Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations

Abstract

Neurobehavioral studies in humans have long concentrated on changes in local activity levels during repetitive executions of a task. Spontaneous neural coupling within extended networks has latterly been found to also influence performance. Here, we intend to uncover the underlying mechanisms, the relative importance, and the interaction between spontaneous coupling and task-induced activations. To do so, we recorded two groups of healthy participants (male and female) during rest and while they performed either a visual perception or a motor sequence task. We demonstrate that, for both tasks, stronger activations during the task as well as greater network coupling through spontaneous α rhythms at rest predict performance. However, high performers present an absence of classical task-induced activations and, instead, stronger spontaneous network coupling. Activations were thus a compensation mechanism needed only in subjects with lower spontaneous network interactions. This challenges classical models of neural processing and calls for new strategies in attempts to train and enhance performance.SIGNIFICANCE STATEMENT Our findings challenge the widely accepted notion that task-induced activations are of paramount importance for behavior. This will have an important impact on interpretations of human neurobehavioral research. They further link the widely used techniques of quantifying network communication in the brain with classical neuroscience methods and demonstrate possible ways of how network communication influences human behavior. Traditional training methods attempt to enhance neural activations through task repetitions. Our findings suggest a more efficient neural target for learning: enhancing spontaneous neural interactions. This will be of major interest for a large variety of scientific fields with very broad applications in schools, work, and others.

Keywords: event-related desynchronization; motor planning; neural coupling; visual perception.

Copyright © 2020 the authors.

Figures

Figure 1.

Schematic illustration of the relations between neural activity patterns and visual perception.

Figure 2.

Experimental paradigm and time course of a single block. The task consisted of blocks starting with a resting period followed by seven trials of a target detection task (for details, see Materials and Methods). For visualization purposes, white represents the background, and dark gray represents the background of the task.

Figure 3.

Behavioral results. Sensitivity curves for 1 subject (A), threshold contrast values (B), and detection rates (C) for all subjects and conditions. *p < 0.05.

Figure 4.

Characteristics of investigated neural processes. Average event-related in-task power changes induced by targets presented to the left (A) and right (B) with white overlaid ROI cutout. Blue represents regions with significant power decrease from baseline in the α band after the presentation of a target (p < 0.05, 5% FDR-corrected over all voxels) on a 3D rendering of a template brain. Time-frequency decomposition of power changes in a bilateral occipital ROI compared with a baseline at 500-300 ms before target onset, as induced by a left or right visual stimulus (C; p < 0.05, 5% FDR-corrected over time and frequency). D, The topography of resting-state α band FC shows an occipitofrontal gradient. E, Frequency spectrum of resting-state FC in a bilateral occipital ROI. F, Α band FC variation over the interblock resting periods. Error bars indicate mean ± 95% CI.

Figure 5.

Neural processes predicting visual perception. Seen stimuli were associated, on average across all subjects, with greater power changes in θ, α, and β bands during the presentation of seen stimuli than missed trials (A; p < 0.05, 5% FDR-corrected), in accordance with the classical pattern of task-induced neural activations. We also observed greater α band coherence between the ROI and the entire cortex during resting periods before perceived stimuli than before missed trials (B; *p = 0.035, 5% FDR-corrected), in accordance with previous findings of a positive impact of α band coherence during rest for task performance (Guggisberg et al., 2015). Error bars indicate mean ± 95% CI.

Figure 6.

Between-subject covariation of neural processing and target perception. A, Subjects with greater spontaneous α band WND in the occipital ROI showed proportionally better visual detection rates. B–D, Conversely, subjects with greater in-task θ, α, and β power difference (seen – missed) tended to have proportionally lower detection rates. E, Dividing the sample in two equal groups based on their levels of resting-state α-FC (median cutoff) showed that only subjects with low α band FC level demonstrate the classical task-induced power decrease. Black line indicates time windows in which the difference between seen and missed trials is significant (p < 0.05, 5% FDR-corrected over time). F, Conversely, subjects with high α band FC show an absence of α power decrease following stimulus presentation, regardless of their awareness of the target. Association between resting-state α band FC and in-task α power difference (G) and same association for all bands (H; Pearson correlations, p < 0.05, 5% FDR-corrected).

Figure 7.

Generalization to task-induced power in other frequency bands. Power variation over the peristimulus interval for seen and missed targets in β, γ, and high-γ in the 10 subjects with the lowest (A–C) or highest (D–F) resting-state α WND. Black line indicates time windows in which the difference between seen and missed trials is significant (p < 0.05). Association between resting-state α WND and in-task power difference in β (G), γ (H), and high-γ frequency bands (I; Pearson correlations).

Figure 8.

Comparison of the influence of α band FC traits and FC states on task-induced α band ERD and visual perception. Subjects with high α band FC traits show low α band ERD amplitudes and stable perception performance regardless of the momentary FC state (A,C). Subjects with low FC traits had blocks with higher α band FC states, which were associated with significantly greater α band ERDs (B). Horizontal lines indicate significant differences (p < 0.05, Tukey–Kramer HSD) and, on average, a nonsignificantly larger proportion of perceived stimuli (D).

Figure 9.

Spontaneous network correlates of leftward spatial attention bias. A, Parietal ROI. B, Pearson correlation between resting-state α-WND of the parietal ROI and visuospatial leftward bias in bilateral stimulus presentations. C, Difference in α-WND preceding unilateral stimulus presentations. *p < 0.05 (5% FDR-corrected).

Figure 10.

Generalization to motor planning. A, Mean event-related α band power decrease before execution of a motor sequence across all 20 patients (p < 0.05, 5% FDR-corrected over voxels) with white overlaid ROI cutout. B, A time-frequency decomposition at the contralateral motor ROI shows, on average, prominent power decrease in all examined bands (p < 0.05, 5% FDR-corrected over time-frequency windows). Time 0 indicates the onset cue. C, Correlation between spontaneous α band FC and number of correct motor sequences per minute. D, Greater α band FC was in turn associated with more years of experience with piano playing. E, Participants with low spontaneous α band FC needed significantly greater event-related α power decrease than subjects with high FC. Black line indicates time points with significant difference (5% FDR-corrected). Thus, greater spontaneous α band FC correlated with proportionally smaller task-induced power decrease at all examined frequency bands (F; p < 0.05, corrected).