Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping

Zhao, Yu; Gao, Yurui; Zu, Zhongliang; Li, Muwei; Schilling, Kurt G.; Anderson, Adam W.; Ding, Zhaohua; Gore, John C.

doi:10.1016/j.neuroimage.2022.119399

Cited by 5 publications

(8 citation statements)

References 60 publications

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“…The ODF-based encoding used in the design of voxel-wise brain has been exploited for inpainting WM fMRI from cortical fMRI data (Tarun et al, 2020) revealing the mediation of WM structures across temporally-coherent gray matter areas. It has also been shown that voxel-wise diffusion informed graphs can effectively enhance fMRI activation mapping in WM (Abramian et al, 2021; Zhao et al, 2022), in-line with previous findings on voxel-wise gray matter graphs (Behjat et al, 2015, 2021). It yet remains to be studied how functional connectivity (FC) and their associated measures can be extended to accurately integrate structural information.…”

Section: Discussionsupporting

confidence: 85%

“…The proposed voxel-wise graphs can be leveraged to perform whole-brain anatomically-informed spatial filtering and interpolation of fMRI data, operations that are inherent within numerous fMRI processing pipelines; e.g. spatial smoothing to enhance whole-brain fMRI activation mapping, as done using tissue-specific designs in gray matter (Behjat et al, 2015, 2021) and white matter (Abramian et al, 2021; Zhao et al, 2022). Moreover, it yet remains to be studied how functional connectivity (FC) and their associated measures can be extended to accurately integrate structural information.…”

Section: Discussionmentioning

confidence: 99%

“…The majority of existing applications of GSP in functional brain imaging are limited to region-wise analyses, where regions are defined using a priori brain atlases having 100 to 1000 regions. Motivated by the promising results from existing region-wise GSP studies in linking brain structure and function, and novel recent methods for high-resolution connectomics (Mansour-L et al, 2021) and activation mapping (Zhao et al, 2022), it is fitting to further explore the benefits of large-scale brain graphs at the resolution of voxels . Here we present models to derive the strength of connection between adjacent voxels using diffusion MRI data, one based on tensors estimated from diffusion tensor imaging (DTI) (Basser et al, 1994), and the other based on diffusion orientation distribution functions (ODF) estimated from high angular resolution diffusion imaging (HARDI) data (Tuch, 2004); we consider the two signal representation settings to make the methodology applicable to a larger set of available diffusion MRI data.…”

Section: Introductionmentioning

confidence: 99%

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Voxel-Wise Brain Graphs from Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

Behjat

Tarun

Abramian

et al. 2022

Preprint

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Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion-weighted MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show, firstly, that brain activity can be well approximated by a small subset of low-frequency components, and secondly, that spectral graph energy profiles differ under different functional loads. The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Voxel-Wise Brain Graphs from Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

Behjat

Tarun

Abramian

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The majority of existing applications of GSP in functional brain imaging are limited to region-wise analyses, where regions are defined using a priori brain atlases having 100 to 1000 regions. Motivated by the promising results from existing region-wise GSP studies in linking brain structure and function, and novel recent methods for high-resolution connectomics [30] and activation mapping [31], it is fitting to further explore the benefits of large-scale brain graphs at the resolution of voxels. Here we present models to derive the strength of connection between adjacent voxels using diffusion MRI data, one based on tensors estimated from diffusion tensor imaging (DTI) [32], and the other based on diffusion orientation distribution functions (ODF) estimated from high angular resolution diffusion imaging (HARDI) data [33]; we consider the two signal representation settings to make the methodology applicable to a larger set of available diffusion MRI data.…”

Section: Introductionmentioning

confidence: 99%

Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

Behjat

Tarun

Abramian

et al. 2025

IEEE Open J. Eng. Med. Biol.

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Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. Methods: We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. Results: The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on lowfrequency graph Laplacian eigenmodes, we show that brain activity can be well approximated by a small subset of lowfrequency components. Conclusions: The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level.

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“…The majority of conventional functional MRI (fMRI) studies have focused on neural activity in gray matter (GM) while ignoring the blood oxygenation level dependent (BOLD) signal in white matter (WM), at times even considering the WM signal as a nuisance regressor. Recently, a growing body of evidence has demonstrated that BOLD signals in WM, although weaker, are reliably detectable and related to neural activity; moreover, local signal changes and inter-regional correlations within WM can be robustly measured with appropriate methods [1][2][3][4][5][6] . These findings stimulate the demand for more elaborate studies of BOLD signals in WM in different clinical and developmental populations 7,8 .…”

Section: Introductionmentioning

confidence: 99%

Automatic preprocessing pipeline for white matter functional analyses of large-scale databases

Gao

Lawless

et al. 2023

Medical Imaging 2023: Image Processing

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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 highperformance 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 timecourses, 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.

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Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping

Cited by 5 publications

References 60 publications

Voxel-Wise Brain Graphs from Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

Voxel-Wise Brain Graphs from Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

Automatic preprocessing pipeline for white matter functional analyses of large-scale databases

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