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Macrovascular biases have been a long-standing challenge for fMRI, limiting its ability to detect spatially specific neural activity. Recent experimental studies, including our own (Huck et al., 2023; Zhong et al., 2023), found substantial resting-state macrovascular 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 (mVANs) derived from angiograms. Our results demonstrate that: 1) with the availability of mVANs, it is feasible to model macrovascular BOLD FC using both the mVAN-based model and 3D infinite-cylinder models, though the former performed better; 2) biophysical modelling can accurately predict the BOLD pairwise 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.
RESUMO
Introduction: In the context of functional magnetic resonance imaging (fMRI), carbon dioxide (CO2) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO2) reflects changes in arterial CO2 and has been demonstrated as the largest physiological noise source for denoising the low-frequency range of the resting-state fMRI (rs-fMRI) signal. However, the majority of rs-fMRI studies do not involve CO2 recordings, and most often only heart rate and respiration are recorded. While the intrinsic link between these latter metrics and CO2 led to suggested possible analytical models, they have not been widely applied. Methods: In this proof-of-concept study, we propose a deep-learning (DL) approach to reconstruct CO2 and PETCO2 data from respiration waveforms in the resting state. Results: We demonstrate that the one-to-one mapping between respiration and CO2 recordings can be well predicted using fully convolutional networks (FCNs), achieving a Pearson correlation coefficient (r) of 0.946 ± 0.056 with the ground truth CO2. Moreover, dynamic PETCO2 can be successfully derived from the predicted CO2, achieving r of 0.512 ± 0.269 with the ground truth. Importantly, the FCN-based methods outperform previously proposed analytical methods. In addition, we provide guidelines for quality assurance of respiration recordings for the purposes of CO2 prediction. Discussion: Our results demonstrate that dynamic CO2 can be obtained from respiration-volume using neural networks, complementing the still few reports in DL of physiological fMRI signals, and paving the way for further research in DL based bio-signal processing.
RESUMO
Resting-state functional magnetic resonance imaging (rs-fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs-fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age-sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs-fMRI signal frequency using the Leipzig mind-brain-body data set. Over a frequency band of up to 0.3 Hz, we found that the rs-fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs-fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological-noise components. This higher rs-fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)-frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs-fMRI frequency bands, the fMRI-EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs-fMRI signal and may lead to the development of new, frequency-based rs-fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age- and sex-related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link.