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Wang X, Yan C, Yang PY, Xia Z, Cai XL, Wang Y, Kwok SC, Chan RCK. Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data. Psychiatry Clin Neurosci 2024; 78:157-168. [PMID: 38013639 DOI: 10.1111/pcn.13625] [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: 05/12/2023] [Revised: 11/01/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023]
Abstract
The emergence of machine learning (ML) techniques has opened up new avenues for identifying biomarkers associated with schizophrenia (SCZ) using task-related fMRI (t-fMRI) designs. To evaluate the effectiveness of this approach, we conducted a comprehensive meta-analysis of 31 t-fMRI studies using a bivariate model. Our findings revealed a high overall sensitivity of 0.83 and specificity of 0.82 for t-fMRI studies. Notably, neuropsychological domains modulated the classification performance, with selective attention demonstrating a significantly higher specificity than working memory (β = 0.98, z = 2.11, P = 0.04). Studies involving older, chronic patients with SCZ reported higher sensitivity (P <0.015) and specificity (P <0.001) than those involving younger, first-episode patients or high-risk individuals for psychosis. Additionally, we found that the severity of negative symptoms was positively associated with the specificity of the classification model (β = 7.19, z = 2.20, P = 0.03). Taken together, these results support the potential of using task-based fMRI data in combination with machine learning techniques to identify biomarkers related to symptom outcomes in SCZ, providing a promising avenue for improving diagnostic accuracy and treatment efficacy. Future attempts to deploy ML classification should consider the factors of algorithm choice, data quality and quantity, as well as issues related to generalization.
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Affiliation(s)
- Xuan Wang
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Chao Yan
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
| | | | - Zheng Xia
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Xin-Lu Cai
- Institute of Brain Science and Department of Physiology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Sze Chai Kwok
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
- Phylo-Cognition Laboratory, Division of Natural and Applied Sciences, Data Science Research Center, Duke Kunshan University, Kunshan, China
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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2
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Bannai D, Reuter M, Hegde R, Hoang D, Adhan I, Gandu S, Pong S, Raymond N, Zeng V, Chung Y, He G, Sun D, van Erp TGM, Addington J, Bearden CE, Cadenhead K, Cornblatt B, Mathalon DH, McGlashan T, Jeffries C, Stone W, Tsuang M, Walker E, Woods SW, Cannon TD, Perkins D, Keshavan M, Lizano P. Linking enlarged choroid plexus with plasma analyte and structural phenotypes in clinical high risk for psychosis: A multisite neuroimaging study. Brain Behav Immun 2024; 117:70-79. [PMID: 38169244 DOI: 10.1016/j.bbi.2023.12.021] [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: 07/19/2023] [Revised: 12/04/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Choroid plexus (ChP) enlargement exists in first-episode and chronic psychosis, but whether enlargement occurs before psychosis onset is unknown. This study investigated whether ChP volume is enlarged in individuals with clinical high-risk (CHR) for psychosis and whether these changes are related to clinical, neuroanatomical, and plasma analytes. METHODS Clinical and neuroimaging data from the North American Prodrome Longitudinal Study 2 (NAPLS2) was used for analysis. 509 participants (169 controls, 340 CHR) were recruited. Conversion status was determined after 2-years of follow-up, with 36 psychosis converters. The lateral ventricle ChP was manually segmented from baseline scans. A subsample of 31 controls and 53 CHR had plasma analyte and neuroimaging data. RESULTS Compared to controls, CHR (d = 0.23, p = 0.017) and non-converters (d = 0.22, p = 0.03) demonstrated higher ChP volumes, but not in converters. In CHR, greater ChP volume correlated with lower cortical (r = -0.22, p < 0.001), subcortical gray matter (r = -0.21, p < 0.001), and total white matter volume (r = -0.28,p < 0.001), as well as larger lateral ventricle volume (r = 0.63,p < 0.001). Greater ChP volume correlated with makers functionally associated with the lateral ventricle ChP in CHR [CCL1 (r = -0.30, p = 0.035), ICAM1 (r = 0.33, p = 0.02)], converters [IL1β (r = 0.66, p = 0.004)], and non-converters [BMP6 (r = -0.96, p < 0.001), CALB1 (r = -0.98, p < 0.001), ICAM1 (r = 0.80, p = 0.003), SELE (r = 0.59, p = 0.026), SHBG (r = 0.99, p < 0.001), TNFRSF10C (r = 0.78, p = 0.001)]. CONCLUSIONS CHR and non-converters demonstrated significantly larger ChP volumes compared to controls. Enlarged ChP was associated with neuroanatomical alterations and analyte markers functionally associated with the ChP. These findings suggest that the ChP may be a key an important biomarker in CHR.
