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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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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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Tang SX, Cong Y, Nikzad AH, Mehta A, Cho S, Hänsel K, Berretta S, Dhar AA, Kane JM, Malhotra AK. Clinical and computational speech measures are associated with social cognition in schizophrenia spectrum disorders. Schizophr Res 2023; 259:28-37. [PMID: 35835710 DOI: 10.1016/j.schres.2022.06.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 12/15/2022]
Abstract
In this study, we compared three domains of social cognition (emotion processing, mentalizing, and attribution bias) to clinical and computational language measures in 63 participants with schizophrenia spectrum disorders. Based on the active inference model for discourse, we hypothesized that emotion processing and mentalizing, but not attribution bias, would be related to language disturbances. Clinical ratings for speech disturbance assessed disorganized and underproductive dimensions. Computational features included speech graph metrics, use of modal verbs, use of first-person pronouns, cosine similarity of adjacent utterances, and measures of sentiment; these were represented by four principal components. We found that higher clinical ratings for disorganized speech were predicted by greater impairments in both emotion processing and mentalizing, and that these relationships remained significant when accounting for demographic variables, overall psychosis symptoms, and verbal ability. Similarly, a computational speech component reflecting insular speech was consistently predicted by impairment in emotion processing. There were notable trends for computational speech components reflecting underproductive speech and decreased content-rich speech predicting mentalizing ability. Exploratory longitudinal analyses in a small subset of participants (n = 17) found that improvements in both emotion processing and mentalizing predicted improvements in disorganized speech. Attribution bias did not demonstrate strong relationships with language measures. Altogether, our findings are consistent with the active inference model of discourse and suggest greater emphasis on treatments that target social cognitive and language systems.
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Affiliation(s)
- Sunny X Tang
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Yan Cong
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Amir H Nikzad
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Aarush Mehta
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Sunghye Cho
- University of Pennsylvania, Linguistic Data Consortium, 3600 Market St., Suite 810, Philadelphia, PA 19104, United States of America.
| | - Katrin Hänsel
- Yale University, Department of Laboratory Medicine, 195 Church Street, New Haven, CT 06510, United States of America.
| | - Sarah Berretta
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Aamina A Dhar
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America
| | - John M Kane
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
| | - Anil K Malhotra
- Zucker Hillside Hospital, Department of Psychiatry, Feinstein Institutes for Medical Research, 75-59 263rd St., Glen Oaks, NY 11004, United States of America.
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4
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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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5
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Palaniyappan L, Homan P, Alonso-Sanchez MF. Language Network Dysfunction and Formal Thought Disorder in Schizophrenia. Schizophr Bull 2023; 49:486-497. [PMID: 36305160 PMCID: PMC10016399 DOI: 10.1093/schbul/sbac159] [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: 11/13/2022]
Abstract
BACKGROUND Pathophysiological inquiries into schizophrenia require a consideration of one of its most defining features: disorganization and impoverishment in verbal behavior. This feature, often captured using the term Formal Thought Disorder (FTD), still remains to be one of the most poorly understood and understudied dimensions of schizophrenia. In particular, the large-scale network level dysfunction that contributes to FTD remains obscure to date. STUDY DESIGN In this narrative review, we consider the various challenges that need to be addressed for us to move towards mapping FTD (construct) to a brain network level account (circuit). STUDY RESULTS The construct-to-circuit mapping goal is now becoming more plausible than it ever was, given the parallel advent of brain stimulation and the tools providing objective readouts of human speech. Notwithstanding this, several challenges remain to be overcome before we can decisively map the neural basis of FTD. We highlight the need for phenotype refinement, robust experimental designs, informed analytical choices, and present plausible targets in and beyond the Language Network for brain stimulation studies in FTD. CONCLUSIONS Developing a therapeutically beneficial pathophysiological model of FTD is a challenging endeavor, but holds the promise of improving interpersonal communication and reducing social disability in schizophrenia. Addressing the issues raised in this review will be a decisive step in this direction.
