Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants (15 women) by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3–4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of the Dice coefficients and Silhouette values. The resulting parcellation of auditory cortices showed intraindividual reproducibility of 69–78% between resting-state sessions and 62–73% between resting-state and task sessions, indicating moderate reproducibility. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%–68%, p < 0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of the auditory cortex. Together, our results show that auditory areas in STC can be segmented into functional subareas based on functional connectivity. Our findings also suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.

In functional magnetic resonance imaging (fMRI), neural activity is inferred from the associated hemodynamic response. However, the degree to which hemodynamics can track dynamic changes in neuronal activity, and thus the ultimate temporal resolution of fMRI, remains unknown. To evaluate the detectability of stimulus-driven high-frequency blood oxygenation level dependent (BOLD) signal oscillations in functionally and vascularly distinct cerebral cortical areas, stimuli up to 0.5 Hz were used to evoke activation in the primary somatosensory and motor cortex. Despite their functional and vascular differences, a similar frequency dependence was observed in both cortical areas. We then proceeded to investigate these signals at different levels of the cortical vascular hierarchy, using cortical depth as a proxy. We observed that, above 0.33 Hz, the BOLD response amplitude decreased faster with increasing frequency near the pial surface than in the parenchyma, suggesting that, in addition to exhibiting high spatial specificity, parenchymal signals—accessible with high spatial resolution imaging—also attenuate less rapidly when the stimulus frequency is increased. In addition, as the stimulus frequency increased, we observed larger relative phase differences in the BOLD oscillations across cortical depths. When averaged across depths, these signals can thus interfere destructively, suggesting that high spatial resolutions can avoid this phase cancellation and thereby aid in the detection of rapid BOLD oscillations.

The ability to detect fast responses with functional MRI depends on the speed of hemodynamic responses to neural activity, because hemodynamic responses act as a temporal low-pass filter which blurs rapid changes. However, the shape and timing of hemodynamic responses are highly variable across the brain and across stimuli. This heterogeneity of responses implies that the temporal specificity of functional MRI (fMRI) signals, or the ability of fMRI to preserve fast information, could also vary substantially across the cortex. In this work we investigated how local differences in hemodynamic response timing affect the temporal specificity of fMRI. We used ultra-high-field (7T) fMRI at high spatiotemporal resolution, studying the primary visual cortex (V1) as a model area for investigation. We used visual stimuli oscillating at slow and fast frequencies to probe the temporal specificity of individual voxels. As expected, we identified substantial variability in temporal specificity, with some voxels preserving their responses to fast neural activity more effectively than others. We investigated which voxels had the highest temporal specificity, and tested whether voxel timing was related to anatomical and vascular features. We found that low temporal specificity is only weakly explained by the presence of large veins or cerebral cortical depth. Notably, however, temporal specificity depended strongly on a voxel’s position along the anterior-posterior anatomical axis of V1, with voxels within the calcarine sulcus being capable of preserving close to 25% of their amplitude as the frequency of stimulation increased from 0.05 Hz to 0.20 Hz, and voxels nearest to the occipital pole preserving less than 18%. These results indicate that detection biases in high-resolution fMRI will depend on the anatomical and vascular features of the area being imaged, and that these biases will differ depending on the timing of the underlying neuronal activity. While we attribute this variance primarily to hemodynamic effects, neuronal non-linearities may also influence response timing. Importantly, this spatial heterogeneity of temporal specificity suggests that it could be exploited to achieve higher specificity in some locations, and that tailored data analysis strategies may help improve the detection and interpretation of fast fMRI responses.

Macrovascular biases have been a long-standing challenge for functional magnetic resonance imaging (fMRI), limiting its ability to detect spatially specific neural activity. Recent experimental studies, including our own, found substantial resting-state macrovascular blood-oxygenation level-dependent (BOLD) fMRI contributions from large veins and arteries, extending into the perivascular tissue at 3 T and 7 T. The objective of this study is to demonstrate the feasibility of predicting, using a biophysical model, the experimental resting-state BOLD fluctuation amplitude (RSFA) and associated functional connectivity (FC) values at 3 Tesla. We investigated the feasibility of both 2D and 3D infinite-cylinder Models as well as macrovascular anatomical networks (macro-VANs) derived from angiograms. Our results demonstrate that (1) with the availability of macro-VANs, it is feasible to model macrovascular BOLD FC using both the macro-VAN-based model and 3D infinite-cylinder Models, though the former performed better; (2) biophysical modelling can accurately predict the BOLD pair-wise correlation near to large veins (with R 2 ranging from 0.53 to 0.93 across different subjects), but not near to large arteries; (3) compared with FC, biophysical modelling provided less accurate predictions for RSFA; (4) modelling of perivascular BOLD connectivity was feasible at close distances from veins (with R 2 ranging from 0.08 to 0.57), but not arteries, with performance deteriorating with increasing distance. While our current study demonstrates the feasibility of simulating macrovascular BOLD in the resting state, our methodology may also apply to understanding task-based BOLD. Furthermore, these results suggest the possibility of correcting for macrovascular bias in resting-state fMRI and other types of fMRI using biophysical modelling based on vascular anatomy.