@article{2e0a5a1a4fd342e2bb516a65442bfa57,
title = "Fronto-cerebellar connectivity mediating cognitive processing speed",
abstract = "Processing speed is an important construct in understanding cognition. This study was aimed to control task specificity for understanding the neural mechanisms underlying cognitive processing speed. Forty young adult subjects performed attention tasks of two modalities (auditory and visual) and two levels of task rules (compatible and incompatible). Block-design fMRI captured BOLD signals during the tasks. Thirteen regions of interest were defined with reference to publicly available activation maps for processing speed tasks. Cognitive speed was derived from task reaction times, which yielded six sets of connectivity measures. Mixed-effect LASSO regression revealed six significant paths suggestive of a cerebello-frontal network predicting the cognitive speed. Among them, three are long range (two fronto-cerebellar, one cerebello-frontal), and three are short range (fronto-frontal, cerebello-cerebellar, and cerebello-thalamic). The long-range connections are likely to relate to cognitive control, and the short-range connections relate to rule-based stimulus-response processes. The revealed neural network suggests that automaticity, acting on the task rules and interplaying with effortful top–down attentional control, accounts for cognitive speed.",
keywords = "Cerebellum, Connectivity, Individual differences, Medial frontal cortex, Processing speed",
author = "Wong, {Clive H.Y.} and Jiao Liu and Lee, {Tatia M.C.} and Jing Tao and Wong, {Alex W.K.} and Chau, {Bolton K.H.} and Lidian Chen and Chan, {Chetwyn C.H.}",
note = "Funding Information: The ICA-cleaned imaging dataset, ANTs high-dimensional deformation and scripts for applying the deformation, cluster masks obtaioned with watershed method, and the extracted timeseries for all subjects are available in GitHub (https://github.com/clivehywong/2021CPS). The processing speed tasks activation maps were obtained from NeuroVault (https://identifiers.org/neurovault.collection:857). Watershed-based parcellation of activation map is available from https://www.med.upenn.edu/cmroi/shed.html. Vector Autoregression connectivity indices were estimated with 1dGC (https://afni.nimh.nih.gov/1dgc). The R scripts for estimating other connectivity indices and the mixed effect modeling are available in GitHub (https://github.com/clivehywong/2021CPS). This study was supported by the 12th Five-year Plan project of Ministry of Science and Technology of the People's Republic of China (grant number 2013BAI10B01) and the Fujian Rehabilitation Tech Co-Innovation of China. It was also supported by The University of Hong Kong May Endowed Professorship in Neuropsychology and The Science and Technology Program of Guangdong (2018B030334001). The authors thank the University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University for the equipment and technical supports rendered throughout the study. Funding Information: This study was supported by the 12th Five-year Plan project of Ministry of Science and Technology of the People's Republic of China (grant number 2013BAI10B01 ) and the Fujian Rehabilitation Tech Co-Innovation of China. It was also supported by The University of Hong Kong May Endowed Professorship in Neuropsychology and The Science and Technology Program of Guangdong (2018B030334001). The authors thank the University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University for the equipment and technical supports rendered throughout the study. Publisher Copyright: {\textcopyright} 2020 Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2021",
month = feb,
day = "1",
doi = "10.1016/j.neuroimage.2020.117556",
language = "English",
volume = "226",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
}