1
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Preller KH, Scholpp J, Wunder A, Rosenbrock H. Neuroimaging Biomarkers for Drug Discovery and Development in Schizophrenia. Biol Psychiatry 2024; 96:666-673. [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] [MESH Headings] [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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2
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Redlich Bossy M, Müller DR, Jabat M, Burrer A, Spiller TR, Vetter S, Seifritz E, Egger ST. Relationship between psychopathological symptoms and dimensions in inpatients with schizophrenia: A network analysis. Schizophr Res 2024; 272:36-38. [PMID: 39181009 DOI: 10.1016/j.schres.2024.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 08/14/2024] [Accepted: 08/16/2024] [Indexed: 08/27/2024]
Affiliation(s)
- Mona Redlich Bossy
- Department of Psychiatry, Faculty of Medicine, University of Zurich, Zurich, Switzerland; Department of Psychology, Faculty of Humanities, University of Fribourg, Fribourg, Switzerland
| | - Daniel R Müller
- Department of Psychology, Faculty of Humanities, University of Fribourg, Fribourg, Switzerland; Department of Psychiatry, Faculty of Medicine, University of Bern, Bern, Switzerland
| | - Mounira Jabat
- Department of Psychiatry, Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Achim Burrer
- Department of Psychiatry, Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Tobias R Spiller
- Department of Psychiatry, Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Stefan Vetter
- Department of Psychiatry, Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Stephan T Egger
- Department of Psychiatry, Faculty of Medicine, University of Zurich, Zurich, Switzerland.
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3
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Di Camillo F, Grimaldi DA, Cattarinussi G, Di Giorgio A, Locatelli C, Khuntia A, Enrico P, Brambilla P, Koutsouleris N, Sambataro F. Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis. Psychiatry Clin Neurosci 2024. [PMID: 39290174 DOI: 10.1111/pcn.13736] [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: 04/29/2024] [Revised: 07/31/2024] [Accepted: 08/19/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective. METHODS We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables. RESULTS A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance. CONCLUSIONS Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.
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Affiliation(s)
- Fabio Di Camillo
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | | | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Annabella Di Giorgio
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Clara Locatelli
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Adyasha Khuntia
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Paolo Enrico
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCSS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCSS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nikolaos Koutsouleris
- Max-Planck-Institute of Psychiatry, Munich, Germany
- Department of Psychiatry, Munich University Hospital, Munich, Germany
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
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4
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Auvergne A, Traut N, Henches L, Troubat L, Frouin A, Boetto C, Kazem S, Julienne H, Toro R, Aschard H. Multitrait analysis to decipher the intertwined genetic architecture of neuroanatomical phenotypes and psychiatric disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00266-0. [PMID: 39260564 DOI: 10.1016/j.bpsc.2024.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/28/2024] [Accepted: 08/12/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven challenging, and new approaches are needed to infer potential genetic structure underlying those phenotypes. Multivariate analyses is arising as a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches. METHODS We first conducted univariate and multivariate genome-wide association studies (GWAS) for nine MRI-derived brain volume phenotypes in 20K UK Biobank participants. We next performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches can distinguish disorder-associated and non-disorder-associated variants from six psychiatric disorders: bipolarity, attention-deficit/hyperactivity disorder (ADHD), autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized k-medoids approach. RESULTS Univariate MRI GWAS displayed only negligible genetic correlation with psychiatric disorders, while multitrait GWAS identified multiple new associations and showed significant enrichment for variants related to both ADHD and schizophrenia. Clustering analyses further detected two clusters displaying not only enrichment for association with ADHD and schizophrenia, but also consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophins pathways on both MRI and schizophrenia. CONCLUSIONS Our results show that multitrait association signature can be used to infer genetically-driven latent MRI variables associated with psychiatric disorders, opening paths for future biomarker development.
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Affiliation(s)
- Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France.
| | - Nicolas Traut
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Léo Henches
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Arthur Frouin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Sayeh Kazem
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Roberto Toro
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA.
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5
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Wright SN, Anticevic A. Generative AI for precision neuroimaging biomarker development in psychiatry. Psychiatry Res 2024; 339:115955. [PMID: 38909415 PMCID: PMC11321914 DOI: 10.1016/j.psychres.2024.115955] [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: 01/02/2024] [Revised: 04/19/2024] [Accepted: 05/08/2024] [Indexed: 06/25/2024]
Abstract
The explosion of generative AI offers promise for neuroimaging biomarker development in psychiatry, but effective adoption of AI methods requires clarity with respect to specific applications and challenges. These center on dataset sizes required to robustly train AI models along with feature selection that capture neural signals relevant to symptom and treatment targets. Here we discuss areas where generative AI could improve quantification of robust and reproducible brain-to-symptom associations to inform precision psychiatry applications, especially in the context of drug discovery. Finally, this communication discusses some challenges that need solutions for generative AI models to advance neuroimaging biomarkers in psychiatry.
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Affiliation(s)
- Susan N Wright
- Division of Neuroscience and Behavior, National Institute on Drug Abuse, National Institutes of Health, 11601 Landsdown St., Three White Flint North (3WFN), MSC 6018, Rockville, MD 20852, United States.
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, United States; Department of Psychology, Yale University, New Haven, CT, 06511, United States
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6
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Liddle PF, Sami MB. The mechanisms of persisting disability in schizophrenia: imprecise predictive coding via cortico-striatal-thalamo-cortical loop dysfunction. Biol Psychiatry 2024:S0006-3223(24)01535-X. [PMID: 39181388 DOI: 10.1016/j.biopsych.2024.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 08/05/2024] [Accepted: 08/14/2024] [Indexed: 08/27/2024]
Abstract
Persisting symptoms and disability remain a problem for an appreciable proportion of people with schizophrenia despite treatment with antipsychotic medication. Improving outcomes requires an understanding of the nature and mechanisms of the pathological processes underlying persistence. Classical features of schizophrenia, which include disorganization and impoverishment of mental activity, are well recognised early clinical features that predict poor long-term outcome. Substantial evidence indicates that these features reflect imprecise predictive coding. Predictive coding provides an over-arching framework for understanding efficient function of the nervous system. Imprecise predictive coding also has the potential to precipitate acute psychosis characterised by reality distortion (delusions and hallucinations) at times of stress. On the other hand, substantial evidence indicates that persistent reality distortion itself gives rise to poor occupational and social function in the long term. Furthermore, abuse of psychotomimetic drugs, which exacerbate reality distortion, contributes to poor long-term outcome in schizophrenia. Neural circuits involved in modulating volitional acts are well understood to be implicated in addiction. Plastic changes in these circuits may account for the association between psychotomimetic drug abuse and poor outcomes in schizophrenia. We propose a mechanistic model according to which unbalanced inputs to the corpus striatum disturb the precision of sub-cortical modulation of cortical activity supporting volitional action. This model accounts for the evidence that early classical symptoms predict poor outcome, while in some circumstances, persistent reality distortion also predicts poor outcome. This model has implications for the development of novel treatments that address the risk of persisting symptoms and disabilities in schizophrenia.
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Affiliation(s)
- Peter F Liddle
- Institute of Mental Health, University of Nottingham, Triumph Rd, Nottingham, NG7 2TU.
| | - Musa B Sami
- Institute of Mental Health, University of Nottingham, Triumph Rd, Nottingham, NG7 2TU
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7
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Sun H, Liu N, Qiu C, Tao B, Yang C, Tang B, Li H, Zhan K, Cai C, Zhang W, Lui S. Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility. J Magn Reson Imaging 2024. [PMID: 38946400 DOI: 10.1002/jmri.29470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 07/02/2024] Open
Abstract
Schizophrenia is a severe mental illness that significantly impacts the lives of affected individuals and with increasing mortality rates. Early detection and intervention are crucial for improving outcomes but the lack of validated biomarkers poses great challenges in such efforts. The use of magnetic resonance imaging (MRI) in schizophrenia enables the investigation of the disorder's etiological and neuropathological substrates in vivo. After decades of research, promising findings of MRI have been shown to aid in screening high-risk individuals and predicting illness onset, and predicting symptoms and treatment outcomes of schizophrenia. The integration of machine learning and deep learning techniques makes it possible to develop intelligent diagnostic and prognostic tools with extracted or selected imaging features. In this review, we aimed to provide an overview of current progress and prospects in establishing clinical utility of MRI in schizophrenia. We first provided an overview of MRI findings of brain abnormalities that might underpin the symptoms or treatment response process in schizophrenia patients. Then, we summarized the ongoing efforts in the computer-aided utility of MRI in schizophrenia and discussed the gap between MRI research findings and real-world applications. Finally, promising pathways to promote clinical translation were provided. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital of Sichuan University, Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Biqiu Tang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hongwei Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Kongcai Zhan
- Department of Radiology, Zigong Affiliated Hospital of Southwest Medical University, Zigong Psychiatric Research Center, Zigong, China
| | - Chunxian Cai
- Department of Radiology, the Second People's Hospital of Neijiang, Neijiang, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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8
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Sharkey RJ, Bacon C, Peterson Z, Rootes-Murdy K, Salvador R, Pomarol-Clotet E, Karuk A, Homan P, Ji E, Omlor W, Homan S, Georgiadis F, Kaiser S, Kirschner M, Ehrlich S, Dannlowski U, Grotegerd D, Goltermann J, Meinert S, Kircher T, Stein F, Brosch K, Krug A, Nenadic I, Sim K, Spalletta G, Banaj N, Sponheim SR, Demro C, Ramsay IS, King M, Quidé Y, Green MJ, Nguyen D, Preda A, Calhoun V, Turner J, van Erp T, Nickl-Jockschat T. Differences in the neural correlates of schizophrenia with positive and negative formal thought disorder in patients with schizophrenia in the ENIGMA dataset. Mol Psychiatry 2024:10.1038/s41380-024-02563-z. [PMID: 38671214 DOI: 10.1038/s41380-024-02563-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: 07/17/2023] [Revised: 04/04/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024]
Abstract
Formal thought disorder (FTD) is a clinical key factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, the relationship between FTD symptom dimensions and patterns of regional brain volume loss in schizophrenia remains to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles by enrolling a large multi-site cohort acquired by the ENIGMA Schizophrenia Working Group (752 schizophrenia patients and 1256 controls), to unravel the neuroanatomy of FTD in schizophrenia and using virtual histology tools on implicated brain regions to investigate the cellular basis. Based on the findings of previous clinical and neuroimaging studies, we decided to separately explore positive, negative and total formal thought disorder. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but positive and negative FTD demonstrated a dissociation: negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD also showed associations with microglial cell types. These results provide an important step towards linking FTD to brain structural changes and their cellular underpinnings, providing an avenue for a better mechanistic understanding of this syndrome.
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Affiliation(s)
- Rachel J Sharkey
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Chelsea Bacon
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Zeru Peterson
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | | | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM ISCIII, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM ISCIII, Barcelona, Spain
| | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM ISCIII, Barcelona, Spain
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Zurich, 8008, Switzerland
| | - Ellen Ji
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Zurich, 8008, Switzerland
| | - Wolfgang Omlor
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Zurich, 8008, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Zurich, 8008, Switzerland
| | - Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Zurich, 8008, Switzerland
| | - Stefan Kaiser
- Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Matthias Kirschner
- Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Igor Nenadic
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
| | | | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Scott R Sponheim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Ian S Ramsay
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | | | - Yann Quidé
- School of Psychiatry, University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
| | - Melissa Jane Green
- School of Psychiatry, University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
| | - Dana Nguyen
- Department of Pediatric Neurology, University of California Irvine, Irvine, CA, USA
| | - Adrian Preda
- Department of Pediatric Neurology, University of California Irvine, Irvine, CA, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GE, USA
| | - Jessica Turner
- Department of Psychiatry and Behavioral Medicine, Ohio State University, Columbus, OH, USA
| | - Theo van Erp
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, USA
| | - Thomas Nickl-Jockschat
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA.
- Department of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany.
- German Center for Mental Health (DZPG), partner site Halle-Jena-Magdeburg, Magdeburg, Germany.
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Halle-Jena-Magdeburg, Magdeburg, Germany.
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9
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Wiersch L, Friedrich P, Hamdan S, Komeyer V, Hoffstaedter F, Patil KR, Eickhoff SB, Weis S. Sex classification from functional brain connectivity: Generalization to multiple datasets. Hum Brain Mapp 2024; 45:e26683. [PMID: 38647035 PMCID: PMC11034006 DOI: 10.1002/hbm.26683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 03/19/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results.
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Affiliation(s)
- Lisa Wiersch
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Patrick Friedrich
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Sami Hamdan
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Vera Komeyer
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
- Department of Biology, Faculty of Mathematics and Natural SciencesHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Felix Hoffstaedter
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Kaustubh R. Patil
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Simon B. Eickhoff
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Susanne Weis
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
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10
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Wiersch L, Friedrich P, Hamdan S, Komeyer V, Hoffstaedter F, Patil KR, Eickhoff SB, Weis S. Sex classification from functional brain connectivity: Generalization to multiple datasets Generalizability of sex classifiers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.30.555495. [PMID: 37693374 PMCID: PMC10491190 DOI: 10.1101/2023.08.30.555495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.
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Affiliation(s)
- Lisa Wiersch
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Patrick Friedrich
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Sami Hamdan
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Vera Komeyer
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Department of Biology, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Susanne Weis
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
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11
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van der Vaart AD, Ma Y, Chiappelli J, Bruce H, Kvarta MD, Warner A, Du X, Adhikari BM, Sampath H, Kochunov P, Hong LE. Revisiting delusion subtypes in schizophrenia based on their underlying structures. J Psychiatr Res 2024; 171:75-83. [PMID: 38246028 PMCID: PMC10923062 DOI: 10.1016/j.jpsychires.2023.12.025] [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: 08/15/2023] [Revised: 12/10/2023] [Accepted: 12/12/2023] [Indexed: 01/23/2024]
Abstract
A clear understanding of the pathophysiology of schizophrenia and related spectrum disorders has been limited by clinical heterogeneity. We investigated whether relative severity and predominance of one or more delusion subtypes might yield clinically differentiable patient profiles. Patients (N = 286) with schizophrenia spectrum disorders (SSD) completed the 21-item Peters et al. Delusions Inventory (PDI-21). We performed factor analysis followed by k-means clustering to identify delusion factors and patient subtypes. Patients were further assessed via the Brief Psychiatric Rating Scale, Brief Negative Symptom Scale, Digit Symbol and Digit Substitution tasks, use of cannabis and tobacco, and stressful life events. The overall patient sample clustered into subtypes corresponding to Low-Delusion, Grandiose-Predominant, Paranoid-Predominant, and Pan-Delusion patients. Paranoid-Predominant and Pan-Delusion patients showed significantly higher burden of positive symptoms, while Low-Delusion patients showed the highest burden of negative symptoms. The Paranoia delusion factor score showed a positive association with Digit Symbol and Digit Substitution tasks in the overall sample, and the Paranoid-Predominant subtype exhibited the best performance on both tasks. Grandiose-Predominant patients showed significantly higher tobacco smoking severity than other subtypes, while Paranoid-Predominant patients were significantly more likely to have a lifetime diagnosis of Cannabis Use Disorder. We suggest that delusion self-report inventories such as the PDI-21 may be of utility in identifying sub-syndromes in SSD. From the current study, a Paranoid-Predominant form may be most distinctive, with features including less cognitive impairment and a stronger association with cannabis use.
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Affiliation(s)
- Andrew D van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mark D Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alia Warner
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoming Du
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bhim M Adhikari
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hemalatha Sampath
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Peter Kochunov
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - L Elliot Hong
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
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12
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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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13
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Okruszek Ł, Jarkiewicz M, Piejka A, Chrustowicz M, Krawczyk M, Schudy A, Harvey PD, Penn DL, Ludwig K, Green MF, Pinkham AE. Loneliness is associated with mentalizing and emotion recognition abilities in schizophrenia, but only in a cluster of patients with social cognitive deficits. J Int Neuropsychol Soc 2024; 30:27-34. [PMID: 37154103 DOI: 10.1017/s1355617723000206] [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] [Indexed: 05/10/2023]
Abstract
OBJECTIVE Loneliness is a concern for patients with schizophrenia. However, the correlates of loneliness in patients with schizophrenia are unclear; thus, the aim of the study is to investigate neuro- and social cognitive mechanisms associated with loneliness in individuals with schizophrenia. METHOD Data from clinical, neurocognitive, and social cognitive assessments were pooled from two cross-national samples (Poland/USA) to examine potential predictors of loneliness in 147 patients with schizophrenia and 103 healthy controls overall. Furthermore, the relationship between social cognition and loneliness was explored in clusters of patients with schizophrenia differing in social cognitive capacity. RESULTS Patients reported higher levels of loneliness than healthy controls. Loneliness was linked to increased negative and affective symptoms in patients. A negative association between loneliness and mentalizing and emotion recognition abilities was found in the patients with social-cognitive impairments, but not in those who performed at normative levels. CONCLUSIONS We have elucidated a novel mechanism which may explain previous inconsistent findings regarding the correlates of loneliness in individuals with schizophrenia.