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Affiliation(s)
- Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Western University, London, Canada
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital of the University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
| | - Maria F Alonso-Sanchez
- Robarts Research Institute, Western University, London, Ontario, Canada
- CIDCL, Fonoaudiología, Facultad de Medicina, Universidad de Valparaíso, Valparaiso, Chile
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6
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Yang L, Du Y, Yang W, Liu J. Machine learning with neuroimaging biomarkers: Application in the diagnosis and prediction of drug addiction. Addict Biol 2023; 28:e13267. [PMID: 36692873 DOI: 10.1111/adb.13267] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/19/2022] [Accepted: 12/14/2022] [Indexed: 01/18/2023]
Abstract
Drug abuse is a serious problem worldwide. Owing to intermittent intake of certain substances and the early inconspicuous clinical symptoms, this brings huge challenges for timely diagnosing addiction status and preventing substance use disorders (SUDs). As a non-invasive technique, neuroimaging can capture neurobiological signatures of abnormality in multiple brain regions caused by drug consumption in each clinical stage, like parenchymal morphology alteration as well as aberrant functional activity and connectivity of cerebral areas, making it realizable to diagnosis, prediction and even preemptive therapy of addiction. Machine learning (ML) algorithms primarily used for classification have been extensively applied in analysing medical imaging datasets. Significant neurobiological characteristics employed and revealed by classifiers were used to diagnose addictive states and predict initiation and vulnerability to drug usage, treatment abstinence, relapse and resilience of addicts and the risk of SUD. In this review, we summarize application of ML methods in neuroimaging focusing on addicts' diagnosis of clinical status and risk prediction and elucidate the discriminative neurobiological features from brain electrophysiological, morphological and functional perspectives that contribute most to the classifier, finally highlighting the auxiliary role of ML in addiction treatment.
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Affiliation(s)
- Longtao Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yanyao Du
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Wenhan Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China.,Department of Radiology Quality Control Center in Hunan Province, Changsha, China
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7
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Chang X, Zhao W, Kang J, Xiang S, Xie C, Corona-Hernández H, Palaniyappan L, Feng J. Language abnormalities in schizophrenia: binding core symptoms through contemporary empirical evidence. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:95. [PMID: 36371445 PMCID: PMC9653408 DOI: 10.1038/s41537-022-00308-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Both the ability to speak and to infer complex linguistic messages from sounds have been claimed as uniquely human phenomena. In schizophrenia, formal thought disorder (FTD) and auditory verbal hallucinations (AVHs) are manifestations respectively relating to concrete disruptions of those abilities. From an evolutionary perspective, Crow (1997) proposed that "schizophrenia is the price that Homo sapiens pays for the faculty of language". Epidemiological and experimental evidence points to an overlap between FTD and AVHs, yet a thorough investigation examining their shared neural mechanism in schizophrenia is lacking. In this review, we synthesize observations from three key domains. First, neuroanatomical evidence indicates substantial shared abnormalities in language-processing regions between FTD and AVHs, even in the early phases of schizophrenia. Second, neurochemical studies point to a glutamate-related dysfunction in these language-processing brain regions, contributing to verbal production deficits. Third, genetic findings further show how genes that overlap between schizophrenia and language disorders influence neurodevelopment and neurotransmission. We argue that these observations converge into the possibility that a glutamatergic dysfunction in language-processing brain regions might be a shared neural basis of both FTD and AVHs. Investigations of language pathology in schizophrenia could facilitate the development of diagnostic tools and treatments, so we call for multilevel confirmatory analyses focused on modulations of the language network as a therapeutic goal in schizophrenia.
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Affiliation(s)
- Xiao Chang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Hugo Corona-Hernández
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada.
- Lawson Health Research Institute, London, Ontario, Canada.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Shanghai Center for Mathematical Sciences, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
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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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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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10
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Sun Y, Zhang Z, Kakkos I, Matsopoulos GK, Yuan J, Suckling J, Xu L, Cao S, Chen W, Hu X, Li T, Sim K, Qi P, Sun Y. Inferring the Individual Psychopathologic Deficits with Structural Connectivity in a Longitudinal Cohort of Schizophrenia. IEEE J Biomed Health Inform 2022; 26:2536-2546. [PMID: 34982705 DOI: 10.1109/jbhi.2021.3139701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis.
