Sodium (23Na) MRI provides unique information about ionic homeostasis in the brain. However, in vivo quantification of regional brain sodium is highly challenging due to low SNR and limited spatial resolution. Here, we use our novel anatomically guided reconstruction (AGR) method to overcome these challenges and enable precise quantification of regional brain total sodium concentration (TSC). Thirty-four healthy subjects were studied by using a 3T clinical MRI scanner with a dual-tuned (1H-23Na) birdcage coil. 23Na images were acquired by using a twisted projection imaging sequence (TR = 100 ms, TE1/TE2 = 0.5/5 ms), while proton (1H) images were obtained with a standard T1-weighted MPRAGE sequence. AGR was performed with regularization parameters βr = 0.67, 2.0, and 6.0. As a baseline comparison, standard reconstruction (SR) was also performed by using a regridding algorithm with compensation for nonuniform sampling. To assess partial volume effects (PVEs) on the reconstruction methods, an erosion experiment was conducted. Internal linear calibration using noise-only background and vitreous humor regions was applied to calculate TSC in ROIs including lobar cortical GM, subcortical (including hippocampus, caudate, pallidum, putamen and thalamus), callosal, and whole-brain WM. Bonferroni-corrected pair-wise comparison was performed by using Multivariate Analysis of Variance at a significance level P < .05. The WM erosion experiments confirmed that TSCAGR was stabilized beyond 1-voxel erosion in the WM, but TSCSR was decreasing with erosion increasing, showing a reduced PVE in the AGR images. AGR also shows greater separation in TSC between GM and WM compared with SR (GM TSCSR = 49.2 ± 4.6 mmol/L, WM TSCSR = 38.1 ± 3.0 mmol/L; GM TSCAGR = 48.6 ± 4.9 mmol/L, WM TSCAGR = 30.5 ± 2.8 mmol/L). We also found smaller variance of TSCAGR in WM and GMsubcortical compared with TSCSR. The AGR helps sodium quantification in healthy human brains by reducing the PVE and variance of TSC in noncortical brain regions. Our normative values of TSC in the brain regions set the stage to better understand derangements of 23Na metabolism and homeostasis in neurologic disease.

Coordination of N1 and N2-methylated regioisomers of 2-(1H-tetrazol-5-yl)pyridine and 2-(1H-tetrazol-5-yl)quinoline to ReBr(CO)3 and Ir(ppy)2+ (ppy = cyclometalated 2-phenylpyridine) fragments resulted in the isolation of a small family of Re(I) and Ir(III) luminescent complexes. The complexes display phosphorescent emission from their triplet ligand-to-metal charge transfer excited states in degassed solution at room temperature. Notably, the position of the methyl substituent has a profound effect on the photophysical properties. The N1-methylated complexes in all cases display a significant redshift in the emission band. The shift is ascribed to a stabilisation of the π* orbitals of the tetrazole ligands, which is also supported by cyclic voltammetry and time-dependent density functional theory (TD-DFT) calculations. The complexes were used as luminescent labels for the staining of mouse brain tissue. The Re(I) complexes did not show any evident staining. On the other hand, the Ir(III) complexes - particularly those bound to the ligand containing the quinoline substituent - demonstrated affinity for lipid-rich myelinated regions in brain tissues and white matter in cerebellum tissues. The specificity of the Ir(III) complexes was further demonstrated by means of correlative optical microscopy and Fourier transform infrared (FTIR) microscopy.

The unique human ability to process complex language requires the interaction of multiple brain areas located in the inferior frontal and posterior temporal cortex connected by white matter fiber tracts. These fiber tracts underlie the transfer of information between brain regions. In recent years, MRI of white matter brain networks has provided important insights into the plasticity of the structural brain basis underlying language. This structural network is shaped during childhood as a function of language learning by strengthening the connectivity between language-relevant regions mainly in the left hemisphere. In this way, the specific linguistic properties of the native language tongue lead to a modulation of the core language network observable in the adult brain. The brain basis underlying language processing also changes when a second language is learned, as shown by differences in brain connectivity between bilingual and monolingual individuals and by dynamic adaptations during second language learning. Studies of people who use sign language as their native language have shown a domain specificity of the sensory and motor systems rather than the core language network. This separation of the core language system from the sensory-motor system is evolutionarily based. Although the basic auditory-motor interface system is also present in monkeys and apes, the core language system reveals key differences between humans and nonhuman primates. Understanding the function and plasticity of this network is of paramount importance for human cognitive processes, including development and developmental disorders.

PurposeThe aim of this study is to evaluate the changes of white matter microstructure in breast cancer patients before and after chemotherapy based on automated fiber quantification (AFQ),as well as determine if these dispersion indexes are significantly correlated with clinical data.Materials and MethodsTwenty-four breast cancer patients scheduled for chemotherapy were enrolled. Diffusion tensor imaging (DTI), neuropsychological tests and self-report measures, and hematological tests were conducted before chemotherapy(time 0,T0) and within one week after chemotherapy(time 1,T1). AFQ was used to track 20 fiber tracts in the brain. The correlation between average abnormal tracts and changes in neuropsychological tests and self-report measures and blood indicators was analyzed.ResultsCompared to T0, subjects at T1 showed decreased scores on the verbal fluency test; increased scores on the self-rating anxiety scale(SAS)and self-rating depression scale (SDS). Estrogen concentration was lower while luteinizing hormone(LH), follicle-stimulating hormone, and triglyceride levels were higher. Mean fractional anisotropy (FA) value decreased in the right cingulum cingulate(CGC)while mean radial diffusivity (RD) increased in the right CGC; mean axial diffusivity (AD) value decreased in callosum forceps major and callosum forceps minor. Changes in FA with in the right CGC were positively correlated with changes in SDS and LH, while changes in RD with in the right CGC were negatively correlated with changes in SDS and LH.ConclusionEarly changes observed in brain white matter fiber tracts, along with persistent hormone and triglyceride metabolism disorders, could potentially serve as neurobiological markers for monitoring chemotherapy-induced cognitive impairment.

Schizophrenia is highly heritable, and polygenic risk score for schizophrenia (PRSSCZ) has been associated with brain and behavior in healthy populations. However, the full associations of PRSSCZ with brain white matter microstructure and cognitive and mental health outcomes, the potential effects of sex and areal deprivation on these associations, and the mediation of white matter microstructure for the associations between PRSSCZ and behavioral outcomes remain largely unknown. In up to 300,000 participants from the UK Biobank, we investigated the associations of PRSSCZ with eight white matter microstructure metrics of 48 tracts and 14 cognitive and mental health phenotypes, and we further tested the moderation of sex and index of multiple deprivation (IMD) on these associations and the mediation of brain white matter phenotypes for the associations between PRSSCZ and behavioral outcomes. We found that higher PRSSCZ was associated with decreased white matter integrity in 26 tracts, such as cingulum, corona radiata, and fornix. We also found that higher PRSSCZ was associated with poorer mental health and worse cognitive performance. These associations were not significantly moderated by sex and IMD. Causal mediation analyses revealed that these adverse effects of PRSSCZ on cognitive and mental health outcomes were partially mediated by brain white matter phenotypes. These results indicate that genetic risk for schizophrenia affects the integrity of white matter tracts, which may account for its adverse effects on cognitive and mental health outcomes.

Background/Objectives: Identifying pathological distinctions among mild cognitive impairment (MCI) subtypes is important for differentiating dementia. The purpose of this study is to investigate subtype-specific structural alterations in amnestic MCI (aMCI) and non-amnestic MCI (naMCI) and evaluate their potential as imaging biomarkers for subtype classification. Methods: T1 and DTI MRI data from two independent cohorts were analyzed, including a discovery dataset (58 aMCI, 35 naMCI, and 95 NC) and a replication dataset (61 aMCI, 39 naMCI, and 67 NC). Surface-based morphometry and automated fiber quantification (AFQ) were used to examine cortical thickness and white matter microstructure. Mediation models were used to explore the links between brain structure and cognitive outcomes. A logistic regression model was applied to evaluate classification performance. Results: The aMCI exhibited right hippocampal atrophy. In the naMCI, reduced cortical thickness was observed in the right anterior cingulate cortex (rACC) and opercular inferior frontal gyrus, along with increased fractional anisotropy (FA) in the right inferior fronto-occipital fasciculus (IFOF). These alterations were linked to domain-specific cognitive deficits. Moreover, partial mediation effects of IFOF FA values were observed in the link between rACC thickness and cognitive outcomes. Furthermore, these structural alterations effectively distinguished between aMCI and naMCI, showing stable performance across independent datasets (Accuracy = 0.821, AUC = 0.904). Conclusions: Our findings reveal distinct structural alterations across MCI subtypes, providing deeper insight into the heterogeneous mechanisms of dementia and supporting the potential of imaging markers for the diagnosis of MCI subtypes.

