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Cortico-Thalamic-Interplay: Representation of Cortical Resting-State Networks in the Thalamus

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Kumar,  V       
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Grodd,  W
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kumar, V., Beckmann, C., & Grodd, W. (2016). Cortico-Thalamic-Interplay: Representation of Cortical Resting-State Networks in the Thalamus. Poster presented at 5th Biennial Conference On Resting State And Brain Connectivity 2016, Wien, Austria.


Cite as: https://hdl.handle.net/21.11116/0000-000B-179F-9
Abstract
Background: The thalamus relays sensory and motor signals to the cortex and is involved in
regulating consciousness, sleep, and alertness by temporally binding information from
various subcortical and cortical areas. It has been hypothesized that the cortical areas cannot
self-sustain cortical dynamics without tightly locked thalamic activity (Alonso and Swadlow,
2015).
Resting state fMRI is able to characterise different cortical networks (c-RSNs) according to
their behavioral domain (Smith et al., 2009) . It remains, however, underexplored how these
c-RSNs communicate w ith the thalamus. Therefore, in this study we investigated how
specific cortical network’s functional connectivity is represented on the thalamus.
Methods: Four resting state sessions (Gradient-echo EPI, 1200 scans/session, duration: 14:33
min, TR: 720 ms, TE: 33.1 ms, resolution: 2 mm iso, 72 slices, multiband factor: 8) of 130
subjects were chosen from the HCP dataset (Van Essen et al., 2012). Data were preprocessed
and ICA denoised using the available HCP pipeline (Glasser et al., 2013) and FSL Fix. Data
were smoothed with 3 mm kernel. Ten c-RSN templates (Smith 2009) were used for
classification of thalamic voxels by performing correlation-using fsl_sbca (O'Reilly 2009) .
The group correlation maps were calculated using th e fixed-effect analysis. The fixed effect
group maps were used to calculate the strongest and dominant representations (winner-takes-
all).
Results: The c-RSNs corresponding thalamic WTA maps showed a topographic organization
within left and right thalamus . Furthermore, these maps reveal hemispheric variability . (s .
Fig.1 & 2 ). The Sensorimotor and cerebellum -cortico displayed a higher degree of spatial
spread compared to other RSNs.
Conclusions: We found dominant spatial pattern in the left and right thalamus for different c-
RSNs. Furthermore, we observed laterality differences, possibly suggesting different
hemispheric thalamo-cortical communication requirements during rest.