Education Moderates the Biomarker–Cortical-Thickness Relationship in Females With Cognitive Decline: A Descriptive Study With Sex Comparisons

Journal of Clinical Neurology · 2026-01-01

Background and Purpose Education is a well-established proxy for cognitive reserve and hence may influence the relationship between Alzheimer's disease pathology and brain structure.This is particularly relevant in South Korea, where historical sex disparities in educational level among current elderly populations create unique cohort characteristics.This study examined how educational level moderates the biomarker-cortical-thickness relationship in females with cognitive decline, with descriptive sex comparisons used to contextualize these findings within a cohort reflecting disparities in the history of educational opportunities in South Korea.Methods In 80 patients (58 females, 22 males) with cognitive decline, we assessed plasma amyloid- 42 (A42), phosphorylated tau 181 (p-tau), and total tau levels, p-tau/A42 ratio, regional cortical thickness, and scores on the Mini-Mental State Examination.Given the substantial confounding between sex and educational level in this cohort, moderated mediation analyses examining the biomarker-cortical-thickness-cognition pathway were restricted to females (n=53 with complete data).Results Females had a higher p-tau/A42 ratio (p=0.026) and thicker frontal cortex (p=0.049)despite the absence of a significant difference in age-adjusted cognition.No significant sex educational level interaction was observed for the biomarkers or cognition.In females, educational level moderated the relationship between the p-tau/A42 ratio and cortical thickness (p<0.05 in all five brain regions), with high-educational level females showing the expected cortical thinning with pathology, while low-educational level females exhibited preserved thickness, which did not mediate the cognitive performance.Conclusions Descriptive sex comparisons revealed a higher biomarker burden yet greater cortical thickness in females.Importantly, educational level significantly moderated the biomarkercortical thickness relationship specifically in females, with lower-educated individuals showing structural preservation despite the presence of pathology.These findings suggest that measurements of the cortical thickness can underestimate the disease burden in lower-educated females, which highlights the need for educational level-stratified interpretations of structural biomarkers.

Longitudinal resting-state EEG–based modeling predicts phenoconversion and delineates heterogeneity in isolated REM sleep behavior disorder

Research Square · 2026-03-26

Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study

The Lancet Digital Health · 2026-01-01

BACKGROUND: Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain predicted age difference (PAD) has emerged as a sensitive biomarker of both sensorimotor and cognitive function after stroke. Our previous study showed a higher global brain PAD associated with poorer motor function after stroke. However, the association between local stroke lesion load, regional brain age, and motor impairment is unclear. This study aimed to investigate the associations between focal lesion damage, regional brain PAD in both hemispheres, and motor outcomes in chronic stroke, and to identify key predictors of motor impairment. METHODS: In this multicohort, retrospective, observational study, we included individuals with chronic unilateral stroke (>180 days post stroke) from the ENIGMA Stroke Recovery Working Group dataset and used individuals from the UK Biobank cohort to train the regional brain age prediction model. Structural T1-weighted MRI scans were used to estimate regional brain PAD in 18 predefined functional subregions via a graph convolutional network algorithm. Lesion load for each region was calculated on the basis of lesion overlap. Linear mixed-effects models assessed associations between lesion size, local lesion load, and regional brain PAD. Machine learning classifiers predicted motor outcomes using lesion loads and regional brain PADs. Structural equation modelling examined directional relationships among corticospinal tract lesion load, ipsilesional brain PAD, motor outcomes, and contralesional brain PAD. FINDINGS: We included 501 individuals from the ENIGMA Stroke Recovery Working Group dataset (34 cohorts in eight countries) and 17 791 individuals from the UK Biobank dataset. Larger total lesion size was positively associated with higher ipsilesional regional brain PADs (older brain age) across most regions (β=0·5420 to 0·9458 across significantly correlated regions, false discovery rate [FDR]-corrected p<0·05), and with lower brain PAD in the contralesional ventral attention and language network region (β=-0·3747, 95% CI -0·6961 to -0·0534, FDR-corrected p<0·05). Higher local lesion loads showed similar patterns. Specifically, lesion load in the salience network significantly influenced regional brain PADs across both hemispheres. Machine learning models identified corticospinal tract lesion load (adjusted mean difference -0·0905, 95% CI -0·1221 to -0·0589, p<0·0001), salience network lesion load (-0·0632, -0·0906 to -0·0358, p<0·0001), and regional brain PAD in the contralesional frontoparietal network (0·9939, 0·4929 to 1·4950, p=0·0001) as the top three predictors of motor outcomes. Structural equation modelling revealed that higher corticospinal tract lesion load was associated with poorer motor outcomes (β=-0·355, 95% CI -0·446 to -0·267, p<0·0001), which were further linked to younger contralesional brain age (0·204, 0·111 to 0·295, p<0·0001), suggesting that severe motor impairment is linked to compensatory decreases in contralesional brain age. INTERPRETATION: Our findings reveal that larger stroke lesions are associated with accelerated ageing in the ipsilesional hemisphere and paradoxically decelerated brain ageing in the contralesional hemisphere, suggesting compensatory neural mechanisms. Assessing regional brain age might serve as a biomarker for neuroplasticity and inform targeted interventions to enhance motor recovery after stroke. FUNDING: US National Institutes of Health.

