Resting-state functional magnetic resonance imaging study of voxel-mirrored homotopy connections in patients with schizophrenia.

Surface-based morphometry in lesioned brains after virtual brain grafting: CAT12 versus FreeSurfer in low-grade glioma.

The human brain relies on a complex network of connections to function, with white matter acting as the primary communication highway between different brain regions1,2. Disruptions in these critical communication pathways are linked to several neurological, psychiatric and developmental disorders3,4. Although clinicians have long used standard growth charts to track physical development5, with more recent work translating these to whole-brain and grey matter measurements6-9, there has been no equivalent reference standard for white matter. Establishing a readily available normative reference is an imperative first step if we hope to utilize these white matter structural biomarkers clinically. Here we present lifespan reference charts for human brain white matter. By processing and standardizing 35,120 brain scans from diverse global studies, we mapped the typical growth, maturation and age-related decline of specific brain pathways from birth to 100 years of age. These reference charts establish a fundamental benchmark for healthy brain development and ageing, allowing researchers and clinicians to quantify how an individual's braindeviates from typical patterns and highlighting disorder-related alterations. Furthermore, the accompanying open access charts enable the scientific and clinical communities to evaluate new patient and research data against these normative baselines, facilitating future clinical and neuroscience studies.

Cerebellar Abscess Caused by Rare Bacteria: A Case Report.

Normative models of brain morphometry quantify individual deviations from typical anatomical patterns and hold promise for enhancing clinical decision-making. However, their clinical utility depends critically on demonstrating generalizability across diverse ethnoracial populations. We previously developed sex-specific, race-neutral normative models for cortical thickness, surface area, and subcortical volumes using brain scans from a large international sample of healthy individuals, as part of the CentileBrain Project, a global initiative to provide open-access, neuroimaging reference models. The primary aim of the present study was to empirically evaluate the generalizability and accuracy of these pretrained models across multiple ethnoracial groups. To this end, we tested model performance in independent samples of healthy individuals from Africa, Asia, Europe, and the Americas, with ethnoracial classification defined either by self-identification or genetic ancestry (N = 4,862). We further compared performance against normative models developed exclusively from a single-population Chinese cohort. Across all groups, as well as in the pooled sample, the pretrained CentileBrain models demonstrated consistently high accuracy, with relative mean absolute error values below 10% for subcortical volume and surface area and below 5% for cortical thickness. Model performance was highly concordant across self-identified and ancestry-defined groups. In a separate analysis, the CentileBrain models performed comparably to a population-specific model when applied to an independent ancestry-matched sample. These findings provide empirical support for the generalizability of race-neutral normative models developed on large and diverse samples and underscore their potential utility for individualized neuroimaging assessment across ethnoracially diverse populations.

Quality control (QC) of magnetic resonance imaging (MRI) data before preprocessing is fundamental, because substandard data are known to introduce additional variability in the form of noise to subsequent analyses. This can result in spurious results of a false effect or the obstruction of a true effect. Consequently, there is a need for a reliable and robust method to identify subpar images, given pre-specified exclusion criteria. Here, we describe how to carry out the visual assessment of T1-weighted, T2-weighted, functional and diffusion MRI scans of the human brain with visual reports generated by MRIQC ( https://mriqc.readthedocs.io/en/stable/ ). We provide guidance and instructions for using the MRIQC software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). This includes installing MRIQC, configuring datasets (30-45 min active, plus 10-15 min of compute time per scan) and executing MRIQC (10-15 min compute time per scan). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the 'rating widget', a utility that enables rapid annotation and minimizes bookkeeping errors (1-5 min per participant). Integrating proper QC checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged.

Current magnetic resonance imaging (MRI) requires the subject to remain stationary to limit motion artifacts and avoid unwanted field-induced brain stimulation. However, imaging during large-scale motion could enable studies in which motion itself is central. One example is the study of brain networks involved in vestibular function, which senses head motion. Here, we demonstrate Moving MRI (mMRI), a system that enables imaging during large-scale motion by moving the subject and scanner together to minimize relative motion. We implemented a proof-of-concept platform using a compact, cryogen-free superconducting magnet mounted on a pneumatically actuated tilt mechanism that moves the magnet, gradients, and RF coil as a unit during scanning. Phantom and in vivo rat brain scans were acquired during repetitive tilting. We characterized artifacts arising from tilt-induced field shifts and residual subject-scanner motion, and partially reduced these effects. mMRI enables imaging during large-scale movement and may broaden access to naturalistic vestibular paradigms while providing a foundation for future human systems.

