Minimal or inconsistent behavioral responses to command make it challenging to accurately diagnose the level of awareness of a patient with a Disorder of consciousness (DOC). By identifying markers of mental imagery being covertly performed to command, functional neuroimaging (fMRI), electroencephalography (EEG) has shown that some of these patients are aware despite their lack of behavioral responsiveness. We report the findings of behavioral, fMRI, and EEG approaches to detecting command-following in a group of patients with DOC. From an initial sample of 14 patients, complete data across all tasks was obtained in six cases. Behavioral evaluations were performed with the Coma Recovery Scale-Revised. Both fMRI and EEG evaluations involved the completion of previously validated mental imagery tasks-i.e., motor imagery (EEG and fMRI) and spatial navigation imagery (fMRI). One patient exhibited statistically significant evidence of motor imagery in both the fMRI and EEG tasks, despite being unable to follow commands behaviorally. Two behaviorally non-responsive patients produced appropriate activation during the spatial navigation fMRI task. However, neither of these patients successfully completed the motor imagery tasks, likely due to specific motor area damage in at least one of these cases. A further patient demonstrated command following only in the EEG motor imagery task, and two patients did not demonstrate command following in any of the behavioral, EEG, or fMRI assessments. Due to the heterogeneity of etiology and pathology in this group, DOC patients vary in terms of their suitability for some forms of neuroimaging, the preservation of specific neural structures, and the cognitive resources that may be available to them. Assessments of a range of cognitive abilities supported by spatially-distinct brain regions and indexed by multiple neural signatures are therefore required in order to accurately characterize a patient's level of residual cognition and awareness.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4244609 | PMC |
http://dx.doi.org/10.3389/fnhum.2014.00950 | DOI Listing |
Clin Rehabil
January 2025
School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Objective: To map evidence on the characteristics, effectiveness, and potential mechanisms of motor imagery interventions targeting cognitive function and depression in adults with neurological disorders and/or mobility impairments.
Data Sources: Six English databases (The Cochrane Library, PubMed, Embase, Scopus, Web of Sciences, and PsycINFO), two Chinese databases (CNKI and WanFang), and a gray literature database were searched from inception to December 2024.
Review Methods: This scoping review followed the Joanna Briggs Institute Scoping Review methodology.
Comput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
View Article and Find Full Text PDFJ Neurol
January 2025
Western Institute of Neuroscience, Western University, London, Canada.
Background: Repeat neurological assessment is standard in cases of severe acute brain injury. However, conventional measures rely on overt behavior. Unfortunately, behavioral responses may be difficult or impossible for some patients.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Department of Biomedical Engineering, The University of Melbourne, Parkville, Melbourne, Victoria, 3010, AUSTRALIA.
Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair.
View Article and Find Full Text PDFJ Neural Eng
January 2025
ECE & Neurology, University of Texas at Austin, 301 E. Dean Keeton St. C2100, Austin, Texas, 78712-1139, UNITED STATES.
Objective: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!