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

BACKGROUND: Recent 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. METHODS: Our 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. RESULTS: Higher 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. CONCLUSIONS: Exposure 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.

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

BACKGROUND: Maternal 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. METHODS: The 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. RESULTS: High 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). CONCLUSIONS: Maternal 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.

Current Auditory Hallucinations Are Not Associated With Specific White Matter Diffusion Alterations in Schizophrenia.

Background and Hypothesis: Studies have linked auditory hallucinations (AH) in schizophrenia spectrum disorders (SCZ) to altered cerebral white matter microstructure within the language and auditory processing circuitry (LAPC). However, the specificity to the LAPC remains unclear. Here, we investigated the relationship between AH and DTI among patients with SCZ using diffusion tensor imaging (DTI). Study Design: We included patients with SCZ with (AH+; n = 59) and without (AH-; n = 81) current AH, and 140 age- and sex-matched controls. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were extracted from 39 fiber tracts. We used principal component analysis (PCA) to identify general factors of variation across fiber tracts and DTI metrics. Regression models adjusted for sex, age, and age2 were used to compare tract-wise DTI metrics and PCA factors between AH+, AH-, and healthy controls and to assess associations with clinical characteristics. Study Results: Widespread differences relative to controls were observed for MD and RD in patients without current AH. Only limited differences in 2 fiber tracts were observed between AH+ and controls. Unimodal PCA factors based on MD, RD, and AD, as well as multimodal PCA factors, differed significantly relative to controls for AH-, but not AH+. We did not find any significant associations between PCA factors and clinical characteristics. Conclusions: Contrary to previous studies, DTI metrics differed mainly in patients without current AH compared to controls, indicating a widespread neuroanatomical distribution. This challenges the notion that altered DTI metrics within the LAPC is a specific feature underlying AH.

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

BACKGROUND: Observational 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 (ß = -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.

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer's disease.

OBJECTIVE: To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI. METHODS: A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort. RESULTS: Based on multivariate logistic regression, clinical dementia rating and Alzheimer's disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341). CONCLUSION: The present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

White matter fiber morphology in persisting postconcussive symptoms and posttraumatic headache after pediatric concussion: a fixel-based analysis.

OBJECTIVE: Posttraumatic headache (PTH) represents the most common acute and persistent postconcussive symptom (PCS) in children after concussion, yet there remains a lack of valid and objective biomarkers to facilitate risk stratification and early intervention in this patient population. Fixel-based analysis of diffusion-weighted imaging, which overcomes constraints of traditional diffusion tensor imaging analyses, can improve the sensitivity and specificity of detecting white matter changes postconcussion. The aim of this study was to investigate whole-brain and tract-based differences in white matter morphology, including fiber density (FD) and fiber bundle cross-section (FC) area in children with PCSs and PTH at 2 weeks after concussion. METHODS: This prospective longitudinal study recruited children aged 5-18 years who presented to the emergency department of a tertiary pediatric hospital with a concussion sustained within the previous 48 hours. Participants underwent diffusion-weighted MRI at 2 weeks postinjury. Whole-brain white matter statistical analysis was performed at the level of each individual fiber population within an image voxel (fixel) to compute FD, FC, and a combined metric (FD and bundle cross-section [FDC]) using connectivity-based fixel enhancement. Tract-based Bayesian analysis was performed to examine FD in 23 major white matter tracts. RESULTS: Comparisons of 1) recovered (n = 27) and symptomatic (n = 16) children, and those with 2) PTH (n = 13) and non-PTH (n = 30; overall mean age 12.99 ± 2.70 years, 74% male) found no fiber-specific white matter microstructural differences in FD, FC, or FDC at 2 weeks postconcussion, when adjusting for age and sex (family-wise error rate corrected p value > 0.05). Tract-based Bayesian analysis showed evidence of no effect of PTH on FD in 10 major white matter tracts, and evidence of no effect of recovery group on FD in 3 white matter tracts (Bayes factor < 1/3). CONCLUSIONS: Using whole-brain fixel-wise and tract-based analyses, these findings indicate that fiber-specific properties of white matter microstructure are not different between children with persisting PCSs compared with recovered children 2 weeks after concussion. These data extend the limited research on white matter fiber-specific morphology while overcoming limitations inherent to traditional diffusion models. Further validation of our findings with a large-scale cohort is warranted.

