Brain connectivity signatures of cognitive impairment in temporal lobe epilepsy identified by robotic assessment.

BACKGROUND

Subjects with temporal lobe epilepsy (TLE) often experience cognitive impairment in different domains. Currently, the mechanisms underlying neuropsychological dysfunction in TLE remain poorly understood. The main objective is to characterize the multivariate relationship between brain connectivity patterns and cognitive impairment detected by robotic testing in subjects with TLE.

METHODS

Kinarm robotic technology was used to evaluate motor, cognitive, and sensory domains of healthy controls and individuals with TLE. Structural connectivity (SC) and functional connectivity (FC) were obtained from multi-shell diffusion MRI and resting-state fMRI, respectively. After principal component analysis for dimension reduction of connectivity features, sparse canonical correlation analyses were used to identify the patterns of multivariate association between brain connectivity and cognitive dysfunctions.

RESULTS

Patients with TLE demonstrated worse performance mainly in the domains of memory, executive function and attention, and to a lesser extent in the perceptual-motor domain. We found that memory and executive function alterations were associated with an intra-hemispheric SC pattern between somatomotor network and default, limbic and frontoparietal networks. We also found that an intra-hemispheric SC pattern of the posterior parietal cortex was related to perceptual-motor and attention skills with FC between this region and the precentral ventral region of DAN and frontal operculum insula of VAN also associated to impairment in these domains.

CONCLUSIONS

This study identifies multivariate patterns of structural and functional connectivity that correlate with domain-specific cognitive impairment, as measured by robotic screening, in individuals with TLE. These findings support the conceptualization of TLE as a network disorder, contextualizing multidomain cognitive deficits within a network-level framework rather than interrogating specific functional circuits. This may in the future permit more personalized treatments or prediction of cognitive changes in response to planned treatment changes.