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Smucny J, Wylie KP, Lesh TA, Carter CS, Tregellas JR. Whole-brain intrinsic functional connectivity predicts symptoms and functioning in early psychosis. J Psychiatr Res 2024; 175:411-417. [PMID: 38781675 PMCID: PMC11374471 DOI: 10.1016/j.jpsychires.2024.05.042] [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: 04/04/2024] [Revised: 05/13/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
Theories of psychotic illness suggest that abnormal intrinsic functional connectivity may explain its characteristic positive and disorganization symptoms as well as lead to impaired general functioning. Here we used resting state functional magnetic resonance imaging (fMRI) to evaluate associations between these symptoms and the degree to which global connectivity is abnormal in early psychosis (EP). Eighty-six healthy controls (HCs) and 108 individuals with EP with resting state fMRI data were included in primary analyses. The EP group included 83 participants with schizophrenia-spectrum disorders and 25 with bipolar disorder type I with psychotic features. A global intrinsic connectivity "similarity index" for each EP individual was determined by calculating its correlation with the average HC connectivity matrix extracted using Schaefer atlases of multiple parcellations (100, 200, 300, and 400 region parcellations). As hypothesized, connectivity similarity with the average HC matrix was negatively associated with Brief Psychiatric Rating Scale total score, Scale for the Assessment of Positive Symptoms total score, and disorganization symptoms. Similarity was also positively associated with Global Assessment of Functioning score. Results were not driven by sex or diagnosis effects and were consistent across parcellation schemes. These results support the hypothesis that changes in whole-brain connectivity patterns are associated with psychosis symptoms and support the use of functional connectivity as a biomarker for these symptoms in EP.
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Affiliation(s)
- Jason Smucny
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, USA.
| | - Korey P Wylie
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, USA
| | - Tyler A Lesh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, USA
| | - Cameron S Carter
- Department of Psychiatry and Human Behavior, University of California, Irvine, USA
| | - Jason R Tregellas
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, USA; Research Service, Rocky Mountain Regional VA Medical Center, USA
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Cattarinussi G, Di Camillo F, Grimaldi DA, Sambataro F. Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01838-4. [PMID: 38914853 DOI: 10.1007/s00406-024-01838-4] [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: 02/12/2024] [Accepted: 06/03/2024] [Indexed: 06/26/2024]
Abstract
Schizophrenia (SCZ) and bipolar disorders (BD) show significant neurobiological and clinical overlap. In this study, we wanted to identify indexes of intrinsic brain activity that could differentiate these disorders. We compared the diagnostic value of the fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) estimated from resting-state functional magnetic resonance imaging in a support vector machine classification of 59 healthy controls (HC), 40 individuals with SCZ, and 43 individuals with BD type I. The best performance, measured by balanced accuracy (BAC) for binary classification relative to HC was achieved by a stacking model (87.4% and 90.6% for SCZ and BD, respectively), with ReHo performing better than fALFF, both in SCZ (86.2% vs. 79.4%) and BD (89.9% vs. 76.9%). BD were better differentiated from HC by fronto-temporal ReHo and striato-temporo-thalamic fALFF. SCZ were better classified from HC using fronto-temporal-cerebellar ReHo and insulo-tempo-parietal-cerebellar fALFF. In conclusion, we provided evidence of widespread aberrancies of spontaneous activity and local connectivity in SCZ and BD, demonstrating that ReHo features exhibited superior discriminatory power compared to fALFF and achieved higher classification accuracies. Our results support the complementarity of these measures in the classification of SCZ and BD and suggest the potential for multivariate integration to improve diagnostic precision.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), Padova Neuroscience Center (PNC), University of Padova, Azienda Ospedaliera di Padova, Via Giustiniani, 2, Padua, I-35128, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fabio Di Camillo
- Department of Neuroscience (DNS), Padova Neuroscience Center (PNC), University of Padova, Azienda Ospedaliera di Padova, Via Giustiniani, 2, Padua, I-35128, Italy
| | - David Antonio Grimaldi
- Department of Neuroscience (DNS), Padova Neuroscience Center (PNC), University of Padova, Azienda Ospedaliera di Padova, Via Giustiniani, 2, Padua, I-35128, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), Padova Neuroscience Center (PNC), University of Padova, Azienda Ospedaliera di Padova, Via Giustiniani, 2, Padua, I-35128, Italy.
- Padova Neuroscience Center, University of Padova, Padua, Italy.
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3
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Hagihara H, Shoji H, Hattori S, Sala G, Takamiya Y, Tanaka M, Ihara M, Shibutani M, Hatada I, Hori K, Hoshino M, Nakao A, Mori Y, Okabe S, Matsushita M, Urbach A, Katayama Y, Matsumoto A, Nakayama KI, Katori S, Sato T, Iwasato T, Nakamura H, Goshima Y, Raveau M, Tatsukawa T, Yamakawa K, Takahashi N, Kasai H, Inazawa J, Nobuhisa I, Kagawa T, Taga T, Darwish M, Nishizono H, Takao K, Sapkota K, Nakazawa K, Takagi T, Fujisawa H, Sugimura Y, Yamanishi K, Rajagopal L, Hannah ND, Meltzer HY, Yamamoto T, Wakatsuki S, Araki T, Tabuchi K, Numakawa T, Kunugi H, Huang FL, Hayata-Takano A, Hashimoto H, Tamada K, Takumi T, Kasahara T, Kato T, Graef IA, Crabtree GR, Asaoka N, Hatakama H, Kaneko S, Kohno T, Hattori M, Hoshiba Y, Miyake R, Obi-Nagata K, Hayashi-Takagi A, Becker LJ, Yalcin I, Hagino Y, Kotajima-Murakami H, Moriya Y, Ikeda K, Kim H, Kaang BK, Otabi H, Yoshida Y, Toyoda A, Komiyama NH, Grant SGN, Ida-Eto M, Narita M, Matsumoto KI, Okuda-Ashitaka E, Ohmori I, Shimada T, Yamagata K, Ageta H, Tsuchida K, Inokuchi K, Sassa T, Kihara A, Fukasawa M, Usuda N, Katano T, Tanaka T, Yoshihara Y, Igarashi M, Hayashi T, Ishikawa K, Yamamoto S, Nishimura N, Nakada K, Hirotsune S, Egawa K, Higashisaka K, Tsutsumi Y, Nishihara S, Sugo N, Yagi T, Ueno N, Yamamoto T, Kubo Y, Ohashi R, Shiina N, Shimizu K, Higo-Yamamoto S, Oishi K, Mori H, Furuse T, Tamura M, Shirakawa H, Sato DX, Inoue YU, Inoue T, Komine Y, Yamamori T, Sakimura K, Miyakawa T. Large-scale animal model study uncovers altered brain pH and lactate levels as a transdiagnostic endophenotype of neuropsychiatric disorders involving cognitive impairment. eLife 2024; 12:RP89376. [PMID: 38529532 DOI: 10.7554/elife.89376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024] Open
Abstract
Increased levels of lactate, an end-product of glycolysis, have been proposed as a potential surrogate marker for metabolic changes during neuronal excitation. These changes in lactate levels can result in decreased brain pH, which has been implicated in patients with various neuropsychiatric disorders. We previously demonstrated that such alterations are commonly observed in five mouse models of schizophrenia, bipolar disorder, and autism, suggesting a shared endophenotype among these disorders rather than mere artifacts due to medications or agonal state. However, there is still limited research on this phenomenon in animal models, leaving its generality across other disease animal models uncertain. Moreover, the association between changes in brain lactate levels and specific behavioral abnormalities remains unclear. To address these gaps, the International Brain pH Project Consortium investigated brain pH and lactate levels in 109 strains/conditions of 2294 animals with genetic and other experimental manipulations relevant to neuropsychiatric disorders. Systematic analysis revealed that decreased brain pH and increased lactate levels were common features observed in multiple models of depression, epilepsy, Alzheimer's disease, and some additional schizophrenia models. While certain autism models also exhibited decreased pH and increased lactate levels, others showed the opposite pattern, potentially reflecting subpopulations within the autism spectrum. Furthermore, utilizing large-scale behavioral test battery, a multivariate cross-validated prediction analysis demonstrated that poor working memory performance was predominantly associated with increased brain lactate levels. Importantly, this association was confirmed in an independent cohort of animal models. Collectively, these findings suggest that altered brain pH and lactate levels, which could be attributed to dysregulated excitation/inhibition balance, may serve as transdiagnostic endophenotypes of debilitating neuropsychiatric disorders characterized by cognitive impairment, irrespective of their beneficial or detrimental nature.
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Affiliation(s)
- Hideo Hagihara
- Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
| | - Hirotaka Shoji
- Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
| | - Satoko Hattori
- Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
| | - Giovanni Sala
- Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
| | - Yoshihiro Takamiya
- Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
| | - Mika Tanaka
- Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
| | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Mihiro Shibutani
- Laboratory of Genome Science, Biosignal Genome Resource Center, Institute for Molecular and Cellular Regulation, Gunma University, Maebashi, Japan
| | - Izuho Hatada
- Laboratory of Genome Science, Biosignal Genome Resource Center, Institute for Molecular and Cellular Regulation, Gunma University, Maebashi, Japan
| | - Kei Hori
- Department of Biochemistry and Cellular Biology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Mikio Hoshino
- Department of Biochemistry and Cellular Biology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Akito Nakao
- Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan
| | - Yasuo Mori
- Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan
| | - Shigeo Okabe
- Department of Cellular Neurobiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Masayuki Matsushita
- Department of Molecular Cellular Physiology, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
| | - Anja Urbach
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Yuta Katayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Akinobu Matsumoto
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Keiichi I Nakayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Shota Katori
- Laboratory of Mammalian Neural Circuits, National Institute of Genetics, Mishima, Japan
| | - Takuya Sato
- Laboratory of Mammalian Neural Circuits, National Institute of Genetics, Mishima, Japan
| | - Takuji Iwasato
- Laboratory of Mammalian Neural Circuits, National Institute of Genetics, Mishima, Japan
| | - Haruko Nakamura
- Department of Molecular Pharmacology and Neurobiology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Yoshio Goshima
- Department of Molecular Pharmacology and Neurobiology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Matthieu Raveau
- Laboratory for Neurogenetics, RIKEN Center for Brain Science, Wako, Japan
| | - Tetsuya Tatsukawa
- Laboratory for Neurogenetics, RIKEN Center for Brain Science, Wako, Japan
| | - Kazuhiro Yamakawa
- Laboratory for Neurogenetics, RIKEN Center for Brain Science, Wako, Japan
- Department of Neurodevelopmental Disorder Genetics, Institute of Brain Sciences, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Noriko Takahashi
- Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Physiology, Kitasato University School of Medicine, Sagamihara, Japan
| | - Haruo Kasai
- Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan
| | - Johji Inazawa
- Research Core, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ikuo Nobuhisa
- Department of Stem Cell Regulation, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tetsushi Kagawa
- Department of Stem Cell Regulation, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tetsuya Taga
- Department of Stem Cell Regulation, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mohamed Darwish
- Department of Biochemistry, Faculty of Pharmacy, Cairo University, Cairo, Egypt
- Department of Behavioral Physiology, Graduate School of Innovative Life Science, University of Toyama, Toyama, Japan
| | | | - Keizo Takao
- Department of Behavioral Physiology, Graduate School of Innovative Life Science, University of Toyama, Toyama, Japan
- Department of Behavioral Physiology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Kiran Sapkota
- Department of Neuroscience, Southern Research, Birmingham, United States
| | - Kazutoshi Nakazawa
- Department of Neuroscience, Southern Research, Birmingham, United States
| | - Tsuyoshi Takagi
- Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
| | - Haruki Fujisawa
- Department of Endocrinology, Diabetes and Metabolism, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Yoshihisa Sugimura
- Department of Endocrinology, Diabetes and Metabolism, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Kyosuke Yamanishi
- Department of Neuropsychiatry, Hyogo Medical University School of Medicine, Nishinomiya, Japan
| | - Lakshmi Rajagopal
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, United States
| | - Nanette Deneen Hannah
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, United States
| | - Herbert Y Meltzer
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, United States
| | - Tohru Yamamoto
- Department of Molecular Neurobiology, Faculty of Medicine, Kagawa University, Kita-gun, Japan
| | - Shuji Wakatsuki
- Department of Peripheral Nervous System Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Toshiyuki Araki
- Department of Peripheral Nervous System Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Katsuhiko Tabuchi
- Department of Molecular & Cellular Physiology, Shinshu University School of Medicine, Matsumoto, Japan
| | - Tadahiro Numakawa
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Hiroshi Kunugi
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
- Department of Psychiatry, Teikyo University School of Medicine, Tokyo, Japan
| | - Freesia L Huang
- Program of Developmental Neurobiology, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
| | - Atsuko Hayata-Takano
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Japan
- Department of Pharmacology, Graduate School of Dentistry, Osaka University, Suita, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Suita, Japan
| | - Hitoshi Hashimoto
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Suita, Japan
- Division of Bioscience, Institute for Datability Science, Osaka University, Suita, Japan
- Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Department of Molecular Pharmaceutical Science, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Kota Tamada
- RIKEN Brain Science Institute, Wako, Japan
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Kobe, Japan
| | - Toru Takumi
- RIKEN Brain Science Institute, Wako, Japan
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Kobe, Japan
| | - Takaoki Kasahara
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Wako, Japan
- Institute of Biology and Environmental Sciences, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
| | - Tadafumi Kato
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Wako, Japan
- Department of Psychiatry and Behavioral Science, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Isabella A Graef
- Department of Pathology, Stanford University School of Medicine, Stanford, United States
| | - Gerald R Crabtree
- Department of Pathology, Stanford University School of Medicine, Stanford, United States
| | - Nozomi Asaoka
- Department of Pharmacology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hikari Hatakama
- Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Shuji Kaneko
- Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Takao Kohno
- Department of Biomedical Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya, Japan
| | - Mitsuharu Hattori
- Department of Biomedical Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya, Japan
| | - Yoshio Hoshiba
- Laboratory of Medical Neuroscience, Institute for Molecular and Cellular Regulation, Gunma University, Maebashi, Japan
| | - Ryuhei Miyake
- Laboratory for Multi-scale Biological Psychiatry, RIKEN Center for Brain Science, Wako, Japan
| | - Kisho Obi-Nagata
- Laboratory for Multi-scale Biological Psychiatry, RIKEN Center for Brain Science, Wako, Japan
| | - Akiko Hayashi-Takagi
- Laboratory of Medical Neuroscience, Institute for Molecular and Cellular Regulation, Gunma University, Maebashi, Japan
- Laboratory for Multi-scale Biological Psychiatry, RIKEN Center for Brain Science, Wako, Japan
| | - Léa J Becker
- Institut des Neurosciences Cellulaires et Intégratives, Centre National de la Recherche Scientifique, Université de Strasbourg, Strasbourg, France
| | - Ipek Yalcin
- Institut des Neurosciences Cellulaires et Intégratives, Centre National de la Recherche Scientifique, Université de Strasbourg, Strasbourg, France
| | - Yoko Hagino
- Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | | | - Yuki Moriya
- Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Kazutaka Ikeda
- Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Hyopil Kim
- Department of Biological Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States
| | - Bong-Kiun Kaang
- Department of Biological Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
- Center for Cognition and Sociality, Institute for Basic Science (IBS), Daejeon, Republic of Korea
| | - Hikari Otabi
- College of Agriculture, Ibaraki University, Ami, Japan
- United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, Fuchu, Japan
| | - Yuta Yoshida
- College of Agriculture, Ibaraki University, Ami, Japan
| | - Atsushi Toyoda
- College of Agriculture, Ibaraki University, Ami, Japan
- United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, Fuchu, Japan
- Ibaraki University Cooperation between Agriculture and Medical Science (IUCAM), Ibaraki, Japan
| | - Noboru H Komiyama
- Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Simons Initiative for the Developing Brain, Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Seth G N Grant
- Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Simons Initiative for the Developing Brain, Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Michiru Ida-Eto
- Department of Developmental and Regenerative Medicine, Mie University, Graduate School of Medicine, Tsu, Japan
| | - Masaaki Narita
- Department of Developmental and Regenerative Medicine, Mie University, Graduate School of Medicine, Tsu, Japan
| | - Ken-Ichi Matsumoto
- Department of Biosignaling and Radioisotope Experiment, Interdisciplinary Center for Science Research, Organization for Research and Academic Information, Shimane University, Izumo, Japan
| | - Emiko Okuda-Ashitaka
- Department of Biomedical Engineering, Osaka Institute of Technology, Osaka, Japan
| | - Iori Ohmori
- Department of Physiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Tadayuki Shimada
- Child Brain Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Kanato Yamagata
- Child Brain Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Hiroshi Ageta
- Division for Therapies Against Intractable Diseases, Center for Medical Science, Fujita Health University, Toyoake, Japan
| | - Kunihiro Tsuchida
- Division for Therapies Against Intractable Diseases, Center for Medical Science, Fujita Health University, Toyoake, Japan
| | - Kaoru Inokuchi
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
- Department of Biochemistry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
- Core Research for Evolutionary Science and Technology (CREST), Japan Science and Technology Agency (JST), University of Toyama, Toyama, Japan
| | - Takayuki Sassa
- Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan
| | - Akio Kihara
- Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan
| | - Motoaki Fukasawa
- Department of Anatomy II, Fujita Health University School of Medicine, Toyoake, Japan
| | - Nobuteru Usuda
- Department of Anatomy II, Fujita Health University School of Medicine, Toyoake, Japan
| | - Tayo Katano
- Department of Medical Chemistry, Kansai Medical University, Hirakata, Japan
| | - Teruyuki Tanaka
- Department of Developmental Medical Sciences, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshihiro Yoshihara
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Japan
| | - Michihiro Igarashi
- Department of Neurochemistry and Molecular Cell Biology, School of Medicine, and Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
- Transdiciplinary Research Program, Niigata University, Niigata, Japan
| | - Takashi Hayashi
- Biomedical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Kaori Ishikawa
- Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Satoshi Yamamoto
- Integrated Technology Research Laboratories, Pharmaceutical Research Division, Takeda Pharmaceutical Company, Ltd, Fujisawa, Japan
| | - Naoya Nishimura
- Integrated Technology Research Laboratories, Pharmaceutical Research Division, Takeda Pharmaceutical Company, Ltd, Fujisawa, Japan
| | - Kazuto Nakada
- Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Shinji Hirotsune
- Department of Genetic Disease Research, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Kiyoshi Egawa
- Department of Pediatrics, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Kazuma Higashisaka
- Laboratory of Toxicology and Safety Science, Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Japan
| | - Yasuo Tsutsumi
- Laboratory of Toxicology and Safety Science, Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Japan
| | - Shoko Nishihara
- Glycan & Life Systems Integration Center (GaLSIC), Soka University, Tokyo, Japan
| | - Noriyuki Sugo
- Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
| | - Takeshi Yagi
- Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
| | - Naoto Ueno
- Laboratory of Morphogenesis, National Institute for Basic Biology, Okazaki, Japan
| | - Tomomi Yamamoto
- Division of Biophysics and Neurobiology, National Institute for Physiological Sciences, Okazaki, Japan
| | - Yoshihiro Kubo
- Division of Biophysics and Neurobiology, National Institute for Physiological Sciences, Okazaki, Japan
| | - Rie Ohashi
- Laboratory of Neuronal Cell Biology, National Institute for Basic Biology, Okazaki, Japan
- Department of Basic Biology, SOKENDAI (Graduate University for Advanced Studies), Okazaki, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Japan
| | - Nobuyuki Shiina
- Laboratory of Neuronal Cell Biology, National Institute for Basic Biology, Okazaki, Japan
- Department of Basic Biology, SOKENDAI (Graduate University for Advanced Studies), Okazaki, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Japan
| | - Kimiko Shimizu
- Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan
| | - Sayaka Higo-Yamamoto
- Healthy Food Science Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Katsutaka Oishi
- Healthy Food Science Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
- Department of Applied Biological Science, Graduate School of Science and Technology, Tokyo University of Science, Noda, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
- School of Integrative and Global Majors (SIGMA), University of Tsukuba, Tsukuba, Japan
| | - Hisashi Mori
- Department of Molecular Neuroscience, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Tamio Furuse
- Mouse Phenotype Analysis Division, Japan Mouse Clinic, RIKEN BioResource Research Center (BRC), Tsukuba, Japan
| | - Masaru Tamura
- Mouse Phenotype Analysis Division, Japan Mouse Clinic, RIKEN BioResource Research Center (BRC), Tsukuba, Japan
| | - Hisashi Shirakawa
- Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Daiki X Sato
- Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Yukiko U Inoue
- Department of Biochemistry and Cellular Biology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Takayoshi Inoue
- Department of Biochemistry and Cellular Biology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Yuriko Komine
- Young Researcher Support Group, Research Enhancement Strategy Office, National Institute for Basic Biology, National Institute of Natural Sciences, Okazaki, Japan
- Division of Brain Biology, National Institute for Basic Biology, Okazaki, Japan
| | - Tetsuo Yamamori
- Division of Brain Biology, National Institute for Basic Biology, Okazaki, Japan
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Japan
| | - Kenji Sakimura
- Department of Cellular Neurobiology, Brain Research Institute, Niigata University, Niigata, Japan
- Department of Animal Model Development, Brain Research Institute, Niigata University, Niigata, Japan
| | - Tsuyoshi Miyakawa
- Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
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Abreu-Mendes P, Dias D, Magno F, Silva G, Rodrigues-Fonseca J, Dinis P, Cruz F, Almeida Pinto R. A Pilot Study of Functional Brain Magnetic Resonance Imaging in BPS/IC Patients: Evidence of Central Sensitization. UROLOGY RESEARCH & PRACTICE 2024; 50:53-57. [PMID: 39115335 PMCID: PMC11059973 DOI: 10.5152/tud.2024.23209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 01/16/2024] [Indexed: 08/12/2024]
Abstract
Bladder pain syndrome/Interstitial cystitis (BPS/IC) is characterized by increased activity in bladder afferent pathways, recruitment of silent nociceptive neurons, and sensitization of the brain areas responsible for pain amplification. Default mode network (DMN) is a set of regions activated during the resting state, which reflect the brain's intrinsic activity. Conversely, the sensorimotor network (SMN) plays a key role in structural neuroplasticity. This study aimed to evaluate DMN and SMN activity in BPS/IC patients, both with and without bladder noxious stimulus, using functional brain magnetic resonance imaging (MRI). Six BPS/IC female patients underwent 3 Tesla fMRI brain scanners. Acquisitions consisted of 10-minute blood oxygen level-dependent echo-planar imaging. The first acquisition was with an empty bladder, painless, and the second was with suprapubic pain. Data were processed using the independent component analysis method with the MELODIC tool from the functional brain MRI of the Brain Software Library (FSL). A semi-quantitative analysis was performed afterward. The patients' age was 42.6 ± 5 years, pain intensity was 7 ± 0.7 (0-10), day and night frequency were 9.2 ± 2.2 and 2.8 ± 1.0, and maximal bladder capacity was 260 ± 54 mL. One patient was unable to complete the study. All patients showed a comparable DMN activation in both empty and full bladder states, and all presented high SMN activation whether the bladder was empty or full. The activation of DMN at both bladder states, empty and full, and constant SMN activation without and with pain supports the role of these networks in BPS/IC. Similar findings have been reported in other chronic pain syndromes.
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Affiliation(s)
- Pedro Abreu-Mendes
- Department of Urology, Centro Hospitalar e Universitário de São João, Porto, Portugal
- Department of Surgery and Physiology, University of Porto, Faculty of Medicine, Porto, Portugal
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
| | - Diogo Dias
- Department of Urology, Centro Hospitalar e Universitário de São João, Porto, Portugal
| | - Francisca Magno
- Department of Gynecology and Obstetrics, Maternidade Alfredo da Costa, Lisbon, Portugal
| | - Guilherme Silva
- Department of Neuroradiology, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - José Rodrigues-Fonseca
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
| | - Paulo Dinis
- Department of Urology, Centro Hospitalar e Universitário de São João, Porto, Portugal
- Department of Surgery and Physiology, University of Porto, Faculty of Medicine, Porto, Portugal
| | - Francisco Cruz
- Department of Urology, Centro Hospitalar e Universitário de São João, Porto, Portugal
- Department of Surgery and Physiology, University of Porto, Faculty of Medicine, Porto, Portugal
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
| | - Rui Almeida Pinto
- Department of Urology, Centro Hospitalar e Universitário de São João, Porto, Portugal
- Department of Surgery and Physiology, University of Porto, Faculty of Medicine, Porto, Portugal
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
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Levi PT, Chopra S, Pang JC, Holmes A, Gajwani M, Sassenberg TA, DeYoung CG, Fornito A. The effect of using group-averaged or individualized brain parcellations when investigating connectome dysfunction in psychosis. Netw Neurosci 2023; 7:1228-1247. [PMID: 38144692 PMCID: PMC10631788 DOI: 10.1162/netn_a_00329] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/27/2023] [Indexed: 12/26/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is widely used to investigate functional coupling (FC) disturbances in a range of clinical disorders. Most analyses performed to date have used group-based parcellations for defining regions of interest (ROIs), in which a single parcellation is applied to each brain. This approach neglects individual differences in brain functional organization and may inaccurately delineate the true borders of functional regions. These inaccuracies could inflate or underestimate group differences in case-control analyses. We investigated how individual differences in brain organization influence group comparisons of FC using psychosis as a case study, drawing on fMRI data in 121 early psychosis patients and 57 controls. We defined FC networks using either a group-based parcellation or an individually tailored variant of the same parcellation. Individualized parcellations yielded more functionally homogeneous ROIs than did group-based parcellations. At the level of individual connections, case-control FC differences were widespread, but the group-based parcellation identified approximately 7.7% more connections as dysfunctional than the individualized parcellation. When considering differences at the level of functional networks, the results from both parcellations converged. Our results suggest that a substantial fraction of dysconnectivity previously observed in psychosis may be driven by the parcellation method, rather than by a pathophysiological process related to psychosis.
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Affiliation(s)
- Priscila T. Levi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - James C. Pang
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Alexander Holmes
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Mehul Gajwani
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | | | - Colin G. DeYoung
- Department of Psychology, University of Minnesota, Minnesota, MN, USA
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
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Danks D, Davis I. Causal inference in cognitive neuroscience. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2023; 14:e1650. [PMID: 37032464 DOI: 10.1002/wcs.1650] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 03/06/2023] [Accepted: 03/21/2023] [Indexed: 04/11/2023]
Abstract
Causal inference is a key step in many research endeavors in cognitive science and neuroscience, and particularly cognitive neuroscience. Statistical knowledge is sufficient for prediction and diagnosis, but causal knowledge is required for action and intervention. Most statistics courses and textbooks emphasize the difficulty of causal inference, focusing on the maxim that "correlation does not mean causation": there can be multiple causal possibilities, often many of them, consistent with given observed statistics. This paper focuses instead on the conceptual issues and assumptions that confront causal and other kinds of inference, primarily focusing on cognitive neuroscience. We connect inference methods with goals and challenges, and provide concrete guidance about how to select appropriate tools for the scientific task. This article is categorized under: Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science.
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Affiliation(s)
- David Danks
- Halicioglu Data Science Institute, Department of Philosophy, University of California San Diego, La Jolla, California, USA
| | - Isaac Davis
- Department of Psychology, Yale University, New Haven, Connecticut, USA
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Seitz-Holland J, Nägele FL, Kubicki M, Pasternak O, Cho KIK, Hough M, Mulert C, Shenton ME, Crow TJ, James ACD, Lyall AE. Shared and distinct white matter abnormalities in adolescent-onset schizophrenia and adolescent-onset psychotic bipolar disorder. Psychol Med 2023; 53:4707-4719. [PMID: 35796024 PMCID: PMC11119277 DOI: 10.1017/s003329172200160x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need. METHODS Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FAT) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL's Randomise. FAT and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence. RESULTS Individuals with adolescent-onset psychosis exhibited pronounced FW and FAT abnormalities compared to HCs. FAT reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FAT was positively related to age. CONCLUSIONS We observe evidence for cellular (FAT) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis.
