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. 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.

An image-computable model for the stimulus selectivity of gamma oscillations

“…We observed that percent signal change in gamma responses is much larger than that for broadband responses. Broadband responses are thought to arise from asynchronous neural activity (Miller et al, 2009b), which results in substantial cancellation in the pooled field potential (Butler et al, 2017; Hermes et al, 2017b; Krusienski et al, 2011; Winawer et al, 2013). Narrowband gamma responses, in contrast, are thought to reflect synchronous activity across a neuronal population (Hasenstaub et al, 2005; Jia et al, 2013).…”

“…Narrowband gamma responses, in contrast, are thought to reflect synchronous activity across a neuronal population (Hasenstaub et al, 2005; Jia et al, 2013). The synchronous response, even if it comes from a much smaller neuronal population, can result in a much larger macroscopic field potential (Butler et al, 2017; Hermes et al, 2017b; Winawer et al, 2013). This likely explains why gamma oscillations, even when the percentage signal change is quite large, can show little correlation with the BOLD signal (Butler et al, 2017; Hermes et al, 2017b) or multiunit activity (Jia et al, 2013; Ray and Maunsell, 2011).…”

Determining Excitatory and Inhibitory Neuronal Activity from Multimodal fMRI Data Using a Generative Hemodynamic Model

“…Furthermore, P-DCM accommodated the magnetic field strength and sequence parameters differences between experimental studies that also influence the size and nonlinearity of BOLD response transients (Havlicek et al, 2015 , 2017a ; Uludağ and Blinder, 2017 ). We think that P-DCM and its emphasis on response transients may be useful to also explain other combinations of multimodal data (e.g., neuronal activity recordings, CBF, CBV and BOLD) or to play an important role in combining EEG and fMRI data (Valdes Sosa et al, 2009 ; Riera and Sumiyoshi, 2010 ; Rosa et al, 2011 ; Butler et al, 2017 ; Friston et al, 2017 ). The neuronal mass activity of post-synaptic signals can be decomposed to non-overlapping frequency bands.…”

Temporal coupling of field potentials and action potentials in the neocortex

“…Frequencies up to approximately 80 Hz can be transferred, whereas above this frequency spike coupling with afferent drive is rare (Vaidya & Johnston, 2013;Buzsáki & Schomburg, 2015). Yet, spatially resolved gamma power in identified dendritic regions can provide important information about the magnitude of upstream drive (Colgin et al, 2009;Lindén et al, 2011;Schomburg et al, 2014;Butler et al, 2017;Fernandez-Ruiz et al, 2017). A second mechanism for induction of oscillations is that irregular patterns can be induced by local circuit interactions.…”