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Schleifer CH, Chang SE, Amir CM, O'Hora KP, Fung H, Kang JWD, Kushan-Wells L, Daly E, Di Fabio F, Frascarelli M, Gudbrandsen M, Kates WR, Murphy D, Addington J, Anticevic A, Cadenhead KS, Cannon TD, Cornblatt BA, Keshavan M, Mathalon DH, Perkins DO, Stone W, Walker E, Woods SW, Uddin LQ, Kumar K, Hoftman GD, Bearden CE. Unique functional neuroimaging signatures of genetic versus clinical high risk for psychosis. Biol Psychiatry 2024:S0006-3223(24)01538-5. [PMID: 39181389 DOI: 10.1016/j.biopsych.2024.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 08/05/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND 22q11.2 Deletion Syndrome (22qDel) is a copy number variant (CNV) associated with psychosis and other neurodevelopmental disorders. Adolescents at clinical high risk for psychosis (CHR) are identified based on the presence of subthreshold psychosis symptoms. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped results to biological pathways. METHODS We analyzed two large multi-site cohorts with resting-state functional MRI (rs-fMRI): 1) 22qDel (n=164, 47% female) and typically developing (TD) controls (n=134, 56% female); 2) CHR individuals (n=244, 41% female) and TD controls (n=151, 46% female) from the North American Prodrome Longitudinal Study-2. We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions, testing case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation. RESULTS BSV, LC, and GBC are significantly disrupted in 22qDel compared with TD controls (False Discovery Rate q<0.05). Spatial maps of BSV and LC differences are highly correlated with each other, unlike GBC. In CHR, only LC is significantly altered versus controls, with a different spatial pattern compared to 22qDel. Group differences map onto biological gradients, with 22qDel effects strongest in regions with high predicted blood flow and metabolism. CONCLUSION 22qDel and CHR exhibit divergent effects on fMRI temporal variability and multi-scale functional connectivity. In 22qDel, strong and convergent disruptions in BSV and LC not seen in CHR individuals suggest distinct functional brain alterations.
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Affiliation(s)
- Charles H Schleifer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Sarah E Chang
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Carolyn M Amir
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Kathleen P O'Hora
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Hoki Fung
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jee Won D Kang
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Leila Kushan-Wells
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Eileen Daly
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK
| | - Fabio Di Fabio
- Department of Human Neurosciences, Sapienza University, Rome, Italy
| | | | - Maria Gudbrandsen
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK; Centre for Psychological Research (CREW), School of Psychology, University of Roehampton, London, UK
| | - Wendy R Kates
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Declan Murphy
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK
| | - Jean Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Alan Anticevic
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | | | - Tyrone D Cannon
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Barbara A Cornblatt
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, and San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - William Stone
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Elaine Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Scott W Woods
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Kuldeep Kumar
- Centre de Recherche du CHU Sainte-Justine, University of Montreal, Montreal, Canada
| | - Gil D Hoftman
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA; Department of Psychology, University of California, Los Angeles, CA, USA.
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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: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [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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Ikeda Y, Funayama T, Okubo Y, Suzuki H. The role of left insula mediating impaired error processing in response inhibition in adult heavy drinkers. Cereb Cortex 2022; 33:5991-5999. [PMID: 36533543 DOI: 10.1093/cercor/bhac477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 11/11/2022] [Accepted: 11/17/2022] [Indexed: 12/23/2022] Open
Abstract
Abstract
Identification of neurobiological mechanisms underlying development of alcohol use disorder is critical to ensuring the appropriate early-phase treatment and prevention of the disorder. To this aim, we tried to elucidate the disturbance of neural functions in heavy drinking, which can lead to alcohol use disorder. Because response inhibition is affected by alcohol use disorder, we examined neural activation and task performance for response inhibition using the Go/No-Go task in an fMRI paradigm in adult non-dependent heavy and light drinkers. We examined the neural activation for error processing and inhibitory control, components of response inhibition. We then investigated the mediating effect of the relevant neural substrate on the relationship between the level of alcohol drinking and task performance using mediation analysis. We found that heavy drinking significantly decreased activation in the left insula during error processing and increased the mean commission error rate for No-Go trials compared with light drinking. Mediation analysis demonstrated full mediation of the left insula activation during error processing for the relationship between drinking level and commission error rate. Our results suggested that left insula activation may be a neural marker pivotal for potential conversion to alcohol use disorder in individuals with high clinical risk such as heavy drinking.
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Affiliation(s)
- Yumiko Ikeda
- Nippon Medical School Department of Pharmacology, Graduate School of Medicine, , Tokyo 113-8602 , Japan
| | - Takuya Funayama
- Tokyo Medical and Dental University Department of Dental Anesthesiology and Orofacial Pain Management, Graduate School of Medical and Dental Sciences, , Tokyo 113-8549 , Japan
| | - Yoshiro Okubo
- Nippon Medical School Department of Neuropsychiatry, Graduate School of Medicine, , Tokyo 113-8602 , Japan
| | - Hidenori Suzuki
- Nippon Medical School Department of Pharmacology, Graduate School of Medicine, , Tokyo 113-8602 , Japan
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5
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Zhao Y, Chen T, Cai J, Lichenstein S, Potenza MN, Yip SW. Bayesian network mediation analysis with application to the brain functional connectome. Stat Med 2022; 41:3991-4005. [PMID: 35795965 PMCID: PMC10131252 DOI: 10.1002/sim.9488] [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: 07/20/2021] [Revised: 04/12/2022] [Accepted: 05/18/2022] [Indexed: 11/10/2022]
Abstract
The brain functional connectome, the collection of interconnected neural circuits along functional networks, facilitates a cutting-edge understanding of brain functioning, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network-variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the article, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub-networks.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Yale Center for Analytical Sciences, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Tianqi Chen
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Jiachen Cai
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Sarah Lichenstein
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Marc N Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut, USA
- Connecticut Mental Health Center, New Haven, Connecticut, USA
- Connecticut Council on Problem Gambling, Wethersfield, Connecticut, USA
- Wu Tsai Institute, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sarah W Yip
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut, USA
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