Retinal structural thicknesses reflect clinically relevant microstructural white matter abnormalities in neuromyelitis optica spectrum disorders.

BACKGROUND: Thinning of retinal thickness seen on optical coherence tomography (OCT) is frequent in patients with neuromyelitis optica spectrum disorder (NMOSD). We explored the association between OCT metrics, MRI measurements and clinical outcomes in NMOSD. METHODS: 44 NMOSD and 60 controls underwent OCT and MR imaging. Mean peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell complex (GCC) thicknesses were measured. Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) was used to measure the white matter microstructural integrity. In NMOSD patients, Expanded Disability Status Scale (EDSS) was used to quantify disability. Visual acuity (VA) was also performed for all participants. RESULTS: pRNFL thickness was positively associated with mean diffusivity in left posterior thalamic radiation (pp = 0.010) and axial kurtosis in inferior cerebellar peduncle (p = 0.023). Similarly, GCC thickness in NMOSD patients was positively associated with fractional anisotropy in right superior longitudinal fascicules (p = 0. 041) and axial kurtosis of left cerebellar peduncle (p = 0.011). CONCLUSIONS: In NMOSD, pRNFL and GCC reflect integrity of clinically relevant white matter structures underlying the value of OCT metrics as markers of neuronaxonal loss and disability.

Body Satisfaction, Exercise Dependence, and White Matter Microstructure in Young Adults.

BACKGROUND: Self-body satisfaction is considered a psychological factor for exercise dependence (EXD). However, the potential neuropsychological mechanisms underlying this association remain unclear. PURPOSE: To investigate the role of white matter microstructure in the association between body satisfaction and EXD. STUDY TYPE: Prospective. POPULATION: One hundred eight regular exercisers (age 22.11 ± 2.62 years; 58 female). FIELD STRENGTH/SEQUENCE: 3.0 Tesla; diffusion-weighted echo planar imaging with 30 directions. ASSESSMENT: The Body Shape Satisfaction (BSS) and Exercise Dependence Scale (EDS); whole-brain tract-based spatial statistics (TBSS) and correlational tractography analyses; average fractional anisotropy (FA) and quantitative anisotropy (QA) values of obtained tracts. STATISTICAL TESTS: The whole-brain regression model, mediation analysis, and simple slope analysis. P values <0.05 were defined as statistically significant. RESULTS: The BSS and EDS scores were 37.33 ± 6.32 and 68.22 ± 13.88, respectively. TBSS showed negative correlations between EDS and FA values in the bilateral corticospinal tract (CST, r = -0.41), right cingulum (r = -0.41), and left superior thalamic radiation (STR, r = -0.50). Correlational tractography showed negative associations between EDS and QA values of the left inferior frontal occipital fasciculus (r = -0.35), STR (r = -0.42), CST (r = -0.31), and right cingulum (r = -0.28). The FA values, rather than QA values, mediated the BSS-EDS association (indirect effects = 0.30). The BSS was significantly associated with the EDS score at both low (ß = 1.02) and high (ß = 0.43) levels of FA value, while the association was significant only at the high level of QA value (ß = 1.26). DATA CONCLUSION: EXD was correlated with white matter in frontal-subcortical and sensorimotor networks, and these tracts mediated the body satisfaction-EXD association. White matter microstructure could be a promising neural signature for understanding the underlying neuropsychological mechanisms of EXD. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated With Psychopathology Across Independent Cohorts.

BACKGROUND: Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions. METHODS: A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. RESULTS: In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use. CONCLUSIONS: This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

Facial emotion recognition function and white matter microstructural alterations in drug-naive, comorbidity-free autism.