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Affiliation(s)
- Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Felix L. Nägele
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kang Ik K. Cho
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Morgan Hough
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
- Highfield Unit, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Christoph Mulert
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
- Centre for Psychiatry and Psychotherapy, Justus-Liebig-University, Giessen, Germany
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Timothy J. Crow
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Anthony C. D. James
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
- Highfield Unit, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Xue K, Chen J, Wei Y, Chen Y, Han S, Wang C, Zhang Y, Song X, Cheng J. Impaired large-scale cortico-hippocampal network connectivity, including the anterior temporal and posterior medial systems, and its associations with cognition in patients with first-episode schizophrenia. Front Neurosci 2023; 17:1167942. [PMID: 37342466 PMCID: PMC10277613 DOI: 10.3389/fnins.2023.1167942] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 06/23/2023] Open
Abstract
Background and objective The cortico-hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico-hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition. Methods A total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico-hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores. Results Compared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico-hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico-hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC). Conclusion Schizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico-hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia.
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Affiliation(s)
- Kangkang Xue
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
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Iasevoli F, D’Ambrosio L, Ciccarelli M, Barone A, Gaudieri V, Cocozza S, Pontillo G, Brunetti A, Cuocolo A, de Bartolomeis A, Pappatà S. Altered Patterns of Brain Glucose Metabolism Involve More Extensive and Discrete Cortical Areas in Treatment-resistant Schizophrenia Patients Compared to Responder Patients and Controls: Results From a Head-to-Head 2-[18F]-FDG-PET Study. Schizophr Bull 2023; 49:474-485. [PMID: 36268829 PMCID: PMC10016407 DOI: 10.1093/schbul/sbac147] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND AND HYPOTHESIS Treatment resistant schizophrenia (TRS) affects almost 30% of patients with schizophrenia and has been considered a different phenotype of the disease. In vivo characterization of brain metabolic patterns associated with treatment response could contribute to elucidate the neurobiological underpinnings of TRS. Here, we used 2-[18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) to provide the first head-to-head comparative analysis of cerebral glucose metabolism in TRS patients compared to schizophrenia responder patients (nTRS), and controls. Additionally, we investigated, for the first time, the differences between clozapine responders (Clz-R) and non-responders (Clz-nR). STUDY DESIGN 53 participants underwent FDG-PET studies (41 patients and 12 controls). Response to conventional antipsychotics and to clozapine was evaluated using a standardized prospective procedure based on PANSS score changes. Maps of relative brain glucose metabolism were processed for voxel-based analysis using Statistical Parametric Mapping software. STUDY RESULTS Restricted areas of significant bilateral relative hypometabolism in the superior frontal gyrus characterized TRS compared to nTRS. Moreover, reduced parietal and frontal metabolism was associated with high PANSS disorganization factor scores in TRS (P < .001 voxel level uncorrected, P < .05 cluster level FWE-corrected). Only TRS compared to controls showed significant bilateral prefrontal relative hypometabolism, more extensive in CLZ-nR than in CLZ-R (P < .05 voxel level FWE-corrected). Relative significant hypermetabolism was observed in the temporo-occipital regions in TRS compared to nTRS and controls. CONCLUSIONS These data indicate that, in TRS patients, altered metabolism involved discrete brain regions not found affected in nTRS, possibly indicating a more severe disrupted functional brain network associated with disorganization symptoms.
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Affiliation(s)
- Felice Iasevoli
- Section of Psychiatry, Unit of Treatment Resistant Psychosis, Laboratory of Molecular and Translational Psychiatry, Department of Neuroscience, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Luigi D’Ambrosio
- Section of Psychiatry, Unit of Treatment Resistant Psychosis, Laboratory of Molecular and Translational Psychiatry, Department of Neuroscience, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Mariateresa Ciccarelli
- Section of Psychiatry, Unit of Treatment Resistant Psychosis, Laboratory of Molecular and Translational Psychiatry, Department of Neuroscience, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Annarita Barone
- Section of Psychiatry, Unit of Treatment Resistant Psychosis, Laboratory of Molecular and Translational Psychiatry, Department of Neuroscience, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
| | - Valeria Gaudieri
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80131, Naples, Italy
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80131, Naples, Italy
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80131, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80131, Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80131, Naples, Italy
| | - Andrea de Bartolomeis
- Section of Psychiatry, Unit of Treatment Resistant Psychosis, Laboratory of Molecular and Translational Psychiatry, Department of Neuroscience, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy
- UNESCO Chair on Health Education and Sustainable Development - University of Naples Federico II, Naples, Italy
| | - Sabina Pappatà
- Institute of Biostructure and Bioimaging, National Research Council, Via T. De Amicis 95, 80145, Naples, Italy
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de Sousa TR, Dt C, Novais F. Exploring the Hypothesis of a Schizophrenia and Bipolar Disorder Continuum: Biological, Genetic and Pharmacologic Data. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:161-171. [PMID: 34477537 DOI: 10.2174/1871527320666210902164235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 07/19/2021] [Accepted: 08/08/2021] [Indexed: 12/16/2022]
Abstract
Present time nosology has its roots in Kraepelin's demarcation of schizophrenia and bipolar disorder. However, accumulating evidence has shed light on several commonalities between the two disorders, and some authors have advocated for the consideration of a disease continuum. Here, we review previous genetic, biological and pharmacological findings that provide the basis for this conceptualization. There is a cross-disease heritability, and they share single-nucleotide polymorphisms in some common genes. EEG and imaging patterns have a number of similarities, namely reduced white matter integrity and abnormal connectivity. Dopamine, serotonin, GABA and glutamate systems have dysfunctional features, some of which are identical among the disorders. Finally, cellular calcium regulation and mitochondrial function are, also, impaired in the two.
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Affiliation(s)
- Teresa Reynolds de Sousa
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
| | - Correia Dt
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
- Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ISAMB - Instituto de Saúde Ambiental, Lisboa, Portugal
| | - Filipa Novais
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
- Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ISAMB - Instituto de Saúde Ambiental, Lisboa, Portugal
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11
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Hagihara H, Murano T, Miyakawa T. The gene expression patterns as surrogate indices of pH in the brain. Front Psychiatry 2023; 14:1151480. [PMID: 37200901 PMCID: PMC10185791 DOI: 10.3389/fpsyt.2023.1151480] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/11/2023] [Indexed: 05/20/2023] Open
Abstract
Hydrogen ion (H+) is one of the most potent intrinsic neuromodulators in the brain in terms of concentration. Changes in H+ concentration, expressed as pH, are thought to be associated with various biological processes, such as gene expression, in the brain. Accumulating evidence suggests that decreased brain pH is a common feature of several neuropsychiatric disorders, including schizophrenia, bipolar disorder, autism spectrum disorder, and Alzheimer's disease. However, it remains unclear whether gene expression patterns can be used as surrogates for pH changes in the brain. In this study, we performed meta-analyses using publicly available gene expression datasets to profile the expression patterns of pH-associated genes, whose expression levels were correlated with brain pH, in human patients and mouse models of major central nervous system (CNS) diseases, as well as in mouse cell-type datasets. Comprehensive analysis of 281 human datasets from 11 CNS disorders revealed that gene expression associated with decreased pH was over-represented in disorders including schizophrenia, bipolar disorder, autism spectrum disorders, Alzheimer's disease, Huntington's disease, Parkinson's disease, and brain tumors. Expression patterns of pH-associated genes in mouse models of neurodegenerative disease showed a common time course trend toward lower pH over time. Furthermore, cell type analysis identified astrocytes as the cell type with the most acidity-related gene expression, consistent with previous experimental measurements showing a lower intracellular pH in astrocytes than in neurons. These results suggest that the expression pattern of pH-associated genes may be a surrogate for the state- and trait-related changes in pH in brain cells. Altered expression of pH-associated genes may serve as a novel molecular mechanism for a more complete understanding of the transdiagnostic pathophysiology of neuropsychiatric and neurodegenerative disorders.
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12
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Northoff G. Spatiotemporal Psychopathology - A Novel Approach to Brain and Symptoms. Noro Psikiyatr Ars 2022; 59:S3-S9. [PMID: 36578984 PMCID: PMC9767129 DOI: 10.29399/npa.28146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 03/13/2022] [Indexed: 12/31/2022] Open
Abstract
How can we characterize psychopathological symptoms and connect them to the brain? Current psychopathological symptoms only focus on either the symptoms themselves or predominantly on the brain. This leaves open their intimate connection. A novel approach, Spatiotemporal Psychopathology, proposes that the brain inner spatiotemporal organisation of its neural activity provides the spatiotemporal organization of the psychopathological symptoms. Specifically, the brains' neuronal topography and dynamic is manifest in a more or less analogous spatiotemporal organisation on the mental level, i.e., mental topography and dynamic. This is strongly supported by various examples including major depressive disorder, bipolar disorder, schizophrenia, and autism. We therefore conclude that Spatiotemporal Psychopathology provides a promising approach to intimately connect brain and symptoms.
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Affiliation(s)
- Georg Northoff
- University of Ottawa, Institute of Mental Health Research, Ontario, Canada,Correspondence Address: Georg Northoff, 1145 Carling Avenue, Ottawa, K1L 8K9 Ontario, Canada • E-mail:
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13
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Zhang J, Northoff G. Beyond noise to function: reframing the global brain activity and its dynamic topography. Commun Biol 2022; 5:1350. [PMID: 36481785 PMCID: PMC9732046 DOI: 10.1038/s42003-022-04297-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/24/2022] [Indexed: 12/13/2022] Open
Abstract
How global and local activity interact with each other is a common question in complex systems like climate and economy. Analogously, the brain too displays 'global' activity that interacts with local-regional activity and modulates behavior. The brain's global activity, investigated as global signal in fMRI, so far, has mainly been conceived as non-neuronal noise. We here review the findings from healthy and clinical populations to demonstrate the neural basis and functions of global signal to brain and behavior. We show that global signal (i) is closely coupled with physiological signals and modulates the arousal level; and (ii) organizes an elaborated dynamic topography and coordinates the different forms of cognition. We also postulate a Dual-Layer Model including both background and surface layers. Together, the latest evidence strongly suggests the need to go beyond the view of global signal as noise by embracing a dual-layer model with background and surface layer.
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Affiliation(s)
- Jianfeng Zhang
- grid.263488.30000 0001 0472 9649Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China ,grid.263488.30000 0001 0472 9649School of Psychology, Shenzhen University, Shenzhen, China
| | - Georg Northoff
- grid.13402.340000 0004 1759 700XMental Health Center, Zhejiang University School of Medicine, Hangzhou, China ,grid.28046.380000 0001 2182 2255Institute of Mental Health Research, University of Ottawa, Ottawa, Canada ,grid.410595.c0000 0001 2230 9154Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
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14
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Chen J, Fu Z, Bustillo JR, Perrone-Bizzozero NI, Lin D, Canive J, Pearlson GD, Stephen JM, Mayer AR, Potkin SG, van Erp TGM, Kochunov P, Elliot Hong L, Adhikari BM, Andreassen OA, Agartz I, Westlye LT, Sui J, Du Y, Macciardi F, Hanlon FM, Jung RE, Turner JA, Liu J, Calhoun VD. Genome-Transcriptome-Functional Connectivity-Cognition Link Differentiates Schizophrenia From Bipolar Disorder. Schizophr Bull 2022; 48:1306-1317. [PMID: 35988022 PMCID: PMC9673262 DOI: 10.1093/schbul/sbac088] [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: 12/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) and bipolar disorder (BD) share genetic risk factors, yet patients display differential levels of cognitive impairment. We hypothesized a genome-transcriptome-functional connectivity (frontoparietal)-cognition pathway linked to SZ-versus-BD differences, and conducted a multiscale study to delineate this pathway. STUDY DESIGNS Large genome-wide studies provided single nucleotide polymorphisms (SNPs) conferring more risk for SZ than BD, and we identified their regulated genes, namely SZ-biased SNPs and genes. We then (a) computed the polygenic risk score for SZ (PRSSZ) of SZ-biased SNPs and examined its associations with imaging-based frontoparietal functional connectivity (FC) and cognitive performances; (b) examined the spatial correlation between ex vivo postmortem expressions of SZ-biased genes and in vivo, SZ-related FC disruptions across frontoparietal regions; (c) investigated SZ-versus-BD differences in frontoparietal FC; and (d) assessed the associations of frontoparietal FC with cognitive performances. STUDY RESULTS PRSSZ of SZ-biased SNPs was significantly associated with frontoparietal FC and working memory test scores. SZ-biased genes' expressions significantly correlated with SZ-versus-BD differences in FC across frontoparietal regions. SZ patients showed more reductions in frontoparietal FC than BD patients compared to controls. Frontoparietal FC was significantly associated with test scores of multiple cognitive domains including working memory, and with the composite scores of all cognitive domains. CONCLUSIONS Collectively, these multiscale findings support the hypothesis that SZ-biased genetic risk, through transcriptome regulation, is linked to frontoparietal dysconnectivity, which in turn contributes to differential cognitive deficits in SZ-versus BD, suggesting that potential biomarkers for more precise patient stratification and treatment.
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Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Juan R Bustillo
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Nora I Perrone-Bizzozero
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Jose Canive
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
- Department of Psychiatry and Neuroscience, Yale University, New Haven, CT, USA
| | | | | | - Steven G Potkin
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, Clinical Translational Neuroscience Laboratory, School of Medicine, University of California, Irvine, CA, USA
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - Bhim M Adhikari
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - Ole A Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Lars T Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | | | - Rex E Jung
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Jessica A Turner
- Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
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15
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Lucia M, Romanella SM, Di Lorenzo G, Demchenko I, Bhat V, Rossi S, Santarnecchi E. Neural correlates of N-back task performance and proposal for corresponding neuromodulation targets in psychiatric and neurodevelopmental disorders. Psychiatry Clin Neurosci 2022; 76:512-524. [PMID: 35773784 PMCID: PMC10603255 DOI: 10.1111/pcn.13442] [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: 10/05/2021] [Revised: 06/21/2022] [Accepted: 06/23/2022] [Indexed: 11/29/2022]
Abstract
AIM Working memory (WM) deficit represents the most common cognitive impairment in psychiatric and neurodevelopmental disorders, making the identification of its neural substrates a crucial step towards the conceptualization of restorative interventions. We present a meta-analysis focusing on neural activations associated with the most commonly used task to measure WM, the N-back task, in patients with schizophrenia, depressive disorder, bipolar disorder, and attention-deficit/hyperactivity disorder. Showing qualitative similarities and differences in WM processing between patients and healthy controls, we propose possible targets for cognitive enhancement approaches. METHODS Selected studies, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, were analyzed through the activation likelihood estimate statistical framework, with subsequent generation of disorder-specific N-back activation maps. RESULTS Despite similar WM deficits shared across all disorders, results highlighted different brain activation patterns for each disorder compared with healthy controls. In general, results showed brain activity in frontal, parietal, subcortical, and cerebellar regions; however, reduced engagement of specific nodes of the fronto-parietal network emerged in patients compared with healthy controls. In particular, neither bipolar nor depressive disorders showed detectable activations in the dorsolateral prefrontal cortices, while their parietal activation patterns were lateralized to the left and right hemispheres, respectively. On the other hand, patients with attention-deficit/hyperactivity disorder showed a lack of activation in the left parietal lobe, whereas patients with schizophrenia showed lower activity over the left prefrontal cortex. CONCLUSION These results, together with biophysical modeling, were then used to discuss the design of future disorder-specific cognitive enhancement interventions based on noninvasive brain stimulation.