Magnetic resonance imaging (MRI) abnormalities have been reported in individuals who later die from sudden unexpected death in epilepsy (SUDEP), but their specificity and predictive value remain uncertain. Postmortem MRI (PM-MRI) offers a unique opportunity to distinguish structural features associated with SUDEP from changes related to epilepsy, comorbid illness, or the postmortem interval. We performed PM-MRI in nine individuals: five with suspected SUDEP, two with epilepsy who died from non-seizure-related causes, and two without epilepsy who died from sudden cardiac death. Hippocampal, amygdala, and subcortical volumes were quantified using validated segmentation methods and compared with 70 healthy invivo controls. Compared with non-SUDEP cases, SUDEP cases showed significantly larger hippocampal (p = 0.014) and amygdala (p = 0.023) volumes, with most exceeding the healthy control mean, whereas non-SUDEP cases consistently demonstrated volume reductions. These findings parallel invivo MRI observations in individuals at high risk of SUDEP and are consistent with transient peri- or postictal structural changes. In the context of recent large-scale studies showing few validated clinical SUDEP biomarkers beyond frequent generalized tonic-clonic seizures and sleeping alone, PM-MRI may provide an objective approach to positively identifying individuals who died from SUDEP. PLAIN LANGUAGE SUMMARY: We used magnetic resonance imaging (MRI) scans taken after death to study brain changes in people who died from sudden unexpected death in epilepsy (SUDEP). We found that certain brain areas involved in seizure control and automatic body functions, such as breathing and heart rate, were larger in people who died from SUDEP than in people who died from other causes. These changes were similar to those previously seen on brain scans of people with epilepsy who are known to be at higher risk of SUDEP while they were still alive. Our findings suggest that postmortem MRI may help identify brain changes linked to SUDEP.

Modernly speaking, reviewing large numbers of Magnetic Resonance Imaging (MRI) images and manually discovering a brain tumor by a person is a slow and inaccurate process. It may have effects on the correct medical treatment of the patient. Additionally, it could be a slow and laborious task due to the numerous amounts of image datasets involved. Because brain tumors appear similarly to healthy tissue, tumor region segmentation can be difficult. Therefore, there is a need for a high-quality automatic tumor detection system. CNNs are one type of deep learning technique which are often used for image recognition and image classification tasks currently. CNNs are also commonly used to identify Brain Tumors. In our research, we proposed a CNN model for the purpose of classifying images from MRI scans of brains into two classes (Normal or Tumor). Our proposed model was able to achieve a recall of 97.51%, accuracy of 97.889%, F1-score of 97.84%, precision of 98.18%, specificity of 97.62% and an AUC of 97.57%. Our CNN model will help doctors to find brain tumors in MRI images with great efficiency , therefore, greatly increasing the amount of time saved when treating patients

Real-world comparison of brain [18F]FDG-PET imaging with CSF Alzheimer's disease biomarkers in a tertiary memory clinic setting.

Mild behavioral impairment (MBI) occurs in absence of dementia, but no studies explored whether MBI may limit the individuals' ability to complete everyday tasks necessary to live independently. In this study we elucidated on the relevance of specific behavioral markers on functional decline in both cognitively normal (CN) older adults and with mild cognitive impairment (MCI), and evaluated whether individuals with MBI present worse cognition and neurodegenerative dysfunctions compared to subjects without MBI. Observational cross-sectional study. Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Amnesic MCI (aMCI; n = 232) participants and CN (n = 418) individuals. Neuropsychological assessment, volumetric MR brain scan, Flortaucipir PET for in vivo assessment of regional tau deposition, functional assessment questionnaire (FAQ), and neuropsychiatric inventory. MBI occurred in 53.5% aMCI and 19.6% CN. In aMCI, the most prevalent behaviors were affective dysregulation (34.4%), impulse dyscontrol (30.6%), and decreased drive/motivation (15.9%), whereas affective dysregulation (13.1%) and impulse dyscontrol (10.2%) were most prevalent in CN. In aMCI, affective dysregulation, decreased drive, social inappropriateness and abnormal perception MBI domains significantly predicted the FAQ score, whereas only decreased drive/motivation MBI domain showed a predicted role on FAQ in CN. No effects of MBI were detected on regional tau deposition or brain volumes in aMCI. Our findings suggest that the occurrence of MBI might predict a high risk of dysfunction in daily life in both aMCI and CN. An early detection of functional impairment may improve the success of disease-modifying interventions.

