Functional Connectivity during Elbow Movements: Comparison between Motor and Non-motor Task

Author(s):  
Alexsandro S. T. Silva ◽  
Antonio Maricio F. L. Miranda de Sá ◽  
Carlos Julio Tierra-Criollo
2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhiguo Jiang ◽  
Xiao-Feng Wang ◽  
Guang H. Yue

The present study examined functional connectivity (FC) between functional MRI (fMRI) signals of the primary motor cortex (M1) and each of the three subcortical neural structures, cerebellum (CB), basal ganglia (BG), and thalamus (TL), during muscle fatigue using the quantile regression technique. Understanding activation relation between the subcortical structures and the M1 during prolonged motor performance should help delineate how central motor control network modulates acute perturbations at peripheral sensorimotor system such as muscle fatigue. Ten healthy subjects participated in the study and completed a 20-minute intermittent handgrip motor task at 50% of their maximal voluntary contraction (MVC) level. Quantile regression analyses were carried out to compare the FC between the contralateral (left) M1 and CB, BG, and TL in the minimal (beginning 100 s) versus significant (ending 100 s) fatigue stages. Widespread, statistically significant increases in FC were found in bilateral BG, CB, and TL with the left M1 during significant versus minimal fatigue stages. Our results imply that these subcortical nuclei are critical components in the motor control network and actively involved in modulating voluntary muscle fatigue, possibly, by working together with the M1 to strengthen the descending central command to prolong the motor performance.


NeuroImage ◽  
2013 ◽  
Vol 78 ◽  
pp. 316-324 ◽  
Author(s):  
Kuang-Chi Tung ◽  
Jinsoo Uh ◽  
Deng Mao ◽  
Feng Xu ◽  
Guanghua Xiao ◽  
...  

2004 ◽  
Vol 22 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Tianzi Jiang ◽  
Yong He ◽  
Yufeng Zang ◽  
Xuchu Weng

2018 ◽  
Vol 8 (5) ◽  
pp. 268-275 ◽  
Author(s):  
Michael Todd Jurkiewicz ◽  
Adrian Philip Crawley ◽  
David John Mikulis

2021 ◽  
Vol 168 ◽  
pp. S227
Author(s):  
YueHan Wang ◽  
Zetao Liu ◽  
Jian Hu ◽  
SiSi Jiang ◽  
Dezhong Yao ◽  
...  

2019 ◽  
Vol 37 (02) ◽  
pp. 137-145
Author(s):  
Stephanie L. Merhar ◽  
Elveda Gozdas ◽  
Jean A. Tkach ◽  
Nehal A. Parikh ◽  
Beth M. Kline-Fath ◽  
...  

Objective The accuracy of structural magnetic resonance imaging (MRI) to predict later cerebral palsy (CP) in newborns with perinatal brain injury is variable. Diffusion tensor imaging (DTI) and task-based functional MRI (fMRI) show promise as predictive tools. We hypothesized that infants who later developed CP would have reduced structural and functional connectivity as compared with those without CP. Study Design We performed DTI and fMRI using a passive motor task at 40 to 48 weeks' postmenstrual age in 12 infants with perinatal brain injury. CP was diagnosed at age 2 using a standardized examination. Results Five infants had CP at 2 years of age, and seven did not have CP. Tract-based spatial statistics showed a widespread reduction of fractional anisotropy (FA) in almost all white matter tracts in the CP group. Using the median FA value in the corticospinal tracts as a cutoff, FA was 100% sensitive and 86% specific to predict CP compared with a sensitivity of 60 to 80% and a specificity of 71% for structural MRI. During fMRI, the CP group had reduced functional connectivity from the right supplemental motor area as compared with the non-CP group. Conclusion DTI and fMRI obtained soon after birth are potential biomarkers to predict CP in newborns with perinatal brain injury.


Author(s):  
Stefan Frässle ◽  
Zina M. Manjaly ◽  
Cao T. Do ◽  
Lars Kasper ◽  
Klaas P. Pruessmann ◽  
...  

ABSTRACTConnectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation.Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.


2011 ◽  
Vol 122 (8) ◽  
pp. 1569-1579 ◽  
Author(s):  
Chia-Feng Lu ◽  
Shin Teng ◽  
Chih-I Hung ◽  
Po-Jung Tseng ◽  
Liang-Ta Lin ◽  
...  

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