White Matter Microstructure Research Articles

Prenatal tobacco and alcohol exposure, white matter microstructure, and early language skills in toddlers from a South African birth cohort.

Tobacco and alcohol are the two most common substances used during pregnancy, and both can disrupt neurodevelopment, resulting in cognitive and behavioral deficits including language difficulties. Previous studies show that children with prenatal substance exposure exhibit microstructural alterations in major white matter pathways, though few studies have investigated the impact of prenatal substance exposure on white matter microstructure and language skills during the toddler years. In this study, 93 children (34 exposed to alcohol and/or tobacco) aged 23 years from the Drakenstein Child Health Study, South Africa, completed Expressive and Receptive Communication assessments from the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) and underwent diffusion MRI scans. Diffusion images were preprocessed, and 11 major white matter tracts were isolated. Fractional anisotropy (FA) and mean diffusivity (MD) were extracted for each white matter tract. Linear regression was used to examine differences between the tobacco/alcohol exposed group and unexposed controls for FA, MD, and language scores, as well as relationships between brain metrics and language. There were no significant group differences in language scores or FA. Children with alcohol or tobacco exposure had lower average MD in the splenium of the corpus callosum compared to unexposed controls. Significant interactions between prenatal substance exposure and language scores were seen in 7 tracts but did not survive multiple comparisons correction. Our findings show that prenatal alcohol and/or tobacco exposure appear to alter the relationship between white matter microstructure and early language skills in this population of toddlers, potentially laying the basis of language deficits observed later in older children with prenatal substance exposure, which may have implications for learning and interventions.

Understanding the mechanisms of fatigue in multiple sclerosis: linking interoception, metacognition and white matter dysconnectivity.

One of the most prominent symptoms in multiple sclerosis is pathological fatigue, often described by sufferers as one of the most debilitating symptoms, affecting quality of life and employment. However, the mechanisms of both, physical and cognitive fatigue in multiple sclerosis remain elusive. Here, we use behavioural tasks and quantitative MRI to investigate the neural correlates of interoception (the ability to sense internal bodily signals) and metacognition (the ability of the brain to assess its own performance), in modulating cognitive fatigue. Assuming that structural damage caused by multiple sclerosis pathology might impair the neural pathways subtending interoception and/or metacognition, we considered three alternative hypotheses to explain fatigue as a consequence of, respectively: (i) reduced interoceptive accuracy, (ii) reduced interoceptive insight or (iii) reduced global metacognition. We then explored associations between these behavioural measures and white matter microstructure, assessed by diffusion and magnetisation transfer MRI. Seventy-one relapsing-remitting multiple sclerosis patients participated in this cross-sectional study (mean age 43, 62% female). Patient outcomes relevant for fatigue were measured, including disability, disease duration, depression, anxiety, sleepiness, cognitive function, disease modifying treatment and quality of life. Interoceptive and metacognitive parameters were measured using heartbeat tracking and discrimination tasks, and metacognitive visual and memory tasks. MRI was performed in 69 participants, including diffusion tensor MRI, neurite orientation dispersion and density imaging and quantitative magnetisation transfer. Associations between interoception and metacognition and the odds of high cognitive fatigue were tested by unconditional binomial logistic regression. The odds of cognitive fatigue were higher in the people with low interoceptive insight (P = 0.03), while no significant relationships were found between fatigue and other interoceptive or metacognitive parameters, suggesting a specific impairment in interoceptive metacognition, rather than interoception generally, or metacognition generally. Diffusion MRI-derived fractional anisotropy and neurite density index showed significant (P < 0.05) negative associations with cognitive fatigue in a widespread bilateral white matter network. Moreover, there was a significant (P < 0.05) interaction between cognitive fatigue and interoceptive insight, suggesting that the poorer the white matter structure, the lower the interoceptive insight, and the worse the fatigue. The results point towards metacognitive impairment confined to the interoceptive domain, in relapsing-remitting patients with cognitive fatigue. The neural basis of this impairment is supported by a widespread white matter network in which loss of neurite density plays a role.

Glymphatic function assessment with diffusion tensor imaging along the perivascular space in patients with major depressive disorder and its relation to cerebral white-matter alteration.