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Affiliation(s)
- Deepthi Bannai
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Martin Reuter
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Rachal Hegde
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dung Hoang
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Iniya Adhan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Swetha Gandu
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sovannarath Pong
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Nick Raymond
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Victor Zeng
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoonho Chung
- Department of Psychology, Yale University, New Haven, CT, USA
| | - George He
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Daqiang Sun
- Semel Institute for Neuroscience and Human Behavior and Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, UC Irvine, Irvine, CA, USA
| | - Jean Addington
- Hotchkins Brain Institute, Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Carrie E Bearden
- Semel Institute for Neuroscience and Human Behavior and Department of Psychology, UCLA, Los Angeles, CA, USA
| | | | | | | | | | - Clark Jeffries
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA
| | - William Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming Tsuang
- Department of Psychiatry, UCSD, San Diego, CA, USA
| | - Elaine Walker
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Diana Perkins
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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3
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Walker EF, Aberizk K, Yuan E, Bilgrami Z, Ku BS, Guest RM. Developmental perspectives on the origins of psychotic disorders: The need for a transdiagnostic approach. Dev Psychopathol 2024:1-11. [PMID: 38406831 DOI: 10.1017/s0954579424000397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Research on serious mental disorders, particularly psychosis, has revealed highly variable symptom profiles and developmental trajectories prior to illness-onset. As Dante Cicchetti pointed out decades before the term "transdiagnostic" was widely used, the pathways to psychopathology emerge in a system involving equifinality and multifinality. Like most other psychological disorders, psychosis is associated with multiple domains of risk factors, both genetic and environmental, and there are many transdiagnostic developmental pathways that can lead to psychotic syndromes. In this article, we discuss our current understanding of heterogeneity in the etiology of psychosis and its implications for approaches to conceptualizing etiology and research. We highlight the need for examining risk factors at multiple levels and to increase the emphasis on transdiagnostic developmental trajectories as a key variable associated with etiologic subtypes. This will be increasingly feasible now that large, longitudinal datasets are becoming available and researchers have access to more sophisticated analytic tools, such as machine learning, which can identify more homogenous subtypes with the ultimate goal of enhancing options for treatment and preventive intervention.
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Affiliation(s)
- Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Katrina Aberizk
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Emerald Yuan
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Zarina Bilgrami
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Benson S Ku
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Ryan M Guest
- Department of Psychology, Emory University, Atlanta, GA, USA
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4
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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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Caballero N, Machiraju S, Diomino A, Kennedy L, Kadivar A, Cadenhead KS. Recent Updates on Predicting Conversion in Youth at Clinical High Risk for Psychosis. Curr Psychiatry Rep 2023; 25:683-698. [PMID: 37755654 PMCID: PMC10654175 DOI: 10.1007/s11920-023-01456-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE OF REVIEW This review highlights recent advances in the prediction and treatment of psychotic conversion. Over the past 25 years, research into the prodromal phase of psychotic illness has expanded with the promise of early identification of individuals at clinical high risk (CHR) for psychosis who are likely to convert to psychosis. RECENT FINDINGS Meta-analyses highlight conversion rates between 20 and 30% within 2-3 years using existing clinical criteria while research into more specific risk factors, biomarkers, and refinement of psychosis risk calculators has exploded, improving our ability to predict psychotic conversion with greater accuracy. Recent studies highlight risk factors and biomarkers likely to contribute to earlier identification and provide insight into neurodevelopmental abnormalities, CHR subtypes, and interventions that can target specific risk profiles linked to neural mechanisms. Ongoing initiatives that assess longer-term (> 5-10 years) outcome of CHR participants can provide valuable information about predictors of later conversion and diagnostic outcomes while large-scale international biomarker studies provide hope for precision intervention that will alter the course of early psychosis globally.
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Affiliation(s)
- Noe Caballero
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Siddharth Machiraju
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Anthony Diomino
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Leda Kennedy
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Armita Kadivar
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA.
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6
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Aberizk K, Sefik E, Addington J, Anticevic A, Bearden CE, Cadenhead KS, Cannon TD, Cornblatt BA, Keshavan M, Mathalon DH, Perkins DO, Stone WS, Tsuang MT, Woods SW, Walker EF. Hippocampal Connectivity with the Default Mode Network is Linked to Hippocampal Volume in the Clinical High Risk for Psychosis Syndrome and Healthy Individuals. Clin Psychol Sci 2023; 11:801-818. [PMID: 37981950 PMCID: PMC10656030 DOI: 10.1177/21677026221138819] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
Reduced hippocampal volume (HV) is an established brain morphological feature of psychiatric conditions. HV is associated with brain connectivity in humans and non-human animals and altered connectivity is associated with risk for psychiatric illness. Associations between HV and connectivity remain poorly characterized in humans, and especially in phases of psychiatric illness that precede disease onset. This study examined associations between HV and hippocampal functional connectivity (FC) during rest in 141 healthy controls and 248 individuals at-risk for psychosis. Significant inverse associations between HV and hippocampal FC with the inferior parietal lobe (IPL) and thalamus were observed. Select associations between hippocampal FC and HV were moderated by diagnostic group. Significant moderation results shifted from implicating the IPL to the temporal pole after excluding participants on antipsychotic medication. Considered together, this work implicates hippocampal FC with the temporoparietal junction, within a specialized subsystem of the default mode network, as sensitive to HV.
