Functional Connectivity Differences in Distinct Dentate-Cortical Networks in Alzheimer’s Disease and Mild Cognitive Impairment

Recent research found increased functional connectivity (FC) between the (DN) and cerebral cortex in Alzheimer’s disease (AD; Olivito, 2020) and points to the potential importance of cerebrocerebellar connectivity as a biomarker in the progression of the disease. The cerebellum, which projects to the thalamus via the dentate nucleus (DN), is connected to both cognitive and motor regions of the cerebral cortex via separate, trans-thalamic, reciprocal loops (Kelly & Strick, 2003). Research has delineated at least two distinct dentate-cortical networks in both the non-human primate and human brain: the dorsal DN, associated with motor functions and circuits, and the ventral DN, associated with cognitive processing and the corresponding circuits (Dum & Strick, 2003; Bernard et al., 2014; Steele et al., 2017). Importantly, cerebellar regions prone to atrophy in AD (e.g., Crus I/II) are functionally connected to atrophied regions of the cerebral cortex (e.g., primary motor cortex and regions related to the default mode network; de Jong et al. 2008, Suvà et al., 1999, Guo, 2016), indicating dysfunction occurs at a network level. A crucial step in elucidating the role of the cerebellum in AD was to investigate functional connectivity alterations between cerebral structures and the DN (Olivito, 2020) and an important next step is to investigate functional connectivity differences in the individual networks within the DN. Additionally, loss of motor function and the presence of motor symptoms can predict AD outcomes and the risk of developing AD (Buchman& Bennett, 2011; Scarmeas, 2005; Albers, 2015). Therefore, it is of interest to explore whether the dorsal and ventral dentate-cortical networks have differences in functional connectivity in the presence of mild cognitive impairment (MCI) and AD and whether any differences seen in these networks are related to scores on motor and cognitive tasks. This work may potentially inform future clinical work in the diagnosis and understanding of AD, as well as help decipher AD subtypes and the progression of symptoms as they relate to brain network dysfunction.