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.
Reviewing Editor
- Michael Breakspear, QIMR Berghofer Medical Research Institute, Australia
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.
Version 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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- Neuroscience
The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.
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The brain regulates food intake in response to internal energy demands and food availability. However, can internal energy storage influence the type of memory that is formed? We show that the duration of starvation determines whether Drosophila melanogaster forms appetitive short-term or longer-lasting intermediate memories. The internal glycogen storage in the muscles and adipose tissue influences how intensely sucrose-associated information is stored. Insulin-like signaling in octopaminergic reward neurons integrates internal energy storage into memory formation. Octopamine, in turn, suppresses the formation of long-term memory. Octopamine is not required for short-term memory because octopamine-deficient mutants can form appetitive short-term memory for sucrose and to other nutrients depending on the internal energy status. The reduced positive reinforcing effect of sucrose at high internal glycogen levels, combined with the increased stability of food-related memories due to prolonged periods of starvation, could lead to increased food intake.