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Affiliation(s)
- Ł Okruszek
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland
| | - M Jarkiewicz
- Third Department of Psychiatry, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - A Piejka
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland
| | - M Chrustowicz
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland
| | - M Krawczyk
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland
| | - A Schudy
- Faculty of Psychology, University of Warsaw, Warsaw, Poland
| | - P D Harvey
- University of Miami Miller School of Medicine, Miami VA Healthcare System, Miami, FL, USA
| | - D L Penn
- Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- School of Psychology, Australian Catholic University, Melbourne, Victoria, Australia
| | - K Ludwig
- Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - M F Green
- Desert Pacific Mental Illness Research, Education, and Clinical Center, VA Greater Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - A E Pinkham
- Department of Psychology, University of Texas at Dallas, Richardson, TX, USA
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
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14
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Hofer A, Biedermann F, Kaufmann A, Kemmler G, Pfaffenberger NM, Yalcin-Siedentopf N. Self-esteem in stabilized individuals with chronic schizophrenia: association with residual symptoms and cognitive functioning. Eur Arch Psychiatry Clin Neurosci 2023; 273:1737-1746. [PMID: 36602648 PMCID: PMC10713693 DOI: 10.1007/s00406-022-01538-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023]
Abstract
Low self-esteem is regarded as a barrier to recovery from schizophrenia and the identification of factors affecting this psychological characteristic may help to implement effective therapeutic interventions. To this end, the present study aimed to assess whether residual symptoms of the disorder and performance on a comprehensive neuropsychological test battery might differently impact self-esteem among 70 stabilized outpatients with chronic schizophrenia from public outpatient mental health services. Self-esteem inter-correlated with the severity of overall symptomatology, affective and negative symptoms, with premorbid intelligence, and with performance in the domains of verbal learning and memory, visual memory, working memory, and verbal fluency. Residual affective symptoms, premorbid intelligence, and female sex predicted poorer self-esteem in multiple linear regression analysis. The findings of this study implicate that next to psychological interventions therapeutic strategies that specifically target affective symptoms of schizophrenia may have a beneficial impact on patients' self-esteem.
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Affiliation(s)
- Alex Hofer
- Division of Psychiatry I, Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Medical University Innsbruck, Anichstr. 35, 6020, Innsbruck, Austria.
| | - Falko Biedermann
- Division of Psychiatry I, Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Medical University Innsbruck, Anichstr. 35, 6020, Innsbruck, Austria
| | - Alexandra Kaufmann
- Division of Psychiatry I, Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Medical University Innsbruck, Anichstr. 35, 6020, Innsbruck, Austria
| | - Georg Kemmler
- Division of Psychiatry I, Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Medical University Innsbruck, Anichstr. 35, 6020, Innsbruck, Austria
| | - Nicole M Pfaffenberger
- Division of Psychiatry I, Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Medical University Innsbruck, Anichstr. 35, 6020, Innsbruck, Austria
| | - Nursen Yalcin-Siedentopf
- Division of Psychiatry I, Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Medical University Innsbruck, Anichstr. 35, 6020, Innsbruck, Austria
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15
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Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Khosravi A, Zare A, Gorriz JM, Chale-Chale AH, Khadem A, Rajendra Acharya U. Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cogn Neurodyn 2023; 17:1501-1523. [PMID: 37974583 PMCID: PMC10640504 DOI: 10.1007/s11571-022-09897-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/23/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.
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Affiliation(s)
- Afshin Shoeibi
- FPGA Lab, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Navid Ghassemi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Juan M. Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
| | | | - Ali Khadem
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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16
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Gao J, Jiang R, Tang X, Chen J, Yu M, Zhou C, Wang X, Zhang H, Huang C, Yang Y, Zhang X, Cui Z, Zhang X. A neuromarker for deficit syndrome in schizophrenia from a combination of structural and functional magnetic resonance imaging. CNS Neurosci Ther 2023; 29:3774-3785. [PMID: 37288482 PMCID: PMC10651988 DOI: 10.1111/cns.14297] [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: 01/09/2023] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023] Open
Abstract
AIM Deficit schizophrenia (DS), defined by primary and enduring negative symptoms, has been proposed as a promising homogeneous subtype of schizophrenia. It has been demonstrated that unimodal neuroimaging characteristics of DS were different from non-deficit schizophrenia (NDS), however, whether multimodal-based neuroimaging features could identify deficit syndrome remains to be determined. METHODS Functional and structural multimodal magnetic resonance imaging of DS, NDS and healthy controls were scanned. Voxel-based features of gray matter volume, fractional amplitude of low-frequency fluctuations, and regional homogeneity were extracted. The support vector machine classification models were constructed using these features separately and jointly. The most discriminative features were defined as the first 10% of features with the greatest weights. Moreover, relevance vector regression was applied to explore the predictive values of these top-weighted features in predicting negative symptoms. RESULTS The multimodal classifier achieved a higher accuracy (75.48%) compared with the single modal model in distinguishing DS from NDS. The most predictive brain regions were mainly located in the default mode and visual networks, exhibiting differences between functional and structural features. Further, the identified discriminative features significantly predicted scores of diminished expressivity factor in DS but not NDS. CONCLUSIONS The present study demonstrated that local properties of brain regions extracted from multimodal imaging data could distinguish DS from NDS with a machine learning-based approach and confirmed the relationship between distinctive features and the negative symptoms subdomain. These findings may improve the identification of potential neuroimaging signatures and improve the clinical assessment of the deficit syndrome.
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Affiliation(s)
- Ju Gao
- Institute of Mental HealthSuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouChina
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
| | - Rongtao Jiang
- Department of Radiology & Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - Xiaowei Tang
- Department of PsychiatryWutaishan Hospital of YangzhouYangzhouChina
| | - Jiu Chen
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
| | - Miao Yu
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
| | - Chao Zhou
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya HospitalChangshaChina
| | - Hongying Zhang
- Department of RadiologySubei People's Hospital of Jiangsu ProvinceYangzhouChina
| | - Chengbing Huang
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
- Department of PsychiatryHuai'an No. 3 People's HospitalHuai'anChina
| | - Yong Yang
- Institute of Mental HealthSuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouChina
| | - Xiaobin Zhang
- Institute of Mental HealthSuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouChina
| | - Zaixu Cui
- Chinese Institute for Brain ResearchBeijingChina
| | - Xiangrong Zhang
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
- Department of PsychiatryThe Affiliated Xuzhou Oriental Hospital of Xuzhou Medical UniversityXuzhouChina
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17
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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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18
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Nickl-Jockschat T, Sharkey R, Bacon C, Peterson Z, Rootes-Murdy K, Salvador R, Pomarol E, Karuk A, Homan P, Ji E, Omlor W, Homan S, Georgiadis F, Kaiser S, Kirschner M, Ehrlich S, Dannlowski U, Grotegerd D, Goltermann J, Meinert S, Kircher T, Stein F, Brosch K, Krug A, Nenadic I, Sim K, Piras F, Banaj N, Sponheim S, Demro C, Ramsay I, King M, Quidé Y, Green M, Nguyen D, Preda A, Calhoun V, Turner J, van Erp T, Spalletta G. Neural Correlates of Positive and Negative Formal Thought Disorder in Individuals with Schizophrenia: An ENIGMA Schizophrenia Working Group Study. RESEARCH SQUARE 2023:rs.3.rs-3179362. [PMID: 37841855 PMCID: PMC10571603 DOI: 10.21203/rs.3.rs-3179362/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Formal thought disorder (FTD) is a key clinical factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, relationship between FTD symptom dimensions and patterns of regional brain volume deficiencies in schizophrenia remain to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles based on a large multi-site cohort through the ENIGMA Schizophrenia Working Group (752 individuals with schizophrenia and 1256 controls), to unravel the neuroanatomy of positive, negative and total FTD in schizophrenia and their cellular bases. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks for positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD was also linked to microglial cell types. These findings relate different dimensions of FTD to distinct brain structural changes and their cellular underpinnings, improve our mechanistic understanding of these key psychotic symptoms.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster
| | | | | | | | | | | | | | | | - Igor Nenadic
- Philipps University Marburg / Marburg University Hospital
| | | | | | | | | | | | | | | | | | | | | | | | - Vince Calhoun
- Georgia Institute of Technology, Emory University and Georgia State University
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19
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Chai C, Ding H, Du X, Xie Y, Man W, Zhang Y, Ji Y, Liang M, Zhang B, Ning Y, Zhuo C, Yu C, Qin W. Dissociation between neuroanatomical and symptomatic subtypes in schizophrenia. Eur Psychiatry 2023; 66:e78. [PMID: 37702075 PMCID: PMC10594537 DOI: 10.1192/j.eurpsy.2023.2446] [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: 10/31/2022] [Revised: 05/20/2023] [Accepted: 08/01/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Schizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated. METHODS A total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using K-means and hierarchical methods, respectively. RESULTS Patients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either K-means or Hierarchical methods. In contrast, patients with symptomatic subtype-1 had severe PANSS scores than subtype-2. No differences in PANSS scores were shown between the two neuroanatomical subtypes; similarly, no GMV differences were found between the two symptomatic subtypes. Cohen's Kappa test further demonstrated an apparent dissociation between the ML-based neuroanatomical and symptomatic subtypes (P > 0.05). The dissociation patterns were validated in four independent sites with diverse disease progressions (chronic vs. first episodes) and ancestors (Chinese vs. Western). CONCLUSIONS These findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.
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Affiliation(s)
- Chao Chai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hao Ding
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yi Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Bin Zhang
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuping Ning
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chuanjun Zhuo
- Department of Psychiatry, Tianjin Fourth Center Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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20
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Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, Bayer J, Menssink JM, Wang T, Bergmeir C, Wood S, Cotton SM. An overview of clustering methods with guidelines for application in mental health research. Psychiatry Res 2023; 327:115265. [PMID: 37348404 DOI: 10.1016/j.psychres.2023.115265] [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: 12/15/2022] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/24/2023]
Abstract
Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.
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Affiliation(s)
- Caroline X Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia; Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Dominic Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Ye Zhu
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Catherine L Smith
- Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Lan Du
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Kate M Filia
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Johanna Bayer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Jana M Menssink
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Teresa Wang
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Christoph Bergmeir
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia; Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Stephen Wood
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Sue M Cotton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
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21
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Lv Q, Zeljic K, Zhao S, Zhang J, Zhang J, Wang Z. Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning. Neurosci Bull 2023; 39:1309-1326. [PMID: 37093448 PMCID: PMC10387015 DOI: 10.1007/s12264-023-01057-2] [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: 09/02/2022] [Accepted: 02/17/2023] [Indexed: 04/25/2023] Open
Abstract
Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
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Affiliation(s)
- Qian Lv
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Kristina Zeljic
- School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK
| | - Shaoling Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Jiangtao Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Jianmin Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
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22
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Sharkey RJ, Bacon C, Peterson Z, Rootes-Murdy K, Salvador R, Pomarol-Clotet E, Karuk A, Homan P, Ji E, Omlor W, Homan S, Georgiadis F, Kaiser S, Kirschner M, Ehrlich S, Dannlowski U, Grotegerd D, Goltermann J, Meinert S, Kircher T, Stein F, Brosch K, Krug A, Nenadić I, Sim K, Spalletta G, Piras F, Banaj N, Sponheim SR, Demro C, Ramsay IS, King M, Quidé Y, Green MJ, Nguyen D, Preda A, Calhoun VD, Turner JA, van Erp TGM, Nickl-Jockschat T. Neural Correlates of Positive and Negative Formal Thought Disorder in Individuals with Schizophrenia: An ENIGMA Schizophrenia Working Group Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.06.23291034. [PMID: 37333179 PMCID: PMC10274967 DOI: 10.1101/2023.06.06.23291034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Formal thought disorder (FTD) is a key clinical factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, relationship between FTD symptom dimensions and patterns of regional brain volume deficiencies in schizophrenia remain to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles based on a large multi-site cohort through the ENIGMA Schizophrenia Working Group (752 individuals with schizophrenia and 1256 controls), to unravel the neuroanatomy of positive, negative and total FTD in schizophrenia and their cellular bases. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks for positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD was also linked to microglial cell types. These findings relate different dimensions of FTD to distinct brain structural changes and their cellular underpinnings, improve our mechanistic understanding of these key psychotic symptoms.
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23
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Okruszek Ł, Piejka A, Chrustowicz M, Krawczyk M, Jarkiewicz M, Schudy A, Ludwig K, Pinkham A. Social cognitive bias increases loneliness both directly and by decreasing social connection in patients with schizophrenia. Schizophr Res 2023; 256:72-78. [PMID: 37163867 DOI: 10.1016/j.schres.2023.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/04/2023] [Accepted: 04/30/2023] [Indexed: 05/12/2023]
Abstract
While considerable emphasis has been put on investigating the mechanisms that drive reduced social connection in patients with schizophrenia (SCZ), recent studies have increasingly focused on the issue of loneliness in SCZ. As both social cognitive bias and self-reported empathy predict loneliness in non-clinical populations, the current study aims to examine the relationship between loneliness, reduced social connection and social cognitive biases, and self-reported empathy in SCZ. Ninety-three adult SCZ and sixty-six matched healthy individuals completed a battery of questionnaires measuring loneliness and social connection (Revised-UCLA Loneliness Scale, Lubben-Social Network Scale, Social Disconnectedness Scale), cognitive biases (Ambiguous Intentions Hostility Questionnaire, Davos Assessment of Cognitive Biases Scale, Cognitive Biases Questionnaire for psychosis) and self-reported empathy (Interpersonal Reactivity Index). Significant predictors of loneliness in SCZ were entered into two latent variables ("Social Threat Bias", "Social Connection"), and structural equation modeling was used to explore the direct and indirect relationships between Social Threat Bias, symptoms. and loneliness in SCZ. Patients reported higher levels of loneliness, cognitive biases and personal distress compared to controls. Furthermore, SCZ reported less social connection and perspective taking compared to controls. Structural equation modeling revealed that Social Threat Bias was linked to increased loneliness in SCZ both directly and indirectly via decreased social connection. Negative symptoms were directly linked with loneliness, while the association between affective symptoms and loneliness was mediated via Social Threat Bias. The results of the current study suggest that social threat bias should be considered while planning the interventions aimed to reduce loneliness in schizophrenia.
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Affiliation(s)
- Ł Okruszek
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland.
| | - A Piejka
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland
| | - M Chrustowicz
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland
| | - M Krawczyk
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland
| | - M Jarkiewicz
- Third Department of Psychiatry, Institute of Psychiatry and Neurology, Poland
| | - A Schudy
- Faculty of Psychology, University of Warsaw, Poland
| | - K Ludwig
- Department of Psychology, University of North Carolina at Chapel Hill, United States of America
| | - A Pinkham
- Department of Psychology, University of Texas at Dallas, United States of America; Department of Psychiatry, UT Southwestern Medical Center, United States of America
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24
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Dhamala E, Yeo BTT, Holmes AJ. One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry. Biol Psychiatry 2023; 93:717-728. [PMID: 36577634 DOI: 10.1016/j.biopsych.2022.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022]
Abstract
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
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25
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Kogler L, Regenbogen C, Müller VI, Kohn N, Schneider F, Gur RC, Derntl B. Cognitive Stress Regulation in Schizophrenia Patients and Healthy Individuals: Brain and Behavior. J Clin Med 2023; 12:jcm12072749. [PMID: 37048832 PMCID: PMC10095473 DOI: 10.3390/jcm12072749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 03/30/2023] [Accepted: 04/01/2023] [Indexed: 04/14/2023] Open
Abstract
Stress is an important factor in the development, triggering, and maintenance of psychotic symptoms. Still, little is known about the neural correlates of cognitively regulating stressful events in schizophrenia. The current study aimed at investigating the cognitive down-regulation of negative, stressful reactions during a neuroimaging psychosocial stress paradigm (non-regulated stress versus cognitively regulated stress). In a randomized, repeated-measures within-subject design, we assessed subjective reactions and neural activation in schizophrenia patients (SZP) and matched healthy controls in a neuroimaging psychosocial stress paradigm. In general, SZP exhibited an increased anticipation of stress compared to controls (p = 0.020). During non-regulated stress, SZP showed increased negative affect (p = 0.033) and stronger activation of the left parietal operculum/posterior insula (p < 0.001) and right inferior frontal gyrus/anterior insula (p = 0.005) than controls. Contrarily, stress regulation compared to non-regulated stress led to increased subjective reactions in controls (p = 0.003) but less deactivation in SZP in the ventral anterior cingulate cortex (p = 0.027). Our data demonstrate stronger reactions to and anticipation of stress in patients and difficulties with cognitive stress regulation in both groups. Considering the strong association between mental health and stress, the investigation of cognitive regulation in individuals vulnerable to stress, including SZP, has crucial implications for improving stress intervention trainings.
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Affiliation(s)
- Lydia Kogler
- Department of Psychiatry and Psychotherapy, Tübingen Centre for Mental Health (TüCMH), Medical Faculty, University of Tübingen, Calwerstrasse 14, 72076 Tübingen, Germany
| | - Christina Regenbogen
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Veronika I Müller
- Institute of Neuroscience und Medicine, INM-7, Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Nils Kohn
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Postbus 9101, 6500 HB Nijmegen, The Netherlands
| | - Frank Schneider
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
- Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Ruben C Gur
- Neuropsychiatry Division, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Tübingen Centre for Mental Health (TüCMH), Medical Faculty, University of Tübingen, Calwerstrasse 14, 72076 Tübingen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction (IMPRS-MMFD), Otfried-Müller-Str. 27, 72076 Tübingen, Germany
- LEAD Graduate School and Network, University of Tübingen, Walter-Simon-Straße 12, 72074 Tübingen, Germany
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26
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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27
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Kernbach JM, Delev D, Neuloh G, Clusmann H, Bzdok D, Eickhoff SB, Staartjes VE, Vasella F, Weller M, Regli L, Serra C, Krayenbühl N, Akeret K. Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival. Brain Commun 2022; 5:fcac336. [PMID: 36632188 PMCID: PMC9830987 DOI: 10.1093/braincomms/fcac336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/06/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
The current World Health Organization classification integrates histological and molecular features of brain tumours. The aim of this study was to identify generalizable topological patterns with the potential to add an anatomical dimension to the classification of brain tumours. We applied non-negative matrix factorization as an unsupervised pattern discovery strategy to the fine-grained topographic tumour profiles of 936 patients with neuroepithelial tumours and brain metastases. From the anatomical features alone, this machine learning algorithm enabled the extraction of latent topological tumour patterns, termed meta-topologies. The optimal part-based representation was automatically determined in 10 000 split-half iterations. We further characterized each meta-topology's unique histopathologic profile and survival probability, thus linking important biological and clinical information to the underlying anatomical patterns. In neuroepithelial tumours, six meta-topologies were extracted, each detailing a transpallial pattern with distinct parenchymal and ventricular compositions. We identified one infratentorial, one allopallial, three neopallial (parieto-occipital, frontal, temporal) and one unisegmental meta-topology. Each meta-topology mapped to distinct histopathologic and molecular profiles. The unisegmental meta-topology showed the strongest anatomical-clinical link demonstrating a survival advantage in histologically identical tumours. Brain metastases separated to an infra- and supratentorial meta-topology with anatomical patterns highlighting their affinity to the cortico-subcortical boundary of arterial watershed areas.Using a novel data-driven approach, we identified generalizable topological patterns in both neuroepithelial tumours and brain metastases. Differences in the histopathologic profiles and prognosis of these anatomical tumour classes provide insights into the heterogeneity of tumour biology and might add to personalized clinical decision-making.
