Ahmad Mheich

A new algorithm for spatiotemporal analysis of brain functional connectivity.

Specific networks of interacting neuronal assemblies distributed within and across distinct brain regions underlie brain functions. In most cognitive tasks, these interactions are dynamic and take place at the millisecond time scale. Among neuroimaging techniques, magneto/electroencephalography - M/EEG - allows for detection of very short-duration events and offers the single opportunity to follow, in time, the dynamic properties of cognitive processes (sub-millisecond temporal resolution). In this paper, we propose a new algorithm to track the functional brain connectivity dynamics. During a picture naming task, this algorithm aims at segmenting high-resolution EEG signals (hr-EEG) into functional connectivity microstates. The proposed algorithm is based on the K-means clustering of the connectivity graphs obtained from the phase locking value (PLV) method applied on hr-EEG. Results show that the analyzed evoked responses can be divided into six clusters representing distinct networks sequentially involved during the cognitive task, from the picture presentation and recognition to the motor response.

Identification of Interictal Epileptic Networks from Dense-EEG.

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r2, h2, PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.

Spatiotemporal Analysis of Brain Functional Connectivity

Brain functions are based on interactions between neural assemblies distributed within and across distinct cerebral regions. During cognitive tasks, these interactions are dynamic and take place at the millisecond time scale. In this context, the excellent temporal resolution (<1 ms) of the Electroencephalographic –EEG- signals allows for detection of very short-duration events and therefore, offers the unique opportunity to follow, over time, the dynamic properties of cognitive processes.

A novel algorithm for measuring graph similarity: Application to brain networks

Measuring similarity among graphs is a challenging issue in many disciplines including neuroscience. Several algorithms, mainly based on vertices or edges properties, were proposed to address this issue. Most of them ignore the physical location of the vertices, which is a crucial factor in the analysis of brain networks. Indeed, functional brain networks are usually represented as graphs composed of vertices (brain regions) connected by edges (functional connectivity). In this paper, we propose a novel algorithm to measure a similarity between graphs. The novelty of our approach is to account for vertices, edges and spatiality at the same time. The proposed algorithm is evaluated using synthetic graphs. It shows high ability to detect and measure similarity between graphs. An application to real functional brain networks is then described. The algorithm allows for quantification of the inter-subjects variability during a picture naming task.

Graph-based analysis of brain connectivity during spelling task

Most of the brain functions are based on interactions between neuronal assemblies distributed within and across distinct cerebral regions. A major challenge in neuroscience is to identify these networks from neuroimaging data. In this paper, we investigate the brain connectivity during a specific cognitive task: spelling the name of an object represented on a picture. By using high-resolution electroencephalography (hr-EEG) and phase synchrony analysis combined with graph theory-based analysis, we show that the topographic distribution of the phase synchrony and the graph parameters are very powerful tools to disclose the activated macro-regions involved in such a complex task.

SimiNet: A Novel Method for Quantifying Brain Network Similarity

Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the properties of nodes and edges. Interestingly, most of these algorithms ignore the physical location of the nodes, which is a key factor in the context of brain networks involving spatially defined functional areas. In this paper, we present a novel algorithm called “SimiNet” for measuring similarity between two graphs whose nodes are defined a priori within a 3D coordinate system. SimiNet provides a quantified index (ranging from 0 to 1) that accounts for node, edge and spatiality features. Complex graphs were simulated to evaluate the performance of SimiNet that is compared with eight state-of-art methods. Results show that SimiNet is able to detect weak spatial variations in compared graphs in addition to computing similarity using both nodes and edges. SimiNet was also applied to real brain networks obtained during a visual recognition task. The algorithm shows high performance to detect spatial variation of brain networks obtained during a naming task of two categories of visual stimuli: animals and tools. A perspective to this work is a better understanding of object categorization in the human brain.

Brain network modules of meaningful and meaningless objects

Network modularity is a key feature for efficient information processing in the human brain. This information processing is however dynamic and networks can reconfigure at very short time period (few hundreds of millisecond). This requires neuroimaging techniques with sufficient time resolution. Here the dense electroencephalography (EEG) source connectivity methods were used to identify cortical networks with excellent time resolution (in the order of millisecond). Functional networks were identified during picture naming task. Two categories of visual stimuli were presented: meaningful (tools, animals...) and meaningless (scrambled) objects. In this paper, we report the reconfiguration of brain network modularity for meaningful and meaningless objects. Results showed mainly that networks of meaningful objects were more modular than those of meaningless objects. Networks of the ventral visual pathway were activated in both cases; however a strong occipito-temporal functional connectivity appeared for meaningful object but not for meaningless object. We believe that this approach will give new insights into the dynamic behavior of the brain networks during fast information processing.

Identification of epileptogenic networks from dense EEG: A model-based study.

Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG inverse problem and ii) the method used to measure the functional connectivity. We evaluate three inverse solutions algorithms (dSPM, wMNE and cMEM) and three connectivity measures (r2, h2 and MI) on data simulated from a combined biophysical/physiological model able to generate realistic interictal epileptic spikes reflected in scalp EEG. The performance criterion used here is the similarity between the network identified by each of the inverse/connectivity combination and the original network used in the source model. Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMNE.