White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer’s Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLDFMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between subject populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.
KEYWORDS: Functional magnetic resonance imaging, Matrices, Databases, White matter, Signal processing, Signal detection, Reliability, Quality control, Neuroimaging, Correlation coefficients
Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.
Background: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder, in which pathological alterations are seen in both gray matter (GM) and white matter (WM). To date functional MRI (fMRI) studies of AD have been exclusively focused on GM, since blood oxygenation level dependent (BOLD) signals in WM are relatively weak and thus ignored in practice. Our recent work provides compelling evidence that BOLD fluctuations in brain WM are reliably detectable and reflect neural activities, offering the potential of investigating the functional connectivity in WM. Purpose: In this study, we aim to apply our fMRI analysis method to the investigation of functional alterations in WM during the progression of AD. Method: Raw resting state fMRI data of normal subjects and patients (total n=290, 5 diagnostic groups) were obtained from the Alzheimer’s Disease Neuroimaging Initiative database. Each fMRI image was parcellated into 82 GM regions and 48 WM bundles. Temporal correlation between each pair of GM and WM was calculated and the correlations of all pairs constituted a functional correlation matrix (FCM) for each subject. The FCMs were averaged within each diagnostic group, and differences in the averaged FCMs between the normal group and each disease group were sought. Result: Differences in functional correlations progressively enlarge as the disease evolves, and fornix and ventral entorhinal cortices exhibited most pronounced differences between the normal and disease groups. Conclusion: Functional connectivity in WM may serve as a novel neuroimaging biomarker for the progression of AD.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.