A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity
- Advanced Telecommunications Research Institute International, Japan
- Kyoto University Graduate School of Medicine, Japan
- Showa University, Japan
- The University of Tokyo, Japan
- Hiroshima University Graduate School of Biomedical and Health Sciences, Japan
- University of Cambridge, United Kingdom
Abstract
Working memory deficits are present in many neuropsychiatric diseases with diagnosis-related severity. However, it is unknown whether this common behavioral abnormality is a continuum explained by a neural mechanism shared across diseases or a set of discrete dysfunctions. Here, we performed predictive modeling to examine working memory ability (WMA) as a function of normative whole-brain connectivity across psychiatric diseases. We built a quantitative model for letter 3-back task performance in healthy participants, using resting state functional magnetic resonance imaging (rs-fMRI). This normative model was applied to independent participants (N = 965) including four psychiatric diagnoses. Individual's predicted WMA significantly correlated with a measured WMA in both healthy population and schizophrenia. Our predicted effect size estimates on WMA impairment were comparable to previous meta-analysis results. These results suggest a general association between brain connectivity and working memory ability applicable commonly to health and psychiatric diseases.
Data availability
The following dataset was generated: Yamashita, M, Yoshihara, Y, Hashimoto, R, Yahata, N, Ichikawa, N, Sakai, Y, ... Imamizu, H, 2018, Working Memory Prediction Database, https://bicr.atr.jp/dcn/en/download/database-wmp/. A download link for the open access dataset will be sent after the application form for data usage is completed (https://bicr.atr.jp/dcn/wp-content/uploads/Application_Form_for_Data_Usage_WMP-3.pdf). MATLAB code used to build the prediction model are also shared via this download link. You can send the completed application form to dcn_db@atr.jp. The Human Connectome Project 500 Subjects Release Open Access dataset is available from ConnectomeDB (https://db.humanconnectome.org/app/template/Login.vm) after the creation of a free account. Before accessing the dataset, users must agree with the Open Access Data Use Terms from ConnectomeDB (further information can be found here https://www.humanconnectome.org/study/hcp-young-adult/document/500-subjects-data-release and here https://www.humanconnectome.org/study/hcp-young-adult/data-use-terms).
Article and author information
Author details
Masahiro Yamashita
Ryuichiro Hashimoto
Funding
Ministry of Education, Culture, Sports, Science, and Technology (JP18dm0307008)
- Mitsuo Kawato
Wellcome
- Ben Seymour
ImPACT Program of Council for Science, Technology and Innovation
- Masahiro Yamashita
- Mitsuo Kawato
- Hiroshi Imamizu
JSPS KAKENHI (26120002)
- Hiroshi Imamizu
Arthritis Research UK (21357)
- Ben Seymour
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: ATR dataset was acquired using protocol (#12-101) according to the Declaration of Helsinki and approved by the Ethics Committee at Advanced Telecommunication Research Institute International. All participants gave written informed consent.Data from SCZ group was acquired by study design that was approved by the Committee on Medical Ethics (#R0027) of Kyoto University and was conducted in accordance with the Code of Ethics of the World Medical Association. All participants gave written informed consent.Data from MDD group was acquired by study protocol (#E-38) that was approved by the Ethics Committee of Hiroshima University. All participants gave written informed consent.Data from OCD group was acquired by study protocol (#RBMR-C-1098-5) that was approved by the Medical Committee on Human Studies at the Kyoto Prefectural University of Medicine. All participants gave written informed consent.Data from ASD group at the University of Tokyo was acquired by study protocol (#3048 and #3150) approved by the Ethics Committee of the Graduate School of Medicine and Faculty of Medicine at the University of Tokyo. All participants gave written informed consent.Data from ASD group at Showa University was aqcuired by study protocol (#893) that was approved by Ethics Committee of the Faculty of Medicine of Showa University. All participants gave written informed consent.
Reviewing Editor
- Michael Breakspear, QIMR Berghofer Medical Research Institute, Australia
Publication history
- Received: June 1, 2018
- Accepted: December 8, 2018
- Accepted Manuscript published: December 10, 2018 (version 1)
- Version of Record published: January 8, 2019 (version 2)
Copyright
© 2018, Yamashita et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity
eLife 7:e38844.
https://doi.org/10.7554/eLife.38844
Further reading
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Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of'bilateral symmetry' to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures.