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Affiliation(s)
- Mencarelli Lucia
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy
- Precision Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Sara M Romanella
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy
- Precision Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Giorgio Di Lorenzo
- IRCCS Fondazione Santa Lucia, Rome, Italy
- Laboratory of Psychophysiology and Cognitive Neuroscience, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Ilya Demchenko
- Interventional Psychiatry Program, Centre for Depression & Suicide Studies, St. Michael’s Hospital, Toronto, Ontario, Canada
| | - Venkat Bhat
- Interventional Psychiatry Program, Centre for Depression & Suicide Studies, St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Simone Rossi
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy
- Human Physiology Section, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Emiliano Santarnecchi
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy
- Precision Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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16
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Canales T, Rodman S, Conklin D, Sarna K, Sajatovic M, Levin JB. Combining Medication Adherence Support Plus Long-Acting Injectable Antipsychotic Medication: A Post-Hoc Analysis of 3 Pilot Studies. PSYCHOPHARMACOLOGY BULLETIN 2022; 52:41-57. [PMID: 35815176 PMCID: PMC9235317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Patients with severe mental illness (SMI) who do not adhere to treatment have a lower quality of life, with more hospitalizations, interpersonal relationship conflict, homelessness, substance use problems, and incarceration compared to patients who adhere to treatment. Nonadherence to psychiatric medications has been studied for over a decade in patients diagnosed with bipolar, schizoaffective, and schizophrenia disorders with long-acting injectable antipsychotics (LAI) becoming a mainstay of adherence-focused treatment. Previous studies have shown that LAI treatment can be further optimized with the inclusion of the behavioral intervention, Customized Adherence Enhancement (CAE). It was unclear if outcomes improved similarly across the studies that varied by demographics, diagnoses, and CAE + LAI protocols. We aimed to evaluate CAE + LAI adherence outcomes in SMI by pooling three studies to better understand response to treatment in the setting of varied circumstances. Our findings show that adherence improved similarly across studies despite these differences. Furthermore, it was demonstrated that CAE + LAI improved adherence to a similar degree when primary mood and psychotic disorder cohorts were compared. As the use of LAI expands, our findings show the versatility and effectiveness of including CAE to further optimize adherence and improve other outcomes.
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Affiliation(s)
- Thomas Canales
- Canales, BS, Case Western Reserve University School of Medicine
| | | | - Danette Conklin
- Conklin, PhD, The MetroHealth System, Department of Psychiatry, Psychologist
| | - Kaylee Sarna
- Sarna, MS, Data Manager, Case Western Reserve University School of Medicine
| | - Martha Sajatovic
- Sajatovic, MD, Professor of Psychiatry and of Neurology. Case Western Reserve University School of Medicine and University Hospitals Cleveland Medical Center
| | - Jennifer B Levin
- Levin, PhD, Professor, Department of Psychiatry, Case Western Reserve University School of Medicine, University Hospitals Cleveland Medical Center, Cleveland
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17
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Szeszko PR, Gohel S, Vaccaro DH, Chu KW, Tang CY, Goldstein KE, New AS, Siever LJ, McClure M, Perez-Rodriguez MM, Haznedar MM, Byne W, Hazlett EA. Frontotemporal thalamic connectivity in schizophrenia and schizotypal personality disorder. Psychiatry Res Neuroimaging 2022; 322:111463. [PMID: 35240516 PMCID: PMC9018622 DOI: 10.1016/j.pscychresns.2022.111463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/22/2022]
Abstract
Schizotypal personality disorder (SPD) resembles schizophrenia, but with attenuated brain abnormalities and the absence of psychosis. The thalamus is integral for processing and transmitting information across cortical regions and widely implicated in the neurobiology of schizophrenia. Comparing thalamic connectivity in SPD and schizophrenia could reveal an intermediate schizophrenia-spectrum phenotype to elucidate neurobiological risk and protective factors in psychosis. We used rsfMRI to investigate functional connectivity between the mediodorsal nucleus (MDN) and pulvinar, and their connectivity with frontal and temporal cortical regions, respectively in 43 healthy controls (HCs), and individuals in the schizophrenia-spectrum including 45 psychotropic drug-free individuals with SPD, and 20 individuals with schizophrenia-related disorders [(schizophrenia (n = 10), schizoaffective disorder (n = 8), schizophreniform disorder (n = 1) and psychosis NOS (n = 1)]. Individuals with SPD had greater functional connectivity between the MDN and pulvinar compared to individuals with schizophrenia. Thalamo-frontal (i.e., between the MDN and rostral middle frontal cortex) connectivity was comparable in SPD and HCs; in SPD greater connectivity was associated with less symptom severity. Individuals with schizophrenia had less thalamo-frontal connectivity and thalamo-temporal (i.e., pulvinar to the transverse temporal cortex) connectivity compared with HCs. Thalamo-frontal functional connectivity may be comparable in SPD and HCs, but abnormal in schizophrenia, and that this may be protective against psychosis in SPD.
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Affiliation(s)
- Philip R Szeszko
- Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, 130 West Kingsbridge Road, Bronx, NY 10468, USA; Mental Health Patient Care Center, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Suril Gohel
- Department of Health Informatics, Rutgers University, Newark, NJ, USA
| | - Daniel H Vaccaro
- Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, 130 West Kingsbridge Road, Bronx, NY 10468, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - King-Wai Chu
- Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, 130 West Kingsbridge Road, Bronx, NY 10468, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Cheuk Y Tang
- Translational and Molecular Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kim E Goldstein
- Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, 130 West Kingsbridge Road, Bronx, NY 10468, USA
| | - Antonia S New
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Larry J Siever
- Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, 130 West Kingsbridge Road, Bronx, NY 10468, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Margaret McClure
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychology, Fairfield University, Fairfield, CT, USA
| | | | - M Mehmet Haznedar
- Mental Health Patient Care Center, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - William Byne
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Erin A Hazlett
- Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, 130 West Kingsbridge Road, Bronx, NY 10468, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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18
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Yan W, Palaniyappan L, Liddle PF, Rangaprakash D, Wei W, Deshpande G. Characterization of Hemodynamic Alterations in Schizophrenia and Bipolar Disorder and Their Effect on Resting-State fMRI Functional Connectivity. Schizophr Bull 2022; 48:695-711. [PMID: 34951473 PMCID: PMC9077436 DOI: 10.1093/schbul/sbab140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Common and distinct neural bases of Schizophrenia (SZ) and bipolar disorder (BP) have been explored using resting-state fMRI (rs-fMRI) functional connectivity (FC). However, fMRI is an indirect measure of neural activity, which is a convolution of the hemodynamic response function (HRF) and latent neural activity. The HRF, which models neurovascular coupling, varies across the brain within and across individuals, and is altered in many psychiatric disorders. Given this background, this study had three aims: quantifying HRF aberrations in SZ and BP, measuring the impact of such HRF aberrations on FC group differences, and exploring the genetic basis of HRF aberrations. We estimated voxel-level HRFs by deconvolving rs-fMRI data obtained from SZ (N = 38), BP (N = 19), and matched healthy controls (N = 35). We identified HRF group differences (P < .05, FDR corrected) in many regions previously implicated in SZ/BP, with mediodorsal, habenular, and central lateral nuclei of the thalamus exhibiting HRF differences in all pairwise group comparisons. Thalamus seed-based FC analysis revealed that ignoring HRF variability results in false-positive and false-negative FC group differences, especially in insula, superior frontal, and lingual gyri. HRF was associated with DRD2 gene expression (P < .05, 1.62 < |Z| < 2.0), as well as with medication dose (P < .05, 1.75 < |Z| < 3.25). In this first study to report HRF aberrations in SZ and BP, we report the possible modulatory effect of dopaminergic signalling on HRF, and the impact that HRF variability can have on FC studies in clinical samples. To mitigate the impact of HRF variability on FC group differences, we suggest deconvolution during data preprocessing.
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Affiliation(s)
- Wenjing Yan
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, USA
- Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China
| | - Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
| | - Peter F Liddle
- Centre for Translational Neuroimaging, Division of Mental Health and Clinical Neuroscience, Institute of Mental Health, University of Nottingham, UK
| | - D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Wei
- Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL
- Alabama Advanced Imaging Consortium, Birmingham, AL
- Center for Neuroscience, Auburn University, AL, USA
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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19
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Takatsuru Y, Motegi S, Nishikata T, Sato H, Yonemochi K. Frontal medial cortex and angular gyrus functional connectivity is related to sex and age differences in odor sensitivity. J Neuroimaging 2022; 32:611-616. [PMID: 35355361 DOI: 10.1111/jon.12994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/23/2022] [Accepted: 03/13/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Odor preference is one of the key factors for the rehabilitation of the swallowing function. On the other hand, sensitivity to odor differs between sexes and decreases with age. These factors rely on brain neuronal circuits. However, it remains not fully clarified which neuronal circuit determines the sex and age differences in odor sensitivity. In this study, we carried out both the odor sensitivity test and functional MRI (fMRI) to find the key neuronal circuits determining sex and age differences in odor sensitivity. METHODS Healthy volunteers (28 males, aged 27-62 years, and 30 females, aged 21-59 years) participated in this study. Some of them (seven males and seven females) underwent fMRI. We prepared five odorous test substances and presented each substance at 1 minute intervals. After 5 minutes of questioning about food intake, the subjects were asked to recall each of the test substances presented from the list. In the fMRI study, all the subjects underwent 15 minutes of the prestimulation, stimulation with peppermint odor, and poststimulation sessions. RESULTS The odor test score was significantly higher in females than in males and showed an age-dependent decrease. We found four functional connectivities whose degrees were significantly different between males and females. One of them, the functional connectivity between the frontal medial cortex (MedFC) and the left angular gyrus (AG. l), showed an age-dependent change. CONCLUSIONS The functional MedFC-AG.l connectivity is one of the important neuronal circuits that affect the sex- and age-dependent odor sensitivity.
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Affiliation(s)
- Yusuke Takatsuru
- Division of Multidimensional Clinical Medicine, Department of Nutrition and Health Sciences, Toyo University, Itakura, Japan.,Department of Medicine, Johmoh Hospital, Maebashi, Japan
| | - Shunichi Motegi
- Department of Radiological Sciences, International University of Health and Welfare, Otawara, Japan
| | | | - Hideyasu Sato
- Department of Food Life Sciences, Toyo University, Itakura, Japan
| | - Keita Yonemochi
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan
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20
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The Limits between Schizophrenia and Bipolar Disorder: What Do Magnetic Resonance Findings Tell Us? Behav Sci (Basel) 2022; 12:bs12030078. [PMID: 35323397 PMCID: PMC8944966 DOI: 10.3390/bs12030078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/10/2022] [Accepted: 03/10/2022] [Indexed: 02/01/2023] Open
Abstract
Schizophrenia and bipolar disorder, two of the most severe psychiatric illnesses, have historically been regarded as dichotomous entities but share many features of the premorbid course, clinical profile, genetic factors and treatment approaches. Studies focusing on neuroimaging findings have received considerable attention, as they plead for an improved understanding of the brain regions involved in the pathophysiology of schizophrenia and bipolar disorder. In this review, we summarize the main magnetic resonance imaging findings in both disorders, aiming at exploring the neuroanatomical and functional similarities and differences between the two. The findings show that gray and white matter structural changes and functional dysconnectivity predominate in the frontal and limbic areas and the frontotemporal circuitry of the brain areas involved in the integration of executive, cognitive and affective functions, commonly affected in both disorders. Available evidence points to a considerable overlap in the affected regions between the two conditions, therefore possibly placing them at opposite ends of a psychosis continuum.
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El Nagar Z, El Shahawi HH, Effat SM, El Sheikh MM, Adel A, Ibrahim YA, Aufa OM. Single episode brief psychotic disorder versus bipolar disorder: A diffusion tensor imaging and executive functions study. Schizophr Res Cogn 2022; 27:100214. [PMID: 34557386 PMCID: PMC8446778 DOI: 10.1016/j.scog.2021.100214] [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: 06/07/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Despite fast progress in neuroscientific approaches, the neurobiological continuum links psychotic spectrum, and affective disorder is obscure. White matter WM abnormalities found utilizing Diffusion Tensor Imaging (DTI) showing impaired communication in both disorders have been consistently demonstrated; however, direct comparisons of findings between them are scarce. This study aims to study WM abnormalities in single episode bipolar I disorder, and single episode brief psychotic disorder related to healthy control with the association of executive function. METHODS A cross-sectional case-control study was used to assess 60 subjects divided into 20 patients with single episode bipolar I disorder, 20 individuals with single episode brief psychotic disorder (both groups of patients were in remission), and 20 healthy controls. The present study examined the superior longitudinal fasciculus (SLF), and cingulum bundle fractional anisotropy (FA) determined from DTI images symmetrically and connected these results with cognitive functions as assessed by the trail making test (TMT) and Wisconsin card sorting test (WCST). RESULTS DTI data indicated that the psychotic group had a significant decrease in FA of the right SLF (p-value less than 0.001), left SLF (p-value less than 0.001), and left cingulum (p-value less than 0.001) than the bipolar I group. In terms of executive functioning, the psychotic group performed significantly worse than the bipolar I group on the TMT part B (p-value less than 0.001), the WCST (number of classifications fulfilled) (p-value less than 0.001), and perseverative errors (p-value less than 0.001). CONCLUSION Even after clinical remission, individuals with single episode brief psychotic disorder had more pronounced white matter impairments and executive function deficiencies than individuals with single episode bipolar I disorder.
