Construction of embedded fMRI resting state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations

EEG source localization analysis in epileptic children during a visual working-memory task

We localize the sources of brain activity of children with epilepsy based on EEG recordings acquired during a visual discrimination working memory task. For the numerical solution of the inverse problem, with the aid of age-specific MRI scans processed from a publicly available database, we use and compare three regularization numerical methods, namely the standarized Low Resolution Electromagnetic Tomography (sLORETA), the weighted Minimum Norm Estimation (wMNE) and the dynamic Statistical Parametric Mapping (dSPM). We show that all three methods provide the same spatio-temporal patterns of differences between epileptic and control children. In particular, our analysis reveals statistically significant differences between the two groups in regions of the Parietal Cortex indicating that these may serve as "biomarkers" for diagnostic purposes and ultimately localized treatment

Modelling and Analysis of Functional Connectivity in EEG source level in Chlidren with Epilepsy

Epilepsy affects more than 65 million people worldwide and around 10.5 million of them are children. Although many children self-heal before adulthood, it has been shown that children with epilepsy confront various problems in learning, attention as well as in memory capacity. Thus, the systematic study of the brain (dys)functionality, and ultimately the design of proper treatments is one of the most challenging problems in neuroscience. Towards this aim, neuroimaging techniques and in particular EEG recordings, most commonly used for clinical assessment play an important role. However, an analysis at the scalp level does not give insight to the functionality and interactions of the “true” brain regions. On the other hand, the inverse problem, i.e. that of identifying the involved brain regions from scalp recordings is an ill-defined problem and as such a comparison between various numerical methods that solve it is critical. Here, we reconstruct the functional connectivity of brain activity of children with epilepsy based on EEG recordings of one-back matching visual discrimination working memory task. We first solve the inverse source localisation problem by using three methods, namely the standarized Low Resolution Electromagnetic Tomography (sLORETA), the weighted Minimum Norm Estimation (wMNE), and the dynamic Statistical Parametric Mapping (dSPM). Then using both linear and nonlinear causality models we reconstruct the functional connectivity network between the sources. A comparative analysis between methods and groups (epileptic vs. children) reveals different spatio-temporal patterns that may serve as “biomarkers” for diagnostic purposes and ultimately localised treatment

Electroencephalography source localization analysis in epileptic children during a visual working-memory task

We localize the sources of brain activity of children with epilepsy based on electroencephalograph (EEG) recordings acquired during a visual discrimination working memory task. For the numerical solution of the inverse problem, with the aid of age-specific MRI scans processed from a publicly available database, we use and compare three regularization numerical methods, namely the standardized low resolution brain electromagnetic tomography (sLORETA), the weighted minimum norm estimation (wMNE) and the dynamic statistical parametric mapping (dSPM). We show that all three methods provide the same spatio-temporal patterns of differences between the groups of epileptic and control children. In particular, our analysis reveals statistically significant differences between the two groups in regions of the parietal cortex indicating that these may serve as "biomarkers" for diagnostic purposes and ultimately localized treatment