Primary angle-closure glaucoma (PACG) has traditionally been regarded as an ocular disorder, but accumulating evidence suggests broader central nervous system involvement. Although previous neuroimaging studies have identified static functional abnormalities, the dynamic properties of large-scale brain networks and their associated molecular signatures in PACG remain insufficiently understood. We applied Leading Eigenvector Dynamics Analysis to resting-state functional MRI data from 44 patients with PACG and 57 healthy controls to characterize recurrent whole-brain dynamic states. State-specific temporal metrics and spatial patterns were further evaluated using multiple machine learning models. To explore potential biological correlates, imaging-derived spatial patterns were linked to cortical gene expression profiles from the Allen Human Brain Atlas using partial least squares regression, followed by pathway enrichment, cell-type enrichment, and neurotransmitter receptor/transporter mapping analyses. Compared with healthy controls, PACG patients showed prolonged dwell time in one recurrent dynamic state, suggesting reduced flexibility of large-scale brain dynamics. Machine learning models showed promising classification performance within the current dataset, with the most informative features primarily located in default mode network regions. Transcriptomic decoding revealed enrichment of genes related to synaptic signaling, ion channel activity, neurotransmitter transport, and neuronal communication. Cell-type enrichment analyses further implicated excitatory neurons, inhibitory neurons, and astrocytes. In addition, a significant spatial association with VMAT2 suggested that monoaminergic systems may be relevant to the observed imaging phenotype. PACG is associated with altered large-scale brain dynamics, particularly involving default mode network-related state instability. These imaging abnormalities show spatial associations with molecular, cellular, and neurotransmitter-related signatures.

Automated Alzheimer’s detection using MRI data: A novel approach with Siamese neural networks

Sleep disturbances and their clinical and psychiatric correlates in pediatric-onset multiple sclerosis.

Finite element analysis of the impact of running foot strike pattern on patellar cartilage stress.

Precise estimation of tissue microstructure with hybrid graph transformer.

Revealing brain network hierarchy and molecular correlates in temporal lobe epilepsy: A multimodal neuroimaging study.

Neuroanatomical variability in brain surface area and cortical volumes associated with adolescent e-cigarette use.

Real-world transition from uncontrolled disease to stability: Three-year Pre-post natalizumab comparison in relapsing multiple sclerosis.

Validation of eleven federated learning strategies for multi-contrast image-to-image MRI data synthesis from heterogeneous sources

Proper monitoring of tumor progression and evaluation of treatment responses highly depend on longitudinal brain tumor segmentation from MRI data. Current deep learning methodologies have mainly concentrated on analyzing single-time-point images, which restricts the ability to incorporate temporal dynamics during the segmentation process. The study proposes a new approach called Temporal-Spatial Transformer Network (TST-Net), which can be used for longitudinal brain tumor segmentation. The proposed methodology involves a temporal attention module to integrate the progression-aware information across temporally successive MRI images and a spatial attention module to improve tumor detection. Preprocessing of BraTS longitudinal MRI data and subsequent training of TST-Net on pre-aligned datasets were done in an end-to-end fashion. Evaluation was done by Dice similarity coefficients and compared with current methods. TST-Net proved to be more effective than previous solutions, obtaining 86.0%, 88.0%, and 91.0% Dice scores for improving tumor segmentation, tumor core, and overall tumor segmentation, respectively. This study reveals that incorporating the temporal dimension along with spatial attention leads to increased accuracy of brain tumor segmentation. Incorporating temporal information and applying spatial attention can effectively decrease inconsistencies between follow-up images and contribute to improved tumor area detection. TST-Net can serve as an important tool for clinical applications. TST-Net presents an efficient solution to brain tumor segmentation due to its ability to combine both spatial and temporal attention.

The neurobiological mechanisms underlying the therapeutic effects of meditation therapy in patients with schizophrenia remain poorly understood, therefore, we aim to investigate the relationship between clinical symptoms and the structural changes in brain gray matter in a meditation intervention trial. Han inpatients with schizophrenia admitted to the Shanghai First Civil Affairs Mental Health Centre in 2018 were recruited and randomly assigned to the meditation (Med) or the conventional (CON) treatment group in an eight-months trial. MRI data was collected at the outset, three months, and eight months of treatment. Fifty-eight male subjects completed all evaluations and MRI scans, including 30 in Med and 28 in CON. The findings indicate that meditation can significantly inhibit the extensive regional atrophy of gray matter volume (GMV). A positive correlation was observed between the GMV change rate in the right thalamus and the reduction rate of PANSS positive score (r = 0.3850, p = 0.0028), negative score (r = 0.3789, p = 0.0034) and total score (r = 0.3705, p = 0.0042); The GMV change rate in right insula was positively correlated with PANSS positive score (r = 0.2976, p = 0.0233), negative score (r = 0.3987, p = 0.0019), general score (r = 0.3216, p = 0.0138) and total score (r = 0.3765, p = 0.0035). Additionally, the GMV change rate in the right supplementary motor area was positively correlated with negative scores (r = 0.3633, p = 0.0051). while the right paracentral lobule positively correlated with PANSS general score (r = 0.2791, p = 0.0338). These findings demonstrate the effectiveness of meditation in chronic schizophrenia and suggest it as a promising method for the alleviation of clinical symptoms in schizophrenia.

Studies have shown that patients with chronic migraine exhibit abnormalities in structural and functional brain networks. However, the underlying brain mechanisms that govern structural-to-functional connectivity coupling in this population remain unclear. Multimodal functional MRI data were collected from 36 patients with chronic migraine without aura, 94 patients with episodic migraine without aura and 51 healthy controls. Spearman's correlation analysis was used to calculate SC-FC coupling. The Kruskal-Wallis test was used to compare differences in SC-FC coupling between the three groups. We also performed a correlation analysis to investigate the associations between SC-FC coupling and clinical variables. Compared with the healthy control group, patients with episodic migraine exhibited abnormal SC-FC coupling in the right rolandic operculum, left lingual gyrus, left pallidum, and vermis 7. In contrast, patients with chronic migraine exhibited abnormal SC-FC coupling in the left cerebellum 3, vermis 7, and pulvinar nucleus of the thalamus. Patients with chronic migraine exhibited abnormal SC-FC coupling in the left pulvinar nucleus of the thalamus compared with patients with episodic migraine. SC-FC coupling in the left cerebellum 3 of patients with chronic migraine was significantly associated with disability scores (r = -0.3731, p = 0.0250). Our study identified SC-FC coupling alterations associated with chronic migraine. This suggests that abnormalities in SC-FC coupling in subcortical nuclei and the cerebellum may represent underlying mechanisms of chronic migraine.

Classification of human gut microbiome into distinct enterotypes based on gut microbial community composition has provided an attractive framework for population stratification. While empirical evidence indicates that microbial enterotype is related to brain function and memory, the neural processes underlying this interaction remain to be further characterized. In 510 healthy young adults, we used 16S rDNA amplicon sequencing to perform enterotyping, acquired resting-state functional MRI data to calculate brain functional measures, and assessed both episodic and working memories. Inter-enterotype differences in brain functional measures were examined, followed by performance of correlation and mediation analyses to disentangle the potential associations between enterotype, brain function, and memory. We found significant differences in multiple brain functional measures in the parietal and occipital cortices across Bacteroides, Prevotella, and Ruminococcaceae enterotypes. Moreover, these differential brain functional measures were correlated with both episodic and working memories, and further mediated the relationship between enterotype and memory. Our findings not only establish brain function as the mediating factor between enterotype and memory, but also hold translational potential for informing novel treatment for cognitive dysfunction via targeting the microbiota-gut-brain axis.

Prostate MRI is central to the diagnostic pathway for prostate cancer (PCa), reducing unnecessary biopsies, improving the detection of clinically significant disease (csPCa), and limiting overdiagnosis of indolent tumours. The Prostate Imaging Reporting and Data System (PIRADS) version 2.1 is the current international standard for MRI data acquisition and interpretation. This review synthesises the evidence for its use, its strengths and limitations, and future developments. PI-RADS v2.1 improved the assessments of multiparametric MRI (mpMRI) by clarifying technical requirements, zone-specific interpretation rules, and structured reporting recommendations. Its diagnostic performance is well established, particularly for ruling out csPCa and guiding risk-adapted biopsy strategies. However, significant gaps remain. MRI image quality varies substantially across centres, and PI-RADS lacks a mechanism to exclude non-diagnostic scans, resulting in inconsistent classification. Several scoring challenges persist, most notably the ambiguous definition and heterogeneous management of PI-RADS 3 lesions, imprecise lesion measurement standards, and limited guidance for central-zone and atypical lesions. Gaps include the absence of strategies for assessing background tissue changes and infiltrative patterns. Cancer detection specificity and inter-reader agreement remain moderate, with significant discrepancies in transition-zone assessments. Artificial intelligence (AI) holds promise for reducing variability, improving lesion detection, and optimising workflow, though rigorous validation and clear human-AI integration frameworks are needed. Advances in deep learning reconstructions improve image quality and enable shorter protocols, thereby supporting the adoption of biparametric MRI. A shift toward risk-based pathways, integrating MRI findings with PSA density and clinical parameters, is reflected in the forthcoming PI-RADS Pathway 2026, which aims to standardize global biopsy practice and reduce unnecessary interventions.

Cortical folding emerges in the late prenatal period and undergoes rapid reorganization during early childhood. However, the long-term impact of folding alterations associated with preterm birth remains unclear. Herein, we analysed the structural MRI data of 56 preterm children and 206 full-term peers aged 1–7 years. We derived cortical metrics from the reconstructed cortical surfaces using a vertex-wise computation framework to characterize regional folding patterns. We then conducted a combined analysis of the local gyrification index and sulcal depth to explain folding patterns in the preterm brain. Compared with their full-term peers, preterm children exhibited a region-specific impairment pattern characterized by a significantly reduced local gyrification index and sulcal depth in the bilateral superior temporal gyrus and left superior frontal gyrus (P < 0.05). Notably, the sulcal depth in the superior temporal cortex showed significant differences between preterm and full-term children in its association with neurodevelopmental outcomes (P < 0.05), indicating an atypical structure–function relationship in preterm children. The local gyrification index was significantly reduced in the right isthmus cingulate and posterior cingulate gyri (P < 0.05), reflecting a simplified gyral configuration. The study findings suggest several folding patterns that capture diverse mechanisms of morphogenetic disruption, indicating that preterm birth induces persistent region-specific impairments in cortical folding that may affect neurodevelopmental domains. These folding-sensitive markers provide critical insights into the development of targeted interventions to optimize long-term neurodevelopmental outcomes.

Multiple lines of research have studied how complex brain dynamics emerge from underlying connectivity by using Ising models as simplified neural mass models. However, limitations on parameter estimation have prevented their use with individual, high-resolution human neuroimaging data. Furthermore, most studies focus only on connectivity, ignoring node heterogeneity, even though real brain regions have different structural and dynamical properties. Here we present an improved approach to fitting Ising models to 360-region functional MRI data: derivation of an initial guess model from group data, optimization of simulation temperature, and two stages of Boltzmann learning, first with group data, then with individual data. Our implementation uses GPU acceleration to mitigate the high computational cost of this approach. We then analyze how data binarization threshold affects goodness-of-fit, the role of the external field in model behavior, consistency among models fitted to different scans of the same individual, and correlations between model parameters and features from structural MRI, including measures of myelination and cortical folding. We find that binarizing fMRI data at higher thresholds decreases correlation between model and data functional connectivity but increases the heterogeneity of node external fields and their correlations with structural features. A choice of threshold that achieves both goodness-of-fit and intrinsic heterogeneity of regions results in a model that better reflects the reality of the brain as a network of intrinsically heterogeneous nodes. By enabling personalized, biophysically interpretable modeling of structure-function mapping across the whole brain, this approach can aid understanding of individual differences in brain network organization and bridge the gap between the network-focused methodology of connectomics and the region-focused paradigm typical of translational research.

Childhood and adolescence are marked by protracted developmental remodeling of cortico-cortical structural connectivity. However, the spatiotemporal variability of white matter connectivity development across the human connectome and its relevance to cognition and psychopathology remains unclear. Using diffusion MRI data from three independent developmental cohorts spanning youth, we identified a robust divergence in structural connectivity maturation along a predefined sensorimotor-association (S-A) connectional axis during youth (http://connectcharts.cibr.ac.cn). This developmental continuum ranged from early childhood increases in sensorimotor-sensorimotor connectivity strength to late adolescent increases in association-association connectivity strength, with the transition occurring around age 15. The S-A connectional axis also captured spatial variations in the associations between structural connectivity and both higher-order cognition and general psychopathology. Moreover, group-level developmental trajectories of structural connectivity differed by cognitive and psychopathological levels, with psychopathological effects predominantly observed in association connections. These findings delineate a spatiotemporal continuum of structural connectivity development during youth, providing a normative reference for quantifying developmental variability in psychiatric disorders.

