Leveraging large-scale Granger causality and neural networks to measure the level of consciousness in DoC patients

Ali Vosoughi; Theresa Raiser; Tina Luther; Lina Willacker; Martin Rosenfelder; Akhil Kasturi; Jacobo Diego Sitt; Dragana Manasova; Angela Comanducci; Andreas Bender; Axel Wismüller

doi:10.1117/12.2677317

28 September 2023 Leveraging large-scale Granger causality and neural networks to measure the level of consciousness in DoC patients

Ali Vosoughi, Theresa Raiser, Tina Luther, Lina Willacker, Martin Rosenfelder, Akhil Kasturi, Jacobo Diego Sitt, Dragana Manasova, Angela Comanducci, Andreas Bender, Axel Wismüller

Author Affiliations +

Proceedings Volume 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; 1265507 (2023) https://doi.org/10.1117/12.2677317
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

Evaluating disorders of consciousness (DoC) in patients typically involves traditional medical exams like the Coma Recovery Scale Revised and clinical EEG. DoC can range from coma to unresponsive wakefulness syndrome (UWS) and minimally conscious states (MCS- & MCS+). This raises the question of whether resting state fMRI and machine learning can help determine the level of consciousness in these patients. However, there are challenges with machine learning due to the scarcity of data from UWS patients and the severely unbalanced distribution of consciousness classes. Additionally, medical diagnoses may be inaccurate, particularly for UWS patients who can have covert consciousness. Our study proposes a combination of large-scale Granger causality as a biomarker and neural networks as a downstream machine learning pipeline to classify and regression problems in DoC patients. The proposed method provides a baseline for the task at hand, using causally explainable biomarkers of brain region interactions, and achieves more than 80.0% accuracy on 87 neuroimaging data with four classes of the level of unconsciousness.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ali Vosoughi, Theresa Raiser, Tina Luther, Lina Willacker, Martin Rosenfelder, Akhil Kasturi, Jacobo Diego Sitt, Dragana Manasova, Angela Comanducci, Andreas Bender, and Axel Wismüller "Leveraging large-scale Granger causality and neural networks to measure the level of consciousness in DoC patients", Proc. SPIE 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, 1265507 (28 September 2023); https://doi.org/10.1117/12.2677317

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