Predicting postoperative language outcome using presurgical fMRI, MEG, TMS, and high gamma ECoG

Autor: Asim F. Choudhri, Stephen P. Fulton, James W. Wheless, Abbas Babajani-Feremi, Frederick A. Boop, Shalini Narayana, Christen Holder
Rok vydání: 2017
Předmět:
Adult
Male
medicine.medical_specialty
Adolescent
medicine.medical_treatment
Brain mapping
050105 experimental psychology
Neurosurgical Procedures
03 medical and health sciences
Young Adult
0302 clinical medicine
Physical medicine and rehabilitation
Physiology (medical)
medicine
Humans
0501 psychology and cognitive sciences
Postoperative Period
Electrocorticography
Brain Mapping
Language Disorders
Modalities
Epilepsy
medicine.diagnostic_test
business.industry
05 social sciences
Neuropsychology
Magnetoencephalography
Magnetic resonance imaging
Magnetic Resonance Imaging
Transcranial Magnetic Stimulation
digestive system diseases
Sensory Systems
Support vector machine
Transcranial magnetic stimulation
Neurology
Preoperative Period
Female
Neurology (clinical)
business
030217 neurology & neurosurgery
Zdroj: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 129(3)
ISSN: 1872-8952
Popis: Objective To predict the postoperative language outcome using the support vector regression (SVR) and results of multimodal presurgical language mapping. Methods Eleven patients with epilepsy received presurgical language mapping using functional MRI (fMRI), magnetoencephalography (MEG), transcranial magnetic stimulation (TMS), and high-gamma electrocorticography (hgECoG), as well as pre- and postoperative neuropsychological evaluation of language. We constructed 15 (24–1) SVR models by considering the extent of resected language areas identified by all subsets of four modalities as input feature vector and the postoperative language outcome as output. We trained and cross-validated SVR models, and compared the cross-validation (CV) errors of all models for prediction of language outcome. Results Seven patients had some level of postoperative language decline and two of them had significant postoperative decline in naming. Some parts of language areas identified by four modalities were resected in these patients. We found that an SVR model consisting of fMRI, MEG, and hgECoG provided minimum CV error, although an SVR model consisting of fMRI and MEG was the optimal model that facilitated the best trade-off between model complexity and prediction accuracy. Conclusions A multimodal SVR can be used to predict the language outcome. Significance The developed multimodal SVR models in this study can be utilized to calculate the language outcomes of different resection plans prior to surgery and select the optimal surgical plan.
Databáze: OpenAIRE
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