The link between glymphatic system function in the brain and alterations in white-matter microstructure among individuals with major depressive disorder (MDD) remains unclear. This study aimed to examine the assessment of glymphatic system function in patients with MDD using the diffusion tensor imaging along the perivascular space (DTI-ALPS) index and to evaluate its association with cerebral-white-matter abnormalities and neuropsychological scores. From February 2023 to November 2023, this cross-sectional study recruited 35 patients with MDD from the Psychosomatic Diseases Department of the First Affiliated Hospital of Dalian Medical University. In this time period, 23 healthy controls (HCs) were enlisted from the community and matched with the MDD cohort in terms of years of education, gender, and age. All participants underwent magnetic resonance imaging, depression, anxiety, and cognitive assessments. The tract-based spatial statistics (TBSS) analyzed DTI parameters and identified significant clusters. Automated fiber quantification (AFQ) was used to automatically identify fiber bundles with statistical differences. Mann-Whitney tests or two-sample t-tests were used for comparisons. Interobserver consistency of the DTI-ALPS measurements was evaluated using the interclass correlation coefficient (ICC). Partial correlation analyses and linear regression analyses were used to examine relationships. A comparison of the DTI-ALPS index was made between the two groups. Correlations among diffusion characteristics, neuropsychological scores, and the DTI-ALPS index were analyzed. Compared to HCs, patients with MDD exhibited a lower DTI-ALPS score (P=0.001). According to using linear regression analysis, the ALPS index was found to be an independent predictor of the Hamilton Depression Rating Scale [B=-25.32; P=0.001; 95% confidence interval (CI): -40.35 to -11.55], Hamilton Anxiety Rating Scale (B=-33.48; P=0.003; 95% CI: -55.38 to -11.24), and Montreal Cognitive Assessment total score (B=8.59; P=0.008; 95% CI: 2.38 to 14.79). According to the TBSS analysis, there were clusters of increased axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) in patients with MDD as compared to HCs (all P values <0.05). A lower DTI-ALPS score was correlated with higher AD (r=-0.592; P<0.001), MD (cluster 1: r=-0.567, P=0.001; cluster 2: r=-0.581, P<0.001), and RD (r=-0.491; P=0.004) values. AFQ analysis identified the significantly different diffusion indicators in the left cingulum bundle (CB_L), left inferior longitudinal fasciculus (ILF_L), and left uncinate fasciculus (UF_L) between the two groups (all false discovery rate P values <0.05). DTI-ALPS score was negatively correlated with the AD value of CB_L (r=-0.304; P=0.024), ILF_L (r=-0.35; P=0.008), and UF_L (r=-0.354; P=0.008) in AFQ tract-level analysis. In point-wise analysis, the MD value of CB_L at nodes 33 to 36 was negatively correlated with DTI-ALPS score (r ranging from -0.504 to -0.535; P<0.01). Our results indicated a decrease in DTI-ALPS index score in patients with MDD. DTI-ALPS score was associated with depression, anxiety, declined cognitive ability, and white-matter microstructural abnormalities and may thus be a promising biomarker for the partial evaluation of glymphatic system function in patients with MDD.

Correlation between brain microstructural white matter integrity and qualitative gait outcome after stroke: A research protocol

Correlation between brain microstructural white matter integrity and qualitative gait outcome after stroke: A research protocol

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. Specifically, there is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Harmonized submissions are evaluated on the reproducibility and comparability of cross-acquisition bundlewise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences. A machine learning approach that learned voxelwise cross-acquisition relationships was the most effective at harmonizing connectomic, microstructure, and macrostructure features, but requires the same subject be scanned at each site co-registered. NeSH, a spatial and angular resampling method, was also effective and has generalizable framework not reliant co-registration. Our code is available at &lt;a href='https://github.com/nancynewlin-masi/QuantConn/'&gt;https://github.com/nancynewlin-masi/QuantConn/&lt;/a&gt;