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Affiliation(s)
- Zeinab El Nagar
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Heba H. El Shahawi
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Safeya M. Effat
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Mona M. El Sheikh
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Ahmed Adel
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Yosra A. Ibrahim
- Radiology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Ola M. Aufa
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
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22
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Jeon EJ, Kang SH, Piao YH, Kim SW, Kim JJ, Lee BJ, Yu JC, Lee KY, Won SH, Lee SH, Kim SH, Kim ET, Kim CT, Oliver D, Fusar-Poli P, Rami FZ, Chung YC. Development of the Korea-Polyenvironmental Risk Score for Psychosis. Psychiatry Investig 2022; 19:197-206. [PMID: 35196829 PMCID: PMC8958209 DOI: 10.30773/pi.2021.0328] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/26/2021] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE Comprehensive understanding of polyenvironmental risk factors for the development of psychosis is important. Based on a review of related evidence, we developed the Korea Polyenvironmental Risk Score (K-PERS) for psychosis. We investigated whether the K-PERS can differentiate patients with schizophrenia spectrum disorders (SSDs) from healthy controls (HCs). METHODS We reviewed existing tools for measuring polyenvironmental risk factors for psychosis, including the Maudsley Environmental Risk Score (ERS), polyenviromic risk score (PERS), and Psychosis Polyrisk Score (PPS). Using odds ratios and relative risks for Western studies and the "population proportion" (PP) of risk factors for Korean data, we developed the K-PERS, and compared the scores thereon between patients with SSDs and HCs. In addition, correlation was performed between the K-PERS and Positive and Negative Syndrome Scale (PANSS). RESULTS We first constructed the "K-PERS-I," comprising five factors based on the PPS, and then the "K-PERS-II" comprising six factors based on the ERS. The instruments accurately predicted participants' status (case vs. control). In addition, the K-PERS-I and -II scores exhibited significant negative correlations with the negative symptom factor score of the PANSS. CONCLUSION The K-PERS is the first comprehensive tool developed based on PP data obtained from Korean studies that measures polyenvironmental risk factors for psychosis. Using pilot data, the K-PERS predicted patient status (SSD vs. HC). Further research is warranted to examine the relationship of K-PERS scores with clinical outcomes of psychosis and schizophrenia.
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Affiliation(s)
- Eun-Jin Jeon
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Shi-Hyun Kang
- Department of Social Psychiatry and Rehabilitation, National Center for Mental Health, Seoul, Republic of Korea
| | - Yan-Hong Piao
- Department of Psychiatry, Jeonbuk National University Medical School, Jeonju, Republic of Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Jung-Jin Kim
- Department of Psychiatry, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Republic of Korea
| | - Bong-Ju Lee
- Department of Psychiatry, Inje University Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Je-Chun Yu
- Department of Psychiatry, Eulji University School of Medicine, Eulji University Hospital, Daejeon, Republic of Korea
| | - Kyu-Young Lee
- Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul, Republic of Korea
| | - Seung-Hee Won
- Department of Psychiatry, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Seung-Hwan Lee
- Department of Psychiatry, Inje University Ilsan Paik Hospital, Goyang, Republic of Korea
| | - Seung-Hyun Kim
- Department of Psychiatry, Korea University College of Medicine, Guro Hospital, Seoul, Republic of Korea
| | - Eui-Tae Kim
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Clara Tammy Kim
- Institute of Life and Death Studies, Hallym University, Chuncheon, Republic of Korea
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,OASIS Service, South London and the Maudsley NHS Foundation Trust, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,OASIS Service, South London and the Maudsley NHS Foundation Trust, London, United Kingdom.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Fatima Zahra Rami
- Department of Psychiatry, Jeonbuk National University Medical School, Jeonju, Republic of Korea
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Republic of Korea.,Department of Psychiatry, Jeonbuk National University Medical School, Jeonju, Republic of Korea.,Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
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23
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Kremneva E, Sinitsyn D, Dobrynina L, Suslina A, Krotenkova M. Resting state functional MRI in neurology and psychiatry. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:5-14. [DOI: 10.17116/jnevro20221220215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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24
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Gallos IK, Mantonakis L, Spilioti E, Kattoulas E, Savvidou E, Anyfandi E, Karavasilis E, Kelekis N, Smyrnis N, Siettos CI. The relation of integrated psychological therapy to resting state functional brain connectivity networks in patients with schizophrenia. Psychiatry Res 2021; 306:114270. [PMID: 34775295 DOI: 10.1016/j.psychres.2021.114270] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/22/2021] [Accepted: 10/31/2021] [Indexed: 01/05/2023]
Abstract
Functional brain dysconnectivity measured with resting state functional magnetic resonance imaging (rsfMRI) has been linked to cognitive impairment in schizophrenia. This study investigated the effects on functional brain connectivity of Integrated Psychological Therapy (IPT), a cognitive behavioral oriented group intervention program, in 31 patients with schizophrenia. Patients received IPT or an equal intensity non-specific psychological treatment in a non-randomized design. Evidence of improvement in executive and social functions, psychopathology and overall level of functioning was observed after treatment completion at six months only in the IPT treatment group and was partially sustained at one-year follow up. Independent Component Analysis and Isometric Mapping (ISOMAP), a non-linear manifold learning algorithm, were used to construct functional connectivity networks from the rsfMRI data. Functional brain dysconnectivity was observed in patients compared to a group of 17 healthy controls, both globally and specifically including the default mode (DMN) and frontoparietal network (FPN). DMN and FPN connectivity were reversed towards healthy control patterns only in the IPT treatment group and these effects were sustained at follow up for DMN but not FPN. These data suggest the use of rsfMRI as a biomarker for accessing and monitoring the therapeutic effects of cognitive remediation therapy in schizophrenia.
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Affiliation(s)
- I K Gallos
- School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - L Mantonakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Spilioti
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Kattoulas
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece
| | - E Savvidou
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece
| | - E Anyfandi
- First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Karavasilis
- Second Department of Radiology, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece
| | - N Kelekis
- Second Department of Radiology, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece
| | - N Smyrnis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; Second Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece.
| | - C I Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Università degli Studi di Napoli Federico II, Naples, Italy
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25
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Altered Dynamic Functional Connectivity of Cuneus in Schizophrenia Patients: A Resting-State fMRI Study. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112311392] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.
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Hwang M, Roh YS, Talero J, Cohen BM, Baker JT, Brady RO, Öngür D, Shinn AK. Auditory hallucinations across the psychosis spectrum: Evidence of dysconnectivity involving cerebellar and temporal lobe regions. Neuroimage Clin 2021; 32:102893. [PMID: 34911197 PMCID: PMC8636859 DOI: 10.1016/j.nicl.2021.102893] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/29/2021] [Accepted: 11/19/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Auditory hallucinations (AH) are typically associated with schizophrenia (SZ), but they are also prevalent in bipolar disorder (BD). Despite the large body of research on the neural correlates of AH in SZ, the pathophysiology underlying AH remains unclear. Few studies have examined the neural substrates associated with propensity for AH in BD. Investigating AH across the psychosis spectrum has the potential to inform about the neural signature associated with the trait of AH, irrespective of psychiatric diagnosis. METHODS We compared resting state functional magnetic resonance imaging data in psychosis patients with (n = 90 AH; 68 SZ, 22 BD) and without (n = 55 NAH; 16 SZ, 39 BD) lifetime AH. We performed region of interest (ROI)-to-ROI functional connectivity (FC) analysis using 91 cortical, 15 subcortical, and 26 cerebellar atlas-defined regions. The primary aim was to identify FC differences between patients with and without lifetime AH. We secondarily examined differences between AH and NAH within each diagnosis. RESULTS Compared to the NAH group, patients with AH showed higher FC between cerebellum and frontal (left precentral gyrus), temporal [right middle temporal gyrus (MTG), left inferior temporal gyrus (ITG), left temporal fusiform gyrus)], parietal (bilateral superior parietal lobules), and subcortical (left accumbens, left palldium) brain areas. AH also showed lower FC between temporal lobe regions (between right ITG and right MTG and bilateral superior temporal gyri) relative to NAH. CONCLUSIONS Our findings suggest that dysconnectivity involving the cerebellum and temporal lobe regions may be common neurofunctional elements associated with AH propensity across the psychosis spectrum. We also found dysconnectivity patterns that were unique to lifetime AH within SZ or bipolar psychosis, suggesting both common and distinct mechanisms underlying AH pathophysiology in these disorders.
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Affiliation(s)
- Melissa Hwang
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA
| | - Youkyung S Roh
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA
| | - Jessica Talero
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA
| | - Bruce M Cohen
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA; Program for Neuropsychiatric Research, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Justin T Baker
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Roscoe O Brady
- Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215, USA; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Dost Öngür
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Ann K Shinn
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA.
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27
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Pelgrim TAD, Bossong MG, Cuiza A, Alliende LM, Mena C, Tepper A, Ramirez-Mahaluf JP, Iruretagoyena B, Ornstein C, Fritsch R, Cruz JP, Tejos C, Repetto G, Crossley N. Abnormal nodal and global network organization in resting state functional MRI from subjects with the 22q11 deletion syndrome. Sci Rep 2021; 11:21623. [PMID: 34732759 PMCID: PMC8566599 DOI: 10.1038/s41598-021-00873-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/05/2021] [Indexed: 12/31/2022] Open
Abstract
The 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and is therefore considered a neurodevelopmental model for studying the pathogenesis of schizophrenia. Studies have shown that localized abnormal functional brain connectivity is present in 22q11 deletion syndrome like in schizophrenia. However, it is less clear whether these abnormal cortical interactions lead to global or regional network disorganization as seen in schizophrenia. We analyzed from a graph-theory perspective fMRI data from 40 22q11 deletion syndrome patients and 67 healthy controls, and reconstructed functional networks from 105 brain regions. Between-group differences were examined by evaluating edge-wise strength and graph theoretical metrics of local (weighted degree, nodal efficiency, nodal local efficiency) and global topological properties (modularity, local and global efficiency). Connectivity strength was globally reduced in patients, driven by a large network comprising 147 reduced connections. The 22q11 deletion syndrome network presented with abnormal local topological properties, with decreased local efficiency and reductions in weighted degree particularly in hub nodes. We found evidence for abnormal integration but intact segregation of the 22q11 deletion syndrome network. Results suggest that 22q11 deletion syndrome patients present with similar aberrant local network organization as seen in schizophrenia, and this network configuration might represent a vulnerability factor to psychosis.
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Affiliation(s)
- Teuntje A D Pelgrim
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Matthijs G Bossong
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Analía Cuiza
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Luz María Alliende
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Carlos Mena
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Angeles Tepper
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | | | - Claudia Ornstein
- Departamento de Psiquiatria y Salud Mental, Hospital Clinico Universidad de Chile, Santiago, Chile
| | - Rosemarie Fritsch
- Departamento de Psiquiatria y Salud Mental, Hospital Clinico Universidad de Chile, Santiago, Chile
| | - Juan Pablo Cruz
- Department of Radiology, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian Tejos
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Gabriela Repetto
- Genetic and Genomic Center, Universidad del Desarrollo, Santiago, Chile
| | - Nicolas Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile.
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Escuela de Medicina, Pontificia Universidad Católica, Diagonal Paraguay 362, Santiago, Chile.
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Griffin AL. The nucleus reuniens orchestrates prefrontal-hippocampal synchrony during spatial working memory. Neurosci Biobehav Rev 2021; 128:415-420. [PMID: 34217746 DOI: 10.1016/j.neubiorev.2021.05.033] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 05/07/2021] [Accepted: 05/10/2021] [Indexed: 12/11/2022]
Abstract
Spatial working memory, the ability to temporarily maintain an internal representation of spatial information for use in guiding upcoming decisions, has been shown to be dependent upon a network of brain structures that includes the hippocampus, a region known to be critical for spatial navigation and episodic memory, and the prefrontal cortex (PFC), a region known to be critical for executive function and goal directed behavior. Oscillatory synchronization between the hippocampus and the prefrontal cortex (PFC) is known to increase in situations of high working memory demand. Most of our knowledge about the anatomical connectivity between the PFC and hippocampus comes from the rodent literature. Thus, most of the findings that will be discussed here model human working memory using spatial working memory-dependent maze navigation tasks in rodents. It has been demonstrated that the ventral midline thalamic nucleus reuniens (Re) is reciprocally connected to both the infralimbic and prelimbic subregions of the PFC, collectively referred to as the medial PFC (mPFC), and the hippocampus. Given that the Re serves as a major anatomical route between the mPFC and hippocampus, it is perhaps not surprising that Re has been shown to be critical for spatial working memory. This review will describe the latest findings and ideas on how the Re contributes to prefrontal-hippocampal synchronization and spatial working memory in rodents. The review will conclude with possible future directions that will advance the understanding of the mechanisms that enable the Re to orchestrate long range synchrony in the prefrontal-hippocampal network.
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Affiliation(s)
- Amy L Griffin
- University of Delaware, Newark, DE, 19711, United States.
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29
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Broeders TAA, Schoonheim MM, Vink M, Douw L, Geurts JJG, van Leeuwen JMC, Vinkers CH. Dorsal attention network centrality increases during recovery from acute stress exposure. NEUROIMAGE-CLINICAL 2021; 31:102721. [PMID: 34134017 PMCID: PMC8214139 DOI: 10.1016/j.nicl.2021.102721] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 05/19/2021] [Accepted: 06/04/2021] [Indexed: 12/17/2022]
Abstract
Stress is a major risk factor for the development of almost all psychiatric disorders. In addition to the acute stress response, an efficient recovery in the aftermath of stress is important for optimal resilience. Increased stress vulnerability across psychiatric disorders may therefore be related to altered trajectories during the recovery phase following stress. Such recovery trajectories can be quantified by changes in functional brain networks. This study therefore evaluated longitudinal functional network changes related to stress in healthy individuals (N = 80), individuals at risk for psychiatric disorders (healthy siblings of schizophrenia patients) (N = 39), and euthymic bipolar I disorder (BD) patients (N = 36). Network changes were evaluated before and at 20 and 90 min after onset of an experimental acute stress task (Trier Social Stress Test) or a control condition. Whole-brain functional networks were analyzed using eigenvector centrality as a proxy for network importance, centrality change over time was related to the acute stress response and recovery for each group. In healthy individuals, centrality of the dorsal attention network (DAN; p = 0.007) changed over time in relation to stress. More specifically, DAN centrality increased during the recovery phase after acute stress exposure (p = 0.020), while no DAN centrality change was observed during the initial stress response (p = 0.626). Such increasing DAN centrality during stress recovery was also found in healthy siblings (p = 0.016), but not in BD patients (p = 0.554). This study highlights that temporally complex and precise changes in network configuration are vital to understand the response to and recovery from stress.
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Affiliation(s)
- T A A Broeders
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - M M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M Vink
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Experimental, Utrecht University, Utrecht, The Netherlands; Department Developmental Psychology, Utrecht University, Utrecht, The Netherlands
| | - L Douw
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - J J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - J M C van Leeuwen
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - C H Vinkers
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
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30
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Canario E, Chen D, Biswal B. A review of resting-state fMRI and its use to examine psychiatric disorders. PSYCHORADIOLOGY 2021; 1:42-53. [PMID: 38665309 PMCID: PMC10917160 DOI: 10.1093/psyrad/kkab003] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 02/17/2021] [Accepted: 03/08/2021] [Indexed: 04/28/2024]
Abstract
Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.
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Affiliation(s)
- Edgar Canario
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
| | - Donna Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
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31
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Gallos IK, Gkiatis K, Matsopoulos GK, Siettos C. ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia. AIMS Neurosci 2021; 8:295-321. [PMID: 33709030 PMCID: PMC7940114 DOI: 10.3934/neuroscience.2021016] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/18/2021] [Indexed: 11/18/2022] Open
Abstract
We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan-Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme (consisting of an outer and an inner loop of "Leave one out" cross-validation (LOOCV)). The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus.
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Affiliation(s)
- Ioannis K Gallos
- School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Greece
| | - Kostakis Gkiatis
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”, Università degli Studi di Napoli Federico II, Italy
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32
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Zhu D, Yuan T, Gao J, Xu Q, Xue K, Zhu W, Tang J, Liu F, Wang J, Yu C. Correlation between cortical gene expression and resting-state functional network centrality in healthy young adults. Hum Brain Mapp 2021; 42:2236-2249. [PMID: 33570215 PMCID: PMC8046072 DOI: 10.1002/hbm.25362] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 01/11/2021] [Accepted: 01/21/2021] [Indexed: 12/18/2022] Open
Abstract
Resting‐state functional connectivity in the human brain is heritable, and previous studies have investigated the genetic basis underlying functional connectivity. However, at present, the molecular mechanisms associated with functional network centrality are still largely unknown. In this study, functional networks were constructed, and the graph‐theory method was employed to calculate network centrality in 100 healthy young adults from the Human Connectome Project. Specifically, functional connectivity strength (FCS), also known as the “degree centrality” of weighted networks, is calculated to measure functional network centrality. A multivariate technique of partial least squares regression (PLSR) was then conducted to identify genes whose spatial expression profiles best predicted the FCS distribution. We found that FCS spatial distribution was significantly positively correlated with the expression of genes defined by the first PLSR component. The FCS‐related genes we identified were significantly enriched for ion channels, axon guidance, and synaptic transmission. Moreover, FCS‐related genes were preferentially expressed in cortical neurons and young adulthood and were enriched in numerous neurodegenerative and neuropsychiatric disorders. Furthermore, a series of validation and robustness analyses demonstrated the reliability of the results. Overall, our results suggest that the spatial distribution of FCS is modulated by the expression of a set of genes associated with ion channels, axon guidance, and synaptic transmission.
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Affiliation(s)
- Dan Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Tengfei Yuan
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Junfeng Gao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Tang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Junping Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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33
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All roads lead to the motor cortex: psychomotor mechanisms and their biochemical modulation in psychiatric disorders. Mol Psychiatry 2021; 26:92-102. [PMID: 32555423 DOI: 10.1038/s41380-020-0814-5] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 06/01/2020] [Accepted: 06/05/2020] [Indexed: 02/08/2023]
Abstract
Psychomotor abnormalities have been abundantly observed in psychiatric disorders like major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCH). Although early psychopathological descriptions highlighted the truly psychomotor nature of these abnormalities, more recent investigations conceive them rather in purely motor terms. This has led to an emphasis of dopamine-based abnormalities in subcortical-cortical circuits including substantia nigra, basal ganglia, thalamus, and motor cortex. Following recent findings in MDD, BD, and SCH, we suggest a concept of psychomotor symptoms in the literal sense of the term by highlighting three specifically psychomotor (rather than motor) mechanisms including their biochemical modulation. These include: (i) modulation of dopamine- and substantia nigra-based subcortical-cortical motor circuit by primarily non-motor subcortical raphe nucleus and serotonin via basal ganglia and thalamus (as well as by other neurotransmitters like glutamate and GABA); (ii) modulation of motor cortex and motor network by non-motor cortical networks like default-mode network and sensory networks; (iii) global activity in cortex may also shape regional distribution of neural activity in motor cortex. We demonstrate that these three psychomotor mechanisms and their underlying biochemical modulation are operative in both healthy subjects as well as in MDD, BD, and SCH subjects; the only difference consists in the fact that these mechanisms are abnormally balanced and thus manifest in extreme values in psychiatric disorders. We conclude that psychomotor mechanisms operate in a dimensional and cross-nosological way as their degrees of expression are related to levels of psychomotor activity (across different disorders) rather than to the diagnostic categories themselves. Psychomotor mechanisms and their biochemical modulation can be considered paradigmatic examples of a dimensional approach as suggested in RDoC and the recently introduced spatiotemporal psychopathology.
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34
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Yamada Y, Matsumoto M, Iijima K, Sumiyoshi T. Specificity and Continuity of Schizophrenia and Bipolar Disorder: Relation to Biomarkers. Curr Pharm Des 2020; 26:191-200. [PMID: 31840595 PMCID: PMC7403693 DOI: 10.2174/1381612825666191216153508] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 12/13/2019] [Indexed: 01/24/2023]
Abstract
Schizophrenia and bipolar disorder overlap considerably in terms of symptoms, familial patterns, risk genes, outcome, and treatment response. This article provides an overview of the specificity and continuity of schizophrenia and mood disorders on the basis of biomarkers, such as genes, molecules, cells, circuits, physiology and clinical phenomenology. Overall, the discussions herein provided support for the view that schizophrenia, schizoaffective disorder and bipolar disorder are in the continuum of severity of impairment, with bipolar disorder closer to normality and schizophrenia at the most severe end. This approach is based on the concept that examining biomarkers in several modalities across these diseases from the dimensional perspective would be meaningful. These considerations are expected to help develop new treatments for unmet needs, such as cognitive dysfunction, in psychiatric conditions.
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Affiliation(s)
- Yuji Yamada
- Department of Psychiatry, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Madoka Matsumoto
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Kazuki Iijima
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tomiki Sumiyoshi
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
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35
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Lv Y, Wu S, Lin Y, Wang X, Wang J, Cai S, Huang L. Association of rs1059004 polymorphism in the OLIG2 locus with functional brain network in first-episode negative schizophrenia. Psychiatry Res Neuroimaging 2020; 303:111130. [PMID: 32563948 DOI: 10.1016/j.pscychresns.2020.111130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 05/29/2020] [Accepted: 06/11/2020] [Indexed: 01/10/2023]
Abstract
Schizophrenia has often been viewed as a disorder of connectivity. The single nucleotide polymorphism rs1059004 in the oligodendrocyte lineage transcription factor 2 gene locus has been reported to be associated with schizophrenia. We measured the functional connectivity and functional brain network topology properties in 49 schizophrenic patients and 47 healthy controls. We compared the strength and diversity of the functional connectivity and topological properties of functional networks between different genotypes. The correlations among functional connectivity, topological properties and behavioral performances were also investigated in this study. We found that the connectivity strength of schizophrenic patients carrying the risk A allele was generally decreased whereas connectivity diversity was increased. Regarding topological properties, all groups showed small-world properties, the nodal efficiency showed significant differences in the right precuneus and left middle temporal pole between different genotypes in schizophrenic patients. Moreover, the nodal efficiency in the left middle temporal pole was positively correlated with the neuropsychological assessment battery results of the schizophrenic patients who were homozygous for the C allele. Our results elucidate the contribution of rs1059004 to the functional brain network, and may help enhance the present understanding of the role of risk gene in the functional dysconnectivity of schizophrenia.
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Affiliation(s)
- Yahui Lv
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Sijia Wu
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Yanyan Lin
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Xuwen Wang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai jiaotong university, Shanghai 200030, China
| | - Suping Cai
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
| | - Liyu Huang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
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36
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Penadés R, Segura B, Inguanzo A, García-Rizo C, Catalán R, Masana G, Bernardo M, Junqué C. Cognitive remediation and brain connectivity: A resting-state fMRI study in patients with schizophrenia. Psychiatry Res Neuroimaging 2020; 303:111140. [PMID: 32693320 DOI: 10.1016/j.pscychresns.2020.111140] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 07/05/2020] [Accepted: 07/14/2020] [Indexed: 02/06/2023]
Abstract
Cognitive remediation is able to improve activation patterns in the frontal lobe but only few data on neuroconnectivity has been reported yet. Resting-state approach is a neuroimaging methodology with potentiality for testing neuroconnectivity in the context of cognitive remediation in schizophrenia. A resting-state fMRI data was acquired in part of the sample (n = 26 patients, n = 10 healthy controls) of a partner study (NCT02341131) testing the effects of cognitive remediation. A data-driven approach using independent component analysis (ICA) was used to identify functional brain networks, which were compared between groups and group per time using a dual-regression approach. ICA results revealed reduced functional connectivity between patients and controls in sensorimotor, basal ganglia, default mode and visual networks at baseline (p<0.05 FWE-corrected). After treatment, time per group analyses evidenced increased connectivity in sensorimotor network. Furthermore, group comparison at follow-up showed similar connectivity patterns between patients and healthy controls in sensorimotor network, but also in default mode and basal ganglia networks. No differences between treatment groups were found. Our results add some evidence to the hypothesis of altered connectivity in schizophrenia, and the possibility to modify some aspects of brain connectivity networks after psychological interventions.
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Affiliation(s)
- Rafael Penadés
- Barcelona Clinic Schizophrenia Unit, Hospital Clinic Barcelona, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain.
| | - Bàrbara Segura
- Medical Psychology Unit, Department of Medicine. Institute of Neuroscience, University of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Anna Inguanzo
- Medical Psychology Unit, Department of Medicine. Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Clemente García-Rizo
- Barcelona Clinic Schizophrenia Unit, Hospital Clinic Barcelona, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Rosa Catalán
- Barcelona Clinic Schizophrenia Unit, Hospital Clinic Barcelona, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Guillem Masana
- Barcelona Clinic Schizophrenia Unit, Hospital Clinic Barcelona, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Miquel Bernardo
- Barcelona Clinic Schizophrenia Unit, Hospital Clinic Barcelona, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Carme Junqué
- Medical Psychology Unit, Department of Medicine. Institute of Neuroscience, University of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Spain; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
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37
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van Dellen E, Börner C, Schutte M, van Montfort S, Abramovic L, Boks MP, Cahn W, van Haren N, Mandl R, Stam CJ, Sommer I. Functional brain networks in the schizophrenia spectrum and bipolar disorder with psychosis. NPJ SCHIZOPHRENIA 2020; 6:22. [PMID: 32879316 PMCID: PMC7468123 DOI: 10.1038/s41537-020-00111-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/23/2020] [Indexed: 12/22/2022]
Abstract
Psychotic experiences have been proposed to lie on a spectrum, ranging from subclinical experiences to treatment-resistant schizophrenia. We aimed to characterize functional connectivity and brain network characteristics in relation to the schizophrenia spectrum and bipolar disorder with psychosis to disentangle neural correlates to psychosis. Additionally, we studied antipsychotic medication and lithium effects on network characteristics. We analyzed functional connectivity strength and network topology in 487 resting-state functional MRI scans of individuals with schizophrenia spectrum disorder (SCZ), bipolar disorder with a history of psychotic experiences (BD), treatment-naïve subclinical psychosis (SCP), and healthy controls (HC). Since differences in connectivity strength may confound group comparisons of brain network topology, we analyzed characteristics of the minimum spanning tree (MST), a relatively unbiased backbone of the network. SCZ and SCP subjects had a lower connectivity strength than BD and HC individuals but showed no differences in network topology. In contrast, BD patients showed a less integrated network topology but no disturbances in connectivity strength. No differences in outcome measures were found between SCP and SCZ, or between BD patients that used antipsychotic medication or lithium and those that did not. We conclude that functional networks in patients prone to psychosis have different signatures for chronic SCZ patients and SCP compared to euthymic BD patients, with a limited role for medication. Connectivity strength effects may have confounded previous studies, as no functional network alterations were found in SCZ after strict correction for connectivity strength.
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Affiliation(s)
- Edwin van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
- Department of Intensive Care Medicine and UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Corinna Börner
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maya Schutte
- University of Groningen, Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - Simone van Montfort
- Department of Intensive Care Medicine and UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lucija Abramovic
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marco P Boks
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center, Sophia Children's Hospital, Rotterdam, The Netherlands
| | - René Mandl
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Iris Sommer
- University of Groningen, Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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38
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Li W, Zhou FC, Zhang L, Ng CH, Ungvari GS, Li J, Xiang YT. Comparison of cognitive dysfunction between schizophrenia and bipolar disorder patients: A meta-analysis of comparative studies. J Affect Disord 2020; 274:652-661. [PMID: 32663999 DOI: 10.1016/j.jad.2020.04.051] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 04/19/2020] [Accepted: 04/27/2020] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Cognitive dysfunction is common in both schizophrenia and bipolar disorder. This is a meta-analysis of studies that compared cognitive dysfunction between schizophrenia and bipolar disorder. METHODS Both international and Chinese databases were systematically searched. Studies that compared cognitive function between schizophrenia and bipolar disorder with the MATRICS Consensus Cognitive Battery (MCCB) were analyzed using the random-effects model. RESULTS Twelve studies with 9,518 participants (4,411 schizophrenia and 5,107 bipolar patients) were included in the analyses. Schizophrenia patients performed significantly worse than bipolar patients on the MCCB total scores with a large effect size (SMD=-0.80, 95%CI: -1.21 to -0.39), as well as on all the 7 subscale scores; attention (SMD=-2.56, 95%CI: -3.55 to -1.57) and social cognition (SMD=-0.86, 95%CI: -1.13 to -0.58) with large effect sizes; and speed of processing (SMD=-0.75, 95%CI: -1.00 to -0.49), working memory (SMD=-0.68, 95%CI: -0.91 to -0.45), verbal learning (SMD=-0.78, 95%CI: -0.95 to -0.61), visual learning (SMD=-0.65, 95%CI: -0.83 to -0.48), and reasoning and problem solving (SMD=-0.61, 95%CI: -0.93 to -0.29) with medium effect sizes. CONCLUSION Compared to bipolar patients, patients with schizophrenia had more severe cognitive dysfunction in this meta-analysis, particularly in attention and social cognition. Timely assessment and treatment of cognitive dysfunction should be part of standard management protocols in both schizophrenia and bipolar disorder.
