Understanding conical penetration into layered biological materials requires capturing the coupled influences of anisotropy, curvature, layer architecture, and developmental evolution of material properties. However, existing computational studies typically assume adult bone, neglect multilayer skull structure, or simplify cortical anisotropy. Here, we develop a multilayer finite element framework that integrates age-dependent cortical thickness, diploë formation, anisotropic elastic behavior, and Hill-type anisotropic yield to resolve penetration mechanics across developmental stages. A data-driven strategy is used to estimate geometry and material properties by fitting a monomolecular growth model to experimental measurements of thickness, modulus, and strength spanning infancy through adulthood, producing a continuous and physiologically realistic map of skull property evolution. The model is validated against independent wedge-indentation experiments and reference finite element simulations, demonstrating close agreement in force-displacement behavior and subsurface stress distributions. Results reveal that age-driven changes in cortical thickness and stiffness produce more than a three-fold variation in penetration depth and a four-fold variation in penetration depth as a percentage of the outer cortical layer thickness, under identical loading. Marked differences in shear-stress localization and plastic-zone morphology highlight how layer geometry and anisotropic stiffness collectively govern penetration resistance. These findings provide new mechanistic insight into the indentation response and pin slippage of layered cranial bone and underscore the importance of age-specific material modeling. The framework has direct implications for biomechanical safety when using head-immobilization devices, particularly in pediatric neurosurgery, where predictive modeling of tool-bone interaction can inform improved device design, force recommendations, and clinical practice.

Major depressive disorder and bipolar disorder are affective disorders that carry substantial disease burdens, yet the structural brain alterations due to recent mood episodes remain unclear. To identify acute effects on brain structure, we compared cortical thickness in adults with and without a mood episode in the past year. Participants were 30 adults who met lifetime DSM-5 criteria for MDD (n=21) or BD (n=9), divided into a Past Year Mood Episode (PYME) group (n=18), and no-PYME group (n=12). Participants completed the Hamilton Rating Scale for Depression (HAM-D), Young Mania Rating Scale (YMRS) and the semi-structural Longitudinal Interval Follow-up Evaluation (LIFE) interview over the year preceding their structural MRI. The PYME group exhibited greater cortical thickness than the no-PYME group in the left medial orbitofrontal cortex, bilateral V1 primary visual cortex (more extensive in the left hemisphere than the right), left V3 and right V2 visual cortices, and the bilateral hippocampus and left presubiculum. Recent mood episodes are linked to increased cortical thickness, possibly reflecting acute compensatory inflammatory responses. Cortical thickness thus shows promise as a transdiagnostic biomarker of recent mood episodes, aiding interpretation of studies that include individuals who are currently euthymic but recently symptomatic.

Contributions of regional cortical thickness and surface area to cognitive functioning in psychosis-risk.

Linking allostatic load, heart rate variability and brain functional networks and structures in healthy men.

Distinct neural correlates between suicide attempters with major depressive disorder and other psychiatric disorders: a multimodal imaging study.

Electroconvulsive therapy induces cortical and subcortical structural changes in adolescents with major depressive disorder.

Biotypes of deeply phenotyped depressed patients reflect signatures of adverse childhood experience and depressive cognitive biases.

Free-water imaging predicts cognitive decline and clinical progression in cognitively normal older adults.

Cortical microstructural alterations in first-episode and recurrent depression and their associations with gene expression profiles.

Does lumbar vertebra bone microstructure relate to combined loading fracture tolerance and inform fracture initiation site?

Cognition beyond relapses: cognitive progression independent of relapse activity reflects early smoldering neurodegeneration in multiple sclerosis.

Widespread cortical thinning and subcortical alterations in mood and psychotic disorders across first-episode and chronic stages.