Individuals with autism spectrum disorder have deficits in facial emotion recognition and white matter microstructural alterations. Nonetheless, most previous studies were confounded by different variables, such as psychiatric comorbidities and psychotropic medications used by ASD participants. Also, it remains unclear how exactly FER deficits are related to white matter microstructural alterations in ASD. Accordingly, we aimed to investigate the FER functions, white matter microstructure, and their relationship in drug-naive and comorbidity-free ASD individuals. 59 ASD individuals and 59 typically developed individuals were included, where 46 ASD and 50 TD individuals completed FER tasks. Covariance analysis showed scores were lower in both basic and complex FER tasks in the ASD group. Tract-Based Spatial Statistics showed FA values in widespread white matter fibers were lower in the ASD group than in the TD group, including forceps major and forceps minor of the corpus callosum, anterior thalamic radiation, corticospinal tract, cingulum, inferior frontal-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus. Moreover, in the TD group but not the ASD group, the performance in the complex FER task was negatively correlated with the FA value in some white matter fibers, including forceps major of the corpus callosum, ATR, CT, cingulum, IFOF, ILF, SLF. Our study suggests children with ASD may experience deficits in facial emotion recognition and exhibit alterations in white matter microstructure. More importantly, our study indicates that white matter microstructural alterations may be involved in FER deficits in children with ASD.

Machine-learning-based feature selection to identify attention-deficit hyperactivity disorder using whole-brain white matter microstructure: A longitudinal study.

BACKGROUND: We aimed to identify important features of white matter microstructures collectively distinguishing individuals with attention-deficit/hyperactivity disorder (ADHD) from those without ADHD using a machine-learning approach. METHODS: Fifty-one ADHD patients and 60 typically developing controls (TDC) underwent diffusion spectrum imaging at two time points. We evaluated three models to classify ADHD and TDC using various machine-learning algorithms. Model 1 employed baseline white matter features of 45 white matter tracts at Time 1; Model 2 incorporated features from both time points; and Model 3 (main analysis) further included the relative rate of change per year of white matter tracts. RESULTS: The random forest algorithm demonstrated the best performance for classification. Model 1 achieved an area-under-the-curve (AUC) of 0.67. Model 3, incorporating Time 2 variables and relative rate of change per year, improved the performance (AUC = 0.73). In addition to identifying several white matter features at two time points, we found that the relative rate of change per year in the superior longitudinal fasciculus, frontal aslant tract, stria terminalis, inferior fronto-occipital fasciculus, thalamic and striatal tracts, and other tracts involving sensorimotor regions are important features of ADHD. A higher relative change rate in certain tracts was associated with greater improvement in visual attention, spatial short-term memory, and spatial working memory. CONCLUSIONS: Our findings support the significant diagnostic value of white matter microstructure and the developmental change rates of specific tracts, reflecting deviations from typical development trajectories, in identifying ADHD.

Association of past and current sleep duration with structural brain differences: A large population-based study from the UK Biobank.

OBJECTIVE: This study aimed to examine the association between past/current sleep duration and macro-/micro-structural brain outcomes and explore whether hypertension or social activity plays a role in such association. METHODS: Within the UK Biobank, 40 436 dementia-free participants (age 40-70 years) underwent a baseline assessment followed by a brain magnetic resonance imaging (MRI) scan 9 years later. Past (baseline) and current (MRI scans) sleep duration (hours/day) were recorded and classified as short (≤5), intermediate (6-8), and long (≥9). Brain structural volumes and diffusion markers were assessed by MRI scans. RESULTS: Compared with past intermediate sleep, past short sleep was related to smaller cortex volumes (standardized ß [95 % CI]: -0.04 [-0.07, -0.02]) and lower regional fractional anisotropy (FA) (-0.08 [-0.13, -0.03]), while past long sleep was related to smaller regional subcortical volumes (standardized ß: -0.04 to -0.07 for thalamus, accumbens, and hippocampus). Compared to current intermediate sleep, current short sleep was associated with smaller cortex volumes (-0.03 [-0.05, -0.01]), greater white matter hyperintensities (WMH) volumes (0.04 [0.01, 0.08]), and lower regional FA (-0.07 [-0.11, -0.02]). However, current long sleep was related to smaller total brain (-0.03 [-0.05, -0.02]), grey matter (-0.05 [-0.07, -0.03]), cortex (-0.05 [-0.07, -0.03]), regional subcortical volumes [standardized ß: -0.05 to -0.09 for putamen, thalamus, hippocampus, and accumbens]), greater WMH volumes (0.06 [0.03, 0.09]), as well as lower regional FA (-0.05 [-0.09, -0.02]). The association between current long sleep duration and poor brain health was stronger among people with hypertension or low frequency of social activity (all Pinteraction <0.05). CONCLUSIONS: Both past and current short/long sleep are associated with smaller brain volume and poorer white matter health in the brain, especially in individuals with hypertension and low frequency of social activity. Our findings highlight the need to maintain 6-8 h' sleep duration for healthy brain aging.

Dynamic Mechanism of Cerebral Venous Disruption: Longitudinal Evidence From a Community-Based Cohort.

BACKGROUND: This study aims to investigate the temporal and spatial patterns of structural brain injury related to deep medullary veins (DMVs) damage. METHODS AND RESULTS: This is a longitudinal analysis of the population-based Shunyi cohort study. Baseline DMVs numbers were identified on susceptibility-weighted imaging. We assessed vertex-wise cortex maps and diffusion maps at both baseline and follow-up using FSL software and the longitudinal FreeSurfer analysis suite. We performed statistical analysis of global measurements and voxel/vertex-wise analysis to explore the relationship between DMVs number and brain structural measurements. A total of 977 participants were included in the baseline, of whom 544 completed the follow-up magnetic resonance imaging (age 54.97±7.83 years, 32% men, mean interval 5.56±0.47 years). A lower number of DMVs was associated with a faster disruption of white matter microstructural integrity, presented by increased mean diffusivity and radial diffusion (ß=0.0001 and SE=0.0001 for both, P=0.04 and 0.03, respectively), in extensive deep white matter (threshold-free cluster enhancement P<0.05, adjusted for age and sex). Of particular interest, we found a bidirectional trend association between DMVs number and change in brain volumes. Specifically, participants with mild DMVs disruption showed greater cortical enlargement, whereas those with severe disruption exhibited more significant brain atrophy, primarily involving clusters in the frontal and parietal lobes (multiple comparison corrected P<0.05, adjusted for age, sex, and total intracranial volume). CONCLUSIONS: Our findings posed the dynamic pattern of brain parenchymal lesions related to DMVs injury, shedding light on the interactions and chronological roles of various pathological mechanisms.

Prefrontal-Limbic Circuitry Is Associated With Reward Sensitivity in Nonhuman Primates.

BACKGROUND: Abnormal reward sensitivity is a risk factor for psychiatric disorders, including eating disorders such as overeating and binge-eating disorder, but the brain structural mechanisms that underlie it are not completely understood. Here, we sought to investigate the relationship between multimodal whole-brain structural features and reward sensitivity in nonhuman primates. METHODS: Reward sensitivity was evaluated through behavioral economic analysis in which monkeys (adult rhesus macaques; 7 female, 5 male) responded for sweetened condensed milk (10%, 30%, 56%), Gatorade, or water using an operant procedure in which the response requirement increased incrementally across sessions (i.e., fixed ratio 1, 3, 10). Animals were divided into high (n = 6) or low (n = 6) reward sensitivity groups based on essential value for 30% milk. Multimodal magnetic resonance imaging was used to measure gray matter volume and white matter microstructure. Brain structural features were compared between groups, and their correlations with reward sensitivity for various stimuli was investigated. RESULTS: Animals in the high sensitivity group had greater dorsolateral prefrontal cortex, centromedial amygdaloid complex, and middle cingulate cortex volumes than animals in the low sensitivity group. Furthermore, compared with monkeys in the low sensitivity group, high sensitivity monkeys had lower fractional anisotropy in the left dorsal cingulate bundle connecting the centromedial amygdaloid complex and middle cingulate cortex to the dorsolateral prefrontal cortex, and in the left superior longitudinal fasciculus 1 connecting the middle cingulate cortex to the dorsolateral prefrontal cortex. CONCLUSIONS: These results suggest that neuroanatomical variation in prefrontal-limbic circuitry is associated with reward sensitivity. These brain structural features may serve as predictive biomarkers for vulnerability to food-based and other reward-related disorders.

