Neurobiology of Aging

Age-related hearing loss is a leading modifiable risk factor for dementia and is increasingly recognized as a non-motor feature of Parkinson's disease (PD). The apolipoprotein E (APOE) E4 allele is the strongest genetic risk factor for Alzheimer's disease and is associated with cognitive decline in PD, yet its relationship to hearing loss remains unclear. Therefore, we examined the independent and interactive effects of PD status and APOE E4 carrier status on age-related hearing loss using a validated web-based speech-in-noise (SIN) assessment in 239,620 23andMe Research Institute participants without PD and 4,361 PD cases. Generalized additive models for location, scale, and shape (GAMLSS) showed that both PD and APOE E4 independently exacerbated age-related hearing decline, with speech reception thresholds (SRTs) worsening non-linearly with advancing age, but without evidence of synergistic interaction. However, longitudinal analyses in a subcohort completing at least two assessments (1,434 PD cases; 36,242 controls) using GAMLSS mixed models showed a significant three-way interaction between PD status, APOE E4, and age2, such that SIN hearing loss accelerated more steeply with age in APOE E4 carriers with PD. Males and individuals with lower educational attainment also exhibited worse SIN hearing loss. These results identify APOE E4 carriers with PD as a priority population for hearing screening and intervention, and support the integration of SIN assessments into routine PD care to detect hearing decline that may compound cognitive and communicative burden in aging.

Using data from a randomized clinical trial, we examined whether daily biofeedback training that modulates heart rate oscillations is associated with changes in microstructural brain texture in Alzheimer's disease signature cortical (ADSC) and hippocampal regions. Younger and older adults were randomly assigned to one of two daily biofeedback practices for five weeks: slow-paced breathing designed to increase heart rate oscillations (Osc+) or self-selected strategies aimed at decreasing oscillations (Osc-). Intervention effects were observed in both ADSC and hippocampus regions and were confined to a composite texture factor dominated by uniformity and entropy. Across regions, effects were expressed primarily as Time x Condition interactions, indicating differential texture trajectories between Osc+ and Osc-. In the hippocampus, this pattern was further qualified by a Time x Condition x Age Group interaction, reflecting more pronounced effects in older adults, whereas younger adults showed no reliable texture modulation. Partial least squares correlation analyses further demonstrated that training-related texture changes in the left hippocampus, right fusiform gyrus, and right entorhinal cortex covaried with concurrent changes in plasma AD-related biomarkers, with tau- and p-tau related measures contributing most strongly to the multivariate association. Together, these findings suggest that HRV biofeedback may selectively influence specific dimensions of brain microstructural texture and that such changes are meaningfully coupled with plasma AD-related biomarker profiles.

Young onset Parkinson's disease may be caused by biallelic mutations in PRKN or other autosomal recessive Parkinson's disease genes, but the majority of patients do not carry known monogenic variants. Previous studies have found an increased cumulative burden of common genetic risk variants for Parkinson's disease in young onset patients, but the specific genetic architecture of non-monogenic young onset Parkinson's disease is not well characterized. We conducted a genome-wide association study of 1,528 Parkinson's disease patients with symptom onset between 18 and 40 years and 20,408 controls of European ancestry using data from The Global Parkinson's Genetic Program, the International Parkinson's Disease Genomics Consortium, and the NeuroGenetics Research Consortium. We performed meta-analyses of additive and recessive regression models and investigated associations between age at onset groups and different polygenic risk scores. An additive model meta-analysis identified six independent loci passing a genome-wide significance threshold, including three loci identified in previous genome-wide association studies (near SNCA, GBA1, and HIP1R) and two loci not previously associated with Parkinson's disease (rs74950462, P = 1.24e-8 and rs72848817, P = 4.89e-8). Furthermore, we identified a significant signal at the PRKN locus, prompting a follow-up analysis employing a recessive model. The recessive genome-wide association meta-analysis identified nine loci passing a genome-wide significance threshold, including SNCA, PRKN, and seven novel variants. Patients with onset between 18 and 40 years had significantly higher polygenic risk scores than later onset patients when the score was modelled specifically on genome-wide association statistics from independent young onset Parkinson's disease participants versus healthy controls. This increased polygenic burden was driven in part by loci harbouring mitochondrial pathway genes. Our results indicate that previously unidentified common and low-frequency variants contribute specifically to the young onset subgroup of Parkinson's disease. Association signals detected uniquely with a recessive model suggest that genetic susceptibility to young onset Parkinson's disease may be partially driven by homozygous variation, in line with previous reports of increased runs of homozygosity in this particular group of patients and may be consistent with a loss of function mechanism. The findings support the notion of young onset Parkinson's disease as a partly distinct subphenotype and highlight the mitochondrial pathway. These results may have implications for future precision medicine but should be interpreted with caution pending independent replication.