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Affiliation(s)
- Katrina Aberizk
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Esra Sefik
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Alan Anticevic
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Carrie E. Bearden
- Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California, Los Angeles, CA, USA
| | | | | | | | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School, Harvard University, Cambridge, MA, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Diana O. Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - William S. Stone
- Department of Psychiatry, Harvard Medical School, Harvard University, Cambridge, MA, USA
| | - Ming T. Tsuang
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Scott W. Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
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7
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Cao H, Barber AD, Rubio JM, Argyelan M, Gallego JA, Lencz T, Malhotra AK. Effects of phase encoding direction on test-retest reliability of human functional connectome. Neuroimage 2023; 277:120238. [PMID: 37364743 PMCID: PMC10529794 DOI: 10.1016/j.neuroimage.2023.120238] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023] Open
Abstract
The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.
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Affiliation(s)
- Hengyi Cao
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States.
| | - Anita D Barber
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Jose M Rubio
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Miklos Argyelan
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Juan A Gallego
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Todd Lencz
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Anil K Malhotra
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
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8
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Mamah D. A Review of Potential Neuroimaging Biomarkers of Schizophrenia-Risk. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2023; 8:e230005. [PMID: 37427077 PMCID: PMC10327607 DOI: 10.20900/jpbs.20230005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The risk for developing schizophrenia is increased among first-degree relatives of those with psychotic disorders, but the risk is even higher in those meeting established criteria for clinical high risk (CHR), a clinical construct most often comprising of attenuated psychotic experiences. Conversion to psychosis among CHR youth has been reported to be about 15-35% over three years. Accurately identifying individuals whose psychotic symptoms will worsen would facilitate earlier intervention, but this has been difficult to do using behavior measures alone. Brain-based risk markers have the potential to improve the accuracy of predicting outcomes in CHR youth. This narrative review provides an overview of neuroimaging studies used to investigate psychosis risk, including studies involving structural, functional, and diffusion imaging, functional connectivity, positron emission tomography, arterial spin labeling, magnetic resonance spectroscopy, and multi-modality approaches. We present findings separately in those observed in the CHR state and those associated with psychosis progression or resilience. Finally, we discuss future research directions that could improve clinical care for those at high risk for developing psychotic disorders.
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Affiliation(s)
- Daniel Mamah
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, 63110, USA
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9
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Long Y, Ouyang X, Yan C, Wu Z, Huang X, Pu W, Cao H, Liu Z, Palaniyappan L. Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks. Hum Brain Mapp 2023; 44:2191-2208. [PMID: 36637216 PMCID: PMC10028647 DOI: 10.1002/hbm.26202] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/25/2022] [Accepted: 01/01/2023] [Indexed: 01/14/2023] Open
Abstract
The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of PsychologyChinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression Research, Institute of PsychologyChinese Academy of SciencesBeijingChina
| | - Zhipeng Wu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xiaojun Huang
- Department of PsychiatryJiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Weidan Pu
- Medical Psychological InstituteThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hengyi Cao
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
- Division of Psychiatry ResearchZucker Hillside HospitalGlen OaksNew YorkUSA
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Lena Palaniyappan
- Department of PsychiatryUniversity of Western OntarioLondonOntarioCanada
- Robarts Research InstituteUniversity of Western OntarioLondonOntarioCanada
- Lawson Health Research InstituteLondonOntarioCanada
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10
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Cao H, Wei X, Zhang W, Xiao Y, Zeng J, Sweeney JA, Gong Q, Lui S. Cerebellar Functional Dysconnectivity in Drug-Naïve Patients With First-Episode Schizophrenia. Schizophr Bull 2023; 49:417-427. [PMID: 36200880 PMCID: PMC10016395 DOI: 10.1093/schbul/sbac121] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Cerebellar functional dysconnectivity has long been implicated in schizophrenia. However, the detailed dysconnectivity pattern and its underlying biological mechanisms have not been well-charted. This study aimed to conduct an in-depth characterization of cerebellar dysconnectivity maps in early schizophrenia. STUDY DESIGN Resting-state fMRI data were processed from 196 drug-naïve patients with first-episode schizophrenia and 167 demographically matched healthy controls. The cerebellum was parcellated into nine functional systems based on a state-of-the-art atlas, and seed-based connectivity for each cerebellar system was examined. The observed connectivity alterations were further associated with schizophrenia risk gene expressions using data from the Allen Human Brain Atlas. STUDY RESULTS Overall, we observed significantly increased cerebellar connectivity with the sensorimotor cortex, default-mode regions, ventral part of visual cortex, insula, and striatum. In contrast, decreased connectivity was shown chiefly within the cerebellum, and between the cerebellum and the lateral prefrontal cortex, temporal lobe, and dorsal visual areas. Such dysconnectivity pattern was statistically similar across seeds, with no significant group by seed interactions identified. Moreover, connectivity strengths of hypoconnected but not hyperconnected regions were significantly correlated with schizophrenia risk gene expressions, suggesting potential genetic underpinnings for the observed hypoconnectivity. CONCLUSIONS These findings suggest a common bidirectional dysconnectivity pattern across different cerebellar subsystems, and imply that such bidirectional alterations may relate to different biological mechanisms.
