BACKGROUND: Current pharmacological treatments offer only limited benefits in altering the course of Alzheimer's disease (AD). Given these limitations, nonpharmacological interventions have emerged as potential therapeutic strategies. This study investigates the therapeutic effects of 40 Hz light stimulation in AD and analyzes blood biomarkers to explore its potential disease-modifying effects. METHODS: This longitudinal study examined the effects of 40 Hz light stimulation on clinical symptoms and blood biomarkers in AD patients. Fourteen individuals were enrolled, with 11 completing the 3-month light stimulation, and 6 continuing to 6 months for the final blood biomarker analysis, including amyloid beta (Aβ) oligomers, Aβ-40, Aβ-42, tau phosphorylated at threonine 181 (p-tau181) and 217 (p-tau217), and neurofilament light chain. RESULTS: At 3 months, cognitive function remained stable or improved in 63.6% of participants, depressive symptoms improved in 54.5%, caregiver burden decreased in 72.7%, and sleep quality improved in 90.9% ( = .014). At 6 months, cognitive function and neuropsychiatric symptoms remained stable or improved in 33.3% and 66.7% of participants, respectively. Biomarker analysis showed decreased Aβ oligomers, increased Aβ-42 and reduced p-tau, suggesting potential disease-modifying effects. CONCLUSIONS: 40 Hz light stimulation demonstrated short-term benefits in cognitive stability, caregiver burden relief, and sleep improvement, with biomarker findings indicating possible neuroprotective effects.

Machine learning can unravel heterogeneous patterns of brain aging and neurodegeneration, but existing methods offer limited insights into disease progression due to reliance on cross-sectional data. We introduce Coupled Cross-sectional and Longitudinal Non-negative Matrix Factorization (CCL-NMF) to capture dominant brain aging patterns by simultaneously leveraging cross-sectional and longitudinal neuroimaging data. CCL-NMF allows individuals to co-express multiple patterns, capturing mixed neuropathologic processes. Applied to neuroimaging data from 48,949 individuals from the harmonized iSTAGING study, CCL-NMF identifies seven distinct, reproducible, and biologically relevant neuroanatomical patterns. Subject-specific loading coefficients quantifying the individual expression of these patterns show distinct associations with cognition, genetic, and lifestyle factors. To support broader application, a regression-based tool was developed to estimate loadings in external cohorts without rerunning the full framework. By enabling individualized estimation of distinct brain aging patterns, these findings may improve risk assessment and therapeutic evaluation in neurodegenerative diseases. Although demonstrated using structural MRI, this framework is generalizable to other imaging modalities and biomarker types.

Some schizophrenia patients share characteristics with behavioral variant frontotemporal dementia (bvFTD) including gray matter volume (GMV) similarities, suggesting overlapping brain mechanisms that may contribute to disease heterogeneity in schizophrenia. However, it is not yet understood whether schizophrenia patients with a bvFTD-like GMV signature also show additional features of bvFTD. Seventy-six patients with schizophrenia (mean age 31.2 years; 71% male) from a previous clinical trial (NCT04038957) underwent structural magnetic resonance imaging (MRI) to measure GMV. Healthy controls (n = 79) were used for age-related dynamic standardisation. Subsets of patients also completed [¹⁸F]-DOPA positron emission tomography (PET; n = 40) for striatal dopamine synthesis, neuromelanin-sensitive MRI (NM-MRI; n = 68) for midbrain dopaminergic integrity, and quantitative susceptibility mapping (QSM; n = 69) for brain iron accumulation. A previously validated machine-learning-based GMV classifier quantified bvFTD-like pattern expression in each schizophrenia patient. Associations with neurochemical, clinical, and medication measures were examined using correlation analyses. Higher bvFTD scores were associated with lower striatal dopamine synthesis capacity (r = -0.343, p = 0.032) and higher striatal QSM values (r = 0.282, p = 0.020), but showed no significant association with QSM or NM-MRI values in the dopaminergic midbrain (p = 0.903 and p = 0.102, respectively). No significant associations were found with negative symptom severity or with medication. Schizophrenia patients expressing a stronger bvFTD-like GMV pattern show lower striatal dopamine synthesis and elevated striatal iron, both hallmark features of bvFTD. This contrasts with the hyperdopaminergic model of schizophrenia and suggests distinct, potentially neurodegenerative mechanisms.