Introduction: Brain-predicted age, estimated from structural MRI data, is a machine-learning biomarker of biological brain aging. Greater brain age gap (BAG) indicates advanced brain aging and is associated with cognitive decline and mortality. Cardiometabolic risk factors, including elevated blood glucose, body mass index (BMI), blood pressure, and cholesterol, increase risk of cognitive impairment and dementia in aging. Their relationship with BAG in severe obesity remains poorly characterized despite increased prevalence of cardiometabolic risk factors among this population. Methods: T1-weighted MRI data from 97 adults (BMI 35-73) were used to calculate BAG using ENIGMA and Pyment brain age models. Associations between BAG and HbA1c, BMI, hypertension, and hyperlipidemia were examined using multiple linear regression and MM-estimation robust regression, adjusting for age, sex, and race. Post hoc analyses stratified models by clinical HbA1c cutoffs (normoglycemic, prediabetic, diabetic). Results: Higher HbA1c was associated with greater BAGENIGMA (B = 1.58, p = .014) and BAGPyment (B = 0.93, p = .013) in linear regression models. In robust models, HbA1c remained significantly associated with BAGENIGMA (B = 1.70, p = .002) but not BAGPyment (B = 0.71, p = .13). BMI, hypertension, and hyperlipidemia were not associated with BAG in either linear or robust models. HbA1c was associated with greater BAGENIGMA (B = 2.15, p = .01) and BAGPyment (B =1.21, p = .04) in those at or above prediabetic levels and with BAGENIGMA (B = 2.49, p = .047) in those with diabetes. Conclusions: Elevated HbA1c is associated with accelerated brain aging in individuals with severe obesity. BAG was not associated with BMI, hypertension, and hyperlipidemia, which may reflect the restricted BMI range inherent to the sample with severe obesity.

Background: While genetic factors strongly influence brain aging trajectories, variants conferring cognitive resilience remain poorly characterized. The neurokinin-3 receptor (NK3-R), encoded by Tachykinin Receptor 3 (TACR3), modulates cholinergic signaling in memory circuits vulnerable to aging. Previous studies linked the non-WT expression of the TACR3 variant rs2765 with cognitive decline and reduced volume of the hippocampus and basal forebrain, but systematic replication and mechanistic validation were lacking. Methods: We investigated rs2765 in the preregistered AgeGain cohort of cognitively healthy older adults (n=188) with independent validation in the ADNI cohort (n=809) which includes persons with and without Alzheimers Disease (AD) that show healthy cognition, mild cognitive impairment or dementia. Analyses integrated structural neuroimaging, longitudinal cognitive assessments, epigenetic aging (PhenoAge), genome-wide methylation profiling, and mechanistic validation through luciferase assays and cross-species protein expression studies. Results: The infrequent protective rs2765 WT variant, found in 12.8% of Europeans, conferred 49% slower cognitive decline (p = 0.002) for amyloid-positive individuals of the ADNI cohort and 3.7 years younger epigenetic age (p = 0.013, 95% CI: 0.79-6.67 years) in the cognitively healthy AgeGain cohort. WT carriers showed larger hippocampal and basal forebrain volumes across cohorts, with Allen Brain Atlas integration revealing these outcomes to occur exclusively in regions where TACR3 expression positively correlated with gray matter volume. Mechanistically, the non-WT variant ameliorated RBMX-mediated post-transcriptional regulation, reducing NK3-R protein expression by 25-40% in vitro and ex vivo murine brain slice models. Senescence-accelerated mice exhibited reduced endogenous NK3-R expression, phenocopying the predicted functional consequences of the variant. In AgeGain participants, genome-wide methylation profiling identified 2,313 differentially methylated CpGs affecting 228 pathways spanning glutamatergic signaling, acetylcholine receptor pathways, chromatin remodeling, and angiogenesis, suggesting coordinated molecular reprogramming from synaptic function to systemic aging. Conclusions: rs2765 WT confers resilience to age- and AD-related cognitive decline through RBMX-dependent regulation of NK3-R expression, with effects of remarkable size cascading from memory to systemic aging. rs2765 genotyping could stratify individuals for NK3-R modulator therapy (e.g., fezolinetant or senktides) and identify those maintaining function despite pathological burden, complementing APOE-based risk assessment in precision geromedicine.

