Dataset Information

Functional connectivity-based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI.

ABSTRACT: This study aimed to identify subunits of the basal ganglia and thalamus and to investigate the functional connectivity among these anatomically segregated subdivisions and the cerebral cortex in healthy subjects. For this purpose, we introduced multilevel independent component analysis (ICA) of the resting-state functional magnetic resonance imaging (fMRI). After applying ICA to the whole brain gray matter, we applied second-level ICA restrictively to the basal ganglia and the thalamus area to identify discrete functional subunits of those regions. As a result, the basal ganglia and the thalamus were parcelled into 31 functional subdivisions according to their temporal activity patterns. The extracted parcels showed functional network connectivity between hemispheres, between subdivisions of the basal ganglia and thalamus, and between the extracted subdivisions and cerebral functional components. Grossly, these findings correspond to cortico-striato-thalamo-cortical circuits in the brain. This study also showed the utility of multilevel ICA of resting state fMRI in brain network research.

SUBMITTER: Kim DJ

PROVIDER: S-EPMC6870335 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Functional connectivity-based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI.

Kim Dae-Jin DJ Park Bumhee B Park Hae-Jeong HJ

Human brain mapping 20120214 6

This study aimed to identify subunits of the basal ganglia and thalamus and to investigate the functional connectivity among these anatomically segregated subdivisions and the cerebral cortex in healthy subjects. For this purpose, we introduced multilevel independent component analysis (ICA) of the resting-state functional magnetic resonance imaging (fMRI). After applying ICA to the whole brain gray matter, we applied second-level ICA restrictively to the basal ganglia and the thalamus area to i ...[more]

PMID: 22331611

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Project description:IntroductionIndependent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.ObjectiveHere, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.MethodsFirst, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network.ResultsNetwork graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness.ConclusionsThis novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

Project description:Basal ganglia and the cerebellum are part of a densely interconnected network. While both subcortical structures process information in basically segregated loops that primarily interact in the neocortex, direct subcortical interaction has been recently confirmed by neuroanatomical studies using viral transneuronal tracers in non-human primate brains. The thalamus is thought to be the main relay station of both projection systems. Yet, our understanding of subcortical basal ganglia and cerebellar interconnectivity within the human thalamus is rather sparse, primarily due to limitation in the acquisition of in vivo tracing. Consequently, we strive to characterize projections of both systems and their potential overlap within the human thalamus by diffusion MRI and tractography. Our analysis revealed a decreasing anterior-to-posterior gradient for pallido-thalamic connections in: (1) the ventral-anterior thalamus, (2) the intralaminar nuclei, and (3) midline regions. Conversely, we found a decreasing posterior-to-anterior gradient for dentato-thalamic projections predominantly in: (1) the ventral-lateral and posterior nucleus; (2) dorsal parts of the intralaminar nuclei and the subparafascicular nucleus, and (3) the medioventral and lateral mediodorsal nucleus. A considerable overlap of connectivity pattern was apparent in intralaminar nuclei and midline regions. Notably, pallidal and cerebellar projections were both hemispherically lateralized to the left thalamus. While strikingly consistent with findings from transneuronal studies in non-human primates as well as with pre-existing anatomical studies on developmentally expressed markers or pathological human brains, our assessment provides distinctive connectional fingerprints that illustrate the anatomical substrate of integrated functional networks between basal ganglia and the cerebellum. Thereby, our findings furnish useful implications for cerebellar contributions to the clinical symptomatology of movement disorders.

Dataset Information

Functional connectivity-based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI.

Publications

Functional connectivity-based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI.

Similar Datasets

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