Language Dysfunction Associated with Pathological Brain Connectivity in Children with Epilepsy

Cortical Network Overconnectivity Relates to Sensory, Cognitive, and Social Dimensions in Young Children With Autism Spectrum Disorder.

RCCA revealed three distinct FC patterns in recurrent ASD-related networks, each contributing to predict individual differences in cognitive, social and sensory features, which may shed light on atypical brain network topology associated with specific phenotypic manifestations of ASD.

Uncovering Individual Language Vulnerability in Epilepsy Through Functional Connectivity Laterality.

BACKGROUND Language processing is organized in brain networks, generally lateralized to the left hemisphere. In clinical routine, task-based functional MRI (fMRI) is the gold standard for non-invasive evaluation of language lateralization. However, standard fMRI does not account for the individual heterogeneity of language networks. fMRI-based functional connectivity analysis (FCA) has addressed this shortcoming by identifying correlates of dysfunctional language networks in epilepsy patients. In order to individualize FCA, translation of group-based network analysis to individual epilepsy patients is necessary. Here, we introduce the functional connectivity laterality index (LIFC) as a potential clinical parameter. METHODS This retrospective study included adults with unilateral temporal lobe epilepsy (TLE) who underwent task-based fMRI and a control group. To account for individual variations, fMRI was performed at a 3T scanner and processed entirely in native space. Laterality indices were computed from both activation maps and seed-based functional connectivity across frontal, temporal, and combined language regions. Group comparisons and associations with neuropsychological language performance were analysed. RESULTS Forty patients with unilateral TLE and 25 healthy controls were included. Groups were comparable in terms of sex, handedness, imaging findings, and epilepsy duration, although the controls were younger. Classical LI showed predominantly left-lateralization, while LIFC showed mostly bilateral language lateralization. Neuropsychological performance did not differ between TLE-left and TLE-right patients. While a difference in semantic fluency was initially observed, it did not survive correction for multiple comparisons. However, analyses based on functional network organization revealed a critical pattern: Patients with language networks lateralized to the seizure side-as quantified by LIFC-showed significantly poorer semantic fluency and lower composite language scores. This potential clinically relevant result was exclusive to FCA and was not captured by activation data alone. CONCLUSION Our findings suggest an individual vulnerability of language networks to seizure burden, although this uncorrected, exploratory finding requires replication in larger, independent samples. Incorporating functional connectivity measures into presurgical clinical fMRI analysis may enhance our ability to predict and protect language function in patients with TLE by prioritizing further therapies or surgical interventions for those at higher risk of language decline.

fMRI Network Features Identify Speech and Language Critical Cortex

Opposing associations of default mode network and subcortical network efficiency with IQ in children with drug-resistant epilepsy.

Paediatric drug-resistant epilepsy (DRE) is associated with IQ deficits only partially explained by clinical seizure variables (for example, age of onset, seizure burden, seizure location). Given that intelligence depends on efficient large-scale brain networks, structural connectomics provides a complementary mechanistic framework for explaining residual IQ variance. We tested whether brain-network architecture explains additional IQ variance using global and network-averaged graph-theoretic metrics derived from streamline-count weighted diffusion MRI connectomes. Seventy-one children with DRE (50 focal epilepsy; 21 multifocal epilepsy) and 15 control participants underwent diffusion and T1-weighted MRI. For each participant, a 253 × 253 structural connectome was constructed, edge weights were defined as the number of streamlines, and graph metrics were computed using the Brain Connectivity Toolbox. Nodal metrics were averaged within Yeo's seven functional networks plus a Subcortical network. Associations with IQ were examined using correlations and general linear models (single-network and eight-predictor models). Finally, mediation analyses tested whether network metrics explained IQ differences between controls and children with DRE. Global metrics were not associated with IQ (all p > 0.05). Regionally, higher Salience-network betweenness centrality showed a nominal negative association with IQ in the multifocal subgroup (p = 0.040*, adjusted R2 = 0.142). In the eight-network GLM, higher nodal efficiency within the Default Mode Network (DMN) was positively associated with IQ (B = 122.406, p = .013), whereas higher nodal efficiency within the Subcortical Network was negatively associated with IQ (B = -51.942, p = .012). These regional associations did not survive Bonferroni correction. Exploratory mediation analyses suggested that opposing DMN and Subcortical Network effects partially accounted for the observed group difference in IQ. These findings are hypothesis-generating, as the regional associations with IQ were nominal and did not survive family-wise correction. Nevertheless, the mediation effects survived correction, highlighting opposing regional, rather than global, network alterations as candidate correlates of IQ variability in paediatric DRE.

Deciphering Physiological and Pathological Influences on Amygdala-Hippocampus Connectivity

Objective. Temporal lobe epilepsy (TLE) is associated with disrupted functional integrity in the amygdala-hippocampus complex. Cortico-cortical evoked potentials (CCEPs) can characterize this disruption and have been proposed as biomarkers of the epileptogenic zone (EZ), but their study is typically limited by the spatial sampling bias inherent to whole-brain intracranial EEG. We investigated how epileptogenicity shapes effective connectivity in the amygdala-hippocampus complex, whether structural connectivity underlies it, and ultimately derived a multimodal EZ biomarker. Methods. We retrospectively included 71 patients (50 adults, 21 children) who underwent single-pulse electrical stimulation protocols with intracranial contacts in the amygdala or hippocampus; 15 also underwent diffusion MRI. CCEPs were visually detected, and the latency and amplitude of the first response peak (D1) were extracted. Structural connectivity metrics (tract length, quantitative and fractional anisotropy, mean diffusivity) were derived between the same contacts. A Bayesian linear mixed model (BLMM) related D1 latency to clinical, neurophysiological, and structural predictors, handling missing DTI values jointly within the model. A corrected latency score was then built to discriminate epileptogenic from non-epileptogenic contacts. Results. Among 6257 possible stimulation-recording pairs, 1027 CCEPs were detected with a significantly higher rate in the hippocampus compared to the amygdala. The BLMM identified robust associations between D1 latency and epileptogenicity, epilepsy type, ipsilateral stimulation, stimulation site (hippocampus/amygdala), and quantitative and fractional anisotropy. The resulting EZ score, obtained by extracting the EZ term's contribution from the BLMM equation, demonstrated an ability to discriminate epileptogenic contacts, showing a balanced accuracy of 78% (sensitivity 86%, specificity 71%), and the resulting EZ probability, based on an elastic net logistic regression, showed a balanced accuracy of 84% (sensitivity 86%, specificity 82%). Discussion. These findings suggest that effective connectivity results from the interplay of opposing physiological (here amygdala vs. hippocampus) and pathological (epilepsy-related) influences rather than a simple facilitation within the EZ, and that white matter microstructure independently contributes to this timing. Connectivity is slower within the EZ itself, with an even greater delay observed in its vicinity compared to other brain areas. The resulting EZ score offers a practical, closed-form tool to strengthen EZ localization, and paves the way toward a structurally informed, CCEP-based framework extendable to other brain regions.

Nucleus basalis functional connectivity aberrations in temporal lobe epilepsy and improvements after successful epilepsy surgery.

Successful TLE surgery is associated with partial restoration of cholinergic arousal network connectivity, supporting the idea that seizure cessation enables recovery of brain networks disrupted by recurrent seizures.