Voxel-based morphometry in patients with cryptogenic occipital epilepsies. Preliminary data.

We evaluated the differences in grey matter concentration (GMC) by voxel-based morphometry (VBM) in patients with cryptogenic occipital epilepsies. VBM analysis was performed in 11 patients with cryptogenic occipital epilepsies compared to 11 healthy controls. VBM analysis in patients revealed focal areas of reduced GMC in the occipital cortex and, more interestingly, increased GMC in the midbrain tegmentum and basal ganglia (globus pallidus and thalamus). VBM may disclose slight structural abnormalities in the brain of cryptogenic epilepsy patients, not evident with standard MRI. To the best of our knowledge, this is the first literature report describing areas of altered GMC in patients with occipital epilepsy. We hypothesize that these findings might be related to epileptic discharges and/or their diffusion and suggest that midbrain, globus pallidus and thalamus may be part of a functional network originating from the occipital areas.

Does the default-mode functional connectivity of the brain correlate with working-memory performances?

The "default-mode" network is an ensemble of cortical regions that are typically deactivated during demanding cognitive tasks in functional magnetic resonance imaging (fMRI) studies. Using functional connectivity analysis, this network can be studied as a "stand-alone" brain system whose functional role is supposed to consist in the dynamic control of intrinsic processing activities like attention focusing and task-unrelated thought generation and suppression. Independent component analysis (ICA) is the method of choice for generating a statistical image of the "default-mode" network (DMN) using a task- and seed-independent distributed model of fMRI functional connectivity without prior specification of node region extent and timing of neural activation. We used a standard graded working-memory task (n-back) to induce fMRI changes in the default-mode regions and ICA to evaluate to DMN functional connectivity in nineteen healthy volunteers. Based on the known spatial variability of the ICA-DMN maps with the task difficulty levels, we hypothesized the ICA-DMN may also correlate with the subject performances. We confirmed that the relative extent of the anterior and posterior midline spots within the DMN were oppositely (resp. positively in the anterior and negatively in the posterior cingulate cortex) correlated with the level of task difficulty and found out that the spatial distribution of DMN also correlates with the individual task performances. We conclude that the working-memory function is related to a spatial re-configuration of the DMN functional connectivity, and that the relative involvement of the cingulate regions within the DMN might function as a novel predictor of the working-memory efficiency.

Non-Inferential Multi-Subject Study of Functional Connectivity during Visual Stimulation.

Independent component analysis (ICA) is a powerful technique for the multivariate, non-inferential, data-driven analysis of functional magnetic resonance imaging (fMRI) data-sets. The non-inferential nature of ICA makes this a suitable technique for the study of complex mental states whose temporal evolution would be difficult to describe analytically in terms of classical statistical regressors. Taking advantage of this feature, ICA can extract a number of functional connectivity patterns regardless of the task executed by the subject. The technique is so powerful that functional connectivity patterns can be derived even when the subject is just resting in the scanner, opening the opportunity for functional investigation of the human mind at its basal "default" state, which has been proposed to be altered in several brain disorders. However, one major drawback of ICA consists in the difficulty of managing its results, which are not represented by a single functional image as in inferential studies. This produces the need for a classification of ICA results and exacerbates the difficulty of obtaining group "averaged" functional connectivity patterns, while preserving the interpretation of individual differences. Addressing the subject-level variability in the very same framework of "grouping" appears to be a favourable approach towards the clinical evaluation and application of ICA-based methodologies. Here we present a novel strategy for group-level ICA analyses, namely the self-organizing group-level ICA (sog-ICA), which is used on visual activation fMRI data from a block-design experiment repeated on six subjects. We propose the sog-ICA as a multi-subject analysis tool for grouping ICA data while assessing the similarity and variability of the fMRI results of individual subject decompositions.