Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Age-related cortical changes and cognitive performance in healthy adults

Aging is a continuous process with cortical thinning as a common consequence. This study aimed to evaluate cortical thickness, volume and area differences associated with age in healthy population. Seventy-six healthy individuals were divided into three age groups: younger (25-40 years, n = 25), middle-aged (41-55 years, n = 24), and older (56-80 years, n = 27). The elderly group exhibited significantly reduced cortical gray matter in frontal regions (left rostral middle frontal, bilateral lateral orbitofrontal, precentral gyri), temporal (middle temporal, right superior temporal, right inferior temporal), limbic regions (left insula, left posterior cingulate gyrus), occipital (right cuneus, lateral occipital, right lateral occipital), and parietal (precuneus and left postcentral gyri) compared to the younger group. Older adults exhibited age-related decline in performance of auditory verbal learning (AVL) and recall memory, working memory, visuo-motor coordination, compared to younger adults. Thinning of the left posterior cingulate gyrus is positively correlated with auditory verbal learning performance in middle and older age groups. Total and bilateral cortical thickness and volumes were found to be negatively correlated with age. The present study shows the impact of aging on cortical thickness, volume and cognitive performance and have implications in the management of cognitive decline in the ageing population including prophylactic interventions thereof.

Structural signatures of language reorganization after left hemispherotomy in patients with Rasmussen's encephalitis

Rasmussen's encephalitis (RE) is a rare neurological disorder affecting a single cerebral hemisphere, often requiring hemispherotomy as a curative treatment. While significant brain plasticity occurs due to the pathology and surgical intervention, the mechanisms underlying cognitive functioning in the remaining hemisphere remain poorly understood. This multiple-case study longitudinally investigates neurocognitive reorganization in childhood after left hemispherotomy for RE and identifies structural patterns in the right hemisphere associated with language recovery. Indeed, the mechanisms that allow the right hemisphere to support language, after left hemispherotomy remain unclear. Cognitive trajectories were analyzed in three RE patients, and their cortical thickness (CT) changes were compared with data from a publicly available cohort of 393 healthy subjects. Language neuropsychological scores and T1-weighted MRI data were assessed in the healthy right hemisphere before hemispherotomy, one year, and five years post-surgery. Specifically, principal component analysis, structural covariance, and graph theory approaches were employed to investigate language network organization in patients and controls. Results reveal diverse language recovery trajectories among the three patients. Regarding CT, three potential signatures associated with favorable language outcomes were identified: (1) normal or below-normal CT values in cortical regions; (2) a more associative and integrative organization of the language network; and (3) increased global efficiency. These preliminary longitudinal findings provide novel insights into the mechanisms of neurocognitive reorganization following left hemispherotomy in childhood. By emphasizing structural patterns linked to favorable postoperative language recovery, this study highlights their value for guiding future research and clinical interventions.

Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize ( https://github.com/ahzhu/eharmonize ).

Obesity accelerates age-related memory deficits and alters white matter tract integrity in Ldlr-/-.Leiden mice

Background

Obesity in mid-adulthood has been suggested to promote brain aging and is associated with progressive cognitive impairment later in life. However, the structural and functional alterations that underlie obesity-related cognitive dysfunction are still poorly understood, partly owing to the lack of translational models replicating age- and obesity-related brain pathology.

Methods

The effect of age and high-fat diet (HFD)-induced obesity was investigated in adult Ldlr-/-.Leiden mice, an established translational model for obesity and its comorbidities. During mid-adulthood, from three to eight months of age, brain structure and function (hippocampal volume, cortical thickness, white matter integrity, cerebral blood flow (CBF), resting-state functional connectivity) were monitored with brain magnetic resonance imaging, and cognitive function was evaluated using cognitive tests. Brain pathology was further examined with histopathological and gene expression analyses.

Results

Ldlr-/-.Leiden mice showed age-related decreases in cortical thickness, CBF, brain connectivity, and neurogenesis along with the development of neuroinflammation and (short-term) memory impairments. On HFD feeding, Ldlr-/-.Leiden mice exhibited similar features, but memory deficits started at a younger age than in chow-fed mice. HFD-fed mice additionally showed a rise in CBF with concomitant decline in fractional anisotropy in white matter tracts. Analyses of hippocampal gene expression further revealed an age-related suppression of processes related to metabolic and neuronal function while HFD feeding strongly activated neuroinflammatory pathways.

Conclusions

Ldlr-/-.Leiden mice show similar critical age-related changes in brain structure and function as observed in humans. In this mouse model, HFD feeding particularly trigger disturbances in brain blood perfusion and white matter tract integrity, which may underlie an accelerated cognitive decline in obesity.

Brain Age Prediction in Type II GM1 Gangliosidosis

GM1 gangliosidosis is an inherited, progressive, and fatal neurodegenerative lysosomal storage disorder with no approved treatment. We calculated a predicted brain ages and Brain Structures Age Gap Estimation (BSAGE) for 81 MRI scans from 41 Type II GM1 gangliosidosis patients and 897 MRI scans from 556 neurotypical controls (NC) utilizing BrainStructuresAges, a machine learning MRI analysis pipeline. NC showed whole brain aging at a rate of 0.83 per chronological year compared with 1.57 in juvenile GM1 patients and 12.25 in late-infantile GM1 patients, accurately reflecting the clinical trajectories of the two disease subtypes. Accelerated and distinct brain aging was also observed throughout midbrain structures including the thalamus and caudate nucleus, hindbrain structures including the cerebellum and brainstem, and the ventricles in juvenile and late-infantile GM1 patients compared to NC. Predicted brain age and BSAGE both correlated with cross-sectional and longitudinal clinical assessments, indicating their importance as a surrogate neuroimaging outcome measures for clinical trials in GM1 gangliosidosis.

Automatic segmentation and quantitative analysis of brain CT volume in 2-year-olds using deep learning model

Objective

Our research aims to develop an automated method for segmenting brain CT images in healthy 2-year-old children using the ResU-Net deep learning model. Building on this model, we aim to quantify the volumes of specific brain regions and establish a normative reference database for clinical and research applications.

Methods

In this retrospective study, we included 1,487 head CT scans of 2-year-old children showing normal radiological findings, which were divided into training (n = 1,041) and testing (n = 446) sets. We preprocessed the Brain CT images by resampling, intensity normalization, and skull stripping. Then, we trained the ResU-Net model on the training set and validated it on the testing set. In addition, we compared the performance of the ResU-Net model with different kernel sizes (3 × 3 × 3 and 1 × 3 × 3 convolution kernels) against the baseline model, which was the standard 3D U-Net. The performance of the model was evaluated using the Dice similarity score. Once the segmentation model was established, we derived the regional volume parameters. We then conducted statistical analyses to evaluate differences in brain volumes by sex and hemisphere, and performed a Spearman correlation analysis to assess the relationship between brain volume and age.

