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Subregional mesiotemporal network regularization and fragmentation in temporal lobe epilepsy


Bernhardt,  Boris C.
Department Social Neuroscience, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Bernhardt, B. C., Kim, H., Hong, S., Dery, S., Bernasconi, A., & Bernasconi, N. (2014). Subregional mesiotemporal network regularization and fragmentation in temporal lobe epilepsy. Poster presented at 20th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Hamburg, Germany.

Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-2788-2
Introduction: Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults and associated with hippocampal atrophy on MRI. Structural MRI network analyses have shown an increased clustering and path length of cortico-cortical networks in TLE, a finding indicative of a more regularized global topology (Bernhardt et al., 2011). Likewise, functional MRI data has also shown shifts in the topological large-scale network organization (Liao et al., 2010). Collectively, these findings suggest that TLE is a system disorder related to alterations in large-scale networks. Yet, and somewhat surprisingly, the few previous network assessments focused on cortico-cortical networks derived from macroscopic parcellations and either omitted the mesiotemporal lobe completely, or assessed this critical subnetwork in a very coarse manner. The current work specifically assesses the impact of TLE on MRI covariance networks in the mesiotemporal lobe. Methods: We studied 134 consecutive patients referred to our hospital for the investigation of drug-resistant TLE (63 males; 16 to 57 years; 64/70 left/right TLE) and 46 age- and sex-matched healthy controls. To quantify volume change at a subregional level, we carried out surface-shape mapping of manual MRI segmentations in all subjects from three key mesiotemporal regions (i.e., hippocampus, entorhinal cortex, and amygdala) (Styner et al., 2006, Kim et al., 2008). Using two alternative schemes, we subdivided mesiotemporal surfaces into 24 and 198 parcels. Within each parcellation scheme, we generated structural MRI covariance networks from patterns of parcel-to-parcel volume correlations across subjects (Figure 1). Graph-theoretical measures then quantified topological, modular, and nodal features of network organization (Bernhardt et al., 2011). Findings were corrected for multiple comparisons using the false discovery rate procedure at FDR<0.05 (Benjamini and Hochberg, 1995). Results: Compared to controls, patients showed marked network regularization (i.e.. elevated path length and clustering; Figure 2). Findings were consistent in left and right TLE, seen across both parcellation schemes (with, however, more marked effects when using 198 parcels), and remained robust in a split-half reliability assessment. Patients consistently showed lower nodal efficiency of the ipsilateral hippocampus and increased modularity indicative of network fragmentation (Figure 3). While modules in controls generally extended across different structures, in TLE they were largely identical to the embedding anatomical structure. In relation to post-surgical outcome, path length, but not clustering was increased in seizure-free patients compared to those with post-operative seizures. Conclusions: Our results demonstrate a striking reorganization of subregional mesiotemporal circuitries in TLE. Network regularization and fragmentation may relate to combined effects of axonal sprouting and decreased inter-structure connectivity. Findings showing increased path length in seizure-free patients, suggest that graph-theoretical assessment of network topology may provide diagnostic biomarkers in TLE.