Abstract Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast (in vivo or ex vivo) at a fraction of the computational cost of the original method (<5 minutes on a GPU). We validate our tool on four different modalities (in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation).

Abdominal epilepsy is rare but under-recognised, characterised by paroxysmal abdominal symptoms with no identifiable gastrointestinal cause. A 77-year-old woman had a lifelong history of brief nocturnal episodes of abdominal cramping followed by transient confusion and speech difficulty. An MR scan of brain within 24 hours of an event identified cortical diffusion restriction and increased FLAIR signal in the left frontal operculum and insular cortex, consistent with peri-ictal MRI abnormalities. Electroencephalography was normal. Her symptoms completely resolved after starting antiseizure medication. This case highlights the importance of considering neurological causes in unexplained recurrent abdominal pain and expands the phenotypic and radiological spectrum of abdominal epilepsy. The involvement of the insular cortex, a central hub for autonomic and viscerosensory processing, provides imaging-based evidence supporting its role as a key anatomical substrate in abdominal epilepsy.

The distinction between reactive and proactive balance control mechanisms in terms of age-related structural neural correlates is still scarce. From a biomechanical perspective, reactive stepping is a rapid response to sudden loss of stability, whereas proactive self-induced stepping requires anticipatory postural adjustments and longer duration. This study aims to explore how cortical and sub-cortical gray matter volume correlates with variables of reactive and proactive stepping responses among older and young adults; and whether these stepping responses can be distinguished from one another in terms of their structural neural correlates (i.e., cortical and sub-cortical gray matter volume). Twenty-six older adults and nine young adults underwent structural MRI brain scanning. Self-induced stepping variables were derived from ground reaction force data, while kinematic parameters of reactive stepping, including step thresholds, were obtained using a three-dimensional motion capture system. Age-related differences in ground reaction force measures, stepping kinematics, and gray-matter volume in ten regions of interest were examined, followed by partial correlation analyses. Age-related impairments in reactive and proactive stepping performance were accompanied by significantly smaller gray-matter volume among older adults across all regions of interest (p≤0.003), except for the brainstem (p=0.026; post-correction significance level: p<0.005). Partial correlation analyses including both older and young adults revealed significant associations whereby longer balance recovery durations and lower stepping thresholds were associated with lower gray-matter volume in prefrontal and cortical regions, and in the putamen and amygdala (r=-0.41 to -0.60, p≤0.037; and r=0.38 to 0.43, p≤0.048, respectively). In self-induced stepping performance, longer preparation and step durations were significantly associated with lower pre-central, cerebellar and amygdala gray-matter volume (r=-0.51 - -0.56, p≤0.006). Age-related differences in reactive stepping deficits were primarily associated with lower gray-matter volume in prefrontal, cortical, putamen, and amygdala regions, whereas self-induced stepping impairments were associated with precentral, cerebellar, and amygdala gray matter volumes. These findings suggest distinct neural substrates underlying reactive versus self-initiated balance control. Further investigation needs to explore whether intervention programs in older adults may change gray matter volume in these regions.