Results

The ResU-Net model we proposed achieved a Dice coefficient of 0.94 for the training set and 0.96 for the testing set, demonstrating robust segmentation performance. When comparing different models, ResU-Net (3,3,3) model achieved the highest Dice coefficient of 0.96 in the testing set, followed by ResU-Net (1,3,3) model with 0.92, and the baseline 3D U-Net with 0.88. Statistical analysis showed that the brain volume of males was significantly larger than that of females in all brain regions (p < 0.05), and age was positively correlated with the volume of each brain region. In addition, specific structural asymmetries were observed between the right and left hemispheres.

Conclusion

This study highlights the effectiveness of deep learning for automatic brain segmentation in pediatric CT imaging, providing a reliable reference for normative brain volumes in 2-year-old children. The findings may serve as a benchmark for clinical assessment and research, complementing existing MRI-based reference data and addressing the need for accessible, population-based standards in pediatric neuroimaging.

A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans

BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.

Beyond case-control study in neuroimaging for psychiatric disorders: Harmonizing and utilizing the brain images from multiple sites

Recent magnetic resonance imaging (MRI) research has advanced our understanding of brain pathophysiology in psychiatric disorders. This progress necessitates re-evaluation of the diagnostic system for psychiatric disorders based on MRI-based biomarkers, with implications for precise clinical diagnosis and optimal therapeutics. To achieve this goal, large-scale multi-site studies are essential to develop a standardized MRI database, with the analysis of several thousands of images and the incorporation of new data. A critical challenge in these studies is to minimize sampling and measurement biases in MRI studies to accurately capture the diversity of disease-derived biomarkers. Various techniques have been employed to consolidate datasets from multiple sites in case-control studies. Traveling subject harmonization stands out as a powerful tool that can differentiate measurement bias from sample variety and sampling bias. A non-linear statistical model for a normative trajectory across the lifespan also strengthens the database to mitigate sampling bias from known factors such as age and sex. These approaches can enhance the alterations between psychiatric disorders and integrate new data and follow-up scans into existing life-course trajectory, enhancing the reliability of machine learning classification and subtyping. Although this approach has been developed using T1-weighted structural image features, future research may extend this framework to other modalities and measures. The required sample size and methodological establishment are needed for future investigations, leading to novel insights into the brain pathophysiology of psychiatric disorders and the development of optimal therapeutics for bedside clinical applications. Sharing big data and their findings also need to be considered.

Vulnerability to memory decline in aging - a mega-analysis of structural brain change

Brain atrophy is a key factor behind episodic memory loss in aging, but the nature and ubiquity of this relationship remains poorly understood. This study leveraged 13 longitudinal datasets, including 3,737 cognitively healthy adults (10,343 MRI scans; 13,460 memory assessments), to determine whether brain change-memory change associations are more pronounced with age and genetic risk for Alzheimer's Disease. Both factors are associated with accelerated brain decline, yet it remains unclear whether memory loss is exacerbated beyond what atrophy alone would predict. Additionally, we assessed whether memory decline aligns with a global pattern of atrophy or stems from distinct regional contributions. Our mega-analysis revealed a nonlinear relationship between memory decline and brain atrophy, primarily affecting individuals with above-average brain structural decline. The associations were stronger in the hippocampus but also spread across diverse cortical and subcortical regions. The associations strengthened with age, reaching moderate associations in participants in their eighties. While APOE ε4 carriers exhibited steeper brain and memory loss, genetic risk had no effect on the change-change associations. These findings support the presence of common biological macrostructural substrates underlying memory function in older age which are vulnerable to multiple age-related factors, even in the absence of overt pathological changes.

Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis

Introduction

Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, brain age research in psychiatry has primarily examined mood and psychotic disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group.

Methods

3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1,134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effect models examined group differences in brain-PAD and moderation by age.

Results

No significant group difference in brain-PAD manifested (β diagnosis (SE)=0.37 years (0.43), p=0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (β diagnosis-by-age=-0.08 (0.03), pFDR=0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post-hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; β diagnosis=1.20 (0.60), p<0.05, mixed-effects d (95% confidence interval)=0.14 (0.00-0.28)), but not older participants (36-75 years; β diagnosis=0.07 (0.65), p=0.91).

Conclusions

Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post-hoc analyses showed a subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

EEG Microstate Dynamics during Different Physiological Developmental Stages and the Effects of Medication in Schizophrenia

BACKGROUND

Schizophrenia (SCZ) is associated with abnormal neural activities and brain connectivity. Electroencephalography (EEG) microstate is a voltage topographical representation of temporary brain network activations. Most research on EEG microstates in SCZ has focused on differences between patients and healthy controls (HC). However, changes in EEG microstates among SCZ patients across various stages of physiological and cognitive development have not been thoroughly assessed. Consequently, we stratified patients with SCZ into four age-specific cohorts (20-29 years (brain maturation), 30-39 years (stabilization), 40-49 years (early aging), and 50-59 years (advanced aging)) to evaluate EEG microstate alterations. Additionally, we assessed changes in EEG microstates in first-episode psychosis (FEP) before and after an 8-week treatment period.

METHODS

We acquired 19-channel resting-state EEG from 140 chronic SCZ patients, aged 20 to 59 years, as well as from 19 FEP and 20 healthy controls. FEP patients underwent an 8-week inpatient follow-up. After pre-processing, EEG data from different groups were subjected to microstate analysis, and the K-Means clustering algorithm was applied to classify the data into 4 microstates. Subsequently, templates of these microstates were used to fit EEG signals from each patient, and the collected microstate parameters were analyzed.

RESULTS

Patients with SCZ aged 20 to 29 years demonstrated an increased time coverage of microstate class D compared to other age cohorts. In individuals aged 30-39 years, the parameters of microstate class B-specifically time coverage and occurrence-exhibited significant reductions relative to those in the 40-49 and 50-59 years age groups. Compared to healthy controls, microstates class A parameters were significantly reduced in SCZ patients, while microstates class C parameters were prolonged; after 8 weeks of treatment, microstates class A parameters increased and microstates class C parameters decreased.

CONCLUSIONS

Alterations in microstate dynamics were observed among SCZ patients across developmental stages, suggesting potential changes in brain activity patterns. Changes in microstates A and C may serve as potential biomarkers for evaluating treatment efficacy, establishing a foundation for personalized therapeutic approaches.

Internet use, unhealthy diet, and obesity in rural school-aged youth: a cross-sectional study in Henan Province, China

BACKGROUND

In rural areas of China, the prevalence of obesity in children has grown continuously, becoming a major problem in the field of pediatrics. The purpose of this study is to investigate the relationship between Internet use and obesity in rural children and explore the mediating role of unhealthy dietary preferences.

METHODS

This study empirically tested the impact of Internet use on obesity in rural children and its mechanism by using the survey data of Chinese rural primary and secondary school students, the OLS model, the two-stage least squares method, and the mediation effect model.

RESULTS

This study provides new evidence that the prevalence of obesity is higher when more internet time is spent. When length of Internet use increased by one unit, the BMI-Z value of rural children increased by 11.2%. Analysis shows that Internet use has a significant impact on obesity through three types of unhealthy diets: "fast food preference", "snack food preference" and "soft drink and sugary fruit drink preference" (all at the 1% level). Heterogeneity analysis found that non-left behind (NLBC), male and depressed rural children's obesity was more significantly affected by Internet use (significant at 1%, 10% and 10%, respectively).