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Affiliation(s)
| | | | - Georg Neuloh
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany,Center for Integrated Oncology, Düsseldorf (CIO ABCD), Universities Aachen, Bonn, Cologne, Germany
| | - Hans Clusmann
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany,Center for Integrated Oncology, Düsseldorf (CIO ABCD), Universities Aachen, Bonn, Cologne, Germany
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer Science, McGill University, 845 Sherbrooke St W, Montreal, Quebec H3A 0G4, Canada,Mila—Quebec Artificial Intelligence Institute, 6666 Rue Saint-Urbain, Montreal, Quebec H2S 3H1, Canada
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Wilhelm Johnen Strasse, 52428 Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany
| | - Victor E Staartjes
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
| | - Flavio Vasella
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
| | - Michael Weller
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
| | - Carlo Serra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
| | - Niklaus Krayenbühl
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland,Division of Pediatric Neurosurgery, University Children's Hospital, Steinwiesstrasse 75, 8032 Zurich, Switzerland
| | - Kevin Akeret
- Correspondence to: Kevin Akeret, MD PhD Department of Neurosurgery, Clinical Neuroscience Center University Hospital Zurich and University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland E-mail:
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28
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Cheng B, Chen J, Königsberg A, Mayer C, Rimmele L, Patil KR, Gerloff C, Thomalla G, Eickhoff SB. Mapping the deficit dimension structure of the National Institutes of Health Stroke Scale. EBioMedicine 2022; 87:104425. [PMID: 36563488 PMCID: PMC9800288 DOI: 10.1016/j.ebiom.2022.104425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The National Institutes of Health Stroke Scale (NIHSS) is the most frequently applied clinical rating scale for standardized assessment of neurological deficits in acute stroke in both clinical and research settings. Notwithstanding this prominent role, important questions regarding its validity remain insufficiently addressed: Investigations of the underlying dimensional structure of the NIHSS yielded inconsistent results that are largely not generalizable across studies. Neurobiological validations by linking measured deficit dimensions to brain anatomy and function are missing. METHODS We, therefore, employ advanced machine learning to identify an optimal representation of the dimensional structure of the NIHSS across two independent and heterogeneous stroke datasets (N = 503 and N = 690). Associated lesion locations are identified by multivariate lesion-deficit mapping (LDM) and their functional relevance is profiled based on a-priori task activation meta-data analysis, to provide an independent link to the behavioural level. FINDINGS A five-factor structure of the NIHSS was identified as the most robust and generalizable representation of stroke deficit dimensions across study populations, settings, and clinical phenotypes. Specifically, the identified dimensions comprised NIHSS items for (F1) left motor deficits, (F2) right motor deficits, (F3) dysarthria and facial palsy, (F4) language, and (F5) deficits in spatial attention and gaze. LDM linked four of these factors to differentially localized, eloquent neuroanatomical areas. Functional characterization of LDM results aligned with detected deficit dimensions, revealing associations with motor functions, language processing, and various functions in the perception domain. INTERPRETATION By cross-validating machine learning in heterogeneous multi-site stroke cohorts, we report evidence on the validity of the NIHSS: We identified an overarching structure of the NISHS containing a five-dimensional representation of stroke deficits. We provide an anatomical map of the NIHSS that is of value for future applications of individualized stroke treatment and rehabilitation. FUNDING This research was supported by the National Key R&D Program of China (Grant No. 2021YFC2502200), the National Human Brain Project of China (Grant No. 2022ZD0214000)", the German Research Foundation (Deutsche Forschungsgemeinschaft), Project 178316478 (A1, C1, C2), and Project 454012190 of the SPP 2041, the Helmholtz Portfolio Theme "Supercomputing and Modelling for the Human Brain" and Helmholtz Imaging Platform grant NimRLS (ZT-I-PF-4-010).
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Affiliation(s)
- Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,Corresponding author.
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Corresponding author.
| | - Alina Königsberg
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Leander Rimmele
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Gerloff
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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29
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Moujaes F, Preller KH, Ji JL, Murray JD, Berkovitch L, Vollenweider FX, Anticevic A. Towards mapping neuro-behavioral heterogeneity of psychedelic neurobiology in humans. Biol Psychiatry 2022:S0006-3223(22)01805-4. [PMID: 36715317 DOI: 10.1016/j.biopsych.2022.10.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/10/2022] [Accepted: 10/31/2022] [Indexed: 12/12/2022]
Abstract
Precision psychiatry aims to identify markers of inter-individual variability that allow predicting the right treatment for each patient. However, bridging the gap between molecular-level manipulations and neural systems-level functional alterations remains an unsolved problem in psychiatry. After decades of low success rates in pharmaceutical R&D for psychiatric drugs, multiple studies now point to the potential of psychedelics as a promising fast-acting and long-lasting treatment for some psychiatric symptoms. Yet, given the highly psychoactive nature of these substances, a precision medicine approach is essential to map the neural signals related to clinical efficacy in order to identify patients who can maximally benefit from this treatment. Recent studies have shown that bridging the gap between pharmacology, systems-level neural response in humans and individual experience is possible for psychedelic substances, therefore paving the way for a precision neuropsychiatric therapeutic development. Specifically, it has been shown that the integration of brain-wide PET or transcriptomic data, i.e. receptor distribution for the serotonin 2A receptor, with computational neuroimaging methods can simulate the effect of psychedelics on the human brain. These novel 'computational psychiatry' approaches allow for modeling inter-individual differences in neural as well as subjective effects of psychedelic substances. Collectively, this review provides a deep dive into psychedelic pharmaco-neuroimaging studies with a core focus on how recent computational psychiatry advances in biophysically based circuit modeling can be leveraged to predict individual responses. Finally, we emphasize the importance of human pharmacological neuroimaging for the continued precision therapeutic development of psychedelics.
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Affiliation(s)
- Flora Moujaes
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, Lenggstr. 31, 8032 Zurich, Switzerland; Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States
| | - Katrin H Preller
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, Lenggstr. 31, 8032 Zurich, Switzerland; Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States
| | - John D Murray
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States; Department of Physics, Yale University, New Haven, CT, 06511, United States; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, United States
| | - Lucie Berkovitch
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States; Université de Paris, 15 Rue de l'École de Médecine, F-75006 Paris, France; Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie & Neurosciences, 1 rue Cabanis, F-75014, Paris, France
| | - Franz X Vollenweider
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, Lenggstr. 31, 8032 Zurich, Switzerland
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, United States.
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30
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Ferrara M, Franchini G, Funaro M, Cutroni M, Valier B, Toffanin T, Palagini L, Zerbinati L, Folesani F, Murri MB, Caruso R, Grassi L. Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment. Curr Psychiatry Rep 2022; 24:925-936. [PMID: 36399236 PMCID: PMC9780131 DOI: 10.1007/s11920-022-01399-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/12/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE OF REVIEW This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment. RECENT FINDINGS Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.
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Affiliation(s)
- Maria Ferrara
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy.
- Department of Psychiatry, Yale School of Medicine, 34 Park Street, New Haven, CT, USA.
| | - Giorgia Franchini
- Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Via Campi 213/B, Modena, Italy
- Department of Mathematics and Computer Science, University of Ferrara, Via Macchiavelli 33, Ferrara, Italy
| | - Melissa Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, 333 Cedar St., New Haven, CT, USA
| | - Marcello Cutroni
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Beatrice Valier
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Tommaso Toffanin
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Laura Palagini
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Luigi Zerbinati
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Federica Folesani
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Martino Belvederi Murri
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Rosangela Caruso
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Luigi Grassi
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
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31
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Okruszek Ł, Chrustowicz M, Jarkiewicz M, Krawczyk M, Manera V, Piejka A, Schudy A, Wiśniewska M, Wysokiński A. Mentalizing abilities mediate the impact of the basic social perception on negative symptoms in patients with schizophrenia. J Psychiatr Res 2022; 155:85-89. [PMID: 35995018 DOI: 10.1016/j.jpsychires.2022.07.069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/13/2022] [Accepted: 07/30/2022] [Indexed: 10/31/2022]
Abstract
Social cognitive deficits are currently considered as one of the main predictors of clinical symptoms and functional outcome in patients with schizophrenia. Multiple studies have suggested that a two-factor solution (low-level vs. high-level) best describes the structure of social cognitive processes in patients. While higher-order processes have been repeatedly linked to negative symptoms, no such association was found for lower-level processes. Thus, the aim of the current study is to examine whether the association between basic social perception processes and symptoms in patients with schizophrenia is mediated by mentalizing abilities. One hundred thirty-nine patients have completed basic social perception (Communicative Interactions Database task CID-12) and mentalizing (Reading the Mind in the Eyes task) tasks. In line with our hypothesis, we have observed full mediation of the effects of basic social perception abilities on negative symptoms via mentalizing abilities in patients. This effect suggests that, similarly as in the case of positive symptoms, a hierarchical nature of social cognitive processes should be considered while investigating negative symptoms of schizophrenia.
