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Hoops D, Kyne R, Salameh S, MacGowan D, Avramescu RG, Ewing E, He AT, Orsini T, Durand A, Popescu C, Zhao JM, Shatz K, Li L, Carroll Q, Liu G, Paul MJ, Flores C. The scheduling of adolescence with Netrin-1 and UNC5C. eLife 2024; 12:RP88261. [PMID: 39056276 PMCID: PMC11281785 DOI: 10.7554/elife.88261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024] Open
Abstract
Dopamine axons are the only axons known to grow during adolescence. Here, using rodent models, we examined how two proteins, Netrin-1 and its receptor, UNC5C, guide dopamine axons toward the prefrontal cortex and shape behaviour. We demonstrate in mice (Mus musculus) that dopamine axons reach the cortex through a transient gradient of Netrin-1-expressing cells - disrupting this gradient reroutes axons away from their target. Using a seasonal model (Siberian hamsters; Phodopus sungorus) we find that mesocortical dopamine development can be regulated by a natural environmental cue (daylength) in a sexually dimorphic manner - delayed in males, but advanced in females. The timings of dopamine axon growth and UNC5C expression are always phase-locked. Adolescence is an ill-defined, transitional period; we pinpoint neurodevelopmental markers underlying this period.
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Affiliation(s)
- Daniel Hoops
- Department of Psychiatry, McGill UniversityMontréalCanada
- Douglas Mental Health University InstituteMontréalCanada
| | - Robert Kyne
- Neuroscience Program, University at BuffaloSUNYUnited States
| | - Samer Salameh
- Douglas Mental Health University InstituteMontréalCanada
- Integrated Program in Neuroscience, McGill UniversityMontrealCanada
| | - Del MacGowan
- Douglas Mental Health University InstituteMontréalCanada
- Integrated Program in Neuroscience, McGill UniversityMontrealCanada
| | - Radu Gabriel Avramescu
- Department of Psychiatry, McGill UniversityMontréalCanada
- Douglas Mental Health University InstituteMontréalCanada
| | - Elise Ewing
- Douglas Mental Health University InstituteMontréalCanada
- Integrated Program in Neuroscience, McGill UniversityMontrealCanada
| | - Alina Tao He
- Douglas Mental Health University InstituteMontréalCanada
- Integrated Program in Neuroscience, McGill UniversityMontrealCanada
| | - Taylor Orsini
- Douglas Mental Health University InstituteMontréalCanada
- Integrated Program in Neuroscience, McGill UniversityMontrealCanada
| | - Anais Durand
- Douglas Mental Health University InstituteMontréalCanada
- Integrated Program in Neuroscience, McGill UniversityMontrealCanada
| | - Christina Popescu
- Douglas Mental Health University InstituteMontréalCanada
- Integrated Program in Neuroscience, McGill UniversityMontrealCanada
| | - Janet Mengyi Zhao
- Douglas Mental Health University InstituteMontréalCanada
- Integrated Program in Neuroscience, McGill UniversityMontrealCanada
| | - Kelcie Shatz
- Department of Psychology, University at BuffaloSUNYUnited States
| | - LiPing Li
- Department of Psychology, University at BuffaloSUNYUnited States
| | - Quinn Carroll
- Department of Psychology, University at BuffaloSUNYUnited States
| | - Guofa Liu
- Department of Biological Sciences, University of ToledoToledoUnited States
| | - Matthew J Paul
- Neuroscience Program, University at BuffaloSUNYUnited States
- Department of Psychology, University at BuffaloSUNYUnited States
| | - Cecilia Flores
- Department of Psychiatry, McGill UniversityMontréalCanada
- Douglas Mental Health University InstituteMontréalCanada
- Department of Neurology and Neurosurgery, McGill UniversityMontréalCanada
- Ludmer Centre for Neuroinformatics & Mental Health, McGill UniversityMontréalCanada
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2
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Akhgari A, Michel TM, Vafaee MS. Dendritic spines and their role in the pathogenesis of neurodevelopmental and neurological disorders. Rev Neurosci 2024; 35:489-502. [PMID: 38440811 DOI: 10.1515/revneuro-2023-0151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/02/2024] [Indexed: 03/06/2024]
Abstract
Since Cajal introduced dendritic spines in the 19th century, they have attained considerable attention, especially in neuropsychiatric and neurologic disorders. Multiple roles of dendritic spine malfunction and pathology in the progression of various diseases have been reported. Thus, it is inevitable to consider these structures as new therapeutic targets for treating neuropsychiatric and neurologic disorders such as autism spectrum disorders, schizophrenia, dementia, Down syndrome, etc. Therefore, we attempted to prepare a narrative review of the literature regarding the role of dendritic spines in the pathogenesis of aforementioned diseases and to shed new light on their pathophysiology.
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Affiliation(s)
- Aisan Akhgari
- Student Research Committee, Tabriz University of Medical Sciences, Golgasht Street, Tabriz 5166616471, Iran
| | - Tanja Maria Michel
- Research Unit for Psychiatry, Odense University Hospital, J. B. Winsløws Vej 4, Odense 5000, Denmark
- Clinical Institute, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark
| | - Manouchehr Seyedi Vafaee
- Research Unit for Psychiatry, Odense University Hospital, J. B. Winsløws Vej 4, Odense 5000, Denmark
- Clinical Institute, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark
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Williams LM, Whitfield Gabrieli S. Neuroimaging for precision medicine in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01917-z. [PMID: 39039140 DOI: 10.1038/s41386-024-01917-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/24/2024]
Abstract
Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.
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Affiliation(s)
- Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, 94304, USA.
| | - Susan Whitfield Gabrieli
- Department of Psychology, Northeastern University, 805 Columbus Ave, Boston, MA, 02120, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
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Dwyer GE, Johnsen E, Hugdahl K. NMDAR dysfunction and the regulation of dopaminergic transmission in schizophrenia. Schizophr Res 2024; 271:19-27. [PMID: 39002526 DOI: 10.1016/j.schres.2024.07.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/27/2024] [Accepted: 07/07/2024] [Indexed: 07/15/2024]
Abstract
A substantial body of evidence implicates dysfunction in N-methyl-d-aspartate receptors (NMDARs) in the pathophysiology of schizophrenia. This article illustrates how NMDAR dysfunction may give rise to many of the neurobiological phenomena frequently associated with schizophrenia with a particular focus on how NMDAR dysfunction affects the thalamic reticular nucleus (nRT) and pedunculopontine tegmental nucleus (PPTg). Furthermore, this article presents a model for schizophrenia illustrating how dysfunction in the nRT may interrupt prefrontal regulation of midbrain dopaminergic neurons, and how dysfunction in the PPTg may drive increased, irregular burst firing.
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Affiliation(s)
- Gerard Eric Dwyer
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.
| | - Erik Johnsen
- NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kenneth Hugdahl
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway; Department of Radiology, Haukeland University Hospital, Bergen, Norway
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5
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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2024:sbae110. [PMID: 38982882 DOI: 10.1093/schbul/sbae110] [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: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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Berthet P, Haatveit BC, Kjelkenes R, Worker A, Kia SM, Wolfers T, Rutherford S, Alnaes D, Dinga R, Pedersen ML, Dahl A, Fernandez-Cabello S, Dazzan P, Agartz I, Nesvåg R, Ueland T, Andreassen OA, Simonsen C, Westlye LT, Melle I, Marquand A. A 10-Year Longitudinal Study of Brain Cortical Thickness in People with First-Episode Psychosis Using Normative Models. Schizophr Bull 2024:sbae107. [PMID: 38970378 DOI: 10.1093/schbul/sbae107] [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: 07/08/2024]
Abstract
BACKGROUND Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms. DESIGN Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms. RESULTS LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores. CONCLUSIONS This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.
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Affiliation(s)
- Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Beathe C Haatveit
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Rikka Kjelkenes
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Amanda Worker
- Department of Psychosis Studies, Institute of Psychiatry, King's College, London, UK
| | - Seyed Mostafa Kia
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, Utrecht University Medical Center, Utrecht, the Netherlands
- Department Cognitive Science and Artificial Intelligence, Tilburg University, the Netherlands
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Saige Rutherford
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Dag Alnaes
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Richard Dinga
- Department Cognitive Science and Artificial Intelligence, Tilburg University, the Netherlands
| | - Mads L Pedersen
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Andreas Dahl
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Sara Fernandez-Cabello
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, King's College, London, UK
| | - Ingrid Agartz
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ragnar Nesvåg
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Torill Ueland
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Carmen Simonsen
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Andre Marquand
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
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Sheehan N, Bannai D, Silverstein SM, Lizano P. Neuroretinal Alterations in Schizophrenia and Bipolar Disorder: An Updated Meta-analysis. Schizophr Bull 2024:sbae102. [PMID: 38954839 DOI: 10.1093/schbul/sbae102] [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: 07/04/2024]
Abstract
Schizophrenia (SZ) and bipolar disorder (BD) are characterized by major symptomatic, cognitive, and neuroanatomical changes. Recent studies have used optical coherence tomography (OCT) to investigate retinal changes in SZ and BD, but their unique and shared changes require further evaluation. Articles were identified using PubMed and Google Scholar. 39 studies met the inclusion criteria. Diagnostic groups were proband (SZ/BD combined), SZ, BD, and healthy control (HC) eyes. Meta-analyses utilized fixed and random effects models when appropriate, and publication bias was corrected using trim-and-fill analysis ("meta" package in R). Results are reported as standardized mean differences with 95% CIs. Data from 3145 patient eyes (1956 SZ, 1189 BD) and 3135 HC eyes were included. Studies identified thinning of the peripapillary retinal nerve fiber layer (pRNFL, overall and in 2 subregions), m-Retina (overall and all subregions), mGCL-IPL, mIPL, and mRPE in SZ patients. BD showed thinning of the pRNFL (overall and in each subregion), pGCC, and macular Retina (in 5 subregions), but no changes in thickness or volume for the total retina. Neither SZ nor BD patients demonstrated significant changes in the fovea, mRNFL, mGCL, mGCC, mINL, mOPL, mONL, or choroid thicknesses. Moderating effects of age, illness duration, and smoking on retinal structures were identified. This meta-analysis builds upon previous literature in this field by incorporating recent OCT studies and examining both peripapillary and macular retinal regions with respect to psychotic disorders. Overall, this meta-analysis demonstrated both peripapillary and macular structural retinal abnormalities in people with SZ or BD compared with HCs.
