Purpose: Cortical and subcortical functional activity stimulated via saccade and vergence eye movements were investigated to examine the similarities and differences between networks and regions of interest (ROIs).
Methods: Blood oxygenation level-dependent (BOLD) signals from stimulus-induced functional Magnetic Resonance Imaging (MRI) experiments were analyzed studying 16 healthy subjects. Six types of oculomotor experiments were conducted using a block design to study both saccade and vergence circuits. The experiments included a simple eye movement task and a more cognitively demanding prediction task. A hierarchical independent component analysis (ICA) process began by analyzing individual subject data sets with spatial ICA to extract spatial independent components (sIC), which resulted in three ROIs. Using the time series from each of the three ROIs per subject, per oculomotor experiment, a temporal ICA was used to compute individual temporal independent components (tICs). For each of the three ROIs, the individual tICs from multiple subjects were entered into a second temporal ICA to compute group-level tICs for comparison.
Results: Two independent spatial maps were observed for each subject (one sIC showing activity in the frontoparietal regions and another sIC in the cerebellum) during the six oculomotor tasks. Analysis of group-level tICs revealed an increased latency in the cerebellar region when compared to the frontoparietal region.
Conclusion: Shared neuronal behavior has been reported in the frontal and parietal lobes, which may in part explain the segregation of frontoparietal functional activity into one sIC. The cerebellum uses multiple time scales for motor learning. This may result in an increased latency observed in the BOLD signal of the cerebellar group-level tIC when compared to the frontal and parietal group-level tICs. The increased latency offers a possible explanation to why ICA dissects the cerebellar activity into an sIC. The hierarchical ICA process used to calculate group-level tICs can yield insight into functional connectivity within complex neural networks.