No significant alteration in white matter microstructure in first-degree relatives of patients with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is characterized by structural alteration within white matter tissues of cortico-striato-thalamo-cortical, temporal and occipital circuits. However, the presence of microstructural changes in the white matter tracts of unaffected first-degree relatives of patients with OCD as a vulnerability marker remains unclear. Therefore, here, diffusion-tensor magnetic resonance imaging (DTI) data were obtained from 29 first-degree relatives of patients with OCD and 59 healthy controls. We investigated the group differences in FA using whole-brain analysis (DTI analysis). For additional regions of interest (ROI) analysis, we focused on the posterior thalamic radiation and sagittal stratum, shown in recent meta-analysis of patients with OCD. In both whole-brain and ROI analyses, using a strict statistical threshold (family-wise error rate [FWE] corrected p<.05 for whole-brain analyses, and p<.0125 (0.05/4) with Bonferroni correction for ROI analyses), we found no significant group differences in FA. Subtle reductions were observed in the anterior corona radiata, forceps minor, cingulum bundle, and corpus callosum only when a lenient statistical was applied (FWE corrected p<.20). These findings suggest that alterations in the white matter microstructure of first-degree relatives, as potential vulnerability markers for OCD, are likely subtle.

Inflammatory bowel disease and white matter microstructure: A bidirectional Mendelian randomization study

BackgroundObservational studies have reported changes in the brain white matter (WM) microstructure in patients with inflammatory bowel disease (IBD); however, it remains uncertain whether the relationship between them is causative. The aim of this study is to reveal the potential causal relationship between IBD and WM microstructure through a bidirectional two-sample Mendelian randomization (MR) analysis. Methods: We extracted genome-wide association study (GWAS) summary statistics for IBD and WM microstructure from published GWASs. Two-sample MR analysis was conducted to explore the bidirectional causal associations between IBD and WM microstructure, followed by a series of sensitivity analyses to assess the robustness of the results. Results: Although forward MR analysis results showed no evidence of causality from microstructural WM to IBD, reverse MR showed that genetically predicted IBD, consisting of ulcerative colitis and Crohn’s disease, has a significant causal effect on the orientation dispersion index (OD) of the right tapetum (β = −0.029, 95% CI = −0.045 to −0.013, p = 3.63 × 10−4). Further sensitivity analysis confirmed the robustness of the association. Conclusion: Our results suggested the potentially causal association of IBD with reduced OD in the right tapetum.

Exploring White Matter Microstructure with Symptom Severity and Outcomes Following Deep Brain Stimulation in Tremor Syndromes.

Essential tremor (ET) and dystonic tremor (DT) are movement disorders that cause debilitating symptoms, significantly impacting daily activities and quality of life. A poor understanding of their pathophysiology, as well as the mediators of clinical outcomes following deep brain stimulation (DBS), highlights the need for biomarkers to accurately characterise and optimally treat patients. We assessed the white matter microstructure of pathways implicated in the pathophysiology and therapeutic intervention in a retrospective cohort of patients with DT (n = 17) and ET (n = 19). We aimed to identity associations between white matter microstructure, upper limb tremor severity, and tremor improvement following DBS. A fixel-based analysis pipeline was implemented to investigate white matter microstructural metrics in the whole brain, cerebello-thalamic pathways and tracts connected to stimulation volumes following DBS. Associations with preoperative and postoperative severity were analysed within each disorder group and across combined disorder groups. DBS led to significant improvements in both groups. No group differences in stimulation positions were identified. When white matter microstructural data was aligned according to the maximally affected upper limb, increased fiber density, and combined fiber density & cross-section of fixels in the left cerebellum were associated with greater tremor severity across DT and ET patients. White matter microstructure did not show associations with postoperative changes in cerebello-thalamic pathways, or tracts connected to stimulation volumes. Diffusion changes of the cerebellum are associated with the severity of upper limb tremor and appear to overlap in essential or dystonic tremor disorders.

Differential Tractography: A Biomarker for Neuronal Function in Neurodegenerative Disease.

GM1 gangliosidosis is an ultra-rare inherited neurodegenerative lysosomal storage disorder caused by biallelic mutations in the GLB1 gene. GM1 is uniformly fatal and has no approved therapies, although clinical trials investigating gene therapy as a potential treatment for this condition are underway. Novel outcome measures or biomarkers demonstrating the longitudinal effects of GM1 and potential recovery due to therapeutic intervention are urgently needed to establish efficacy of potential therapeutics. One promising tool is differential tractography, a novel imaging modality utilizing serial diffusion weighted imaging (DWI) to quantify longitudinal changes in white matter microstructure. In this study, we present the novel use of differential tractography in quantifying the progression of GM1 alongside age-matched neurotypical controls. We analyzed 113 DWI scans from 16 GM1 patients and 32 age-matched neurotypical controls to investigate longitudinal changes in white matter pathology. GM1 patients showed white matter degradation evident by both the number and size of fiber tract loss. In contrast, neurotypical controls showed longitudinal white matter improvements as evident by both the number and size of fiber tract growth. We also corroborated these findings by documenting significant correlations between cognitive global impression (CGI) scores of clinical presentations and our differential tractography derived metrics in our GM1 cohort. Specifically, GM1 patients who lost more neuronal fiber tracts also had a worse clinical presentation. This result demonstrates the importance of differential tractography as an important biomarker for disease progression in GM1 patients with potential extension to other neurodegenerative diseases and therapeutic intervention.

