期刊
BRAIN STRUCTURE & FUNCTION
卷 228, 期 8, 页码 1811-1834出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s00429-022-02603-w
关键词
Thalamus; Systems-level decoding; Structure-Function relationships; BrainMap database; ALE meta-analysis; Neurosynth
The human thalamus plays a crucial role in relaying sensory signals, sensorimotor functions and various cognitive functions. However, its full functional repertoire and internal anatomical structure are still not fully understood. In this study, we developed a novel systems-level decoding approach that combines traditional neuroinformatics methods to investigate the functional profile of the thalamus. By decoding the functional repertoire of thalamic subregions in conjunction with functionally connected cortical regions, we identified key structure-function relationships and discovered associations with language, memory, and locomotion.
The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.
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