Diffusion magnetic resonance imaging has emerged as an opportunity to explore brain white matter fiber tracts (WMFTs) through 3D digital reconstruction. This method could be useful in investigating the relationship between positional plagiocephaly and developmental problems; however, this has not been fully explored. Evaluate WMFTs of healthy infants in two age groups with a range of positional plagiocephaly from normal to severe. This exploratory study, conducted at a free-standing, quaternary pediatric hospital in the Northeastern United States, utilized an existing database of healthy infants' MRIs obtained between 1 month and 4 months of age. MRIs were included if deemed good quality and had complete T1- and diffusion-weighted sequences and excluded if there were measurement disagreements or MRI data processing problems. Positional plagiocephaly severity was calculated using the Cranial Vault Asymmetry Index (CVAI). A repeated-measures regression model was constructed to assess the association of positional plagiocephaly severity with WMFTs fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Median age of 18 infants was 64.5 (IQR 71) days at the time of MRI. FA had a negative association with CVAI overall (β±SE=-0.53±0.51% per unit CVAI, P=0.32) and in both age groups. MD and RD had a positive association with CVAI overall (β±SE=1.31±0.46% per unit CVAI, P=0.013; β±SE=1.54±0.54% per unit CVAI, P=0.012) and in both age groups and all pathways. As the severity of positional plagiocephaly increases, differences in WMFT formation are observed, suggesting the need for longitudinal studies with cognitive and behavioral assessments.

BackgroundSubjective cognitive decline can be an early indicator of underlying brain pathology. We investigated associations of subjective cognitive decline with imaging biomarkers of neurodegeneration, cerebrovascular injury, and amyloid burden.MethodHarmonized sample of ethno‐racially diverse study participants from Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE), LifeAfter90 (LA90), and the Study of Healthy Aging in African Americans (STAR) aged 50‐101. Subjective cognitive decline was measured via the 12‐item Everyday Cognition (ECog) scale (range=1‐4), where higher scores suggest more subjective cognitive decline. Regional brain volumes and white matter integrity were measured using 3T MRI. Amyloid was measured using florbetapir PET among a random subset of KHANDLE and LA90. Measures of cerebrum and gray matter volume (cerebrum gray, hippocampus, frontal, occipital, parietal, temporal), cerebrospinal fluid volume (lateral, third), and log‐white matter hyperintensities were normalized with intracranial volume. We also examined free water fraction and fractional anisotropy. All MRI measures were z‐standardized. Amyloid burden was quantified using standard uptake value ratios (SUVR) and amyloid positivity was defined as SUVR ≥1.1. Linear regression and logistic models estimated associations of subjective cognitive decline with global and regional brain volumes and white matter injury, SUVR, and amyloid‐positivity. All models adjusted for sex, race/ethnicity, age, education, and cohort.ResultParticipants’ (N = 822) mean±SD age was 77.8±10.2, 60% were women, 50% had at most a high school education and 18% identified as Asian, 43% as Black, 16% as Hispanic/Latino (Table 1). Overall, the mean ECog was 1.36±0.36, mean amyloid SUVR was 1.09±0.17, and 33.7% were amyloid‐positive). Higher (worse) ECog was associated with smaller volumes in cerebrum (β=‐0.18,95%CI=‐0.31,‐0.05), cerebrum gray (β=‐0.30,95%CI=‐0.47,‐0.13), hippocampus (β=‐0.35,95%CI=‐0.51,‐0.18), and temporal cortex (β=‐0.21,95%CI=‐0.39,‐0.03); larger volumes in lateral (β=0.27,95%=CI 0.11,0.43) and third ventricles (β=0.17,95%CI 0.01,0.33); greater log‐white matter hyperintensities (β=0.29,95%CI=0.12,0.45) and free water (β=0.17,95%CI=0.004,0.33); and lower fractional anisotropy (β=‐0.15,95%CI ‐0.22,‐0.08) (Table 2). Greater ECog was also associated with higher amyloid‐SUVR (β=0.05,95%CI 0.01,0.09) and higher odds of amyloid‐positivity (OR=1.71,95%CI 1.05,2.80) (Table 2).ConclusionIn this diverse cohort of older adults, greater subjective cognitive decline was associated with worse late‐life brain health. These findings highlight the importance of addressing subjective cognitive decline as potential indicators of neurodegeneration, cerebrovascular injury, and amyloid burden.

Abstract This review synthesizes current evidence on the relationship between structural brain changes and driving safety in cognitively healthy older adults. Using magnetic resonance imaging (MRI) structural biomarkers—such as white matter hyperintensities (WMH), brain atrophy (BA), and regional gray matter volumes (rGMVs)—recent studies have demonstrated that even mild or asymptomatic alterations are associated with impaired driving behavior and increased crash risk. Emerging machine learning models that incorporate rGMVs have achieved high accuracy and specificity in identifying high-risk drivers, though sensitivity and precision remain limited when relying solely on structural MRI data. Japan’s Brain Dock system, an MRI-based brain healthcare screening program developed uniquely in Japan, provides a valuable infrastructure for large-scale neuroimaging-based risk assessment. Importantly, structural MRI biomarkers can be influenced by lifestyle improvements, such as quitting smoking and reducing alcohol consumption, as well as by the treatment of lifestyle-related diseases, including diabetes and hypertension. Overall, this review highlights the potential of neuroimaging-informed approaches to identify at-risk drivers and to support targeted preventive strategies, thereby contributing to both brain healthcare program and traffic safety in aging societies.

This study presents a novel approach to improve fiber orientation estimation in diffusion tensor imaging (DTI), a widely used MRI technique for mapping brain white matter fibers (WMFs) by analyzing water diffusion patterns. Traditional DTI estimates the diffusion tensor matrix (DT-matrix) via linear regression, which effectively detects a single fiber per voxel but fails in regions with crossing fibers. To address this limitation, multicompartment mixture models have been introduced, typically assuming fixed eigenvalues m /ms based on normative WMF data. However, such fixed assumptions may lead to inaccuracies in regions with complex microstructures. In contrast, the proposed method dynamically computes the eigenvalues of the DT-matrix for each voxel, allowing for a voxel-specific characterization of diffusion properties. This adaptability accounts for spatial variability in fiber geometry, improving the accuracy of fiber orientation detection. Simulations and experiments on human and rat brain datasets demonstrate that the method achieves improved white matter reconstruction and reduced angular error compared with traditional DTI and fixed-eigenvalue models. By tailoring diffusion modeling to each voxel, this approach enhances neuroimaging analysis, refines tissue microstructure characterization, and advances the precision of diffusion MRI.

Early-onset Total Cholesterol Abnormality is Associated with Gray Matter Atrophy and Decreased Microstructural Integrity.

BackgroundLow-density lipoprotein cholesterol (LDL-C) has been associated with Alzheimer's disease (AD) pathology and other neuroimaging measures, such as brain volume and white matter hyperintensity (WMH) volume.ObjectiveIn this exploratory study, we examined cross-sectional associations between LDL cargo proteins and AD-related outcomes.MethodsWe randomly selected 65 participants without dementia with amyloid PET and brain MRI data available in the Atherosclerosis Risk in Communities. We used a mass spectrometry-based technique to quantify proteins in LDL isolated from plasma collected at the ARIC Visit 5. Linear or logistic regression was applied to evaluate the associations between individual LDL protein or LDL-C and (1) brain amyloid deposition (yes or no), (2) temporal-parietal meta-ROI brain volume (a biomarker of AD-related neurodegeneration), or (3) WMH volume, adjusting for covariates.ResultsParticipants' average age was 76.3 years with a standard deviation of 5.4, and females comprised 53.8% (35 out of 65). The estimated effect sizes of many LDL protein's associations with these neuroimaging measures were larger than that of LDL-C. The strongest association was higher LDL apolipoprotein C1 (apoC1) and lower WMH volume. Each SD increment of LDL apoC1 LDL was associated with a lower WMH volume of 7243.1 mm3 (95% CI [-10482.0, -4004.1]; a BH-adjusted p = 0.002). In comparison, each SD increment of LDL-C (in mg/dL) was associated with a lower WMH volume of 1676.1 mm3 (95% CI [-5425.9, 2073.7]; a BH-adjusted p of 0.90).ConclusionsThis study suggests that increased LDL apoC1 was linked to decreased WMH volume in older adults without dementia.

Maternal diabetes may affect neonates' long-term neurodevelopment and cognitive behavior. Brain biochemistry and white matter fiber tracts may reveal early changes of brain abnormality. The purpose of this study was to compare brain metabolites and fiber structures in infants of mothers with diabetes with those of mothers without diabetes during the early stage of neonatal neurodevelopment. Pregnant women with diabetes mellitus (DM) and healthy controls were recruited prospectively. Baseline characteristics including maternal and gestational age, weight, height, body mass index (BMI), and types of diabetes were extracted from the mothers and neonates in the clinical record. Infants of mothers with diabetes and healthy controls underwent scans by using a 3T MRI scanner. T2-weighted anatomic images were acquired for voxel positioning. 1H-MR spectroscopy from the right frontal lobe and whole-brain DTI data were acquired from the participants. Independent sample t-tests were used to compare anthropometric data and a general linear model was used to compare brain metabolites and diffusion measures in the neonates. MRI data were successfully obtained from 77 neonates (41 healthy controls and 36 infants of mothers with diabetes). Gestational diabetes was the most common diagnosis (53%), followed by type 2 (33%) and type 1 pregestational DM (14%). Weight (P = .02) and BMI (P < .01) were significantly higher in mothers with diabetes with earlier birth gestational age. Myo-inositol (mI) measurements were significantly higher (P = .02) in infants of mothers with diabetes versus controls but this was not observed in the excitatory neurotransmitter of γ-aminobutyric acid (GABA) and other high-concentration metabolites. Mean diffusivity values were significantly higher in the anterior part (P = .03) and posterior gray matter (P < .05) of the cingulate gyrus for infants of mothers with diabetes compared with controls. Axial diffusivity values were significantly higher (P = .03) in the anterior part right gray matter of the cingulate gyrus for infants of mothers with diabetes compared with controls. The increase of myo-inositol in infants of mothers with diabetes may reflect a compensatory mechanism related to altered blood glucose to preserve and promote β-cell growth to enhance the production of insulin. The increase in mean diffusivity is consistent with decreased water diffusion, which potentially reflects abnormal changes in myelination and axonal loss.

ABSTRACTStructural deficits in white matter fibre have been linked to psychosis. However, it remains unclear whether these aberrations are present in individuals that experience non‐clinical psychotic‐like experiences, predating illness onset. While previous research demonstrates that alterations in white matter in schizotypy are consistent with those in clinical psychosis, these studies often dichotomise healthy samples into high and low schizotypy, which may reduce statistical sensitivity. Previous research is also confounded by the investigation of diffusion MRI parameters that fail to account for complex crossing fibre populations. In this work, we treat psychotic‐like experiences as a continuous variable, and applied Fixel‐Based Analysis (FBA), a framework for investigating microstructural and morphological effects in brain white matter using diffusion‐weighted imaging data. Across two independent cohorts of healthy participants with varied psychotic‐like experiences including data from the IMAGEN consortium (Study 1 n = 41; Study 2 n = 1098), we hypothesized that greater psychotic‐like experiences would be associated with FBA metrics sensitive to microstructural fibre density and/or cross‐sectional morphological effects. Contrary to our hypothesis, we did not find significant correlations between psychotic‐like experiences and FBA metrics across either dataset (FWE p < 0.05). Bayesian analysis of tract‐aggregated data showed substantial evidence of no association (Bayes factor < 1/3) between psychotic‐like experiences and fibre density, nor cross‐sectional morphology, across several white matter tracts of interest, pre‐defined from prior neuroimaging literature. These findings suggest that the relationship between non‐clinical psychotic‐like experiences and white matter microstructure may not be as robust as previously thought. This raises the possibility that white matter alterations across the psychosis spectrum echo clinical diagnostic thresholding, with observable effects in clinical but not sub‐clinical presentations. Our findings show no association between whole‐brain fibre‐specific properties of white matter microstructure and sub‐clinical psychotic‐like experiences. Further, we show evidence for the lack of an association within tract‐aggregated fibre‐specific metrics. Future research should integrate longitudinal designs to explore whether fibre‐specific white matter attributes provide clinically meaningful insight into the risk of psychosis onset.

This study investigated the therapeutic potential and mechanisms of Acer truncatum Bunge seed oil (ATSO) against cuprizone-induced white matter injury and cognitive decline. A comprehensive analysis integrating techniques from animal behavior, histopathology, immunology and network pharmacology was performed. The results showed that ATSO ameliorated the cuprizone-induced cognitive impairment, corpus callosum demyelination, synaptic loss, and inflammatory activation of microglia and astrocytes. Mechanistically, ATSO reversed the cuprizone-induced demyelination by enhancing sphingomyelin metabolism and activating the TREM2-APOE signaling pathway. The network pharmacology study revealed that the cognitive protection, sphingomyelin metabolism regulation, and anti-inflammatory effects of ATSO were mediated by its unsaturated fatty acid components. Validated by experimental data, the core targets of ATSO were found to be centered on the TREM2 network. In conclusion, ATSO ameliorates cuprizone-induced cognitive impairment and brain white matter lesions by improving sphingomyelin metabolism and alleviating neuroinflammation through activation of the TREM2 signaling pathway.

Leukodystrophies are rare genetic disorders that affect the brain's white matter, making diagnosis and management difficult. One type, leukoencephalopathy with vanishing white matter, shows a variety of neurological symptoms with different levels of severity. Advances in diagnostics, like identifying genetic mutations in the EIF2B gene family, help uncover the genetic mechanisms behind the condition. In this case, a 5-year-old Iraqi boy with prenatal symptoms, including seizures, intrauterine growth retardation, decreased movements, and oligohydramnios, was examined through exome-sequencing and magnetic resonance imaging. A novel mutation (hg38: chr1-44941633-T-C; c.327A&gt;G; p.Ile109Met) in exon 4 of the EIF2B3 gene was identified, causing a substitution of Isoleucine with Methionine. In silico analysis predicted the potential effects of this mutation. This case highlights the significance of finding a novel mutation, contributing to the understanding of vanishing white matter genetics and showing how advanced sequencing techniques can improve diagnostics and personalised treatments for genetic disorders.

BackgroundSporadic cerebral small vessel disease (SVD) has a heterogeneous underlying pathology, and current SVD magnetic resonance imaging (MRI) markers do not accurately capture this heterogeneity. Novel ultrahigh-field (7T) brain MRI markers provide a window of opportunity to study early changes and potential determinants of SVD. White matter hyperintensity (WMH) shape is a relatively novel MRI marker of SVD and has shown prognostic potential. However, the exact microstructural changes within or surrounding WMHs or potential causes related to WMH shape variations are unknown. Furthermore, impaired brain clearance via the recently discovered brain clearance system may be another early change or potential cause of SVD.ObjectiveIn the White Matter Hyperintensity Shape and Brain Clearance (WHIMAS) study, we aim to assess the link between WMHs—their shape in particular—and brain clearance and other MRI markers on ultrahigh-field (7T) brain MRI and show whether these markers are associated with cognitive functioning in older adults with memory complaints.MethodsThis is a cross-sectional study conducted at the Leiden University Medical Center. A total of 50 outpatients from the memory or geriatric clinic aged ≥65 years will be recruited for a 3T and 7T MRI scan (including clinical structural scans, eg, 3D T1-weighted, 3D fluid-attenuated inversion recovery), and experimental scans such as cerebrospinal fluid (CSF)–selective T2-prepared readout with acceleration and mobility encoding (CSF-STREAM) and the relationship between blood oxygen level–dependent [BOLD] signals and CSF flow) and magnetic resonance fingerprinting, as well as a standardized neuropsychological test battery (domains: memory, executive function, visuoconstruction, and processing speed). We will assess WMH shape markers (solidity, convexity, concavity index, fractal dimension, and eccentricity) and brain clearance markers (CSF mobility and the relationship between blood oxygen level–dependent signals and CSF flow) and study their relationship to other MRI markers and cognitive functioning using multivariable regression analyses.ResultsPatient inclusion started in January 2023, and study enrollment of patients is expected to finish in the second quarter of 2027, whereas the main results are expected to be published in the first quarter of 2028.ConclusionsWe aim to understand variations in WMH shape and find their relationship to cerebral SVD and markers of brain clearance and cognitive functioning. WMH shape and brain clearance markers early in the disease process of SVD are extremely important as they may represent a basis for future patient selection for lifestyle interventions or for treatment trials aimed at the prevention of dementia.Trial RegistrationClinicalTrials.gov NCT06010511; https://clinicaltrials.gov/study/NCT06010511International Registered Report Identifier (IRRID)DERR1-10.2196/77681

The white matter of the brain is a dynamic and active tissue that coordinates brain function through the transmission and modulation of information between regions via action potentials. Although scientific and clinical interest in studying white matter via diffusion imaging has grown rapidly, an electrophysiological understanding of white matter in healthy and disease states remains elusive. And although human white matter recordings are acquired in intracranial EEG studies in epilepsy, clinical evaluation typically focuses on grey matter structures to understand seizure generation and plan surgical intervention despite the role of white matter in coordinating and propagating epileptic activity.Here, we study white matter recordings in 29 patients with drug-resistant epilepsy who underwent stereo EEG for surgical evaluation. We elucidate properties of both electrical activity and network connectivity within white matter and its relationship to cortical connectivity. We also integrate tractography and stereo EEG recordings to demonstrate that our observed white matter dynamics reflect underlying structural connectivity patterns between grey matter structures, emphasizing the role of white matter in information transmission during seizures. Finally, we find that increased white matter connectivity to the presumed seizure onset zone is associated with poor surgical outcome after epilepsy surgery.Our findings support the distributed epileptic network hypothesis, which states that the true seizure onset zone is not a single focal anatomical location per se (in some cases), but rather a distributed epileptic network where many brain regions interact together, allowing a patient to enter a seizure state. Therefore, we propose that white matter electrophysiology and connectivity might represent information on the spatial distribution of epilepsy and thus its amenability to intervention, whether through focal ablation or modulation through devices. Overall, white matter functional recordings might provide a wealth of currently untapped knowledge about the neurobiology of disease and could guide clinical decision-making in treatment of drug-resistant epilepsy patients.

Leukodystrophies are a group of inherited disorders that predominantly and selectively affect the white matter of the central nervous system. Their overlapping clinical and imaging manifestations make a timely and accurate diagnosis challenging. In this study, brain MRI images from 115 patients with confirmed Leukodystrophy representing five major subtypes were analyzed. The imaging pipeline began with comprehensive pre-processing, which included tilt correction, noise reduction, skull stripping, brain segmentation, intensity normalization, and registration. This process ensured consistency throughout the dataset. Subsequently, two main classification strategies were investigated: (1) five traditional machine learning algorithms trained on four sets of handcrafted features extracted from the white matter and whole-brain regions, and (2) deep learning models using pre-trained convolutional neural networks fine-tuned on 3D MRI volumes. The CNN-based methods consistently outperformed traditional approaches, demonstrating a greater ability to learn complex hierarchical and spatial patterns. The InceptionV3 architecture achieved the highest performance on whole-brain images, with an accuracy of 93.41%, precision of 85.49%, recall of 83.95%, specificity of 95.77%, F1-score of 84.48%, and AUC of 89.86%. These findings indicate that machine learning-based approaches provide a reliable automated tool that can support neurologists in the differential diagnosis of Leukodystrophies, facilitating targeted confirmatory genetic testing and guiding patient management strategies.

Background/Objectives: Recurrence of glioblastoma (GBM) mostly occurs in close vicinity to the resection cavity. Therefore, our group has previously designed an implant to locally apply repetitive photodynamic therapy to mitigate tumor recurrence. The penetration depths of different wavelengths in brain tissue were exhaustively studied before. However, the PDT-induced biological effects of 5-ALA-based PDT against GBM cells at different depths have not been evaluated yet. Methods: Therefore, a synthetic brain substitute material of 1–10 mm thickness and with optical properties comparable to the white or gray matter of pig brain was developed. Tumor cell viability was assessed in spheroids from six GBM cell lines using disks of varying thickness prepared from pig brain substitute material to mimic in vivo radiation attenuation. Results: Using an artificial brain tissue optical model based on material science, we have established a relationship between the PDT-induced effect of our PDT implant and the distance of migrating GBM cells from the resection cavity wall. Conclusions: This model may be helpful to aid optimization of the irradiation doses and fractionation required to attain the maximal therapeutic effect by long-term PDT applications.