Maternal adverse childhood experiences and infant visual-limbic white matter development

BackgroundMaternal adverse childhood experiences (ACEs) are robust predictors of mental health for both the exposed individual and the next generation; however, the pathway through which such intergenerational risk is conferred remains unknown. The current study evaluated the association between maternal ACEs and infant brain development, including an a priori focus on circuits implicated in emotional and sensory processing. MethodsThe sample included 101 mother-infant dyads from a longitudinal study. Maternal ACEs were assessed with the Adverse Childhood Questionnaire dichotomized into low (0 or 1) and high (≥2) groups. White matter microstructure, as indexed by fractional anisotropy (FA), was assessed using structural magnetic resonance imaging in infants (41.6–46.0 weeks' postconceptional age) within a priori tracts (the cingulum, fornix, uncinate, inferior frontal occipital fasciculus, and inferior longitudinal fasciculus). Exploratory analyses were also conducted across the whole brain. ResultsHigh maternal ACEs (≥2) were associated with decreased infant left inferior longitudinal fasciculus (ILF) FA (F(1,94) = 7.78, p < .006) relative to infants of low ACE mothers. No group difference was observed within the right ILF following correction for multiple comparisons (F(1,95) = 4.29, p < .041). Follow-up analyses within the left ILF demonstrated associations between high maternal ACEs and increased left radial diffusivity (F(1,95) = 5.10, p < .006). Exploratory analyses demonstrated preliminary support for differences in visual processing networks (e.g., optic tract) as well as additional circuits less frequently examined in the context of early life adversity exposure (e.g., corticothalamic tract). ConclusionsMaternal ACEs predict neural circuit development of the inferior longitudinal fasciculus. Findings suggest that early developing sensory circuits within the infant brain are susceptible to maternal adverse childhood experiences and may have implications for the maturation of higher-order emotional and cognitive circuits.

Diffusion tensor imaging in Behcet's disease with and without neurological involvement patients: evaluation of microstructural white matter abnormality with a tract-based spatial statistical analysis.

This study aims to assess the microstructural abnormalities in white matter (WM) among Behcet's disease (BD) patients, both with and without neurological involvement, utilising tract-based spatial statistics (TBSS) to elucidate the underlying causes of WM microstructural changes. This prospective study comprised 43 BD patients without neurological involvement, 15 neuro-Behcet's disease (NBD) patients with normal conventional MRI, and 54 healthy controls matched for age and sex. TBSS was applied in this diffusion tensor imaging study to conduct a whole-brain voxel-wise analysis of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of WM. Compared to the control group, BD patients exhibited decreased FA and increased MD and RD in nearly all WM tracts, along with increased AD in the left corticospinal tract (CST), left inferior longitudinal fasciculus (ILF), and left superior longitudinal fasciculus (SLF). NBD patients also showed a widespread decrease in FA and increased MD and RD, similar to BD patients without neurological involvement. Additionally, NBD patients had increased AD in the left CST, left ILF, left SLF, left inferior fronto-occipital fasciculus (IFOF), and right CST. Compared to BD patients without neurological involvement, NBD patients exhibited a greater reduction in FA and an increase in MD and RD in WM tracts, with no significant differences in AD. These results suggest that the main mechanism of microstructural changes in the WM of BD patients may be related to impaired fibre integrity, demyelination, and decreased myelin sheath integrity. This study demonstrated BD patients without neurological involvement and NBD patients a decrease in FA and an increase in MD and RD were observed in larger areas of major WM tracts, while an increase in AD values was observed in fewer tracts. Our findings may be useful in understanding the pathophysiology underlying subclinical parenchymal involvement and neurological dysfunction in BD patients and the management of BD patients.