Type 2 Diabetes Mellitus (T2DM) confers a significant risk for Mild Cognitive Impairment (MCI), yet robust biomarkers for early detection remain limited. In this study, 150 age-matched participants (50 Healthy Controls, 50 T2DM, 50 T2DM with MCI) were assessed using high-resolution structural MRI, neuropsychological testing, and serological profiling to identify sensitive neuroanatomical and cognitive markers. Cortical thinning was observed, most prominently in the Left Pars Opercularis (LPO), which exhibited stepwise unidirectional atrophy across the diagnostic continuum, highlighting its potential as a structural marker for cognitive deterioration in T2DM. Significant deficits in episodic memory, processing speed, executive function, and verbal memory were also observed, reflecting disruptions in medial-temporal and frontoparietal networks. A Random Forest classifier integrating multimodal features achieved high discriminatory performance (AUC-ROC = 0.95) for distinguishing T2DM with MCI from T2DM patients. SHAP, an Explainable AI method, identified cortical thickness at LPO, and executive function assessed by TMTB as the most influential predictors. These findings establish the LPO as a key neuroanatomical substrate of T2DM-related cognitive impairment and demonstrate that combining targeted neuroimaging with domain-specific cognitive assessments provides a clinically viable framework for early identification of at-risk T2DM patients, offering critical opportunities for preventive intervention.

BackgroundAlzheimer's disease (AD) is the most common form of dementia, marked by progressive cognitive decline. Low-density lipoprotein cholesterol (LDL-C) has been implicated in AD pathology, but findings remain inconsistent. Apolipoprotein E4 (APOE4) status and sex may contribute to this variability.ObjectiveTo examine how LDL-C association with neurodegeneration in AD patients, differ according to APOE4 status and gender.MethodsWe stratified 106 AD patients by APOE4 status and sex into four subgroups: male APOE4+, female APOE4+, male APOE4-, and female APOE4-. Longitudinal cortical thickness changes were assessed using magnetic resonance imaging (MRI). We examined the association between LDL-C levels and cortical thinning within each subgroup.ResultsIn APOE4-positive females, higher LDL-C levels were significantly associated with accelerated cortical thinning in several regions, including the parahippocampal (β = -0.0075, p = 0.017), medial orbitofrontal (β = -0.0025, p = 0.028), fusiform (β = -0.0047, p = 0.034), posterior cingulate (β = -0.0097, p = 0.006), and inferior temporal cortices (β = -0.0085, p = 0.019). This subgroup also showed a significant association between LDL-C and MMSE decline (β = -1.409, p = 0.014) as well as longitudinal increases in cerebrospinal fluid phosphorylated tau181 (β = 0.014, p = 0.039). These effects were not observed in other subgroups.ConclusionsElevated LDL-C is associated with increased neurodegeneration and cognitive decline in female AD patients carrying the APOE4 allele. These exploratory findings highlight a subgroup-specific vulnerability to lipid-related neurodegeneration in AD and underscore the importance of considering both sex and genetic background in future studies.

Microcephaly with simplified gyral pattern (MSGP) represents an often subtle subtype of malformation of cortical development that is commonly accompanied by epilepsy and neurodevelopmental delay. However, whether gyral simpflication scales to reduced brain volume, and if MRI features predict clinical outcome still remains unclear. The aims of this study are therefore to apply a quantitative morphometric approach to distinguish simplified gyral pattern from microcephaly-related effects, and to correlate quantitative MRI-derived features with clinical outcome. From a single-center cohort of 34 patients with microcephaly and visually confirmed simplified gyral pattern (SGP) (shallow sulci, normal cortical thickness), 18 subjects (mean age 7.1±6.5 years) that fulfilled the eligibility criteria were retrospectively included for final analysis. 26 age- and sex-matched healthy controls were included. Volumetric gray/white-matter volumes (GMV/WMV) and surface-based measures were derived using FreeSurfer. Scaling corrections based on log-log robust regression and cortical folding analyses were subsequently analyzed. Principal component analyses clustered along morphometric phenotypes. ANCOVA, Kendall τ correlations, and logistic regression were applied to evaluated group differences and clinical outcome predictors. Compared with healthy controls, patients showed significantly reduced WMV, cortical surface area, local gyrification index (LGI) (all q<0.003), and mildly increased cortical thickness. Allometric modeling indicated that while WMV and LGI scaled proportionally to total brain volume, cortical surface area was disproportionately reduced, suggesting deviation beyond microcephalic scaling. Spectral folding analysis showed loss of specifically macro-scale (primary) folding power. Morphometric clustering separated four subtypes differing in cortical thickness and area, of which the cluster with lowest surface area and LGI exhibited universal developmental delay and included both early deaths. Across the cohort, neurodevelopmental delay correlated negatively with surface area. Logistic regression suggested lower surface area, WMV, and LGI as predictors of adverse clinical outcome (q<0.02). The presence of SGP may reflect additional disturbance of primary sulcation and tangential cortical expansion beyond scaling to microcephaly alone. Our results suggest reduced cortical surface area as a key morphometric marker and strong MRI feature predictive of neurodevelopmental delay, underscoring the clinical value of quantitative morphometry for stratifying disease severity in microcephaly with simplified gyral pattern.