BackgroundIntracranial hemorrhage is a potentially fatal neurological emergency. It requires rapid diagnosis to guide the management plan. Non-contrast computed tomography (NCCT) is the primary imaging method for detecting acute intracranial bleeding due to its speed and accessibility. Recent advances in artificial intelligence (AI), including large language models like ChatGPT (OpenAI, San Francisco, CA, USA), offer new opportunities to support radiological interpretation.ObjectiveThis study evaluated ChatGPT's ability to detect intracranial hemorrhages on NCCT brain images and compared its diagnostic performance with that of radiologists.MethodsA retrospective case-control study analyzed 276 computed tomography (CT) brain scans obtained from December 2025 to February 2026. The dataset comprised 138 cases with confirmed intracranial hemorrhage and 138 control cases without hemorrhage. CT images were evaluated using ChatGPT with a structured prompt. Radiologist reports served as the reference standard. Diagnostic performance was measured by sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). Statistical analyses included chi-squared tests and Cohen's kappa coefficient.ResultsChatGPT identified 124 of 138 hemorrhage-positive cases and 117 of 138 hemorrhage-negative cases, resulting in 89.9% sensitivity, 84.8% specificity, and 87.3% diagnostic accuracy. Subtype analysis revealed the highest sensitivity for intraparenchymal hemorrhage (88.2%), followed by subarachnoid (73.8%), epidural (66.7%), and subdural (61.5%) hemorrhages, respectively. A statistically significant association was found between ChatGPT predictions and radiologist diagnoses (χ² = 154.1; p < 0.001). The agreement between ChatGPT and radiologist interpretations was good (κ = 0.75). McNemar's test showed no statistically significant difference between ChatGPT and radiologist diagnoses (p = 0.31).ConclusionChatGPT exhibited promising sensitivity in detecting intracranial hemorrhage on NCCT brain scans. However, its moderate specificity suggests it should serve as an adjunct to, rather than a substitute for, radiologist interpretation. Additional research involving larger datasets and model optimization is necessary prior to clinical implementation.

Despite evidence of group-level differences in striatal morphometry among persons with Huntington's Disease (PwHD), current models of HD progression used for participant selection and assessment of treatment outcomes in clinical trials do not leverage shape information. We first validated the capability of a discriminative deep neural network to derive descriptors of shape from all subcortical structures affected by HD, utilizing 2,932 brain scans in 615 PwHD across three longitudinal datasets (TRACK-HD, PREDICT-HD, and IMAGE-HD). We then trained a conditional generative model that used shape descriptors, alongside conventional volumetric, genetic, as well as composite cognitive, motor, and functional features at baseline to predict biomarkers of disease progression at subsequent time points. We observed that the anatomical shapes of subcortical structures, including putamen, lateral ventricle, pallidum, caudate, thalamus, and accumbens, exhibited strong associations with HD progression, as measured by a commonly used prognostic score. Furthermore, within-stage heterogeneity, along the continuum of disease progression, was better captured: when shape descriptors were aggregated using principal component analysis, they showed a high correlation with disease stage (Spearman's correlation: ρ = 0.72), compared to volumetric measurements in cubic millimetres (ρ = 0.45). Finally, incorporating subcortical shape into the generative model improved predictive performance, compared to the same model that relied solely on brain volumes. This study demonstrates that subcortical brain shape is associated with HD progression, enables capturing fine-grained within-stage variability, and improves the predictability of characteristic biomarkers. The findings could potentially optimize future clinical trials through more targeted participant recruitment and more objective post-intervention assessments of treatment efficacy.

Childhood inhibited temperament (cIT) is associated with an increased risk for developing internalizing psychopathology. Neurobiological characteristics identified by structural magnetic resonance imaging (MRI) may elucidate the neural substrates for cIT, but studies are scarce and often focus on particular regions of interest. Moreover, current findings lack replication. This preregistered analysis from the ENIGMA-Anxiety Working Group examined structural brain characteristics associated with cIT using a comprehensive whole-brain approach. Temperament assessments (behavioral observations, parent/teacher reports or self-reports on cIT before age 13 years) and MRI data (age at scan, 6-25 years) from international research sites (Europe, North America, South America) were pooled for mega-analysis. Following image processing and quality control, associations between cIT and brain structure were examined in 3,803 participants. Subcortical volumes, cortical thickness, and surface area (main analyses) and detailed subcortical characteristics (eg, subnuclei, subfields, partial volume effects; exploratory analyses) were considered. In the full sample, cIT showed no relation with brain structure, either as a main effect or in interactions with sex or age. Subgroup analyses (based on cIT assessment type) revealed cIT by sex interactions on mean cortical thickness (pMC-FWER = .037) and thickness of the right superior parietal region (pMC-FWER = .029) in youth with parent/teacher reports on cIT levels. Exploratory analyses revealed findings in the hippocampus, putamen, and caudate, but most did not survive statistical correction for multiple testing. This mega-analysis found no consistent associations between cIT and regional brain structure, although the role of parietal regions warrants further investigation. Future studies should consider brain function in cIT, preferably using longitudinal designs. Inhibited temperament during childhood is a risk factor for the development of anxiety and depression later in life. A preregistered study from the international ENIGMA-Anxiety Working Group investigated whether characteristics of brain structure are associated with the level of childhood inhibited temperament, using brain scans and data on temperamental traits from participants aged 6 to 25 years, which have been previously acquired at research sites worldwide (total sample > 3,800 subjects). Analyses revealed no consistent correlations between brain structure and inhibited temperament. Structural Brain Correlates of Childhood Inhibited Temperament: An ENIGMA-Anxiety Mega-analysis. https://www.jaacap.org/article/S0890-8567(22)00299-4/fulltext.

