Technologies to Better Study Cerebrospinal Fluid Movement in The Human Brain
| dc.contributor.advisor | Aazhang, Behnaam | en_US |
| dc.creator | Banta, Anton | en_US |
| dc.date.accessioned | 2024-05-20T20:21:38Z | en_US |
| dc.date.created | 2024-05 | en_US |
| dc.date.issued | 2023-12-14 | en_US |
| dc.date.submitted | May 2024 | en_US |
| dc.date.updated | 2024-05-20T20:21:38Z | en_US |
| dc.description.abstract | This work describes the development of data processing pipelines and machine learning algorithms to better study the movement of cerebrospinal fluid (CSF) in the human brain. This was done by approaching two limitations of current technology: an inability to measure CSF flow without bulky and expensive magnetic resonance imaging (MRI) and a lack of image processing algorithms to measure CSF in perivascular spaces of the brain from MRI. First, we developed a method to estimate CSF flow in the cerebral aqueduct of healthy control subjects using a set of simpler, portable, and non-invasive modalities. To do this, we extracted novel feature sets from signals acquired during a sleep study, including electrocardiogram (ECG), photoplethysmography (PPG), respiration, electroencephalogram (EEG), hypnograms, and body position as input into three machine learning models to estimate CSF flow. The estimates are compared to the ground truth CSF flow as determined by 7-Tesla phase contrast MRI (PC-MRI). We achieve a mean Pearson correlation coefficient of $0.910$, $0.932$, and $0.933$ for the three models (partial least squares regression, least absolute shrinkage and selection operator regression, and ridge regression) across all input feature sets for the development cohort. We also perform CSF estimation on a separate independent validation set with some models performing over $0.85$ and up to $0.9$ Pearson correlation coefficient. Thus, the regressor achieves high accuracy and is generalizeable making it more than adequate for clinical use. To the best of our knowledge, this is the first instance of CSF estimation without using MRI based signals. This lays the foundation towards a method to assess CSF movement in the brain with a portable and non-invasive device. Second, we developed a novel image processing pipeline for extracting the velocity of cerebrospinal fluid (CSF) in perivascular spaces of humans using $7$ Tesla (7T) Phase Contrast Magnetic Resonance Imaging (PC-MRI). We applied the processing pipeline to a group of $40$ subjects ($29$ healthy controls, $7$ normal pressure hydrocephalus, $2$ subarachnoid hemorrhage, $1$ stroke, and $1$ Alzheimer's disease) acquired by Houston Methodist Hospital. Using the healthy control subjects, we investigate if there exists differences in perivascular CSF movement between age groups and sex. We found that there were no significant differences amongst these groups, which is in alignment with findings for CSF velocity in the cerebral aqueduct. Next we investigated the relationship between CSF velocity in the cerebral aqueduct and the perivascular spaces and found a mean Pearson correlation coefficient between these two curves of $0.54$ across the subjects, indicating a real association between the ventricular and perivascular system of CSF movement. Lastly, we investigated the differences in perivascular CSF movement across the disease cases and found significant differences in the standard deviation of perivascular velocity between the healthy controls and normal pressure hydrocephalus. To our knowledge, this is the first example of quantitatively measuring CSF velocity in the perivascular spaces of humans and the first time anyone has compared the movement of CSF between the ventricular and perivascular systems. Our pipeline can be applied to both healthy and diseased subjects for accelerated discovery of differences in CSF and glymphatic function across populations of subjects. These two bodies of work demonstrate the application and development of data processing and machine learning algorithms to studying CSF movement in the human brain to pave the way towards new biomarkers for brain health and disease etiology. | en_US |
| dc.embargo.lift | 2024-11-01 | en_US |
| dc.embargo.terms | 2024-11-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Banta, Anton. Technologies to Better Study Cerebrospinal Fluid Movement in The Human Brain. (2024). PhD diss., Rice University. https://hdl.handle.net/1911/115920 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/115920 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | Cerebrospinal fluid | en_US |
| dc.subject | machine learning | en_US |
| dc.title | Technologies to Better Study Cerebrospinal Fluid Movement in The Human Brain | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Electrical and Computer Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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