Anastasi, G. P. | Search bioRxiv

bioRxiv2021

Ventral Intermediate Nucleus structural connectivity-derived segmentation: anatomical reliability and variability

The Ventral intermediate nucleus (Vim) of thalamus is the most targeted structure for the treatment of drug-refractory tremors. Since methodological differences across existing studies are remarkable and no gold-standard pipeline is available, in this study, we tested different parcellation pipelines for tractography-derived putative Vim identification. Thalamic parcellation was performed on a high quality, multi-shell dataset and a downsampled, clinical-like dataset using two different diffusion signal modeling techniques and two different voxel classification criteria, thus implementing a total of four parcellation pipelines. The most reliable pipeline in terms of inter-subject variability has been picked and parcels putatively corresponding to motor thalamic nuclei have been selected by calculating similarity with a histology-based mask of Vim. Then, spatial relations with optimal stimulation points for the treatment of essential tremor have been quantified. Finally, effect of data quality and parcellation pipelines on a volumetric index of connectivity clusters has been assessed. We found that the pipeline characterized by higher-order signal modeling and threshold-based voxel classification criteria was the most reliable in terms of inter-subject reliability regardless data quality. The maps putatively corresponding to Vim were those derived by precentral- and dentate nucleus-thalamic connectivity. However, tractography-derived functional targets showed remarkable differences in shape and sizes when compared to a ground truth model based on histochemical staining on seriate sections of human brain. Thalamic voxels connected to contralateral dentate nucleus resulted to be the closest to literature-derived stimulation points for essential tremor but at the same time showing the most remarkable inter-subject variability. Finally, the volume of connectivity parcels resulted to be significantly influenced by data quality and parcellation pipelines. Hence, caution is warranted when performing thalamic connectivity-based segmentation for stereotacting targeting.

neuroscience↗

bioRxiv2021

In vivo super-resolution track-density imaging for thalamic nuclei identification

The development of novel techniques for the in vivo, non-invasive visualization and identification of thalamic nuclei has represented a major challenge for human neuroimaging research in the last decades. Thalamic nuclei have important implications in various key aspects of brain physiology and many of them show selective alterations in various neurologic and psychiatric disorders. In addition, both surgical stimulation and ablation of specific thalamic nuclei have been proven to be useful for the treatment of different neuropsychiatric diseases. The present work aimed at describing a novel protocol for histologically-guided delineation of thalamic nuclei based on short-tracks track-density imaging (stTDI), which is an advanced imaging technique that exploits high angular resolution diffusion tractography to obtain super-resolved white matter maps with high anatomical information. We tested this protocol on i) six healthy individual 3T MRI scans from the Human Connectome Project database, and on ii) a group population template reconstructed by averaging 100 unrelated healthy subjects scans from the same repository. We demonstrated that this approach can identify up to 13 distinct thalamic nuclei bilaterally with very high reliability (intraclass correlation coefficient: 0.996, 95% CI: 0.993-0.998; total accumulated overlap: 0.43) and that both subject-based and group-level thalamic parcellation show a fair share of similarity to a recent standard-space histological thalamic atlas. Finally, we showed that stTDI-derived thalamic maps can be successfully employed to study thalamic structural and functional connectivity, and may have potential implications both for basic and translational research, as well as for pre-surgical planning purposes.

neuroscience↗