Background and Objectives: Older adults with epilepsy are at increased risk for Alzheimer's disease (AD), yet the mechanisms underlying this association remain poorly understood. We applied a validated AD neuroimaging signature to older adults with epilepsy to examine 1) whether older adults with epilepsy mirror AD-related changes, 2) associations with clinical, cognitive, and plasma biomarker outcomes, and 3) utility for identifying subgroups at heightened risk for cognitive decline. Our multicenter, prospectively enrolled cohort allowed for direct examination of differences in AD signatures between those with early-onset and late-onset unexplained epilepsy. Methods: Participants included 449 older adults: 87 with focal epilepsy from the multicenter Brain Aging and Cognition in Epilepsy (BrACE) cohort (age=66.10 [SD=6.86], including early-onset (<55 years at seizure onset) and late-onset ([&ge;]55 years at seizure onset) epilepsy); 362 from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU) healthy controls and individuals with mild cognitive impairment (MCI) or AD dementia. An AD signature was derived from regional cortical thickness and hippocampal volume weighted by their sensitivity to AD-related neurodegeneration in prior work. Associations between the AD signature, epilepsy characteristics, plasma biomarkers ({beta}-amyloid 42/40, phosphorylated tau [pTau217, pTau181], neurofilament light chain [NfL]), and cognition were evaluated in BrACE. Results: Participants with epilepsy demonstrated more AD-like signatures compared to ADNI CU controls ({beta}= -0.43, p<0.001), reflecting reduced thickness/volume in AD-vulnerable regions. This effect was stronger among early-onset ({beta}= -0.57) versus late-onset ({beta}= -0.26) epilepsy. In BrACE, the AD signature correlated with NfL ({beta}= -0.30, p=0.050), memory performance ({beta}= 0.30, p=0.006), and predicted greater odds of cognitive impairment specifically among those with early-onset, but not late-onset, epilepsy (interaction p=0.043). Further, among those with early-onset epilepsy, the AD signature significantly improved identification of cognitive impairment over and beyond the effects of plasma AD biomarkers (p=0.041). Findings were similar when examining the effects of epilepsy duration rather than epilepsy onset age. Discussion: AD neuroimaging signatures may help identify clinically meaningful subgroups among older adults with epilepsy, particularly when integrated with AD biomarkers. Findings support a multimodal framework for assessing AD-related risk in epilepsy and highlight interactive effects of epilepsy chronicity and AD-related processes that can influence cognitive outcomes.

Introduction: Variants in the polymerase gamma (POLG) gene are associated with a wide range of mitochondrial disorders. Emerging evidence suggests a potential link between POLG variants and Parkinson's disease (PD); yet, results remain inconclusive. Objectives: To investigate the genetic spectrum and prevalence of POLG variants in PD across diverse ancestries. Methods: We leveraged multi-ancestry genetic data from the Global Parkinson's Genetics Program (GP2), including genotyping data from 98,589 and short-read sequencing data from 36,022 individuals. We performed a POLG rare variant screen, case-control association, and gene-level burden analyses. Results: Five PD cases carried potentially biallelic rare pathogenic/likely pathogenic POLG variants. Additionally, 228 individuals (<1%; 161 PD cases, 28 individuals with other neurological disorders, and 39 controls) carried 34 distinct rare pathogenic/likely pathogenic heterozygous variants, with no significant frequency differences between cases and controls, except for the p.Ala467Thr variant in the European population. The co-inherited pathogenic variants p.Thr251Ile and p.Pro587Leu were present in <1% of both cases and controls, with no significant group differences. Burden and variant-level association analyses showed no association between rare POLG variant burden or common POLG variant enrichment and PD. Conclusions: POLG variants are overall rare in PD. The identification of rare pathogenic variants among PD cases suggests that POLG-related mitochondrial dysfunction may contribute to PD in isolated instances, particularly under recessive inheritance. Our findings support a role for POLG variants in select cases and underscore the need for larger-scale sequencing and functional studies.