Neurofibrillary tangles (NFTs) progressively damage gray matter in Alzheimer disease (AD). Resulting cortical microstructural alterations might not be detectable using macrostructural metrics but may be studied using isotropic water diffusion, as it reflects extracellular free water content. The aim of this study was to examine the effect of NFTs on cortical microstructure by investigating whether cortical free water increases as a function of tau load. We also investigated whether phosphorylated tau in blood plasma also indicated cortical microstructural abnormalities. For this cross-sectional study, we sampled participants with T1 MRI, multishell diffusion-weighted MRI, amyloid PET ([18F]AZD4694), tau PET, and plasma phosphorylated tau 217+ (p-tau217) from the Translational biomarkers in Aging and Dementia cohort at McGill University; participants were recruited between 2017 and 2024. We used the Neurite Orientation Dispersion and Density Imaging algorithm to calculate isotropic free water images ("free water"). FreeSurfer was used to calculate cortical thickness in the entorhinal, fusiform, inferior temporal, and middle temporal gyri regions of interest ("meta-ROI"); Automatic Segmentation of Hippocampal Subfields was used to calculate hippocampal volumes. We grouped participants by amyloid PET positivity (A), plasma p-tau217 positivity (T1), and tau PET positivity (T2). We performed voxel-wise correlation analyses between free water and these proteinopathy markers, as well as ROI-based analyses in the meta-ROI. A total of 303 participants (mean age 67 years, 58.7% female) were included in this study (168 cognitively normal individuals, 43 with mild cognitive impairment, 23 with AD dementia, 68 not diagnosed). Tau PET was positively correlated with free water in gray matter predominantly in the temporal lobe (partial R2 = 0.39, p < 0.001), and the correlation of p-tau217 with the meta-ROI free water was entirely mediated by tau PET (p < 0.001). In addition, medial temporal and hippocampal free water was negatively correlated with Montreal Cognitive Assessment scores in the A-T1+ and A+T2+ groups. The strongest ROI-based multilinear models for predicting temporal gray matter and hippocampal tau PET burden used both cortical thickness and free water as predictors (temporal gray matter partial R2 = 0.62; hippocampal partial R2 = 0.64). In AD-relevant regions, increased free water correlates with tau load independently of macrostructural metrics or amyloid load. Free water may serve as an imaging marker for microstructural changes in gray matter resulting from NFT accumulation, complementary to macrostructural metrics.

Accurate and automated reconstruction of cortical surfaces across the human lifespan is essential for studying brain development, aging, and the early diagnosis of neurological disorders. However, traditional neuroimaging pipelines require hours per subject, limiting scalability. Existing deep learning methods typically target narrow age ranges, struggling to generalize due to substantial age-related anatomical variability. This leads to inaccurate quantification of cortical properties, such as curvature and cortical thickness, thereby undermining their potential as reliable biomarkers for routine clinical brain analysis. To address these challenges, we present uBrainSurf, a unified curvature-aware deformation framework for lifespan cortical surface reconstruction. Specifically, uBrainSurf learns a sequence of stationary velocity fields (SVFs) from volumetric MR images, gradually deforming a smooth template mesh to subject-specific white-matter and pial surfaces through a coarse-to-fine strategy. To enhance the reconstruction accuracy, we introduce an auxiliary curvature prediction branch that provides an anatomical prior, guiding the model to prioritize anatomically important regions. Furthermore, we propose a novel curvature-driven loss function that encourages consistency between the curvatures of corresponding points on predicted and target surfaces, ensuring the reconstructed surfaces are directly suitable for downstream analyses. The uBrainSurf is evaluated on a large-scale brain dataset comprising 2,132 subjects spanning 0-100 years. Experimental results demonstrate that uBrainSurf achieves superior performance and generalizability while being several orders of magnitude faster than traditional pipelines. Our code is available at https://github.com/TL9792/CCF.

