scholarly journals Multidimensional analysis and detection of informative features in human brain white matter

2021 ◽  
Vol 17 (6) ◽  
pp. e1009136
Author(s):  
Adam Richie-Halford ◽  
Jason Yeatman ◽  
Noah Simon ◽  
Ariel Rokem
Keyword(s):  
White Matter ◽  
Statistical Power ◽  
Brain Regions ◽  
Multidimensional Analysis ◽  
Tissue Properties ◽  
Brain White Matter ◽  
Multivariate Relationships ◽  
Weighted Magnetic Resonance Imaging ◽  
Health And Disease ◽  
Brain Age

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts “brain age.” In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.

scholarly journals Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter

2019 ◽  
Author(s):  
Adam Richie-Halford ◽  
Jason Yeatman ◽  
Noah Simon ◽  
Ariel Rokem
Keyword(s):  
White Matter ◽  
Statistical Power ◽  
Mean Diffusivity ◽  
Brain Regions ◽  
Multidimensional Analysis ◽  
Tissue Properties ◽  
Weighted Magnetic Resonance Imaging ◽  
Health And Disease ◽  
Brain Age ◽  
Lateral Sclerosis

AbstractThe white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) data to quantify tissue properties (e.g. fractional anisotropy (FA), mean diffusivity (MD), etc.), along the trajectories of these connections [1]. Statistical inference from tractometry usually either (a) averages these quantities along the length of each bundle in each individual, or (b) performs analysis point-by-point along each bundle, with group comparisons or regression models computed separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. In the present work, we developed a method based on the sparse group lasso (SGL) [2] that takes into account tissue properties measured along all of the bundles, and selects informative features by enforcing sparsity, not only at the level of individual bundles, but also across the entire set of bundles and all of the measured tissue properties. The sparsity penalties for each of these constraints is identified using a nested cross-validation scheme that guards against over-fitting and simultaneously identifies the correct level of sparsity. We demonstrate the accuracy of the method in two settings: i) In a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls [3]. Furthermore, SGL automatically identifies FA in the corticospinal tract as important for this classification – correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, dMRI is used to accurately predict “brain age” [4, 5]. In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change with development and contribute to the prediction of age. Thus, SGL makes it possible to leverage the multivariate relationship between diffusion properties measured along multiple bundles to make accurate predictions of subject characteristics while simultaneously discovering the most relevant features of the white matter for the characteristic of interest.

scholarly journals Large-Scale Evidence for an Association Between Peripheral Inflammation and White Matter Free Water in Schizophrenia and Healthy Individuals

2020 ◽  
Author(s):  
Maria A Di Biase ◽  
Andrew Zalesky ◽  
Suheyla Cetin-Karayumak ◽  
Yogesh Rathi ◽  
Jinglei Lv ◽  
...  
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Interleukin 2 ◽  
Free Water ◽  
Markers Of Inflammation ◽  
Brain White Matter ◽  
Inflammatory Processes ◽  
Weighted Magnetic Resonance Imaging

Abstract Introduction Clarifying the role of neuroinflammation in schizophrenia is subject to its detection in the living brain. Free-water (FW) imaging is an in vivo diffusion-weighted magnetic resonance imaging (dMRI) technique that measures water molecules freely diffusing in the brain and is hypothesized to detect inflammatory processes. Here, we aimed to establish a link between peripheral markers of inflammation and FW in brain white matter. Methods All data were obtained from the Australian Schizophrenia Research Bank (ASRB) across 5 Australian states and territories. We first tested for the presence of peripheral cytokine deregulation in schizophrenia, using a large sample (N = 1143) comprising the ASRB. We next determined the extent to which individual variation in 8 circulating pro-/anti-inflammatory cytokines related to FW in brain white matter, imaged in a subset (n = 308) of patients and controls. Results Patients with schizophrenia showed reduced interleukin-2 (IL-2) (t = −3.56, P = .0004) and IL-12(p70) (t = −2.84, P = .005) and increased IL-6 (t = 3.56, P = .0004), IL-8 (t = 3.8, P = .0002), and TNFα (t = 4.30, P < .0001). Higher proinflammatory signaling of IL-6 (t = 3.4, P = .0007) and TNFα (t = 2.7, P = .0007) was associated with higher FW levels in white matter. The reciprocal increases in serum cytokines and FW were spatially widespread in patients encompassing most major fibers; conversely, in controls, the relationship was confined to the anterior corpus callosum and thalamic radiations. No relationships were observed with alternative dMRI measures, including the fractional anisotropy and tissue-related FA. Conclusions We report widespread deregulation of cytokines in schizophrenia and identify inflammation as a putative mechanism underlying increases in brain FW levels.

