Thalamotemporal impairment in temporal lobe epilepsy: a combined MRI analysis of structure, integrity, and connectivity.

OBJECTIVE: Thalamic abnormality in temporal lobe epilepsy (TLE) is well known from imaging studies, but evidence is lacking regarding connectivity profiles of the thalamus and their involvement in the disease process. We used a novel multisequence magnetic resonance imaging (MRI) protocol to elucidate the relationship between mesial temporal and thalamic pathology in TLE. METHODS: For 23 patients with TLE and 23 healthy controls, we performed T1 -weighted (for analysis of tissue structure), diffusion tensor imaging (tissue connectivity), and T1 and T2 relaxation (tissue integrity) MRI across the whole brain. We used connectivity-based segmentation to determine connectivity patterns of thalamus to ipsilateral cortical regions (occipital, parietal, prefrontal, postcentral, precentral, and temporal). We subsequently determined volumes, mean tractography streamlines, and mean T1 and T2 relaxometry values for each thalamic segment preferentially connecting to a given cortical region, and of the hippocampus and entorhinal cortex. RESULTS: As expected, patients had significant volume reduction and increased T2 relaxation time in ipsilateral hippocampus and entorhinal cortex. There was bilateral volume loss, mean streamline reduction, and T2 increase of the thalamic segment preferentially connected to temporal lobe, corresponding to anterior, dorsomedial, and pulvinar thalamic regions, with no evidence of significant change in any other thalamic segments. Left and right thalamotemporal segment volume and T2 were significantly correlated with volume and T2 of ipsilateral (epileptogenic), but not contralateral (nonepileptogenic), mesial temporal structures. SIGNIFICANCE: These convergent and robust data indicate that thalamic abnormality in TLE is restricted to the area of the thalamus that is preferentially connected to the epileptogenic temporal lobe. The degree of thalamic pathology is related to the extent of mesial temporal lobe damage in TLE.

Segmentation of the thalamus in MRI based on T1 and T2.

Reliable identification of thalamic nuclei is required to improve positioning of electrodes in Deep Brain Stimulation (DBS), and to allow the role of individual thalamic nuclei in health and disease to be fully investigated. In this work, a previously proposed method for identifying sub-regions within the thalamus based on differences in their T1 and T2 values is explored in detail. The effect on the segmentation of T1 and T2 dependence weighted against priors for spatial position and extent was investigated. When T1 and T2 dependence was highly weighted, good distinction between identified regions was obtained in T1/T2 feature-space, but no contiguous anatomically distinct regions were identified within the thalamus. Incorporating spatial priors was necessary to ensure anatomically distinct regions were defined. Optimal values for segmentation parameters were obtained by assessing performance on a 'synthetic thalamus'. Using these optimum input parameters, within- and between-subjects reproducibility was assessed. Good reproducibility was obtained when six regions were specified to be identified in the thalamus. The six regions identified were similar in the majority of the normal subject group. However, intriguingly these regions were different from those obtained in the same subjects using a well-known connectivity-based segmentation technique. This method shows promise to identify intrathalamic structures on the basis of T1 and T2 signal. A comprehensive characterisation of thalamic nuclei may require a fully multi-modal approach.

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.

Reproducibility of thalamic segmentation based on probabilistic tractography.

Reliable identification of thalamic nuclei is required to improve targeting of electrodes used in Deep Brain Stimulation (DBS), and for exploring the role of thalamus in health and disease. A previously described method using probabilistic tractography to segment the thalamus based on connections to cortical target regions was implemented. Both within- and between-subject reproducibility were quantitatively assessed by the overlap of the resulting segmentations; the effect of two different numbers of target regions (6 and 31) on reproducibility of the segmentation results was also investigated. Very high reproducibility was observed when a single dataset was processed multiple times using different starting conditions. Thalamic segmentation was also very reproducible when multiple datasets from the same subject were processed using six cortical target regions. Within-subject reproducibility was reduced when the number of target regions was increased, particularly in medial and posterior regions of the thalamus. A large degree of overlap in segmentation results from different subjects was obtained, particularly in thalamic regions classified as connecting to frontal, parietal, temporal and pre-central cortical target regions.