Interactions between the anterior cingulate-insula network and the fronto-parietal network during perceptual decision-making
Introduction
Previous neuroimaging investigations have described large-scale, intrinsically organized brain networks underlying a broad range of brain functions, from sensory to motor and to higher-level cognitive functions (Deco et al., 2011, Power et al., 2011, Seeley et al., 2007). The anterior cingulate-insula network (aCIN) and the fronto-parietal network (FPN) are known to be central for cognitive functions (Chen et al., 2013, Uddin, 2015). Functional magnetic resonance imaging (fMRI) studies (Goulden et al., 2014, Sridharan et al., 2008) have demonstrated that the aCIN sends a dominant information flow to the FPN in goal-directed tasks. However, what remains poorly understood is how these networks interact in the time-scales of human cognitive processes.
The individual functional roles of the brain areas in the aCIN have not been precisely resolved. The dorsal anterior cingulate cortex (DACC) of aCIN is known to monitor performance, to signal the need for behavioral adaptation (Ridderinkhof et al., 2004), and in concert with the lateral prefrontal cortex to signal enhanced cognitive control and implement behavioral changes (Egner, 2009, Ridderinkhof et al., 2004). The anterior insula (AI), especially the right AI (R AI), is known as cortical outflow hub of the aCIN to coordinate a change in activity across multiple brain networks, including the FPN (Bonnelle et al., 2012, Menon and Uddin, 2010, Sridharan et al., 2008). Here, we seek to examine the interactions between the aCIN and the FPN in millisecond time-scale. Since multiple frequency bands of neural oscillations have important implications for cognitive processes (Diener et al., 2012, Hipp et al., 2011, Siegel et al., 2012), we also seek to examine the spectra of information flow between the aCIN and the FPN during perceptual decision-making.
We performed electroencephalography (EEG) experiments by using clear and degraded face-house images in perceptual categorization task. We reconstructed waveforms of EEG sources associated with perceptual decisions and applied spectral Granger causality methods (Dhamala et al., 2008a, Dhamala et al., 2008b) to examine at the frequency-specific network interactions between the aCIN and the FPN. We hypothesized that the FPN would be under Granger causal control from the aCIN, this causal influence would change during a course of a perceptual decision, and this casual influence would be retained by the task difficulty.
Section snippets
Participants
In this study, 26 human volunteers (21 males, 5 females) of age ranged from 22 to 38 years (mean: 26.3 years, standard deviation: 4.7 years) participated. Each participant reported that he or she is not currently on medication for mental illness or had a prior history of mental illness affecting decision-making abilities. Out of 26 participants, 4 participants reported that they are left-handed. We collected a written informed consent from each participant prior to the data collection. The
Behavioral results
The overall accuracy percent is defined as the ratio of the number of correctly responded trials to the total number of presented trials multiplied by hundred. The accuracy percent was significantly higher for 0% noise compared with that of 40% and 55% noises. The average response times for 0%, 40%, and 55% noisy stimuli were 434.02 ms, 484.28 ms, and 565.70 ms, respectively. We limited our analysis (electrophysiological results below) within 425 ms to exclude the possibility of finger movement
Discussion
Our analyses demonstrated that beta (~22 Hz) and gamma (~80 Hz) bands of neural activity involve between the aCIN and the FPN interactions for both easier and harder decisions. In behaviorally easier task, our gamma Granger causal outflow calculations in 25–225 ms indicated that the aCIN played a Granger causal control to the FPN consistent with previous fMRI studies (Goulden et al., 2014, Sridharan et al., 2008). In contrast, beta Granger causal outflow calculations further uncovered that the FPN
Acknowledgements
This work was financially supported by a National Science Foundation CAREER award to MD (BCS 0955037).
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