Rationale:
Mesial temporal lobe epilepsy (MTLE) is a disorder of neural networks. Although MTLE is often amenable to surgical treatment, focal resection of the seizure-onset zone can still be non-curative or impermanent. Seizure recurrence is likely attributable to extrahippocampal/distributed network-mediation of ictogenesis. In order to address reasons for surgical failure, it is crucial to understand higher level network properties in MTLE pathology. Graph theory models (GTM) can interrogate these properties by quantifying features of network topology. Imaging studies have leveraged graph theory to detect compensatory network changes (Chiang et al 2014) and predict seizure-onset laterality in MTLE (Amiri et al 2020), portending its utility in biomarker development. This technique was recently adapted for coordinate-based meta-analysis (CBMA) of voxel-based morphometry (VBM) and voxel-based physiology (VBP) studies, instantiating a multi-variate extension of activation likelihood estimation (ALE; mass-univariate CBMA). We applied meta-analytic GTM (Meta-GTM; Cauda et al 2018) to VBM and VBP studies of MTLE, to infer organizational properties of the MTLE co-alteration network.
Methods:
Meta-GTM was used to quantify the topological organization of MTLE network pathology. A co-alteration network model was derived from 74 experiments, modeling published coordinates of MTLE pathology as spatial probability distributions, defining nodes at peaks in the joint distribution of pathology, and computing edges as co-alterations in individual studies (Patel et al 2006). Clusters of network pathology (modules) were detected via spectral partition (Newman et al 2006), functionally interpreted, and structurally compared with pathology via Regional Behavioral & Diseases Analyses of 21,435 task-based (n=107,167) and 4,398 VBM (n=115,627) experiments from the BrainMap database. MATLAB/Cytoscape were used to compute graph and modular sub-graph topology metrics. Nodal influence on MTLE network topology was totaled per-node as an average of normalized metrics.
Results:
Two distributed networks were identified as modules in the MTLE co-alteration network, connected only via three most influential nodes: hippocampus, MDN thalamus, medial frontal gyrus. Module 1 regions (M1: mesial temporal, deep nuclear, frontal, pre/post central, cingulate, inferior parietal) were associated with emotion (Z=3.7) and weakly with social cognition/explicit memory (Z=2.5; 2.3). Module 2 (M2: cerebellar, occipital, temporoparietal, precentral, inferior, medial frontal) associations included language/speech/semantic cognition (Z=4.1; 3.0; 2.3). M1 was associated with known VBM patterns in Alzheimer’s (Z=4.1); both M1/M2 matched those of structural epilepsy pathology (Z=3.4; 3.6). A 2-node module (M3: parahippocampus, amygdala) and disconnected pair (M4: mid-frontal, cingulate gyri) were noted.
Conclusions:
Discrete co-alteration networks exist in MTLE. The medial frontal gyrus likely mediates interactions and evolution of limbic (M1) and verbal (M2) symptoms in MTLE. Pathology modules and intermodular connections represent potential targets for disease monitoring and therapeutic modulation.
Funding:
R01MH074457, T32GM113896, F31NS131025