Time scale properties of task and resting-state functional connectivity: Detrended partial cross-correlation analysis

Abstract

Functional connectivity analysis is an essential tool for understanding brain function. Previous studies showed that brain regions are functionally connected through low-frequency signals both within the default mode network (DMN) and task networks. However, no studies have directly compared the time scale (frequency) properties of network connectivity during task versus rest, or examined how they relate to task performance. Here, using fMRI data collected from sixty-eight subjects at rest and during a stop signal task, we addressed this issue with a novel functional connectivity measure based on detrended partial cross-correlation analysis (DPCCA). DPCCA has the advantage of quantifying correlations between two variables in different time scales while controlling for the influence of other variables. The results showed that the time scales of within-network connectivity of the DMN and task networks are modulated in opposite directions across rest and task, with the time scale increased during rest vs. task in the DMN and vice versa in task networks. In regions of interest analysis, the within-network connectivity time scale of the pre-supplementary motor area - a medial prefrontal cortical structure of the task network and critical to proactive inhibitory control - correlated inversely with Barratt impulsivity and stop signal reaction time. Together, these findings demonstrate that time scale properties of brain networks may vary across mental states and provide evidence in support of a role of low frequency fluctuations of BOLD signals in behavioral control.

Noradrenergic Dysfunction in Alzheimer's and Parkinson's Diseases—An Overview of Imaging Studies

“…Third, the LC projects to multiple brain regions and the interaction of LC with these neural networks are likely complex and defy simple correlation analyses. More sophisticated analytical tools such as Granger causality analysis (Duann et al, 2009 ; Ide and Chiang-shan, 2011 ; Ide and Li, 2011 ; Hu et al, 2015 ) and detrended partial cross correlation (Ide et al, 2017 ; Ide and Li, 2018 ) will be useful in delineating the direction of influence and distinguish direct functional interaction from influences via a common “third party.” These analyses would be tremendously useful in confirming the hypothesis that the LC response to saliency and its projection to the SAN facilitates activity transition from the DMN to frontoparietal network for goal-directed behavior. Together, these new studies will address many unanswered questions in cognitive and clinical neuroscience, and the findings would not only advance knowledge but also better patient care.…”

Brain sources’ activity in resting state before a visuo-motor task

“…MRI is also a more expensive and invasive method compared to the readiness, economic, less invasive and more available procedure of the EEG. In different experimental conditions, such as Go/NoGo visuo-spatial task, MRI was applied to explore different brain areas, such as prefrontal and superior parietal cortex (Fellrath et al 2016), and without using laterality indices (Ide and Li 2018). It was also applied to classify handedness based on individual resting-state maps (Pool et al 2015), and to study changes of connectivity exclusively in resting state EEG and not before a task (Solesio-Jofre et al 2018).…”

Cortical electrophysiological evidence for individual‐specific temporal organization of brain functional networks

“…Moreover, to exclude the possibility that significant identification was caused primarily by subject‐specific head movement patterns, we performed identification based on head motion feature only. Specifically, we calculated framewise displacement (FD) similar to fMRI studies (Ide & Li, ; Power, Barnes, Snyder, Schlaggar, & Petersen, ) for each subject in each session. For each time point t , the FD was calculated as: FD ( t ) = | Δd x ( t )| + | Δd y ( t )| + | Δd z ( t )| + r | Δα ( t )| + r | Δβ ( t )| + r | Δγ ( t )|, where ( d x , d y , d z ) and ( α , β , γ ) are the translation and rotation transformation parameters; r = 50 mm, a constant that approximates the mean distance from the cerebral cortex to the center of the head and used to covert rotations into displacement; and Δd x ( t ) = d x ( t ) − d x ( t − 1) (other parameters, i.e., d y , d z , α , β , γ , could be expressed in the same way).…”