Abnormal thalamocortical structural and functional connectivity in juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy is the most common idiopathic generalized epilepsy, characterized by frequent myoclonic jerks, generalized tonic-clonic seizures and, less commonly, absences. Neuropsychological and, less consistently, anatomical studies have indicated frontal lobe dysfunction in the disease. Given its presumed thalamo-cortical basis, we investigated thalamo-cortical structural connectivity, as measured by diffusion tensor imaging, in a cohort of 28 participants with juvenile myoclonic epilepsy and detected changes in an anterior thalamo-cortical bundle compared with healthy control subjects. We then investigated task-modulated functional connectivity from the anterior thalamic region identified using functional magnetic resonance imaging in a task consistently shown to be impaired in this group, phonemic verbal fluency. We demonstrate an alteration in task-modulated connectivity in a region of frontal cortex directly connected to the thalamus via the same anatomical bundle, and overlapping with the supplementary motor area. Further, we show that the degree of abnormal connectivity is related to disease severity in those with active seizures. By integrating methods examining structural and effective interregional connectivity, these results provide convincing evidence for abnormalities in a specific thalamo-cortical circuit, with reduced structural and task-induced functional connectivity, which may underlie the functional abnormalities in this idiopathic epilepsy.

Clustering probabilistic tractograms using independent component analysis applied to the thalamus.

The connectivity information contained in diffusion tensor imaging (DTI) has previously been used to parcellate cortical and subcortical regions based on their connectivity profiles. The aim of the current study is to investigate the utility of a novel approach to connectivity based parcellation of the thalamus using probabilistic tractography and independent component analysis (ICA). We use ICA to identify spatially coherent tractograms as well as their underlying seed regions, in a single step. We compare this to seed-based tractography results and to an established and reliable approach to parcellating the thalamus based on the dominant cortical connection from each thalamic voxel (Behrens et al., 2003a,b). The ICA approach identifies thalamo-cortical pathways that correspond to known anatomical connections, as well as parcellating the underlying thalamus in a spatially similar way to the connectivity based parcellation. We believe that the use of such a multivariate method to interpret the complex datasets created by probabilistic tractography may be better suited than other approaches to parcellating brain regions.