EEG-based unsupervised learning uncovers an insomnia subtype with sleep-state misperception and associated brain and mental health risks

SSRN Electronic Journal · 2026-01-01

Multimodal Free-Water Imaging Links Cardiometabolic Risk to Periarterial Dysfunction and Amyloid Accumulation in Early Alzheimer’s

Research Square · 2026-05-07

Lower fat-free mass is independently linked to restless legs syndrome in men: a cross-sectional PSG–BIA study

Frontiers in Neurology · 2026-03-06

Objective: Restless legs syndrome (RLS) is a common sensorimotor disorder that disrupts sleep and quality of life. Sarcopenia-reduced skeletal muscle mass and function-has been linked to sleep disturbances, but its relationship with RLS remains unclear. We examined whether sarcopenia is associated with RLS, with a focus on sex-specific effects. Methods: We conducted a cross-sectional analysis of 5,752 adults who underwent both type-I polysomnography (PSG) and bioelectrical impedance analysis (BIA) at a tertiary sleep center. RLS was diagnosed by IRLSSG criteria. Sarcopenia was defined using skeletal muscle index (SMI) and fat-free mass index (FFMI) thresholds. Multivariable models adjusted for age, physical activity, caffeine/alcohol intake, and apnea-hypopnea index (AHI). Results: RLS prevalence was 6.6% in females and 2.9% in males. Sarcopenia was more frequent in the RLS group than in non-RLS (10.6 vs. 6.8%), particularly among males (8.7 vs. 3.2%). In males, lower SMI and FFMI were independently associated with higher odds of RLS; sex interaction for FFMI was significant. Conclusions: Reduced muscle mass is independently associated with RLS in men, suggesting a male-specific muscle phenotype relevant to RLS pathophysiology. Incorporating BIA-based screening and muscle-preserving interventions may benefit the management of male patients with RLS.

0799 Resting-State EEG Subtype and Stage Inference Identifies Distinct Prodromal Biotypes and Staging in iRBD

SLEEP · 2026-05-01

Abstract Introduction Idiopathic REM Sleep Behavior Disorder (iRBD) provides a critical preclinical window into α-synucleinopathy. Given the substantial heterogeneity in clinical trajectories, developing a biologically grounded framework for staging this prodromal phase is essential. We applied SuStaIn (Subtype and Stage Inference) modeling to resting-state electroencephalography (EEG) to identify distinct prodromal biotypes and evaluate their relevance using long-term phenoconversion outcomes. Methods A total of 285 participants (51 healthy controls, 234 iRBD) were drawn from a prospective cohort with standardized assessments and baseline/biannual EEG. Among iRBD participants, 39 converted (iRBD-CV) and 195 remained non-converted (iRBD-NC). Twenty-four EEG spectral-power features across eight cortical regions, standardized to healthy controls, were entered into the SuStaIn model to infer latent subtypes and stage trajectories. Longitudinal EEG was used to validate subtype stability, track stage progression, and examine associations with cognitive and clinical outcomes. Results SuStaIn modeling revealed two distinct EEG-based prodromal biotypes. Subtype 1 exhibited a focal reduction in posterior beta power that remained stable across stages. During follow-up, the majority of this group (89.5%) transitioned toward “Subtype 0,” indicating reversal of the posterior beta abnormality, and all individuals remained non-converted. Subtype 1 also showed higher educational attainment and a greater proportion of males. In contrast, Subtype 2 demonstrated a progressive trajectory beginning with early frontal theta elevations and extending to delta abnormalities. Within Subtype 2, higher stages were significantly associated with lower MoCA scores. Longitudinal EEG demonstrated significant stage advancement (p = 0.00013), with the most pronounced progression occurring in Subtype 2 and among converters. Conclusion SuStaIn modeling of resting-state EEG delineated biologically prodromal biotypes in iRBD, revealing two mechanistically distinct progression pathways: an adaptive–compensatory subtype (Subtype 1) and a progressive–degenerative subtype (Subtype 2). Subtype 1, marked by isolated posterior beta reduction without abnormalities in other regions and subsequent reversal over time, appears to reflect a compensatory mechanism underlying clinical stability. In contrast, Subtype 2, characterized by early and widespread theta–delta increases, represents an actively degenerating trajectory associated with cognitive decline. These findings demonstrate that early EEG-monitoring–based SuStaIn staging can stratify stable versus progressing iRBD, underscoring the prodromal period as a key window for disease-modifying interventions. Support (if any)

