ABCD children, traits within internalizing and externalizing behavioral categories are predicted by more similar network features concatenated across task and resting states than those between different categories. We did not observe this pattern in HCP adults. Distinct network features predict internalizing and externalizing behaviors in ABCD children and HCP adults. These data reveal shared and unique brain network features accounting for individual variation within broad internalizing and externalizing categories across developmental stages.
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Affiliation(s)
- Yueyue Lydia Qu
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Jianzhong Chen
- Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Center for Translational MR Research, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Center for Translational MR Research, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Center for Translational MR Research, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, CT, USA
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, USA
- Kavli Institute for Neuroscience, Yale University, New Haven, USA
| | - Carrisa Cocuzza
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Connor Lawhead
- Department of Psychology, Yale University, New Haven, CT, USA
| | - B T Thomas Yeo
- Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Center for Translational MR Research, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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Thomasson M, Voruz P, Cionca A, Jacot de Alcântara I, Nuber-Champier A, Allali G, Benzakour L, Lalive PH, Lövblad KO, Braillard O, Nehme M, Coen M, Serratrice J, Reny JL, Pugin J, Guessous I, Landis BN, Griffa A, Van De Ville D, Assal F, Péron JA. Markers of limbic system damage following SARS-CoV-2 infection. Brain Commun 2023; 5:fcad177. [PMID: 37415776 PMCID: PMC10320753 DOI: 10.1093/braincomms/fcad177] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/21/2023] [Accepted: 06/09/2023] [Indexed: 07/08/2023] Open
Abstract
Alterations of the limbic system may be present in the chronic phase of SARS-CoV-2 infection. Our aim was to study the long-term impact of this disease on limbic system-related behaviour and its associated brain functional connectivity, according to the severity of respiratory symptoms in the acute phase. To this end, we investigated the multimodal emotion recognition abilities of 105 patients from the Geneva COVID-COG Cohort 223 days on average after SARS-CoV-2 infection (diagnosed between March 2020 and May 2021), dividing them into three groups (severe, moderate or mild) according to respiratory symptom severity in the acute phase. We used multiple regressions and partial least squares correlation analyses to investigate the relationships between emotion recognition, olfaction, cognition, neuropsychiatric symptoms and functional brain networks. Six to 9 months following SARS-CoV-2 infection, moderate patients exhibited poorer recognition abilities than mild patients for expressions of fear (P = 0.03 corrected), as did severe patients for disgust (P = 0.04 corrected) and irritation (P < 0.01 corrected). In the whole cohort, these performances were associated with decreased episodic memory and anosmia, but not with depressive symptoms, anxiety or post-traumatic stress disorder. Neuroimaging revealed a positive contribution of functional connectivity, notably between the cerebellum and the default mode, somatosensory motor and salience/ventral attention networks. These results highlight the long-term consequences of SARS-Cov-2 infection on the limbic system at both the behavioural and neuroimaging levels.
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Affiliation(s)
| | | | - Alexandre Cionca
- Clinical and Experimental Neuropsychology Laboratory, Faculty of Psychology, University of Geneva, Geneva 1205, Switzerland
| | - Isabele Jacot de Alcântara
- Clinical and Experimental Neuropsychology Laboratory, Faculty of Psychology, University of Geneva, Geneva 1205, Switzerland
- Neurology Department, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva 1205, Switzerland
| | - Anthony Nuber-Champier
- Clinical and Experimental Neuropsychology Laboratory, Faculty of Psychology, University of Geneva, Geneva 1205, Switzerland
| | - Gilles Allali
- Leenaards Memory Centre, Lausanne University Hospital and University of Lausanne, Lausanne 1205, Switzerland
| | - Lamyae Benzakour
- Psychiatry Department, Geneva University Hospitals, Geneva 1205, Switzerland
| | - Patrice H Lalive
- Neurology Department, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva 1205, Switzerland
- Faculty of Medicine, University of Geneva, Geneva 1011, Switzerland
| | - Karl-Olof Lövblad
- Faculty of Medicine, University of Geneva, Geneva 1011, Switzerland
- Diagnostic and Interventional Neuroradiology Department, Geneva University Hospitals, Geneva 1205, Switzerland
| | - Olivia Braillard
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva 1205, Switzerland
| | - Mayssam Nehme
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva 1205, Switzerland
| | - Matteo Coen
- Division of General Internal Medicine, Department of Medicine, Geneva University Hospitals and Geneva University, Geneva 1205, Switzerland
| | - Jacques Serratrice
- Division of General Internal Medicine, Department of Medicine, Geneva University Hospitals and Geneva University, Geneva 1205, Switzerland
| | - Jean-Luc Reny
- Faculty of Medicine, University of Geneva, Geneva 1011, Switzerland
- Division of General Internal Medicine, Department of Medicine, Geneva University Hospitals and Geneva University, Geneva 1205, Switzerland
| | - Jérôme Pugin
- Faculty of Medicine, University of Geneva, Geneva 1011, Switzerland
- Intensive Care Department, Geneva University Hospitals, Geneva 1205, Switzerland
| | - Idris Guessous
- Faculty of Medicine, University of Geneva, Geneva 1011, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva 1205, Switzerland
| | - Basile N Landis
- Faculty of Medicine, University of Geneva, Geneva 1011, Switzerland
