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Yet, typical analyses of functional connectivity, such as spatial independent components analysis (ICA) for resting\u2010state data, often ignore sources of between\u2010subject variability, which may be crucial for identifying functional connectivity patterns associated with MDD. Typically, methods like spatial ICA will identify a single component to represent a network like the default mode network (DMN), even if groups within the data show differential DMN coactivation. To address this gap, this project applies a tensorial extension of ICA (tensorial ICA)\u2014which explicitly incorporates between\u2010subject variability\u2014to identify functionally connected networks using functional MRI data from the Human Connectome Project (HCP). Data from the HCP included individuals with a diagnosis of MDD, a family history of MDD, and healthy controls performing a gambling and social cognition task. Based on evidence associating MDD with blunted neural activation to rewards and social stimuli, we predicted that tensorial ICA would identify networks associated with reduced spatiotemporal coherence and blunted social and reward\u2010based network activity in MDD. Across both tasks, tensorial ICA identified three networks showing decreased coherence in MDD. All three networks included ventromedial prefrontal cortex, striatum, and cerebellum and showed different activation across the conditions of their respective tasks. However, MDD was only associated with differences in task\u2010based activation in one network from the social task. Additionally, these results suggest that tensorial ICA could be a valuable tool for understanding clinical differences in relation to network activation and connectivity.<\/jats:p>","DOI":"10.1002\/hbm.26254","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T07:51:30Z","timestamp":1678175490000},"page":"2905-2920","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Tensorial independent component analysis reveals social and reward networks associated with major depressive disorder"],"prefix":"10.1002","volume":"44","author":[{"given":"Jeff B.","family":"Dennison","sequence":"first","affiliation":[{"name":"Department of Psychology &amp; Neuroscience Temple University  Philadelphia Pennsylvania USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lindsey J.","family":"Tepfer","sequence":"additional","affiliation":[{"name":"Department of Psychological and Brain Science Dartmouth University  Hanover New Hampshire USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5754-9633","authenticated-orcid":false,"given":"David V.","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Psychology &amp; Neuroscience Temple University  Philadelphia Pennsylvania USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291714001123"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(15)00461-4"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.033"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2004.10.043"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-020-0751-3"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2015.11.023"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41583-018-0002-7"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2015.06.041"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nn.3423"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.10.057"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.10032"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2000.0612"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/0891988717700509"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnsys.2010.00008"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2018.01.024"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.2000.84.6.3072"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101796"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/nm.4246"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0811879106"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.31887\/DCNS.2016.18.3\/efinn"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcs.1546"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1186\/2045-5380-1-11"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2021.12.129"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.12.036"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/npp.2009.129"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291717003348"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.03.016"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bandc.2013.12.004"},{"key":"e_1_2_8_31_1","unstructured":"Himberg J. &Hyvarinen A.(2003).Icasso: Software for investigating the reliability of ICA estimates by clustering and visualization. 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