Summary Functional Magnetic Resonance Imaging data has been a mainstay of neuroscience research for more than two decades, as it allows rapid, continuous, noninvasive monitoring of neuronal function. However, a substantial portion of the fMRI signal arises from purely physiological cerebral hemodynamic signals in the low and cardiac frequency bands. Historically, these have simply been considered noise sources that complicate fMRI analysis. We have developed two novel, retrospective analyses to separate the neuronal and hemodynamic portions of BOLD fMRI data to not only model and remove low and cardiac frequency systemic noise, but to make use of this “noise” to characterize bulk and pulsatile blood flow with high precision. These techniques have been extensively tested and validated and have shown great flexibility in processing fMRI data from a number of sources. The first technique, Regressor Interpolation at Progressive Time Delays (“RIPTiDe”), isolates and characterizes the low frequency global hemodynamic signal in fMRI data[2]; as this is a bloodborne signal, RIPTiDe can be used to measure blood arrival time and rCBV throughout the brain in normal and pathological circulation[3-8]. without a separate perfusion scan. Moreover, this generates voxelwise noise regressors which remove confound signal without generating the spurious correlations arising from global signal regression[10], substantially increasing the specificity of resting state and task fMRI analyses for detecting neuronal, rather than hemodynamic[10, 11]. The second technique, “happy”, allows retrospective extraction of the plethysmogram signal from multiband fMRI data[12], allowing R-R interval and heart rate variability measurements even in subjects where no plethysmogram was recorded (or failed to record), and mapping and/or removing of the cardiac pulsation waveform as it moves through the brain. These two tools together both enhance and extend existing datasets by removing a substantial proportion of previously intractable in-band noise, while providing new, entirely separate windows into cerebral hemodynamics and autonomic function from existing data. Both have been released in the open source “rapidtide” package. This project will take these tools to the next level, by improving their documentation, capabilities and reliability, optimizing them for retrospective analysis of the multiple HCP and ABCD datasets, and making them available to the widest audience by developing a cloud-based platform to allow users from institutions of all sizes (not only large, well-funded universities with extensive computing infrastructure) to use this software.

Narrative Characterization, modelling, and interpretation of hemodynamic “noise” in fMRI data can be used both as a probe of cerebrovascular structure and function, and as a powerful denoising method for increasing the specificity of estimating neuronal activity. This project will refine the reliability and documentation of hemodynamic modelling tools developed in our lab and determine the best parameters to use when applying them to Human Connectome Project (HCP) and Adolescent Brain Cognitive Development study (ABCD) data. We will develop a turnkey cloud-based virtual compute cluster to make these tools available to any researcher.