Knowledge atlas of white matter microstructure: a bibliometric analysis.

Background: White matter microstructure is valued for being an indicator of neural network integrity, which plays an indispensable role in the execution of advanced brain functions. Although the number of publications has increased in the past 10 years, no comprehensive analysis has yet been conducted of this field. Therefore, this study aimed to identify the research hotspots and trends in research on white matter microstructure using a bibliometric analysis of the related literature published from 2013 to 2022. Methods: VOSviewer and CiteSpace were used to objectively analyze the research articles concerning white matter microstructure, which were retrieved from the Web of Science Core Collection (WoSCC). Results: A total of 5,806 publications were obtained, with the number of published articles increasing annually over the past decade. The United States, China, the United Kingdom, and Canada maintained the top positions worldwide and had strong cooperative relationships. The top institution and journal were Harvard Medical School and Neuroimage, respectively. Alexander Leemans, Marek Kubicki, and Martha E Shenton were the most productive authors. Thematic keywords mainly included "diffusion tensor imaging" (DTI), "white matter integrity", and "connectivity". The keyword analysis revealed that DTI has a critical role in detecting white matter microstructure integrity and that fractional anisotropy is the main parameter in the assessment process. Keyword burst detection identified four research hotspots: movement, distortion correction, voxelwise analysis, and fixel-based analysis. Conclusions: This bibliometric analysis provided a systematic understanding of the research on white matter microstructure and identified the current frontiers. This may help clinicians and researchers comprehensively identify hotspots and trends in this field.

The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood.

Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.

A normative modeling approach to quantify white matter changes and predict functional outcomes in stroke patients.

Objectives: The diverse nature of stroke necessitates individualized assessment, presenting challenges to case-control neuroimaging studies. The normative model, measuring deviations from a normal distribution, provides a solution. We aim to evaluate stroke-induced white matter microstructural abnormalities at group and individual levels and identify potential prognostic biomarkers. Methods: Forty-six basal ganglia stroke patients and 46 healthy controls were recruited. Diffusion-weighted imaging and clinical assessment were performed within 7 days after stroke. We used automated fiber quantification to characterize intergroup alterations of segmental diffusion properties along 20 fiber tracts. Then each patient was compared to normative reference (46 healthy participants) by Mahalanobis distance tractometry for 7 significant fiber tracts. Mahalanobis distance-based deviation loads (MaDDLs) and fused MaDDLmulti were extracted to quantify individual deviations. We also conducted correlation and logistic regression analyses to explore relationships between MaDDL metrics and functional outcomes. Results: Disrupted microstructural integrity was observed across the left corticospinal tract, bilateral inferior fronto-occipital fasciculus, left inferior longitudinal fasciculus, bilateral thalamic radiation, and right uncinate fasciculus. The correlation coefficients between MaDDL metrics and initial functional impairment ranged from 0.364 to 0.618 (p < 0.05), with the highest being MaDDLmulti. Furthermore, MaDDLmulti demonstrated a significant enhancement in predictive efficacy compared to MaDDL (integrated discrimination improvement [IDI] = 9.62%, p = 0.005) and FA (IDI = 34.04%, p < 0.001) of the left corticospinal tract. Conclusion: MaDDLmulti allows for assessing behavioral disorders and predicting prognosis, offering significant implications for personalized clinical decision-making and stroke recovery. Importantly, our method demonstrates prospects for widespread application in heterogeneous neurological diseases.

Interactions between overweight/obesity and alcohol dependence impact human brain white matter microstructure: evidence from DTI.