Synapse loss is an early feature of neurodegeneration and may provide sensitive biomarkers for experimental medicine. Positron emission tomography (PET) with the synaptic vesicle glycoprotein 2A radioligand [11C]UCB-J shows widespread signal reduction across dementias. However, it remains unclear which aspects of synaptic integrity [11C]UCB-J PET measures. We developed a histological-imaging pipeline to quantify structurally intact synapses in post-mortem brain tissue. We applied it to six donors with the tauopathy progressive supranuclear palsy (PSP) who had ante-mortem [11C]UCB-J-PET, alongside six controls across 11 brain regions. Synapse loss in PSP was widespread but region-specific across cortical, subcortical, and brainstem regions. Greater synapse loss was associated with higher tau burden and pathology, and cortical synaptic density correlated with ante-mortem cognition. Post-mortem synaptic density correlated with in vivo [11C]UCB-J-PET signal. This study provides validation of SV2A PET as a biomarker of synaptic density and supports integration of imaging with histopathology in neurodegenerative disease research.

High placebo response is an obstacle in developing drugs to treat agitation in Alzheimer's disease (AAD), a prevalent and burdensome symptom. However, it has proved challenging to develop actionable models of placebo response that 1) can be applied prospectively, requiring only information available at screening or baseline, 2) yield strategies for reducing placebo response without equally depressing drug response, and 3) show generalizability across trials. Here, we first investigated placebo response in AAD at the trial level using meta-regression applied to 23 clinical trials. Meta-regression identified several factors associated with increased placebo response, but most of these factors were non-specific such that they predicted improvements in drug response as well. We therefore turned to individual level clinical trial datasets and applied causal modeling to predict which participants would have high placebo response relative to predicted drug response. We successfully built and validated the causal model across two independent clinical trials of risperidone and haloperidol at the level of individual patients (ability to predict subsequent improvement on drug or placebo). Crucially, we also found efficacy improvements in the overall trial through in silico exclusion/screen failing of high placebo-predicted subjects. We further characterized features most associated with placebo response to improve explainability and, lastly, validated the effect of these features at the trial level in clinical trials of galantamine, an acetylcholinesterase inhibitor (hence in a different class of drugs than those in the other two trials used). Taken together, we have developed and applied a causal modeling framework for reducing placebo response and increasing trial-level efficacy in neuropsychiatry clinical trials using historical trial datasets.

Background: Cognitive impairment in Parkinson's disease (PD) is linked to degeneration of the cholinergic basal forebrain, particularly cholinergic nucleus 4 (Ch4) in the nucleus basalis of Meynert. Structural and diffusion MRI separately detect this degeneration, but few studies have combined these modalities across the PD cognitive spectrum. Methods: We analyzed 92 participants: 14 healthy controls (HC), 35 PD with normal cognition (PD-NC), 33 with mild cognitive impairment (PD-MCI), and 10 with dementia (PDD). For Ch4 and cholinergic nuclei 1, 2, and 3 (Ch1-3) in the medial septal/diagonal band complex, we determined TIV-normalized gray matter density (GMD) and free-water (FW) fraction. We evaluated group differences, cognitive correlations, adjusted multivariable regression, and exploratory ROC discrimination. Results: Ch4 GMD was significantly lower in PDD compared to PD-MCI (p=0.007), PD-NC (p<0.001), and HC (p<0.001). Ch4 GMD was also lower in PD-MCI versus HC (p=0.028); the PD-MCI versus PD-NC difference was not significant after correction (p=0.074). Ch1-3 GMD was lower in PDD versus PD-NC (p=0.008) and HC (p=0.009). Ch4 and Ch1-3 FW were elevated in PDD versus all other groups (all p<0.01). Among PD patients (n=78), MoCA was positively correlated with Ch4 GMD ({rho}=0.49) and Ch1-3 GMD ({rho}=0.42) and negatively correlated with Ch4 FW ({rho}=-0.51) and Ch1-3 FW ({rho}=-0.40; all p<0.001). In the full four-metric model, Ch4 GMD and Ch4 FW were the only independent basal forebrain predictors (Ch4 GMD {beta}=+2.04, p<0.001; Ch4 FW {beta}=-1.46, p=0.005) of MoCA score. The combined Ch4 GMD + Ch4 FW model showed high discrimination for PDD versus non-demented PD (AUC=0.934; optimism-corrected AUC=0.925). Conclusions: Structural and free-water diffusion MRI provide complementary information about Ch4 degeneration in PD. The combined Ch4 model showed promising exploratory discrimination of PDD; validation in larger independent samples is needed.

PURPOSE: Alzheimer disease (AD) is associated with cognitive impairment, brain atrophy, and elevated amyloid-beta and tau. The study aimed to characterize regional atrophy associated with elevated amyloid-beta and tau, as measured by [18F]florbetapir (FBP) and [18F]flortaucipir (FTP) positron emission tomography (PET), respectively, and determine whether combining PET and atrophy data improves the prediction of cognitive impairment. METHODS: Alzheimer Disease Neuroimaging Initiative data (n = 381) were retrospectively analyzed. PET results were correlated with cortical thickness, gray matter (GM) volumes, Mini-Mental State Examination, and Montreal Cognitive Assessment. Linear/logistic regression and area under the curve (AUC) were used to evaluate for significant correlations and compare performances in distinguishing cognitive impairment, respectively. RESULTS: Incremental loss of cortical thickness and GM volume was observed from FBP-/FTP- (n = 205) to single PET-positive (FBP+/FTP-, n = 133; FBP-/FTP+, n = 5) and FBP+/FTP+ (n = 38) groups, particularly in the temporal and parietal lobes. FBP+/FTP+ showed the most severe cortical thickness loss in the entorhinal cortex, temporal lobe GM atrophy, and cognitive impairment. Adding brain atrophy as the third variable resulted in higher odds ratios and improved AUCs for cognitive impairment, with FBP+/FTP+/temporal GM or entorhinal cortical atrophy+ demonstrating the strongest associations with cognitive impairment. CONCLUSION: A multimodal approach combining PET and MRI may help improve the assessment of cognitive impairment in AD.

Background and Objectives: Converging evidence hints at neurodevelopmental effects in genetic frontotemporal degeneration (FTD). In cross-sectional studies, for some genes, young adult FTD variant carriers show differences in brain volumes and cognition compared to familial non-carriers. However, longitudinal trajectories may more sensitively capture FTD-related neurodevelopmental vs. neurodegenerative changes than cross-sectional approaches. This study examined longitudinal trajectories of brain volumes, executive function, and plasma biomarkers in young adult carriers compared to familial non-carriers, as measures of neurodevelopmental and neurodegenerative outcomes of FTD-causing variants. Methods: This longitudinal cohort study comprised participants, aged 18-30 years, from the FTD Prevention Initiative across Europe, Canada, and the USA. Genetic groups included C9orf72 (47%), MAPT (30%), and GRN (23%). Linear mixed-effects models were computed to assess longitudinal outcomes across age between groups, controlling for sex, scanner (for brain volumes), and education (for executive function); random effects accounted for between-subject variability nested within family membership. Results: Variant carriers (n=147) and familial non-carriers (n=113) did not differ in age (mean{+/-}SD, 25.9{+/-}3.2 years), sex (53% female), or number of visits (2.1{+/-}1.7). Young adult C9orf72 repeat expansion carriers exhibited smaller thalamic volumes than non-carriers at the reference age of 26 years (b=-982.8mm3, SE=317.0, p=0.0046, f2=0.32), with relatively stable trajectories across ages 18-30 (i.e., no change over time). Trajectories of rostral anterior cingulate volumes differed in C9orf72 carriers and non-carriers across age, where carriers showed relatively stable trajectories and non-carriers showed age-appropriate declines (b=64.4mm3, SE=29.9, p=0.035, f2=0.07). For MAPT and GRN, there were little to no differences in total brain, cortical, or subcortical volumes between groups and over time. No longitudinal differences were observed between carriers and non-carriers in executive function, or plasma NfL or GFAP for any genetic group. Discussion: C9orf72 repeat expansions were linked to smaller average thalamic volumes and stable trajectories between ages 18 to 30, supporting potential neurodevelopmental origins. The modest evidence supporting an absence of difference in neurodegenerative biomarkers and executive function suggests minimal early neurodegeneration and functional preservation in young adulthood.

BACKGROUND: Neuropsychiatric symptoms (NPS) are prevalent and debilitating in Alzheimer's disease (AD). Existing pharmacologic treatments are often ineffective and associated with serious adverse events. Identifying modifiable risk factors (MRFs) is critical for prevention and treatment. METHODS: Capitalizing on data from 14,497 individuals with AD from the National Alzheimer's Coordinating Center (NACC) database, we examined longitudinal associations between modifiable risk factors, APOE genotype and NPI-Q-assessed NPS using Cox proportional hazards models adjusted for demographics. RESULTS: Diabetes, alcohol consumption, smoking, and TBI were associated with an increased risk of specific NPS in AD. APOE{varepsilon}4 carrier status was linked to multiple NPS, showing a dose-response relationship. Education, LDL-C, and corrective lenses were protective; hypertension showed no associations. CONCLUSION: These findings strongly suggest that individual MRFs are associated with specific NPS in line with a complex etiology underlying these symptoms. Early detection and management of vascular, lifestyle and sensory factors could reduce NPS.

Background: Vascular cognitive impairment (VCI) and small vessel disease (SVD) involve many interconnected factors influencing multiple outcomes, also beyond cognitive decline. Bayesian networks (BNs) can help unravel these complex interrelations, which we demonstrate in this proof-of-concept study in the Heart-Brain Connection cohort, including memory-clinic patients with SVD, patients with heart failure, carotid occlusive disease, and reference participants. Methods: We trained BNs and jointly modelled cognitive decline (Clinical Dementia Rating (CDR) increase) and major adverse cardiovascular events (MACE) over five years as outcomes in relation to multiple demographic and disease factors and emerging imaging and plasma biomarkers, also considering possible non-random dropout. Results: Of 566 individuals (median age 68, 64% men), 134 had MACE and 112 experienced CDR increase. Diagnostic group and baseline cognition were key determinants of both outcomes. The BN identified baseline clinical severity as a non-random dropout source. Plasma biomarkers formed an interconnected subnetwork, linked to demographic and vascular factors, but without direct dependencies with outcomes. The trained BN also provides individualized inference under partial evidence, informing on outcome probabilities. Conclusion: This proof-of-concept study demonstrates how BNs quantify and visualize the dependency structure underlying prognostic heterogeneity in VCI and SVD, including non-random dropout and positioning of emerging biomarkers.

Background: Parkinson's disease (PD) is the second most common progressive neurological disorder that is pathologically characterized by the loss of dopaminergic neurons within the substantia nigra (SN). However, disease progression probably involves coordinated changes across both neuronal and glial cell populations. Although single-nucleus RNA-seq resolved cell-type-specific transcriptional profiling, differential expression and regulatory interpretation are commonly reported separately; however, they may limit the mechanistic prioritization to uncover novel therapeutic targets. Methods: Here, we performed sample-aware pseudobulk framework analysis on single-nucleus transcriptomes obtained SN of PD and control donors. Cell-type-specific differential expression for PD vs. control was identified using edgeR quasi-likelihood modeling (FDR < 0.05; |log2FC| > 0.5). Further, to quantify disease-specific remodelling, we computed one-vs-rest cell-type specificity scores in each condition and defined delta-specificity as the PD-control shift. We further prioritized the gene-set for dopaminergic neurons and microglia based on edge R significance and delta-specificity shifts, followed by upstream regulatory assessment using transcription factor enrichment and subnetwork visualization using ChEA-KG. Moreover, we used Cellchat to identify altered cell-cell communication networks to infer differences between both conditions. Results: Dopaminergic neurons demonstrated upregulation of neuronal-state remodeling transcriptional programs related gene sets in PD group, including receptor signaling and contact/guidance pathways (e.g., CHRM3, ROBO1, PLXNA4, UNC5D, EFNA5), neuronal excitability homeostatsis, RNA components, cellular traffickings and proteostasis, suggesting coordinated remodeling in surviving neuronal population. Microglia exhibited a compact PD-associated signature enriched for regulatory and activation state-related genes. TF networks analysis revealed distinct regulatory subnetwork in each population,including BNC2-centered network in microglia and an NPAS3-centered network in dopaminergic neurons with embedded ZNF804A and chromatin-associated components. Conclusions: In summary, integrating pseudobulk, delta-specificity scoring and TF-network enrichment analysis provides coherent dopaminergic and microglial programs in PD substantia nigra. This framework prioritizes cell-type-specific potential candidate mechanisms for downstream validation. The inferred regulatory networks and interactions are hypothesis generating and need orthogonal validation, such as spatial or proteomics approaches and independent cohorts.

