Motor and higher‐order functions topography of the human dentate nuclei identified with tractography and clustering methods

• Published in: Human brain mapping Vol. 42; no. 13; pp. 4348 - 4361
• Main Authors: Palesi, Fulvia, Ferrante, Matteo, Gaviraghi, Marta, Misiti, Anastasia, Savini, Giovanni, Lascialfari, Alessandro, D'Angelo, Egidio, Gandini Wheeler‐Kingshott, Claudia A. M.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2021
• Subjects: Adult; Algorithms; Brain; Cerebellar Cortex - anatomy & histology; Cerebellar Nuclei - anatomy & histology; Cerebellum; Clustering; Cognitive ability; connectivity; Cortex (somatosensory); dentate nuclei; diffusion imaging; Diffusion Tensor Imaging - methods; Female; Humans; Machine learning; Magnetic resonance imaging; Male; Methods; Microstructure; MRI; Nerve Net - anatomy & histology; Neural networks; Nuclei; Statistical analysis; Substantia grisea; thalamus; Topography; tractography; tractography; microstructure; connectivity; MRI; dentate nuclei; diffusion imaging; cerebellum; thalamus
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25551

Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher‐order functions in humans in vivo. Whole‐brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion‐derived metrics in an unsupervised fuzzy c‐means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule‐specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non‐motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule‐specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher‐order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non‐motor functions and that connectivity‐based and microstructure‐based atlases provide complementary information. These results represent a step‐ahead for the interpretation of pathological conditions involving cerebro‐cerebellar circuits. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity, in turn with either the cerebral or the cerebellar cortex, as well as their microstructural features, inputting them in an unsupervised fuzzy c‐means classification algorithm. The three approaches provided quite overlapping results in terms of geometrical distribution and, for the first time, indicate that more than half of the DN volumes is involved in non‐motor higher‐order functions.