CONCLUSIONS

This study provides new evidence that the prevalence rate of obesity is higher when more internet time is spent, especially in NLBC, boys and depressed children.

Causal Association Between Major Depressive Disorder and Cortical Structure: A Bidirectional Mendelian Randomization Study and Mediation Analysis

BACKGROUND

Previous observational studies have reported a possible association between major depressive disorder (MDD) and abnormal cortical structure. However, it is unclear whether MDD causes reductions in global cortical thickness (CT) and global area (SA).

OBJECTIVE

We aimed to test the bidirectional causal relationship between MDD and CT and SA using a Mendelian randomization (MR) design and performed exploratory analyses of MDD on CT and SA in different brain regions.

METHODS

Summary-level data were obtained from two GWAS meta-analysis studies: one screening for single nucleotide polymorphisms (SNPs) predicting the development of MDD (n = 135,458) and the other identifying SNPs predicting the magnitude of cortical thickness (CT) and surface area (SA) (n = 51,665).

RESULTS

The results showed that MDD caused a decrease in CT in the medial orbitofrontal region, a decrease in SA in the paracentral region, and an increase in SA in the lateral occipital region. C-reactive protein, tumor necrosis factor alpha (TNF-α), interleukin-1β, and interleukin-6 did not mediate the reduction. We also found that a reduction in CT in the precentral region and a reduction in SA in the orbitofrontal regions might be associated with a higher risk of MDD.

CONCLUSION

Our study did not suggest an association between MDD and cortical CT and SA.

Metabolic Status Modulates Global and Local Brain Age Estimates in Overweight and Obese Adults

BACKGROUND

As people live longer, maintaining brain health becomes essential for extending health span and preserving independence. Brain degeneration and cognitive decline are major contributors to disability. In this study, we investigated how metabolic health influences the brain age gap estimate (brainAGE), which measures the difference between neuroimaging-predicted brain age and chronological age.

METHODS

K-means clustering was applied to fasting metabolic markers including insulin, glucose, leptin, cortisol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol, steady-state plasma glucose, and body mass index of 114 physically and cognitively healthy adults. The homeostatic model assessment for insulin resistance served as a reference. T1-weighted brain magnetic resonance imaging was used to calculate voxel-level and global brainAGE. Longitudinal data were available for 53 participants over a 3-year interval.

RESULTS

K-means clustering divided the sample into 2 groups, those with favorable (n = 58) and those with suboptimal (n = 56) metabolic health. The suboptimal group showed signs of insulin resistance and dyslipidemia (false discovery rate-corrected p < .05) and had older global brainAGE and local brainAGE, with deviations most prominent in cerebellar, ventromedial prefrontal, and medial temporal regions (familywise error-corrected p < .05). Longitudinal analysis revealed group differences but no significant time or interaction effects on brainAGE measures.

CONCLUSIONS

Suboptimal metabolic status is linked to accelerated brain aging, particularly in brain regions rich in insulin receptors. These findings highlight the importance of metabolic health in maintaining brain function and suggest that promoting metabolic well-being may help extend health span.

Multifactorial influences on childhood insomnia: Genetic, socioeconomic, brain development and psychopathology insights

Insomnia is the most prevalent sleep disturbance during childhood and can result in extensively detrimental effects. Children's insomnia involves a complex interplay of biological, neurodevelopmental, social-environmental, and behavioral variables, yet remains insufficiently addressed. This study aimed to investigate the multifactorial etiology of childhood insomnia from its genetic architecture and social-environmental variables to its neural instantiation and the relationship to mental health. This cohort study uses 4340 participants at baseline and 2717 participants at 2-year follow-up from the Adolescent Brain Cognitive Development (ABCD) Study. We assessed the joint effects of polygenic risk score (PRS) and socioeconomic status (SES) on insomnia symptoms and then investigated the underlying neurodevelopmental mechanisms. Structural equation model (SEM) was applied to investigate the directional relationships among these variables. SES and PRS affected children's insomnia symptoms independently and additively (SES: β = -0.089, P = 1.91 × 10-8; PRS: β = 0.041, P = 0.008), which was further indirectly mediated by the deviation of inferior precentral sulcus (β = 0.0027, P = 0.0071). SEM revealed that insomnia (β = 0.457, P < 0.001) and precentral development (β = -0.039, P = 0.009) significantly mediated the effect of SES_PRS (accumulated risks of PRS and SES) on psychopathology symptoms. Furthermore, baseline insomnia symptoms, SES_PRS, and precentral deviation significantly predicted individual total psychopathology syndromes (r = 0.346, P < 0.001). These findings suggest the additive effects of genetic and socioenvironmental factors on childhood insomnia via precentral development and highlight potential targets in early detection and intervention for childhood insomnia.

Mapping Structural Neuroimaging Trajectories in Bipolar Disorder: Neurobiological and Clinical Implications

Neuroimaging is a powerful noninvasive method for studying brain alterations in bipolar disorder (BD). To date, most neuroimaging studies of BD have included smaller cross-sectional samples reporting case versus control comparisons, revealing small to moderate effect sizes. In this narrative review, we discuss the current state of structural neuroimaging studies using magnetic resonance imaging, which inform our understanding of altered brain trajectories in BD across the lifespan. Alternative methodologies such as those that model patient deviations from age-specific norms are discussed, which may help derive new markers of BD pathophysiology. We discuss evidence from neuroimaging genetics and transcriptomics studies, which attempt to bridge the gap between macroscale brain variations and underlying microscale neurodevelopmental mechanisms. We conclude with a look toward the future and how ambitious investments in longitudinal, deeply phenotyped, population-based cohorts can improve modeling of complex clinical factors and provide more clinically actionable brain markers for BD.

Altered surface-based brain morphometry in type 1 diabetes and neuropathic pain

This study explored surface brain morphometry in type 1 diabetes including focus on painful diabetic peripheral neuropathy (DPN). Brain MRI was obtained from 56 individuals with diabetes (18 without DPN, 19 with painless DPN, 19 with painful DPN) and 20 healthy controls. Cortical thickness, sulcus depth, and gyrification were analysed globally and regionally in each group and in the combined diabetes group. Associations with clinical characteristics and pain were assessed. Globally, cortical thickness was reduced in the combined diabetes group and in painful DPN compared to healthy controls. No differences in sulcus depth and gyrification were found. Several regions, including the middle frontal gyrus showed reduced cortical thickness in the combined diabetes- and painful DPN group. The postcentral gyrus exhibited reduced cortical thickness in painful DPN compared to healthy controls, and reduced sulcus depth compared to painless DPN correlating with higher pain intensity. Cortical thinning manifested across the brain cortex in diabetes, especially for painful DPN. Altered postcentral gyrus morphometry may be associated with neuropathic pain. Assessing cortical morphometry may be critical for comprehending central neuropathy and the manifestation of painful DPN in diabetes.