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Affiliation(s)
- Nora Sheehan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Deepthi Bannai
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Steven M Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, NY, USA
| | - Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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Keane BP, Abrham YT, Hearne LJ, Bi H, Hu B. Increased whole-brain functional heterogeneity in psychosis during rest and task. Neuroimage Clin 2024; 43:103630. [PMID: 38875745 PMCID: PMC11225660 DOI: 10.1016/j.nicl.2024.103630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/09/2024] [Accepted: 06/06/2024] [Indexed: 06/16/2024]
Abstract
Past work has shown that people with schizophrenia exhibit more cross-subject heterogeneity in their functional connectivity patterns. However, it remains unclear whether specific brain networks are implicated, whether common confounds could explain the results, or whether task activations might also be more heterogeneous. Unambiguously establishing the existence and extent of functional heterogeneity constitutes a first step toward understanding why it emerges and what it means clinically. METHODS We first leveraged data from the HCP Early Psychosis project. Functional connectivity (FC) was extracted from 718 parcels via principal components regression. Networks were defined via a brain network partition (Ji et al., 2019). We also examined an independent data set with controls, later-stage schizophrenia patients, and ADHD patients during rest and during a working memory task. We quantified heterogeneity by averaging the Pearson correlation distance of each subject's FC or task activity pattern to that of every other subject of the same cohort. RESULTS Affective and non-affective early psychosis patients exhibited more cross-subject whole-brain heterogeneity than healthy controls (ps < 0.001, Hedges' g > 0.74). Increased heterogeneity could be found in up to seven networks. In-scanner motion, medication, nicotine, and comorbidities could not explain the results. Later-stage schizophrenia patients exhibited heterogeneous connectivity patterns and task activations compared to ADHD and control subjects. Interestingly, individual connection weights, parcel-wise task activations, and network averages thereof were not more variable in patients, suggesting that heterogeneity becomes most obvious over large-scale patterns. CONCLUSION Whole-brain cross-subject functional heterogeneity characterizes psychosis during rest and task. Developmental and pathophysiological consequences are discussed.
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Affiliation(s)
- Brian P Keane
- Department of Psychiatry, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642, USA; Department of Neuroscience, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642, USA; Center for Visual Science, University of Rochester, 601 Elmwood Ave, Rochester, NY 14642, USA; Department of Brain & Cognitive Science, University of Rochester, 358 Meliora Hall, P.O. Box 270268, Rochester, NY 14627, USA.
| | - Yonatan T Abrham
- Center for Visual Science, University of Rochester, 601 Elmwood Ave, Rochester, NY 14642, USA; Department of Brain & Cognitive Science, University of Rochester, 358 Meliora Hall, P.O. Box 270268, Rochester, NY 14627, USA
| | - Luke J Hearne
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Howard Bi
- Department of Psychiatry, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642, USA
| | - Boyang Hu
- Department of Brain & Cognitive Science, University of Rochester, 358 Meliora Hall, P.O. Box 270268, Rochester, NY 14627, USA
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Jang YJ, Yassin W, Mesholam-Gately R, Gershon ES, Keedy S, Pearlson GG, Tamminga CA, McDowell J, Parker DA, Sauer K, Keshavan MS. Characterizing the Relationship between Personality Dimensions and Psychosis-Specific Clinical Characteristics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.06.24308169. [PMID: 38883764 PMCID: PMC11178011 DOI: 10.1101/2024.06.06.24308169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Background Past studies associating personality with psychosis have been limited by small nonclinical samples and a focus on general symptom burden. This study uses a large clinical sample to examine personality's relationship with psychosis-specific features and compare personality dimensions across clinically and neurobiologically defined categories of psychoses. Methods A total of 1352 participants with schizophrenia, schizoaffective disorder, and bipolar with psychosis, as well as 623 healthy controls (HC), drawn from the Bipolar-Schizophrenia Network for Intermediate Phenotypes (BSNIP-2) study, were included. Three biomarker-derived biotypes were used to separately categorize the probands. Mean personality factors (openness, conscientiousness, extraversion, agreeableness, and neuroticism) were compared between HC and proband subgroups using independent sample t-tests. A robust linear regression was utilized to determine personality differences across biotypes and diagnostic subgroups. Associations between personality factors and cognition were determined through Pearson's correlation. A canonical correlation was run between the personality factors and general functioning, positive symptoms, and negative symptoms to delineate the relationship between personality and clinical outcomes of psychosis. Results There were significant personality differences between the proband and HC groups across all five personality factors. Overall, the probands had higher neuroticism and lower extraversion, agreeableness, conscientiousness, and openness. Openness showed the greatest difference across the diagnostic subgroups and biotypes, and greatest correlation with cognition. Openness, agreeableness, and extraversion had the strongest associations with symptom severity. Conclusions Individuals with psychosis have different personality profiles compared to HC. In particular, openness may be relevant in distinguishing psychosis-specific phenotypes and experiences, and associated with biological underpinnings of psychosis, including cognition. Further studies should identify potential causal factors and mediators of this relationship.
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Zheng J, Zong X, Tang L, Guo H, Zhao P, Womer FY, Zhang X, Tang Y, Wang F. Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders. Psychol Med 2024:1-11. [PMID: 38804091 DOI: 10.1017/s0033291724000886] [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: 05/29/2024]
Abstract
BACKGROUND Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. METHODS We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. RESULTS Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. CONCLUSIONS Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.
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Affiliation(s)
- Junjie Zheng
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Xiaofen Zong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Lili Tang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Huiling Guo
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Pengfei Zhao
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Fay Y Womer
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
- Department of Gerontology, The First Hospital of China Medical University, Shenyang, China
- Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
- Department of Mental Health, School of Public Health, Nanjing Medical University, Nanjing, China
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Andrés-Camazón P, Diaz-Caneja CM, Ballem R, Chen J, Calhoun VD, Iraji A. Neurobiology-based Cognitive Biotypes Using Multi-scale Intrinsic Connectivity Networks in Psychotic Disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.14.24307341. [PMID: 38798576 PMCID: PMC11118619 DOI: 10.1101/2024.05.14.24307341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Objective Understanding the neurobiology of cognitive dysfunction in psychotic disorders remains elusive, as does developing effective interventions. Limited knowledge about the biological heterogeneity of cognitive dysfunction hinders progress. This study aimed to identify subgroups of patients with psychosis with distinct patterns of functional brain alterations related to cognition (cognitive biotypes). Methods B-SNIP consortium data (2,270 participants including participants with psychotic disorders, relatives, and controls) was analyzed. Researchers used reference-informed independent component analysis and the NeuroMark 100k multi-scale intrinsic connectivity networks (ICN) template to obtain subject-specific ICNs and whole-brain functional network connectivity (FNC). FNC features associated with cognitive performance were identified through multivariate joint analysis. K-means clustering identified subgroups of patients based on these features in a discovery set. Subgroups were further evaluated in a replication set and in relatives. Results Two biotypes with different functional brain alteration patterns were identified. Biotype 1 exhibited brain-wide alterations, involving hypoconnectivity in cerebellar-subcortical and somatomotor-visual networks and worse cognitive performance. Biotype 2 exhibited hyperconnectivity in somatomotor-subcortical networks and hypoconnectivity in somatomotor-high cognitive processing networks, and better preserved cognitive performance. Demographic, clinical, cognitive, and FNC characteristics of biotypes were consistent in discovery and replication sets, and in relatives. 70.12% of relatives belonged to the same biotype as their affected family members. Conclusions These findings suggest two distinctive psychosis-related cognitive biotypes with differing functional brain patterns shared with their relatives. Patient stratification based on these biotypes instead of traditional diagnosis may help to optimize future research and clinical trials addressing cognitive dysfunction in psychotic disorders.
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Affiliation(s)
- Pablo Andrés-Camazón
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Covadonga Martínez Diaz-Caneja
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Ram Ballem
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
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12
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Pettorruso M, Di Lorenzo G, De Risio L, Di Carlo F, d'Andrea G, Martinotti G. Addiction biotypes: a paradigm shift for future treatment strategies? Mol Psychiatry 2024; 29:1450-1452. [PMID: 38243073 DOI: 10.1038/s41380-024-02423-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 01/01/2024] [Accepted: 01/08/2024] [Indexed: 01/21/2024]
Affiliation(s)
- Mauro Pettorruso
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy.
| | - Giorgio Di Lorenzo
- Chair of Psychiatry, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
- IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Luisa De Risio
- Department of Psychiatry and Addiction, ASL Roma 5, Colleferro (Rome), Italy
| | - Francesco Di Carlo
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
| | - Giacomo d'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
| | - Giovanni Martinotti
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
- Department of Pharmacy, Pharmacology, Clinical Science, University of Hertfordshire, Herts, UK
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13
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Demro C, Lahud E, Burton PC, Purcell JR, Simon JJ, Sponheim SR. Reward anticipation-related neural activation following cued reinforcement in adults with psychotic psychopathology and biological relatives. Psychol Med 2024; 54:1441-1451. [PMID: 38197294 DOI: 10.1017/s0033291723003343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
BACKGROUND Schizophrenia is associated with hypoactivation of reward sensitive brain areas during reward anticipation. However, it is unclear whether these neural functions are similarly impaired in other disorders with psychotic symptomatology or individuals with genetic liability for psychosis. If abnormalities in reward sensitive brain areas are shared across individuals with psychotic psychopathology and people with heightened genetic liability for psychosis, there may be a common neural basis for symptoms of diminished pleasure and motivation. METHODS We compared performance and neural activity in 123 people with a history of psychosis (PwP), 81 of their first-degree biological relatives, and 49 controls during a modified Monetary Incentive Delay task during fMRI. RESULTS PwP exhibited hypoactivation of the striatum and anterior insula (AI) during cueing of potential future rewards with each diagnostic group showing hypoactivations during reward anticipation compared to controls. Despite normative task performance, relatives demonstrated caudate activation intermediate between controls and PwP, nucleus accumbens activation more similar to PwP than controls, but putamen activation on par with controls. Across diagnostic groups of PwP there was less functional connectivity between bilateral caudate and several regions of the salience network (medial frontal gyrus, anterior cingulate, AI) during reward anticipation. CONCLUSIONS Findings implicate less activation and connectivity in reward processing brain regions across a spectrum of disorders involving psychotic psychopathology. Specifically, aberrations in striatal and insular activity during reward anticipation seen in schizophrenia are partially shared with other forms of psychotic psychopathology and associated with genetic liability for psychosis.
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Affiliation(s)
- Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Elijah Lahud
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Philip C Burton
- College of Liberal Arts, University of Minnesota, Minneapolis, MN, USA
| | - John R Purcell
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA
| | - Joe J Simon
- Department of General Internal Medicine and Psychosomatics, Centre for Psychosocial Medicine, Heidelberg, Germany
| | - Scott R Sponheim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA
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14
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Garakh Z, Larionova E, Shmukler A, Horáček J, Zaytseva Y. EEG alpha reactivity on eyes opening discriminates patients with schizophrenia and schizoaffective disorder. Clin Neurophysiol 2024; 161:211-221. [PMID: 38522267 DOI: 10.1016/j.clinph.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 02/15/2024] [Accepted: 03/05/2024] [Indexed: 03/26/2024]
Abstract
OBJECTIVE Alpha activity in the electroencephalogram (EEG) is typically dominant during rest with closed eyes but suppressed by visual stimulation. Previous research has shown that alpha-blockade is less pronounced in schizophrenia patients compared to healthy individuals, but no studies have examined it in schizoaffective disorder. METHODS A resting state EEG was used for the analysis of the alpha-reactivity between the eyes closed and the eyes opened conditions in overall (8 - 13 Hz), low (8 - 10 Hz) and high (10 - 13 Hz) alpha bands in three groups: schizophrenia patients (SC, n = 30), schizoaffective disorder (SA, n = 30), and healthy controls (HC, n = 36). All patients had their first psychotic episode and were receiving antipsychotic therapy. RESULTS A significant decrease in alpha power was noted across all subjects from the eyes-closed to eyes-open condition, spanning all regions. Alpha reactivity over the posterior regions was lower in SC compared to HC within overall and high alpha. SA showed a trend towards reduced alpha reactivity compared to HC, especially evident over the left posterior region within the overall alpha. Alpha reactivity was more pronounced over the middle and right posterior regions of SA as compared to SC, particularly in the high alpha. Alpha reactivity in SC and SA patients was associated with various negative symptoms. CONCLUSIONS Our findings imply distinct alterations in arousal mechanisms in SC and SA and their relation to negative symptomatology. Arousal is more preserved in SA. SIGNIFICANCE This study is the first to compare the EEG features of arousal in SC and SA.
