A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data.

ABSTRACT: Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Grad

SUBMITTER: Nguyen RD 

PROVIDER: S-EPMC8330002 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature