UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

BACKGROUND: Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions.
METHODS: A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.
RESULTS: In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS 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 WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

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.

BACKGROUND: Self-body satisfaction is considered a psychological factor for exercise dependence (EXD). However, the potential neuropsychological mechanisms underlying this association remain unclear.
OBJECTIVE: To investigate the role of white matter microstructure in the association between body satisfaction and EXD.
METHODS: Prospective.
METHODS: One hundred eight regular exercisers (age 22.11 ± 2.62 years; 58 female).
UNASSIGNED: 3.0 Tesla; diffusion-weighted echo planar imaging with 30 directions.
RESULTS: 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.
METHODS: 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 (β = 1.02) and high (β = 0.43) levels of FA value, while the association was significant only at the high level of QA value (β = 1.26).
CONCLUSIONS: 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.
METHODS: 2 TECHNICAL EFFICACY: Stage 1.

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.

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.

BACKGROUND: This study aims to investigate the temporal and spatial patterns of structural brain injury related to deep medullary veins (DMVs) damage.
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 (β=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.

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 β [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 β: -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 β: -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.

UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.