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Abstract

Introduction

Most of neurological disorders are network-based diseases. The networks associated with these diseases usually involve spatially disturbed brain regions. Thus efforts were recently evolving from identifying pathological “zones” toward identifying “networks”. In a very recent review, Fornito and colleagues revealed that the identification of alterations in brain networks is one of the most promising paradigms in brain disorders research (Fornito and Bullmore, 2014; Fornito et al., 2015). So far, approaches based on graph theory have characterized the brain networks as sets of nodes connected by edges (Bullmore and Sporns, 2009). Once the nodes (brain regions) and edges (functional/structural connections between regions) are defined from neuroimaging technique, methods based on graph theory may be used to describe the topological properties of the identified networks. This network-based analysis has been largely used to investigate normal (Bressler and Menon, 2010) and pathological (Fornito et al., 2015) brain activities from numerous modalities. It has been used in many clinical applications such as Alzheimer's disease (He et al., 2008; Lo et al., 2010; Mallio et al., 2015; Stam et al., 2007), schizophrenia (Fornito et al., 2011; Liu et al., 2008; van den Heuvel et al., 2013) and autism (Guye et al., 2010; Li et al., 2014). However, a precise tracking of the spatiotemporal dynamics of large-scale pathological networks is still an unsolved issue (Hutchison et al., 2013). The only noninvasive technique that provides the sufficient temporal resolution to track dynamics of brain activity at millisecond scale is the Electro/Magneto encephalogram (EEG/MEG).

Methods

Therefore, the focus of our work is to develop advanced methods to, noninvasively, identify dynamics of functional brain networks using dense EEG signals (256 electrodes). The key advantage of our method, called ‘EEG source connectivity’ (Fig. 1), is the possibility of identifying functional brain networks with excellent temporal (∼ 1 ms) and spatial resolutions (Hassan et al., 2015a; Hassan et al., 2014; Hassan et al., 2015b). The EEG source connectivity involves two main steps: i) the reconstruction of regional time series by solving the EEG inverse problem and ii) the estimation of the functional connectivity using phase synchronization among oscillations present in the time-courses of reconstructed regional time series. The method was originally developed to track dynamics of functional brain networks during short time cognitive activity such as picture naming task ( < 1 second) (Hassan et al., 2015a). In this abstract, we show the first results of applying EEG source connectivity method to two neurological disorders: Epilepsy and Parkinson's disease. Figure 1: Illustrative structure of the proposed method. Dense EEG were used to record data from patients (and healthy control). Structural MRI images will be also recorded, segmented and then anatomically parcellated into a desired number of brain regions (regions of interest). The functional connectivity was then computed giving rise to high-resolution functional brain networks. These networks (graphs) will be finally characterized (quantified) using approach from graph theory to characterize the brain networks (regions and sub regions) involved in the analyzed pathology.

Results

First, in the context of epilepsy, data were recorded from epileptic patients who underwent a full pre-surgical evaluation for drug-resistant partial epilepsy. The patients had a comprehensive evaluation including detailed history and neurological examination, neuropsychological testing, structural MRI, standard 32-channels (Micromed) as well as High-Resolution 256-channels (EGI, Electrical Geodesic Inc.) scalp EEG with video recordings and intracerebral EEG recordings (SEEG). We applied our method to identify epileptogenic networks from the scalp dense-EEG. We also estimated the functional network from depth-EEG. The results revealed a very good matching between networks identified from noninvasive EEG and those obtained using the intra-cerebral electrodes (ground truth). We were able, for the first time from scalp recordings, to identify the network of brain regions (nodes) involved in the generation of the seizure as selected by the epileptologist. Second, we recently applied the EEG source connectivity method to detect alterations in functional networks involved in cognitive impairments. Dense-EEG (128 electrodes) were recorded during task-free paradigm (resting state, eye closed) to produce whole-brain functional connectivity networks in patients with different cognitive phenotypes (Dujardin et al., 2013): 1) cognitively intact patients, 2) patients with mild cognitive impairment and 3) patients with very severe cognitive impairment. Functional connectivity was assessed between each pair of 68 brain regions in 124 patients at different EEG frequency bands. Network measures were realized at global level topology, region-wise connectivity and edge-wise connectivity. Using Network Based Statistics (NBS) (Zalesky et al., 2010), results showed significant differences at alpha band (7–13 Hz) between all groups. The quantification of the networks showed that most of significant alterations (decreased connections) were frontotemporal (functional connections between frontal and temporal lobes) which perfectly match with previous findings of cognitive impairments in patient with neurodegenerative disorders (Pievani et al., 2011).

Discussion

We consider that the identification of pathological brain networks from noninvasive EEG recordings is a topic of great interest. The results of the EEG source connectivity methods on cognitive task (Hassan et al., 2015a) as well on neurological disorders such as epilepsy and Parkinson's disease as presented here, may open new perspectives in the clinical use of such approach. These network dysfunctions may be specific to the clinical syndromes and, in Parkinson's disease and frontotemporal dementia for instance, network disruption may track the pattern of pathological alterations. We speculate that our findings might have practical repercussions for diagnostic purpose, allowing earlier detection of neurodegenerative diseases and tracking of disease development.

Acknowledgment

This work has received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01.

References

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/content/papers/10.5339/qfarc.2016.HBPP1544
2016-03-21
2020-09-19
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