Enhancing cognitive control with transcranial magnetic stimulation in subject-specific frontoparietal networks

BACKGROUND

Cognitive control processes, including those involving frontoparietal networks, are highly variable between individuals, posing challenges to basic and clinical sciences. While distinct frontoparietal networks have been associated with specific cognitive control functions such as switching, inhibition, and working memory updating functions, there have been few basic tests of the role of these networks at the individual level.

METHODS

To examine the role of cognitive control at the individual level, we conducted a within-subject excitatory transcranial magnetic stimulation (TMS) study in 19 healthy individuals that targeted intrinsic ("resting") frontoparietal networks. Person-specific intrinsic networks were identified with resting state functional magnetic resonance imaging scans to determine TMS targets. The participants performed three cognitive control tasks: an adapted Navon figure-ground task (requiring set switching), n-back (working memory), and Stroop color-word (inhibition).

OBJECTIVE

Hypothesis: We predicted that stimulating a network associated with externally oriented control [the "FPCN-B" (fronto-parietal control network)] would improve performance on the set switching and working memory task relative to a network associated with attention (the Dorsal Attention Network, DAN) and cranial vertex in a full within-subjects crossover design.

RESULTS

We found that set switching performance was enhanced by FPCN-B stimulation along with some evidence of enhancement in the higher-demand n-back conditions.

CONCLUSION

Higher task demands or proactive control might be a distinguishing role of the FPCN-B, and personalized intrinsic network targeting is feasible in TMS designs.

EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio

EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.

Alerting attention is sufficient to induce a phase-dependent behavior that can be predicted by frontal EEG

Recent studies suggest that attention is rhythmic. Whether that rhythmicity can be explained by the phase of ongoing neural oscillations, however, is still debated. We contemplate that a step toward untangling the relationship between attention and phase stems from employing simple behavioral tasks that isolate attention from other cognitive functions (perception/decision-making) and by localized monitoring of neural activity with high spatiotemporal resolution over the brain regions associated with the attentional network. In this study, we investigated whether the phase of electroencephalography (EEG) oscillations predicts alerting attention. We isolated the alerting mechanism of attention using the Psychomotor Vigilance Task, which does not involve a perceptual component, and collected high resolution EEG using novel high-density dry EEG arrays at the frontal region of the scalp. We identified that alerting attention alone is sufficient to induce a phase-dependent modulation of behavior at EEG frequencies of 3, 6, and 8 Hz throughout the frontal region, and we quantified the phase that predicts the high and low attention states in our cohort. Our findings disambiguate the relationship between EEG phase and alerting attention.