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Affiliation(s)
- Wen Li
- Unit of Psychiatry, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China; Center for Cognition and Brain Sciences, University of Macau, Macao SAR, China
| | - Fu-Chun Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ling Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Chee H Ng
- Department of Psychiatry, The Melbourne Clinic and St Vincent's Hospital, University of Melbourne, Richmond, Victoria, Australia
| | - Gabor S Ungvari
- Division of Psychiatry, School of Medicine, University of Western Australia, Perth, Australia; University of Notre Dame Australia, Fremantle, Australia
| | - Jun Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China
| | - Yu-Tao Xiang
- Unit of Psychiatry, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China; Center for Cognition and Brain Sciences, University of Macau, Macao SAR, China.
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39
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Goswami S, Beniwal RP, Kumar M, Bhatia T, Gur RE, Gur RC, Khushu S, Deshpande SN. A preliminary study to investigate resting state fMRI as a potential group differentiator for schizophrenia. Asian J Psychiatr 2020; 52:102095. [PMID: 32339919 PMCID: PMC10154078 DOI: 10.1016/j.ajp.2020.102095] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Revised: 03/13/2020] [Accepted: 04/07/2020] [Indexed: 02/03/2023]
Abstract
Schizophrenia (SZ) is found to be associated with dysconnectivity between the various regions of the brain. These aberrant connections in brain networks responsible for various mental processes in schizophrenia. We examined differences in functional connectivity among persons with SZ (n = 30) and an equal number of their unaffected relatives using resting state functional Magnetic Resonance Imaging (rsfMRI). Subjects were interviewed using the Diagnostic Interview for Genetic Studies (DIGS) and Family Interview for Genetic Studies (FIGS). Cognition was assessed using the Computerized Neuropsychological Battery (CNB) and Trail Making Tests A and B. The resting state functional data were acquired using 3.0 T Magnetic Resonance Imaging system and analysed using Statistical Package for the Social Sciences (SPSS) version 21 and FSL version 5.01 (FMRIB's) Software. The persons with SZ performed significantly worse on tasks of cognition and executive functioning. On rsfMRI, a significantly reduced connectivity was noted in the case group in right and left precentral gyri, right post central gyrus, right and left middle temporal gyrus, left paracingulate gyrus, anterior and posterior cingulate, right planum temporale, right pallidum, left cerebellum-6,7b and 8 lobules. Increased connectivity was noted between areas of right temporal pole and left hippocampus, posterior cingulate and the precuneus, right planum polare and right amygdala, right Heschl's gyrus and left posterior supramarginal gyrus, right amygdala with right insular cortex and left cerebellum 6 with bilateral postcentral gyrus in the same group. These differences in connectivity could be utilised as potential group differentiator for schizophrenia.
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Affiliation(s)
- Seujee Goswami
- Department of Psychiatry, Assam Medical College and Hospital, Dibrugarh, Assam, India.
| | - Ram Pratap Beniwal
- Department of Psychiatry, Centre of Excellence in Mental Health, Atal Bihari Vajpayee Institute of Medical Sciences & Dr RML Hospital, New Delhi, India.
| | - Mukesh Kumar
- NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences (I.N.M.A.S), Timarpur, Delhi, India.
| | - Triptish Bhatia
- Indo-US Projects, Department of Psychiatry, Centre of Excellence in Mental Health, Atal Bihari Vajpayee Institute of Medical Sciences & Dr RML Hospital, New Delhi, India.
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA.
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA.
| | - Subhash Khushu
- NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences (I.N.M.A.S), Timarpur, Delhi, India.
| | - Smita N Deshpande
- Department of Psychiatry, Centre of Excellence in Mental Health, Atal Bihari Vajpayee Institute of Medical Sciences & Dr RML Hospital, New Delhi, India.
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40
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Miao Q, Pu C, Wang Z, Yan CG, Shi C, Cao Q, Wang X, Cheng Z, Han X, Yang L, Lai Y, Yuan Y, Ma H, Li K, Hong N, Yu X. Influence of More Than 5 Years of Continuous Exposure to Antipsychotics on Cerebral Functional Connectivity of Chronic Schizophrenia. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2020; 65:463-472. [PMID: 32027178 PMCID: PMC7298577 DOI: 10.1177/0706743720904815] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To explore the effect of long-term antipsychotics use on the strength of functional connectivity (FC) in the brains of patients with chronic schizophrenia. METHOD We collected resting-state functional magnetic resonance imaging from 15 patients with continuously treated chronic schizophrenia (TCS), 19 patients with minimally TCS (MTCS), and 20 healthy controls (HCs). Then, we evaluated and compared the whole-brain FC strength (FCS; including full-range, short-range, and long-range FCS) among patients with TCS, MTCS, and HCs. RESULTS Patients with TCS and MTCS showed reduced full-/short-range FC compared with the HCs. No significant differences in the whole-brain FCS (including full-range, short-range, and long-range FCS) or clinical characteristics were identified between patients with TCS and MTCS. Additionally, the FCS in the right fusiform gyrus, right inferior temporal gyrus, and right inferior occipital gyrus negatively correlated with the duration of illness and positively correlated with onset age across all patients with chronic schizophrenia. CONCLUSIONS Regardless of the long-term use of antipsychotics, patients with chronic schizophrenia show decreased FC compared with healthy individuals. For some patients with chronic schizophrenia, the influence of long-term and minimal/short-term antipsychotic exposure on resting-state FC was similar. The decreased full- and short-range FCS in the right fusiform gyrus, right inferior temporal gyrus, and right inferior occipital gyrus may be an ongoing pathological process that is not altered by antipsychotic interventions in patients with chronic schizophrenia. Large-sample, long-term follow-up studies are still needed for further exploration.
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Affiliation(s)
- Qi Miao
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Chengcheng Pu
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Zhijiang Wang
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Chuan Shi
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Qingjiu Cao
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Xijin Wang
- The First Psychiatric Hospital of Harbin, Heilongjiang, China
| | - Zhang Cheng
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Xue Han
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Lei Yang
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Yunyao Lai
- Department of Radiology, People's Hospital, Peking University, Beijing, China
| | - Yanbo Yuan
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Hong Ma
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
| | - Keqing Li
- The Sixth People's Hospital of Hebei Province, Baoding, China
| | - Nan Hong
- Department of Radiology, People's Hospital, Peking University, Beijing, China
| | - Xin Yu
- Peking University Sixth Hospital, Beijing, China.,Peking University Institute of Mental Health, Beijing, China.,NHC Key Laboratory of Mental Health, Peking University, Beijing, China.,National Clinical Research Center for Mental Health Disorders, Peking University Sixth Hospital, Beijing, China
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41
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Vostrikov VM, Uranova NA. Reduced density of oligodendrocytes and oligodendrocyte clusters in the caudate nucleus in major psychiatric illnesses. Schizophr Res 2020; 215:211-216. [PMID: 31653579 DOI: 10.1016/j.schres.2019.10.027] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 09/05/2019] [Accepted: 10/10/2019] [Indexed: 12/15/2022]
Abstract
Functional dysconnectivity in schizophrenia and affective disorders may be associated with myelin and oligodendrocyte abnormalities. Altered network integration involving the caudate nucleus (CN) and metabolic abnormalities in fronto-striatal-thalamic white matter tracts have been reported in schizophrenia and impaired patterns of cortico-caudate functional connectivity have been found in both bipolar disorder (BPD) and schizophrenia compared to healthy controls. Postmortem studies have found ultrastructural dystrophy and degeneration of oligodendrocytes and dysmyelination in the CN in schizophrenia and BPD. We aimed to test the hypothesis that oligodendrocyte density may be reduced in the CN in major psychiatric disorders and may thereby form the cellular basis for the functional dysconnectivity observed in these disorders. Optical disector was used to estimate the numerical density (Nv) of oligodendrocytes and oligodendrocyte clusters (OLC) in the CN of cases with schizophrenia, BPD and major depressive disorder (MDD) and in normal controls (15 cases per group). A significant reduction in the Nv of oligodendrocytes was found in schizophrenia and BPD as compared to the control group (p < 0.05), and the Nv of OLC was significantly lowered in schizophrenia and BPD compared to controls (p < 0.05). There were no significant differences between MDD and control groups. The Nv of OLC was significantly decreased in the left hemisphere in schizophrenia as compared to the left hemisphere of the control group (-52%, p < 0.01). The data indicates that a decreased density of oligodendrocytes and OLC could contribute to the altered functional connectivity of the CN in subjects with severe mental illnesses.
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Affiliation(s)
- V M Vostrikov
- Laboratory of Clinical Neuropathology, Mental Health Research Centre, Zagorodnoe shosse 2, Moscow, Russia
| | - N A Uranova
- Laboratory of Clinical Neuropathology, Mental Health Research Centre, Zagorodnoe shosse 2, Moscow, Russia.
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42
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Ilzarbe D, de la Serna E, Baeza I, Rosa M, Puig O, Calvo A, Masias M, Borras R, Pariente JC, Castro-Fornieles J, Sugranyes G. The relationship between performance in a theory of mind task and intrinsic functional connectivity in youth with early onset psychosis. Dev Cogn Neurosci 2019; 40:100726. [PMID: 31791005 PMCID: PMC6974903 DOI: 10.1016/j.dcn.2019.100726] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Revised: 09/06/2019] [Accepted: 11/03/2019] [Indexed: 12/15/2022] Open
Abstract
Psychotic disorders are characterized by theory of mind (ToM) impairment. Although ToM undergoes maturational changes throughout adolescence, there is a lack of studies examining ToM performance and its brain functional correlates in individuals with an early onset of psychosis (EOP; onset prior to age 18), and its relationship with age. Twenty-seven individuals with EOP were compared with 41 healthy volunteers using the "Reading-the-Mind-in-the-Eyes" Test, as a measure of ToM performance. A resting-state functional MRI scan was also acquired, in which the default mode network was used to identify areas relevant to ToM processing employing independent component analysis. Group effects revealed worse ToM performance and less intrinsic functional connectivity in the medial prefrontal cortex in EOP relative to healthy volunteers. Group by age interaction revealed age-positive associations in ToM task performance and in intrinsic connectivity in the medial prefrontal cortex in healthy volunteers, which were not present in EOP. Differences in ToM performance were partially mediated by intrinsic functional connectivity in the medial prefrontal cortex. Poorer ToM performance in EOP, coupled with less medial prefrontal cortex connectivity, could be associated with the impact of psychosis during a critical period of development of the social brain, limiting normative age-related maturation.
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Affiliation(s)
- Daniel Ilzarbe
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neurosciences, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Medicine, Universitat de Barcelona, Barcelona, Spain; Department of Child and Adolescent Psychiatry, Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
| | - Elena de la Serna
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neurosciences, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Medicine, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
| | - Inmaculada Baeza
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neurosciences, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Medicine, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
| | - Mireia Rosa
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neurosciences, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Olga Puig
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neurosciences, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Medicine, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
| | - Anna Calvo
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Mireia Masias
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Roger Borras
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neurosciences, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Jose C Pariente
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Josefina Castro-Fornieles
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neurosciences, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Medicine, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
| | - Gisela Sugranyes
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neurosciences, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Medicine, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain.
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43
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Lerman-Sinkoff DB, Kandala S, Calhoun VD, Barch DM, Mamah DT. Transdiagnostic Multimodal Neuroimaging in Psychosis: Structural, Resting-State, and Task Magnetic Resonance Imaging Correlates of Cognitive Control. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:870-880. [PMID: 31327685 PMCID: PMC6842450 DOI: 10.1016/j.bpsc.2019.05.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 03/14/2019] [Accepted: 05/01/2019] [Indexed: 11/17/2022]
Abstract
BACKGROUND Disorders with psychotic features, including schizophrenia and some bipolar disorders, are associated with impairments in regulation of goal-directed behavior, termed cognitive control. Cognitive control-related neural alterations have been studied in psychosis. However, studies are typically unimodal, and relationships across modalities of brain function and structure remain unclear. Thus, we performed transdiagnostic multimodal analyses to examine cognitive control-related neural variation in psychosis. METHODS Structural, resting, and working memory task imaging for 31 control participants, 27 participants with bipolar disorder, and 23 participants with schizophrenia were collected and processed identically to the Human Connectome Project, enabling identification of relationships with prior multimodal work. Two cognitive control-related independent components (ICs) derived from the Human Connectome Project using multiset canonical correlation analysis with joint IC analysis were used to predict performance in psychosis. De novo multiset canonical correlation analysis with joint IC analysis was performed, and the results were correlated with cognitive control. RESULTS A priori working memory and cortical thickness maps significantly predicted cognitive control in psychosis. De novo multiset canonical correlation analysis with joint IC analysis identified an IC correlated with cognitive control that also discriminated groups. Structural contributions included insular and cingulate regions; task contributions included precentral, posterior parietal, cingulate, and visual regions; and resting-state contributions highlighted canonical network organization. Follow-up analyses suggested that correlations with cognitive control were primarily influenced by participants with schizophrenia. CONCLUSIONS A priori and de novo imaging replicably identified a set of interrelated patterns across modalities and the healthy-to-psychosis spectrum, suggesting robustness of these features. Relationships between imaging and cognitive control performance suggest that shared symptomatology may be key to identifying transdiagnostic relationships in psychosis.
