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However, thalamo\u2010cortical connectivity differences between vegetative state (VS), minimally conscious state minus (MCS\u2212, i.e., low\u2010level behavior such as visual pursuit), and minimally conscious state plus (MCS+, i.e., high\u2010level behavior such as language processing) remain unclear. Probabilistic tractography in a sample of 25 DOC patients was employed to assess whether structural connectivity in various thalamo\u2010cortical circuits could differentiate between VS, MCS\u2212, and MCS+ patients. First, the thalamus was individually segmented into seven clusters based on patterns of cortical connectivity and tested for univariate differences across groups. Second, reconstructed whole\u2010brain thalamic tracks were used as features in a multivariate searchlight analysis to identify regions along the tracks that were most informative in distinguishing among groups. At the univariate level, it was found that VS patients displayed reduced connectivity in most thalamo\u2010cortical circuits of interest, including frontal, temporal, and sensorimotor connections, as compared with MCS+, but showed more pulvinar\u2010occipital connections when compared with MCS\u2212. Moreover, MCS\u2212 exhibited significantly less thalamo\u2010premotor and thalamo\u2010temporal connectivity than MCS+. At the multivariate level, it was found that thalamic tracks reaching frontal, parietal, and sensorimotor regions, could discriminate, up to 100% accuracy, across each pairwise group comparison. Together, these findings highlight the role of thalamo\u2010cortical connections in patients' behavioral profile and level of consciousness. Diffusion tensor imaging combined with machine learning algorithms could thus potentially facilitate diagnostic distinctions in DOC and shed light on the neural correlates of consciousness. <jats:italic>Hum Brain Mapp 38:431\u2013443, 2017<\/jats:italic>. \u00a9 <jats:bold>2016 Wiley Periodicals, Inc.<\/jats:bold><\/jats:p>","DOI":"10.1002\/hbm.23370","type":"journal-article","created":{"date-parts":[[2016,9,13]],"date-time":"2016-09-13T13:47:57Z","timestamp":1473774477000},"page":"431-443","source":"Crossref","is-referenced-by-count":92,"title":["Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning"],"prefix":"10.1002","volume":"38","author":[{"given":"Zhong S.","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Psychology University of California Los Angeles Los Angeles California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicco","family":"Reggente","sequence":"additional","affiliation":[{"name":"Department of Psychology University of California Los Angeles Los Angeles California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evan","family":"Lutkenhoff","sequence":"additional","affiliation":[{"name":"Department of Psychology University of California Los Angeles Los Angeles California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian M.","family":"Owen","sequence":"additional","affiliation":[{"name":"The Brain and Mind Institute, University of Western Ontario London Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin M.","family":"Monti","sequence":"additional","affiliation":[{"name":"Department of Psychology University of California Los Angeles Los Angeles California"},{"name":"Department of Neurosurgery University of California Los Angeles Los Angeles California"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2016,9,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1006\/ccog.1999.0423"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.313.7048.13"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nn1075"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.20672"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00415-011-6114-x"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00415-011-6303-7"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.313.7062.944"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2008.06.011"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2012.00029"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000000666"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2011.03.018"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.08.035"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/ana.23635"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1097\/00001199-200501000-00005"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.58.3.349"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apmr.2004.02.033"},{"key":"e_1_2_8_19_1","doi-asserted-by":"crossref","unstructured":"GoodlettC FletcherPT LinW GerigG(2007): Quantification of measurement error in DTI: Theoretical predictions and validation. 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