Background. Fairness-aware machine learning increasingly targets demographic performance disparities in clinical prediction, yet whether standard bias mitigation strategies genuinely improve equity in physiological signal analysis remains unclear. Age-based disparities in photoplethysmography (PPG)-based heart rate prediction present a particular challenge, as age-related performance differences may reflect context-dependent physiological structure rather than correctable artifacts. Methods. We evaluated three fairness interventions, inverse-frequency weighting (IF), Group Distributionally Robust Optimization (GroupDRO), and adversarial debiasing (ADV), applied via fine-tuning of a PPG foundation model across three clinical datasets spanning intensive care unit, laboratory, and consumer wearable contexts. Outcomes were assessed using a 2x2 framework classifying each intervention-dataset combination by the joint direction of change in mean absolute error (MAE) and fairness gap (FG) across age groups, yielding four outcome types: genuine improvement (G), leveling down (L), selective benefit (S), and both worse (W). Results. Across nine intra-domain conditions, no intervention simultaneously improved both MAE and FG (0/9 genuine improvement). The dominant pattern was leveling down (5/9): FG decreased but was accompanied by MAE degradation, indicating that apparent fairness gains were achieved at the cost of overall predictive performance. Age-group difficulty ordering varied across clinical contexts at baseline and was not preserved under intervention. In 18 cross-domain transfer conditions, genuine improvement was rare (4/18) and observed exclusively in non-MIMIC source configurations; models fine-tuned on MIMIC-sourced data yielded no genuine improvements (0/6). Embedding-level representation changes following fine-tuning did not reliably predict fairness outcomes. Conclusions. Age-based fairness interventions in PPG heart rate prediction indicate a leveling-down pattern rather than genuine equity improvement, suggesting that age-related performance gaps reflect context-dependent physiological structure not fully addressable through standard bias mitigation. Cross-domain transfer further amplifies this instability. These findings suggest that fairness evaluation frameworks for age-stratified physiological prediction should account for context-dependent performance structure rather than treating observed gaps as correctable bias.

As genetic testing becomes increasingly integrated into Parkinson disease (PD) research, including targeted testing for variants in LRRK2 and GBA1, the return of individual research results is becoming more common. However, limited qualitative data exists regarding how research participants experience genetic results disclosure and post-test genetic counseling in PD research settings. We conducted semi-structured qualitative interviews with participants (n=13) enrolled in the Parkinson Precision Medicine Initiative (formerly Parkinson Progression Markers Initiative; PPMI) who had received PD-related genetic test results and post-test genetic counseling. Interviews were conducted 1 to 3 weeks following result disclosure and analyzed using thematic analysis with a primarily deductive coding approach informed by study aims and inductive identification of emergent themes. Four primary themes were identified: (1) personal connection and motivations for participation, (2) centrality of result disclosure and information preferences, (3) emotional experiences and support needs, and (4) communication quality and alignment with participant needs. Overall, our findings underscore the importance of person-centered genetic counseling within PD research. As return of genetic and biomarker results in research and clinical trial contexts expand, thoughtful integration of relational, informational, and communication-focused practices will be essential to support participant engagement and trust.

Background: Mono-allelic Dehydrodolichyl Diphosphate Synthase (DHDDS) variants are associated with juvenile Parkinsonism, developmental delay and seizures. Symptoms are progressive, and various mechanisms, such as defective glycosylation, lysosomal dysfunction and cholesterol accumulation have been hypothesized to underlie disease symptoms. There is no treatment for DHDDS-related disease. Methods: Patient-derived cortical forebrain organoids were created to elucidate disease mechanisms and evaluate potential treatments. In these neuronal models, glycosylation, lipidomics, proteomics, cholesterol/ganglioside accumulation, mitochondrial function and electrophysiological activity were assessed. Finally, we investigated the effects of nicotinamide mononucleotide (NMN), identified through a yeast-based drug screen, in neuronal cell models and in six patients in an off-label, N-of-1, observational series. Results: DHDDS-patient derived organoids showed visual signs of degeneration after four months of culturing. This was accompanied by significant cholesterol accumulation in astrocytes, decreased mitochondrial respiration and loss of deep-layer neurons. In addition, we identified glycosylation abnormalities, showing for the first time that glycosylation in human tissue is affected by monoallelic DHDDS variants. Proteomic analysis revealed altered protein expression of proteins involved in lipid metabolism, cytoskeletal organization and neuronal development. We found that oral Nicotinamide Mononucleotide supplementation led to significant improvement in mitochondrial respiration and electrophysiological parameters in organoids, concurring with clinical improvements in all of the treated patients, particularly regarding their ataxia and tremor. Conclusion: Our findings reveal a progressive phenotype in DHDDS-patient-derived brain organoids, with mitochondrial dysfunction and astrocyte-specific metabolic alterations contributing to disease pathology. Notably, NMN treatment led to clinical improvements in patients with heterozygous DHDDS variants, highlighting its potential as a therapeutic strategy.