Residential ambient air pollution exposure and the development of white matter microstructure throughout adolescence

BackgroundRecent evidence suggests an association of air pollution exposure with brain development, but evidence on white matter microstructure in children is scarce. We investigated how air pollution exposure during pregnancy and childhood impacts longitudinal development of white matter microstructure throughout adolescence. MethodsOur study population consisted of 4108 participants of Generation R, a large population-based birth cohort from Rotterdam, the Netherlands. Residential air pollution exposure to 14 air pollutants during pregnancy and childhood was estimated with land-use regression models. Diffusion tensor images were obtained around age 10 and 14, resulting in a total of 5422 useable scans (n = 3082 for wave 1 and n = 2340 for wave 2; n = 1314 for participants with data on both waves). We calculated whole-brain fractional anisotropy (FA) and mean diffusivity (MD) and performed single- and multi-pollutant analyses using mixed effects models adjusted for life-style and socioeconomic status variables. ResultsHigher exposure to PM2.5 during pregnancy, and PM10, PM2.5, PM2.5-10, and NOX during childhood was associated with a consistently lower whole-brain FA throughout adolescence (e.g. – 0.07 × 10−2 FA [95%CI -0.12; −0.02] per 1 standard deviation higher PM2.5 exposure during pregnancy). Higher exposure to silicon (Si) in PM2.5 and oxidative potential of PM2.5 during pregnancy, and PM2.5 during childhood was associated with an initial higher MD followed by a faster decrease in MD throughout adolescence (e.g. – 0.02 × 10−5 mm2/s MD [95%CI -0.03; −0.00] per year of age per 1 standard deviation higher Si exposure during pregnancy). Results were comparable when performing the analysis in children with complete data on the outcome for both neuroimaging assessments. ConclusionsExposure to several pollutants was associated with a consistently lower whole-brain FA throughout adolescence. The association of few pollutants with whole-brain MD at baseline attenuated throughout adolescence. These findings suggest both persistent and age-limited associations of air pollution exposure with white matter microstructure.

Deep learning-based segmentation in MRI-(immuno)histological examination of myelin and axonal damage in normal-appearing white matter and white matter hyperintensities.

The major vascular cause of dementia is cerebral small vessel disease (SVD). Its diagnosis relies on imaging hallmarks, such as white matter hyperintensities (WMH). WMH present a heterogenous pathology, including myelin and axonal loss. Yet, these might be only the "tip of the iceberg." Imaging modalities imply that microstructural alterations underlie still normal-appearing white matter (NAWM), preceding the conversion to WMH. Unfortunately, direct pathological characterization of these microstructural alterations affecting myelinated axonal fibers in WMH, and especially NAWM, is still missing. Given that there are no treatments to significantly reduce WMH progression, it is important to extend our knowledge on pathological processes that might already be occurring within NAWM. Staining of myelin with Luxol Fast Blue, while valuable, fails to assess subtle alterations in white matter microstructure. Therefore, we aimed to quantify myelin surrounding axonal fibers and axonal- and microstructural damage in detail by combining (immuno)histochemistry with polarized light imaging (PLI). To study the extent (of early) microstructural damage from periventricular NAWM to the center of WMH, we refined current analysis techniques by using deep learning to define smaller segments of white matter, capturing increasing fluid-attenuated inversion recovery signal. Integration of (immuno)histochemistry and PLI with post-mortem imaging of the brains of individuals with hypertension and normotensive controls enables voxel-wise assessment of the pathology throughout periventricular WMH and NAWM. Myelin loss, axonal integrity, and white matter microstructural damage are not limited to WMH but already occur within NAWM. Notably, we found that axonal damage is higher in individuals with hypertension, particularly in NAWM. These findings highlight the added value of advanced segmentation techniques to visualize subtle changes occurring already in NAWM preceding WMH. By using quantitative MRI and advanced diffusion MRI, future studies may elucidate these very early mechanisms leading to neurodegeneration, which ultimately contribute to the conversion of NAWM to WMH.

What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review.

Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.

Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices

Alterations in white matter (WM) microstructure of the central nervous system have been shown to be pathophysiological presentations of various neurodegenerative disorders. Current methods for measuring such WM features require ex vivo tissue samples analyzed using electron microscopy. Magnetic Resonance Imaging (MRI) diffusion-weighted pulse sequences provide a non-invasive tool for estimating such microstructural features in vivo. The current project investigated the use of two methods of analysis, including the ROI-based (Region of Interest, RBA) and voxel-based analysis (VBA), as well as four mathematical models of WM microstructure, including the ActiveAx Frequency-Independent Extra-Axonal Diffusion (AAI), ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD), AxCaliber Frequency-Independent Extra-Axonal Diffusion (ACI), and AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) models. Two mice samples imaged at 7 T and 15.2 T were analyzed. Both the AAI and AAD models provide a single value for each of the fit parameters, including mean effective axon diameter AxD¯, packing fraction fin, intra-cellular and Din and extra-cellular Dex diffusion coefficients, as well as the frequency dependence of Dex, βex for the AAD model. The ACI and ACD models provide this, in addition to a distribution of axon diameters for a chosen ROI. VBA extends this, providing a parameter value for each voxel within the selected ROI, at the cost of increased computational load and analysis time. Overall, RBA-ACD and VBA-AAD were found to be optimal for parameter fitting to physically relevant values in a reasonable time frame. A full comparison of each combination of RBA and VBA with AAI, AAD, ACI, and ACD is provided to give the reader sufficient information to make an informed decision of which model is best for their own experiments.

More similarity than difference: comparison of within- and between-sex variance in early adolescent brain structure.

Adolescent neuroimaging studies of sex differences in the human brain predominantly examine mean differences between males and females. This focus on between-groups differences without probing relative distributions and similarities may contribute to both conflation and overestimation of sex differences and sexual dimorphism in the developing human brain. We aimed to characterize the variance in brain macro- and micro-structure in early adolescence as it pertains to sex at birth using a large sample of 9-11 year-olds from the Adolescent Brain Cognitive Development (ABCD) Study (N=7,723). Specifically, for global and regional estimates of gray and white matter volume, cortical thickness, and white matter microstructure (i.e., fractional anisotropy and mean diffusivity), we examined: within- and between-sex variance, overlap between male and female distributions, inhomogeneity of variance via the Fligner-Killeen test, and an analysis of similarities (ANOSIM). For completeness, we examined these sex differences using both uncorrected (raw) brain estimates and residualized brain estimates after using mixed-effects modeling to account for age, pubertal development, socioeconomic status, race, ethnicity, MRI scanner manufacturer, and total brain volume, where applicable. The overlap between male and female distributions was universally greater than the difference (overlap coefficient range: 0.585 - 0.985) and the ratio of within-sex and between-sex differences was similar (ANOSIM R range: -0.001 - 0.117). All cortical and subcortical volumes showed significant inhomogeneity of variance, whereas a minority of brain regions showed significant sex differences in variance for cortical thickness, white matter volume, fractional anisotropy, and mean diffusivity. Inhomogeneity of variance was reduced after accounting for other sources of variance. Overlap coefficients were larger and ANOSIM R values were smaller for residualized outcomes, indicating greater within- and smaller between-sex differences once accounting for other covariates. Reported sex differences in early adolescent human brain structure may be driven by disparities in variance, rather than binary, sex-based phenotypes. Contrary to the popular view of the brain as sexually dimorphic, we found more similarity than difference between sexes in all global and regional measurements of brain structure examined. This study builds upon previous findings illustrating the importance of considering variance when examining sex differences in brain structure.

Distinguishing glioblastoma progression from treatment-related changes using DTI directionality growth analysis.

