Mapping the functional connectome traits of levels of consciousness
Enrico Amico, Daniele Marinazzo, Carol DiPerri, Lizette Heine, Jitka, Annen, Charlotte Martial, Mario Dzemidzic, Steven Laureys, and Joaqu\'in, Go\~ni

TL;DR
This study introduces connICA, a novel data-driven method to extract independent functional connectivity traits from brain connectomes, revealing patterns associated with consciousness levels and brain damage severity.
Contribution
The paper presents connICA, a new ICA-based approach for identifying robust FC-traits without prior group stratification, applied to consciousness-related clinical features.
Findings
Identified three main FC-traits linked to consciousness and brain damage.
Connected FC-traits to clinical features like arousal, communication ability, and self-awareness.
Revealed distinct functional processes involved in consciousness degradation.
Abstract
Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connICA, which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. We here apply connICA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of…
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Taxonomy
TopicsFunctional Brain Connectivity Studies · Traumatic Brain Injury Research · EEG and Brain-Computer Interfaces