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Affiliation(s)
- Ł Okruszek
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland.
| | - M Chrustowicz
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland
| | | | - M Krawczyk
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland
| | | | - A Piejka
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland
| | - A Schudy
- Faculty of Psychology, University of Warsaw, Poland
| | - M Wiśniewska
- Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Poland
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32
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Linking cerebellar functional gradients to transdiagnostic behavioral dimensions of psychopathology. Neuroimage Clin 2022; 36:103176. [PMID: 36063759 PMCID: PMC9450332 DOI: 10.1016/j.nicl.2022.103176] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 08/24/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
High co-morbidity and substantial overlap across psychiatric disorders encourage a transition in psychiatry research from categorical to dimensional approaches that integrate neuroscience and psychopathology. Converging evidence suggests that the cerebellum is involved in a wide range of cognitive functions and mental disorders. An important question thus centers on the extent to which cerebellar function can be linked to transdiagnostic dimensions of psychopathology. To address this question, we used a multivariate data-driven statistical technique (partial least squares) to identify latent dimensions linking human cerebellar connectome as assessed by functional MRI to a large set of clinical, cognitive, and trait measures across 198 participants, including healthy controls (n = 92) as well as patients diagnosed with attention-deficit/hyperactivity disorder (n = 35), bipolar disorder (n = 36), and schizophrenia (n = 35). Macroscale spatial gradients of connectivity at voxel level were used to characterize cerebellar connectome properties, which provide a low-dimensional representation of cerebellar connectivity, i.e., a sensorimotor-supramodal hierarchical organization. This multivariate analysis revealed significant correlated patterns of cerebellar connectivity gradients and behavioral measures that could be represented into four latent dimensions: general psychopathology, impulsivity and mood, internalizing symptoms and executive dysfunction. Each dimension was associated with a unique spatial pattern of cerebellar connectivity gradients across all participants. Multiple control analyses and 10-fold cross-validation confirmed the robustness and generalizability of the yielded four dimensions. These findings highlight the relevance of cerebellar connectivity as a necessity for the study and classification of transdiagnostic dimensions of psychopathology and call on researcher to pay more attention to the role of cerebellum in the dimensions of psychopathology, not just within the cerebral cortex.
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33
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Shi W, Fan L, Wang H, Liu B, Li W, Li J, Cheng L, Chu C, Song M, Sui J, Luo N, Cui Y, Dong Z, Lu Y, Ma Y, Ma L, Li K, Chen J, Chen Y, Guo H, Li P, Lu L, Lv L, Wan P, Wang H, Wang H, Yan H, Yan J, Yang Y, Zhang H, Zhang D, Jiang T. Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement. Cereb Cortex 2022; 33:3683-3700. [PMID: 36005854 DOI: 10.1093/cercor/bhac301] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 11/12/2022] Open
Abstract
Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.
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Affiliation(s)
- Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Huiling Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.,Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Jun Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China.,Department of Psychology, Xinxiang Medical University, Xinxiang 453002, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China.,Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100191, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China.,Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences, Beijing 100190, China
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34
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Li ZP, You YS, Wang JD, He LP. Underlying disease may increase mortality risk in users of atypical antipsychotics. World J Psychiatry 2022; 12:1112-1114. [PMID: 36158300 PMCID: PMC9476835 DOI: 10.5498/wjp.v12.i8.1112] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/19/2022] [Accepted: 07/19/2022] [Indexed: 02/05/2023] Open
Abstract
Schizophrenia is a group of the most common types of mental illness. Commonly used antischizophrenia drugs all increase mortality to some extent. The increased risk of death in older individuals and patients with dementia using atypical antipsychotics may be due to myocardial damage, increased mobility and increased risk of stroke.
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Affiliation(s)
- Zhi-Peng Li
- School of Medicine, Taizhou University, Taizhou 318000, Zhejiang Province, China
| | - Yu-Shun You
- School of Medicine, Taizhou University, Taizhou 318000, Zhejiang Province, China
| | - Jun-Dong Wang
- School of Medicine, Taizhou University, Taizhou 318000, Zhejiang Province, China
| | - Lian-Ping He
- School of Medicine, Taizhou University, Taizhou 318000, Zhejiang Province, China
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35
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Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
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36
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Sun H, Zhang W, Cao H, Sun H, Dai J, Li S, Zeng J, Wei X, Tang B, Gong Q, Lui S. Linked brain connectivity patterns with psychopathological and cognitive phenotypes in drug-naïve first-episode schizophrenia. PSYCHORADIOLOGY 2022; 2:43-51. [PMID: 38665967 PMCID: PMC10994520 DOI: 10.1093/psyrad/kkac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 02/05/2023]
Abstract
Background Schizophrenia is considered to be a disorder of dysconnectivity characterized by abnormal functional integration between distinct brain regions. Different brain connection abnormalities were found to be correlated with various clinical manifestations, but whether a common deficit in functional connectivity (FC) in relation to both clinical symptoms and cognitive impairments could present in first-episode patients who have never received any medication remains elusive. Objective To find a core deficit in the brain connectome that is related to both psychopathological and cognitive manifestations. Methods A total of 75 patients with first-episode schizophrenia and 51 healthy control participants underwent scanning of the brain and clinical ratings of behaviors. A principal component analysis was performed on the clinical ratings of symptom and cognition. Partial correlation analyses were conducted between the main psychopathological components and resting-state FC that were found abnormal in schizophrenia patients. Results Using the principal component analysis, the first principal component (PC1) explained 37% of the total variance of seven clinical features. The ratings of GAF and BACS contributed negatively to PC1, while those of PANSS, HAMD, and HAMA contributed positively. The FCs positively correlated with PC1 mainly included connections related to the insula, precuneus gyrus, and some frontal brain regions. FCs negatively correlated with PC1 mainly included connections between the left middle cingulate cortex and superior and middle occipital regions. Conclusion In conclusion, we found a linked pattern of FC associated with both psychopathological and cognitive manifestations in drug-naïve first-episode schizophrenia characterized as the dysconnection related to the frontal and visual cortex, which may represent a core deficit of brain FC in patients with schizophrenia.
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Affiliation(s)
- Hui Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Hengyi Cao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, 11030 Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, 11004 Glen Oaks, NY, USA
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Jing Dai
- Department of Psychoradiology, Chengdu Mental Health Center, 610031 Chengdu, China
| | - Siyi Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Jiaxin Zeng
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Xia Wei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Biqiu Tang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 610041 Chengdu, China
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37
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Divergence of dose-response with asenapine: a cluster analysis of randomized, double-blind, and placebo control study. CNS Spectr 2022; 27:369-377. [PMID: 33461636 DOI: 10.1017/s1092852921000043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Differences in psychiatric background and dose-response to asenapine in patients with schizophrenia were examined based on efficacy and safety, using data obtained in a double-blind, placebo-controlled trial. METHODS Patients with schizophrenia were classified into three clusters by a cluster analysis based on the Positive and Negative Symptom Scale (PANSS) subscores at baseline, using the data from a 6-week, double-blind, placebo-controlled trial. PANSS Marder factor scores were calculated for each cluster. The efficacy of 10 or 20 mg/day of asenapine on PANSS score was used as the primary endpoint, with the incidence of adverse events evaluated as the secondary endpoint. RESULTS A total of 529 asenapine-treated patients were classified into 3 clusters: Cluster-P with the higher scores in positive symptoms, disorganized thoughts, and hostility/excitement, Cluster-N with higher scores in negative symptoms, and Cluster-L with overall lower scores. In Cluster-N and Cluster-L, both 10 and 20 mg/day groups showed significant improvement in PANSS scores, while only the 20 mg/day group showed a significant difference in Cluster-P. Cluster-N and Cluster-L had differences in the incidence of adverse events, but this was not seen in Cluster-P. CONCLUSIONS The efficacy and safety of asenapine 10 and 20 mg/day differed between the 3 clusters of patients. This suggests that background information regarding baseline psychiatric symptoms may affect the therapeutic response in patients with schizophrenia.
