RESUMO
BACKGROUND AND PURPOSE: Temporal lobe epilepsy secondary to hippocampal sclerosis is related to epileptogenic networks rather than a focal epileptogenic source. Graph-theoretical gray and white matter networks may help to identify alterations within these epileptogenic networks. METHODS: Twenty-seven patients with hippocampal sclerosis and 14 controls underwent magnetic resonance imaging, including 3D-T1, fluid-attenuated inversion recovery, and diffusion tensor imaging. Subject-specific structural gray and white matter network properties (normalized path length, clustering, and small-worldness) were reconstructed. Group differences and differences between those with higher and lower seizure burden (<4 vs. ≥4 average monthly seizures in the last year) in network parameters were evaluated. Additionally, correlations between network properties and disease-related variables were calculated. RESULTS: All patients with hippocampal sclerosis as one group did not have altered gray or white matter network properties (all p > .05). Patients with lower seizure burden had significantly lower gray matter small-worldness and normalized clustering compared to controls and those with higher seizure burden (all p < .04). A higher number of monthly seizures was significantly associated with increased gray and white matter small-worldness, indicating a more rigid network. CONCLUSION: Overall, there were no differences in network properties in this group of patients with hippocampal sclerosis. However, patients with lower seizure burden had significantly lower gray matter network indices, indicating a more random organization. The correlation between higher monthly seizures and a more rigid network is driven by those with higher seizure burden, who presented a more rigid network compared to those with a lower seizure burden.
Assuntos
Epilepsia do Lobo Temporal , Substância Branca , Encéfalo , Imagem de Tensor de Difusão , Epilepsia do Lobo Temporal/diagnóstico por imagem , Hipocampo/diagnóstico por imagem , Hipocampo/patologia , Humanos , Imageamento por Ressonância Magnética , Esclerose/patologia , Convulsões/diagnóstico por imagem , Convulsões/patologia , Substância Branca/diagnóstico por imagem , Substância Branca/patologiaRESUMO
BACKGROUND: Coordinated patterns of gray matter morphology can be represented as networks, and network disruptions may explain cognitive dysfunction related to multiple sclerosis (MS). OBJECTIVE: To investigate whether single-subject gray matter network properties are related to impaired cognition in MS. METHODS: We studied 148 MS patients (99 female) and 33 healthy controls (HC, 21 female). Seven network parameters were computed and compared within MS between cognitively normal and impaired subjects, and associated with performance on neuropsychological tests in six cognitive domains with regression models. Analyses were controlled for age, gender, whole-brain gray matter volumes, and education level. RESULTS: Compared to MS subjects with normal cognition, MS subjects with cognitive impairment showed a more random network organization as indicated by lower lambda values (all p < 0.05). Worse average cognition and executive function were associated with lower lambda values. Impaired information processing speed, working memory, and attention were associated with lower clustering values. CONCLUSION: Our findings indicate that MS subjects with a more randomly organized gray matter network show worse cognitive functioning, suggesting that single-subject gray matter graphs may capture neurological dysfunction due to MS.