Mahalanobis distance tractometry (MaD-Tract) – a framework for personalized white matter anomaly detection applied to TBI

Guerrero-González, José; Yeske, Benjamin; Kirk, Gregory R.; Bell, Michael J.; Ferrazzano, Peter; Alexander, Andrew L.

doi:10.1016/j.neuroimage.2022.119475

Cited by 14 publications

(19 citation statements)

References 77 publications

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“…Given the complexity of underlying pathological changes in various brain conditions, multiparametric approaches are a promising avenue to capture the combination of multiple changes in brain properties (Dean et al, 2017; Guberman et al, 2022; Guerrero-Gonzalez et al, 2022; Iturria-Medina et al, 2017; Owen et al, 2021; Taylor et al, 2020). For instance, D2 incorporating fractional anisotropy (FA) in multiple WM tracts in epileptic patients was found to show stronger associations with epilepsy duration than any univariate measure (e.g., mean FA in a single WM tract) (Owen et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, the choice of which dimensions to combine, either MRI-derived metrics or brain regions (e.g., WM tracts), depends on what we want to capture. Combining brain regions within a multivariate measure allows to capture the degree of deviation from a reference even in the presence of high spatial heterogeneity (e.g., Owen et al, 2021; Taylor et al, 2020), while combining features is useful in the presence of mechanistic heterogeneity (i.e, several concomitant underlying biological mechanisms) and when preserving regional specificity is desirable (e.g., Guerrero-Gonzalez et al, 2022; Gyebnár et al, 2019; Lindemer et al, 2015). See Fig.…”

Section: Methodsmentioning

confidence: 99%

“…However, such group analyses are likely to average out interindividual variability and may be problematic when heterogeneity is high (Guberman et al, 2022). Wilk’s criterion is another approach that can be used to define abnormality based on a calculated critical value that accounts for normative sample size, number of features, and multiple comparisons (Guerrero-Gonzalez et al, 2022; Gyebnár et al, 2019; Wilks, 1963).…”

Section: Methodsmentioning

confidence: 99%

Section: D2 In the Study Of Pathologiesmentioning

confidence: 99%

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MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

Tremblay,

Alasmar,

Pirhadi

et al. 2024

Preprint

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Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group-or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: D2 In the Study Of Pathologiesmentioning

confidence: 99%

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MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

Tremblay,

Alasmar,

Pirhadi

et al. 2024

Preprint

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“…As such, dMRI has become the modality of choice for investigating the neural architecture of the brain. dMRI has been used extensively to study white matter changes during brain development and aging (Rathi et al (2014); Gunning-Dixon et al (2009); Madden et al (2009)), in the context of pathological disorders and diseases (Nguyen et al (2022); Zhao et al (2022); Rosenkranz et al (2022)), as well as from traumatic brain injury (Hutchinson et al (2018); Minchew et al (2022); Guerrero-Gonzalez et al (2022); Liu et al (2022)). However, the application of dMRI to cortical gray matter analysis has only recently begun to be explored, with studies reporting age related changes using diffusion imaging based measures (Bhagat and Beaulieu (2004); Pfefferbaum et al (2010)).…”

Section: Introductionmentioning

confidence: 99%

Backscattering Mueller Matrix polarimetry on whole brain specimens shows promise for minimally invasive mapping of microstructural orientation features

Bonaventura

Morara

Carlson

et al. 2022

Preprint

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Understanding microscale physiology and microstructural cellular features of the brain is key to understanding the mechanisms of neurodegenerative diseases and injury, as well as the prominent changes that neurons and glia in the brain undergo in development and aging which reflect functional state. Non-invasive imaging modalities sensitive to the microscale - especially diffusion magnetic resonance imaging (dMRI) - are extremely promising for three-dimensional mapping of cellular microstructure of brain tissues and brain connectivity via tractography; however, there is a need for robust validation techniques to verify and improve the biological accuracy of fiber orientation information derived from these techniques. Recent advances in dMRI acquisition and modeling have moved toward probing of the more complex grey matter architecture, challenging current validation techniques, which are largely based on ex vivo staining and microscopy and focused on white matter. Polarized light imaging (PLI) has been shown to be a successful technique for high resolution, direct, microstructural imaging and has been applied to dMRI validation with clear advantages over conventional staining and microscopy techniques. Conventionally, PLI is applied to thin, sectioned samples in transmission mode, but unlike histologic staining, PLI can be extended to operate with high sensitivity in reflectance mode and even extended to 3D imaging to bridge the gap toward in vivo validation of dMRI measurements of orientation features in both gray and white matter of the brain. In this report we investigate the use of backscattering Mueller Matrix polarimetry to characterize the microstructural content of intact Ferret brain specimens. The experimental results show that backscattering polarimetry can probe white matter fiber coherence and fiber orientation in whole brain specimens, and show promise for probing grey matter microstructure. Ultimately, these preliminary results motivate further study to fully understand how backscattering polarimetry can best be used for validation of in vivo microstructural imaging of the brain.

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Multi-modal MRI of hippocampal morphometry and connectivity after pediatric severe TBI

Guerrero-Gonzalez,

Kirk,

Birn

et al. 2023

Brain Imaging and Behavior

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This investigation explores memory performance using the California Verbal Learning Test in relation to morphometric and connectivity measures of the memory network in severe traumatic brain injury. Twenty-two adolescents with severe traumatic brain injury were recruited for multimodal MRI scanning 1–2 years post-injury at 13 participating sites. Analyses included hippocampal volume derived from anatomical T1-weighted imaging, fornix white matter microstructure from diffusion tensor imaging, and hippocampal resting-state functional magnetic resonance imaging connectivity as well as diffusion-based structural connectivity. A typically developing control cohort of forty-nine age-matched children also underwent scanning and neurocognitive assessment. Results showed hippocampus volume was decreased in traumatic brain injury with respect to controls. Further, hippocampal volume loss was associated with worse performance on memory and learning in traumatic brain injury subjects. Similarly, hippocampal fornix fractional anisotropy was reduced in traumatic brain injury with respect to controls, while decreased fractional anisotropy in the hippocampal fornix also was associated with worse performance on memory and learning in traumatic brain injury subjects. Additionally, reduced structural connectivity of left hippocampus to thalamus and calcarine sulcus was associated with memory and learning in traumatic brain injury subjects. Functional connectivity in the left hippocampal network was also associated with memory and learning in traumatic brain injury subjects. These regional findings from a multi-modal neuroimaging approach should not only be useful for gaining valuable insight into traumatic brain injury induced memory and learning disfunction, but may also be informative for monitoring injury progression, recovery, and for developing rehabilitation as well as therapy strategies.

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Mahalanobis distance tractometry (MaD-Tract) – a framework for personalized white matter anomaly detection applied to TBI

Cited by 14 publications

References 77 publications

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

Backscattering Mueller Matrix polarimetry on whole brain specimens shows promise for minimally invasive mapping of microstructural orientation features

Multi-modal MRI of hippocampal morphometry and connectivity after pediatric severe TBI

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