Beyond cognitive impairment, Alzheimer's disease (AD) is frequently accompanied by apathy, the most prevalent and burdensome neuropsychiatric symptom (NPS). Apathy significantly impacts AD onset and progression, yet its molecular underpinnings remain unclear. Our previous RNA-sequencing analysis revealed abnormal immune gene expression uniquely associated with apathy in AD patients. In this study, we investigated whether changes in these immune related genes are also linked to apathy-like behavior, and whether administration of C3a receptor antagonist SB290157, alone or with methylphenidate, modifies apathy-like behaviors in 5xFAD mice. We first validated the apathy-related immune hub genes identified in human AD in the prefrontal cortex (PFC) of 16-18 month-old 5xFAD mice using RT-qPCR. Then separate cohorts of similarly aged 5xFAD mice received SB290157 and/or methylphenidate for three weeks. Our results showed that elevated immune hub genes Tyrobp, C3, C3ar, C1qa, C1qb, and C1qc were strongly correlated with apathy-like behavior in 5xFAD mice. Combined SB290157 and methylphenidate treatment improved nest-building behavior, reduced C3 and C3ar expression as well as restored dendritic spine density in the PFC. Our results confirm complement-mediated immune dysregulation is linked to apathy and suggest that co-targeting complement and catecholaminergic pathways may offer a novel therapeutic strategy for alleviating apathy in AD.

Passive digital health technologies (DHTs) are increasingly promoted as scalable tools for detecting Alzheimer's disease and related dementias (ADRD) earlier than routine clinic visits. We searched six major databases for English-language studies published between January 2014 and July 2024 that used passively collected, real-world DHT data for ADRD screening or diagnosis. Thirty studies met the criteria. Population sizes were highly skewed (median = 87; range 12-82,829), and most designs were longitudinal (53%) and fully passive (68%). Wrist-worn accelerometers and photoplethysmography sensors dominated, though several studies also used gait, sleep, voice, radar, or posture-tracking devices. A cross-study synthesis showed that those two modalities were primarily applied to memory, attention, and language tasks. Nineteen studies reported median accuracy, sensitivity, specificity, and precision between 80-90%, with F1-score and AUC medians approaching 78%, though relying on in-sample cross-validation rather than external cohorts. Reference standards varied widely, data-quality criteria were seldom reported, and fewer than 5% shared datasets publicly. Classification was the predominant modeling strategy, with regression emerging only in recent years. Overall, passive DHTs show promise as low-burden triage tools for population-level ADRD screening, but routine deployment will require more diverse cohorts, harmonized reporting, multimodal privacy-preserving analytics, and rigorous human-factors evaluation.

Early diagnosis of Alzheimer's disease (AD) is crucial for timely intervention but remains challenging due to subtle and heterogeneous brain alterations, particularly in the mild cognitive impairment (MCI) stage. To address this issue, we propose a pathology-aware Adaptive Neighborhood Aggregation Graph Neural Network (ANA-GNN) to model the brain as a dynamic and task-driven graph for multimodal AD classification. The framework integrates three synergistic components: an adaptive neighborhood aggregation module that aligns each node's receptive field with disease-specific heterogeneity, an importance-weighted pooling mechanism that enhances discriminative graph-level representations by prioritizing biologically relevant regions, and a gated multimodal fusion strategy that adaptively balances imaging and non-imaging information. Evaluated on an expanded cohort of 707 subjects from the ADNI dataset, ANA-GNN achieved an overall accuracy of 85.23% and an F1-score of 85.44%, consistently outperforming state-of-the-art baselines such as BrainGNN and Graph Transformers. Furthermore, the identified high-importance brain regions, including the hippocampus, amygdala, and posterior cingulate cortex, align with known AD biomarkers, demonstrating the model's biological interpretability and potential as a reliable tool for early AD diagnosis.