Background: Neurological, neuropsychiatric, and neurodevelopmental disorders cluster in ALS families, sharing a common genetic architecture with ALS. Pathogenic variants in genes associated with other neurological, neurodevelopmental, or neuropsychiatric disorders may also co-occur in ALS and modify phenotype. We have sought to determine the prevalence and clinical pattern of likely-pathogenic/pathogenic (LP/P) non-ALS neurological, neurodevelopmental, and neuropsychiatric variants, alone and in combination with ALS-gene variants, in two large ALS cohorts. Methods: Whole-genome sequencing (WGS) of 469 Irish and 774 Answer ALS people with ALS (pwALS) was analysed for ClinVar LP/P variants associated with other neurological (n = 15541), neurodevelopmental (n = 9761), and neuropsychiatric (n = 321) phenotypes. Inheritance patterns for associated genes (autosomal recessive/autosomal dominant) along with the associated phenotype were validated using OMIM. Standardised clinical data included family history, site and age of onset, El Escorial category, survival, motor decline, and cognitive and behavioural assessments. Known ALS-gene variants and C9orf72 repeat expansion status were included for each cohort. Results: Non-ALS neurological variants were identified in 47/469 (10.0%) Irish and 69/774 (8.9%) Answer ALS participants, most frequently in hereditary spastic paraplegia-associated genes (3.2% Irish; 2.8% Answer ALS). Irish neurological variant carriers showed higher frequency of respiratory onset (10.6% vs 1.2%, Fisher's exact p = 0.002, {Phi} = 0.20) and fewer premorbid behavioural symptoms (0.92 +/- 0.56 vs 3.08 +/- 0.97, Cohen's d = -0.40). Neurodevelopmental variants occurred in 12/469 (2.6%) Irish and 20/774 (2.6%) Answer ALS participants. In the Irish cohort, neurodevelopmental variant carriers had significantly shorter survival in Cox proportional hazards model (log-rank p = 0.005), corresponding to a more than two-fold increased hazard of death (HR = 2.25, 95% CI 1.26-4.00), and had significantly increased familial burden of neuropsychiatric disorders among first- and second-degree relatives (negative binomial IRR for carriers = 2.41, 95% CI: 1.12-5.18, p = 0.025). Across combined cohorts, 18 individuals (Irish n = 8; Answer ALS n = 10) carried [&ge;]2 LP/P variants spanning ALS and non-ALS genes. Conclusion: Rare LP/P variants in genes associated with other neurological and neurodevelopmental disorders occur in up to 12% of pwALS across two independent cohorts. Carriers show distinct phenotypes, shorter survival, and characteristic family history patterns. These findings suggest that extended pleiotropic and oligogenic architectures may contribute to ALS heterogeneity.

Background: Sleep fragmentation and reduced sleep efficiency are markers of disrupted sleep architecture linked to cognitive and age-related decline. Current assessments rely on subjective reports prone to recall bias, limiting their effectiveness for longitudinal monitoring. Data-driven analysis of sleep using physiological signals such as EEG and EMG remains underutilised, particularly in mid-to-older adults. Objective: We present a deep learning pipeline for automated sleep staging and label-free abnormality scoring, with the primary objective of quantifying deviations in sleep architecture to capture progressive sleep disruption and longitudinal change. Methods: Temporal and attention-based models were benchmarked using datasets from the National Sleep Research Resource and PhysioBank. To improve class-specific performance, we introduce a stacking-based ensemble of sleep stage classifiers, each trained to specialise in a different stage. For longitudinal scoring, we develop a reconstruction loss-based abnormality metric using a temporal convolutional autoencoder trained on hypnograms generated by the sleep staging models. Results: Attention-based models, particularly AttnSleep, achieved the highest performance in both multimodal and single-channel settings (accuracy: 0.85 and 0.83; F1: 0.79 and 0.74, respectively). The encoder-decoder ensemble model improved overall classification accuracy by 3% compared to the best-performing biased base classifier, with a modest gain in N1-stage F1 score (0.444). The proposed abnormality score correlated with Pittsburgh Sleep Quality Index components and showed sensitivity to synthetic hypnogram degradation, highlighting its potential as a label-free indicator of sleep disruption. Conclusion: Automated classification and annotation-free scoring enable an end-to-end multimodal pipeline that supports scalable, objective sleep health monitoring, with relevance for future clinical deployment.