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38
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Geng H, Chen J, Chuan-Peng H, Jin J, Chan RCK, Li Y, Hu X, Zhang RY, Zhang L. Promoting computational psychiatry in China. Nat Hum Behav 2022; 6:615-617. [PMID: 35347241 DOI: 10.1038/s41562-022-01328-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Haiyang Geng
- Department of Psychology, The University of Hong Kong, Hong Kong, China.,The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.,Tianqiao and Chrissy Chen Institute for Translational Research, Shanghai, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China. .,Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
| | - Jingwen Jin
- Department of Psychology, The University of Hong Kong, Hong Kong, China.,The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Li
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xiaoqing Hu
- Department of Psychology, The University of Hong Kong, Hong Kong, China.,The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.,HKU, Shenzhen Institute of Research and Innovation, Shenzhen, China
| | - Ru-Yuan Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,Institute of Psychology and Behavioral Science, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China.
| | - Lei Zhang
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
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39
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Stein F, Buckenmayer E, Brosch K, Meller T, Schmitt S, Ringwald KG, Pfarr JK, Steinsträter O, Enneking V, Grotegerd D, Heindel W, Meinert S, Leehr EJ, Lemke H, Thiel K, Waltemate L, Winter A, Hahn T, Dannlowski U, Jansen A, Nenadić I, Krug A, Kircher T. Dimensions of Formal Thought Disorder and Their Relation to Gray- and White Matter Brain Structure in Affective and Psychotic Disorders. Schizophr Bull 2022; 48:902-911. [PMID: 35064667 PMCID: PMC9212109 DOI: 10.1093/schbul/sbac002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Factorial dimensions and neurobiological underpinnings of formal thought disorders (FTD) have been extensively investigated in schizophrenia spectrum disorders (SSD). However, FTD are also highly prevalent in other disorders. Still, there is a lack of knowledge about transdiagnostic, structural brain correlates of FTD. In N = 1071 patients suffering from DSM-IV major depressive disorder, bipolar disorder, or SSD, we calculated a psychopathological factor model of FTD based on the SAPS and SANS scales. We tested the association of FTD dimensions with 3 T MRI measured gray matter volume (GMV) and white matter fractional anisotropy (FA) using regression and interaction models in SPM12. We performed post hoc confirmatory analyses in diagnostically equally distributed, age- and sex-matched sub-samples to test whether results were driven by diagnostic categories. Cross-validation (explorative and confirmatory) factor analyses revealed three psychopathological FTD factors: disorganization, emptiness, and incoherence. Disorganization was negatively correlated with a GMV cluster comprising parts of the middle occipital and angular gyri and positively with FA in the right posterior cingulum bundle and inferior longitudinal fascicle. Emptiness was negatively associated with left hippocampus and thalamus GMV. Incoherence was negatively associated with FA in bilateral anterior thalamic radiation, and positively with the hippocampal part of the right cingulum bundle. None of the gray or white matter associations interacted with diagnosis. Our results provide a refined mapping of cross-disorder FTD phenotype dimensions. For the first time, we demonstrated that their neuroanatomical signatures are associated with language-related gray and white matter structures independent of diagnosis.
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Affiliation(s)
- Frederike Stein
- To whom correspondence should be addressed; Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; tel: +49-6421-58 63831, fax: +49-6421-58 65197, e-mail:
| | - Elena Buckenmayer
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany,Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Kai Gustav Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany,Core-Facility Brainimaging, Faculty of Medicine, Philipps-University Marburg, Marburg, Germany
| | - Verena Enneking
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Walter Heindel
- Department of Radiology, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany,Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany,Core-Facility Brainimaging, Faculty of Medicine, Philipps-University Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry und Psychotherapy, University of Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
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Miranda L, Paul R, Pütz B, Koutsouleris N, Müller-Myhsok B. Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping. Front Psychiatry 2021; 12:665536. [PMID: 34744805 PMCID: PMC8569315 DOI: 10.3389/fpsyt.2021.665536] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/07/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.
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Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Riya Paul
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Benno Pütz
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Bertram Müller-Myhsok
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Health Data Science, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
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41
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Eickhoff SB, Heinrichs B. [The predictable human : Possibilities and risks of AI-based prediction of cognitive abilities, personality traits and mental illnesses]. DER NERVENARZT 2021; 92:1140-1148. [PMID: 34608537 DOI: 10.1007/s00115-021-01197-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/31/2021] [Indexed: 11/29/2022]
Abstract
New approaches to the use of artificial intelligence (AI) to analyze data from neuroimaging but also passively collected data from so-called wearables, such as smartphones or smartwatches, as well as data that can be extracted from social media and other online activities, already make it possible to predict cognitive abilities, personality traits, and mental illnesses, as well as to reveal acute mental states. In this article, we explain the methodological concepts behind these current developments, illuminate the possibilities and limitations, and address ethical and social aspects arising from the use.
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Affiliation(s)
- Simon B Eickhoff
- Institut für Neurowissenschaften und Medizin: Gehirn und Verhalten (INM-7), Forschungszentrum Jülich, 52425, Jülich, Deutschland. .,Institut für Systemische Neurowissenschaften, Medizinische Fakultät, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland.
| | - Bert Heinrichs
- Institut für Neurowissenschaften und Medizin: Ethik in den Neurowissenschaften (INM-8), Forschungszentrum Jülich, 52425, Jülich, Deutschland.,Institut für Wissenschaft und Ethik (IWE), Universität Bonn, Bonner Talweg 57, 53113, Bonn, Deutschland
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42
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Interindividual variability of functional connectome in schizophrenia. Schizophr Res 2021; 235:65-73. [PMID: 34329851 DOI: 10.1016/j.schres.2021.07.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 07/08/2021] [Accepted: 07/11/2021] [Indexed: 11/21/2022]
Abstract
Schizophrenia is a complex psychiatric disorder that displays an outstanding interindividual variability in clinical manifestation and neurobiological substrates. A better characterization and quantification of this heterogeneity could guide the search for both common abnormalities (linked to lower intersubject variability) and the presence of biological subtypes (leading to a greater heterogeneity across subjects). In the current study, we address interindividual variability in functional connectome by means of resting-state fMRI in a large sample of patients with schizophrenia and healthy controls. Among the different metrics of distance/dissimilarity used to assess variability, geodesic distance showed robust results to head motion. The main findings of the current study point to (i) a higher between subject heterogeneity in the functional connectome of patients, (ii) variable levels of heterogeneity throughout the cortex, with greater variability in frontoparietal and default mode networks, and lower variability in the salience network, and (iii) an association of whole-brain variability with levels of clinical symptom severity and with topological properties of brain networks, suggesting that the average functional connectome overrepresents those patients with lower functional integration and with more severe clinical symptoms. Moreover, after performing a graph theoretical analysis of brain networks, we found that patients with more severe clinical symptoms had decreased connectivity at both whole-brain level and within the salience network, and that patients with higher negative symptoms had large-scale functional integration deficits.
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43
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Wang X, Cheng B, Roberts N, Wang S, Luo Y, Tian F, Yue S. Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder. Hum Brain Mapp 2021; 42:5458-5476. [PMID: 34431584 PMCID: PMC8519858 DOI: 10.1002/hbm.25618] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 07/02/2021] [Accepted: 07/13/2021] [Indexed: 02/05/2023] Open
Abstract
Working memory (WM) impairments are common features of psychiatric disorders. A systematic meta-analysis was performed to determine common and disorder-specific brain fMRI response during performance of WM tasks in patients with SZ and patients with MDD relative to healthy controls (HC). Thirty-four published fMRI studies of WM in patients with SZ and 18 published fMRI studies of WM in patients with MDD, including relevant HC, were included in the meta-analysis. In both SZ and MDD there was common stronger fMRI response in right medial prefrontal cortex (MPFC) and bilateral anterior cingulate cortex (ACC), which are part of the default mode network (DMN). The effects were of greater magnitude in SZ than MDD, especially in prefrontal-temporal-cingulate-striatal-cerebellar regions. In addition, a disorder-specific weaker fMRI response was observed in right middle frontal gyrus (MFG) in MDD, relative to HC. For both SZ and MDD a significant correlation was observed between the severity of clinical symptoms and lateralized fMRI response relative to HC. These findings indicate that there may be common and distinct anomalies in brain function underlying deficits in WM in SZ and MDD, which may serve as a potential functional neuroimaging-based diagnostic biomarker with value in supporting clinical diagnosis, measuring illness severity and assessing the efficacy of treatments for SZ and MDD at the brain level.
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Affiliation(s)
- Xiuli Wang
- Department of Psychiatry, the Fourth People's Hospital of Chengdu, Chengdu, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, China
| | - Neil Roberts
- Edinburgh Imaging Facility, Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Song Wang
- Department of Radiology, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Ya Luo
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Fangfang Tian
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Suping Yue
- Department of Psychiatry, the Fourth People's Hospital of Chengdu, Chengdu, China
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44
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Camilleri JA, Eickhoff SB, Weis S, Chen J, Amunts J, Sotiras A, Genon S. A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System. Sci Rep 2021; 11:16896. [PMID: 34413412 PMCID: PMC8377093 DOI: 10.1038/s41598-021-96342-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 08/09/2021] [Indexed: 02/07/2023] Open
Abstract
While a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis-Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.