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Affiliation(s)
- Hengyi Cao
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Xia Wei
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jiaxin Zeng
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Qiyong Gong
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
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11
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Ouyang X, Long Y, Wu Z, Liu D, Liu Z, Huang X. Temporal Stability of Dynamic Default Mode Network Connectivity Negatively Correlates with Suicidality in Major Depressive Disorder. Brain Sci 2022; 12:brainsci12091263. [PMID: 36138998 PMCID: PMC9496878 DOI: 10.3390/brainsci12091263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/26/2022] Open
Abstract
Previous studies have demonstrated that the suicidality in patients with major depressive disorder (MDD) is related to abnormal brain functional connectivity (FC) patterns. However, little is known about its relationship with dynamic functional connectivity (dFC) based on the assumption that brain FCs fluctuate over time. Temporal stabilities of dFCs within the whole brain and nine key networks were compared between 52 MDD patients and 21 age, sex-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging and temporal correlation coefficients. The alterations in MDD were further correlated with the scores of suicidality item in the Hamilton Rating Scale for Depression (HAMD). Compared with HCs, the MDD patients showed a decreased temporal stability of dFC as indicated by a significantly decreased temporal correlation coefficient at the global level, as well as within the default mode network (DMN) and subcortical network. In addition, temporal correlation coefficients of the DMN were found to be significantly negatively correlated with the HAMD suicidality item scores in MDD patients. These results suggest that MDD may be characterized by excessive temporal fluctuations of dFCs within the DMN and subcortical network, and that decreased stability of DMN connectivity may be particularly associated with the suicidality in MDD.
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Affiliation(s)
- Xuan Ouyang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Yicheng Long
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zhipeng Wu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Dayi Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zhening Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Xiaojun Huang
- Department of Psychiatry, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
- Correspondence:
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12
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Suhas S, Mehta UM. A redux of schizophrenia research in 2021. Schizophr Res 2022; 243:458-461. [PMID: 35300898 PMCID: PMC8919807 DOI: 10.1016/j.schres.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 03/06/2022] [Indexed: 11/19/2022]
Affiliation(s)
- Satish Suhas
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore 560029, India
| | - Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore 560029, India.
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13
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Xu Y, Shang H, Lu H, Zhang J, Yao L, Long Z. Altered Dynamic Functional Connectivity in Subcortical Ischemic Vascular Disease With Cognitive Impairment. Front Aging Neurosci 2021; 13:758137. [PMID: 34955812 PMCID: PMC8704998 DOI: 10.3389/fnagi.2021.758137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 11/17/2021] [Indexed: 11/25/2022] Open
Abstract
Subcortical ischemic vascular disease (SIVD) can cause cognitive impairment and affect the static functional connectivity of resting functional magnetic resonance imaging (fMRI). Numerous previous studies have demonstrated that functional connectivities (FCs) fluctuate dynamically over time. However, little is known about the impact of cognitive impairment on brain dynamic functional connectivity (DFC) in SIVD patients with MCI. In the present study, the DFC analysis method was applied to the resting functional magnetic resonance imaging (fMRI) data of 37 SIVD controls (SIVD-Control) without cognitive impairment, 34 SIVD patients with amnestic MCI (SIVD-aMCI) and 30 SIVD patients with nonamnestic MCI (SIVD-naMCI). The results indicated that the cognitive impairment of SIVD mainly reduced the mean dwell time of State 3 with overall strong positive connections. The reduction degree of SIVD-aMCI was larger than that of SIVD-naMCI. The memory/execution function impairment of SIVD also changed the relationship between the mean dwell time of State 3 and the behavioral performance of the memory/execution task from significant to non-significant correlation. Moreover, SIVD-aMCI showed significantly lower system segregation of FC states than SIVD-Control and SIVD-naMCI. The system segregation of State 5 with overall weak connections was significantly positive correlated with the memory performance. The results may suggest that the mean dwell time of State 3 and the system segregation of State 5 may be used as important neural measures of cognitive impairments of SIVD.
