Janina Wilmskoetter1, John Del Gaizo2, Lorelei Phillip3, Roozbeh Behroozmand3, Ezequiel Gleichgerrcht4, Julius Fridriksson3, Ellyn Riley5, Leonardo Bonilha4. 1. Department of Neurology, Medical University of South Carolina, SC, USA. Electronic address: wilmskoe@musc.edu. 2. Biomedical Data Science and Informatics, Medical University of South Carolina, SC, USA. 3. Department of Communication Sciences and Disorders, University of South Carolina, SC, USA. 4. Department of Neurology, Medical University of South Carolina, SC, USA. 5. Department of Communication Sciences and Disorders, Syracuse University, NY, USA.
Abstract
OBJECTIVE: To investigate whether pre-articulatory neural activity could be used to predict correct vs. incorrect naming responses in individuals with post-stroke aphasia. METHODS: We collected 64-channel high density electroencephalography (hdEEG) data from 5 individuals with chronic post-stroke aphasia (2 female/3 male, median age: 54 years) during naming of 80 concrete images. We applied machine learning on continuous wavelet transformed hdEEG data separately for alpha and beta energy bands (200 ms pre-stimulus to 1500 ms post-stimulus, but before articulation), and determined whether electrode/time-range/energy (ETE) combinations were predictive of correct vs incorrect responses for each participant. RESULTS: The five participants correctly named between 30% and 70% of the 80 stimuli correctly. We observed that pre-articulatory scalp EEG ETE combinations could predict correct vs incorrect responses with accuracies ranging from 63% to 80%. For all but one participant, the prediction accuracies were statistically better than chance. CONCLUSIONS: Our findings indicate that pre-articulatory neural activity may be used to predict correct vs incorrect naming responses for some individuals with aphasia. SIGNIFICANCE: The individualized pre-articulatory neural pattern associated with correct naming responses could be used to both predict naming problems in aphasia and lead to the development of brain stimulation strategies for treatment.
OBJECTIVE: To investigate whether pre-articulatory neural activity could be used to predict correct vs. incorrect naming responses in individuals with post-stroke aphasia. METHODS: We collected 64-channel high density electroencephalography (hdEEG) data from 5 individuals with chronic post-stroke aphasia (2 female/3 male, median age: 54 years) during naming of 80 concrete images. We applied machine learning on continuous wavelet transformed hdEEG data separately for alpha and beta energy bands (200 ms pre-stimulus to 1500 ms post-stimulus, but before articulation), and determined whether electrode/time-range/energy (ETE) combinations were predictive of correct vs incorrect responses for each participant. RESULTS: The five participants correctly named between 30% and 70% of the 80 stimuli correctly. We observed that pre-articulatory scalp EEG ETE combinations could predict correct vs incorrect responses with accuracies ranging from 63% to 80%. For all but one participant, the prediction accuracies were statistically better than chance. CONCLUSIONS: Our findings indicate that pre-articulatory neural activity may be used to predict correct vs incorrect naming responses for some individuals with aphasia. SIGNIFICANCE: The individualized pre-articulatory neural pattern associated with correct naming responses could be used to both predict naming problems in aphasia and lead to the development of brain stimulation strategies for treatment.
Authors: Jessica DeLeon; Rebecca F Gottesman; Jonathan T Kleinman; Melissa Newhart; Cameron Davis; Jennifer Heidler-Gary; Andrew Lee; Argye E Hillis Journal: Brain Date: 2007-03-02 Impact factor: 13.501
Authors: Alon Sinai; Christopher W Bowers; Ciprian M Crainiceanu; Dana Boatman; Barry Gordon; Ronald P Lesser; Frederick A Lenz; Nathan E Crone Journal: Brain Date: 2005-04-07 Impact factor: 13.501