Intrinsic Connectivity Provides the Baseline Framework for Variability in Motor Performance: A Multivariate Fusion Analysis of Low- and High-Frequency Resting-State Oscillations and Antisaccade Performance.
Intrinsic brain activity provides the functional framework for the brain's full repertoire of behavioral responses; that is, a common mechanism underlies intrinsic and extrinsic neural activity, with extrinsic activity building upon the underlying baseline intrinsic activity. The generation of a motor movement in response to sensory stimulation is one of the most fundamental functions of the central nervous system. Since saccadic eye movements are among our most stereotyped motor responses, we hypothesized that individual variability in the ability to inhibit a prepotent saccade and make a voluntary antisaccade would be related to individual variability in intrinsic connectivity. Twenty-three individuals completed the antisaccade task and resting-state functional magnetic resonance imaging (fMRI). A multivariate analysis of covariance identified relationships between fMRI oscillations (0.01-0.2 Hz) of resting-state networks determined using high-dimensional independent component analysis and antisaccade performance (latency, error rate). Significant multivariate relationships between antisaccade latency and directional error rate were obtained in independent components across the entire brain. Some of the relationships were obtained in components that overlapped substantially with the task; however, many were obtained in components that showed little overlap with the task. The current results demonstrate that even in the absence of a task, spectral power in regions showing little overlap with task activity predicts an individual's performance on a saccade task.
1 Monash Biomedical Imaging, Monash University , Melbourne, Australia .|2 Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University , Melbourne, Australia .|3 Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University , Melbourne, Australia .
Sharna D
SD
Jamadar
1 Monash Biomedical Imaging, Monash University , Melbourne, Australia .|2 Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University , Melbourne, Australia .|3 Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University , Melbourne, Australia .
Gary F
GF
Egan
4 The Mind Research Network , Albuquerque, New Mexico.|5 Department of Electrical and Computer Engineering, University of New Mexico , Albuquerque, New Mexico.
Vince D
VD
Calhoun
2 Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University , Melbourne, Australia .
Beth
B
Johnson
2 Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University , Melbourne, Australia .|6 Department of Medicine, University of Melbourne , Melbourne, Australia .
Joanne
J
Fielding
D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D058256:Brain Waves; D005260:Female; D006801:Humans; D007266:Inhibition, Psychological; D008279:Magnetic Resonance Imaging; D008297:Male; D009434:Neural Pathways / Q000502:physiology; D011597:Psychomotor Performance; D012438:Saccades; D012815:Signal Processing, Computer-Assisted
antisaccade; functional magnetic resonance imaging (fMRI); intrinsic connectivity; low-frequency oscillations; multivariate covariate analysis; oculomotor; resting-state fMRI; time-course spectra