TY - JOUR TI - A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity AU - Yamashita, Masahiro AU - Yoshihara, Yujiro AU - Hashimoto, Ryuichiro AU - Yahata, Noriaki AU - Ichikawa, Naho AU - Sakai, Yuki AU - Yamada, Takashi AU - Matsukawa, Noriko AU - Okada, Go AU - Tanaka, Saori C AU - Kasai, Kiyoto AU - Kato, Nobumasa AU - Okamoto, Yasumasa AU - Seymour, Ben AU - Takahashi, Hidehiko AU - Kawato, Mitsuo AU - Imamizu, Hiroshi A2 - Breakspear, Michael A2 - Frank, Michael J VL - 7 PY - 2018 DA - 2018/12/10 SP - e38844 C1 - eLife 2018;7:e38844 DO - 10.7554/eLife.38844 UR - https://doi.org/10.7554/eLife.38844 AB - 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 three-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. KW - working memory KW - rs-fMRI KW - functional connectivity KW - prediction model KW - biomarkers JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -