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A network approach to assessing cognition in disorders of consciousness.

ABSTRACT: Conventional assessments of consciousness rely on motor responses to indicate awareness. However, overt behaviors may be absent or ambiguous in patients with disorders of consciousness (DOC) resulting in underrating capacity for cognition. fMRI during a silent picture-naming task was evaluated as an indicator of command following when conventional methods are not sufficient.A total of 10 patients with and without conventional evidence of awareness, who met diagnostic criteria for the minimally conscious state (MCS) (n = 5), vegetative state (VS) (n = 3), emerged from MCS (EMCS) (n = 1), and locked-in syndrome (LIS) (n = 1), participated in this observational fMRI study.The LIS and EMCS patients engaged a complete network of essential language-related regions during the object-naming task. The MCS and 2 of the VS patients demonstrated both complete and partial preservation of the object-naming system. Patients who engaged a complete network scored highest on the Coma Recovery Scale-Revised.This study supports the view that fMRI during object naming can elicit brain activations in patients with DOC similar to those observed in healthy subjects during command following, and patients can be stratified by completeness of the engaged neural system. These results suggest that activity of the language network may serve as an indicator of high-level cognition and possibly volitional processes that cannot be discerned through conventional behavioral assessment alone.

SUBMITTER: Rodriguez Moreno D

PROVIDER: S-EPMC2995384 | biostudies-other | 2010 Nov

REPOSITORIES: biostudies-other

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A network approach to assessing cognition in disorders of consciousness.

Rodriguez Moreno D D Schiff N D ND Giacino J J Kalmar K K Hirsch J J

Neurology 20101027 21

<h4>Objective</h4>Conventional assessments of consciousness rely on motor responses to indicate awareness. However, overt behaviors may be absent or ambiguous in patients with disorders of consciousness (DOC) resulting in underrating capacity for cognition. fMRI during a silent picture-naming task was evaluated as an indicator of command following when conventional methods are not sufficient.<h4>Methods</h4>A total of 10 patients with and without conventional evidence of awareness, who met diagn ...[more]

PMID: 20980667

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Project description:Background Prolonged Disorders of Consciousness (PDOC) resulting from severe acquired brain injury can lead to complex disabilities that make diagnosis challenging. The role of machine learning (ML) in diagnosing PDOC states and identifying intervention strategies is relatively under-explored, having focused on predicting mortality and poor outcome. This study aims to: (a) apply ML techniques to predict PDOC diagnostic states from variables obtained from two non-invasive neurobehavior assessment tools; and (b) apply network analysis for guiding possible intervention strategies. Methods The Coma Recovery Scale-Revised (CRS-R) is a well-established tool for assessing patients with PDOC. More recently, music has been found to be a useful medium for assessment of coma patients, leading to the standardization of a music-based assessment of awareness: Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC). CRS-R and MATADOC data were collected from 74 PDOC patients aged 16–70 years at three specialist centers in the USA, UK and Ireland. The data were analyzed by three ML techniques (neural networks, decision trees and cluster analysis) as well as modelled through system-level network analysis. Results PDOC diagnostic state can be predicted to a relatively high level of accuracy that sets a benchmark for future ML analysis using neurobehavioral data only. The outcomes of this study may also have implications for understanding the role of music therapy in interdisciplinary rehabilitation to help patients move from one coma state to another. Conclusions This study has shown how ML can derive rules for diagnosis of PDOC with data from two neurobehavioral tools without the need to harvest large clinical and imaging datasets. Network analysis using the measures obtained from these two non-invasive tools provides novel, system-level ways of interpreting possible transitions between PDOC states, leading to possible use in novel, next-generation decision-support systems for PDOC. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-023-02128-0.

Dataset Information

A network approach to assessing cognition in disorders of consciousness.

Publications

A network approach to assessing cognition in disorders of consciousness.

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