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High-frequency bursts facilitate fast communication for human spatial attention

Nature Neuroscience volume 29, pages 435–444 (2026)Cite this article

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Abstract

Brain-wide communication supporting flexible behavior requires coordination between sensory and associative regions but how brain networks route sensory information at fast timescales to guide action remains unclear. Using human intracranial electrophysiology and spiking neural networks during spatial attention tasks, where participants detected targets at cued locations, we show that high-frequency activity bursts (HFAbs) mark temporal windows of elevated population firing that enable fast, long-range communications. HFAbs were evoked by sensory cues and targets, dynamically coupled to low-frequency rhythms. Notably, both the strength of cue-evoked HFAbs and their decoupling from slow rhythms predicted behavioral accuracy. HFAbs synchronized across the brain, revealing distinct cue- and target-activated subnetworks. These subnetworks exhibited lead–lag dynamics following target onset, with cue-activated subnetworks preceding target-activated subnetworks when cues were informative. Computational modeling suggested that HFAbs reflect transitions to population spiking, denoting temporal windows for network communications supporting attentional performance. These findings establish HFAbs as signatures of population state transitions, supporting information routing across distributed brain networks.

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Fig. 1: HFAb activation profile predicts behavioral outcome on a trial-by-trial basis.
Fig. 2: HFAbs dynamically phase lock to low-frequency LFPs and decouple transiently after cue and target onsets.
Fig. 3: Network-level synchronization of HFAbs identifies functionally specialized subnetworks.
Fig. 4: Cue-subnetworks precede target-subnetworks during target processing.
Fig. 5: Computational modeling of HFAbs in spiking neural networks.

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Data availability

Preprocessed data used for generating these results are deposited at figshare at https://doi.org/10.6084/m9.figshare.30434683 (ref. 107). Source data are provided with this paper.

Code availability

Custom programming codes for analysis and modeling are available from https://github.com/banaiek/Attentional_Routing/.

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Acknowledgements

This work was supported by the C.V. Starr Fellowship (K.B.B.), German Research Foundation, Emmy Noether Program (DFG HE8329/2-1; R.F.H.), National Institute of Biomedical Imaging and Bioengineering (P41-EB018783; P.B.), National Institute of Neurological Disorders and Stroke (R01NS021135; R.T.K.), National Eye Institute (2R01EY017699; S.K.), National Institutes of Health (1R01MH137624; SK) and National Institute of Mental Health (2R01MH064043, P50MH132642; S.K.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

    Kianoush Banaie Boroujeni & Sabine Kastner

  2. Departments of Psychology and Neurology and the Wu Tsai Institute, Yale University, New Haven, CT, USA

    Randolph F. Helfrich

  3. Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA

    Ian C. Fiebelkorn

  4. Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, USA

    J. Nicole Bentley

  5. Department of Neurosurgery, Washington University in St Louis, St Louis, MO, USA

    Peter Brunner

  6. Department of Neurology and Center for Mind and Brain, University of California Davis, Davis, CA, USA

    Jack J. Lin

  7. Departments of Psychology and Neuroscience, University of California Berkeley, Berkeley, CA, USA

    Robert T. Knight

  8. Department of Psychology, Princeton University, Princeton, NJ, USA

    Sabine Kastner

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Conceptualization, K.B.B. and S.K.; Methodology, K.B.B.; Formal Analysis, K.B.B.; Modeling, K.B.B.; Investigation, R.F.H., R.T.K. and J.J.L.; Software, R.F.H. and I.C.F.; Visualization, K.B.B; Writing—Original Draft, K.B.B.; Writing—Review and Editing, K.B.B., R.F.H., I.C.F., J.N.B., P.B., J.J.L., R.T.K. and S.K.; and Supervision, S.K.

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Correspondence to Kianoush Banaie Boroujeni.

