High-dimensional Functional Segregation of the Human Thalamus

Introduction: Unarguably, the thalamus is a core structure of the human brain (Sherman, 2016), and all individual thalamic nuclei are integral components of very different functional systems in the brain (Jones, 2007). However, only a few studies have addressed the structural and functional diversity of the thalamic substructures in detail. Using diffusion tensor (DTI) or resting state functional MRI (r-fMRI) most 1.5, 3, and 7 Tesla MRI studies were able to determine 7 to 31 thalamic parcels with different spatial resolution (Calamante et al., 2012; Johansen-Berg et al., 2005; Kim et al., 2013; Kumar et al., 2017). Although most nuclei like the pulvinar complex, the lateral geniculate nucleus (LGN), and others are composed of numerous subnuclei (Baldwin et al., 2011, Andrews et al., 1997), we thought to examine whether a higher imaging resolution could assess a more detailed functional diversity of thalamic substructures. We, therefore, decided to use 1 mm isotopic resting state MRI data acquired at 9.4 Tesla to assess a high dimensional parcellation of the human thalamus. Methods: r-fMRI Acquisition: The r-fMRI was acquired at 9.4 T Siemens (Erlangen, Germany) in six right-handed male volunteers. We used the psf method for the distortion correction. The FOV covered thalamus (s. Fig. 1a) consisted of 45 slices, TR of 2.5 sec., 1 mm isotropic resolution and 220 scans. The volunteers kept their eyes closed during the resting state scans. Besides, two structural scans, i.e., MP2RAGE (600 microns iso) and a 3D GRE (400 microns iso) were acquired. r-fMRI Analysis: The preprocessing was performed with a modified pipeline using SPM12. It included slice timing, motion correction, coregistration, normalization and smoothing (3x3x3mm kernel). ICA Analysis: The normalized subject data were temporally concatenated. We performed the probabilistic-ICA (Beckmann et al., 2005) on the left and right thalamus. The optimal number of component estimation was done using default melodic model order selection. In the last step, the z-stat of all components were used to compute the winner map for the right and left thalamus. Results: The ICA automatic model order selection detected 82 components within the right thalamus and 83 components within the left thalamus (s. Fig 1b). In comparison with the histological atlas of Morel (Morel et al., 1997), which is restricted to a set of 29 bilateral nuclei (s. Fig. 1c), we observed an of 2.8 fold increase of parcels. Furthermore, our parcels varied in size, location, and distribution between both hemispheres (Fig. 2). This variable distribution with different temporal pattern within the left and right thalamus probably reflects functional differences between both hemispheres. The composite analysis in respect to the histological atlas revealed a varying number of parcels for each Morel nucleus; for example, we observed 6-7 left and right sub-parcels within the layered lateral geniculate nuclei (Andrews et al., 1997). Conclusions: Our study revealed that the thalamus exhibits a high-dimensional functional segregation even at rest. The detected parcels differed in size, location, and lateralization. In comparison with the histological defined thalamic nuclei, we observed a variable parcel assignment to all major nuclei groups in both hemispheres. However, further work is required to establish a valid and high-dimensional functional atlas of the thalamus, which could enhance our understanding of the concerted thalamo-cortical interaction at rest and under task conditions