To evaluate visual function in children with cerebral visual impairment (CVI) from various causes, examine its relationship with the severity of cerebral involvement using a semi-quantitative MRI scoring system (sqMRI), and compare the results with strabismic controls without visual impairment. This retrospective study included children diagnosed with CVI and controls without CVI who were followed for strabismus. MRI data were compared using a modified 24-point sqMRI scale. Inter-rater agreement was excellent (ICC = 0.91; 95% CI: 0.79-0.96). Partial correlations controlling for gestational age were computed for primary MRI-BCVA relationships. Forty-two children with CVI and 40 strabismic controls were included. Hypoxic-ischemic encephalopathy was the most common cause (26.1%). Children with CVI had significantly lower gestational ages, birth weights, and best-corrected visual acuity (BCVA) (mean 0.28 ± 0.17 logMAR) compared to controls (0.05 ± 0.05 logMAR) (p < 0.001). The sqMRI scores showed significant correlation with BCVA, especially in the basal ganglia, thalamus, hemispheric, subcortical, and total MRI scores (p < 0.05). The total MRI-BCVA correlation remained strong after adjusting for gestational age (partial r = 0.78, p < 0.001). A strong, prematurity-independent correlation was observed between sqMRI scores and visual impairment in a clinically diverse CVI population. These findings suggest that sqMRI scoring could be a useful additional tool for visual prognosis and early rehabilitation planning.

Identifying individuals with mild cognitive impairment (MCI) who are at risk of progressing to Alzheimer's disease (AD) is crucial for early interventions and understanding disease mechanisms. While previous studies using resting-state fMRI (functional magnetic resonance imaging) have identified various neural markers in MCI, there is limited research on alterations in interhemispheric functional interactions. Fifty-three MCI patients and 68 age-, gender-, and education-matched healthy controls were included in the study. Structural cranial MRI and resting-state fMRI data were collected. We investigated voxel-mirrored homotopic connectivity (VMHC) alterations in patients with mild cognitive impairment (MCI) using resting-state fMRI. Functional connectivity was then calculated using the regions with abnormal VMHC as seed points. Correlation analyses were performed to examine the relationship between altered functional connectivity and Core Neuropsychological Test (CNT) scores. Compared to controls, MCI patients exhibited increased VMHC in the bilateral postcentral gyrus. Functional connectivity was enhanced between the bilateral postcentral gyrus and the left cerebellar Crus I, as well as the right inferior temporal gyrus. In contrast, decreased connectivity was observed with the left Gyrus rectus and right supplementary motor area. Correlation analysis revealed a significant negative relationship between VMHC values and CNT scores, as well as with verbal analogy and Chinese word matching scores. MCI patients not only show disrupted interhemispheric VMHC but also demonstrate significant associations with core neuropsychological impairments. These findings provide insights into cognitive changes in MCI and highlight potential biomarkers for early Alzheimer's disease detection.

Adult adrenoleukodystrophy is a rare X linked disorder with heterogeneous phenotypes, complicating prognosis and trial design. We characterised phenotype and natural history in a large single-centre nationwide cohort and contextualised findings with prior reports. We performed a combined retrospective-prospective observational study of adults (≥18 years) with confirmed ABCD1 variants evaluated at Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan (January 2004-June 2023), with reassessments between July and December 2023. Clinical, genetic, biochemical, MRI and neurophysiological data were analysed. The cohort comprised 140 patients (64 males, 76 females) from 58 families, carrying 50 ABCD1 variants, including 11 novel mutations. Adrenomyeloneuropathy (AMN) predominated in males, with low mortality (<10%) and rare cerebral progression (7%) across 18 years, suggesting protective factors. An intermediate phenotype, adrenoleukomyeloneuropathy (ALMN), showed earlier onset, demyelinating MRI changes without cerebral symptoms at baseline and higher mortality than AMN (HR 4.75, 95% CI 1.60 to 14.11). In females, symptom prevalence increases with age, affecting 57% over 60, although only 37% required walking aids. Males had higher very long-chain fatty acid (VLCFA) levels than females, but intrasex correlations with phenotype were absent. Brainstem auditory evoked potentials (BAEPs) were consistently abnormal, whereas nerve conduction studies were abnormal in ~half of male patients (less often in females). Adult adrenoleukodystrophy comprises distinct phenotypes with variable prognosis. Recognition of ALMN as an intermediate form and the low cerebral progression rate in Italian AMN refine disease classification. Sex-related VLCFA differences may influence severity, although standard assays lack sensitivity. Neurophysiological testing, particularly BAEPs, can support differential diagnosis in patients with hereditary spastic paraplegias. NCT04880356.