The Imaging Database for Epilepsy And Surgery (IDEAS)

OBJECTIVE

Magnetic resonance imaging (MRI) is a crucial tool for identifying brain abnormalities in a wide range of neurological disorders. In focal epilepsy, MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence (AI) algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data.

METHODS

Herein, we release an open-source data set of pre-processed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections and detailed demographic information. We also share scans from 100 healthy controls acquired on the same scanners. The MRI scan data include the preoperative three-dimensional (3D) T1 and, where available, 3D fluid-attenuated inversion recovery (FLAIR), as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age a onset of epilepsy, location of surgery, histopathology of resected specimen, occurrence and frequency of focal seizures with and without impairment of awareness, focal to bilateral tonic-clonic seizures, number of anti-seizure medications (ASMs) at time of surgery, and a total of 1764 patient years of post-surgical followup. Crucially, we also include resection masks delineated from post-surgical imaging.

RESULTS

To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of ~50%. Our imaging data replicate findings of group-level atrophy in patients compared to controls. Resection locations in the cohort were predominantly in the temporal and frontal lobes.

SIGNIFICANCE

We envisage that our data set, shared openly with the community, will catalyze the development and application of computational methods in clinical neurology.

Glymphatic function decline as a mediator of core memory-related brain structures atrophy in aging

Background and Objectives

This study aimed to elucidate the role of the glymphatic system-a crucial pathway for clearing waste in the brain-in the aging process and its contribution to cognitive decline. We specifically focused on the diffusion tensor imaging analysis along the perivascular space (ALPS) index as a noninvasive biomarker of glymphatic function.

Methods

Data were drawn from the Alzheimers Disease Neuroimaging Initiative (ADNI) database and a separate validation cohort to analyze the ALPS index in cognitively normal older adults. The relationships among the ALPS index, brain morphometry, and memory performance were examined.

Results

As a biomarker of glymphatic function, the ALPS index appeared to decline with age in both cohorts. According to the brain morphology analysis, the ALPS index was positively correlated with the thickness of the left entorhinal cortex (r = 0.258, P false discovery rate (FDR) = 2.96 × 10-4), and it played a mediating role between aging and left entorhinal cortex thinning. The independent cohort further validated the correlation between the ALPS index and the left entorhinal cortex thickness (r = 0.414, P FDR = 0.042). Additionally, in both the primary and validation cohorts, the ALPS index played a significant mediating role in the relationship between age and durable or delayed memory decline.

Conclusion

This study highlights the ALPS index as a promising biomarker for glymphatic function and links it to atrophy of the core memory brain regions during aging. Furthermore, these results suggest that targeting glymphatic dysfunction could represent a novel therapeutic approach to mitigate age-related memory decline.

Early Severe Cortical Involvement and Novel FUCA1 Mutations in a Pediatric Fucosidosis Case

BACKGROUND

Biallelic pathogenic variants in the FUCA1 gene are associated with fucosidosis. This report describes a 4-year-old boy presenting with psychomotor regression, spasticity, and dystonic postures.

METHODS AND RESULTS

Trio-based whole exome sequencing revealed two previously unreported loss-of-function variants in the FUCA1 gene. Brain magnetic resonance imaging (MRI) findings included corpus callosum hypoplasia, white matter hypomyelination, and alterations in the globus pallidi, alongside markedly reduced cortical thickness.

CONCLUSIONS

These findings suggest that cortical atrophy may occur in the early stages of fucosidosis. Early diagnosis is imperative for genetic counseling, timely investigations, and initiating early therapeutic interventions to potentially mitigate more extensive brain involvement.

Disentangling the effect of sex from brain size on brain organization and cognitive functioning

Neuroanatomical sex differences estimated in neuroimaging studies are confounded by total intracranial volume (TIV) as a major biological factor. Employing a matching approach widely used for causal modeling, we disentangled the effect of TIV from sex to study sex-differentiated brain aging trajectories, their relation to functional networks and cytoarchitectonic classes, brain allometry, and cognition. Using data from the UK Biobank, we created subsamples that removed, maintained, or exaggerated the TIV differences in the original sample. We compared regional and vertex-level sex estimates across subsamples. The overall sex-related differences diminished in head size-matched subsamples, suggesting that most of the observed variability results from TIV differences. Furthermore, bidirectional sex differences in brain neuroanatomy emerged that were previously masked by the effect of TIV. Allometry remained fairly consistent across lifespan and was not sex-differentiated. Finally, the matching process changed the direction of the estimated sex differences in "verbal and numerical reasoning" and "working memory", suggesting that behavioral sex difference investigations can benefit from additional biological analysis to uncover the underlying factors contributing to cognition. Taken together, we provide new evidence disentangling sex differences from TIV as a relevant biological confound.

Possible compensatory role of cerebellum in bipolar disorder. A cortical thickness study

Recent studies suggested that structural changes in the cerebellum are implicated in the pathophysiology of bipolar disorder (BD). Here, we aimed to characterize the structural alterations of cerebellar lobules in BD, evaluating their possible relation with those occurring in the rest of the brain. One-hundred-fifty-five type I BD patients were recruited and compared with one-hundred-nineteen controls subjects. Cerebral cortical thickness (CT) was evaluated vertex-wise, while cerebellar CT at the level of its twelve lobules. A widespread pattern of cortical thinning was found in several clusters of BD patients. In the cerebellum, we found an anterior thinning (lobule I_II, III, X) and a posterior thickening (crus I, crus II, lobule VI and lobule IX) of its lobules in BD. Exploring the relation between cerebral and cerebellar CT changes in BD patients, after correcting for age and disease duration, the CT of a large subset of cerebral regions, found thinned in BD, were also inversely correlated with the thickening of cerebellar lobule IX. We speculate that this lobule may undergo adaptive changes to compensate the widespread cortical thinning which characterizes BD syndrome. Such a compensatory adaptation of the cerebellum would be similar to that found in other neurological and psychiatric disorders.

In vivo human neurite exchange imaging (NEXI) at 500 mT/m diffusion gradients

Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion-water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength=500 mT/m, maximum slew rate=600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13-30 ms) and b -values up to 17.5 ms/μm2. The anisotropic Kärger model was applied to estimate the exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median±interquartile range) 13±8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the NEXI exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast (≤20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex and visual cortex exhibit longer exchange times compared to other cortical regions. Non-linear fitting for the anisotropic Kärger model was accelerated 100 times using a GPU-based pipeline compared to the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in dMRI data, which can have a substantial impact on the estimated NEXI exchange time and require extra attention when comparing NEXI results between various hardware setups.

Neuroimaging Correlates of the NIH-Toolbox-Driven Cognitive Metrics in Children

BACKGROUND

The National Institutes of Health (NIH) Toolbox Cognition Battery is increasingly being used as a standardized test to examine cognitive functioning in multicentric studies. This study examines the associations between the NIH Toolbox Cognition Battery composite scores with neuroimaging metrics using data from the Adolescent Brain Cognitive Development (ABCD) study to elucidate the neurobiological and neuroanatomical correlates of these cognitive scores.