scholarly journals Aberrant White Matter Networks Mediate Cognitive Impairment in Patients with Silent Lacunar Infarcts in Basal Ganglia Territory

2015 ◽  
Vol 35 (9) ◽  
pp. 1426-1434 ◽  
Author(s):  
Jinfu Tang ◽  
Suyu Zhong ◽  
Yaojing Chen ◽  
Kewei Chen ◽  
Junying Zhang ◽  
...  
Keyword(s):  
White Matter ◽  
Basal Ganglia ◽  
Cognitive Deficits ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Lacunar Infarcts ◽  
Healthy Elderly ◽  
Brain White Matter ◽  
Diffusion Tensor Imaging Tractography

Silent lacunar infarcts, which are present in over 20% of healthy elderly individuals, are associated with subtle deficits in cognitive functions. However, it remains largely unclear how these silent brain infarcts lead to cognitive deficits and even dementia. Here, we used diffusion tensor imaging tractography and graph theory to examine the topological organization of white matter networks in 27 patients with silent lacunar infarcts in the basal ganglia territory and 30 healthy controls. A whole-brain white matter network was constructed for each subject, where the graph nodes represented brain regions and the edges represented interregional white matter tracts. Compared with the controls, the patients exhibited a significant reduction in local efficiency and global efficiency. In addition, a total of eighteen brain regions showed significantly reduced nodal efficiency in patients. Intriguingly, nodal efficiency–behavior associations were significantly different between the two groups. The present findings provide new aspects into our understanding of silent infarcts that even small lesions in subcortical brain regions may affect large-scale cortical white matter network, as such may be the link between subcortical silent infarcts and the associated cognitive impairments. Our findings highlight the need for network-level neuroimaging assessment and more medical care for individuals with silent subcortical infarcts.

scholarly journals Association between in-scanner head motion with cerebral white matter microstructure: a multiband diffusion-weighted MRI study

2014 ◽  
Author(s):  
Xiang-zhen Kong
Keyword(s):  
White Matter ◽  
Sampling Rate ◽  
Motion Artifacts ◽  
Cerebral White Matter ◽  
Head Motion ◽  
Brain White Matter ◽  
Diffusion Weighted ◽  
White Matter Microstructure ◽  
Weighted Magnetic Resonance Imaging ◽  
Mri Study

Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) has emerged as the most popular neuroimaging technique used to depict the biological microstructural properties of human brain white matter. However, like other MRI technique, traditional DW-MRI data remains subject to head motion artifacts during scanning. For example, previous studies have indicated that, with traditional DW-MRI data, head motion artifacts significantly affect the evaluation of diffusion metrics. Actually, DW-MRI data scanned with higher sampling rate are important for accurately evaluating diffusion metrics because it allows for full-brain coverage through the acquisition of multiple slices simultaneously and more gradient directions. Here, we employed a publicly available multiband DW-MRI dataset to investigate the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS). The diffusion metrics used in this study included not only the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also newly proposed inter-voxel metric, local diffusion homogeneity (LDH). We found that the motion effects in FA and MD seems to be mitigated to some extent, but the effect on MD still exists. Furthermore, the effect in LDH is much more pronounced. These results indicate that researchers shall be cautious when conducting data analysis and interpretation. Finally, the motion-diffusion association is discussed.

Abstract WP192: Periventricular White Matter Hyperintensities Are Associated With Functional Decline: The Northern Manhattan Study

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Mandip S Dhamoon ◽  
Ying-Kuen Cheung ◽  
Ahmet M Bagci ◽  
Chensy Marquez ◽  
Noam Alperin ◽  
...  
Keyword(s):  
White Matter ◽  
Repeated Measures ◽  
Brain Mri ◽  
Large Population ◽  
Functional Decline ◽  
Brain Regions ◽  
White Matter Hyperintensity ◽  
Periventricular White Matter ◽  
Brain White Matter