There is inconsistent evidence for an association of obesity with white matter microstructural alterations. Such inconsistent findings may be related to the cumulative effects of obesity and alcohol dependence. This study aimed to investigate the possible interactions between alcohol dependence and overweight/obesity on white matter microstructure in the human brain. A total of 60 inpatients with alcohol dependence during early abstinence (44 normal weight and 16 overweight/obese) and 65 controls (42 normal weight and 23 overweight/obese) were included. The diffusion tensor imaging (DTI) measures [fractional anisotropy (FA) and radial diffusivity (RD)] of the white matter microstructure were compared between groups. We observed significant interactive effects between alcohol dependence and overweight/obesity on DTI measures in several tracts. The DTI measures were not significantly different between the overweight/obese and normal-weight groups (although widespread trends of increased FA and decreased RD were observed) among controls. However, among the alcohol-dependent patients, the overweight/obese group had widespread reductions in FA and widespread increases in RD, most of which significantly differed from the normal-weight group; among those with overweight/obesity, the alcohol-dependent group had widespread reductions in FA and widespread increases in RD, most of which were significantly different from the control group. This study found significant interactive effects between overweight/obesity and alcohol dependence on white matter microstructure, indicating that these two controllable factors may synergistically impact white matter microstructure and disrupt structural connectivity in the human brain.

The potential of blood neurofilament light as a marker of neurodegeneration for Alzheimer's disease.

Over the past several years, there has been a surge in blood biomarker studies examining the value of plasma or serum neurofilament light (NfL) as a biomarker of neurodegeneration for Alzheimer's disease. However, there have been limited efforts to combine existing findings to assess the utility of blood NfL as a biomarker of neurodegeneration for Alzheimer's disease. In addition, we still need better insight into the specific aspects of neurodegeneration that are reflected by the elevated plasma or serum concentration of NfL. In this review, we survey the literature on the cross-sectional and longitudinal relationships between blood-based NfL levels and other, neuroimaging-based, indices of neurodegeneration in individuals on the Alzheimer's continuum. Then, based on the biomarker classification established by the FDA-NIH Biomarker Working group, we determine the utility of blood-based NfL as a marker for monitoring the disease status (i.e. monitoring biomarker) and predicting the severity of neurodegeneration in older adults with and without cognitive decline (i.e. a prognostic or a risk/susceptibility biomarker). The current findings suggest that blood NfL exhibits great promise as a monitoring biomarker because an increased NfL level in plasma or serum appears to reflect the current severity of atrophy, hypometabolism and the decline of white matter integrity, particularly in the brain regions typically affected by Alzheimer's disease. Longitudinal evidence indicates that blood NfL can be useful not only as a prognostic biomarker for predicting the progression of neurodegeneration in patients with Alzheimer's disease but also as a susceptibility/risk biomarker predicting the likelihood of abnormal alterations in brain structure and function in cognitively unimpaired individuals with a higher risk of developing Alzheimer's disease (e.g. those with a higher amyloid-ß). There are still limitations to current research, as discussed in this review. Nevertheless, the extant literature strongly suggests that blood NfL can serve as a valuable prognostic and susceptibility biomarker for Alzheimer's disease-related neurodegeneration in clinical settings, as well as in research settings.

ECHDC3 Variant Regulates the Right Hippocampal Microstructural Integrity and Verbal Memory in Type 2 Diabetes Mellitus.

ECHDC3 is a risk gene for white matter (WM) hyperintensity and is associated with insulin resistance. This study aimed to investigate whether ECHDC3 variants selectively regulate brain WM microstructures and episodic memory in patients with type 2 diabetes mellitus (T2DM). We enrolled 106 patients with T2DM and 111 healthy controls. A voxel-wise general linear model was employed to explore the interaction effect between ECHDC3 rs11257311 polymorphism and T2DM diagnosis on fractional anisotropy (FA). A linear modulated mediation analysis was conducted to examine the potential of FA value to mediate the influence of T2DM on episodic memory in an ECHDC3-dependent manner. We observed a noteworthy interaction between genotype and diagnosis on FA in the right inferior temporal WM, right anterior limb of the internal capsule, right frontal WM, and the right hippocampus. Modulated mediation analysis revealed a significant ECHDC3 modulation on the T2DM â†’ right hippocampal FA â†’ short-term memory pathway, with only rs11257311 G risk homozygote demonstrating significant mediation effect. Together, our findings provide evidence of ECHDC3 modulating the effect of T2DM on right hippocampal microstructural impairment and short-term memory decline, which might be a neuro-mechanism for T2DM related episodic memory impairment.