METHODS

Neuroimaging data from 5290 children (mean age 9.9 years) were analyzed, assessing the correlation of the composite scores with Diffusion Tensor Imaging (DTI), structural Magnetic Resonance Imaging (sMRI), and resting-state functional connectivity (rs-fMRI). Results were adjusted for age, sex, race/ethnicity, head size, body mass index (BMI), and parental income and education.

RESULTS

Higher fluid cognition composite scores were linked to greater white matter (WM) microstructural integrity, lower cortical thickness, greater cortical surface area, and mixed associations with rs-fMRI. Conversely, crystallized cognition composite scores showed more complex associations, suggesting that higher scores correlated with lower WM microstructure integrity. Total cognition scores reflected patterns consistent with a combination of both fluid and crystallized cognition, but with diluted specific insights. Our findings highlight the complexity of the neuroimaging correlates of the NIH Toolbox composite scores.

CONCLUSIONS

The results suggest that fluid cognition composite scores may serve as a marker for cognitive functioning, emphasizing neuroimaging's clinical relevance in assessing cognitive performance in children. These insights can guide early interventions and personalized education strategies. Future ABCD follow-ups will further illuminate these associations into adolescence and adulthood.

Neuroanatomical Variability and Substance Use Initiation in Late Childhood and Early Adolescence

Importance

The extent to which neuroanatomical variability associated with early substance involvement, which is associated with subsequent risk for substance use disorder development, reflects preexisting risk and/or consequences of substance exposure remains poorly understood.

Objective

To examine neuroanatomical features associated with early substance use initiation and to what extent associations may reflect preexisting vulnerability.

Design, Setting, and Participants

Cohort study using data from baseline through 3-year follow-up assessments of the ongoing longitudinal Adolescent Brain Cognitive Development Study. Children aged 9 to 11 years at baseline were recruited from 22 sites across the US between June 1, 2016, and October 15, 2018. Data were analyzed from February to September 2024.

Exposures

Substance use initiation through 3-year follow-up (ie, age <15 years).

Main Outcomes and Measures

Self-reported alcohol, nicotine, cannabis, and other substance use initiation and baseline magnetic resonance imaging (MRI)-derived estimates of brain structure (ie, global and regional cortical volume, thickness, surface area, sulcal depth, and subcortical volume). Covariates included family (eg, familial relationships), pregnancy (eg, prenatal exposure to substances), child (eg, sex and pubertal status), and MRI (eg, scanner model) variables.

Results

Among 9804 children (mean [SD] baseline age, 9.9 [0.6] years; 5160 boys [52.6%]; 213 Asian [2.2%], 1474 Black [15.0%], 514 Hispanic/Latino [5.2%], 29 American Indian [0.3%], 10 Pacific Islander [0.1%], 7463 White [76.1%], and 75 other [0.7%]) with nonmissing baseline neuroimaging and covariate data, 3460 (35.3%) reported substance use initiation before age 15. Initiation of any substance or alcohol use was associated with thinner cortex in prefrontal regions (eg, rostral middle frontal gyrus, β = -0.03; 95% CI, -0.02 to -0.05; P = 6.99 × 10-6) but thicker cortex in all other lobes, larger globus pallidus and hippocampal volumes, as well as greater global indices of brain structure (eg, larger whole brain volume, β = 0.05; 95% CI, 0.03 to 0.06; P = 2.80 × 10-8) following Bonferroni or false discovery rate multiple testing correction. Cannabis use initiation was associated with lower right caudate volume (β = -0.03; 95% CI, -0.01 to -0.05; P = .002). Post hoc examinations restricting to postbaseline initiation suggested that the majority of associations, including thinner prefrontal cortex and greater whole brain volume, preceded initiation.

Conclusions and Relevance

In this cohort study of children, preexisting neuroanatomical variability was associated with substance use initiation. In addition to putative neurotoxic effects of substance exposure, brain structure variability may reflect predispositional risk for initiating substance use earlier in life with potential cascading implications for development of later problems.

The Lifespan Evolution of Individualized Neurophysiological Traits

How do neurophysiological traits that characterize individuals evolve across the lifespan? To address this question, we analyzed brief, task-free magnetoencephalographic recordings from over 1,000 individuals aged 4-89. We found that neurophysiological activity is significantly more similar between individuals in childhood than in adulthood, though periodic patterns of brain activity remain reliable markers of individuality across all ages. The cortical regions most critical for determining individuality shift across neurodevelopment and aging, with sensorimotor cortices becoming increasingly prominent in adulthood. These developmental changes in neurophysiology align closely with the expression of cortical genetic systems related to ion transport and neurotransmission, suggesting a growing influence of genetic factors on neurophysiological traits across the lifespan. Notably, this alignment peaks in late adolescence, a critical period when genetic factors significantly shape brain individuality. Overall, our findings highlight the role of sensorimotor regions in defining individual brain traits and reveal how genetic influences on these traits intensify with age. This study advances our understanding of the evolving biological foundations of inter-individual differences.

Lay summary

This study examines how brain activity reflects the development of individuality across a person's life. Using magnetoencephalography to capture brief recordings of spontaneous brain activity, the researchers distinguished between over 1,000 individuals, spanning ages 4 to 89. They found that the brain regions most associated with individuality change with age: sensory and motor regions become increasingly distinctive in early adulthood, highlighting their role in shaping a person's unique characteristics of brain activity. The study also revealed that changes in brain activity across different ages correspond to specific patterns of gene expression, shedding light on how genetics influence brain individuality. These findings deepen our understanding of the biological foundations of inter-individual differences and how it evolves over the lifespan.

Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology

BACKGROUND

Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD).

METHODS

Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task).

FINDINGS

Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits.

INTERPRETATION

The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life.

FUNDING

The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Development of white matter in young adulthood: The speed of brain aging and its relationship with changes in fractional anisotropy

White matter (WM) development has been studied extensively, but most studies used cross-sectional data, and to the best of our knowledge, none of them considered the possible effects of biological (vs. chronological) age. Therefore, we conducted a longitudinal multimodal study of WM development and studied changes in fractional anisotropy (FA) in the different WM tracts and their relationship with cortical thickness-based measures of brain aging in young adulthood. A total of 105 participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort underwent magnetic resonance imaging (MRI) at the age of 23-24, and the age of 28-30 years. At both time points, FA in the different WM tracts was extracted using the JHU atlas, and brain age gap estimate (BrainAGE) was calculated using the Neuroanatomical Age Prediction using R (NAPR) model based on cortical thickness maps. Changes in FA and the speed of cortical brain aging were calculated as the difference between the respective variables in the late vs. early 20s. We demonstrated tract-specific increases as well as decreases in FA, which indicate that the WM microstructure continues to develop in the third decade of life. Moreover, the significant interaction between the speed of cortical brain aging, tract, and sex on mean FA revealed that a greater speed of cortical brain aging in young adulthood predicted greater decreases in FA in the bilateral cingulum and left superior longitudinal fasciculus in young adult men. Overall, these changes in FA in the WM tracts in young adulthood point out the protracted development of WM microstructure, particularly in men.