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Affiliation(s)
- J A Camilleri
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany.
| | - S B Eickhoff
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
| | - S Weis
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
| | - J Chen
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
| | - J Amunts
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
| | - A Sotiras
- Mallinckrodt Institute of Radiology, Institute for Informatics, Washington University in Saint Louis, Saint Louis, USA
| | - S Genon
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
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The Imbalanced Plasticity Hypothesis of Schizophrenia-Related Psychosis: A Predictive Perspective. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 21:679-697. [PMID: 34050524 DOI: 10.3758/s13415-021-00911-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/28/2021] [Indexed: 02/06/2023]
Abstract
A considerable number of studies have attempted to account for the psychotic aspects of schizophrenia in terms of the influential predictive coding (PC) hypothesis. We argue that the prediction-oriented perspective on schizophrenia-related psychosis may benefit from a mechanistic model that: 1) gives due weight to the extent to which alterations in short- and long-term synaptic plasticity determine the degree and the direction of the functional disruption that occurs in psychosis; and 2) addresses the distinction between the two central syndromes of psychosis in schizophrenia: disorganization and reality-distortion. To accomplish these goals, we propose the Imbalanced Plasticity Hypothesis - IPH, and demonstrate that it: 1) accounts for commonalities and differences between disorganization and reality distortion in terms of excessive (hyper) or insufficient (hypo) neuroplasticity, respectively; 2) provides distinct predictions in the cognitive and electrophysiological domains; and 3) is able to reconcile conflicting PC-oriented accounts of psychosis.
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Calhoun VD, Pearlson GD, Sui J. Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples. Curr Opin Neurol 2021; 34:469-479. [PMID: 34054110 PMCID: PMC8263510 DOI: 10.1097/wco.0000000000000967] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
PURPOSE OF REVIEW The 'holy grail' of clinical applications of neuroimaging to neurological and psychiatric disorders via personalized biomarkers has remained mostly elusive, despite considerable effort. However, there are many reasons to continue to be hopeful, as the field has made remarkable advances over the past few years, fueled by a variety of converging technical and data developments. RECENT FINDINGS We discuss a number of advances that are accelerating the push for neuroimaging biomarkers including the advent of the 'neuroscience big data' era, biomarker data competitions, the development of more sophisticated algorithms including 'guided' data-driven approaches that facilitate automation of network-based analyses, dynamic connectivity, and deep learning. Another key advance includes multimodal data fusion approaches which can provide convergent and complementary evidence pointing to possible mechanisms as well as increase predictive accuracy. SUMMARY The search for clinically relevant neuroimaging biomarkers for neurological and psychiatric disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent examples from studies in our group, and link to other ongoing work in the field. It is critical that access and use of these advanced approaches becomes mainstream, this will help propel the community forward and facilitate the production of robust and replicable neuroimaging biomarkers.
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Affiliation(s)
- 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, Georgia
| | - Godfrey D Pearlson
- Department of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
- Institute of Automation, Chinese Academy of Sciences, and the University of Chinese Academy of Sciences, Beijing, China
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Ji JL, Helmer M, Fonteneau C, Burt JB, Tamayo Z, Demšar J, Adkinson BD, Savić A, Preller KH, Moujaes F, Vollenweider FX, Martin WJ, Repovš G, Cho YT, Pittenger C, Murray JD, Anticevic A. Mapping brain-behavior space relationships along the psychosis spectrum. eLife 2021; 10:e66968. [PMID: 34313219 PMCID: PMC8315806 DOI: 10.7554/elife.66968] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/14/2021] [Indexed: 12/29/2022] Open
Abstract
Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 PSD patients spanning several diagnoses, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results with current sample sizes. However, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and neural gene expression maps derived from the Allen Human Brain Atlas. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale University School of MedicineNew HavenUnited States
| | - Markus Helmer
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | | | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Jure Demšar
- Department of Psychology, University of LjubljanaLjubljanaSlovenia
- Faculty of Computer and Information Science, University of LjubljanaLjubljanaSlovenia
| | - Brendan D Adkinson
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale University School of MedicineNew HavenUnited States
| | | | - Katrin H Preller
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Flora Moujaes
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Franz X Vollenweider
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - William J Martin
- The Janssen Pharmaceutical Companies of Johnson and JohnsonSan FranciscoUnited States
| | - Grega Repovš
- Department of Psychiatry, University of ZagrebZagrebCroatia
| | - Youngsun T Cho
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Child Study Center, Yale University School of MedicineNew HavenUnited States
| | - Christopher Pittenger
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Child Study Center, Yale University School of MedicineNew HavenUnited States
| | - John D Murray
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale University School of MedicineNew HavenUnited States
- Department of Physics, Yale UniversityNew HavenUnited States
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale University School of MedicineNew HavenUnited States
- Department of Psychology, Yale University School of MedicineNew HavenUnited States
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Liu X, Eickhoff SB, Caspers S, Wu J, Genon S, Hoffstaedter F, Mars RB, Sommer IE, Eickhoff CR, Chen J, Jardri R, Reetz K, Dogan I, Aleman A, Kogler L, Gruber O, Caspers J, Mathys C, Patil KR. Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate. Neuroimage 2021; 235:118006. [PMID: 33819611 PMCID: PMC8214073 DOI: 10.1016/j.neuroimage.2021.118006] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 02/10/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.
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Affiliation(s)
- Xiaojin Liu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jianxiao Wu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Ji Chen
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Renaud Jardri
- Division of Psychiatry, University of Lille, CNRS UMR9193, SCALab & CHU Lille, Fontan Hospital, CURE platform, Lille, France
| | - Kathrin Reetz
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Imis Dogan
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - André Aleman
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Lydia Kogler
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany
| | - Julian Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Christian Mathys
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany; Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, Oldenburg, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany.
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Ye H, Zalesky A, Lv J, Loi SM, Cetin-Karayumak S, Rathi Y, Tian Y, Pantelis C, Di Biase MA. Network Analysis of Symptom Comorbidity in Schizophrenia: Relationship to Illness Course and Brain White Matter Microstructure. Schizophr Bull 2021; 47:1156-1167. [PMID: 33693887 PMCID: PMC8266579 DOI: 10.1093/schbul/sbab015] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
INTRODUCTION Recent network-based analyses suggest that schizophrenia symptoms are intricately connected and interdependent, such that central symptoms can activate adjacent symptoms and increase global symptom burden. Here, we sought to identify key clinical and neurobiological factors that relate to symptom organization in established schizophrenia. METHODS A symptom comorbidity network was mapped for a broad constellation of symptoms measured in 642 individuals with a schizophrenia-spectrum disorder. Centrality analyses were used to identify hub symptoms. The extent to which each patient's symptoms formed clusters in the comorbidity network was quantified with cluster analysis and used to predict (1) clinical features, including illness duration and psychosis (positive symptom) severity and (2) brain white matter microstructure, indexed by the fractional anisotropy (FA), in a subset (n = 296) of individuals with diffusion-weighted imaging (DWI) data. RESULTS Global functioning, substance use, and blunted affect were the most central symptoms within the symptom comorbidity network. Symptom profiles for some patients formed highly interconnected clusters, whereas other patients displayed unrelated and disconnected symptoms. Stronger clustering among an individual's symptoms was significantly associated with shorter illness duration (t = 2.7; P = .0074), greater psychosis severity (ie, positive symptoms expression) (t = -5.5; P < 0.0001) and lower fractional anisotropy in fibers traversing the cortico-cerebellar-thalamic-cortical circuit (r = .59, P < 0.05). CONCLUSION Symptom network structure varies over the course of schizophrenia: symptom interactions weaken with increasing illness duration and strengthen during periods of high positive symptom expression. Reduced white matter coherence relates to stronger symptom clustering, and thus, may underlie symptom cascades and global symptomatic burden in individuals with schizophrenia.
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Affiliation(s)
- Hua Ye
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Samantha M Loi
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | | | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Ye Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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50
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Wu XS, Yan TC, Wang XY, Cao Y, Liu XF, Fu YF, Wu L, Jin YC, Yin H, Cui LB. Magnetic Resonance Imaging-Guided and Navigated Individualized Repetitive Transcranial Magnetic Stimulation for Cognitive Impairment in Schizophrenia. Neurosci Bull 2021; 37:1365-1369. [PMID: 34145549 DOI: 10.1007/s12264-021-00727-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 04/12/2021] [Indexed: 12/21/2022] Open
Affiliation(s)
- Xu-Sha Wu
- School of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712046, China
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, 710032, China
| | - Tian-Cai Yan
- School of Medical Imaging, Mudanjiang Medical University, Mudanjiang, 157011, China
| | - Xian-Yang Wang
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, 710032, China
| | - Yang Cao
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, 710032, China
| | - Xiao-Fan Liu
- School of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712046, China
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, 710032, China
| | - Yu-Fei Fu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China
| | - Lin Wu
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, 710032, China
| | - Yin-Chuan Jin
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, 710032, China
| | - Hong Yin
- School of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712046, China.
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, 710032, China.
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.
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