Identification of Subclinical Language Deficit Using Machine Learning Classification Based on Poststroke Functional Connectivity Derived from Low Frequency Oscillations.

Post-stroke neuropsychological evaluation is time-intensive in assessing impairments in subjects without overt clinical deficits. We utilized functional connectivity (FC) from ten-minute non-invasive resting-state functional MRI (rs-fMRI) to identify stroke subjects at risk for subclinical language deficit (SLD) using machine learning. Discriminative ability of FC derived from slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz) and low frequency oscillations (LFO; 0.01-0.1 Hz) was compared. Sixty clinically non-aphasic right-handed subjects were categorized into three subgroups based on stroke status and normalized verbal fluency (NVF) score: 20 ischemic early-stage stroke subjects at higher risk for SLD (LD+; mean VFS=-1.77), 20 ischemic early-stage stroke subjects with at risk for SLD (LD-; mean VFS=-0.05), 20 healthy controls (HC; mean VFS=0.29). T1-weighted and rs-fMRI were acquired within 30 days of stroke onset. Blood-oxygen-level-dependent signal was extracted within the language network. FC was evaluated and used by a multiclass support vector machine to classify test subject into a subgroup which was assessed by nested leave-one-out cross-validation. FC derived from slow-4 (70%) provided the best accuracy relative to LFO (65%) and slow-5 (50%), reasonably higher than random chance (33.33%). Using subgroup-specific accuracy, classification was best realized within slow-4 for LD+ (81.6%) and LD- (78.3%) and slow-4/LFO for HC (80%), i.e., early-stage stroke subjects showed a slow-4 FC dominance whereas HC also indicated the normalized involvement within LFO. While frontal FC differentiated stroke from healthy, occipital FC differentiated between the two stroke subgroups. Thus, stroke subjects at risk for SLD can be identified using rs-fMRI reasonably in an expedited manner.