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Affiliation(s)
- Yuanhang Xu
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huajie Shang
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Hui Lu
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Junying Zhang
- BABRI Centre, Beijing Normal University, Beijing, China.,Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Li Yao
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhiying Long
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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14
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Cao H, Chen OY, McEwen SC, Forsyth JK, Gee DG, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Carrión RE, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Anticevic A, Woods SW, Cannon TD. Cross-paradigm connectivity: reliability, stability, and utility. Brain Imaging Behav 2021; 15:614-629. [PMID: 32361945 DOI: 10.1007/s11682-020-00272-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic "trait" architecture that is independent of any given paradigm. We have previously proposed the use of "cross-paradigm connectivity (CPC)" to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain's "trait" structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional "trait" networks and offer some methodological implications for future CPC studies.
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Affiliation(s)
- Hengyi Cao
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT, USA.
| | - Oliver Y Chen
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Sarah C McEwen
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.,Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Jennifer K Forsyth
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.,Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Dylan G Gee
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.,Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Jean Addington
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Canada
| | - Bradley Goodyear
- Departments of Radiology, Clinical Neuroscience and Psychiatry, University of Calgary, Calgary, Canada
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Heline Mirzakhanian
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Barbara A Cornblatt
- Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Ricardo E Carrión
- Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | | | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
| | - Heidi Thermenos
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Stephan Hamann
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT, USA.,Department of Psychiatry, Yale University, New Haven, CT, USA
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15
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Woods SW, Bearden CE, Sabb FW, Stone WS, Torous J, Cornblatt BA, Perkins DO, Cadenhead KS, Addington J, Powers AR, Mathalon DH, Calkins ME, Wolf DH, Corcoran CM, Horton LE, Mittal VA, Schiffman J, Ellman LM, Strauss GP, Mamah D, Choi J, Pearlson GD, Shah JL, Fusar-Poli P, Arango C, Perez J, Koutsouleris N, Wang J, Kwon JS, Walsh BC, McGlashan TH, Hyman SE, Gur RE, Cannon TD, Kane JM, Anticevic A. Counterpoint. Early intervention for psychosis risk syndromes: Minimizing risk and maximizing benefit. Schizophr Res 2021; 227:10-17. [PMID: 32402605 PMCID: PMC8218020 DOI: 10.1016/j.schres.2020.04.020] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/16/2020] [Accepted: 04/16/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Malhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis. METHOD Response to points of critique. RESULTS We agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c) symptoms used to identify CHR are specific to psychotic illnesses; d) CHR diagnosis is not "extremely difficult"; e) the pattern of progression, although heterogenous, is discernible; f) "psychosis-like symptoms" are common but are not used to identify CHR; and g) on the point described as 'the real risk,' CHR diagnosis does not frequently cause harmful stigma. DISCUSSION Malhi et al.'s arguments do not fairly characterize progress in the CHR field nor efforts to minimize stigma. That said, much work remains in areas of consistent nomenclature, mechanisms of disease, dissecting heterogeneity, and biomarkers. With regard to what the authors term the "real risk" of stigma associated with a CHR "label," however, our view is that avoiding words like "risk" and "psychosis" reinforces the stigma that both they and we mean to oppose. Moreover, patients and their families benefit from being given a term that describes what is happening to them.
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Affiliation(s)
- Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA.
