1 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University Montreal, QC, Canada ; Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University Montreal, QC, Canada ; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Functional Neuroimaging Unit, Université de Montréal Montreal, QC, Canada.
2 Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Functional Neuroimaging Unit, Université de Montréal Montreal, QC, Canada ; Department of Computer Science and Operations Research, University of Montreal Montreal, Quebec, Canada.
3 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University Montreal, QC, Canada ; Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University Montreal, QC, Canada.
4 Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University Montreal, QC, Canada.
5 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University Montreal, QC, Canada ; Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University Montreal, QC, Canada ; Physics Department, PERFORM Center, Concordia University Montreal, QC, Canada.

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

Front Neurosci. 2014;8:419

Authors: Dansereau CL, Bellec P, Lee K, Pittau F, Gotman J, Grova C

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.

PMID: 25565949 [PubMed]