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Your search keyword '"Rebecca J. Williams"' showing total 3 results
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3 results on '"Rebecca J. Williams"'

1. Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population

Author

Abdoljalil Addeh, Fernando Vega, Prathistith Raj Medi, Rebecca J. Williams, G. Bruce Pike, and M. Ethan MacDonald

Subjects
fMRI, Physiological signals, Respiratory variation, Deep-learning, Pediatrics, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connectome Projects, over half of the respiratory recordings may be unusable. As a result, the direct removal of low frequency respiratory variations from the blood oxygen level-dependent (BOLD) signal time series is not possible. This study proposes a deep learning-based method for reconstruction of respiratory variation (RV) waveforms directly from BOLD fMRI data in pediatric participants (aged 5 to 21 years old), and does not require any respiratory measurement device. To do this, the Lifespan Human Connectome Project in Development (HCP-D) dataset, which includes respiratory measurements, was used to both train a convolutional neural network (CNN) and evaluate its performance. Results show that a CNN can capture informative features from the BOLD signal time course and reconstruct accurate RV timeseries, especially when the subject has a prominent respiratory event. This work advances the use of direct estimation of physiological parameters from fMRI, which will eventually lead to reduced complexity and decrease the burden on participants because they may not be required to wear a respiratory bellows.

Published
2023
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3. Modeling hyperoxia-induced BOLD signal dynamics to estimate cerebral blood flow, volume and mean transit time

Author

Erin L. Mazerolle, M. Ethan MacDonald, Avery J.L. Berman, Rebecca J. Williams, and G. Bruce Pike

Subjects
Adult, Male, Cognitive Neuroscience, Models, Neurological, Hyperoxia, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Cerebral circulation, 0302 clinical medicine, medicine, Image Processing, Computer-Assisted, Deoxygenated Hemoglobin, Humans, Cerebral perfusion pressure, Brain Mapping, Blood-oxygen-level dependent, Chemistry, Brain, Magnetic Resonance Imaging, Neurology, Cerebral blood flow, Cerebrovascular Circulation, Limiting oxygen concentration, Female, medicine.symptom, Perfusion, 030217 neurology & neurosurgery, circulatory and respiratory physiology, Biomedical engineering
Abstract

A new method is proposed for obtaining cerebral perfusion measurements whereby blood oxygen level dependent (BOLD) MRI is used to dynamically monitor hyperoxia-induced changes in the concentration of deoxygenated hemoglobin in the cerebral vasculature. The data is processed using kinetic modeling to yield perfusion metrics, namely: cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). Ten healthy human subjects were continuously imaged with BOLD sequence while a hyperoxic (70% O2) state was interspersed with baseline periods of normoxia. The BOLD time courses were fit with exponential uptake and decay curves and a biophysical model of the BOLD signal was used to estimate oxygen concentration functions. The arterial input function was derived from end-tidal oxygen measurements, and a deconvolution operation between the tissue and arterial concentration functions was used to yield CBF. The venous component of the CBV was calculated from the ratio of the integrals of the estimated tissue and arterial concentration functions. Mean gray and white matter measurements were found to be: 61.6 ± 13.7 and 24.9 ± 4.0 ml 100 g−1 min−1 for CBF; 1.83 ± 0.32 and 1.10 ± 0.19 ml 100 g−1 for venous CBV; and 2.94 ± 0.52 and 3.73 ± 0.60 s for MTT, respectively. We conclude that it is possible to derive CBF, CBV and MTT metrics within expected physiological ranges via analysis of dynamic BOLD fMRI acquired during a period of hyperoxia.

Published
2017

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