Resting-state Functional Network Disruptions in a Rodent Model of Mesial Temporal Lobe Epilepsy (TLE)

Mesial temporal lobe epilepsy (TLE) is the most common form of drug-refractory epilepsy. The clinical application of non-invasively mapped networks using resting-state functional magnetic resonance imaging (rsfMRI) in humans has been rather limited due to heterogeneous (varying etiology, drugs, onset, latent period, etc.) patient groups. We employed a pharmacological (kainic acid) rodent model of TLE to measure the extent of functional network disruptions using rsfMRI, and study selected behaviors and olfactory to hippocampus transmission. Graph theoretical network modelling and analysis revealed significant increase in functional connectivity connectivity to the temporal lobe (hippocampus) in epileptic-rats compared to controls in the limbic (nucleus accumbens, medial dorsal thalamus), and ``default mode’’ network (retrosplenial, sensorimotor, auditory and posterior parietal cortices). Loss in righting reflex that occurred in response to a lower isoflurane concentration in kainate-treated rats compared to controls was also revealed. These results suggest extensive disruptions in brain networks affected by TLE

Resting state functional network disruptions in a kainic acid model of temporal lobe epilepsy.

We studied the graph topological properties of brain networks derived from resting-state functional magnetic resonance imaging in a kainic acid induced model of temporal lobe epilepsy (TLE) in rats. Functional connectivity was determined by temporal correlation of the resting-state Blood Oxygen Level Dependent (BOLD) signals between two brain regions during 1.5% and 2% isoflurane, and analyzed as networks in epileptic and control rats. Graph theoretical analysis revealed a significant increase in functional connectivity between brain areas in epileptic than control rats, and the connected brain areas could be categorized as a limbic network and a default mode network (DMN). The limbic network includes the hippocampus, amygdala, piriform cortex, nucleus accumbens, and mediodorsal thalamus, whereas DMN involves the medial prefrontal cortex, anterior and posterior cingulate cortex, auditory and temporal association cortex, and posterior parietal cortex. The TLE model manifested a higher clustering coefficient, increased global and local efficiency, and increased small-worldness as compared to controls, despite having a similar characteristic path length. These results suggest extensive disruptions in the functional brain networks, which may be the basis of altered cognitive, emotional and psychiatric symptoms in TLE

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

International audienceBackground and Objective To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD). Methods We used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2–55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 cases with FCD (13 ± 10 years). Applying the algorithm to 42 healthy controls and 89 controls with temporal lobe epilepsy disease tested specificity. Results Overall sensitivity was 93% (137 of 148 FCD detected) using a leave-one-site-out cross-validation, with an average of 6 false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half, it ranked the highest. Sensitivity in the independent cohort was 83% (19 of 23; average of 5 false positives per patient). Specificity was 89% in healthy and disease controls. Discussion This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification, this classifier may assist clinicians in adjusting hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for presurgical evaluation of MRI-negative epilepsy. Classification of Evidence This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in patients with epilepsy initially diagnosed as MRI negative