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11
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Omelchenko MA, Arutyunova EV, Butashin AD, Ivanova EM. [Testing of the Russian-language version of the Thought, Language and Communication Scale]. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:116-120. [PMID: 36279237 DOI: 10.17116/jnevro2022122101116] [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] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To analyze the possibilities of using the Russian version of the Thought, Language and Communication Scale (TLC) for early recognition of thought disorders in patients at clinical high-risk for schizophrenia. MATERIAL AND METHODS For the main group, the study included 30 adolescent male patients (19.2±2.2 years) hospitalized with the first depressive episode (ICD-10: F32.1, F32.2, F32.28, F32.8), who demonstrated attenuated schizophrenic symptoms (ASS) in the structure of the depression, which made it possible to attribute the patients to the group of clinical high-risk for schizophrenia. The control group consisted of 27 mentally healthy adolescent males (20.0±2.3 years). In both groups, the severity of thought impairment was assessed using the TLC scale. Psychopathological, psychometric and statistical methods were used. RESULTS The median values of the severity of thought impairment using the TLC scale were 20 points [19.75; 26] in the main group, 10.5 points [9.25; 13] in the control group, with a high degree of statistical significance (p<0.001). The most significant differences (p<0.001) were found in following parameters: Incoherence (2 [1; 3] vs 1 [0; 1]), Tangentiality (2 [2; 2] vs 1 [0; 2]), Derailment (2 [1.25; 2] vs 1 [0.5; 2]), Illogical thinking (2 [2; 2.75] vs 0 [0; 1]), Loss of goal (1 [0; 2] vs 0 [0; 0]) and Blocking (1 [0; 1] vs 0 [0; 0] accordingly). CONCLUSION Specific, not related to depression, disorders of thinking in patients of the clinical group, which indicates signs of disorganization of thinking and suggests the beginning of the endogenous process of the schizophrenic pole were found. The results show that the TLC scale can be used to detect early cognitive disorders in patients at risk of schizophrenia.
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Affiliation(s)
| | | | - A D Butashin
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - E M Ivanova
- Pirogov Russian National Research Medical University, Moscow, Russia
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12
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Liu Y, Pu C, Xia S, Deng D, Wang X, Li M. Machine learning approaches for diagnosing depression using EEG: A review. Transl Neurosci 2022; 13:224-235. [PMID: 36045698 PMCID: PMC9375981 DOI: 10.1515/tnsci-2022-0234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/18/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022] Open
Abstract
Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.
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Affiliation(s)
- Yuan Liu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
| | - Changqin Pu
- Queen Mary College, Nanchang University, Nanchang 330031, Jiangxi Province, China
| | - Shan Xia
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
| | - Dingyu Deng
- Department of Internal Neurology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Xing Wang
- School of Life Sciences, Nanchang University, No.999 Xuefu Avenue, Honggutan District, Nanchang 330036, Jiangxi Province, China.,Clinical Diagnostics Laboratory, Clinical Medical Experiment Center, Nanchang University, Nanchang 330036, China
| | - Mengqian Li
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
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13
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Palaniyappan L. Dissecting the neurobiology of linguistic disorganisation and impoverishment in schizophrenia. Semin Cell Dev Biol 2021; 129:47-60. [PMID: 34507903 DOI: 10.1016/j.semcdb.2021.08.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 08/13/2021] [Accepted: 05/06/2021] [Indexed: 12/16/2022]
Abstract
Schizophrenia provides a quintessential disease model of how disturbances in the molecular mechanisms of neurodevelopment lead to disruptions in the emergence of cognition. The central and often persistent feature of this illness is the disorganisation and impoverishment of language and related expressive behaviours. Though clinically more prominent, the periodic perceptual distortions characterised as psychosis are non-specific and often episodic. While several insights into psychosis have been gained based on study of the dopaminergic system, the mechanistic basis of linguistic disorganisation and impoverishment is still elusive. Key findings from cellular to systems-level studies highlight the role of ubiquitous, inhibitory processes in language production. Dysregulation of these processes at critical time periods, in key brain areas, provides a surprisingly parsimonious account of linguistic disorganisation and impoverishment in schizophrenia. This review links the notion of excitatory/inhibitory (E/I) imbalance at cortical microcircuits to the expression of language behaviour characteristic of schizophrenia, through the building blocks of neurochemistry, neurophysiology, and neurocognition.
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Affiliation(s)
- Lena Palaniyappan
- Department of Psychiatry,University of Western Ontario, London, Ontario, Canada; Robarts Research Institute,University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada.
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