Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing
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This manuscript addresses the important issue of hemodynamic response function (HRF) variability across brain areas and will be valuable to researchers who use fMRI and other types of functional imaging that rely on neurovascular coupling. Using simulations and experiments, the authors provide solid evidence that differences in the HRF can impact spectrum-based metrics such as ALFF and fALFF. A better understanding of the variability of the HRF is critical for the proper interpretation of activation onset times and of differences observed in clinical populations where both neural and vascular alterations can be expected.
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Abstract
Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.
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eLife assessment
This manuscript addresses the important issue of hemodynamic response function (HRF) variability across brain areas and will be valuable to researchers who use fMRI and other types of functional imaging that rely on neurovascular coupling. Using simulations and experiments, the authors provide solid evidence that differences in the HRF can impact spectrum-based metrics such as ALFF and fALFF. A better understanding of the variability of the HRF is critical for the proper interpretation of activation onset times and of differences observed in clinical populations where both neural and vascular alterations can be expected.
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Reviewer #1 (Public Review):
The authors first show in simulated data that differences in the speed of the HRF are reflected in the power spectra of the BOLD signal obtained during oscillatory stimulation at different frequencies. They then identified voxels that were fast or slow responders in data obtained from the primary visual cortex and LGN during visual stimulation and found that the fast and slow groups exhibited the same differences in power spectra observed in the simulations. Moreover, resting state data obtained separately from the same areas also exhibited these spectral differences. In contrast, the onset time of a response to a breath hold was less able to differentiate between fast and slow voxels.
The combination of simulations and experiments in this work provides evidence that power spectra from rs-fMRI can provide …
Reviewer #1 (Public Review):
The authors first show in simulated data that differences in the speed of the HRF are reflected in the power spectra of the BOLD signal obtained during oscillatory stimulation at different frequencies. They then identified voxels that were fast or slow responders in data obtained from the primary visual cortex and LGN during visual stimulation and found that the fast and slow groups exhibited the same differences in power spectra observed in the simulations. Moreover, resting state data obtained separately from the same areas also exhibited these spectral differences. In contrast, the onset time of a response to a breath hold was less able to differentiate between fast and slow voxels.
The combination of simulations and experiments in this work provides evidence that power spectra from rs-fMRI can provide information about the HRF in different locations across the brain. However, the simulated HRFs differ in amplitude and duration as well as latency, and all of these features can affect the power spectrum. The authors show that differences remain in the power spectra for amplitude-normalized HRFs, which strengthens their work. However, the entire premise of the work is that the actual HRFs in the brain can be modeled using the range of shapes that were simulated. As the authors point out, we know little about the actual HRF in much of the brain, and it may be that this model does not adequately represent HRFs in other regions. At a minimum, it would be useful to disentangle the effects of latency and duration of the response, in addition to amplitude, because with the current model early onset voxels also have shorter response durations. It is not hard to imagine that a brain area might have a rapid onset but a long duration of HRF, and the power spectrum in this case may look more like that of a slow responder. The current approach was validated in the visual system, which has been the basis for much of what we know about HRFs, and it may not be as accurate in other areas of the brain. This is admittedly a difficult issue to address, but merits consideration as a limitation.
Despite my skepticism of the general applicability of the technique, it remains a significant advance in understanding the variability of HRFs in the brain. The authors make a strong case that cerebrovascular reactivity as measured in response to a breath hold does not accurately capture all of the aspects of neurovascular coupling, an important finding. The work also clearly shows that differences in fALFF or other power-based metrics can reflect differences in neurovascular coupling rather than neural activity, something that is widely suspected but commonly ignored in the interpretation of fALFF data. We still have far to go to fully understand neurovascular coupling throughout the brain and under various conditions, and this manuscript contributes to our knowledge of how two investigative tools perform at the task.
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Reviewer #2 (Public Review):
This study highlights a connection between the power spectra of fMRI signals and the temporal dynamics of the hemodynamic response function (HRF). Using visual stimulation experiments and resting-state scans, the spectral features of resting-state fMRI signals in V1 and LGN are found to have a significant relationship to the relative timing of HRF responses during the task.
Overall, I found this to be an interesting and clearly written study, with high-quality data. The connection between BOLD signal spectra and vascular responses is not discussed in much of the resting-state fMRI literature, and represents an important message and consideration. While the connection between the amplitude of resting-state BOLD fluctuations and the amplitude of task HRFs has been investigated in the past, I am not aware of …
Reviewer #2 (Public Review):
This study highlights a connection between the power spectra of fMRI signals and the temporal dynamics of the hemodynamic response function (HRF). Using visual stimulation experiments and resting-state scans, the spectral features of resting-state fMRI signals in V1 and LGN are found to have a significant relationship to the relative timing of HRF responses during the task.
Overall, I found this to be an interesting and clearly written study, with high-quality data. The connection between BOLD signal spectra and vascular responses is not discussed in much of the resting-state fMRI literature, and represents an important message and consideration. While the connection between the amplitude of resting-state BOLD fluctuations and the amplitude of task HRFs has been investigated in the past, I am not aware of prior work that had considered the timing aspect. The present comparison between resting-state spectra and breath-holding task responses also provides useful data about the hemodynamic information carried by these two conditions.
The present experiments were conducted at 7T with high temporal resolution and focused on a visual experiment. The generalization of the findings to other task conditions, brain regions, and acquisition parameters would be a valuable future step. Understanding the translation to other datasets would be a practical consideration for researchers who are considering applying this method. Regarding the evaluation of the classification models, it currently appears possible that the train/test sets might contain closely spaced and thus correlated voxels. Accounting for this effect could help to better support the conclusions of this analysis. More discussion about the neural or vascular basis of the slow- versus fast-HRF voxels could also bring further insights to the work (for instance, the location of the fast and slow V1 voxels with respect to functional boundaries and vascular anatomy).
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