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Affiliation(s)
- Zhanna Garakh
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, Moscow, Russia
| | - Ekaterina Larionova
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, Moscow, Russia
| | - Alexander Shmukler
- National Medical Research Centre for Psychiatry and Narcology named after V. Serbsky , Moscow, Russia
| | - Jiří Horáček
- National Institute of Mental Health, Klecany, Czechia; Department of Psychiatry and Psychotherapy, 3rd Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Yuliya Zaytseva
- National Institute of Mental Health, Klecany, Czechia; Department of Psychiatry and Psychotherapy, 3rd Faculty of Medicine, Charles University in Prague, Prague, Czechia; Institute of Medical Psychology, Ludwig-Maximilian University, Munich, Germany.
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15
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Hoops D, Kyne RF, Salameh S, MacGowan D, Avramescu RG, Ewing E, He AT, Orsini T, Durand A, Popescu C, Zhao JM, Schatz KC, Li L, Carroll QE, Liu G, Paul MJ, Flores C. The scheduling of adolescence with Netrin-1 and UNC5C. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.19.521267. [PMID: 36711625 PMCID: PMC9882376 DOI: 10.1101/2023.01.19.521267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Dopamine axons are the only axons known to grow during adolescence. Here, using rodent models, we examined how two proteins, Netrin-1 and its receptor, UNC5C, guide dopamine axons towards the prefrontal cortex and shape behaviour. We demonstrate in mice ( Mus musculus ) that dopamine axons reach the cortex through a transient gradient of Netrin-1 expressing cells - disrupting this gradient reroutes axons away from their target. Using a seasonal model (Siberian hamsters; Phodopus sungorus ) we find that mesocortical dopamine development can be regulated by a natural environmental cue (daylength) in a sexually dimorphic manner - delayed in males, but advanced in females. The timings of dopamine axon growth and UNC5C expression are always phase-locked. Adolescence is an ill-defined, transitional period; we pinpoint neurodevelopmental markers underlying this period.
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16
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Bhattacharyya U, John J, Lencz T, Lam M. Dissecting Schizophrenia Biology Using Pleiotropy with Cognitive Genomics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.16.24305885. [PMID: 38699340 PMCID: PMC11065000 DOI: 10.1101/2024.04.16.24305885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Given the increasingly large number of loci discovered by psychiatric GWAS, specification of the key biological pathways underlying these loci has become a priority for the field. We have previously leveraged the pleiotropic genetic relationships between schizophrenia and two cognitive phenotypes (educational attainment and cognitive task performance) to differentiate two subsets of illness-relevant SNPs: (1) those with "concordant" alleles, which are associated with reduced cognitive ability/education and increased schizophrenia risk; and (2) those with "discordant" alleles linked to reduced educational and/or cognitive levels but lower schizophrenia susceptibility. In the present study, we extend our prior work, utilizing larger input GWAS datasets and a more powerful statistical approach to pleiotropic meta-analysis, the Pleiotropic Locus Exploration and Interpretation using Optimal test (PLEIO). Our pleiotropic meta-analysis of schizophrenia and the two cognitive phenotypes revealed 768 significant loci (159 novel). Among these, 347 loci harbored concordant SNPs, 270 encompassed discordant SNPs, and 151 "dual" loci contained concordant and discordant SNPs. Competitive gene-set analysis using MAGMA related concordant SNP loci with neurodevelopmental pathways (e.g., neurogenesis), whereas discordant loci were associated with mature neuronal synaptic functions. These distinctions were also observed in BrainSpan analysis of temporal enrichment patterns across developmental periods, with concordant loci containing more prenatally expressed genes than discordant loci. Dual loci were enriched for genes related to mRNA translation initiation, representing a novel finding in the schizophrenia literature.
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17
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Zheng K, Yu S, Chen L, Dang L, Chen B. BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping. Neuroimage 2024; 292:120594. [PMID: 38569980 DOI: 10.1016/j.neuroimage.2024.120594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/24/2024] [Accepted: 03/27/2024] [Indexed: 04/05/2024] Open
Abstract
Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.
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Affiliation(s)
- Kaizhong Zheng
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
| | - Shujian Yu
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Machine Learning Group, UiT - Arctic University of Norway, Tromsø, Norway.
| | - Liangjun Chen
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
| | - Lujuan Dang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
| | - Badong Chen
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
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18
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Lundin NB, Blouin AM, Cowan HR, Moe AM, Wastler HM, Breitborde NJK. Identification of Psychosis Risk and Diagnosis of First-Episode Psychosis: Advice for Clinicians. Psychol Res Behav Manag 2024; 17:1365-1383. [PMID: 38529082 PMCID: PMC10962362 DOI: 10.2147/prbm.s423865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/07/2024] [Indexed: 03/27/2024] Open
Abstract
Early detection of psychotic-spectrum disorders among adolescents and young adults is crucial, as the initial years after psychotic symptom onset encompass a critical period in which psychosocial and pharmacological interventions are most effective. Moreover, clinicians and researchers in recent decades have thoroughly characterized psychosis-risk syndromes, in which youth are experiencing early warning signs indicative of heightened risk for developing a psychotic disorder. These insights have created opportunities for intervention even earlier in the illness course, ideally culminating in the prevention or mitigation of psychosis onset. However, identification and diagnosis of early signs of psychosis can be complex, as clinical presentations are heterogeneous, and psychotic symptoms exist on a continuum. When a young person presents to a clinic, it may be unclear whether they are experiencing common, mild psychotic-like symptoms, early warning signs of psychosis, overt psychotic symptoms, or symptoms better accounted for by a non-psychotic disorder. Therefore, the purpose of this review is to provide a framework for clinicians, including those who treat non-psychotic disorders and those in primary care settings, for guiding identification and diagnosis of early psychosis within the presenting clinic or via referral to a specialty clinic. We first provide descriptions and examples of first-episode psychosis (FEP) and psychosis-risk syndromes, as well as assessment tools used to diagnose these conditions. Next, we provide guidance as to the differential diagnosis of conditions which have phenotypic overlap with psychotic disorders, while considering the possibility of co-occurring symptoms in which case transdiagnostic treatments are encouraged. Finally, we conclude with an overview of early detection screening and outreach campaigns, which should be further optimized to reduce the duration of untreated psychosis among youth.
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Affiliation(s)
- Nancy B Lundin
- Early Psychosis Intervention Center, Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Alexandra M Blouin
- Early Psychosis Intervention Center, Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Henry R Cowan
- Early Psychosis Intervention Center, Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - Aubrey M Moe
- Early Psychosis Intervention Center, Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Heather M Wastler
- Early Psychosis Intervention Center, Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Nicholas J K Breitborde
- Early Psychosis Intervention Center, Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
- Department of Psychology, The Ohio State University, Columbus, OH, USA
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19
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Warren TL, Tubbs JD, Lesh TA, Corona MB, Pakzad SS, Albuquerque MD, Singh P, Zarubin V, Morse SJ, Sham PC, Carter CS, Nord AS. Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis. Mol Psychiatry 2024:10.1038/s41380-024-02457-0. [PMID: 38491343 DOI: 10.1038/s41380-024-02457-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/18/2024]
Abstract
A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 205 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.
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Affiliation(s)
- Tracy L Warren
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
| | - Justin D Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tyler A Lesh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Mylena B Corona
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
| | - Sarvenaz S Pakzad
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Marina D Albuquerque
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Praveena Singh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Vanessa Zarubin
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Sarah J Morse
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
| | - Cameron S Carter
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA.
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA.
| | - Alex S Nord
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA.
- Center for Neuroscience, University of California, Davis, CA, USA.
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20
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Chen Y, Chen Y, Zheng R, Xue K, Li S, Pang J, Li H, Zhang Y, Cheng J, Han S. Identifying two distinct neuroanatomical subtypes of first-episode depression using heterogeneity through discriminative analysis. J Affect Disord 2024; 349:479-485. [PMID: 38218252 DOI: 10.1016/j.jad.2024.01.091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/06/2023] [Accepted: 01/07/2024] [Indexed: 01/15/2024]
Abstract
BACKGROUND Neurobiological heterogeneity in depression remains largely unknown, leading to inconsistent neuroimaging findings. METHODS Here, we adopted a novel proposed machine learning method ground on gray matter volumes (GMVs) to investigate neuroanatomical subtypes of first-episode treatment-naïve depression. GMVs were obtained from high-resolution T1-weighted images of 195 patients with first-episode, treatment-naïve depression and 78 matched healthy controls (HCs). Then we explored distinct subtypes of depression by employing heterogeneity through discriminative analysis (HYDRA) with regional GMVs as features. RESULTS Two prominently divergent subtypes of first-episode depression were identified, exhibiting opposite structural alterations compared with HCs but no different demographic features. Subtype 1 presented widespread increased GMVs mainly located in frontal, parietal, temporal cortex and partially located in limbic system. Subtype 2 presented widespread decreased GMVs mainly located in thalamus, cerebellum, limbic system and partially located in frontal, parietal, temporal cortex. Subtype 2 had smaller TIV and longer illness duration than Subtype 1. And TIV in Subtype 1 was positively correlated with age of onset while not in Subtype 2, probably implying the different potential neuropathological mechanisms. LIMITATIONS Despite results obtained in this study were validated by employing another brain atlas, the conclusions were acquired from a single dataset. CONCLUSIONS This study revealed two distinguishing neuroanatomical subtypes of first-episode depression, which provides new insights into underlying biological mechanisms of the heterogeneity in depression and might be helpful for accurate clinical diagnosis and future treatment.
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Affiliation(s)
- Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China
| | - Yi Chen
- Clinical Research Service Center, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan 450000, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China
| | - Kangkang Xue
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
| | - Jianyue Pang
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
| | - Hengfen Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China.
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China.
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China.
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21
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De Doncker W, Kuppuswamy A. Lesioned hemisphere-specific phenotypes of post-stroke fatigue emerge from motor and mood characteristics in chronic stroke. Eur J Neurol 2024; 31:e16170. [PMID: 38069662 PMCID: PMC11141786 DOI: 10.1111/ene.16170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/03/2023] [Accepted: 11/14/2023] [Indexed: 02/09/2024]
Abstract
BACKGROUND AND PURPOSE Post-stroke fatigue commonly presents alongside several comorbidities. The interaction between comorbidities and their relationship to fatigue is not known. In this study, we focus on physical and mood comorbidities, alongside lesion characteristics. We predict the emergence of distinct fatigue phenotypes with distinguishable physical and mood characteristics. METHODS In this cross-sectional observational study, in 94 first time, non-depressed, moderate to minimally impaired chronic stroke survivors, the relationship between measures of motor function (grip strength, nine-hole peg test time), motor cortical excitability (resting motor threshold), Hospital Anxiety and Depression Scale and Fatigue Severity Scale-7 (FSS-7) scores, age, gender and side of stroke was established using Spearman's rank correlation. Mood and motor variables were then entered into a k-means clustering algorithm to identify the number of unique clusters, if any. Post hoc pairwise comparisons followed by corrections for multiple comparisons were performed to characterize differences among clusters in the variables included in k-means clustering. RESULTS Clustering analysis revealed a four-cluster model to be the best model (average silhouette score of 0.311). There was no significant difference in FSS-7 scores among the four high-fatigue clusters. Two clusters consisted of only left-hemisphere strokes, and the remaining two were exclusively right-hemisphere strokes. Factors that differentiated hemisphere-specific clusters were the level of depressive symptoms and anxiety. Motor characteristics distinguished the low-depressive left-hemisphere from the right-hemisphere clusters. CONCLUSION The significant differences in side of stroke and the differential relationship between mood and motor function in the four clusters reveal the heterogenous nature of post-stroke fatigue, which is amenable to categorization. Such categorization is critical to an understanding of the interactions between post-stroke fatigue and its presenting comorbid deficits, with significant implications for the development of context-/category-specific interventions.
