Frontiers in Aging Neuroscience

Old age and the apolipoprotein E {varepsilon}4 (APOE4) genotype are two of the greatest risk factors for late-onset Alzheimers disease (LOAD). However, the interaction between these is poorly understood, as most preclinical studies use young mice. Therefore, we assessed the interaction between APOE genotype and age across a comprehensive set of cerebrovascular and related outcomes. We performed in vivo imaging, ex vivo cerebral artery studies, behavioral tests, and molecular analyses in male and female homozygous APOE3 and APOE4 mice at [~]6 months (young) and [~]24 months (old). APOE4 interacted with old age to lead to deficits in brain volume and greater microglia content. Old APOE4 mice also exhibited greater cerebral artery vasoconstriction to endothelin-1 (ET-1) than old APOE3 mice, a response concomitant with age-and genotype-related differences in the expression of ET-1 receptors and endothelin-converting enzyme. While we found several interactions between age and APOE genotype, only age impacted cognitive function, cerebral artery endothelial function, and arterial stiffness. In summary, we found that brain volume, neuroinflammation, and ET-1-related outcomes were influenced by the interaction of APOE genotype and age, while other outcomes were affected only by age. As such, an altered ET-1 response and greater neuroinflammation may contribute to the increased risk for LOAD in APOE4 carriers.

Many nonhuman primate species recapitulate the neuropathological features of sporadic Alzheimer's disease (AD) to varying degrees. As with humans, the assessment of AD-related pathology in nonhuman primates has historically relied on the use of postmortem brain tissues. In vivo alternatives, such as PET imaging tracers and fluid biomarkers, have been developed for use in humans but require further validation in nonhuman primates before replacing postmortem analyses. Here we employed the Nucleic Acid-Linked Immuno-Sandwich Assay (NULISATM) CNS Disease panel to compare age-related changes in plasma biomarkers in two nonhuman primate species (rhesus monkeys and baboons). In addition, we examined whether amyloid and tau biomarkers were associated with brain atrophy, as measured by gray matter volume. We found significant associations between age and multiple biomarkers of neurodegeneration for both species, as well as significant differences in the patterns of these associations between the two species. For the phosphorylated tau measures, though rhesus monkeys had higher values, baboons showed significant and stronger associations with age. By contrast, rhesus monkeys exhibited an earlier age-related decline in A{beta}42/A{beta}40 ratio than baboons. Finally, in both species, lower A{beta}42/A{beta}40 ratios were associated with lower gray matter volumes. This is the first systematic comparative study of age-related changes in neurodegeneration biomarkers in two closely related nonhuman primates using comparable age ranges and sample sizes, and the same multiplex assay. Future studies should examine longitudinal changes in these biomarkers as well as validate the plasma findings using cerebral spinal fluid.

Hippocampal atrophy and altered functional connectivity are prominent features of mild cognitive impairment (MCI), yet their relationships and joint contribution to cognitive impairment remain unclear. We evaluated multidomain cognitive-sensorimotor performance, whole-brain gray matter atrophy patterns, associated functional connectivity, and their coupling in MCI and HC. We conducted this cross-sectional study in 25 clinically diagnosed amnestic and non-amnestic MCI patients and 15 age- and education-matched healthy controls (HC). Participants (57-81 years old) completed a battery assessing general cognition, executive functions, attention, speech-in-noise perception, manual dexterity, and balance, combined with structural and resting-state functional magnetic resonance imaging. Among the 25 MCI patients, 8 (32%) presented with amnestic MCI, 15 (60%) with non-amnestic MCI, and 2 (8%) with subjective cognitive decline. Linear mixed models revealed that semantic fluency (g = 0.74) was the largest discriminator of MCI, followed by attention (g = 0.61), phonemic fluency (g = 0.59), and speech-in-noise perception (g = 0.56). Voxel-based morphometry showed bilateral hippocampal and anteromedial cerebellar gray matter atrophy in MCI. Seed-based functional connectivity analyses indicated that atrophy was not uniformly associated with reduced connectivity. Cortico-subcortical hypoconnectivity emerged only in networks associated with left hippocampal atrophy in MCI compared with HC. Network-based analyses showed disrupted brain-behavior coupling and a stronger detrimental influence of age in MCI than in HC. These findings confirm the critical role of hippocampal structure-function relationships in multidomain MCI symptoms. A decoupled brain-cognition aging profile suggests that multimodal indices integrating hippocampal structure, connectivity, and behavioral performance may strengthen MCI diagnosis.

Canagliflozin (Cana), an SGLT2 inhibitor prescribed for type 2 diabetes, extends median lifespan by 14% in male but not female UM-HET3 mice at 180 ppm, with male-specific neuroprotective effects, despite females accumulating higher drug concentrations in blood and brain. Here, we tested whether reducing the dose to a subclinical level of 60 ppm could provide neuroprotective benefits in females by reducing drug accumulation. Starting treatment at 7 months of age, Cana at 60 ppm improved glucose tolerance in both sexes at 18 months and increased water and food intake, consistent with SGLT2 inhibition, but produced only a transient reduction in fat mass in males after one month on diet, with no sustained effect on body weight in either sex. At 60 ppm, Cana did not improve cognitive function at 18 months or reduce neuroinflammation in males, whereas females showed reduced hippocampal microgliosis and astrogliosis at 24 months. Pharmacokinetic analysis demonstrated that females accumulated 3- to 5-fold higher Cana concentrations than males across brain regions, blood, and liver. Together, these findings demonstrate that neither dose reduction nor greater drug accumulation drives neuroprotective benefit in females, indicating fundamental sex differences in the biological response to SGLT2 inhibition and suggesting that the sex-specific longevity effects of Cana are not simply a matter of dose.

ABSTRACT Dementia classification in heterogeneous populations is complicated by the influence of education, language, socioeconomic position and health status on cognitive test performance. Approaches that rely on fixed cognitive thresholds or isolated predictor sets may therefore perform inconsistently across diverse older adult populations. We developed and internally validated a multidomain classification model using harmonized data from the Longitudinal Ageing Study in India (LASI) and its diagnostic sub-study, LASI-DAD. Clinical dementia status was defined as a binary outcome derived from consensus-based Clinical Dementia Rating (CDR) assessments, averaged across 20 multiply imputed outcome datasets and finalised using a 0.5 threshold. The analytic sample comprised 3,186 participants after exclusion of those with mild cognitive impairment. Twenty-two predictors spanning cognitive performance, informant-reported decline, cardiometabolic biomarkers and sociodemographic characteristics were retained. Missing predictor values were addressed using k-nearest neighbours imputation. Model development used a stratified 70:30 train-test split, with nested cross-validation conducted within the training set only, and class imbalance corrected using the Synthetic Minority Oversampling Technique (SMOTE) applied exclusively within training folds. Five supervised learning approaches were evaluated: logistic regression, random forest, gradient boosting, XGBoost and support vector machines. The final logistic regression model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.932 and an average precision of 0.668 on the held-out set. At the optimal probability threshold of 0.70, sensitivity was 0.771, specificity was 0.905, positive predictive value was 0.325 and negative predictive value was 0.985. A cognition-only comparator, restricted to task-based cognitive measures and run through the same pipeline, yielded a ROC-AUC of 0.908 and average precision of 0.620, indicating incremental discriminatory value from the full multidomain feature set. Dementia prevalence increased progressively across model-derived risk strata, reaching approximately 50% in the highest category. Permutation importance and SHAP analyses identified informant-reported decline and orientation as the strongest contributors to classification, with cardiometabolic variables providing smaller but consistent incremental contributions. Dementia classification in a socially and clinically heterogeneous Indian cohort can be improved by integrating cognitive, informant, cardiometabolic and sociodemographic information within a single interpretable model. The strongest predictive signal was carried by cognitive and informant measures, with non-cognitive features adding structure around that core. The model requires external validation and calibration before broader application can be considered. Keywords - dementia; classification; multidomain modelling; machine learning; interpretability; older adults; India; LASI-DAD

Traumatic brain injury (TBI) is a characteristic feature of neurodegenerative diseases such as Alzheimers disease and chronic traumatic encephalopathy. Small animal models have been used to establish clinically relevant biomarkers of neuropathology, however, they show significant anatomical differences from humans and are affected by artificial experimental manipulations, making them often unsuitable for longitudinal study of repetitive mild TBI. Building on a previous study of neuropathology in headbutting bovids in the wild, this pilot study investigated whether freely headbutting domestic goats, which naturally engage in low-intensity, high-frequency head impacts, accumulate measurable biomarkers of neurodegeneration in cerebrospinal fluid (CSF) and brain tissue. Over a six-month period, three male goats (Capra hircus) were allowed to freely headbutt under continuous video surveillance. Monthly CSF samples were collected, and concentrations of key neurodegeneration biomarkers were measured via multiplex immunoassays, including amyloid {beta} ; peptides (A {beta} 40, A {beta} 42), total and phosphorylated tau (tTau and pTau), glial fibrillary acidic protein (GFAP), S100 calcium-binding protein B (S100B), and neurofilament M (NF-M). Postmortem immunohistochemistry was conducted on prefrontal cortical tissues using antibodies targeting pTau, GFAP, and S100B. Head impact kinematics were quantified using horn-mounted accelerometer and inclinometer sensors that recorded linear acceleration, rotational velocity, and head orientation during naturally occurring headbutting events. Several notable trends were observed. Phosphorylated tau as well as reactive astrocytes were detected in the brain tissue, mirrored by elevated GFAP detected in the CSF. PET TSPO was unsuccessful, however, FDG PET revealed frontal-dominant activity in all goats, and one with asymmetrical activation. Overall, the goats sustained 5,000-7,000 head impacts each over six months, with forces up to 388 N and peak acceleration up to 16.5 g. This multi-modal observational study is the first to characterize neurodegeneration biomarkers and kinematics in headbutting goats. Even at one year old, the combination of pTau and gliosis in both the brain tissue and CSF indicates that the goat s repetitive head impacts begin to show neurodegenerative consequences early in life. Likely, the severity of these consequences increases with headbutts and age, eventually resulting in chronic neurodegeneration. This system shows promise as a large-animal model for the longitudinal study of the onset and progression of neurodegenerative disease.

Alzheimer's disease (AD) is clinically marked by difficulty retaining newly learned information, yet routine memory scores often conflate poor initial encoding with failure to stabilise information after encoding. This ambiguity limits the mechanistic interpretability of cognitive assessment during the transition from mild cognitive impairment to AD. Here we propose a Hippocampal Cortical Consolidation Bottleneck (HCCB) model to computationally separate these two components of new memory failure. The model represents newly presented information as a rapidly formed hippocampal trace and a slowly stabilised cortical trace, predicting a residual bottleneck when delayed recall falls below the level expected from immediate recall. We operationalised this prediction as Consolidation Bottleneck Index*(CBI*), a cognitively normal reference normalised residual index, and evaluated it using Alzheimer's Disease Neuroimaging Initiative (ADNI) cognitive and MRI data, with independent dynamical support from OpenNeuro EEG. Simulations showed recent memory vulnerability when hippocampal vulnerability exceeded cortical vulnerability. In ADNI, CBI* increased from cognitively normal participants to mild cognitive impairment nonconverters, reached Alzheimer like levels in mild cognitive impairment converters, and was associated with hippocampal atrophy. CBI* added minimal discrimination beyond established clinical and structural predictors, supporting its role as a mechanistic phenotype rather than a replacement prognostic model. OpenNeuro EEG further showed increased neurodynamic rigidity in AD. Our findings provide a computational framework for quantifying failed stabilisation of newly encoded information in AD progression.

Ageing is often associated with a decline in cardiovascular and cerebrovascular health. This study examines the relationship between cerebral arterial elasticity and measures of cardiovascular health, as well as longitudinal changes in cerebral arterial elasticity over a 1.5 year interval. The pulse relaxation function (PReFx) is a measure of regional cerebral arterial elasticity derived using diffuse optical tomography (pulse-DOT). PReFx was measured over the anterior brain, including the frontal lobes and anterior sections of temporal and parietal regions that are especially vulnerable to vascular and cognitive ageing. We examined relationships between PReFx and measures of four cardiovascular risk factors (CVRF; i.e., hypertension, cholesterol, diabetes, obesity), as well as CVRF burden (i.e., number of CVRFs) in the highly active and cognitively healthy ACTIVate cohort (60-70 years). We replicated the well-established relationship between PReFx and both age and cardiorespiratory fitness, and examined associations between PReFx, measures of cardiovascular health and CVRF burden. Higher CVRF burden was linearly associated with lower cerebral arterial elasticity. The relationship between age and cerebral arterial elasticity was partially mediated by pulse pressure, an index of hypertension. PReFx declined significantly in as little as 1.5 years, and the effect did not vary with baseline level of any of the four CVRFs. We conclude that PReFx shows promise as a putative biomarker for monitoring cerebrovascular ageing in healthy older adults.

In Parkinsons disease (PD), blood-based (BB) biomarkers ubiquitin c-terminal hydrolase L 1 (UCHL-1), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) correlate with motor or cognitive impairment. However, it is unclear if blood levels of these biomarkers represent changes in nigrostriatal neuron viability or dopamine (DA) signaling. In 6-OHDA and Pink1 knockout (KO) rat models that showed progressive loss of DA tissue and tyrosine hydroxylase (TH) protein, we quantified UCHL-1, GFAP, and NfL expression in striatum and substantia nigra (SN) at 7- and 28-days in the 6-OHDA model and 7- and 18-month old in Pink 1 KO. Substantial changes in all biomarkers occurred with TH loss in SN, but not striatum, in both models. UCHL-1 levels increased against remaining TH protein. Accordingly, serum UCHL-1 levels increased 25% at 28 days post-6-OHDA and 18-month old Pink1 KO. GFAP and NfL levels increased in SN 28 days post-6-OHDA and 18 month-old Pink1 KO. Serum GFAP levels increased 28 days post-6-OHDA and 18 month-old Pink1 KO. Serum levels of NfL increased 28 days post-6-OHDA, and in 18 month-old Pink1 KO and wild-type, without influence by genotype. Expression levels of each biomarker were greater in the SN vs striatum, suggesting the SN contributes greater quantities of biomarkers to the blood and reflect TH loss therein. Taken together, our preclinical results show alignment between serum levels of UCHL-1, GFAP, and NfL and loss of TH and DA in the SN. As such, these biomarkers may be relevant peripheral indicators of deficient nigrostriatal DA signaling, and reflect nigrostriatal function in PD.

Objectives: Poor sleep quality is a risk factor for Alzheimer's disease (AD). Older African Americans experience disproportionately high rates of sleep disturbance and AD. Medial temporal lobe (MTL) flexibility reflects dynamic neural reorganization and may be a marker of generalization performance. This study examined whether sleep quality moderates the association between MTL flexibility and memory generalization. Methods: Fifty older African Americans (MeanAge=69.7{+/-}6.21 years; 80% women) underwent rs-fMRI to quantify MTL flexibility, Rutgers Acquired Equivalence Task for memory generalization, and Pittsburgh Sleep Quality Index for sleep quality. Results: Greater MTL flexibility was associated with better generalization (r=0.367, p=.017). Good sleepers showed higher MTL flexibility (F(1,44)=8.11, p2=.156, p=.007) and superior generalization (F(1,46)= 12.33, p2=.211, p=.001). Sleep quality significantly moderated the MTL flexibility and generalization relationship ({beta}=-1.519, p=.012). Conclusions: Preserved MTL flexibility may confer generalization only in good sleepers, suggesting that sleep disturbance may disrupt the MTL neural resilience among older African Americans.

INTRODUCTION: Tau pathology in Alzheimers disease preferentially afflicts excitatory neurons in the limbic and association cortices that utilize high levels of calcium signaling to perform cognitive operations. This includes the layer III pyramidal cells in the dorsolateral prefrontal cortex (dlPFC) that subserve higher cognition, which express the calcium-binding protein, calbindin, when young and healthy, but lose calbindin and develop tangles and degenerate in Alzheimers disease (AD). These data suggest that loss of calbindin may be associated with the emergence of tau pathology. However, the relationship between calbindin and early-stage, soluble tau pathology is challenging to study in human brains, as soluble pTau dephosphorylates within 15min postmortem. In contrast, the relationship between calbindin and soluble pT217-tau expression can be studied in aging macaques with naturally-occurring tau pathology, where perfusion fixation is possible to capture phosphorylation state in situ. METHODS: The current study used multiple-label-immunofluorescence to label MAP2-positive dlPFC layer III pyramidal cells for calbindin and pT217-tau in macaque brains across the adult age span (8-34.5yrs). The study employed a semi-automated CellProfiler workflow to identify labeled pyramidal cell dendrites the cellular compartment where tau pathology begins in AD. RESULTS: Calbindin expression decreased with age, while pT217Tau increased with age. Specifically, the ratio of calbindin/pT217-tau within a dendrite decreased with age, and was especially prominent in the aged macaques with long-term inflammatory disorders. DISCUSSION: These data suggest that the loss of calbindin in dendrites with advancing age, and especially with inflammation, contributes to the rise of tau pathology and the risk of AD.

Cognitive impairment is a prevalent symptom extending from physiological ageing to disease. It commonly manifests itself in initial memory problems, progressing and co-occurring in more severe conditions such as Mild Cognitive Impairment, Alzheimer's Disease and Major Depressive Disorder. However, current non-invasive screening assessments either lack biological information or are invasive and restricted to specialized centers with complex and cost-intensive set-ups. Here, we conducted an initial validation of mobile pupillometry with Virtual Reality (VR) under experimental conditions as a digital biomarker for cognitive impairment by testing required biomarker-specific properties. For this purpose, we first assessed its construct validity by testing healthy participants (n=43) on an n-back task in VR while pupil size was measured. Mixed effects models revealed that similar to lab-based eye-tracking systems, pupil size increased in a sensible and distinguishable fashion as a function of working memory load. Second, to test the signal's reliability, the same participants were tested on the identical set-up two to three months after their first visit. We observed that the pupil response profile was highly stable over this period. Third, for its clinical validity, we examined patients (n=89) from three different cohorts with varying degrees of cognitive impairment and compared them to healthy control participants (n=81). Mixed-effects models indicated that pupil size was reduced as a function of cognitive impairment levels at higher cognitive load and that this effect was stronger pronounced with increasing age. In conclusion, we provide initial evidence for mobile pupillometry being a sensitive, reliable and clinically valid digital biomarker for cognitive functioning and impairment, which offers desirable properties due to its quick, automatized and location-independent set-up. Keywords: digital biomarker, mobile pupillometry, Virtual Reality, cognition, , Major Depressive Disorder, Mild Cognitive Impairment, Alzheimer's Disease

Declines in the cognitive inhibition that comes with aging impacts daily life and independence. It is still debated whether overactivation during cognitive inhibition tasks in older adults is due to compensation or neural noise. To address this question, we examined age-related changes in inter-subject similarity (ISS) of functional connectivity from task-based and resting-state fMRI. Using Bayesian hierarchical modeling, we identified 27 Stroop task-related regions of interest and found that ISS in these regions was significantly reduced in older adults during task performance. Furthermore, aging is associated with a loss of consistent functional connectivity patterns in frontal regions, accompanied by the emergence of a convergent compensatory mechanism within visual attention regions. Principal component analysis of individual activation deviations from the group-mean pattern showed that task performance in older adults cannot be explained by overall task-related brain activation, but rather by a specific spatial component involving suppression of the default mode network and increased activity in visual attention regions. These findings indicate that task-related brain overactivation in older adults is not due to uniform noise or uniform compensation, but rather a spatially specific pattern of functional reorganization.

Older adults are among the fastest growing groups of traumatic brain injury (TBI) patients and sustain disproportionately poor chronic outcomes. Despite this, the preclinical aging-TBI literature is limited. Beyond the limited presence of aging TBI studies, most studies published in this domain use moderate-to-severe, open head models of TBI, rather than closed head models of mild TBI (mTBI) and repetitive mTBI (rmTBI), the most clinically prevalent presentation. Whether age modulates the chronic behavioral consequences of rmTBI is unknown. In the current study, young (3-4 months) and aged (18-19 months) male C57BL/6 mice received either five mTBIs on alternating days to model rmTBI or sham procedures and underwent behavioral testing in the chronic phase for spatial memory and anxiety-related behavior. Because cross-age behavioral comparisons are confounded by age-related declines in activity and by large sample sizes necessary to detection interaction effects, we applied a three-tier analytical framework combining within-age comparisons, sham-normalized inter-age comparisons, and factorial two-way ANOVA. Contrary to our hypothesis that aging would worsen rmTBI behavioral deficits, age produced domain-divergent effects. Spatial memory deficits were directionally consistent in both young and aged mice but was attenuated in the aged group. Conversely, anxiety-related behavior emerged selectively in the aged mice showing increased thigmotaxis. Locomotion was driven by age alone, with no injury effect, confirming that the aged anxiety signal was not a locomotor artifact. A post-hoc sensitivity analysis indicated that resolving the Age x Injury interaction effect would require at least 44 animals per group. These findings show that age shapes the affective, but not the cognitive, consequences of chronic rmTBI, and underscoring that statistical strategy is inseparable from design in factorial injury studies.

Bimanual coordination declines in late adulthood, but the neural mechanisms underlying these changes remain unclear. Age-related differences in brain activity have been interpreted either as compensatory recruitment of frontal cognitive control regions or as a shift toward feedback-based control, supported by sensory and cerebellar processing systems. To investigate these hypotheses, we examined brain activity, using fMRI in twenty-three younger and twenty-three older adults performing a bimanual visuomotor pinch-force task with different task complexities. Behaviourally, older adults showed lower accuracy than younger adults, particularly when task demands increased. Neuroimaging results revealed general age-dependent increases in activity within posterior cerebellar lobules VI-VII, regions overlapping with the classical oculomotor vermis and implicated in visuomotor adaptation, movement calibration, and error-based motor learning. In addition, during the more demanding task condition, older adults showed a greater increase in activation of anterior cerebellar lobules IV-V and a decrease in activation of the medial frontal pole (BA10). No consistent age-related increases or decreases in task related activation was observed in parieto-frontal regions. Moreover, better task performance across age groups was associated with lower activation in frontal cognitive control regions, including the superior medial frontal gyrus and right inferior frontal gyrus. Together, these results suggest increased feedback- and error-related sensorimotor processing in older adults involving the cerebellum and frontal cortex.

BACKGROUND: Accumulations of AD and LATE-NC both contribute to changes in hippocampal volume, possibly via distinct and/or overlapping mechanisms. Microglia-driven inflammation is a shared pathway associated with both AD and LATE-NC. However, the extent to which microglia inflammation is associated with hippocampal volume is less understood. OBJECTIVE: Examine the relationship between AD and LATE-NC with hippocampal volume in persons with differing levels of microglia inflammation. METHODS: Cerebral hemispheres from 441 older adults who came to autopsy were studied. All hemispheres underwent ex-vivo MRI and detailed neuropathologic examination for neurodegenerative and cerebrovascular pathologies. Microglia were quantified in the hippocampal CA1/subiculum region using machine learning-based classifiers trained on digitized CR3-43-stained images via the HALO digital pathology platform. First, linear regression models examined the association of microglia with hippocampal volume, adjusting for demographics, postmortem interval (PMI), and common age-related pathologies. Second, linear regression models were employed to examine whether microglia density modified associations of {beta}-amyloid, tangle, or LATE-NC on hippocampal volume. RESULTS: Participants had a mean age of 90 years at death with 75% being women. Intermediate or high likelihood ADNC was present in 64% and LATE-NC (stage 2/3) was present in 52%. In linear regression models, adjusting for demographics and PMI, higher microglia density was associated with a lower hippocampal volume to hemisphere ratio (estimate = -0.021 SE=0.01, p=0.002); however, after adjusting for common age-related pathologies the association was attenuated (p=0.70). {beta}-amyloid, tangles, and LATE-NC remained independently associated with a lower hippocampal volume. The association of LATE-NC with hippocampal volume was stronger in brains with greater microglia burden (estimate for the interaction term = -0.016; SE=0.01, p=0.002). No interactions were seen between {beta}-amyloid or tangles with microglia on hippocampal volume. In stratified analyses, microglial density modified the association between LATE-NC and hippocampal volume, independent of AD neuropathologic status. CONCLUSION: Microglia-driven inflammation strengthens the association of LATE-NC, but not AD pathology, on hippocampal volume loss. These findings emphasize the importance of inflammatory pathways [when interpreting MRI-based neurodegeneration markers] in aging and mixed pathology.

Background: Alzheimer's Disease and Related Dementias (ADRD) is a growing global public health challenge, and caregivers experience high rates of burden, unmet needs, and system failures. These challenges vary by caregiver role and relationship to the care recipient, reflecting the heterogeneous nature of caregiving. Yet prior work has largely studied burden, unmet needs, and system failures as separate domains rather than examining how they co-occur within individual caregivers. Methods: We applied an LLM-based classification framework (Claude 3.5 Sonnet) to 7,198 posts from three ALZConnected caregiver forums (general, spouse/partner, and adult child caregivers), coding each post for burden, unmet needs, and system failures across 9, 12, and 10 categories respectively. We compared expression rates by caregiver role (primary vs. secondary) and relationship to the care recipient (spousal vs. child) and used post-level co-occurrence networks to map how categories cluster within and across domains. Results: Burden was expressed in 89.0% of posts and unmet needs in 93.3%, while system failures appeared in 34.8%. Primary caregivers reported burden more often than secondary caregivers (91.6% vs. 84.7%), while secondary caregivers reported more unmet needs (94.6% vs. 92.5%) and more system failures (37.2% vs. 33.4%). Child caregivers reported higher rates than spousal caregivers across all three domains. Co-occurrence networks showed dense within-domain clustering (density 0.61-0.65) and 84 significant cross-domain connections, with the strongest links between behavioral/safety burden and safety-management needs (21.7% of posts) and between emotional burden and emotional-support needs (20.9%). Conclusion: Burden, unmet needs, and system failures are not independent problems but form interconnected challenge ecosystems that vary by caregiver role and relationship. This suggests caregiver support should be designed around these connected patterns rather than treated as separate, single-domain interventions.

Background & Methods: The multifaceted physical nature of heritable cognitive impairment in dementia presents significant challenges for traditional linear frameworks attempting to model synergistic risk. While various loci are identified as contributing to neurocognitive disparities, the emergent phenotypic expression and associated predictive value relative to standard clinical baselines require further investigation. To facilitate dimensional reduction of complex genetic data into identifiable phenotypes, Generalized Low Rank Modeling (GLRM) and K-means clustering are applied to participant data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The utility of these derived archetypes and clusters is assessed, stratifying variance for Mini-Mental State Examination (MMSE) performance. Utilizing generalized additive modeling (GAM) and partial eta squared (p2) effect size, the derived genetic features are compared with other predictors including age, educational attainment, gender, and raw, genetic variant carriage dimensions. Results & Conclusion: In accordance with the hypothesized empirical regularity, age and education persist as primary predictors of MMSE performance. The unsupervised machine learning pipeline successfully identified a composite genetic cluster that emerged as an influential predictor in terms of relative magnitude (p2). Centroid analysis of the GLRM subspace indicated that a particular sub-population (Cluster 2) - defined by a substantial weighting on the EPHA1 target - demonstrated a statistically significant association with MMSE scores, relative to cluster 3. These results suggest that data-driven genetic feature engineering provides an interpretable basis for inference regarding variance in global cognition. By discovering multivariate genetic architecture, this modeling approach captures complexity often missed by individual clinical variable modeling. Such findings implicate the utility of interpretable machine learning for translational dementia research and predictive clinical stratification.

Background: Aging-related changes in motor performance are associated with a widespread reorganization of the motor network. Despite extensive research on altered brain activity patterns in aging, the underlying neurophysiological mechanisms driving these reorganizational changes remain incompletely understood, particularly regarding the role of inhibitory control. Methods: We here combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to examine aging-related alterations in local excitability, oscillatory dynamics, and neural coupling in the motor system. Brain responsivity was probed by applying single-pulse TMS to the primary motor cortex (M1) in younger and older subjects at rest. Results: Our findings indicated reduced cortical excitability in the stimulated M1 of older participants, consistent with an age-related reduction of GABAergic inhibitory control. Phase-locking analyses revealed reduced intra- and interhemispheric coupling in lower frequency bands, suggesting decreased inhibitory output from the stimulated M1. Moreover, older individuals demonstrated less localized event-related desynchronization (ERD) with greater power decreases in the prefrontal cortex contralateral to the stimulation site, indicating enhanced prefrontal involvement in the aging motor system. Conclusions: Together, these findings underline the relevance of alterations in inhibitory processes in the motor network in aging and point to a shift from an automatic, locally driven towards a broader, putatively more cognitively controlled sensorimotor processing in older adults.

Normal brain aging and Alzheimers disease both involve progressive structural brain alterations, making it challenging to distinguish pathological neurodegeneration from normative aging-related atrophy. This study investigated whether Alzheimers disease exhibits radiomic patterns that mimic, diverge from, or selectively perturb age-associated structural brain changes. T1-weighted magnetic resonance imaging scans from the Alzheimers Disease Neuroimaging Initiative were analyzed using a region-wise radiomics framework across 10 anatomically defined brain regions. Radiomic features were extracted following automated segmentation, bias field correction, and intensity normalization. Age-associated radiomic patterns were first identified in cognitively normal subjects using Spearman correlation analysis. Features demonstrating significant age sensitivity were subsequently compared between cognitively normal and Alzheimers disease cohorts across age bins using Welchs two-sample t-tests with permutation-based significance estimation and false discovery rate correction. Medial temporal and limbic regions, particularly the hippocampus, entorhinal cortex, and cingulum, demonstrated consistent age-aligned radiomic trajectories with systematic, statistically significant disease-related shifts across all age bins, supported by large effect sizes and bootstrap-validated confidence intervals. In contrast, several other regions demonstrated more heterogeneous and less stable patterns of group separation across age bins. Secondary analysis using late mild cognitive impairment subjects demonstrated that these radiomic divergences are detectable at the transition from normal cognition to mild cognitive impairment, with statistically significant CN-LMCI separation but no significant LMCI-AD separation, positioning the identified markers as early-stage rather than late-stage indicators of neurodegeneration. These findings indicate that Alzheimers disease does not uniformly mimic normal aging across the brain but instead selectively perturbs radiomic features associated with normative aging trajectories. The identified markers represent promising candidates for age-adjusted radiomic biomarkers, warranting validation in independent cohorts to establish their generalisability. The fully automated nature of the analytical pipeline -- spanning segmentation, feature extraction, and statistical comparison without manual annotation -- may facilitate scalable validation of these biomarkers in larger neuroimaging cohorts.