Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning. 2017

Zhong S Zheng, and Nicco Reggente, and Evan Lutkenhoff, and Adrian M Owen, and Martin M Monti
Department of Psychology, University of California Los Angeles, Los Angeles, California.

Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo-cortical system. However, thalamo-cortical connectivity differences between vegetative state (VS), minimally conscious state minus (MCS-, i.e., low-level behavior such as visual pursuit), and minimally conscious state plus (MCS+, i.e., high-level 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-cortical circuits could differentiate between VS, MCS-, 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-brain 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-cortical circuits of interest, including frontal, temporal, and sensorimotor connections, as compared with MCS+, but showed more pulvinar-occipital connections when compared with MCS-. Moreover, MCS- exhibited significantly less thalamo-premotor and thalamo-temporal 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-cortical 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. Hum Brain Mapp 38:431-443, 2017. © 2016 Wiley Periodicals, Inc.

UI MeSH Term Description Entries
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D009434 Neural Pathways Neural tracts connecting one part of the nervous system with another. Neural Interconnections,Interconnection, Neural,Interconnections, Neural,Neural Interconnection,Neural Pathway,Pathway, Neural,Pathways, Neural
D001931 Brain Mapping Imaging techniques used to colocalize sites of brain functions or physiological activity with brain structures. Brain Electrical Activity Mapping,Functional Cerebral Localization,Topographic Brain Mapping,Brain Mapping, Topographic,Functional Cerebral Localizations,Mapping, Brain,Mapping, Topographic Brain
D002540 Cerebral Cortex The thin layer of GRAY MATTER on the surface of the CEREBRAL HEMISPHERES that develops from the TELENCEPHALON and folds into gyri and sulci. It reaches its highest development in humans and is responsible for intellectual faculties and higher mental functions. Allocortex,Archipallium,Cortex Cerebri,Cortical Plate,Paleocortex,Periallocortex,Allocortices,Archipalliums,Cerebral Cortices,Cortex Cerebrus,Cortex, Cerebral,Cortical Plates,Paleocortices,Periallocortices,Plate, Cortical
D003244 Consciousness Disorders Organic mental disorders in which there is impairment of the ability to maintain awareness of self and environment and to respond to environmental stimuli. Dysfunction of the cerebral hemispheres or brain stem RETICULAR FORMATION may result in this condition. Consciousness, Level Altered,Disorder of Consciousness,Disorders of Consciousness,Semiconsciousness,Altered Level of Consciousness,Consciousness, Level Depressed,Depressed Level of Consciousness,Consciousness Disorder
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000069550 Machine Learning A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data. Transfer Learning,Learning, Machine,Learning, Transfer
D000293 Adolescent A person 13 to 18 years of age. Adolescence,Youth,Adolescents,Adolescents, Female,Adolescents, Male,Teenagers,Teens,Adolescent, Female,Adolescent, Male,Female Adolescent,Female Adolescents,Male Adolescent,Male Adolescents,Teen,Teenager,Youths

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