Cortical thickness analysis combined with CSF dynamics improves diagnostic stratification in idiopathic normal pressure hydrocephalus.

Objective.Transcranial direct current stimulation (tDCS) shows potential for cognitive enhancement and neuromodulation, yet its efficacy is limited by substantial inter-individual variability in the induced electric field (E-field) distribution in targeting brain areas and functional networks. This study aims to develop an anatomy-informed framework to select electrode montage and geometry that optimizes network-levelE-field delivery.Approach.Using high-resolution T1-weighted/T2-weighted MRI from 590 participants (ages 36-80) in the Human Connectome Project-Aging, we extracted anatomical features, including cortical, skull, and cerebrospinal fluid (CSF) thickness, and sulcal depth. We simulatedE-fields with finite-element models (SimNIBS 4.1) across multiple commonly used montages in working memory research. Network-level analyses, based on the Schaefer atlas, were performed to assess the spatial distribution and intensity of tDCS-inducedE-fields.Main results.Montage configuration and individual anatomy strongly shape the spatial distribution and intensity of tDCS-inducedE-fields. For montages targeting the dorsolateral prefrontal cortex, the resulting fields extended beyond the intended site and exhibited considerable variability in their cortical focality and magnitude. Principal component and feature importance analyses indicated local gyrification index, cortical thickness, skull thickness, CSF thickness, and sulcal depth as primary determinants of network-levelE-field distribution, with executive and default mode networks most consistently receiving suprathresholdE-field magnitudes. Importantly, peak inducedE-field varied markedly between individuals even under standardized protocols, highlighting substantial inter-individual variability in both dosing and targeting outcomes.Significance.We present a practical workflow for anatomy-guided montage selection that addresses the need for personalized approaches in tDCS. This framework has the potential to improve the efficacy and reproducibility of tDCS in both research and clinical practice by accounting for individual anatomical differences in stimulation planning.

Obesity is a highly prevalent metabolic risk factor that commonly coincides with additional metabolic, cardiovascular, and inflammatory abnormalities. Obesity has frequently been shown to affect brain physiology at multiple levels, and to increase the risk for the development of neuropsychiatric disorders such as major depression and dementia. Previous large-scale neuroimaging research has consistently shown overlapping brain structural alterations in obesity and neuropsychiatric disorders, with the most pronounced alterations being lower cortical thickness in the frontal and temporal cortex. Yet, the direction of association, and the potential causal effect of obesity on brain structural decline, remains unclear. Moreover, it is imperative to determine which of the multifaceted biological systems impacted by obesity, encompassing metabolic, cardiovascular, and inflammatory aspects, may be implicated in the link between obesity and brain structural decline. In this study, we employed univariate and multivariate Mendelian randomization (MR) as an instrumental variable (IV) approach to clarify the causal direction of the relationship between body mass index (BMI) and brain structure and to disentangle the metabolic, cardiovascular, and inflammatory factors that might underlie this relationship. We found evidence for a potential causal influence of elevated BMI on lower cortical thickness, with the most prominent effects in the precentral and fusiform gyrus. We furthermore found concurrent nominal associations of visceral adipose tissue (VAT) and the inflammatory serum marker CRP with lower cortical thickness which largely overlapped with regions associated with higher BMI. In contrast, very few associations with cortical thickness emerged for blood pressure or metabolic serum markers. Our findings thus corroborate the notion of a causal effect of BMI on lower cortical thickness. Future research should aim to delineate if and how the BMI related effect on brain structural decline conveys an increased risk for the development of neuropsychiatric disorders.