Synthetic data in generalizable, learning-based neuroimaging

Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole-brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution, and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.

Sex differences in trajectories of cortical development in autistic children from 2-13 years of age

Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2-13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.

Interactive roles of preterm-birth and socioeconomic status in cortical thickness of language-related brain structures: Findings from the Adolescent Brain Cognitive Development (ABCD) study

Preterm-born (PTB) children are at an elevated risk for neurocognitive difficulties in general and language difficulties more specifically. Environmental factors such as socio-economic status (SES) play a key role for Term children's language development. SES has been shown to predict PTB children's behavioral developmental trajectories, sometimes surpassing its role for Term children. However, the role of SES in the neurocognitive basis of PTB children's language development remains uncharted. Here, we aimed to evaluate the role of SES in the neural basis of PTB children's language performance. Leveraging the Adolescent Brain Cognitive Development (ABCD) Study, the largest longitudinal study of adolescent brain development and behavior to date, we showed that prematurity status (PTB versus Term) and multiple aspects of SES additively predict variability in cortical thickness, which is in turn related to children's receptive vocabulary performance. We did not find evidence to support the differential role of environmental factors for PTB versus Term children, underscoring that environmental factors are significant contributors to development of both Term and PTB children. Taken together, our results suggest that the environmental factors influencing language development might exhibit similarities across the full spectrum of gestational age.

Hearing Function Moderates Age-Related Differences in Brain Morphometry in the HCP Aging Cohort

There are well-established relationships between aging and neurodegenerative changes, and between aging and hearing loss. The goal of this study was to determine how structural brain aging is influenced by hearing loss. Human Connectome Project Aging data were analyzed, including T1-weighted Magnetic Resonance Imaging (MRI) and Words in noise (WIN) thresholds (n = 623). Freesurfer extracted gray and white matter volume, and cortical thickness, area, and curvature. Linear regression models targeted (1) interactions between age and WIN threshold and (2) correlations with WIN threshold adjusted for age, both corrected for false discovery rate (pFDR < 0.05). WIN threshold moderated age-related increase in volume in bilateral inferior lateral ventricles, with a higher threshold associated with increased age-related ventricle expansion. Age-related differences in the occipital cortex also increased with higher WIN thresholds. When controlling for age, high WIN threshold was correlated with reduced cortical thickness in Heschl's gyrus, calcarine sulcus, and other sensory regions, and reduced temporal lobe white matter. Older volunteers with poorer hearing and cognitive scores had the lowest volume in left parahippocampal white matter. These results suggest that better hearing is associated with reduced age-related differences in medial temporal lobe, while better hearing at any age is associated with greater cortical tissue in auditory and other sensory regions. Future longitudinal studies are needed to assess the causal nature of these relationships, but these results indicate interventions that preserve or protect hearing function may combat some neurodegenerative changes in aging.

Cortical morphological alterations in adolescents with major depression and non-suicidal self-injury

BACKGROUND

Non-suicidal self-injury (NSSI) involves repetitive self-harm without suicidal intent and is common among adolescents, often linked to major depressive disorder (MDD). NSSI can lead to physical harm, cognitive impairments, interpersonal issues, violent behavior, and increased risks of psychological disorders and suicide attempts later in life.

METHODS

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) were performed on 44 NSSI patients and 44 healthy controls (HCs). Differences in GMV, CT, and cortical complexity were compared using the two-sample t-tests and correlated with neuropsychological scales.

RESULTS

NSSI patients exhibited significant GMV atrophy in multiple regions, including the left insula, left anterior cingulate cortex, left putamen, left middle frontal gyrus, and right superior frontal gyrus showing increased GMV in the cerebellum posterior lobe. NSSI patients had increased CT in multiple left hemisphere regions and decreased CT in the right middle frontal gyrus. Additionally, they exhibited reduced cortical complexity, including decreased SD in the right frontal gyrus, and lower GI in the left insula. There were no significant differences between the two groups in terms of fractal dimension (FD). NSSI patients showed negative correlation between the CT of the right middle frontal gyrus and the anger dimension of the BPAQ, as well as the SD of the right superior frontal gyrus and the hostility dimension of the BPAQ.

CONCLUSION

NSSI patients have significant structural changes in the insular cortex, prefrontal cortex, precentral and postcentral gyrus, temporal lobe, putamen, and anterior cingulate cortex, offering a morphological perspective on the pathophysiology of NSSI in MDD.

Altered Subcortical Brain Volume and Cortical Thickness Related to Insulin Resistance in Type 2 Diabetes Mellitus

PURPOSE

The objective of this study is to examine the alterations in subcortical brain volume and cortical thickness among individuals diagnosed with Type 2 diabetes mellitus (T2DM) through the application of morphometry techniques and, additionally, to investigate the potential association between these modifications and insulin resistance (IR).

MATERIALS AND METHODS

The present cross-sectional study comprised a total of 121 participants (n = 48 with healthy controls [HCs] and n = 73 with T2DM) who were recruited and underwent a battery of cognitive testing and structural magnetic resonance imaging (MRI). FreeSurfer was used to process the MRI data. Analysis of covariance compared discrepancies in cortical thickness and subcortical brain volume between T2DM and HCs, adjusting for the potential confounding effects of gender, age, education, and body mass index (BMI). Exploratory partial correlations investigated links between IR and brain structure in T2DM participants.

RESULTS

Compared with HCs, individuals with T2DM demonstrated a cortical thickness decrease in the right caudal middle frontal gyrus, right pars opercularis, left precentral gyrus, and bilateral superior frontal gyrus. Furthermore, this study for T2DM found that the severity of IR was inversely related to the volume of the left putamen and left hippocampus, as well as the thickness of the left pars orbitalis, left pericalcarine, right entorhinal area, and right rostral anterior cingulate gyrus.

CONCLUSION

The evidence for structural brain changes in T2DM was observed, and alterations in cortical thickness were concentrated in the frontal lobes. Correlations between IR and frontal cortical thinning may serve as a potential neuroimaging marker of T2DM and lead to various diabetes-related brain complications.

Effect of maternal hypertensive disorder on their children's neurocognitive functioning in mediated via low birthweight and BMI not by brain cortical thickness

The aim of the study was to examine the association between prenatal exposure to maternal Hypertensive disorder during pregnancy (HDP) on brain structure and neurocognitive functioning (NCF) in singleton children aged between 9 and 10 years using the baseline wave of the Adolescent Brain and Cognitive Development (ABCD) Study. The ABCD Study® interviewed each child (and their parents), measured NCF, and performed neuroimaging. Exposure to maternal high blood pressure (HBP) and preeclampsia or eclampsia (PE/EL) were extracted from the developmental history questionnaire. Differences in cortical thickness (CTh) and five cognitive abilities (two executive functions, working and episodic memory, processing speed, and two language abilities) between exposed and unexposed children were examined using generalized linear models. The mediating effects of CTh, birthweight, and BMI on the relationship between maternal HDP on NCF were also examined. A total of 584-children exposed to HBP, 387-children exposed to PE/EL, and 5,877 unexposed children were included in the analysis. Neither CTh nor NCF differed between the exposed and unexposed children with or without adjusting for the confounders including the child's age, sex, race, education, and birth histories. The whole-brain CTh did not mediate the relationships between HDP and NCF. However, the relationship between HDP and most of the NCF was mediated by the child's birthweight and BMI. Exposure to maternal HDP can affect their offspring's later-life cognitive abilities via low birthweight and BMI during childhood. Prospective longitudinal studies, following up from infancy, are needed to further delineate the association of HDP on children's cognitive abilities.