It is difficult to distinguish between tumor progression (TP) and treatment-related abnormalities (TRA) in treated glioblastoma patients via conventional MRI, but this distinction is crucial for treatment decision making. Glioblastoma is known to exhibit an invasive growth pattern along white matter architecture and vasculature. This study quantified lesion development patterns in treated glioblastoma lesions and their relation to white matter microstructure to distinguish TP from TRA. Glioblastoma patients with confirmed TP or TRA with T1-weighted contrast-enhanced and DTI MR scans from two posttreatment follow-up timepoints were reviewed. The contrast-enhancing regions were segmented, and the regions were coregistered to the DTI data. Lesion increase vectors were categorized into two groups: parallel (0-20 degrees) and perpendicular (70-90 degrees) to white matter. FA-values were also extracted. To test for a statistically significant difference between the TP and TRA groups, a Mann‒Whitney U test was performed. Of 73 glioblastoma patients, fifteen were diagnosed with TRA, whereas 58 patients suffered TP. TP had a 25.8% (95% CI 24.1%-27.6%) increase in parallel lesions, and TRA had a 25.4% (95% CI 20.9%-29.9%) increase in parallel lesions. The perpendicular increase was 14.7% for TP (95% CI 13.0%-16.4%) and 18.0% (95% CI 13.5%-22.5%) for TRA. These results were not significantly different (p = 0.978). FA value for TP showed to be 0.248 (SD = 0.054) and for TRA it was 0.231 (SD = 0.075), showing no statistically significant difference (p = 0.121). Based on our results, quantifying posttreatment contrast-enhancing lesion development directionality with DTI in glioblastoma patients does not appear to effectively distinguish between TP and TRA.

Causal Relationship between Aging and Anorexia Nervosa: A White-Matter-Microstructure-Mediated Mendelian Randomization Analysis.

This study employed a two-step Mendelian randomization analysis to explore the causal relationship between telomere length, as a marker of aging, and anorexia nervosa and to evaluate the mediating role of changes in the white matter microstructure across different brain regions. We selected genetic variants associated with 675 diffusion magnetic resonance imaging phenotypes representing changes in brain white matter. F-statistics confirmed the validity of the instruments, ensuring robust causal inference. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy tests, and leave-one-out tests, validated the results. The results show that telomere length is significantly negatively correlated with anorexia nervosa in a unidirectional manner (p = 0.017). Additionally, changes in specific white matter structures, such as the internal capsule, corona radiata, posterior thalamic radiation, left cingulate gyrus, left longitudinal fasciculus, and left forceps minor (p < 0.05), were identified as mediators. These findings enhance our understanding of the neural mechanisms, underlying the exacerbation of anorexia nervosa with aging; emphasize the role of brain functional networks in disease progression; and provide potential biological targets for future therapeutic interventions.

Transcallosal white matter and cortical gray matter variations in autistic adults ages 30-73 years: A bi-tensor free water imaging approach.

Background: Autism spectrum disorder (ASD) has long been recognized as a lifelong condition, but brain aging studies in autistic adults aged >30 years are limited. Free water, a novel brain imaging marker derived from diffusion MRI (dMRI), has shown promise in differentiating typical and pathological aging and monitoring brain degeneration. We aimed to examine free water and free water corrected dMRI measures to assess white and gray matter microstructure and their associations with age in autistic adults. Methods: Forty-three autistic adults ages 30-73 years and 43 age, sex, and IQ matched neurotypical controls participated in this cross-sectional study. We quantified fractional anisotropy (FA), free water, and free water-corrected FA (fwcFA) across 32 transcallosal white matter tracts and 94 gray matter areas in autistic adults and neurotypical controls. Follow-up analyses assessed age effect on dMRI metrics of the whole brain for both groups and the relationship between dMRI metrics and clinical measures of ASD in regions that significantly differentiated autistic adults from controls. Results: We found globally elevated free water in 24 transcallosal tracts in autistic adults. We identified negligible differences in dMRI metrics in gray matter between the two groups. Age-associated FA reductions and free water increases were featured in neurotypical controls; however, this brain aging profile was largely absent in autistic adults. Additionally, greater autism quotient (AQ) total raw score was associated with increased free water in the inferior frontal gyrus pars orbitalis and lateral orbital gyrus in autistic adults. Limitations: All autistic adults were cognitively capable individuals, minimizing the generalizability of the research findings across the spectrum. This study also involved a cross-sectional design, which limited inferences about the longitudinal microstructural changes of white and gray matter in ASD. Conclusions: We identified differential microstructural configurations between white and gray matter in autistic adults and that autistic individuals present more heterogeneous brain aging profiles compared to controls. Our clinical correlation analysis offered new evidence that elevated free water in some localized white matter tracts may critically contribute to autistic traits in ASD. Our findings underscored the importance of quantifying free water in dMRI studies of ASD.

Association of early life exposure to cold and heat with brain white matter microstructure at age 9-12 years

Association of early life exposure to cold and heat with brain white matter microstructure at age 9-12 years