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Affiliation(s)
- William De Doncker
- Department of Clinical and Movement Neuroscience, Institute of NeurologyUniversity College LondonLondonUK
| | - Annapoorna Kuppuswamy
- Department of Clinical and Movement Neuroscience, Institute of NeurologyUniversity College LondonLondonUK
- Department of Biomedical SciencesUniversity of LeedsLeedsUK
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22
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Dickie EW, Ameis SH, Boileau I, Diaconescu AO, Felsky D, Goldstein BI, Gonçalves V, Griffiths JD, Haltigan JD, Husain MO, Rubin-Kahana DS, Iftikhar M, Jani M, Lai MC, Lin HY, MacIntosh BJ, Wheeler AL, Vasdev N, Vieira E, Ahmadzadeh G, Heyland L, Mohan A, Ogunsanya F, Oliver LD, Zhu C, Wong JKY, Charlton C, Truong J, Yu L, Kelly R, Cleverley K, Courtney DB, Foussias G, Hawke LD, Hill S, Kozloff N, Polillo A, Rotenberg M, Quilty LC, Tempelaar W, Wang W, Nikolova YS, Voineskos AN. Neuroimaging and Biosample Collection in the Toronto Adolescent and Youth Cohort Study: Rationale, Methods, and Early Data. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:275-284. [PMID: 37979944 DOI: 10.1016/j.bpsc.2023.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 11/20/2023]
Abstract
BACKGROUND The Toronto Adolescent and Youth (TAY) Cohort Study will characterize the neurobiological trajectories of psychosis spectrum symptoms, functioning, and suicidality (i.e., suicidal thoughts and behaviors) in youth seeking mental health care. Here, we present the neuroimaging and biosample component of the protocol. We also present feasibility and quality control metrics for the baseline sample collected thus far. METHODS The current study includes youths (ages 11-24 years) who were referred to child and youth mental health services within a large tertiary care center in Toronto, Ontario, Canada, with target recruitment of 1500 participants. Participants were offered the opportunity to provide any or all of the following: 1) 1-hour magnetic resonance imaging (MRI) scan (electroencephalography if ineligible for or declined MRI), 2) blood sample for genomic and proteomic data (or saliva if blood collection was declined or not feasible) and urine sample, and 3) heart rate recording to assess respiratory sinus arrhythmia. RESULTS Of the first 417 participants who consented to participate between May 4, 2021, and February 2, 2023, 412 agreed to participate in the imaging and biosample protocol. Of these, 334 completed imaging, 341 provided a biosample, 338 completed respiratory sinus arrhythmia, and 316 completed all 3. Following quality control, data usability was high (MRI: T1-weighted 99%, diffusion-weighted imaging 99%, arterial spin labeling 90%, resting-state functional MRI 95%, task functional MRI 90%; electroencephalography: 83%; respiratory sinus arrhythmia: 99%). CONCLUSIONS The high consent rates, good completion rates, and high data usability reported here demonstrate the feasibility of collecting and using brain imaging and biosamples in a large clinical cohort of youths seeking mental health care.
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Affiliation(s)
- Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Isabelle Boileau
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Andreea O Diaconescu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Felsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin I Goldstein
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Vanessa Gonçalves
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - John D Griffiths
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - John D Haltigan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Muhammad O Husain
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Dafna S Rubin-Kahana
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Myera Iftikhar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Melanie Jani
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; National Taiwan University Hospital and College of Medicine, Taiwan
| | - Hsiang-Yuan Lin
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Bradley J MacIntosh
- Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Oslo University Hospital, Oslo, Norway
| | - Anne L Wheeler
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Hospital for Sick Children, Neurosciences and Mental Health, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Neil Vasdev
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Erica Vieira
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ghazaleh Ahmadzadeh
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Lindsay Heyland
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Acadia University, Wolfville, Nova Scotia, Canada
| | - Akshay Mohan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Feyi Ogunsanya
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychology, Western University, London, Ontario, Canada
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Cherrie Zhu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Lunenfeld-Tanenbaum Research Institute at Sinai Health, Toronto, Ontario, Canada
| | - Jimmy K Y Wong
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Colleen Charlton
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Jennifer Truong
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Lujia Yu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Rachel Kelly
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Kristin Cleverley
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Darren B Courtney
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lisa D Hawke
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sean Hill
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Nicole Kozloff
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Alexia Polillo
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Martin Rotenberg
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lena C Quilty
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Wanda Tempelaar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Wei Wang
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Yuliya S Nikolova
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
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23
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Georgiadis F, Larivière S, Glahn D, Hong LE, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens FA, Green MJ, Cairns MJ, Michie PT, Rasser PE, Catts S, Tooney P, Scott RJ, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite TD, Karuk A, Pomarol-Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay IS, Sponheim SR, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein DJ, Howells F, Temmingh HS, Diaz Zuluaga AM, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk SL, Thompson P, van Erp TGM, Turner JA, Bernhardt B, Kirschner M. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Mol Psychiatry 2024:10.1038/s41380-024-02442-7. [PMID: 38336840 DOI: 10.1038/s41380-024-02442-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/08/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.
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Affiliation(s)
- Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
| | - Sara Larivière
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - David Glahn
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Bryan Mowry
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia
| | - Carmel Loughland
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia
| | - Frans A Henskens
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Melissa J Green
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Patricia T Michie
- School of Psychological Sciences, University of Newcastle, Newcastle, NSW, Australia
| | - Paul E Rasser
- School of Medicine and Public Health, College of Health, Medicine, and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia
| | - Stanley Catts
- Faculty of Medicine, University of Queensland, St Lucia, QLD, Australia
| | - Paul Tooney
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Rodney J Scott
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Ulrich Schall
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Vaughan Carr
- School of Clinical Medicine, Discipline of Psychiatry, UNSW Sydney, Sydney, NSW, Australia
| | - Yann Quidé
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Axel Krug
- University Hospital Bonn, Department of Psychiatry and Psychotherapy, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Frederike Stein
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Igor Nenadić
- Department. of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Raquel Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ruben Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | | | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | | | - Aristotle Voineskos
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
| | | | - Diana Tordesillas Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental Neurosciences, Technischen Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany
| | - Nicolas Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Kelly Rootes-Murdy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Caroline Demro
- University of Minnesota Department of Psychology, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Ian S Ramsay
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Andre Schmidt
- University of Basel, Department of Psychiatry, Basel, Switzerland
| | | | | | - Irina Lebedeva
- Mental Health Research Center, Moscow, Russian Federation
| | - Cyril Höschl
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Dana Nguyen
- Department of Pediatric Neurology, University of California Irvine, Irvine, CA, USA
| | - Anne Uhlmann
- Department of child and adolescent psychiatry, TU Dresden, Dresden, Germany
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Fleur Howells
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Henk S Temmingh
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Ana M Diaz Zuluaga
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Carlos López Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Felice Iasevoli
- University of Naples, Department of Neuroscience, Naples, Italy
| | - Ellen Ji
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Wolfgang Omlor
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Bratislav Misic
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Sofie L Valk
- Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, the Ohio State University, Columbus, OH, USA
| | - Boris Bernhardt
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
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24
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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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Ji Y, Pearlson G, Bustillo J, Kochunov P, Turner JA, Jiang R, Shao W, Zhang X, Fu Z, Li K, Liu Z, Xu X, Zhang D, Qi S, Calhoun VD. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering. Schizophr Res 2024; 264:130-139. [PMID: 38128344 DOI: 10.1016/j.schres.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 07/19/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences. METHODS 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives. RESULTS Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms. CONCLUSIONS These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.
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Affiliation(s)
- Yixin Ji
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China
| | - Godfrey Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Juan Bustillo
- Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Rongtao Jiang
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China
| | - Xiao Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Department of Electrical and Computer Engineering, Georgia Tech University, Atlanta, GA, USA
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26
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Liang L, Heinrichs RW, Liddle PF, Jeon P, Théberge J, Palaniyappan L. Cortical impoverishment in a stable subgroup of schizophrenia: Validation across various stages of psychosis. Schizophr Res 2024; 264:567-577. [PMID: 35644706 DOI: 10.1016/j.schres.2022.05.013] [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] [Received: 02/01/2022] [Revised: 05/09/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Cortical thinning is a well-known feature in schizophrenia. The considerable variation in the spatial distribution of thickness changes has been used to parse heterogeneity. A 'cortical impoverishment' subgroup with a generalized reduction in thickness has been reported. However, it is unclear if this subgroup is recoverable irrespective of illness stage, and if it relates to the glutamate hypothesis of schizophrenia. METHODS We applied hierarchical cluster analysis to cortical thickness data from magnetic resonance imaging scans of three datasets in different stages of psychosis (n = 288; 160 patients; 128 healthy controls) and studied the cognitive and symptom profiles of the observed subgroups. In one of the samples, we also studied the subgroup differences in 7-Tesla magnetic resonance spectroscopy glutamate concentration in the dorsal anterior cingulate cortex. RESULTS Our consensus-based clustering procedure consistently produced 2 subgroups of participants. Patients accounted for 75%-100% of participants in one subgroup that was characterized by significantly lower cortical thickness. Both subgroups were equally symptomatic in clinically unstable stages, but cortical impoverishment indicated a higher symptom burden in a clinically stable sample and higher glutamate levels in the first-episode sample. There were no subgroup differences in cognitive and functional outcome profiles or antipsychotic exposure across all stages. CONCLUSIONS Cortical thinning does not vary with functioning or cognitive impairment, but it is more prevalent among patients, especially those with glutamate excess in early stages and higher residual symptom burden at later stages, providing an important mechanistic clue to one of the several possible pathways to the illness.
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Affiliation(s)
- Liangbing Liang
- Graduate Program in Neuroscience, Western University, London, Ontario, Canada; Robarts Research Institute, Western University, London, Ontario, Canada
| | | | - Peter F Liddle
- Institute of Mental Health, Division of Mental Health and Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Peter Jeon
- Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Jean Théberge
- Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Psychiatry, Western University, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Psychiatry, Western University, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
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Dunlop K, Grosenick L, Downar J, Vila-Rodriguez F, Gunning FM, Daskalakis ZJ, Blumberger DM, Liston C. Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample. Biol Psychiatry 2024:S0006-3223(24)00055-6. [PMID: 38280408 DOI: 10.1016/j.biopsych.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/21/2023] [Accepted: 01/13/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample. METHODS We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes. RESULTS The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. CONCLUSIONS Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.
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Affiliation(s)
- Katharine Dunlop
- Centre for Depression and Suicide Studies, St Michael's Hospital, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Logan Grosenick
- Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Jonathan Downar
- Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Faith M Gunning
- Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, New York
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Daniel M Blumberger
- Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Weill Cornell Medicine, New York, New York; Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, New York; Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York.
