Effective Connectivity in Depression

Edmund T Rolls; Wei Cheng; Matthieu Gilson; Jiang Qiu; Zicheng Hu; Hongtao Ruan; Yu Li; Chu-Chung Huang; Albert C Yang; Shih-Jen Tsai; Xiaodong Zhang; Kaixiang Zhuang; Ching-Po Lin; Gustavo Deco; Peng Xie; Jianfeng Feng

doi:10.1016/j.bpsc.2017.10.004

Effective Connectivity in Depression

Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Feb;3(2):187-197. doi: 10.1016/j.bpsc.2017.10.004. Epub 2017 Oct 23.

Authors

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

Affiliations

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.

PMID: 29529414
DOI: 10.1016/j.bpsc.2017.10.004

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.

Keywords: Depression; Effective connectivity; Functional connectivity; Medial temporal lobe; Orbitofrontal cortex; Precuneus; Resting-state functional neuroimaging.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cerebral Cortex / physiopathology
Depressive Disorder, Major / physiopathology*
Female
Hippocampus / physiopathology
Humans
Magnetic Resonance Imaging / methods
Male
Nerve Net / physiopathology
Neural Pathways / physiopathology*
Prefrontal Cortex / physiopathology*
Reward
Temporal Lobe / physiopathology*