INTRODUCTION: Previous research suggests some post-COVID patients with neurocognitive complaints (NCC) show neuroinflammation. Like in inflammatory diseases, this may affect network connectivity. This study aimed to compare resting-state functional connectivity in individuals with and without i) neuroinflammation and ii) persistent post-COVID NCC. METHODS: Forty-five participants (mean age 49±9 years, 60% female) who had a SARS-CoV-2 infection 27±9 months earlier completed neurocognitive assessment (Checklist-Individual-Strength), 60-minute dynamic [F]DPA-714 PET scan with arterial sampling for neuroinflammation, and 3T MRI scan for resting-state functional connectivity. Twenty resting-state networks (RSNs) were identified using independent component analysis. Group differences in within-RSN connectivity were analyzed using general linear models. Differences in subcortico-cortical between-RSN connectivity-between brainstem or thalamus and cortical RSNs-were assessed using interaction models. RESULTS: Ten participants (22%) showed neuroinflammation on [F]DPA-714 PET, and 31 (69%) reported persistent NCC. Lower within-RSN connectivity was observed in the visual-peripheral (N=3151, P<0.05, t=1.77 and t=3.34) and dorsal-attention networks (N=29, P<0.05, t=1.59 and t=3.66) in those with neuroinflammation, and in the visual-peripheral (N=721, P<0.05, t=1.89 and t=3.45) and default mode networks (N=123, P<0.05, t=2.24 and t=4.68) in those with NCC. A significant interaction effect showed reduced functional connectivity in a large, bilateral cerebellar cluster (N=2648, P<0.05, t=2.31 and t=3.84) with increasing global [F]DPA-714 binding in participants with NCC. Lastly, thalamic-somatomotor and brainstem-control network connectivity (between-RSN) was altered in both individuals with persistent NCC and those with neuroinflammation, with thalamic-somatomotor changes mainly driven by NCC and brainstem-control changes by neuroinflammation. CONCLUSION: Our results suggest that neuroinflammation in individuals with persistent NCC after SARS-CoV-2 infection is linked to altered functional connectivity in RSNs central to higher-order cognitive functions.

Evidence increasingly links greenspace exposure to better cognitive health, yet its relationship to memory remains unclear, particularly among racial and socioeconomic minority groups. This study examined associations between residential greenspace and memory performance in the Chicago Multiethnic Prevention and Surveillance Study (COMPASS) cohort, predominantly composed of socioeconomically disadvantaged racial minority adults. We analyzed data from 4,048 adults aged 26-94 years residing in Chicago, Illinois (2013-2018). Greenspace exposure within a 1000m buffer of participants' residences was quantified using high-resolution satellite imagery and land cover maps. To account for human mobility, measures of street connectivity and integration were incorporated to capture greenspace accessibility. We reported odds ratios and 95% confidence intervals for associations between greenspace and memory, adjusted for socioeconomic and demographic factors and street networks. Analyses were stratified by age, sex, education, race, and income, and sensitivity analyses were conducted across multiple buffer distances to assess robustness. Both overall greenness, as measured by Normalized Difference Vegetation Index (NDVI), and percentage of grass/shrub cover were positively associated with memory performance. We observed positive associations between interquartile range (IQR) increases in greenspace and memory for both NDVI (OR = 1.14, 95% CI: 1.10-1.19) and grass/shrub cover (OR = 1.18, 95% CI: 1.14-1.23), while no associations were found for tree canopy. These associations were consistent across smaller buffer distances (100m, 500m) and when using access-based measures. Effect sizes varied across sociodemographic subgroups: NDVI effects were stronger among females and those with higher education, whereas grass/shrub cover showed greater benefits among males and individuals with lower education. Both indicators demonstrated stronger associations among non-Hispanic Black, single and lower-income participants. Our findings suggest that both the quantity and type of greenspace contribute to cognitive benefits, particularly among racial and socioeconomic minority groups. Enhancing neighborhood greenspace may therefore be a promising strategy to reduce cognitive health disparities.

