Distinct neurophysiological correlates of the fMRI BOLD signal in the hippocampus and neocortex

Abstract

Functional magnetic resonance imaging (fMRI) is among the foremost methods for mapping human brain function but provides only an indirect measure of underlying neural activity. Recent findings suggest that the neurophysiological correlates of the fMRI blood-oxygen-level-dependent (BOLD) signal might be regionally specific. We examined the neurophysiological correlates of the fMRI BOLD signal in the hippocampus and neocortex, where differences in neural architecture might result in a different relationship between the respective signals. Fifteen human neurosurgical patients (10 female, 5 male) implanted with depth electrodes performed a verbal free recall task while electrophysiological activity was recorded simultaneously from hippocampal and neocortical sites. The same patients subsequently performed a similar version of the task during a later fMRI session. Subsequent memory effects (SMEs) were computed for both imaging modalities as patterns of encoding-related brain activity predictive of later free recall. Linear mixed-effects modelling revealed that the relationship between BOLD and gamma-band SMEs was moderated by the lobar location of the recording site. BOLD and high gamma (70-150 Hz) SMEs positively covaried across much of the neocortex. This relationship was reversed in the hippocampus, where a negative correlation between BOLD and high gamma SMEs was evident. We also observed a negative relationship between BOLD and low gamma (30-70 Hz) SMEs in the medial temporal lobe more broadly. These results suggest that the neurophysiological correlates of the BOLD signal in the hippocampus differ from those observed in the neocortex.Significance StatementThe blood-oxygen-level-dependent (BOLD) signal forms the basis of fMRI but provides only an indirect measure of neural activity. Task-related modulation of BOLD signals are typically equated with changes in gamma-band activity; however, relevant empirical evidence comes largely from the neocortex. We examined neurophysiological correlates of the BOLD signal in the hippocampus, where the differing neural architecture might result in a different relationship between the respective signals. We identified a positive relationship between encoding-related changes in BOLD and gamma-band activity in frontal, temporal, and parietal cortex. This effect was reversed in the hippocampus, where BOLD and gamma-band effects negatively covaried. These results suggest regional variability in the transfer function between neural activity and the BOLD signal in the hippocampus and neocortex.

A role for retro-splenial cortex in the task-related P3 network

“…no hippocampal activity has been shown in fMRI in this (Figure 2c) or previous odd-ball-paradigm fMRI studies [Kim, 2014]. One possible reason for this negative finding could be different neurovascular coupling in medial temporal lobe compared to neocortex [Hill et al, 2021], suggested recently based on combined iEEG and fMRI. While the contribution of a hippocampal source to the parietal P3 in EEG had already been excluded based on lesion studies [Johnson, 1988;Onofrj et al, 1992], this does not exclude that hippocampal activity may generally contribute to other aspects of the M/EEG response [Alberto et al, 2021], even though with only a weak signal-to-noise ratio.…”

Statistical Power or More Precise Insights into Neuro-Temporal Dynamics? Assessing the Benefits of Rapid Temporal Sampling in fMRI

“…Furthermore, in the case of simultaneous EEG/fMRI, the temporal information available from EEG can be used to supplement and better understand the co-occurring fMRI signal (Chang and Chen, 2021; Murta et al, 2015) or potentially confirm novel hemodynamic findings (Lewis et al, 2016). Studies combining EEG and fMRI have spanned the full range experimental paradigms routinely used in fMRI, from naturalistic stimuli (Whittingstall et al, 2010), to resting state (Deligianni et al, 2014; Goldman et al, 2002; Mayhew and Bagshaw, 2017; Meyer et al, 2013; Wirsich et al, 2020) and typical task based approaches (Hill et al, 2021; Mayhew and Bagshaw, 2017).…”