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Affiliation(s)
- Dov B Lerman-Sinkoff
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri; Medical Scientist Training Program, Washington University in St. Louis, St. Louis, Missouri.
| | - Sridhar Kandala
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Vince D Calhoun
- Medical Image Analysis Lab, The Mind Research Network, Albuquerque, New Mexico; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Psychological and Brain Science, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Daniel T Mamah
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
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44
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Lehner KR, Yeagle EM, Argyelan M, Klimaj Z, Du V, Megevand P, Hwang ST, Mehta AD. Validation of corpus callosotomy after laser interstitial thermal therapy: a multimodal approach. J Neurosurg 2019; 131:1095-1105. [PMID: 30497188 DOI: 10.3171/2018.4.jns172588] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Accepted: 04/17/2018] [Indexed: 11/06/2022]
Abstract
Objective Disconnection of the cerebral hemispheres by corpus callosotomy (CC) is an established means to palliate refractory generalized epilepsy. Laser interstitial thermal therapy (LITT) is gaining acceptance as a minimally invasive approach to treating epilepsy, but this method has not been evaluated in clinical series using established methodologies to assess connectivity. The goal in this study was to demonstrate the safety and feasibility of MRI-guided LITT for CC and to assess disconnection by using electrophysiology- and imaging-based methods. Methods Retrospective chart and imaging review was performed in 5 patients undergoing LITT callosotomy at a single center. Diffusion tensor imaging and resting functional MRI were performed in all patients to assess anatomical and functional connectivity. In 3 patients undergoing simultaneous intracranial electroencephalography monitoring, corticocortical evoked potentials and resting electrocorticography were used to assess electrophysiological correlates. Results All patients had generalized or multifocal seizure onsets. Three patients with preoperative evidence for possible lateralization underwent stereoelectroencephalography depth electrode implantation during the perioperative period. LITT ablation of the anterior corpus callosum was completed in a single procedure in 4 patients. One complication involving misplaced devices required a second procedure. Adequacy of the anterior callosotomy was confirmed using contrast-enhanced MRI and diffusion tensor imaging. Resting functional MRI, corticocortical evoked potentials, and resting electrocorticography demonstrated functional disconnection of the hemispheres. Postcallosotomy monitoring revealed lateralization of the seizures in all 3 patients with preoperatively suspected occult lateralization. Four of 5 patients experienced > 80% reduction in generalized seizure frequency. Two patients undergoing subsequent focal resection are free of clinical seizures at 2 years. One patient developed a 9-mm intraparenchymal hematoma at the site of entry and continued to have seizures after the procedure. Conclusions MRI-guided LITT provides an effective minimally invasive alternative method for CC in the treatment of seizures associated with drop attacks, bilaterally synchronous onset, and rapid secondary generalization. The disconnection is confirmed using anatomical and functional neuroimaging and electrophysiological measures.
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Affiliation(s)
- Kurt R Lehner
- 1Department of Neurosurgery, Hofstra Northwell School of Medicine
| | - Erin M Yeagle
- 1Department of Neurosurgery, Hofstra Northwell School of Medicine
- 2The Feinstein Institute for Medical Research; and
| | | | | | - Victor Du
- 1Department of Neurosurgery, Hofstra Northwell School of Medicine
| | | | - Sean T Hwang
- 3Department of Neurology, North Shore University Hospital, Manhasset, New York
| | - Ashesh D Mehta
- 1Department of Neurosurgery, Hofstra Northwell School of Medicine
- 2The Feinstein Institute for Medical Research; and
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45
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Dolleman-van der Weel MJ, Griffin AL, Ito HT, Shapiro ML, Witter MP, Vertes RP, Allen TA. The nucleus reuniens of the thalamus sits at the nexus of a hippocampus and medial prefrontal cortex circuit enabling memory and behavior. Learn Mem 2019; 26:191-205. [PMID: 31209114 PMCID: PMC6581009 DOI: 10.1101/lm.048389.118] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 04/16/2019] [Indexed: 12/12/2022]
Abstract
The nucleus reuniens of the thalamus (RE) is a key component of an extensive network of hippocampal and cortical structures and is a fundamental substrate for cognition. A common misconception is that RE is a simple relay structure. Instead, a better conceptualization is that RE is a critical component of a canonical higher-order cortico-thalamo-cortical circuit that supports communication between the medial prefrontal cortex (mPFC) and the hippocampus (HC). RE dysfunction is implicated in several clinical disorders including, but not limited to Alzheimer's disease, schizophrenia, and epilepsy. Here, we review key anatomical and physiological features of the RE based primarily on studies in rodents. We present a conceptual model of RE circuitry within the mPFC-RE-HC system and speculate on the computations RE enables. We review the rapidly growing literature demonstrating that RE is critical to, and its neurons represent, aspects of behavioral tasks that place demands on memory focusing on its role in navigation, spatial working memory, the temporal organization of memory, and executive functions.
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Affiliation(s)
- Margriet J Dolleman-van der Weel
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam NL-1007MB, The Netherlands
- Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam NL-1098XH, The Netherlands
| | - Amy L Griffin
- Department of Psychological and Brain Sciences, University of Delaware, Newark, Delaware 19716, USA
| | - Hiroshi T Ito
- Max Planck Institute for Brain Research, 60438, Frankfurt am Main, Germany
| | - Matthew L Shapiro
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, New York 12208, USA
| | - Menno P Witter
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, NTNU Norwegian University of Science and Technology, Trondheim NO-7491, Norway
| | - Robert P Vertes
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida 33431, USA
| | - Timothy A Allen
- Cognitive Neuroscience Program, Department of Psychology, Florida International University, Miami, Florida 33199, USA
- Department of Environmental Health Sciences, Florida International University, Miami, Florida 33199, USA
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46
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Maher S, Ekstrom T, Ongur D, Levy DL, Norton DJ, Nickerson LD, Chen Y. Functional disconnection between the visual cortex and right fusiform face area in schizophrenia. Schizophr Res 2019; 209:72-79. [PMID: 31126803 DOI: 10.1016/j.schres.2019.05.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 03/28/2019] [Accepted: 05/06/2019] [Indexed: 11/16/2022]
Abstract
Patients with schizophrenia show impairment in processing faces, including facial affect and face detection, but the underlying mechanisms are unknown. We used functional magnetic resonance imaging (fMRI) to characterize resting state functional connectivity between an independent component analysis (ICA)-defined early visual cortical network (corresponding to regions in V1, V2, V3) and a priori defined face-processing regions (fusiform face area [FFA], occipital face area [OFA], superior temporal sulcus [STS] and amygdala) using dual regression in 20 schizophrenia patients and 26 healthy controls. We also investigated the association between resting functional connectivity and neural responses (fMRI) elicited by a face detection paradigm in a partially overlapping sample (Maher et al., 2016) that used stimuli equated for lower-level perceptual abilities. Group differences in functional connectivity were found in right FFA only; controls showed significantly stronger functional connectivity to an early visual cortical network. Functional connectivity in right FFA was associated with (a) neural responses during face detection in controls only, and (b) perceptual detection thresholds for faces in patients only. The finding of impaired functional connectivity for right FFA (but not other queried domain-specific regions) converges with findings investigating face detection in an overlapping sample in which dysfunction was found exclusively for right FFA in schizophrenia during face detection.
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Affiliation(s)
- S Maher
- McLean Hospital, Harvard Medical School, United States of America.
| | - T Ekstrom
- McLean Hospital, Harvard Medical School, United States of America
| | - D Ongur
- McLean Hospital, Harvard Medical School, United States of America
| | - D L Levy
- McLean Hospital, Harvard Medical School, United States of America
| | - D J Norton
- McLean Hospital, Harvard Medical School, United States of America
| | - L D Nickerson
- McLean Hospital, Harvard Medical School, United States of America
| | - Y Chen
- McLean Hospital, Harvard Medical School, United States of America
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47
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Mitelman SA. Transdiagnostic neuroimaging in psychiatry: A review. Psychiatry Res 2019; 277:23-38. [PMID: 30639090 DOI: 10.1016/j.psychres.2019.01.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 01/07/2019] [Accepted: 01/07/2019] [Indexed: 01/10/2023]
Abstract
Transdiagnostic approach has a long history in neuroimaging, predating its recent ascendance as a paradigm for new psychiatric nosology. Various psychiatric disorders have been compared for commonalities and differences in neuroanatomical features and activation patterns, with different aims and rationales. This review covers both structural and functional neuroimaging publications with direct comparison of different psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, conduct disorder, anorexia nervosa, and bulimia nervosa. Major findings are systematically presented along with specific rationales for each comparison.
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Affiliation(s)
- Serge A Mitelman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Department of Psychiatry, Division of Child and Adolescent Psychiatry, Elmhurst Hospital Center, 79-01 Broadway, Elmhurst, NY 11373, USA.
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48
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Cognitive variability in bipolar I disorder: A cluster-analytic approach informed by resting-state data. Neuropharmacology 2019; 156:107585. [PMID: 30914304 DOI: 10.1016/j.neuropharm.2019.03.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 03/21/2019] [Accepted: 03/22/2019] [Indexed: 01/15/2023]
Abstract
BACKGROUND While the presence of cognitive performance deficits in bipolar disorder I (BD-I) is well established, there is no consensus about which cognitive abilities are affected. Heterogeneous phenotypes displayed in BD-I further suggest the existence of subgroups among the disorder. The present study sought to identify different cognitive profiles among BD-I patients as well as potentially underlying neuronal network changes. METHODS 54 euthymic BD-I patients underwent cognitive testing and resting state neuroimaging. Hierarchical cluster-analysis was performed on executive function scores of bipolar patients. The derived clusters were compared against 54 age-, gender- and IQ-matched healthy controls (HC) to facilitate the interpretation of results. Further, resting state network properties were compared to identify differences probably underlying cognitive profiles. RESULTS A three-cluster solution emerged. Cluster 1 (n = 22) was characterized by deficits in cognitive flexibility and motor inhibition, cluster 2 (n = 12) displayed impulsive decision-making, while cluster 3 (n = 20) showed good visuospatial planning. Weaker connections in cluster 1 compared to cluster 2 were found between regions activated during tasks cluster 1 showed deficits on. Cluster 3 had a higher modularity than cluster 2, which correlated positively with problem solving performance and risk-taking in this cluster. CONCLUSION Obtained clusters showed distinct cognitive profiles, characterized by deficits and strengths, most of which remained precluded in a general comparison. Weaker interregional connections and separated subnetworks might underly behavioral deficits and strengths, respectively. The findings help explain the phenotypic heterogeneity observed in BD-I. This article is part of the Special Issue entitled 'Current status of the neurobiology of aggression and impulsivity'.
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49
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Jensen AM, Tregellas JR, Sutton B, Xing F, Ghosh D. Kernel machine tests of association between brain networks and phenotypes. PLoS One 2019; 14:e0199340. [PMID: 30897094 PMCID: PMC6428401 DOI: 10.1371/journal.pone.0199340] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 02/24/2019] [Indexed: 11/19/2022] Open
Abstract
Applications of quantitative network analysis to functional brain connectivity have become popular in the last decade due to their ability to describe the general topological principles of brain networks. However, many issues arise when standard statistical analysis techniques are applied to functional magnetic resonance imaging (fMRI) connectivity maps. Frequently, summary measures of these maps, such as global efficiency and clustering coefficients, collapse the changing structures of graph topology from many scales to one. This can result in a loss of whole-brain spatio-temporal pattern information that may be significant in association and prediction analyses. Drawing from the electrical engineering field, the resistance perturbation distance is a quantification of similarity between graphs on the same vertex set that has been shown to identify changes in dynamic graphs, such as those from fMRI, while not being computationally expensive or result in a loss of information. This work proposes a novel kernel-based regression scheme that incorporates the resistance perturbation distance to better understand the association with biological phenotypes from fMRI using both simulated and real datasets.
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Affiliation(s)
- Alexandria M. Jensen
- Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Jason R. Tregellas
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- Research Services, Denver VA Medical Center, Aurora, Colorado, United States of America
| | - Brianne Sutton
- Department of Behavioral Health, Denver Health, Denver, Colorado, United States of America
| | - Fuyong Xing
- Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
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50
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Xia M, Womer FY, Chang M, Zhu Y, Zhou Q, Edmiston EK, Jiang X, Wei S, Duan J, Xu K, Tang Y, He Y, Wang F. Shared and Distinct Functional Architectures of Brain Networks Across Psychiatric Disorders. Schizophr Bull 2019; 45:450-463. [PMID: 29897593 PMCID: PMC6403059 DOI: 10.1093/schbul/sby046] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Brain network alterations have increasingly been implicated in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). However, little is known about the similarities and differences in functional brain networks among patients with SCZ, BD, and MDD. A total of 512 participants (121 with SCZ, 100 with BD, 108 with MDD, and 183 healthy controls, matched for age and sex) completed resting-state functional magnetic resonance imaging at a single site. Four global measures (the clustering coefficient, the characteristic shortest path length, the normalized clustering coefficient, and the normalized characteristic path length) were computed at a voxel level to quantify segregated and integrated configurations. Inter-regional functional associations were examined based on the Euclidean distance between regions. Distance strength maps were used to localize regions with altered distances based on functional connectivity. Patient groups exhibited shifts in their network architectures toward randomized configurations, with SCZ>BD>MDD in the degree of randomization. Patient groups displayed significantly decreased short-range connectivity and increased medium-/long-range connectivity. Decreases in short-range connectivity were similar across the SZ, BD, and MDD groups and were primarily distributed in the primary sensory and association cortices and the thalamus. Increases in medium-/long-range connectivity were differentially localized within the prefrontal cortices among the patient groups. We highlight shared and distinct connectivity features in functional brain networks among patients with SCZ, BD, and MDD, which expands our understanding of the common and distinct pathophysiological mechanisms and provides crucial insights into neuroimaging-based methods for the early diagnosis of and interventions for psychiatric disorders.
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Affiliation(s)
- Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, PR China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, PR China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, PR China
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Miao Chang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yue Zhu
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Qian Zhou
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Elliot Kale Edmiston
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Xiaowei Jiang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Shengnan Wei
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Jia Duan
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Ke Xu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yanqing Tang
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, PR China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, PR China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, PR China
| | - Fei Wang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
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