EEG-based unsupervised learning uncovers an insomnia subtype with sleep-state misperception and associated brain and mental health risks

Results in Engineering · 2026-05-01

Insomnia with sleep-state misperception (SSM), defined by a mismatch between subjective complaints and objective polysomnography, lacks a clear neurophysiological explanation despite its substantial clinical burden. Using an unsupervised autoencoder approach, we extracted latent EEG microstructure features and identified two reproducible insomnia subtypes across multiple datasets: an objective sleep disruption (OSD) phenotype marked by macrostructural abnormalities and an SSM phenotype presenting with near-normal polysomnography. Individuals with SSM showed reduced delta activity and elevated alpha activity during early N3 sleep, indicating shallow deep sleep and alpha intrusion. These microstructural alterations were strongly associated with clinically significant outcomes, including accelerated brain aging, impairments in attention and visual memory, and elevated depressive symptoms. Conventional SSM classifications based solely on subjective–objective discrepancy did not observe these pathophysiological abnormalities or their clinical consequences. Because consumer wearables quantify only macrostructural sleep metrics, they overlook these clinically relevant EEG features. Integrating microstructure-based analysis into portable sleep technologies may allow earlier identification of high-risk insomnia phenotypes that remain undetectable with standard approaches.

A composite measure of cerebral small vessel disease predicts cognitive change after stroke

medRxiv · 2026-04-24

Post-stroke cognitive recovery is difficult to predict using focal lesion characteristics alone. The brain's capacity to maintain cognitive function depends also on structural integrity of the whole brain. One way to measure brain health is through the severity of cerebral small vessel disease (CSVD) markers, which reflect aging-related pathologies that erode structural integrity. Here, we propose a composite measure of CSVD (cCSVD) integrating three independently validated biomarkers automatically quantified using T1-weighted MRIs: white matter hyperintensity volume (WMH; representing vascular injury), perivascular space count (PVS; putative glymphatic clearance), and brain-predicted age difference (brain-PAD; structural atrophy). We hypothesize that cCSVD, which captures the shared variance across these CSVD biomarkers, will be a robust indicator of whole-brain structural integrity and predict cognitive changes 3 months after stroke. We analyzed 65 early subacute stroke survivors with assessments within 21 days (baseline) and at 90 days (follow-up) post-stroke. WMH volume, PVS count, and brain-PAD were quantified from baseline T1-weighted MRIs, and then residualized for age, sex, days since stroke, and intracranial volume. Principal component analysis (PCA) of the residualized biomarkers was used to derive cCSVD. Beta regression with stability selection using LASSO was used to model three outcomes: baseline Montreal Cognitive Assessment (MoCA) scores, follow-up MoCA scores, and longitudinal change (follow-up score adjusted for baseline score). Logistic regression was used to test if baseline cCSVD predicted improvement in those with baseline cognitive impairment (MoCA < 26). The PCA revealed that the first principal component (PC1) explained 43.1% of the total variance among WMH volume, PVS count, and brain-PAD. The three biomarkers contributed nearly equally to PC1, which was subsequently used as the baseline cCSVD score. Lower baseline cCSVD was significantly associated with better MoCA scores at follow-up (β = -0.19, p = 0.009), even after adjusting for baseline MoCA (β = -0.12, p = 0.042), and, importantly, outperformed all individual biomarkers. Furthermore, lower cCSVD at baseline significantly increased the likelihood of improving to cognitively unimpaired status at three months (OR = 0.34, p = 0.036), independent of age and education. The composite CSVD captures the additive impact of vascular injury, glymphatic dysfunction, and structural atrophy on recovery in a way that individual measures do not. cCSVD accounts for shared variance across these domains, reflecting a patient's latent capacity for cognitive recovery, where relative integrity in one CSVD domain may mitigate effects of another. This automated, T1-based framework offers a scalable tool for predicting post-stroke recovery.

The association between sleep EEG-based brain age and cognitive function

Journal of the Neurological Sciences · 2025-12-01