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, USA; Department Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, USA
| | - Fred W Sabb
- Lewis Center for Neuroimaging, University of Oregon, Eugene, USA
| | - William S Stone
- Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA; Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, China
| | - John Torous
- Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, USA
| | | | - Jean Addington
- Department of Psychiatry, University of Calgary, Alberta, Canada
| | - Albert R Powers
- Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, USA
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Psychology, Northwestern University, Chicago, IL, USA
| | - Jason Schiffman
- Department of Psychology, University of Maryland, Baltimore County, USA
| | - Lauren M Ellman
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | | | - Daniel Mamah
- Department of Psychiatry, Washington University in Saint Louis, MO, USA
| | - Jimmy Choi
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, CT, USA
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, CT, USA
| | - Jai L Shah
- Department of Psychiatry, McGill University, Montreal, Canada
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, King's College London, UK; Department of Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Celso Arango
- Dept. of Child and Adolescent Psychiatry, Universidad Complutense de Madrid, Spain
| | - Jesus Perez
- Department of Psychiatry, University of Cambridge, UK
| | | | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, China
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, South Korea
| | - Barbara C Walsh
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | | | | | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA
| | - John M Kane
- Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA
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16
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Identifying neural signatures mediating behavioral symptoms and psychosis onset: High-dimensional whole brain functional mediation analysis. Neuroimage 2020; 226:117508. [PMID: 33157263 PMCID: PMC7836235 DOI: 10.1016/j.neuroimage.2020.117508] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 11/26/2022] Open
Abstract
Along the pathway from behavioral symptoms to the development of psychotic disorders sits the multivariate mediating brain. The functional organization and structural topography of large-scale multivariate neural mediators among patients with brain disorders, however, are not well understood. Here, we design a high-dimensional brain-wide functional mediation framework to investigate brain regions that intermediate between baseline behavioral symptoms and future conversion to full psychosis among individuals at clinical high risk (CHR). Using resting-state functional magnetic resonance imaging (fMRI) data from 263 CHR subjects, we extract an α brain atlas and a β brain atlas: the former underlines brain areas associated with prodromal symptoms and the latter highlights brain areas associated with disease onset. In parallel, we identify and separate mediators that potentially positively and negatively mediate symptoms and psychosis, respectively, and quantify the effect of each neural mediator on disease development. Taken together, these results paint a brain-wide picture of neural markers that are potentially mediating behavioral symptoms and the development of psychotic disorders; additionally, they underscore a statistical framework that is useful to uncover large-scale intermediating variables in a regulatory biological system.
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17
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Long Y, Liu Z, Chan CKY, Wu G, Xue Z, Pan Y, Chen X, Huang X, Li D, Pu W. Altered Temporal Variability of Local and Large-Scale Resting-State Brain Functional Connectivity Patterns in Schizophrenia and Bipolar Disorder. Front Psychiatry 2020; 11:422. [PMID: 32477194 PMCID: PMC7235354 DOI: 10.3389/fpsyt.2020.00422] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 04/24/2020] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia and bipolar disorder share some common clinical features and are both characterized by aberrant resting-state functional connectivity (FC). However, little is known about the common and specific aberrant features of the dynamic FC patterns in these two disorders. In this study, we explored the differences in dynamic FC among schizophrenia patients (n = 66), type I bipolar disorder patients (n = 53), and healthy controls (n = 66), by comparing temporal variabilities of FC patterns involved in specific brain regions and large-scale brain networks. Compared with healthy controls, both patient groups showed significantly increased regional FC variabilities in subcortical areas including the thalamus and basal ganglia, as well as increased inter-network FC variability between the thalamus and sensorimotor areas. Specifically, more widespread changes were found in the schizophrenia group, involving increased FC variabilities in sensorimotor, visual, attention, limbic and subcortical areas at both regional and network levels, as well as decreased regional FC variabilities in the default-mode areas. The observed alterations shared by schizophrenia and bipolar disorder may help to explain their overlapped clinical features; meanwhile, the schizophrenia-specific abnormalities in a wider range may support that schizophrenia is associated with more severe functional brain deficits than bipolar disorder. Together, these findings highlight the potentials of using dynamic FC as an objective biomarker for the monitoring and diagnosis of either schizophrenia or bipolar disorder.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | | | - Guowei Wu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Zhimin Xue
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Yunzhi Pan
- Mental Health Institute of Central South University, Changsha, China
| | - Xudong Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Xiaojun Huang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Dan Li
- Department of Geriatrics, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Weidan Pu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
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18
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Ellis JK, Walker EF, Goldsmith DR. Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion. Front Psychiatry 2020; 11:567534. [PMID: 33173516 PMCID: PMC7538833 DOI: 10.3389/fpsyt.2020.567534] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 08/31/2020] [Indexed: 12/19/2022] Open
Abstract
First episode psychosis (FEP), and subsequent diagnosis of schizophrenia or schizoaffective disorder, predominantly occurs during late adolescence, is accompanied by a significant decline in function and represents a traumatic experience for patients and families alike. Prior to first episode psychosis, most patients experience a prodromal period of 1-2 years, during which symptoms first appear and then progress. During that time period, subjects are referred to as being at Clinical High Risk (CHR), as a prodromal period can only be designated in hindsight in those who convert. The clinical high-risk period represents a critical window during which interventions may be targeted to slow or prevent conversion to psychosis. However, only one third of subjects at clinical high risk will convert to psychosis and receive a formal diagnosis of a primary psychotic disorder. Therefore, in order for targeted interventions to be developed and applied, predicting who among this population will convert is of critical importance. To date, a variety of neuroimaging modalities have identified numerous differences between CHR subjects and healthy controls. However, complicating attempts at predicting conversion are increasingly recognized co-morbidities, such as major depressive disorder, in a significant number of CHR subjects. The result of this is that phenotypes discovered between CHR subjects and healthy controls are likely non-specific to psychosis and generalized for major mental illness. In this paper, we selectively review evidence for neuroimaging phenotypes in CHR subjects who later converted to psychosis. We then evaluate the recent landscape of machine learning as it relates to neuroimaging phenotypes in predicting conversion to psychosis.
