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Matrix Gaussian graphical models are a useful tool to characterize the conditional dependence structure of rows and columns. We employ non-convex penalization to tackle the estimation of multiple graphs from matrix-valued data under a matrix normal distribution. We propose a highly efficient non-convex optimization algorithm that can scale up for graphs with hundreds of nodes. We establish the asymptotic properties of the estimator, which requires less stringent conditions and has a sharper probability error bound than existing results. We demonstrate the efficacy of our proposed method through both simulations and real functional magnetic resonance imaging analyses.<\/jats:p>","DOI":"10.1111\/rssb.12278","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T01:31:26Z","timestamp":1529026286000},"page":"927-950","source":"Crossref","is-referenced-by-count":24,"title":["Multiple Matrix Gaussian Graphs Estimation"],"prefix":"10.1093","volume":"80","author":[{"given":"Yunzhang","family":"Zhu","sequence":"first","affiliation":[{"name":"Ohio State University , Columbus , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lexin","family":"Li","sequence":"additional","affiliation":[{"name":"University of California at Berkeley \u00a0 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,6,14]]},"reference":[{"key":"2023022101105523700_","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1016\/j.biopsych.2004.08.019","article-title":"Neurobiology of executive functions: catecholamine influences on prefrontal cortical functions","volume":"57","author":"Arnsten","year":"2005","journal-title":"Biol. 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