- Rhinology-Olfactology Unit, Otorhinolaryngology Department, Geneva University Hospitals, Geneva 1205, Switzerland
| | - Alessandra Griffa
- Leenaards Memory Centre, Lausanne University Hospital and University of Lausanne, Lausanne 1205, Switzerland
- Institute of Bioengineering, Centre for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1205, Switzerland
| | - Dimitri Van De Ville
- Faculty of Medicine, University of Geneva, Geneva 1011, Switzerland
- Institute of Bioengineering, Centre for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Frederic Assal
- Neurology Department, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva 1205, Switzerland
- Faculty of Medicine, University of Geneva, Geneva 1011, Switzerland
| | - Julie A Péron
- Correspondence to: Julie Péron Clinical and Experimental Neuropsychology Laboratory Faculté de Psychologie et des Sciences de l’Education Université de Genève, 40 bd du Pont d’Arve 1205 Geneva, Switzerland E-mail:
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Saviola F, Deste G, Barlati S, Vita A, Gasparotti R, Corbo D. The Effect of Physical Exercise on People with Psychosis: A Qualitative Critical Review of Neuroimaging Findings. Brain Sci 2023; 13:923. [PMID: 37371403 DOI: 10.3390/brainsci13060923] [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: 05/13/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Recently, genuine motor abnormalities have been recognized as prodromal and predictive signs of psychosis onset and progression. Therefore, physical exercise could represent a potentially relevant clinical tool in promoting the reshaping of neural connections in motor circuitry. The aim of this review is to provide an overview of the literature on neuroimaging findings as a result of physical treatment in psychosis cohorts. Twenty-one studies, all research articles, were included and discussed in this narrative review. Here, we first outlined how the psychotic brain is susceptible to structural plastic changes after aerobic physical training in pathognomic brain areas (i.e., temporal, hippocampal and parahippocampal regions). Secondly, we focused on functional changes, both region-specific and in terms of connections, to gain insights into the involvement of distant but inter-related neural regions in the plastic process occurring after treatment. Third, we attempted to bridge neural plastic changes occurring after physical interventions with clinical and cognitive outcomes of psychotic patients in order to assess the relevance of such neural reshaping in the psychiatric rehabilitation field. In conclusion, we suggest that the current state of the art is presenting physical intervention as effective in promoting neural changes for patients with psychosis; it is not only useful at the onset of the pathology but also in improving the course of the illness and its functional outcome. However, more evidence is needed to improve our knowledge of the efficacy of physical exercise in plastically reorganizing the psychotic brain in the long term, especially within regions lacking specific investigations, such as motor circuitry.
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Affiliation(s)
- Francesca Saviola
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy
| | - Giacomo Deste
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy
- Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, 25123 Brescia, Italy
| | - Stefano Barlati
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy
- Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, 25123 Brescia, Italy
| | - Antonio Vita
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy
- Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, 25123 Brescia, Italy
| | - Roberto Gasparotti
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy
- Neuroradiology Unit, ASST Spedali Civili of Brescia, 25123 Brescia, Italy
| | - Daniele Corbo
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy
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46
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Lei W, Xiao Q, Wang C, Cai Z, Lu G, Su L, Zhong Y. The disruption of functional connectome gradient revealing networks imbalance in pediatric bipolar disorder. J Psychiatr Res 2023; 164:72-79. [PMID: 37331260 DOI: 10.1016/j.jpsychires.2023.05.084] [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: 01/20/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023]
Abstract
OBJECTIVE Pediatric bipolar disorder (PBD) is a psychiatric disorder marked by alteration of brain networks. However, the understanding of these alterations in topological organization still unclear. This study aims to leverage the functional connectome gradient to examine changes in functional network hierarchy in PBD. METHOD Connectome gradients were used to scrutinize the differences between functional gradient map in PBD patients (n = 68, aged 11 to 18) and healthy controls (HC, n = 37, aged 11 to 18). The association between regional altered gradient scores and clinical factors was examined. We further used Neurosynth to determine the correlation of the cognitive terms with the PBD principal gradient changes. RESULTS Global topographic alterations were exhibited in the connectome gradient in PBD patients, involving gradient variance, explanation ratio, gradient range, and gradient dispersion in the principal gradient. Regionally, PBD patients revealed that the default mode network (DMN) held the most majority of the brain areas with higher gradient scores, whereas a higher proportion of brain regions with lower gradient scores in the sensorimotor network (SMN). These regional gradient differences exhibited significant correlation with clinical features and meta-analysis terms including cognitive behavior and sensory processing. CONCLUSION Functional connectome gradient presents a thorough investigation of large-scale networks hierarchy in PBD patients. This exhibited excessive segregation between DMN and SMN supports the theory of imbalance in top-down control and bottom-up in PBD and provides a possible biomarker for diagnostic assessment.