Age-related changes in sleep spindle characteristics in individuals over 75 years of age: a retrospective and comparative study

BACKGROUND

Sleep and its architecture are affected and changing through the whole lifespan. We know main modifications of the macro-architecture with a shorter sleep, occurring earlier and being more fragmented. We have been studying sleep micro-architecture through its pathological modification in sleep, psychiatric or neurocognitive disorders whereas we are still unable to say if the sleep micro-architecture of an old and very old person is rather normal, under physiological changes, or a concern for a future disorder to appear. We wanted to evaluate age-related changes in sleep spindle characteristics in individuals over 75 years of age compared with younger individuals.

METHODS

This was an exploratory study based on retrospective and comparative laboratory-based polysomnography data registered in the normal care routine for people over 75 years of age compared to people aged 65-74 years. We were studying their sleep spindle characteristics (localization, density, frequency, amplitude, and duration) in the N2 and N3 sleep stages. ANOVA and ANCOVA using age, sex and OSA were applied.

RESULTS

We included 36 participants aged > 75 years and 57 participants aged between 65 and 74 years. An OSA diagnosis was most common in both groups. Older adults receive more medication to modify their sleep. Spindle localization becomes more central after 75 years of age. Changes in the other sleep spindle characteristics between the N2 and N3 sleep stages and between the slow and fast spindles were conformed to literature data, but age was a relevant modifier only for density and duration.

CONCLUSION

We observed the same sleep spindle characteristics in both age groups except for localization. We built our study on a short sample, and participants were not free of all sleep disorders. We could establish normative values through further studies with larger samples of people without any sleep disorders to understand the modifications in normal aging and pathological conditions and to reveal the predictive biomarker function of sleep spindles.

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next-generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct ~10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer's disease and healthy brain ageing based on ~4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 μm isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

Brain structure and activity predicting cognitive maturation in adolescence

Cognitive abilities of primates, including humans, continue to improve through adolescence 1,2. While a range of changes in brain structure and connectivity have been documented 3,4, how they affect neuronal activity that ultimately determines performance of cognitive functions remains unknown. Here, we conducted a multilevel longitudinal study of monkey adolescent neurocognitive development. The developmental trajectory of neural activity in the prefrontal cortex accounted remarkably well for working memory improvements. While complex aspects of activity changed progressively during adolescence, such as the rotation of stimulus representation in multidimensional neuronal space, which has been implicated in cognitive flexibility, even simpler attributes, such as the baseline firing rate in the period preceding a stimulus appearance had predictive power over behavior. Unexpectedly, decreases in brain volume and thickness, which are widely thought to underlie cognitive changes in humans 5 did not predict well the trajectory of neural activity or cognitive performance changes. Whole brain cortical volume in particular, exhibited an increase and reached a local maximum in late adolescence, at a time of rapid behavioral improvement. Maturation of long-distance white matter tracts linking the frontal lobe with areas of the association cortex and subcortical regions best predicted changes in neuronal activity and behavior. Our results provide evidence that optimization of neural activity depending on widely distributed circuitry effects cognitive development in adolescence.

Sex-related variability of white matter tracts in the whole HCP cohort

Behavioral differences between men and women have been studied extensively, as have differences in brain anatomy. However, most studies have focused on differences in gray matter, while white matter has been much less studied. We conducted a comprehensive study of 77 deep white matter tracts to analyze their volumetric and microstructural variability between men and women in the full Human Connectome Project (HCP) cohort of 1065 healthy individuals aged 22-35 years. We found a significant difference in total brain volume between men and women (+ 12.6% in men), consistent with the literature. 16 tracts showed significant volumetric differences between men and women, one of which stood out due to a larger effect size: the corpus callosum genu, which was larger in women (+ 7.3% in women, p = 5.76 × 10-19). In addition, we found several differences in microstructural parameters between men and women, both using standard Diffusion Tensor Imaging (DTI) parameters and more complex microstructural parameters from the Neurite Orientation Dispersion and Density Imaging (NODDI) model, with the tracts showing the greatest differences belonging to motor (cortico-spinal tracts, cortico-cerebellar tracts) or limbic (cingulum, fornix, thalamo-temporal radiations) systems. These microstructural differences may be related to known behavioral differences between the sexes in timed motor performance, aggressiveness/impulsivity, and social cognition.

Normative Modeling of Thalamic Nuclear Volumes and Characterization of Lateralized Volume Alterations in Alzheimer's Disease Versus Schizophrenia

BACKGROUND

Thalamic nuclei facilitate a wide range of complex behaviors, emotions, and cognition and have been implicated in neuropsychiatric disorders including Alzheimer's disease (AD) and schizophrenia (SCZ). The aim of this work was to establish novel normative models of thalamic nuclear volumes and their laterality indices and investigate their changes in SCZ and AD.

METHODS

Volumes of bilateral whole thalami and 10 thalamic nuclei were generated from T1 magnetic resonance imaging data using a state-of-the-art novel segmentation method in healthy control participants (n = 2374) and participants with early mild cognitive impairment (n = 211), late mild cognitive impairment (n = 113), AD (n = 88), and SCZ (n = 168). Normative models for each nucleus were generated from healthy control participants while controlling for sex, intracranial volume, and site. Extreme z-score deviations (|z| > 1.96) and z-score distributions were compared across phenotypes. z Scores were associated with clinical descriptors.

RESULTS

Increased infranormal and decreased supranormal z scores were observed in SCZ and AD. z Score shifts representing reduced volumes were observed in most nuclei in SCZ and AD, with strong overlap in the bilateral pulvinar, medial dorsal, and centromedian nuclei. Shifts were larger in AD, with evidence of a left-sided preference in early mild cognitive impairment while a predilection for right thalamic nuclei was observed in SCZ. The right medial dorsal nucleus was associated with disorganized thought and daily auditory verbal hallucinations.

CONCLUSIONS

In AD, thalamic nuclei are more severely and symmetrically affected, while in SCZ, the right thalamic nuclei are more affected. We highlight the right medial dorsal nucleus, which may mediate multiple symptoms of SCZ and is affected early in the disease course.

ComBatLS: A location- and scale-preserving method for multi-site image harmonization

Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters

BACKGROUND

Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases.

PURPOSE

This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion.

METHODS

Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm.

RESULTS

With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline.