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Extended data

Extended Data Fig. 1 Behavioral performance and HFAb activation patterns across experiments.

a, Task structure of experiment 2. Subjects hold their gaze fixation to the center of a screen (a white plus sign) with red circles turning on and off. A spatial cue endogenously cues subject’s attention to a hemifield. A target appears at one hemifield and subjects should report whether the target was seen in the cued hemifield. The brain shows the localization of electrodes across all subjects. b, Reaction times (top) and accuracy (bottom) for individual subjects (n = 7 in experiment 1, and n = 5 in experiment 2) across valid, invalid, and catch trials (trials with no targets in experiment 1). Box plots show median (center line), interquartile range (box bounds = 25th–75th percentile), and whiskers extending to non-outlier minima and maxima (values within 1.5×IQR). c, Population-averaged HFAb density around cue (top) and target (bottom) onsets, similar to Fig. 1b in experiment 2 (mean ± SEM). d, HFAb responses grouped by trial outcomes (correct hit, correct reject, miss, false alarm) for cue (top) and target (bottom) epochs [median ± SEM], (n = 5). Asterisks indicate significant differences (Kruskal–Wallis test p = 0.003 for cue, and p < 0.001 for target response; followed by Dunn’s test, p < 0.05). e, Group topography of cue (left) and target (right) response for incorrect trials. f, Individual subject topographies for correct and incorrect trials, similar to Fig. 1h. g,h, HFAb responses comparing valid/invalid cues and contra/ipsi laterality conditions for experiments 1 (g, n = 6, GLME, n.s.) and 2 (h, n = 5), similar to Fig. 1i,j (median ± SEM). Horizontal lines indicate a significant main effect of validity on target (GLME, t = 5.03 p < 0.001), and laterality on target (GLME, t = 2.23, p = 0.025). i, Classifier accuracy predicting trial outcomes for cue-responsive (Cue + ) and cue-unresponsive (Cue-) in experiment 2 (error bars: SEM across realizations and cross-validations). Red line indicates significant prediction above baseline and chance (two-sided binomial test, p < 0.05, FDR-corrected for multiple dependent comparisons).

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Extended Data Fig. 2 HFAb phase-locking to low-frequency rhythms across subjects.

a, Individual examples showing HFAb phase distributions at the peak (left), phase-locking value (PLV, middle), and HFAb-triggered spectrum (right) from different subjects. Peak frequencies noted for each example in red and blue circles. b, Phase histograms of HFAbs at theta/alpha (8 Hz, purple) and beta (20 Hz, green) frequencies for all subjects. Orange and red lines indicate circular means. c, Distribution of peak phases across all electrodes. d, Distribution of phase-locking peak widths (mean: 7.3 ± 0.3 Hz). e, Relationship between peak frequency and power ratio (HFAb-triggered/baseline), colored by phase value. f, Distribution of power enhancement ratios at phase-locking frequencies (median: 2.1-fold increase), dashed line shows 1.

Extended Data Fig. 3 HFAb synchronization with low-frequency activity in experiment 1 and 2.

a, Group-average spatial pattern of observed frequency peak of HFAb phase-locking to LFP. b, Individual examples showing the spatial pattern of the observed frequency peaks of the HFAb phase-locking to LFP. c, Corresponding to Fig. 2e, showing the proportion of time-frequency points where phase-locking was significantly lower than baseline (p < 0.05, two-sided random permutation test). d, Similar to Fig. 2e, but after removing event-related potential from the LFP (see Methods). e, Group-average heatmaps of pairwise phase consistency (PPC) as a measure for HFAb synchronization to LFPs, Similar to Fig. 2e. f, Examples of the coupling ratio between HFAb and low-frequency (4–25 Hz) LFP following cue and target onsets in correct and incorrect trials. g, Similar to Fig. 2e for experiment 2. h,i, Regression plots showing correlation of coupling ratios following cue onset with target responses (green, h), and coupling ratios following target onset with cue responses (purple, i). Scatter points denote electrodes, lines indicate individual subjects with orange line showing the regression across all subjects.

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Extended Data Fig. 4 A network clustering approach based on the synchronization of HFAbs between electrodes.

a, The average HFAb-triggered HFA for individuals in experiment 2. b, The normalized PSD for HFAb-triggered HFA in experiment 2. c, HFAb-triggered HFA for electrodes distanced in 4 different quantiles (25,50, 75, 100 mm), ranging from green (short) to red (long) in experiment 2, similar to Fig. 3c. d, The first and second principal components of HFAb-triggered HFA for individual subjects in experiment 2. e, A schematic demonstration of network clustering algorithm. We used HFAbs outside of cue/delay and target/response periods. The network synchrony matrix shows the loading values for each electrode pair on the synchronized component. A K-means clustering was performed on randomly selected electrode samples for different cluster numbers (K = 2 to 8). We calculated a pairwise grouping probability matrix in which each element indicates how likely it is that two electrodes will be grouped together. The next step was clustering with network subsampling, similar to the previous step but based on the pairwise grouping likelihood matrix. The final clustering of the pairwise grouping likelihood results indicated stable clusters for each K (see Methods).