Acute alcohol use reduces brain glucose metabolism while increasing uptake of acetate, a byproduct of alcohol. This metabolic shift persists in individuals with alcohol use disorder (AUD) and may offer a treatment target. Recent studies show that ketone therapies can lessen alcohol withdrawal and cravings. In this study, we tested whether a single dose of a ketone ester (KE) supplement affects brain energy use and alcohol craving. Ten participants (five with AUD, five healthy controls) received two FDG-PET brain scans-one after taking 395 mg/kg KE and one at baseline-in a randomized order. Additionally, five AUD participants underwent magnetic resonance spectroscopy to measure cingulate β-hydroxybutyrate (BHB). KE lowered blood glucose and increased BHB in both groups. Brain scans revealed a 17% reduction in glucose metabolism, especially in the frontal, occipital, and cingulate cortices, as well as the hippocampus, amygdala, and insula. No major differences were observed between AUD and control groups. KE significantly reduced alcohol craving in AUD participants and tripled cingulate BHB levels. These findings suggest that a single KE dose can rapidly shift brain energy use from glucose to ketones, and may help reduce cravings in AUD, supporting its potential as a therapeutic approach.

Brain tumor classification using hybrid spinal-EfficientNet using MRI images.

Intracranial internal carotid artery calcification (iICAC) is a form of intracranial arteriosclerosis and is associated with an elevated risk of stroke and dementia. However, iICAC’s relationship with brain atrophy remains poorly understood. We aimed to automatically quantify iICAC morphometric characteristics and evaluate their associations with regional brain volumes (BVs). We developed an automated approach to compute iICAC surface area and thickness from CT brain scans in a sample of physically active South American subsistence farmers (n = 1,232, age range: 40 years to 92 years, 48.1% female, 794 Tsimane and 438 Moseten). Linear regression models were used to assess associations between two iICAC features and regional BVs, adjusted for age, sex, population, and total intracranial volume. Significant negative relationships were found between regional BVs and iICAC surface area, but not iICAC thickness. Frontal, parietal, temporal, and subcortical BVs exhibited significant negative associations with iICAC surface area (standardized $$\beta$$ range: -0.146 to -0.066, p ≤ 0.013), while the occipital BV did not (standardized $${\beta}_{left}$$ = -0.035, p = 0.249; $${\beta}_{right}$$ = 0.007, p = 0.810). Subcortical BVs demonstrated the strongest negative associations with iICAC surface area (standardized $${\beta}_{left}$$ = -0.146, p < 0.001; $${\beta}_{right}$$ = -0.139, p < 0.001). iICAC surface area—assumed to reflect arterial stiffness—shows a stronger relationship with regional BV loss than iICAC thickness—assumed to indicate arterial stenosis. The findings suggest that brain regions primarily supplied by the anterior circulation are more vulnerable to iICAC-related atrophy. Subcortical BVs showed the strongest negative associations with iICAC surface area, with region-specific analyses identifying significant effects in the putamen, thalamus, hippocampus, amygdala, pallidum, and ventral diencephalon, suggesting heightened vulnerability of deep gray-matter structures to iICAC-related atrophy.