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Preller KH, Scholpp J, Wunder A, Rosenbrock H. Neuroimaging Biomarkers for Drug Discovery and Development in Schizophrenia. Biol Psychiatry 2024:S0006-3223(24)00036-2. [PMID: 38272287 DOI: 10.1016/j.biopsych.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/19/2023] [Accepted: 01/14/2024] [Indexed: 01/27/2024]
Abstract
Schizophrenia is a chronic mental illness that affects up to 1% of the population. While efficacious therapies are available for positive symptoms, effective treatment of cognitive and negative symptoms remains an unmet need after decades of research. New developments in the field of neuroimaging are accelerating our knowledge gain regarding the underlying pathophysiology of symptoms in schizophrenia and psychosis spectrum disorders, inspiring new targets for drug development. However, no validated and qualified biomarkers are currently available to support the development of new therapeutics. This review summarizes the current use of neuroimaging technology in clinical drug development for psychotic disorders. As exemplified by drug development programs that target NMDA receptor hypofunction, neuroimaging results play a critical role in target discovery and establishing target engagement and dose selection. Furthermore, pharmacological neuroimaging may provide response biomarkers that allow for early decision making in proof-of-concept studies that leverage pharmacological challenge models in healthy volunteers. That said, while response and predictive biomarkers are starting to be evaluated in patient populations, they continue to play a limited role. Novel approaches to neuroimaging data acquisition and analysis may aid the establishment of biomarkers that are predictive at the individual level in the future. Nevertheless, various gaps in knowledge need to be addressed and biomarkers need to be validated to establish them as "fit for purpose" in drug development.
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Affiliation(s)
- Katrin H Preller
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany; Boehringer Ingelheim (Schweiz) GmbH, Basel, Switzerland.
| | - Joachim Scholpp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Andreas Wunder
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Holger Rosenbrock
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
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29
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Parker D, Trotti R, McDowell J, Keedy S, Keshavan M, Pearlson G, Gershon E, Ivleva E, Huang LY, Sauer K, Hill S, Sweeny J, Tamminga C, Clementz B. Differentiating Biomarker Features and Familial Characteristics of B-SNIP Psychosis Biotypes. RESEARCH SQUARE 2024:rs.3.rs-3702638. [PMID: 38260530 PMCID: PMC10802686 DOI: 10.21203/rs.3.rs-3702638/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Idiopathic psychosis shows considerable biological heterogeneity across cases. B-SNIP used psychosis-relevant biomarkers to identity psychosis Biotypes, which will aid etiological and targeted treatment investigations. Psychosis probands from the B-SNIP consortium (n = 1907), their first-degree biological relatives (n = 705), and healthy participants (n = 895) completed a biomarker battery composed of cognition, saccades, and auditory EEG measurements. ERP quantifications were substantially modified from previous iterations of this approach. Multivariate integration reduced multiple biomarker outcomes to 11 "bio-factors". Twenty-four different approaches indicated bio-factor data among probands were best distributed as three subgroups. Numerical taxonomy with k-means constructed psychosis Biotypes, and rand indices evaluated consistency of Biotype assignments. Psychosis subgroups, their non-psychotic first-degree relatives, and healthy individuals were compared across bio-factors. The three psychosis Biotypes differed significantly on all 11 bio-factors, especially prominent for general cognition, antisaccades, ERP magnitude, and intrinsic neural activity. Rand indices showed excellent consistency of clustering membership when samples included at least 1100 subjects. Canonical discriminant analysis described composite bio-factors that simplified group comparisons and captured neural dysregulation, neural vigor, and stimulus salience variates. Neural dysregulation captured Biotype-2, low neural vigor captured Biotype-1, and deviations of stimulus salience captured Biotype-3. First-degree relatives showed similar patterns as their Biotyped proband relatives on general cognition, antisaccades, ERP magnitudes, and intrinsic brain activity. Results extend previous efforts by the B-SNIP consortium to characterize biologically distinct psychosis Biotypes. They also show that at least 1100 observations are necessary to achieve consistent outcomes. First-degree relative data implicate specific bio-factor deviations to the subtype of their proband and may inform studies of genetic risk.
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Foucher JR, Hirjak D, Walther S, Dormegny-Jeanjean LC, Humbert I, Mainberger O, de Billy CC, Schorr B, Vercueil L, Rogers J, Ungvari G, Waddington J, Berna F. From one to many: Hypertonia in schizophrenia spectrum psychosis an integrative review and adversarial collaboration report. Schizophr Res 2024; 263:66-81. [PMID: 37059654 DOI: 10.1016/j.schres.2023.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 04/16/2023]
Abstract
Different types of resistance to passive movement, i.e. hypertonia, were described in schizophrenia spectrum disorders (SSD) long before the introduction of antipsychotics. While these have been rediscovered in antipsychotic-naïve patients and their non-affected relatives, the existence of intrinsic hypertonia vs drug-induced parkinsonism (DIP) in treated SSD remains controversial. This integrative review seeks to develop a commonly accepted framework to specify the putative clinical phenomena, highlight conflicting issues and discuss ways to challenge each hypothesis and model through adversarial collaboration. The authors agreed on a common framework inspired from systems neuroscience. Specification of DIP, locomotor paratonia (LMP) and psychomotor paratonia (PMP) identified points of disagreement. Some viewed parkinsonian rigidity to be sufficient for diagnosing DIP, while others viewed DIP as a syndrome that should include bradykinesia. Sensitivity of DIP to anticholinergic drugs and the nature of LPM and PMP were the most debated issues. It was agreed that treated SSD should be investigated first. Clinical features of the phenomena at issue could be confirmed by torque, EMG and joint angle measures that could help in challenging the selectivity of DIP to anticholinergics. LMP was modeled as the release of the reticular formation from the control of the supplementary motor area (SMA), which could be challenged by the tonic vibration reflex or acoustic startle. PMP was modeled as the release of primary motor cortex from the control of the SMA and may be informed by subclinical echopraxia. If these challenges are not met, this would put new constraints on the models and have clinical and therapeutic implications.
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Affiliation(s)
- Jack R Foucher
- ICube - CNRS UMR 7357, Neurophysiology, FMTS, University of Strasbourg, France, EU; CEMNIS - Noninvasive Neuromodulation Center, University Hospital Strasbourg, France, EU.
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany, EU
| | - Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Ludovic C Dormegny-Jeanjean
- ICube - CNRS UMR 7357, Neurophysiology, FMTS, University of Strasbourg, France, EU; CEMNIS - Noninvasive Neuromodulation Center, University Hospital Strasbourg, France, EU
| | - Ilia Humbert
- CEMNIS - Noninvasive Neuromodulation Center, University Hospital Strasbourg, France, EU
| | - Olivier Mainberger
- ICube - CNRS UMR 7357, Neurophysiology, FMTS, University of Strasbourg, France, EU; CEMNIS - Noninvasive Neuromodulation Center, University Hospital Strasbourg, France, EU
| | - Clément C de Billy
- ICube - CNRS UMR 7357, Neurophysiology, FMTS, University of Strasbourg, France, EU; CEMNIS - Noninvasive Neuromodulation Center, University Hospital Strasbourg, France, EU
| | - Benoit Schorr
- Pôle de Psychiatrie, Santé Mentale et Addictologie, University Hospital Strasbourg, France, EU; Physiopathologie et Psychopathologie Cognitive de la Schizophrénie - INSERM 1114, FMTS, University of Strasbourg, France, EU
| | - Laurent Vercueil
- Unité de neurophysiologie clinique, CHU Grenoble Alpes, Université Grenoble Alpes, France, EU; INSERM U1216, Institut de neurosciences, Grenoble, France, EU
| | - Jonathan Rogers
- Division of Psychiatry, University College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Gabor Ungvari
- Section of Psychiatry, School of Medicine, University Notre Dame Australia, Fremantle, Australia
| | - John Waddington
- School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland, EU
| | - Fabrice Berna
- Pôle de Psychiatrie, Santé Mentale et Addictologie, University Hospital Strasbourg, France, EU; Physiopathologie et Psychopathologie Cognitive de la Schizophrénie - INSERM 1114, FMTS, University of Strasbourg, France, EU
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31
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Tian YE. Toward Reproducible, Generalizable, and Clinically Useful Neurophysiological Subtypes of Major Depressive Disorder. Biol Psychiatry 2023; 94:e45-e47. [PMID: 37968030 DOI: 10.1016/j.biopsych.2023.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 11/17/2023]
Affiliation(s)
- Ye Ella Tian
- Melbourne Neuropsychiatric Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.
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32
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Luther L, Jarvis SA, Spilka MJ, Strauss GP. Global reward processing deficits predict negative symptoms transdiagnostically and transphasically in a severe mental illness-spectrum sample. Eur Arch Psychiatry Clin Neurosci 2023:10.1007/s00406-023-01714-7. [PMID: 38051397 DOI: 10.1007/s00406-023-01714-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/29/2023] [Indexed: 12/07/2023]
Abstract
Reward processing impairments are a key factor associated with negative symptoms in those with severe mental illnesses. However, past findings are inconsistent regarding which reward processing components are impaired and most strongly linked to negative symptoms. The current study examined the hypothesis that these mixed findings may be the result of multiple reward processing pathways (i.e., equifinality) to negative symptoms that cut across diagnostic boundaries and phases of illness. Participants included healthy controls (n = 100) who served as a reference sample and a severe mental illness-spectrum sample (n = 92) that included psychotic-like experiences, clinical high-risk for psychosis, bipolar disorder, and schizophrenia participants. All participants completed tasks measuring four RDoC Positive Valence System constructs: value representation, reinforcement learning, effort-cost computation, and hedonic reactivity. A k-means cluster analysis of the severe mental illness-spectrum samples identified three clusters with differential reward processing profiles that were characterized by: (1) global reward processing deficits (22.8%), (2) selective impairments in hedonic reactivity alone (40.2%), and (3) preserved reward processing (37%). Elevated negative symptoms were only observed in the global reward processing cluster. All clusters contained participants from each clinical group, and the distribution of these groups did not significantly differ among the clusters. Findings identified one pathway contributing to negative symptoms that was transdiagnostic and transphasic. Future work further characterizing divergent pathways to negative symptoms may help to improve symptom trajectories and personalized treatments.
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Affiliation(s)
- Lauren Luther
- Department of Psychology, University of Georgia, 125 Baldwin St., Athens, GA, 30602, USA.
| | - Sierra A Jarvis
- Department of Psychology, University of Georgia, 125 Baldwin St., Athens, GA, 30602, USA
| | - Michael J Spilka
- Department of Psychology, University of Georgia, 125 Baldwin St., Athens, GA, 30602, USA
| | - Gregory P Strauss
- Department of Psychology, University of Georgia, 125 Baldwin St., Athens, GA, 30602, USA.
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Rief W, Hofmann SG, Berg M, Forbes MK, Pizzagalli DA, Zimmermann J, Fried E, Reed GM. Do We Need a Novel Framework for Classifying Psychopathology? A Discussion Paper. CLINICAL PSYCHOLOGY IN EUROPE 2023; 5:e11699. [PMID: 38357431 PMCID: PMC10863678 DOI: 10.32872/cpe.11699] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 10/09/2023] [Indexed: 02/16/2024] Open
Abstract
Introduction The ICD-11 and DSM-5 are the leading systems for the classification of mental disorders, and their relevance for clinical work and research, as well as their impact for policy making and legal questions, has increased considerably. In recent years, other frameworks have been proposed to supplement or even replace the ICD and the DSM, raising many questions regarding clinical utility, scientific relevance, and, at the core, how best to conceptualize mental disorders. Method As examples of the new approaches that have emerged, here we introduce the Hierarchical Taxonomy of Psychopathology (HiTOP), the Research Domain Criteria (RDoC), systems and network approaches, process-based approaches, as well as a new approach to the classification of personality disorders. Results and Discussion We highlight main distinctions between these classification frameworks, largely related to different priorities and goals, and discuss areas of overlap and potential compatibility. Synergies among these systems may provide promising new avenues for research and clinical practice.
