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Global network influences on local functional connectivity

Nature Neuroscience volume 18, pages 736–743 (2015)Cite this article

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Abstract

A central neuroscientific pursuit is understanding neuronal interactions that support computations underlying cognition and behavior. Although neurons interact across disparate scales, from cortical columns to whole-brain networks, research has been restricted to one scale at a time. We measured local interactions through multi-neuronal recordings while accessing global networks using scalp electroencephalography (EEG) in rhesus macaques. We measured spike count correlation, an index of functional connectivity with computational relevance, and EEG oscillations, which have been linked to various cognitive functions. We found a non-monotonic relationship between EEG oscillation amplitude and spike count correlation, contrary to the intuitive expectation of a direct relationship. With a widely used network model, we replicated these findings by incorporating a private signal targeting inhibitory neurons, a common mechanism proposed for gain modulation. Finally, we found that spike count correlation explained nonlinearities in the relationship between EEG oscillations and response time in a spatial selective attention task.

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Figure 1: Recording set-up and analysis strategy.
Figure 2: Single-session example of surrogate data sets.
Figure 3: Correlation results for spontaneous task across sessions (N = 24; Monkey B, 14 sessions; Monkey W, 10 sessions).
Figure 4: Firing rate results for spontaneous task across all sessions and subjects.
Figure 5: Amplitude-correlation relationship across all six frequency bands of interest.
Figure 6: Results for evoked task.
Figure 7: Neural network modeling results.
Figure 8: Psychophysical performance versus alpha amplitude.

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Acknowledgements

We are grateful to B. Doiron for helpful advice and discussion, and to S. Cortes and S. Nelson for help with data collection. A.C.S. was supported by a US National Institutes of Health fellowship (F32EY023456). M.J.M. was supported by an US National Institutes of Health undergraduate research training fellowship through the University of Pittsburgh's Program in Neural Computation (R90DA023426). M.A.S. was supported by US National Institutes of Health grants R00EY018894, R01EY022928 and P30EY008098, a career development grant and an unrestricted award from Research to Prevent Blindness, and the Eye and Ear Foundation of Pittsburgh.

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Authors and Affiliations

  1. Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Adam C Snyder, Michael J Morais, Cory M Willis & Matthew A Smith

  2. Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Adam C Snyder & Matthew A Smith

  3. Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Michael J Morais & Matthew A Smith

  4. Fox Center for Vision Restoration, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Matthew A Smith

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Contributions

A.C.S. and M.A.S. designed the experiments, analyzed the data and wrote the manuscript. A.C.S. and C.M.W. conducted the experiments. A.C.S. and M.J.M. conducted the network model simulations. M.A.S. supervised the project.

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Correspondence to Matthew A Smith.

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Integrated supplementary information

Supplementary Figure 1 Average power spectrum for estimated EEG signal derived by summing the EPSPs from the neural network model.

Average power spectrum for estimated EEG signal derived by summing the EPSPs from the neural network model (mean of 54,240 one-second epochs, SEM is contained within the line). Note that the vertical axis of volts is not a veridical representation of scalp EEG because it does not take into account tissue resistances. For this analysis, we had 1 Hz frequency resolution. When we calculated spike count correlation we separated each 1 s epoch of simulated data into five 200 ms epochs, which provided 5 Hz resolution for those analyses.

Supplementary Figure 2 EEG power spectra.

EEG power spectra (right central-parietal sensor) for Monkey W (top) and Monkey B (bottom). Shading represents ± 1 SEM.

Supplementary Figure 3 Task schematic.

Task schematic. While the subject maintained central fixation, a peripheral visual cue indicated the spatial location that was more likely to contain the target (80% validity). Subsequently, two drifting gratings appeared. The task was to detect a change in the drift speed in one of the two gratings, and to make an eye movement to this stimulus. On 40% of trials neither stimulus changed speed and the subject was rewarded for maintaining fixation. The dashed circle and “spotlight” symbol represent the receptive field area and focus of attention, respectively, and were not actually present in the stimulus display. For one subject, the cue was an isoluminant yellow annulus that encircled the aggregate RF area, while for the other subject (shown) the cue was a low-contrast dot at the center of the RF area.

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Snyder, A., Morais, M., Willis, C. et al. Global network influences on local functional connectivity. Nat Neurosci 18, 736–743 (2015). https://doi.org/10.1038/nn.3979

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