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Rolls ET, Cheng W, Gilson M, Gong W, Deco G, Lo CYZ, Yang AC, Tsai SJ, Liu ME, Lin CP, Feng J. Beyond the disconnectivity hypothesis of schizophrenia. Cereb Cortex 2021; 30:1213-1233. [PMID: 31381086 DOI: 10.1093/cercor/bhz161] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 06/24/2019] [Accepted: 06/24/2019] [Indexed: 01/01/2023] Open
Abstract
To go beyond the disconnectivity hypothesis of schizophrenia, directed (effective) connectivity was measured between 94 brain regions, to provide evidence on the source of the changes in schizophrenia and a mechanistic model. Effective connectivity (EC) was measured in 180 participants with schizophrenia and 208 controls. For the significantly different effective connectivities in schizophrenia, on average the forward (stronger) effective connectivities were smaller, whereas the backward connectivities tended to be larger. Further, higher EC in schizophrenia was found from the precuneus and posterior cingulate cortex (PCC) to areas such as the parahippocampal, hippocampal, temporal, fusiform, and occipital cortices. These are backward effective connectivities and were positively correlated with the positive symptoms of schizophrenia. Lower effective connectivities were found from temporal and other regions and were negatively correlated with the symptoms, especially the negative and general symptoms. Further, a signal variance parameter was increased for areas that included the parahippocampal gyrus and hippocampus, consistent with the hypothesis that hippocampal overactivity is involved in schizophrenia. This investigation goes beyond the disconnectivity hypothesis by drawing attention to differences in schizophrenia between backprojections and forward connections, with the backward connections from the precuneus and PCC implicated in memory stronger in schizophrenia.
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Affiliation(s)
- Edmund T Rolls
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Oxford Centre for Computational Neuroscience, Oxford OX1 4BH, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona E-08018, Spain and Brain and Cognition, Pompeu Fabra University, Barcelona, Spain
| | - Weikang Gong
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX1 4BH, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona E-08018, Spain and Brain and Cognition, Pompeu Fabra University, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Mu-En Liu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Institute of Neuroscience, National Yang-Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei 11221, Taiwan
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,School of Mathematical Sciences, School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, PR China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
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Koh MT, Gallagher M. Using internal memory representations in associative learning to study hallucination-like phenomenon. Neurobiol Learn Mem 2020; 175:107319. [PMID: 33010386 PMCID: PMC7655598 DOI: 10.1016/j.nlm.2020.107319] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/24/2020] [Accepted: 09/27/2020] [Indexed: 12/23/2022]
Abstract
Studies of Pavlovian conditioning have enriched our understanding of how relations among events can adaptively guide behavior through the formation and use of internal mental representations. In this review, we illustrate how internal representations flexibly integrate new updated information in reinforcer revaluation to influence relationships to impact actions and outcomes. We highlight representation-mediated learning to show the similarities in properties and functions between internally generated and directly activated representations, and how normal perception of internal representations could contribute to hallucinations. Converging evidence emerges from recent behavioral and neural activation studies using animal models of schizophrenia as well as clinical studies in patients to support increased tendencies in these populations to evoke internal representations from prior associative experience that approximate hallucination-like percepts. The heightened propensity is dependent on dopaminergic activation which is known to be sensitive to hippocampal overexcitability, a condition that has been observed in patients with psychosis. This presents a network that overlaps with cognitive neural circuits and offers a fresh approach for the development of therapeutic interventions targeting psychosis.
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Affiliation(s)
- Ming Teng Koh
- Department of Psychological and Brain Sciences, Johns Hopkins University, USA.
| | - Michela Gallagher
- Department of Psychological and Brain Sciences, Johns Hopkins University, USA
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