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Affiliation(s)
- Winfried Rief
- Clinical Psychology and Psychotherapy Group, Department of Psychology, Philipps-University of Marburg, Marburg, Germany
| | - Stefan G. Hofmann
- Translational Clinical Psychology Group, Department of Psychology, Philipps-University of Marburg, Marburg, Germany
| | - Max Berg
- Clinical Psychology and Psychotherapy Group, Department of Psychology, Philipps-University of Marburg, Marburg, Germany
| | - Miriam K. Forbes
- School of Psychological Sciences, Australian Hearing Hub, Macquarie University Sydney, Sydney, Australia
| | - Diego A. Pizzagalli
- Department of Psychiatry, Center for Depression, Anxiety and Stress Research & McLean Imaging Center, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | | | - Eiko Fried
- Clinical Psychology Group, Department of Psychology, Leiden University, Leiden, The Netherlands
| | - Geoffrey M. Reed
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
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34
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Yassin W, Hoftman GD, Bergen SE, del Re EC. Editorial: Diagnostic and prognostic brain-based biomarkers in psychosis spectrum. Front Psychiatry 2023; 14:1332447. [PMID: 38076681 PMCID: PMC10703481 DOI: 10.3389/fpsyt.2023.1332447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 02/12/2024] Open
Affiliation(s)
- Walid Yassin
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Gil D. Hoftman
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sarah E. Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
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Warren TL, Tubbs JD, Lesh TA, Corona MB, Pakzad S, Albuquerque M, Singh P, Zarubin V, Morse S, Sham PC, Carter CS, Nord AS. Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.24.23290465. [PMID: 37292649 PMCID: PMC10246134 DOI: 10.1101/2023.05.24.23290465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 206 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.
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Affiliation(s)
| | - Justin D. Tubbs
- Department of Psychiatry, The University of Hong Kong
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
- Department of Psychiatry, Harvard Medical School
| | | | | | | | | | | | | | | | - Pak Chung Sham
- Department of Psychiatry, The University of Hong Kong
- Centre for PanorOmic Sciences, The University of Hong Kong
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong
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Beño-Ruiz-de-la-Sierra RM, Arjona-Valladares A, Hernández-García M, Fernández-Linsenbarth I, Díez Á, Fondevila Estevez S, Castaño C, Muñoz F, Sanz-Fuentenebro J, Roig-Herrero A, Molina V. Corollary Discharge Dysfunction as a Possible Substrate of Anomalous Self-experiences in Schizophrenia. Schizophr Bull 2023:sbad157. [PMID: 37951230 DOI: 10.1093/schbul/sbad157] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2023]
Abstract
BACKGROUND AND HYPOTHESIS Corollary discharge mechanism suppresses the conscious auditory sensory perception of self-generated speech and attenuates electrophysiological markers such as the auditory N1 Event-Related Potential (ERP) during Electroencephalographic (EEG) recordings. This phenomenon contributes to self-identification and seems to be altered in people with schizophrenia. Therefore, its alteration could be related to the anomalous self-experiences (ASEs) frequently found in these patients. STUDY DESIGN To analyze corollary discharge dysfunction as a possible substrate of ASEs, we recorded EEG ERP from 43 participants with schizophrenia and 43 healthy controls and scored ASEs with the 'Inventory of Psychotic-Like Anomalous Self-Experiences' (IPASE). Positive and negative symptoms were also scored with the 'Positive and Negative Syndrome Scale for Schizophrenia' (PANSS) and with the 'Brief Negative Symptom Scale' (BNSS) respectively. The N1 components were elicited by two task conditions: (1) concurrent listening to self-pronounced vowels (talk condition) and (2) subsequent non-concurrent listening to the same previously self-uttered vowels (listen condition). STUDY RESULTS The amplitude of the N1 component elicited by the talk condition was lower compared to the listen condition in people with schizophrenia and healthy controls. However, the difference in N1 amplitude between both conditions was significantly higher in controls than in schizophrenia patients. The values of these differences in patients correlated significantly and negatively with the IPASE, PANSS, and BNSS scores. CONCLUSIONS These results corroborate previous data relating auditory N1 ERP amplitude with altered corollary discharge mechanisms in schizophrenia and support corollary discharge dysfunction as a possible underpinning of ASEs in this illness.
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Affiliation(s)
| | | | | | | | - Álvaro Díez
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
| | | | | | - Francisco Muñoz
- UCM-ISCIII Center for Human Evolution and Behaviour, Madrid, Spain
- Psychobiology and Behavioural Sciences Methods Department, Complutense University of Madrid, Madrid, Spain
| | | | - Alejandro Roig-Herrero
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
- Imaging Processing Laboratory, University of Valladolid, Valladolid, Spain
| | - Vicente Molina
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
- Psychiatry Service, University Clinical Hospital of Valladolid, Valladolid, Spain
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Omlor W, Rabe F, Fuchs S, Cecere G, Homan S, Surbeck W, Kallen N, Georgiadis F, Spiller T, Seifritz E, Weickert T, Bruggemann J, Weickert C, Potkin S, Hashimoto R, Sim K, Rootes-Murdy K, Quide Y, Houenou J, Banaj N, Vecchio D, Piras F, Piras F, Spalletta G, Salvador R, Karuk A, Pomarol-Clotet E, Rodrigue A, Pearlson G, Glahn D, Tomecek D, Spaniel F, Skoch A, Kirschner M, Kaiser S, Kochunov P, Fan FM, Andreassen OA, Westlye LT, Berthet P, Calhoun VD, Howells F, Uhlmann A, Scheffler F, Stein D, Iasevoli F, Cairns MJ, Carr VJ, Catts SV, Di Biase MA, Jablensky A, Green MJ, Henskens FA, Klauser P, Loughland C, Michie PT, Mowry B, Pantelis C, Rasser PE, Schall U, Scott R, Zalesky A, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Di Giorgio A, Thomopoulos SI, Jahanshad N, Thompson PM, van Erp T, Turner J, Homan P. Estimating multimodal brain variability in schizophrenia spectrum disorders: A worldwide ENIGMA study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559032. [PMID: 37961617 PMCID: PMC10634976 DOI: 10.1101/2023.09.22.559032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Objective Schizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia. Methods This meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy. Results We found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region. Conclusions Higher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.
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Harvanek ZM, Boks MP, Vinkers CH, Higgins-Chen AT. The Cutting Edge of Epigenetic Clocks: In Search of Mechanisms Linking Aging and Mental Health. Biol Psychiatry 2023; 94:694-705. [PMID: 36764569 PMCID: PMC10409884 DOI: 10.1016/j.biopsych.2023.02.001] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/11/2023]
Abstract
Individuals with psychiatric disorders are at increased risk of age-related diseases and early mortality. Recent studies demonstrate that this link between mental health and aging is reflected in epigenetic clocks, aging biomarkers based on DNA methylation. The reported relationships between epigenetic clocks and mental health are mostly correlational, and the mechanisms are poorly understood. Here, we review recent progress concerning the molecular and cellular processes underlying epigenetic clocks as well as novel technologies enabling further studies of the causes and consequences of epigenetic aging. We then review the current literature on how epigenetic clocks relate to specific aspects of mental health, such as stress, medications, substance use, health behaviors, and symptom clusters. We propose an integrated framework where mental health and epigenetic aging are each broken down into multiple distinct processes, which are then linked to each other, using stress and schizophrenia as examples. This framework incorporates the heterogeneity and complexity of both mental health conditions and aging, may help reconcile conflicting results, and provides a basis for further hypothesis-driven research in humans and model systems to investigate potentially causal mechanisms linking aging and mental health.
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Affiliation(s)
- Zachary M Harvanek
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Marco P Boks
- Department of Psychiatry, University Medical Center Utrecht Brain Center, University of Utrecht, Utrecht, the Netherlands
| | - Christiaan H Vinkers
- Department of Psychiatry, Amsterdam University Medical Center, location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Department of Pathology, Yale University School of Medicine, New Haven, Connecticut.
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Huang LY, Parker DA, Ethridge LE, Hamm JP, Keedy SS, Tamminga CA, Pearlson GD, Keshavan MS, Hill SK, Sweeney JA, McDowell JE, Clementz BA. Double dissociation between P300 components and task switch error type in healthy but not psychosis participants. Schizophr Res 2023; 261:161-169. [PMID: 37776647 PMCID: PMC11015813 DOI: 10.1016/j.schres.2023.09.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/02/2023] [Accepted: 09/13/2023] [Indexed: 10/02/2023]
Abstract
Event-related potentials (ERPs) during oddball tasks and the behavioral performance on the Penn Conditional Exclusion Task (PCET) measure context-appropriate responding: P300 ERPs to oddball targets reflect detection of input changes and context updating in working memory, and PCET performance indexes detection, adherence, and maintenance of mental set changes. More specifically, PCET variables quantify cognitive functions including inductive reasoning (set 1 completion), mental flexibility (perseverative errors), and working memory maintenance (regressive errors). Past research showed that both P300 ERPs and PCET performance are disrupted in psychosis. This study probed the possible neural correlates of 3 PCET abnormalities that occur in participants with psychosis via the overlapping cognitive demands of the two study paradigms. In a two-tiered analysis, psychosis (n = 492) and healthy participants (n = 244) were first divided based on completion of set 1 - which measures subjects' ability to use inductive reasoning to arrive at the correct set. Results showed that participants who failed set 1 produced lower parietal P300, independent of clinical status. In the second tier of analysis, a double dissociation was found among healthy set 1 completers: frontal P300 amplitudes were negatively associated with perseverative errors, and parietal P300 was negatively associated with regressive errors. In contrast, psychosis participants showed global P300 reductions regardless of PCET performance. From this we conclude that in psychosis, overall activations evoked by the oddball task are reduced while the cognitive functions required by PCET are still somewhat supported, showing some level of independence or compensatory physiology in psychosis between neural activities underlying the two tasks.
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Affiliation(s)
- Ling-Yu Huang
- Departments of Psychology & Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - David A Parker
- Departments of Psychology & Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - Lauren E Ethridge
- Department of Psychology and Pediatrics, University of Oklahoma, Norman, OK, USA
| | - Jordan P Hamm
- Department of Neuroscience, Georgia State University, Atlanta, GA, USA
| | - Sarah S Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, IL, USA
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | | | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Jennifer E McDowell
- Departments of Psychology & Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - Brett A Clementz
- Departments of Psychology & Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA.
