Edmund T Rolls1, Wei Cheng2, Matthieu Gilson3, Jiang Qiu4, Zicheng Hu5, Hongtao Ruan6, Yu Li7, Chu-Chung Huang8, Albert C Yang9, Shih-Jen Tsai9, Xiaodong Zhang5, Kaixiang Zhuang7, Ching-Po Lin10, Gustavo Deco11, Peng Xie12, Jianfeng Feng13. 1. Department of Computer Science, University of Warwick, Coventry, United Kingdom; Oxford Centre for Computational Neuroscience, Oxford, United Kingdom. Electronic address: Edmund.Rolls@oxcns.org. 2. Department of Computer Science, University of Warwick, Coventry, United Kingdom; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China. 3. Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain. 4. Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China. 5. Institute of Neuroscience, Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. 6. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China; School of Mathematical Sciences, Fudan University, Shanghai, PR China. 7. Department of Psychology, Southwest University, Chongqing, China. 8. Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan. 9. Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan. 10. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China; Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan; Brain Research Center, National Yang-Ming University, Taipei, Taiwan. Electronic address: cplin@ym.edu.tw. 11. Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona, Spain. 12. Institute of Neuroscience, Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. Electronic address: xiepeng@cqmu.edu.cn. 13. Department of Computer Science, University of Warwick, Coventry, United Kingdom; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China; School of Mathematical Sciences, Fudan University, Shanghai, PR China; School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, PR China. Electronic address: jianfeng64@gmail.com.
Abstract
BACKGROUND: Resting-state functional connectivity reflects correlations in the activity between brain areas, whereas effective connectivity between different brain areas measures directed influences of brain regions on each other. Using the latter approach, we compare effective connectivity results in patients with major depressive disorder (MDD) and control subjects. METHODS: We used a new approach to the measurement of effective connectivity, in which each brain area has a simple dynamical model, and known anatomical connectivity is used to provide constraints. This helps the approach to measure the effective connectivity between the 94 brain areas parceled in the automated anatomical labeling (AAL2) atlas, using resting-state functional magnetic resonance imaging. Moreover, we show how the approach can be used to measure the differences in effective connectivity between different groups of individuals, using as an example effective connectivity in the healthy brain and in individuals with depression. The first brainwide resting-state effective-connectivity neuroimaging analysis of depression, with 350 healthy individuals and 336 patients with major depressive disorder, is described. RESULTS: Key findings are that the medial orbitofrontal cortex, implicated in reward and subjective pleasure, has reduced effective connectivity from temporal lobe input areas in depression; that the lateral orbitofrontal cortex, implicated in nonreward, has increased activity (variance) in depression, with decreased effective connectivity to and from cortical areas contralateral to language-related areas; and that the hippocampus, implicated in memory, has increased activity (variance) in depression and increased effective connectivity from the temporal pole. CONCLUSIONS: Measurements of effective connectivity made using the new method provide a new approach to causal mechanisms in the brain in depression.
BACKGROUND: Resting-state functional connectivity reflects correlations in the activity between brain areas, whereas effective connectivity between different brain areas measures directed influences of brain regions on each other. Using the latter approach, we compare effective connectivity results in patients with major depressive disorder (MDD) and control subjects. METHODS: We used a new approach to the measurement of effective connectivity, in which each brain area has a simple dynamical model, and known anatomical connectivity is used to provide constraints. This helps the approach to measure the effective connectivity between the 94 brain areas parceled in the automated anatomical labeling (AAL2) atlas, using resting-state functional magnetic resonance imaging. Moreover, we show how the approach can be used to measure the differences in effective connectivity between different groups of individuals, using as an example effective connectivity in the healthy brain and in individuals with depression. The first brainwide resting-state effective-connectivity neuroimaging analysis of depression, with 350 healthy individuals and 336 patients with major depressive disorder, is described. RESULTS: Key findings are that the medial orbitofrontal cortex, implicated in reward and subjective pleasure, has reduced effective connectivity from temporal lobe input areas in depression; that the lateral orbitofrontal cortex, implicated in nonreward, has increased activity (variance) in depression, with decreased effective connectivity to and from cortical areas contralateral to language-related areas; and that the hippocampus, implicated in memory, has increased activity (variance) in depression and increased effective connectivity from the temporal pole. CONCLUSIONS: Measurements of effective connectivity made using the new method provide a new approach to causal mechanisms in the brain in depression.
Authors: Patrick S Malone; Silvio P Eberhardt; Klaus Wimmer; Courtney Sprouse; Richard Klein; Katharina Glomb; Clara A Scholl; Levan Bokeria; Philip Cho; Gustavo Deco; Xiong Jiang; Lynne E Bernstein; Maximilian Riesenhuber Journal: Hum Brain Mapp Date: 2019-03-28 Impact factor: 5.038
Authors: Ashish K Sahib; Joana R A Loureiro; Megha M Vasavada; Antoni Kubicki; Shantanu H Joshi; Kai Wang; Roger P Woods; Eliza Congdon; Danny J J Wang; Michael L Boucher; Randall Espinoza; Katherine L Narr Journal: Eur Neuropsychopharmacol Date: 2020-02-12 Impact factor: 5.415