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Extended Data Fig. 5 Organization of clusters based on cluster numbers.

Columns from left to right show the results for cluster numbers K = 2–8. The cluster IDs are sorted by cluster stability.

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Extended Data Fig. 6 Clustering results for individual subjects.

a, Color-coded loading values on the synchronized PC (left) and the pairwise grouping probability (right) for each subject. b, Electrode distributions for optimal cluster numbers. c, Cluster number selection using four metrics with nonparametric voting rank (black). The accuracy is determined by the median diagonals, the confusion by the median nondiagonal, selectivity by the relative rank of the diagonal over the highest nondiagonal rank, and stability by the relative rank of the diagonal over the nondiagonal rank. d, HFAb density around cue and target onsets for each cluster, similar to Fig. 3f, across subjects. Shaded error bars indicate the standard error of the means, thicker lines indicate significant functional subnetworks. e, Leave-one-subject-out classifier analysis, similar to Fig. 3h. f, g, Classifier accuracy predicting correct trials (green), errors (brown), and overall accuracy (gray) for (f) experiment 1 and (g) experiment 2. h, outcome prediction by cue- and target-subnetworks following target onset. Shaded error bars show standard error of the mean. Thick lines in panels e-h indicate time points above baseline and chance (two-sided binomial test, p < 0.05, FDR-corrected for multiple dependent comparisons).

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Extended Data Fig. 7 Temporal precession between cue and target-subnetworks.

a, HFAb-triggered HFA examples for individual subjects (mean ± SEM), similar to Fig. 4a. b, Top: Group-level average of HFAb-triggered HFA in experiment 2 (n = 3), similar to Fig. 4b. Shaded regions indicate significant lead-lag patterns (two-sided permutation test, p < 0.05; red: cue leads, [135-244] ms, blue: target leads, [-298 to -155] ms). Bottom: Peak time-lag distributions in experiment 2 (n = 3, medina ± SEM: 14.9 ± 4.7 ms, two-sided Wilcoxon rank-sum test between directions; *: p < 0.05, **: p < 0.01, ***: p < 0.001). c, HFAb-triggered HFA during cue/delay period, Similar to Fig. 4a. d, Lead-lag pattern topography for individual subjects, similar to Fig. 4c. e, DMI heatmaps for individual subjects, similar to Fig. 4d. f, DMI analysis around cue onset, similar to Fig. 4e. g, Group-average DMI in experiment 2 (n = 3), similar to Fig. 4d. DMI peaked 297.7 ± 19.6 ms after target onset with 125.6 ± 26.2 ms time-lag (n = 3). h, DMI peak distributions for invalid/valid trials in experiment 2 (n = 3), similar to Fig. 4e.

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Extended Data Fig. 8 Computational modeling of iEEG signal.

a, Connection probability between neurons decreases with distance. b, Frequency-dependent attenuation factor for iEEG signals at the recording disks. c, A complete trial simulation example. Raster shows the activity of neurons in one network (bottom, blue and red show excitatory and inhibitory neurons, respectively). Raw and attenuated traces correspond to field dynamics of the same network. d, Network activity with coherent (top) and random (bottom) input phases. e, Connectivity matrix for feedforward network (N1 → N2, each scatter point indicates whether two neurons have excitatory (blue) or inhibitory (red) connections). f, An input design evaluating how a transient stimulus affects HFAb coherence with LFP in a network as shown in E (network 1 (top) receives an impulse input). g, PLV changes in both networks following transient input, similar to Fig. 5g. h In both networks 1 and 2, the PLV drops within 500 ms of stimulus onset (p < 0.001, two-sided Wilcoxon test). I, Normalized HFAb rate relative to stimulus onset. j, Four-network architecture and example raster activity during spatial attention task simulation. k, HFAb rate distributions in target networks for valid versus invalid trials. l, Normalized DMI between cue and target networks following cue onset, similar to Fig. 5l.

Extended Data Table 1 List of brain areas containing electrodes that showed significant HFAb response to cue (cue +) and target (target +, see Methods), as well as electrodes in cue- and target-activated subnetworks
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Extended Data Table 2 Parameters used for modeling different neuron types
Full size table

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Banaie Boroujeni, K., Helfrich, R.F., Fiebelkorn, I.C. et al. High-frequency bursts facilitate fast communication for human spatial attention. Nat Neurosci 29, 435–444 (2026). https://doi.org/10.1038/s41593-025-02160-5

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