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Affiliation(s)
- Justin K Ellis
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, United States
| | - David R Goldsmith
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
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19
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Long Y, Chen C, Deng M, Huang X, Tan W, Zhang L, Fan Z, Liu Z. Psychological resilience negatively correlates with resting-state brain network flexibility in young healthy adults: a dynamic functional magnetic resonance imaging study. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:809. [PMID: 32042825 DOI: 10.21037/atm.2019.12.45] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Psychological resilience is an important personality trait whose decrease is associated with many common psychiatric disorders, but the neural mechanisms underlying it remain largely unclear. In this study, we aimed to explore the neural correlates of psychological resilience in healthy adults by investigating its relationship with functional brain network flexibility, a fundamental dynamic feature of brain network defined by switching frequency of its modular community structures. Methods Resting-state functional magnetic resonance imaging (fMRI) scans were acquired from 41 healthy adults, whose psychological resilience was quantified by the Connor-Davidson Resilience Scale (CD-RISC). Dynamic functional brain network was constructed for each subject, whose flexibility was calculated at all the global, subnetwork and region-of-interest (ROI) levels. After that, the associations between CD-RISC score and brain network flexibility were assessed at all levels by partial correlations controlling for age, sex, education and head motion. Correlation was also tested between the CD-RISC score and modularity of conventional static brain network for comparative purposes. Results The CD-RISC score was significant negatively correlated with the brain network flexibility at global level (r=-0.533, P=0.001), and with flexibility of the visual subnetwork at subnetwork level (r=-0.576, corrected P=0.002). Moreover, significant (corrected P<0.05) or trends for (corrected P<0.10) negative correlations were found between the CD-RISC score and flexibilities of a number of visual and default-mode areas at ROI level. Meanwhile, the modularity of static brain network did not reveal significant correlation with CD-RISC score (P>0.05). Conclusions Our results suggest that excessive fluctuations of the functional brain community structures during rest may be indicative of a lower psychological resilience, and the visual and default-mode systems may play crucial roles in such relationship. These findings may provide important implications for improving our understanding of the psychological resilience.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China.,Mental Health Institute of Central South University, Changsha 410011, China
| | - Chujun Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Mengjie Deng
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Xiaojun Huang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Wenjian Tan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Li Zhang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zebin Fan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China.,Mental Health Institute of Central South University, Changsha 410011, China
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20
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Worthington MA, Cao H, Cannon TD. Discovery and Validation of Prediction Algorithms for Psychosis in Youths at Clinical High Risk. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 5:738-747. [PMID: 31902580 DOI: 10.1016/j.bpsc.2019.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 10/07/2019] [Accepted: 10/26/2019] [Indexed: 12/19/2022]
Abstract
In the past 2 to 3 decades, clinicians have used the clinical high risk for psychosis (CHR-P) paradigm to better understand factors that contribute to the onset of psychotic disorders. While this paradigm is useful to identify individuals at risk, the CHR-P criteria are not sufficient to predict outcomes from the CHR-P population. Because approximately 25% of the CHR-P population will ultimately convert to psychosis, more precise methods of prediction are needed to account for heterogeneity in both risk factors and outcomes in the CHR-P population. To this end, several groups in recent years have used data-driven approaches to refine predictive algorithms to predict both conversion to psychosis and functional outcomes. These models have generally used either clinical and behavioral data, including demographics and measures of symptom severity, neurocognitive functioning, and social functioning, or neuroimaging data, including structural and functional measures, to predict conversion to psychosis in CHR-P samples. This review focuses on the empirical models that have been derived within each of these lines of research and evaluates the performance and methodology of these models. This review also serves to inform best practices for data-driven approaches and directions moving forward to improve our prediction of psychotic disorders and associated outcomes. Because sample size is still the most critical consideration in the current models, we urge that algorithms to predict conversion be conducted using multisite data in order to obtain the power necessary to conclusively determine predictive accuracy without overfitting.
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Affiliation(s)
| | - Hengyi Cao
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, Connecticut.
