The potential value of probabilistic tractography-based for MR-guided focused ultrasound thalamotomy for essential tremor

Abstract

Magnetic Resonance-guided Focused UltraSound (MRgFUS) offers an incisionless approach to treat essential tremor (ET). Due to lack of evident internal anatomy on traditional structural imaging, indirect targeting must still be used to localize the lesion. Here, we investigate the potential predictive value of probabilistic tractography guided thalamic targeting by defining how tractography-defined targets, lesion size and location, and clinical outcomes interrelate. MR imaging and clinical outcomes from 12 ET patients that underwent MRgFUS thalamotomy in a pilot study at the University of Virginia were evaluated in this analysis. FSL was used to evaluate each patient's voxel-wise thalamic connectivity with FreeSurfer generated pre- and post-central gyrus targets, to generate thalamic target maps. Using Receiver Operating Characteristic curves, the overlap between these thalamic target maps and the MRgFUS lesion was systematically evaluated relative to clinical outcome. To further define the connectivity characteristics of effective MRgFUS thalamotomy lesions, we evaluated whole brain probabilistic tractography of lesions (using post-treatment imaging to define the lesion pre-treatment diffusion tensor MRI). The structural connectivity difference was explored between subjects with the best clinical outcome relative to all others. Ten of twelve patients presented high percentage of overlapping between connectivity-based thalamic segmentation maps and lesion area. The improvement of clinical score was predicted (AUC: 0.80) using the volume of intersection between the thalamic target (precentral gyrus) map and MRgFUS induced lesion as feature. The main structural differences between those with different magnitudes of response were observed in connectivity to the pre- and post-central gyri and brainstem/cerebellum. MRgFUS thalamotomy lesions characterized by strong structural connectivity to precentral gyrus demonstrated better responses in a cohort of patients treated with MRgFUS for ET. The intersection between lesion and thalamic-connectivity maps to motor - sensory targets proved to be effective in predicting the response to the therapy. These imaging techniques can be used to increase the efficacy and consistency of outcomes with MRgFUS and potentially shorten treatment times by identifying optimal targets in advance of treatment.

Keywords: Magnetic resonance imaging-guided focused ultrasound; Tractography; Tremor.

Fig. 1

Localization of MRgFUs treatment-induced lesion area and calculation of intersection area between lesion and thalamic probabilistic maps. a) At T2-weighted image registered to pre-treatment T1-weighted image the MRgFUs treatment-induced lesion area was localized and (b) based on the maximum intensity value of the lesion area (upper threshold: maximum intensity value - lower a percentage of 60% of the maximum intensity value), (c) the lesion was delineated (blue color). d) Fiber tract projections from the thalamic region with maximal connectivity with to the target (precentral gyrus in this case) were derived. e) Connectivity-based thalamic segmentation map (red-yellow map) to pre-central gyrus were found (zoom out (f)). (g) Segmentation map was thresholded at 30% of the maximum intensity value (green map) to find the voxels with high probability of connectivity to target area. (h) The lesion area was overlapped with the thalamic segmentation map and the intersection was calculated (light blue) (i).

Fig. 2

Whole brain probabilistic tractography of shared fiber tract of MRgFUS induced lesion area. Tractography from the MRgFUS lesion demonstrates that patients with (a) superior clinical outcome (n = 6) (red common whole brain binary map) present stronger structural connectivity than patients with (b) inferior clinical outcome (n = 6) (blue common whole brain binary map) in pre- and post-central gyri and brainstem/cerebellum areas. The differences (c) between the common whole brain binary map of the two groups were localized in pre- and post-central gyri (green binary map) and to the caudal projection to the cerebellum (green binary map).

Fig. 3

Average clinical efficacy map using clinical improvement scores 1 month and 1 year after MRgFUS. The percentage of improvement in clinical score between baseline and 1 month and 1 year after MRgFUs was assigned to each subject's binary whole brain map. The resulted clinically-weighted maps were then averaged to find the ‘average clinical efficacy map’ at two time points. Each voxel's value in the ‘average clinical efficacy map’ corresponds to the average clinical outcome of the subjects that present connectivity to that specific voxel. In both time points the patients with higher clinical improvement present stronger connectivity to pre- and post-central gyri and brainstem/cerebellum areas (Red-Yellow map).

Fig. 4

Prediction of superior clinical outcome using the volume of overlapping between thalamic segmentation maps and MRgFUS induced lesion. The volume of intersection between the MRgFUs treatment-induced lesion area and thalamic-segmentation maps to both targets precentral (a) and postcentral (b) gyrus ((unthresholded (i)/thresholded maps (ii–iv))) was used as feature to predict the superior clinical outcome. The area under the ROC curve was 0.80 (a-ii, red color) (95% confidence interval:0.54–1; p < 0.005) using as feature the overlapping of thalamic probabilistic map to motor target (at 30% threshold) and (b-ii, red color) 76% (95% confidence interval:0.48–1; p < 0.005) when as feature it was used the thalamic probabilistic map to sensory target (at 30% threshold).

Fig. 1

Correlation of Lesion size with clinical outcome. The correlation between the size of the lesion and the percentage of improvement in clinical outcome 1 month and 1 year after MRgFUS was examined. Results showed that there is not a significant correlation between the two parameters.

Fig. 2

Prediction of superior clinical outcome using the volume of overlapping between the MRgFUS induced lesion and thalamic segmentation maps normalized by the Volume of Lesion (VOL_o/VOL_Lesion) and normalized by the Volume of thalamic segmentation map (VOL_O/VOL_Segmentation Map). The volume of overlapping between the MRgFUS induced lesion and thalamic segmentation maps to both targets precentral (a) and postcentral (b) gyrus ((unthresholded (i)/thresholded maps (ii-iv))) was normalized by the volume of Lesion (VOL_o/VOL_Lesion) and the volume of thalamic segmentation map (VOL_O/VOL_Segmentation Map) and then were used as features to predict the superior clinical outcome. Low accuracy results were found in both cases.

Fig. 3

Prediction using the mean Euclidean distance between lesion area and thalamic segmentation maps. The euclidean distance (a) between the voxel with the maximum intensity value at the lesion area on PostT2_to PreT1 image and the voxel with the maximum intensity value of thalamic segmentation map was calculated. Also, the mean euclidean distance (b) between all voxels of lesion with the voxel with the maximum intensity value of thalamic segmentation maps was found. The resulted measures were used to predict the superior clinical improvement after MRgFUS. ROC analysis revealed low accuracy values for both cases.