Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment

Abstract

The spatial coherence of spontaneous slow fluctuations in the blood-oxygen-level dependent (BOLD) signal at rest is routinely used to characterize the underlying resting-state networks (RSNs). Studies have demonstrated that these patterns are organized in space and highly reproducible from subject to subject. Moreover, RSNs reorganizations have been suggested in pathological conditions. Comparisons of RSNs organization have been performed between groups of subjects but have rarely been applied at the individual level, a step required for clinical application. Defining the notion of modularity as the organization of brain activity in stable networks, we propose Detection of Abnormal Networks in Individuals (DANI) to identify modularity changes at the individual level. The stability of each RSN was estimated using a spatial clustering method: Bootstrap Analysis of Stable Clusters (BASC) (Bellec et al., 2010). Our contributions consisted in (i) providing functional maps of the most stable cores of each networks and (ii) in detecting “abnormal” individual changes in networks organization when compared to a population of healthy controls. DANI was first evaluated using realistic simulated data, showing that focussing on a conservative core size (50% most stable regions) improved the sensitivity to detect modularity changes. DANI was then applied to resting state fMRI data of six patients with focal epilepsy who underwent multimodal assessment using simultaneous EEG/fMRI acquisition followed by surgery. Only patient with a seizure free outcome were selected and the resected area was identified using a post-operative MRI. DANI automatically detected abnormal changes in 5 out of 6 patients, with excellent sensitivity, showing for each of them at least one “abnormal” lateralized network closely related to the epileptic focus. For each patient, we also detected some distant networks as abnormal, suggesting some remote reorganization in the epileptic brain.

Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy

“…Addressing functional connectivity beyond the temporal lobes, several studies have identified abnormal interactions between mesiotemporal seeds and targets in posterior cingulate, precuneus, inferior parietal, and medial prefrontal cortices [79][80][81][82]. Connectivity disruptions in these regions comprising the so-called default mode network (DMN) have been confirmed by studies using data-driven network parcellation techniques [83][84][85][86] and those placing seeds in nontemporal areas [87]. Default mode network connectivity alterations in TLE likely relate to the important role of the hippocampus in this network [88]; they may relate to reorganization of memory circuits in this condition [89,90].…”

Statistical power and prediction accuracy in multisite resting-state fMRI connectivity

“…Figure 1A shows the posterior cingulate cortex connectivity map, averaged across all subjects and all sites. The key regions of the DMN are easily identifiable, and include the posterior cingulate cortex, precuneus, inferior parietal lobule, anterior cingulate cortex, medial pre-frontal cortex (dorsal, anterior and ventral), superior frontal gyri and the medial temporal lobe (Damoiseaux et al, 2006;Dansereau et al, 2014;Yan et al, 2013). The average connectivity map of the DMN was then extracted for each site, Figure 1A.…”

Connectomics and graph theory analyses: Novel insights into network abnormalities in epilepsy

“…From a practical perspective, identifying connectivity alterations in persons with epilepsy can provide valuable information at the individual level regarding neuroanatomic factors that influence the clinical manifestations of the disease . In fact, the International League Against Epilepsy new terminology and classification adopts a “network perspective” to better define focal versus generalized seizures .…”