Decorrelated Input Dissociates Narrow Band γ Power and BOLD in Human Visual Cortex

Abstract

Although fMRI using the BOLD contrast is widely used for noninvasively mapping hemodynamic brain activity in humans, its exact link to underlying neural processing is poorly understood. Whereas some studies have reported that BOLD signals measured in visual cortex are tightly linked to neural activity in the narrow band γ (NBG) range, others have found a weak correlation between the two. To elucidate the mechanisms behind these conflicting findings, we hypothesized that BOLD reflects the strength of synaptic inputs to cortex, whereas NBG is more dependent on how well these inputs are correlated. To test this, we measured NBG, BOLD, and cerebral blood flow responses to stimuli that either correlate or decorrelate neural activity in human visual cortex. Next, we simulated a recurrent network model of excitatory and inhibitory neurons that reproduced in detail the experimental NBG and BOLD data. Results show that the visually evoked BOLD response was solely predicted by the sum of local inputs, whereas NBG was critically dependent on how well these inputs were correlated. In summary, the NBG-BOLD relationship strongly depends on the nature of sensory input to cortex: stimuli that increase the number of correlated inputs to visual cortex will increase NBG and BOLD in a similar manner, whereas stimuli that increase the number of decorrelated inputs will dissociate the two. The NBG-BOLD relationship is therefore not fixed but is rather highly dependent on input correlations that are both stimulus- and state-dependent.SIGNIFICANCE STATEMENT It is widely believed that γ oscillations in cortex are tightly linked to local hemodynamic activity. Here, we present experimental evidence showing how a stimulus can increase local blood flow to the brain despite suppressing γ power. Moreover, using a sophisticated model of cortical neurons, it is proposed that this occurs when synaptic input to cortex is strong yet decorrelated. Because input correlations are largely determined by the state of the brain, our results demonstrate that the relationship between γ and local hemodynamics is not fixed, but rather context dependent. This likely explains why certain neurodevelopmental disorders are characterized by weak γ activity despite showing normal blood flow.

Keywords: CBF; EEG; fMRI; synaptic input.

Figure 1.

Qualitative overview of EEG and BOLD responses to visual stimulation. Five visual stimulus types: from left to right, 5% MC, 33% MC, 100% MC, 10% SR, and 60% SR and corresponding group-average (n = 22) of the (a) EEG time frequency decomposition, (b) NBG (60–70 Hz) scalp topography, and (c) BOLD response maps overlaid on a T1-weighted anatomical template (for visual purposes only, values <±0.5% are masked out). Both NBG and BOLD tend to increase with MC but become dissociated with SR. Examples of stimulus-evoked (d) EEG and (e) V1 BOLD signals from 3 subjects and stimulus conditions. The same trend is observed for both “strong” (Subjects S4 and S5) and “weak” (Subject S8) NBG responders.

Figure 2.

Quantitative analysis of EEG and BOLD responses to MC and SR. Group-average (n = 22) EEG and BOLD modulation for different MC (a, b) and SR (c, d) levels. a, c, Highlighted area represents the represents the frequency range (60–70 Hz) where EEG modulation is statistically significant (p < 0.05, FDR) in both MC and SR conditions. e, Summary of NBG and BOLD response magnitude (averaged within highlighted areas in a–d across all stimulus types). *Significant difference (p < 0.01) relative to 100% MC. Error bars indicate SEM. f, Each dot indicates the mean NBG and BOLD modulation at SR 60% (relative to MC 100%) from a single participant (22 subjects in total). Subject-to-subject variations in NBG and BOLD were not significantly correlated (p > 0.05).

Figure 3.

Control CBF and CMRO₂ measurements. Group-average (n = 8) BOLD, CBF, and CMRO₂ results. a, Like BOLD, 60% SR (red curve) yielded a stronger CBF response compared with 100% MC (black curve) and 5% MC (blue curve). b, CMRO₂ estimation based on the Davis model using M = 0.1, α = 0.25, and β = 1.5.

Figure 4.

Simulations of NBG as a function of input magnitude (I_m) and spatial correlation (I_sc). a, The network consists of reciprocally connected excitatory (E) and inhibitory (I) spiking neurons driven by inputs with different levels of magnitude (I_m) and spatial correlations (I_sc). A feedback pathway (G), representing recurrent inputs from higher cortical areas, further shapes the responses of the excitatory population. b, An example simulated EEG response to different (I_m, I_sc) combinations. Red squares represent a patch of neurons receiving spatially correlated (e.g., MC 100%) or decorrelated (e.g., SR 10%) input. Gray time courses represent the corresponding simulated EEG signal. For MC 100% (left), input is strong (average I_m = 0.3) and spatially correlated (I_sc = 0.6), thus yielding a robust and sustained NBG response (NBG_sim). However, when input strength is left unchanged (average I_m = 0.3) although input correlations are reduced (I_sc = 0.2), NBG_sim is suppressed, similar to that observed during SR 10%. c, Summary of the simulated data used in this study. For each stimulus condition, the spectral profile of the simulated EEG signal (gray line) and corresponding average power in the 60–70 Hz range (gray dots) closely match the experimental observations (NBG_exp). d, I_m and I_sc values underlying the NBG_sim results shown in c. The SR 10% and SR 60% are best characterized as an increase in decorrelated inputs relative to MC 100%. e, Fit of NBG_exp and BOLD_exp data using I_m and I_sc as regressors in a GLM. f, Quantitative summary of GLM fits (R²). Dashed line indicates the mean R² value obtained when using shuffled data. The NBG_exp fit is increased when adding I_sc in the GLM, whereas the same has little effect on the BOLD_exp fit.

Figure 5.

Spatial and temporal analysis of BOLD and NBG responses, respectively. Spatial analysis of BOLD response magnitude for (a) MC 100% > MC 5% and (b) SR 60% > MC 100% (n = 22). In both cases, red areas represent areas where the effect was statistically significant (p ≪ 0.01, cluster corrected for a minimum 200 voxels). Compared with MC 5%, MC 100% yields a stronger BOLD response near LGN (yellow arrow), whereas no differences are observed during SR 60%. A slight decrease in extrastriate cortex was observed during SR 60% (blue voxels). c, During MC 100% (black curve), NBG (averaged over group) was significantly stronger than MC 5% (blue curve) in both the early (t1: 0–0.4 s) and late (t2: 1.0–1.4 s) phases of the response (transparent gray bars). d, On the other hand, compared with SR 60%, only the late phase of the NBG response is significantly different. **p < 0.01. Thick black line indicates the period when the visual stimulus was presented.