Neural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance is consequential to neural oscillations or an emergent property of the networks that interconnect them. Using a network model of loosely-coupled Wilson-Cowan oscillators to simulate a patch of cortical sheet, we demonstrate that the size of the activated network is inversely related to its resonance frequency. Further analysis of the parameter space indicated that the number of excitatory and inhibitory connections, as well as the average transmission delay between units, determined the resonance frequency. The model predicted that if an activated network within the visual cortex increased in size, the resonance frequency of the network would decrease. We tested this prediction experimentally using the steady-state visual evoked potential where we stimulated the visual cortex with different size stimuli at a range of driving frequencies. We demonstrate that the frequency corresponding to peak steady-state response inversely correlated with the size of the network. We conclude that although individual neurons possess resonance properties, oscillatory activity at the macroscopic level is strongly influenced by network interactions, and that the steady-state response can be used to investigate functional networks.

Frequency-dependent plasticity (FDP) describes adaptation at the synapse in response to stimulation at different frequencies. Its consequence on the structure and function of cortical networks is unknown. We tested whether cortical "resonance," favorable stimulation frequencies at which the sensory cortices respond maximally, influenced the impact of FDP on perception, functional topography, and connectivity of the primary somatosensory cortex using psychophysics and functional imaging (fMRI). We costimulated two digits on the hand synchronously at, above, or below the resonance frequency of the somatosensory cortex, and tested subjects' accuracy and speed on tactile localization before and after costimulation. More errors and slower response times followed costimulation at above- or below-resonance, respectively. Response times were faster after at-resonance costimulation. In the fMRI, the cortical representations of the two digits costimulated above-resonance shifted closer, potentially accounting for the poorer performance. Costimulation at-resonance did not shift the digit regions, but increased the functional coupling between them, potentially accounting for the improved response time. To relate these results to synaptic plasticity, we simulated a network of oscillators incorporating Hebbian learning. Two neighboring patches embedded in a cortical sheet, mimicking the two digit regions, were costimulated at different frequencies. Network activation outside the stimulated patches was greatest at above-resonance frequencies, reproducing the spread of digit representations seen with fMRI. Connection strengths within the patches increased following at-resonance costimulation, reproducing the increased fMRI connectivity. We show that FDP extends to the cortical level and is influenced by cortical resonance.

Magnetic resonance spectroscopy (MRS) is a research tool for measuring the concentration of metabolites such as γ‐aminobutyric acid (GABA) and glutamate in the brain. MEGA‐PRESS has been the preferred pulse sequence for GABA measurements due to low physiological GABA concentrations, hence low signal. To compensate, researchers incorporate long acquisition durations (7–10 min) making functional measurements of this metabolite challenging. Here, the acquisition duration and sample sizes required to detect specific concentration changes in GABA using MEGA‐PRESS at 3 T are presented for both between‐groups and within‐session study designs. 75 spectra were acquired during rest using MEGA‐PRESS from 41 healthy volunteers in 6 different brain regions at 3 T with voxel sizes between 13 and 22 cm3. Between‐group and within‐session variance was calculated for different acquisition durations and power calculations were performed to determine the number of subjects required to detect a given percentage change in GABA/NAA signal ratio. Within‐subject variability was assessed by sampling different segments of a single acquisition. Power calculations suggest that detecting a 15% change in GABA using a 2 min acquisition and a 27 cm3 voxel size, depending on the region, requires between 8 and 93 subjects using a within‐session design. A between‐group design typically requires more participants to detect the same difference. In brain regions with suboptimal shimming, the subject numbers can be up to 4‐fold more. Collecting data for longer than 4 min in brain regions examined in this study is deemed unnecessary, as variance in the signal did not reduce further for longer durations.

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