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21
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Cabungcal JH, Steullet P, Kraftsik R, Cuenod M, Do KQ. A developmental redox dysregulation leads to spatio-temporal deficit of parvalbumin neuron circuitry in a schizophrenia mouse model. Schizophr Res 2019; 213:96-106. [PMID: 30857872 DOI: 10.1016/j.schres.2019.02.017] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 02/19/2019] [Accepted: 02/22/2019] [Indexed: 11/26/2022]
Abstract
The fast-spiking parvalbumin (PV) interneurons play a critical role in neural circuit activity and dysfunction of these cells has been implicated in the cognitive deficits typically observed in schizophrenia patients. Due to the high metabolic demands of PV neurons, they are particularly susceptible to oxidative stress. Given the extant literature exploring the pathological effects of oxidative stress on PV cells in cortical regions linked to schizophrenia, we decided to investigate whether PV neurons in other select brain regions, including sub-cortical structures, may be differentially affected by redox dysregulation induced oxidative stress during neurodevelopment in mice with a genetically compromised glutathione synthesis (Gclm KO mice). Our analyses revealed a spatio-temporal sequence of PV cell deficit in Gclm KO mice, beginning with the thalamic reticular nucleus at postnatal day (P) 20 followed by a PV neuronal deficit in the amygdala at P40, then in the lateral globus pallidus and the ventral hippocampus Cornu Ammonis 3 region at P90 and finally the anterior cingulate cortex at P180. We suggest that PV neurons in different brain regions are developmentally susceptible to oxidative stress and that anomalies in the neurodevelopmental calendar of metabolic regulation can interfere with neural circuit maturation and functional connectivity contributing to the emergence of developmental psychopathology.
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Affiliation(s)
- Jan-Harry Cabungcal
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland
| | - Pascal Steullet
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland
| | - Rudolf Kraftsik
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Michel Cuenod
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland.
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Abstract
Statistics plays three important roles in brain studies. They are (1) the study of differences between brains in distinctive populations; (2) the study of the variability in the structure and functioning of the brain; and (3) the study of data reduction on large-scale brain data. I discuss these concepts using examples from past and ongoing research in brain connectivity, brain information flow, information extraction from large-scale neuroimaging data, and neural predictive modeling. Having dispensed with the past, I attempt to present a few areas where statistical science facilitates brain decoding and to write prospectively, in the light of present knowledge and in the quest for artificial intelligence, about questions that statistical and neurobiological communities could work closely together to address in the future.
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Cao H, McEwen SC, Chung Y, Chén OY, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Carrión RE, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Anticevic A, Woods SW, Cannon TD. Altered Brain Activation During Memory Retrieval Precedes and Predicts Conversion to Psychosis in Individuals at Clinical High Risk. Schizophr Bull 2019; 45:924-933. [PMID: 30215784 PMCID: PMC6581134 DOI: 10.1093/schbul/sby122] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Memory deficits are a hallmark of psychotic disorders such as schizophrenia. However, whether the neural dysfunction underlying these deficits is present before the onset of illness and potentially predicts conversion to psychosis is unclear. In this study, we investigated brain functional alterations during memory processing in a sample of 155 individuals at clinical high risk (including 18 subjects who later converted to full psychosis) and 108 healthy controls drawn from the second phase of the North American Prodrome Longitudinal Study (NAPLS-2). All participants underwent functional magnetic resonance imaging with a paired-associate memory paradigm at the point of recruitment and were clinically followed up for approximately 2 years. We found that at baseline, subjects at high risk showed significantly higher activation during memory retrieval in the prefrontal, parietal, and bilateral temporal cortices (PFWE < .035). This effect was more pronounced in converters than nonconverters and was particularly manifested in unmedicated subjects (P < .001). The hyperactivation was significantly correlated with retrieval reaction time during scan in converters (P = .009) but not in nonconverters and controls, suggesting an exaggerated retrieval effort. These findings suggest that hyperactivation during memory retrieval may mark processes associated with conversion to psychosis, and such measures have potential as biomarkers for psychosis prediction.
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Affiliation(s)
- Hengyi Cao
- Department of Psychology, Yale University, New Haven, CT
| | - Sarah C McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA
| | - Yoonho Chung
- Department of Psychology, Yale University, New Haven, CT
| | - Oliver Y Chén
- Department of Psychology, Yale University, New Haven, CT
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Canada
| | - Bradley Goodyear
- Department of Psychiatry, University of Calgary, Calgary, Canada,Department of Radiology, University of Calgary, Calgary, Canada,Department of Clinical Neuroscience, University of Calgary, Calgary, Canada
| | | | | | | | - Ricardo E Carrión
- Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA
| | | | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, Chapel Hill, NC
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, Chapel Hill, NC
| | - Larry J Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Heidi Thermenos
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Ming T Tsuang
- Department of Psychiatry, University of California San Diego, San Diego, CA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA
| | | | | | - Alan Anticevic
- Department of Psychiatry, Yale University, New Haven, CT
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT,Department of Psychiatry, Yale University, New Haven, CT,To whom correspondence should be addressed; Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, US; tel: +1-2034361545, e-mail:
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