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Jones BDM, Zhukovsky P, Hawco C, Ortiz A, Cipriani A, Voineskos AN, Mulsant BH, Husain MI. Protocol for a systematic review and meta-analysis of coordinate-based network mapping of brain structure in bipolar disorder across the lifespan. BJPsych Open 2023; 9:e178. [PMID: 37811544 PMCID: PMC10594157 DOI: 10.1192/bjo.2023.569] [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: 04/17/2023] [Revised: 08/03/2023] [Accepted: 08/22/2023] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND Studies about brain structure in bipolar disorder have reported conflicting findings. These findings may be explained by the high degree of heterogeneity within bipolar disorder, especially if structural differences are mapped to single brain regions rather than networks. AIMS We aim to complete a systematic review and meta-analysis to identify brain networks underlying structural abnormalities observed on T1-weighted magnetic resonance imaging scans in bipolar disorder across the lifespan. We also aim to explore how these brain networks are affected by sociodemographic and clinical heterogeneity in bipolar disorder. METHOD We will include case-control studies that focus on whole-brain analyses of structural differences between participants of any age with a standardised diagnosis of bipolar disorder and controls. The electronic databases Medline, PsycINFO and Web of Science will be searched. We will complete an activation likelihood estimation analysis and a novel coordinate-based network mapping approach to identify specific brain regions and brain circuits affected in bipolar disorder or relevant subgroups. Meta-regressions will examine the effect of sociodemographic and clinical variables on identified brain circuits. CONCLUSIONS Findings from this systematic review and meta-analysis will enhance understanding of the pathophysiology of bipolar disorder. The results will identify brain circuitry implicated in bipolar disorder, and how they may relate to relevant sociodemographic and clinical variables across the lifespan.
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Affiliation(s)
- Brett D. M. Jones
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Abigail Ortiz
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, UK; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK; and Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, UK
| | - Aristotle N. Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Benoit H. Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Muhammad Ishrat Husain
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
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Clementz BA, Chattopadhyay I, Trotti RL, Parker DA, Gershon ES, Hill SK, Ivleva EI, Keedy SK, Keshavan MS, McDowell JE, Pearlson GD, Tamminga CA, Gibbons RD. Clinical characterization and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT)-1. Schizophr Res 2023; 260:143-151. [PMID: 37657281 PMCID: PMC10712427 DOI: 10.1016/j.schres.2023.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
Clinically defined psychosis diagnoses are neurobiologically heterogeneous. The B-SNIP consortium identified and validated more neurobiologically homogeneous psychosis Biotypes using an extensive battery of neurocognitive and psychophysiological laboratory measures. However, typically the first step in any diagnostic evaluation is the clinical interview. In this project, we evaluated if psychosis Biotypes have clinical characteristics that can support their differentiation in addition to obtaining laboratory testing. Clinical interview data from 1907 individuals with a psychosis Biotype were used to create a diagnostic algorithm. The features were 58 ratings from standard clinical scales. Extremely randomized tree algorithms were used to evaluate sensitivity, specificity, and overall classification success. Biotype classification accuracy peaked at 91 % with the use of 57 items on average. A reduced feature set of 28 items, though, also showed 81 % classification accuracy. Using this reduced item set, we found that only 10-11 items achieved a one-vs-all (Biotype-1 or not, Biotype-2 or not, Biotype-3 or not) area under the sensitivity-specificity curve of .78 to .81. The top clinical characteristics for differentiating psychosis Biotypes, in order of importance, were (i) difficulty in abstract thinking, (ii) multiple indicators of social functioning, (iii) conceptual disorganization, (iv) severity of hallucinations, (v) stereotyped thinking, (vi) suspiciousness, (vii) unusual thought content, (viii) lack of spontaneous speech, and (ix) severity of delusions. These features were remarkably different from those that differentiated DSM psychosis diagnoses. This low-burden adaptive algorithm achieved reasonable classification accuracy and will support Biotype-specific etiological and treatment investigations even in under-resourced clinical and research environments.
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Affiliation(s)
- Brett A Clementz
- Department of Psychology, BioImaging Research Center, University of Georgia, Athens, GA 30602, United States of America.
| | - Ishanu Chattopadhyay
- Department of Medicine, Section of Hospital Medicine, University of Chicago, Chicago, IL, United States of America
| | - Rebekah L Trotti
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States of America
| | - David A Parker
- Department of Human Genetics, Emory University School of Medicine, Atlanta VA Medical Center, Atlanta, GA, United States of America
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States of America
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States of America
| | - Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States of America
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States of America
| | - Jennifer E McDowell
- Department of Psychology, Owens Institute for Behavioral Research, University of Georgia, Athens, GA 30602, United States of America
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, United States of America; Olin NeuroPsychiatry Research Center, Institute of Living, Hartford, CT, United States of America
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Robert D Gibbons
- Center for Health Statistics, Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, IL, United States of America
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Yu G, Liu Z, Wu X, Becker B, Zhang K, Fan H, Peng S, Kuang N, Kang J, Dong G, Zhao XM, Schumann G, Feng J, Sahakian BJ, Robbins TW, Palaniyappan L, Zhang J. Common and disorder-specific cortical thickness alterations in internalizing, externalizing and thought disorders during early adolescence: an Adolescent Brain and Cognitive Development study. J Psychiatry Neurosci 2023; 48:E345-E356. [PMID: 37673436 PMCID: PMC10495167 DOI: 10.1503/jpn.220202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 02/13/2023] [Accepted: 05/17/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND A growing body of neuroimaging studies has reported common neural abnormalities among mental disorders in adults. However, it is unclear whether the distinct disorder-specific mechanisms operate during adolescence despite the overlap among disorders. METHODS We studied a large cohort of more than 11 000 preadolescent (age 9-10 yr) children from the Adolescent Brain and Cognitive Development cohort. We adopted a regrouping approach to compare cortical thickness (CT) alterations and longitudinal changes between healthy controls (n = 4041) and externalizing (n = 1182), internalizing (n = 1959) and thought disorder (n = 347) groups. Genome-wide association study (GWAS) was performed on regional CT across 4468 unrelated European youth. RESULTS Youth with externalizing or internalizing disorders exhibited increased regional CT compared with controls. Externalizing (p = 8 × 10-4, Cohen d = 0.10) and internalizing disorders (p = 2 × 10-3, Cohen d = 0.08) shared thicker CT in the left pars opercularis. The somatosensory and the primary auditory cortex were uniquely affected in externalizing disorders, whereas the primary motor cortex and higher-order visual association areas were uniquely affected in internalizing disorders. Only youth with externalizing disorders showed decelerated cortical thinning from age 10-12 years. The GWAS found 59 genome-wide significant associated genetic variants across these regions. Cortical thickness in common regions was associated with glutamatergic neurons, while internalizing-specific regional CT was associated with astrocytes, oligodendrocyte progenitor cells and GABAergic neurons. LIMITATIONS The sample size of the GWAS was relatively small. CONCLUSION Our study provides strong evidence for the presence of specificity in CT, developmental trajectories and underlying genetic underpinnings among externalizing and internalizing disorders during early adolescence. Our results support the neurobiological validity of the regrouping approach that could supplement the use of a dimensional approach in future clinical practice.
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Affiliation(s)
- Gechang Yu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Zhaowen Liu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Xinran Wu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Benjamin Becker
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Kai Zhang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Huaxin Fan
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Songjun Peng
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Nanyu Kuang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jujiao Kang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Guiying Dong
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Xing-Ming Zhao
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Gunter Schumann
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jianfeng Feng
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Barbara J Sahakian
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Trevor W Robbins
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Lena Palaniyappan
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jie Zhang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
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Lizano P, Pong S, Santarriaga S, Bannai D, Karmacharya R. Brain microvascular endothelial cells and blood-brain barrier dysfunction in psychotic disorders. Mol Psychiatry 2023; 28:3698-3708. [PMID: 37730841 DOI: 10.1038/s41380-023-02255-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/31/2023] [Accepted: 09/08/2023] [Indexed: 09/22/2023]
Abstract
Although there is convergent evidence for blood-brain barrier (BBB) dysfunction and peripheral inflammation in schizophrenia (SZ) and bipolar disorder (BD), it is unknown whether BBB deficits are intrinsic to brain microvascular endothelial cells (BMECs) or arise via effects of peripheral inflammatory cytokines. We examined BMEC function using stem cell-based models to identify cellular and molecular deficits associated with BBB dysfunction in SZ and BD. Induced pluripotent stem cells (iPSCs) from 4 SZ, 4 psychotic BD and 4 healthy control (HC) subjects were differentiated into BMEC-"like" cells. Gene expression and protein levels of tight junction proteins were assessed. Transendothelial electrical resistance (TEER) and permeability were assayed to evaluate BBB function. Cytokine levels were measured from conditioned media. BMECs derived from human iPSCs in SZ and BD did not show differences in BBB integrity or permeability compared to HC BMECs. Outlier analysis using TEER revealed a BBB-deficit (n = 3) and non-deficit (n = 5) group in SZ and BD lines. Stratification based on BBB function in SZ and BD patients identified a BBB-deficit subtype with reduced barrier function, tendency for increased permeability to smaller molecules, and decreased claudin-5 (CLDN5) levels. BMECs from the BBB-deficit group show increased matrix metallopeptidase 1 (MMP1) activity, which correlated with reduced CLDN5 and worse BBB function, and was improved by tumor necrosis factor α (TNFα) and MMP1 inhibition. These results show potential deficits in BMEC-like cells in psychotic disorders that result in BBB disruption and further identify TNFα and MMP1 as promising targets for ameliorating BBB deficits.
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Affiliation(s)
- Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Sovannarath Pong
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Stephanie Santarriaga
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Chemical Biology and Therapeutic Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deepthi Bannai
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Rakesh Karmacharya
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Chemical Biology and Therapeutic Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA.
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Molina V, Fernández-Linsenbarth I, Queipo-de-Llano M, Jiménez-Aparicio MT, Vallecillo-Adame C, Aremy-Gonzaga A, de-Andrés-Lobo C, Recio-Barbero M, Díez Á, Beño-Ruiz-de-la-Sierra RM, Martín-Gómez C, Sanz-Fuentenebro J. Real-life outcomes in biotypes of psychotic disorders based on neurocognitive performance. Eur Arch Psychiatry Clin Neurosci 2023; 273:1379-1386. [PMID: 36416961 PMCID: PMC10449979 DOI: 10.1007/s00406-022-01518-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022]
Abstract
Aiming at discerning potential biotypes within the psychotic syndrome, we have recently reported the possible existence of two clusters or biotypes across schizophrenia and bipolar disorder characterized by their cognitive performance using the Brief Assessment of Cognition in Schizophrenia (BACS) instrument and validated with independent biological and clinical indexes (Fernández-Linsenbarth et al. in Schizophr Res 229:102-111, 2021). In this previous work, the group with larger cognitive deficits (N = 93, including 69 chronic schizophrenia, 17 first episodes (FE) of schizophrenia and 7 bipolar disorder patients) showed smaller thalamus and hippocampus volume and hyper-synchronic electroencephalogram than the group with milder deficits (N = 105, including 58 chronic schizophrenia, 25 FE and 22 bipolar disorder patients). We predicted that if these biotypes indeed corresponded to different cognitive and biological substrates, their adaptation to real life would be different. To this end, in the present work we have followed up the patients' population included in that work at 1st and 3rd years after the date of inclusion in the 2021 study and we report on the statistical comparisons of each clinical and real-life outcomes between them. The first cluster, with larger cognitive deficits and more severe biological alterations, showed during that period a decreased capacity for job tenure (1st and 3rd years), more admissions to a psychiatric ward (1st year) and a higher likelihood for quitting psychiatric follow-up (3rd year). Patients in the second cluster, with moderate cognitive deficits, were less compliant with prescribed treatment at the 3rd year. The differences in real-life outcomes may give additional external validity to that yielded by biological measurements to the described biotypes based on neurocognition.
