![\"Working](\"https:\/\/bicr.atr.jp\/dcn\/wp-content\/uploads\/Figure2-300x300.png\")
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A prediction model of working memory ability was developed in Yamashita et al. 2015, Sci Rep<\/a>. As a next step, we demonstrated the model’s generalizability to independent cohorts that included multiple psychiatric disorders in Yamashita et al. 2017 bioRxiv<\/a>.<\/span><\/p>\n <\/p>\n Method summary<\/strong><\/span><\/p>\n In Yamashita et al. 2015, healthy young participants performed a letter-based 3-back task for 25 blocks in a single session (80-90 minutes). A learning curve was quantified by taking moving average of each 5-blocks, and then the learning plateau was estimated by an inverse curve fitting. Resting state functional MRI data was obtained for 5 minutes while they looking at fixation point under dim light. Large-scale network connectivity, including between- and within-network connectivity, was calculated using BrainMap ICA of 20 components. Using a sparse linear regression with leave-one-out cross-validation, individual learning plateaus were predicted by their whole-brain connectivity patterns.<\/span><\/p>\n In Yamashita et al. 2017, we developed a prediction model of working memory ability using the full sample of the dataset used in Yamashita et al., 2015. The model was tested by using external independent datasets that include participants with psychiatric diagnoses: schizophrenia, major depressive disorder, obsessive-compulsive disorder, and autism spectrum disorder.<\/span><\/p>\n <\/p>\n Data<\/strong><\/span><\/p>\n “ATR dataset” includes basic demographic data (age, sex, handedness), behavioral data (estimated learning plateau of the 3-back task), and brain connectivity data (171 connectivity values, 153 of between-network connectivity and 18 within-network connectivity).<\/span><\/p>\n “Multiple Psychiatric Diagnoses dataset” includes basic demographic data (age, sex), and brain connectivity data (171 connectivity values, 153 of between-network connectivity and 18 within-network connectivity). In schizohphrenia group, cognitive test (the Brief Assessment of Cognition in Schizophrenia) subscores are also included.<\/span><\/p>\n <\/p>\n Code<\/strong><\/span><\/p>\n MATLAB code to build and test a prediction model is shared. The code (i) builds the prediction model by using ATR dataset and (ii) predicts individual working memory ability using test data (e.g. Multiple Psychiatric Diagnoses dataset). The scripts are released under GNU General Public License.<\/span><\/p>\n <\/p>\n Download<\/strong><\/span><\/p>\n In order to download data and code, you must agree to abide by the Terms of Use<\/a>. After we approve your request, you will receive a download link via email.<\/span><\/p>\n <\/p>\n Authors<\/strong><\/span><\/p>\n Masahiro Yamashita, Yujiro Yoshihara, Ryuichiro Hashimoto, Noriaki Yahata, Naho Ichikawa, Yuki Sakai, Takashi Yamada, Noriko Matsukawa, Go Okada, Saori C Tanaka, Kiyoto Kasai, Nobumasa Kato, Yasumasma Okamoto, Ben Seymour, Hidehiko Takahashi, Mitsuo Kawato, Hiroshi Imamizu.<\/span><\/p>\n <\/p>\n References<\/strong><\/span><\/p>\n [1] Yamashita, M., Kawato, M., & Imamizu, H. (2015). Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns. Scientific Reports, 5, 7622<\/a>.<\/span><\/p>\n [2] Yamashita, M., Yoshihara, Y., Hashimoto, R., Yahata, N., Ichikawa, N., Sakai, Y., Yamada T., Matsukawa N., Okada G., Tanaka S.C., Kasai K., Kato N., Okamoto Y., Seymour B., Takahashi H., Kawato M., Imamizu H. (2017). A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity. bioRxiv, 222281<\/a>.<\/span><\/p>\n <\/p>\n Funding<\/strong><\/span><\/p>\n JP18dm0307008 (AMED)<\/span> A prediction model of working memory ability was developed in Yamashita et al. 2015, Sci Rep. As a next step, we demonstrated the model’s generalizability to independent cohorts that included multiple psychiatric disorders in Yamashita et al. 2017 bioRxiv. Method summary In Yamashita et al. 2015, healthy young participants performed a letter-based 3-back task for 25 blocks in a single session (80-90 minutes). A learning curve was quantified by taking moving average of each 5-blocks, and then the learning plateau was estimated by an inverse curve fitting. Resting state functional MRI data was obtained for 5 minutes while they looking at fixation point under dim light. Large-scale network connectivity, […]<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":1856,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1808","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/pages\/1808","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/comments?post=1808"}],"version-history":[{"count":16,"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/pages\/1808\/revisions"}],"predecessor-version":[{"id":1949,"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/pages\/1808\/revisions\/1949"}],"up":[{"embeddable":true,"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/pages\/1856"}],"wp:attachment":[{"href":"https:\/\/bicr.atr.jp\/dcn\/en\/wp-json\/wp\/v2\/media?parent=1808"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nDevelopment of BMI Technologies for Clinical Application (MEXT SRPBS)<\/span>
\nNovel and Innovative R&D Making Use of Brain Structures (MIC)<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"