Background: We previously showed that overall brain white matter hyperintensity volume (WMHV) was associated with accelerated long-term functional decline. However, it was unclear whether WMHV in particular brain regions was more predictive of decline. We hypothesized that WMHV in particular brain regions would be more predictive of functional decline. Methods: In the Northern Manhattan MRI study, participants had brain MRI with axial T1, T2, and fluid attenuated inversion recovery sequences, with baseline interview and examination. Volumetric WMHV distribution across 14 brain regions (brainstem, cerebellum, and bilateral frontal, occipital, temporal, and parietal lobes, and bilateral anterior and posterior periventricular white matter [PVWM]) was determined separately by combining bimodal image intensity distribution and atlas based methods. Participants had annual functional assessments with the Barthel index (BI, range 0-100) over a mean of 7.3 years and were followed for stroke and myocardial infarction (MI). Due to multiple collinear variables, lasso regression was used to select regional WMHV variables, and adjusted generalized estimating equations models estimated associations with baseline BI and change over time. Results: Among 1195 participants, mean age was 71 (SD 9) years, 460 (39%) were male, 802 (67%) had hypertension and 224 (19%) diabetes. Using lasso regularization, only right anterior PVWM was selected, and each SD increase was associated with accelerated functional decline, of -0.95 additional BI points per year (95% CI -1.20, -0.70) in an unadjusted model, -0.92 points per year (95% CI -1.18, -0.67) with baseline covariate adjustment, and -0.87 points per year (95% CI -1.12, -0.62) after adjusting for stroke and MI. This decline was in addition to a mean decline of -1.13 (95% CI -1.29, -0.97), -1.19 (95% CI -1.36, -1.01), and -1.04 (95% CI -1.21, -0.88) BI points per year, respectively. Conclusions: In this large population-based study with long-term repeated measures of function, periventricular WMHV was particularly associated with accelerated functional decline. Periventricular WMHV may have a greater effect on mobility due to dysfunction in descending leg motor tracts.

Diffusion tensor imaging in schizophrenia

2008 ◽  
Vol 23 (4) ◽  
pp. 255-273 ◽  
Author(s):  
Marinos Kyriakopoulos ◽  
Theodoros Bargiotas ◽  
Gareth J. Barker ◽  
Sophia Frangou
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Basic Principles ◽  
Future Directions ◽  
White Matter Abnormalities ◽  
Non Invasive ◽  
Brain White Matter ◽  
Diffusion Tensor Imaging Dti

AbstractDiffusion tensor imaging (DTI) is a magnetic resonance imaging technique that is increasingly being used for the non-invasive evaluation of brain white matter abnormalities. In this review, we discuss the basic principles of DTI, its roots and the contribution of European centres in its development, and we review the findings from DTI studies in schizophrenia. We searched EMBASE, PubMed, PsychInfo, and Medline from February 1998 to December 2006 using as keywords ‘schizophrenia’, ‘diffusion’, ‘tensor’, and ‘DTI’. Forty studies fulfilling the inclusion criteria of this review were included and systematically reviewed. White matter abnormalities in many diverse brain regions were identified in schizophrenia. Although the findings are not completely consistent, frontal and temporal white matter seems to be more commonly affected. Limitations and future directions of this method are discussed.

scholarly journals Fluctuating Asymmetry and Individual Variation in Regional Gray and White Matter Volumes: A Voxel-Based Morphometry Study

2008 ◽  
Vol 6 (4) ◽  
pp. 147470490800600 ◽  
Author(s):  
Matthew Euler ◽  
Robert J. Thoma ◽  
Lauren Parks ◽  
Steven W. Gangestad ◽  
Ronald A. Yeo
Keyword(s):  
White Matter ◽  
Fluctuating Asymmetry ◽  
Gray Matter Volume ◽  
Brain Mri ◽  
Brain Regions ◽  
Voxel Based Morphometry ◽  
Whole Brain ◽  
Matter Volume ◽  
Brain White Matter ◽  
Mri Scans

Composite measures of fluctuating asymmetry (FA) of skeletal features are commonly used to estimate developmental instability (DI), the imprecise expression of developmental design due to perturbations during an individual's growth and maturation. Though many studies have detailed important behavioral correlates of FA, very little is known about its possible neuroanatomical correlates. In this study we obtained structural brain MRI scans from 20 adults and utilized voxel-based morphometry (VBM) to identify specific regions linked to FA. Greater FA predicted greater whole brain white matter volume, and a trend in the same direction was noted for whole brain gray matter volume. Greater FA was associated with significantly greater gray and white matter volumes in discrete brain regions, most prominently in the frontal lobes and in the right cerebral hemisphere. Developmental studies are needed to identify when FA-related brain differences emerge and to elucidate the specific neurobiological mechanisms leading to these differences.

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