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Affiliation(s)
- Vicente Molina
- Psychiatry Service, Clinical Hospital of Valladolid, Valladolid, Spain.
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain.
| | - Inés Fernández-Linsenbarth
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain
| | | | | | | | | | | | | | - Álvaro Díez
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain
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Kesler SR, Henneghan AM, Prinsloo S, Palesh O, Wintermark M. Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment. Front Med (Lausanne) 2023; 10:1199605. [PMID: 37720513 PMCID: PMC10499624 DOI: 10.3389/fmed.2023.1199605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/15/2023] [Indexed: 09/19/2023] Open
Abstract
Cancer related cognitive impairment (CRCI) is commonly associated with cancer and its treatments, yet the present binary diagnostic approach fails to capture the full spectrum of this syndrome. Cognitive function is highly complex and exists on a continuum that is poorly characterized by dichotomous categories. Advanced statistical methodologies applied to symptom assessments have demonstrated that there are multiple subclasses of CRCI. However, studies suggest that relying on symptom assessments alone may fail to account for significant differences in the neural mechanisms that underlie a specific cognitive phenotype. Treatment plans that address the specific physiologic mechanisms involved in an individual patient's condition is the heart of precision medicine. In this narrative review, we discuss how biotyping, a precision medicine framework being utilized in other mental disorders, could be applied to CRCI. Specifically, we discuss how neuroimaging can be used to determine biotypes of CRCI, which allow for increased precision in prediction and diagnosis of CRCI via biologic mechanistic data. Biotypes may also provide more precise clinical endpoints for intervention trials. Biotyping could be made more feasible with proxy imaging technologies or liquid biomarkers. Large cross-sectional phenotyping studies are needed in addition to evaluation of longitudinal trajectories, and data sharing/pooling is highly feasible with currently available digital infrastructures.
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Affiliation(s)
- Shelli R. Kesler
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States
- Department of Diagnostic Medicine, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
| | - Ashley M. Henneghan
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
| | - Sarah Prinsloo
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Oxana Palesh
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer, Houston, TX, United States
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Fekih-Romdhane F, Hajje R, Haddad C, Hallit S, Azar J. Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis. BMC Psychiatry 2023; 23:595. [PMID: 37582728 PMCID: PMC10428523 DOI: 10.1186/s12888-023-05101-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 08/10/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Dissecting the heterogeneity of schizophrenia may help foster progress in understanding its etiology and lay the groundwork for the development of new treatment options for primary or enduring negative symptoms (NS). In this regard, the present study aimed to: (1) to use cluster analysis to identify subgroups of Lebanese patients diagnosed with either schizophrenia or schizoaffective disorder based on NS clusters, and (2) to relate the statistically-derived subgroups to clinically relevant external validators (including measures if state and trait depression, stigma, insight, loneliness, social support). METHOD A total of 202 adult long-stay, chronic, and clinically remitted patients (166 diagnosed with schizophrenia and 36 with schizoaffective disorder) were enrolled. A cluster analysis approach was adopted to classify patients based on the five NS domains social withdrawal, emotional withdrawal, alogia, avolition and anhedonia. RESULTS A three-cluster solution was obtained based on unique NS profiles, and divided patients into (1) low NS (LNS; 42.6%) which characterized by the lowest mean scores in all NS domains, (2) moderate NS (MNS; 25.7%), and (3) high NS (HNS; 31.7%). Post-hoc comparisons showed that depression (state and trait), loneliness and social support could accurately distinguish the schizophrenia subgroups. Additionally, individuals in the HNS cluster had longer duration of illness, longer duration of hospitalization, and were given higher dosages of antipsychotic medication compared to those in the other clusters, but these differences did not achieve the statistical significance. CONCLUSION Findings provide additional support to the categorical model of schizophrenia by confirming the existence of three alternate subtypes based on NS. The determination of distinct NS subgroups within the broad heterogeneous population of people diagnosed with schizophrenia may imply that each subgroup possibly has unique underlying mechanisms and necessitates different treatment approaches.
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Affiliation(s)
- Feten Fekih-Romdhane
- The Tunisian Center of Early Intervention in Psychosis, Department of Psychiatry “Ibn Omrane”, Razi hospital, Manouba, 2010 Tunisia
- Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia
| | - Romy Hajje
- Faculty of Science, Lebanese University, Fanar, Lebanon
| | - Chadia Haddad
- Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon
- INSPECT-LB (Institut National de Santé Publique, d’Épidémiologie Clinique et de Toxicologie-Liban), Beirut, Lebanon
- School of Health Sciences, Modern University for Business and Science, Beirut, Lebanon
| | - Souheil Hallit
- Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon
- School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, P.O. Box 446, Jounieh, Lebanon
- Applied Science Research Center, Applied Science Private University, Amman, Jordan
| | - Jocelyne Azar
- School of Medicine, Lebanese American University, Byblos, Lebanon
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Koen JD, Lewis L, Rugg MD, Clementz BA, Keshavan MS, Pearlson GD, Sweeney JA, Tamminga CA, Ivleva EI. Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP). Sci Rep 2023; 13:12980. [PMID: 37563219 PMCID: PMC10415369 DOI: 10.1038/s41598-023-38101-0] [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: 06/09/2022] [Accepted: 07/03/2023] [Indexed: 08/12/2023] Open
Abstract
Traditional diagnostic formulations of psychotic disorders have low correspondence with underlying disease neurobiology. This has led to a growing interest in using brain-based biomarkers to capture biologically-informed psychosis constructs. Building upon our prior work on the B-SNIP Psychosis Biotypes, we aimed to examine whether structural MRI (an independent biomarker not used in the Biotype development) can effectively classify the Biotypes. Whole brain voxel-wise grey matter density (GMD) maps from T1-weighted images were used to train and test (using repeated randomized train/test splits) binary L2-penalized logistic regression models to discriminate psychosis cases (n = 557) from healthy controls (CON, n = 251). A total of six models were evaluated across two psychosis categorization schemes: (i) three Biotypes (B1, B2, B3) and (ii) three DSM diagnoses (schizophrenia (SZ), schizoaffective (SAD) and bipolar (BD) disorders). Above-chance classification accuracies were observed in all Biotype (B1 = 0.70, B2 = 0.65, and B3 = 0.56) and diagnosis (SZ = 0.64, SAD = 0.64, and BD = 0.59) models. However, the only model that showed evidence of specificity was B1, i.e., the model was able to discriminate B1 vs. CON and did not misclassify other psychosis cases (B2 or B3) as B1 at rates above nominal chance. The GMD-based classifier evidence for B1 showed a negative association with an estimate of premorbid general intellectual ability, regardless of group membership, i.e. psychosis or CON. Our findings indicate that, complimentary to clinical diagnoses, the B-SNIP Psychosis Biotypes may offer a promising approach to capture specific aspects of psychosis neurobiology.
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Affiliation(s)
- Joshua D Koen
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA.
- Department of Psychology, University of Notre Dame, Notre Dame, IN, 46556, USA.
| | - Leslie Lewis
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Michael D Rugg
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
- UT Southwestern Medical Center, Dallas, TX, USA
- University of East Anglia, Norwich, UK
| | | | | | - Godfrey D Pearlson
- Institute of Living, Hartford Hospital, Hartford, CT, USA
- Yale School of Medicine, New Haven, CT, USA
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Constable PA, Lim JKH, Thompson DA. Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies. Front Neurosci 2023; 17:1215097. [PMID: 37600004 PMCID: PMC10433210 DOI: 10.3389/fnins.2023.1215097] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a "window to the brain." With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson's, and Alzheimer's disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.
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Affiliation(s)
- Paul A. Constable
- College of Nursing and Health Sciences, Caring Futures Institute, Flinders University, Adelaide, SA, Australia
| | - Jeremiah K. H. Lim
- Discipline of Optometry, School of Allied Health, University of Western Australia, Perth, WA, Australia
| | - Dorothy A. Thompson
- The Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Trust, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
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Goldstein Ferber S, Shoval G, Weller A, Zalsman G. Not one thing at a time: When concomitant multiple stressors produce a transdiagnostic clinical picture. World J Psychiatry 2023; 13:402-408. [PMID: 37547732 PMCID: PMC10401502 DOI: 10.5498/wjp.v13.i7.402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/07/2023] [Accepted: 06/19/2023] [Indexed: 07/17/2023] Open
Abstract
A condition of exposure to multiple stressors resulting in a mixed clinical picture spanning conventional categories without meeting any of them in full, encompasses a risk for a list of comorbidities preventing appropriate prevention and treatment. New transformative transdiagnostic approaches suggest changes spanning conventional categories. They base their systems of classification on biomarkers as well as on brain structural and functional dysregulation as associated with behavioral and emotional symptoms. These new approaches received critiques for not being specific enough and for suggesting a few biomarkers for psychopathology as a whole. Therefore, they put the value of differential diagnosis at risk of avoiding appropriate derived prevention and treatment. Multiplicity of stressors has been considered mostly during and following catastrophes, without considering the resulting mixed clinical picture and life event concomitant stressors. We herewith suggest a new category within the conventional classification systems: The Complex Stress Reaction Syndrome, for a condition of multiplicity of stressors, which showed a mixed clinical picture for daily life in the post coronavirus disease 2019 era, in the general population. We argue that this condition may be relevant to daily, regular life, across the lifespan, and beyond conditions of catastrophes. We further argue that this condition may worsen without professional care and it may develop into a severe mental health disorder, more costly to health systems and the suffering individuals. Means for derived prevention and treatment are discussed.
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Affiliation(s)
- Sari Goldstein Ferber
- Department of Psychology and Brain Sciences, University of Delaware, Newark, DE 19716, United States
- Psychology and Gonda Brain Research Center, Bar Ilan University, Ramat Gan 5317000, Israel
| | - Gal Shoval
- Department of Neuroscience, Princeton University, New Jersey, NJ 08544, United States
- Geha Mental Health Center, Petah Tiqva, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 77096, Israel
| | - Aron Weller
- Psychology and Gonda Brain Research Center, Bar Ilan University, Ramat Gan 5317000, Israel
| | - Gil Zalsman
- Geha Mental Health Center, Petah Tiqva, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 77096, Israel
- Department of Psychiatry, Columbia University, New York, NY 10032, United States
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Kim M, Shim Y, Kwon J, Bae S, Lee J, Cha J, Choi SH, Kim SH, Kang UG, Kwon JS. Resting-state theta-phase gamma amplitude coupling as a biomarker for the transdiagnostic dimensional approach in psychiatric disorders. Psychiatry Clin Neurosci 2023; 77:410-411. [PMID: 37029954 DOI: 10.1111/pcn.13554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 03/25/2023] [Indexed: 04/09/2023]
Affiliation(s)
- Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yurim Shim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Junbeom Kwon
- Department of Psychology, Seoul National University College of Social Sciences, Seoul, Republic of Korea
| | - Sangyoon Bae
- Interdisciplinary Program in Artificial Intelligence, Seoul National University College of Social Sciences, Seoul, Republic of Korea
| | - Junhee Lee
- Department of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Gyeonggi, Republic of Korea
| | - Jiook Cha
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
- Department of Psychology, Seoul National University College of Social Sciences, Seoul, Republic of Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University College of Social Sciences, Seoul, Republic of Korea
| | - Soo-Hee Choi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Se Hyun Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ung Gu Kang
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
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