Constructing fMRI connectivity networks: a whole brain functional parcellation method for node definition.

Background: Functional Magnetic Resonance Imaging (fMRI) is used for exploring brain functionality, and recently it was applied for mapping the brain connection patterns. To give a meaningful neurobiological interpretation to the connectivity network, it is fundamental to properly define the network framework. In particular, the choice of the network nodes may affect the final connectivity results and the consequent interpretation.

A post-stroke rehabilitation system integrating robotics, VR and high-resolution EEG imaging.

We propose a system for the neuro-motor rehabilitation of upper limbs in stroke survivors. The system is composed of a passive robotic device (Trackhold) for kinematic tracking and gravity compensation, five dedicated virtual reality (VR) applications for training of distinct movement patterns, and high-resolution EEG for synchronous monitoring of cortical activity. In contrast to active devices, the Trackhold omits actuators for increased patient safety and acceptance levels, and for reduced complexity and costs.

GMAC: a Matlab toolbox for spectral Granger causality analysis of fMRI data.

Investigation of causal interactions within brain networks using Granger causality analysis (GCA) is a key challenge in studying neural activity on the basis of functional magnetic resonance imaging (fMRI). The article describes an open-source software toolbox GMAC (Granger multivariate autoregressive connectivity) implementing multivariate spectral GCA. Available features are: fMRI data importing/exporting, network nodes definition, time series preprocessing, multivariate autoregressive modeling, spectral Granger causality indexes estimation, statistical significance assessment using surrogate data, network analysis and visualization of connectivity results.

Parcel-based connectivity analysis of fMRI data for the study of epileptic seizure propagation.

The aim of this work is to improve fMRI Granger Causality Analysis (GCA) by proposing and comparing two strategies for defining the topology of the networks among which cerebral connectivity is measured and to apply fMRI GCA for studying epileptic seizure propagation. The first proposed method is based on information derived from anatomical atlas only; the other one is based on functional information and employs an algorithm of hierarchical clustering applied to fMRI data directly. Both methods were applied to signals recorded during seizures on a group of epileptic subjects and two connectivity matrices were obtained for each patient.

Exploring interregional brain interactivity in temporal lobe epilepsy using partial correlation analysis of fMRI data.

Simultaneous recording of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has been recently used to measure metabolic changes related to interictal spikes in temporal lobe epilepsy (TLE). Since blood oxygen level dependent (BOLD) responses have been often observed in extratemporal regions, we propose to explore interregional brain connectivity using a data-driven method based on partial correlation analysis of fMRI data. This approach allows to extract informations about functional interactivity and to differentiate direct from mediated interactions.

The haemodynamic response to the interictal epileptic spikes.

We studied the blood oxygen level dependent (BOLD) response to interictal epileptic spikes in a group of patients with focal epilepsy by simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The detection of activated areas was performed by using an approach based on the theory of the General Linear Models (GLM). Since little is know about the haemodynamic response to the interictal epileptiform activity, for each region involved by fMRI response and for each subject we obtained a robust estimation of the haemodynamic response function (HRF) by using a Bayesian approach.