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Affiliation(s)
- Wenkun Lei
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, 210097, China; Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing, Jiangsu, 210097, China; International Joint Laboratory of Child and Adolescent Psychological Development and Crisis Intervention, Nanjing, Jiangsu, 210097, China
| | - Qian Xiao
- Mental Health Centre of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Chun Wang
- Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Zhen Cai
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, 210097, China; Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing, Jiangsu, 210097, China; International Joint Laboratory of Child and Adolescent Psychological Development and Crisis Intervention, Nanjing, Jiangsu, 210097, China
| | - Guangming Lu
- Department of Medical Imaging, Nanjing General Hospital of Nanjing Military Command, Nanjing, Jiangsu, 210002, China
| | - Linyan Su
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410008, China
| | - Yuan Zhong
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, 210097, China; Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing, Jiangsu, 210097, China; International Joint Laboratory of Child and Adolescent Psychological Development and Crisis Intervention, Nanjing, Jiangsu, 210097, China.
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47
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Saccaro LF, Gaviria J, Ville DVD, Piguet C. Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder. Brain Behav 2023; 13:e3010. [PMID: 37062926 PMCID: PMC10275545 DOI: 10.1002/brb3.3010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 04/18/2023] Open
Abstract
OBJECTIVES Bipolar disorder (BD) is a severe, chronic, affective disorder characterized by recurrent switching between mood states, psychomotor and cognitive symptoms, which can linger in euthymic states as residual symptoms. Hippocampal alterations may play a key role in the neural processing of BD symptoms. However, its dynamic functional connectivity (dFC) remains unclear. Therefore, the present study explores hippocampal dFC in relation to BD symptoms. METHODS We assessed hippocampus-based dFC coactivation patterns (CAPs) on resting-state fMRI data of 25 euthymic BD patients and 25 age- and sex-matched healthy controls (HC). RESULTS Bilateral hippocampal dFC with somatomotor networks (SMN) was reduced in BD, compared to HC, while at the same time dFC between the left hippocampus and midcingulo-insular salience system (SN) was higher in BD. Correlational analysis between CAPs and clinical scores revealed that dFC between the bilateral hippocampus and the default-like network (DMN) correlated with depression scores in BD. Furthermore, pathological hyperconnectivity between the default mode network (DMN) and SMN and the frontoparietal network (FPN) was modulated by the same depression scores in BD. CONCLUSIONS Overall, we observed alterations of large-scale functional brain networks associated with decreased flexibility in cognitive control, salience detection, and emotion processing in BD. Additionally, the present study provides new insights on the neural architecture underlying a self-centered perspective on the environment in BD patients. dFC markers may improve detection, treatment, and follow-up of BD patients and of disabling residual depressive symptoms in particular.
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Affiliation(s)
- Luigi F Saccaro
- Faculty of Medicine, Psychiatry DepartmentUniversity of GenevaGenevaSwitzerland
- Psychiatry DivisionGeneva University HospitalGenevaSwitzerland
| | - Julian Gaviria
- Faculty of Medicine, Psychiatry DepartmentUniversity of GenevaGenevaSwitzerland
- Department of Basic NeurosciencesUniversity of GenevaGenevaSwitzerland
- Swiss Center for Affective SciencesCampus BiotechGenevaSwitzerland
| | - Dimitri Van De Ville
- Swiss Center for Affective SciencesCampus BiotechGenevaSwitzerland
- Faculty of Medicine, Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- Neuro‐X Institute, School of EngineeringEcole Polytechnique Fédérale de Lausanne (EPFL)GenevaSwitzerland
| | - Camille Piguet
- Faculty of Medicine, Psychiatry DepartmentUniversity of GenevaGenevaSwitzerland
- Child and Adolescence Psychiatry DivisionGeneva University HospitalGenevaSwitzerland
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48
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Zhao H, Ge M, Turel O, Bechara A, He Q. Brain modular connectivity interactions can predict proactive inhibition in smokers when facing smoking cues. Addict Biol 2023; 28:e13284. [PMID: 37252878 DOI: 10.1111/adb.13284] [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: 12/19/2022] [Revised: 02/25/2023] [Accepted: 04/14/2023] [Indexed: 06/01/2023]
Abstract
Proactive inhibition is a critical ability for smokers who seek to moderate or quit smoking. It allows them to pre-emptively refrain from seeking and using nicotine products, especially when facing salient smoking cues in daily life. Nevertheless, there is limited knowledge on the impact of salient cues on behavioural and neural aspects of proactive inhibition, especially in smokers with nicotine withdrawal. Here, we seek to bridge this gap. To this end, we recruited 26 smokers to complete a stop-signal anticipant task (SSAT) in two separate sessions: once in the neutral cue condition and once in the smoking cue condition. We used graph-based modularity analysis to identify the modular structures of proactive inhibition-related network during the SSAT and further investigated how the interactions within and between these modules could be modulated by different proactive inhibition demands and salient smoking cues. Findings pointed to three stable brain modules involved in the dynamical processes of proactive inhibition: the sensorimotor network (SMN), cognitive control network (CCN) and default-mode network (DMN). With the increase in demands, functional connectivity increased within the SMN, CCN and between SMN-CCN and decreased within the DMN and between SMN-DMN and CCN-DMN. Salient smoking cues disturbed the effective dynamic interactions of brain modules. The profiles for those functional interactions successfully predicted the behavioural performance of proactive inhibition in abstinent smokers. These findings advance our understanding of the neural mechanisms of proactive inhibition from a large-scale network perspective. They can shed light on developing specific interventions for abstinent smokers.
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Affiliation(s)
- Haichao Zhao
- Faculty of Psychology, MOE Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China
| | - Mengjiao Ge
- Faculty of Psychology, MOE Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China
| | - Ofir Turel
- Computing Information Systems, The University of Melbourne, Parkville, Victoria, Australia
- Department of Psychology, and Brain and Creativity Institute, University of Southern California, Los Angeles, California, USA
| | - Antoine Bechara
- Department of Psychology, and Brain and Creativity Institute, University of Southern California, Los Angeles, California, USA
| | - Qinghua He
- Faculty of Psychology, MOE Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China
- Collaborative Innovation Center of Assessment toward Basic Education Quality, Southwest University Branch, Chongqing, China
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49
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Zhao K, Xie H, Fonzo GA, Tong X, Carlisle N, Chidharom M, Etkin A, Zhang Y. Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression. Mol Psychiatry 2023; 28:2490-2499. [PMID: 36732585 DOI: 10.1038/s41380-023-01958-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/04/2023] [Accepted: 01/11/2023] [Indexed: 02/04/2023]
Abstract
Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.
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Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | | | - Amit Etkin
- Alto Neuroscience, Inc, Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.
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50
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Godel M, Robain F, Journal F, Kojovic N, Latrèche K, Dehaene-Lambertz G, Schaer M. Prosodic signatures of ASD severity and developmental delay in preschoolers. NPJ Digit Med 2023; 6:99. [PMID: 37248317 DOI: 10.1038/s41746-023-00845-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/15/2023] [Indexed: 05/31/2023] Open
Abstract
Atypical prosody in speech production is a core feature of Autism Spectrum Disorder (ASD) that can impact everyday life communication. Because the ability to modulate prosody develops around the age of speech acquisition, it might be affected by ASD symptoms and developmental delays that emerge at the same period. Here, we investigated the existence of a prosodic signature of developmental level and ASD symptom severity in a sample of 74 autistic preschoolers. We first developed an original diarization pipeline to extract preschoolers' vocalizations from recordings of naturalistic social interactions. Using this novel approach, we then found a robust voice quality signature of ASD developmental difficulties in preschoolers. Furthermore, some prosodic measures were associated with one year later outcome in participants who had not acquired speech yet. Altogether, our results highlight the potential benefits of automatized diarization algorithms and prosodic metrics for digital phenotyping in psychiatry, helping clinicians establish early diagnosis and prognosis.
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Affiliation(s)
- Michel Godel
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland.
| | - François Robain
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
| | - Fiona Journal
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
| | - Nada Kojovic
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
| | - Kenza Latrèche
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
| | - Ghislaine Dehaene-Lambertz
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France
| | - Marie Schaer
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
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