CONCLUSIONS

This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Causal associations between chronic heart failure and the cerebral cortex: results from Mendelian randomization study and integrated bioinformatics analysis

Background

Chronic heart failure (CHF) patients exhibit alterations in cerebral cortical structure and cognitive function. However, the mechanisms by which CHF affects cortical structure and functional regions remain unknown. This study aims to investigate potential causal relationship between CHF and cerebral cortical structure through Mendelian randomization (MR).

Methods

The research utilized genome-wide association studies (GWAS) to explore the causal association between CHF and cerebral cortical structure. The results were primarily analyzed using the inverse-variance weighted (IVW). The reliability of the data was verified through horizontal pleiotropy and heterogeneity analysis by MR-Egger intercept test and Cochran's Q-test, respectively. Replication analysis was conducted in the Integrative Epidemiology Unit (IEU) OpenGWAS project for further validation. In addition, we collected mediator genes that mediate causality to reveal potential mechanisms. Integrated bioinformatics analysis was conducted using the Open Target Genetics platform, the STRING database, and Cytoscape software.

Results

The IVW results did not reveal any significant causal association between genetically predicted CHF and the overall structure of the cerebral cortex or the surface area (SA) of the 34 functional regions of the cerebral cortex (P > 0.05). However, the results revealed that CHF increased the thickness (TH) of pars opercularis (IVW: β = 0.015, 95% CI: 0.005-0.025, P = 3.16E-03). Replication analysis supported the causal association between CHF and pars opercularis TH (IVW: β = 0.02, 95% CI: 0.010-0.033, P = 1.84E-04). We examined the degree centrality values of the top 10 mediator genes, namely CDKN1A, CELSR2, NME5, SURF4, PSMA5, TSC1, RPL7A, SURF6, PRDX3, and FTO.

Conclusion

Genetic evidence indicates a positive correlation between CHF and pars opercularis TH.

Neurocognitive and neurobehavioral mechanisms of change following psychological treatment for alcohol use disorder

BACKGROUND

Although modestly effective treatments exist for alcohol use disorder (AUD), many individuals return to heavy drinking after treatment, suggesting the need for better understanding of factors that contribute to maintaining abstinence or drinking reductions. Whereas past studies identified what treatments work for AUD, recent studies focus more on why particular treatments work, and the mechanisms by which treatment leads to change. This focus on mechanisms of behavior change (MOBC) may inform the process by which treatment leads to better outcomes, and also may lead to new treatments or modifications of existing treatments that target empirically supported mechanisms known to lead to change. There is a paucity of studies examining MOBC from a neurocognitive perspective.

METHOD

To address this gap in knowledge, the study described here is examining emotional reactivity, alcohol cue reactivity, and cognitive control as potential MOBC at three levels of analysis - self-report, behavior, and neural.

RESULTS

One hundred ten treatment-seeking individuals with an AUD are being randomized to receive 8 sessions of either Cognitive Behavioral Treatment (CBT) or Mindfulness Based Treatment (MBT) after up to 4 sessions of a platform treatment focused on enhancing motivation to change. To establish the temporal relationship between changes in drinking and changes in MOBC, patients are assessed at baseline, during and immediately after treatment, and 9- and 15-months post-baseline. Relationships between changes in drinking and changes in the proposed MOBC will be examined using advanced mixed modeling techniques.

CONCLUSIONS

Results should advance AUD treatment by targeting treatments to neurocognitive MOBC.

Cortical Structure in Pre-Readers at Cognitive Risk for Dyslexia: Baseline Differences and Response to Intervention

Early childhood is a critical period for structural brain development as well as an important window for the identification and remediation of reading difficulties. Recent research supports the implementation of interventions in at-risk populations as early as kindergarten or first grade, yet the neurocognitive mechanisms following such interventions remain understudied. To address this, we investigated cortical structure by means of anatomical MRI before and after a 12-week tablet-based intervention in: (1) at-risk children receiving phonics-based training (n = 29; n = 16 complete pre-post datasets), (2) at-risk children engaging with AC training (n = 24; n = 15 complete pre-post datasets) and (3) typically developing children (n = 25; n = 14 complete pre-post datasets) receiving no intervention. At baseline, we found higher surface area of the right supramarginal gyrus in at-risk children compared to typically developing peers, extending previous evidence that early anatomical differences exist in children who may later develop dyslexia. Our longitudinal analysis revealed significant post-intervention thickening of the left supramarginal gyrus, present exclusively in the intervention group but not the active control or typical control groups. Altogether, this study contributes new knowledge to our understanding of the brain morphology associated with cognitive risk for dyslexia and response to early intervention, which in turn raises new questions on how early anatomy and plasticity may shape the trajectories of long-term literacy development.

Differences in scalp-to-cortex tissues across age groups, sexes and brain regions: Implications for neuroimaging and brain stimulation techniques

Aging affects the scalp-to-cortex distance (SCD) and the comprising tissues. This is crucial for noninvasive neuroimaging and brain stimulation modalities as they rely on traversing from the scalp to the cortex or vice versa. The specific relationship between aging and these tissues has not been comprehensively investigated. We conducted a study on 250 younger and older adults to examine age-related differences in SCD and its constituent tissues. We identified region-specific differences in tissue thicknesses related to age and sex. Older adults exhibit larger SCD in the frontocentral regions compared to younger adults. Men exhibit greater SCD in the inferior scalp regions, while women show similar-to-greater SCD values in regions closer to the vertex compared to men. Younger adults and men have thicker soft tissue layers, whereas women and older adults exhibit thicker compact bone layers. CSF is considerably thicker in older adults, particularly in men. These findings emphasize the need to consider age, sex, and regional differences when interpreting SCD and its implications for noninvasive neuroimaging and brain stimulation.

Age-related change in cortical thickness in adolescents at clinical high risk for psychosis: a longitudinal study

Progression to psychosis has been associated with increased cortical thinning in the frontal, temporal and parietal lobes in individuals at clinical high risk for the disorder (CHR-P). The timing and spatial extent of these changes are thought to be influenced by age. However, most evidence so far stems from adult samples. Longitudinal studies are essential to understanding the neuroanatomical changes associated to transition to psychosis during adolescence, and their relationship with age. We conducted a longitudinal, multisite study including adolescents at CHR-P and healthy controls (HC), aged 10-17 years. Structural images were acquired at baseline and at 18-month follow-up. Images were processed with the longitudinal pipeline in FreeSurfer. We used a longitudinal two-stage model to compute the regional cortical thickness (CT) change, and analyze between-group differences controlling for age, sex and scan, and corrected for multiple comparisons. Linear regression was used to study the effect of age at baseline. A total of 103 individuals (49 CHR-P and 54 HC) were included in the analysis. During follow-up, the 13 CHR-P participants who transitioned to psychosis exhibited greater CT decrease over time in the right parietal cortex compared to those who did not transition to psychosis and to HC. Age at baseline correlated with longitudinal changes in CT, with younger individuals showing greater cortical thinning in this region. The emergence of psychosis during early adolescence may have an impact on typical neuromaturational processes. This study provides new insights on the cortical changes taking place prior to illness onset.

White matter trajectories over the lifespan

White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.