# Implementation and dissemination of cloud-based retrospective hemodynamic analysis tools to enhance HCP data interpretation

> **NIH NIH RF1** · MCLEAN HOSPITAL · 2022 · $748,762

## Abstract

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.

## Key facts

- **NIH application ID:** 10509534
- **Project number:** 1RF1MH130637-01
- **Recipient organization:** MCLEAN HOSPITAL
- **Principal Investigator:** Blaise deBonneval Frederick
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $748,762
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10509534

## Citation

> US National Institutes of Health, RePORTER application 10509534, Implementation and dissemination of cloud-based retrospective hemodynamic analysis tools to enhance HCP data interpretation (1RF1MH130637-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10509534. Licensed CC0.

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