BACKGROUND: Diabetes mellitus (DM) is associated with an elevated risk of cognitive decline, though trajectories are heterogeneous. This study investigated whether a novel, clinically applicable measure of brain network integrity, the Morphometric Inverse Divergence (MIND) network, could differentiate and predict cognitive progression in DM. METHODS: We retrospectively analyzed 101 DM participants (41 cognitively normal, 60 with mild cognitive impairment) from the Alzheimer's Disease Neuroimaging Initiative, classifying them into stable (DM_S, n=64) or decline (DM_D, n=37) group based on longitudinal diagnostic conversion. MIND networks were constructed from multiple cortical morphological features derived from T1-weighted MRI and graph theory measurements were further analyzed. Using network-based statistics (NBS) and its extension NBS-predict, we tested whether subject-level connectomes were associated with long-term DM-related cognitive worsening. RESULTS: At baseline, DM_D individuals exhibited significantly lower cognitive scores and a focal subnetwork of disrupted morphometric similarity, primarily involving temporal regions. Longitudinally, DM_D individuals showed a more targeted pattern of network change that significantly altered global efficiency, local efficiency, and path length exclusively, while stable individuals, the brain underwent more widespread changes. Crucially, baseline MIND networks significantly predicted long-term cognitive progression status (accuracy = 63.1%, p = 0.034). The predictive subnetwork was rich in transmodal connections involving the temporoparietal, default mode, and limbic networks. CONCLUSION: These findings indicate that cognitive decline in DM is preceded by specific disruptions in the brain's structural connectome. The MIND method shows promise as a network-based biomarker for identifying at-risk individuals and predicting cognitive trajectory, potentially driving advanced network analyses toward real-world applicability.

The existence and functional significance of immature neurons in the adult human brain, particularly in the context of neurodegenerative disorders, remain an open question. Although rodent studies have highlighted active roles for adult-born immature neurons in the hippocampus both under healthy conditions and in Alzheimer's disease (AD), evidence from the human brain is limited and lacks detailed molecular characterization. To address this gap, we performed single-nucleus RNA sequencing in aged healthy, AD, and dementia-resilient human hippocampus samples to probe immature neuronal signatures and gene expression alterations associated with AD pathology and resilience. By applying an integrated experimental and computational pipeline, we identified persistent populations of immature neurons across all donor groups, with transcriptional profiles reflecting "juvenile" cellular functions, which are compromised in AD. Our findings suggest that the presence of these immature neuronal populations per se may actively contribute to maintaining homeostasis within the aged human hippocampus and to cognitive resilience in AD.

BACKGROUND: The mouse model is the most widely used animal model in neuroscience, yet translating findings to humans suffers from the lack of formal models comparing the mouse and the human brain. Here, we devised a framework using mouse and human gene expression to build a quantitative common space and apply it to models of neurodegenerative disease. METHODS: We trained a variational autoencoder on mouse spatial transcriptomics, and embedded mouse and human gene orthologs in the model's latent space. We computed a latent cross-species similarity matrix for translation and compared translated maps to human ground truth evidence. FINDINGS: We established the validity of our model based on anatomical homology. Independent of species, brain areas with similar latent patterns clustered together, improving the homology of known anatomical pairs, and preserving principles of brain organisation. Importantly, translating brain alterations in mouse disease models predicted human patterns of brain changes in Alzheimer's and Parkinson's diseases. We further determined the best mouse model for the AD patients, based on how well the translations matched the patient data, across multiple models and timepoints. INTERPRETATION: Our work provides i) a quantitative bridge across evolutionary divergence between the human and the predominant preclinical species, ii) a predictive framework to help design and evaluate disease models. By highlighting which models are best suited across stages of disease, we effectively support the understanding of disease mechanisms, assist in the workflow of clinical trials, and ultimately accelerate the transformation of findings into improved human outcomes. FUNDING: Supported by the Biotechnology and Biological Sciences Research Council (BBSRC) UK, the Medical Research Council (MRC) UK, the European Research Council, and the NIHR Oxford Health Biomedical Research Centre.

Cerebral amyloid angiopathy (CAA) remains diagnostically challenging, particularly in asymptomatic individuals. While CAA often co-exists with Alzheimer's disease (AD), it may even have a direct impact on AD pathophysiology and the cognitive decline within the clinical course of AD. While fluid biomarkers are well established for AD pathology, reliable markers to characterize CAA are lacking. We analyzed two subsets of participants from the Alzheimer's Disease Neuroimaging Initiative with available plasma biomarker measurements from a 145-analyte multiplex immunoassay panel: one with T2*-weighted gradient-echo magnetic resonance imaging (MRI) data (n = 21) and another with postmortem neuropathological data (n = 24). We defined CAA as ≥ 2 lobar microbleeds on MRI or moderate-to-severe neocortical amyloid angiopathy on neuropathological examination. Plasma analytes were assessed twice per participant, one year apart, with the earlier sample obtained up to 6.6 years prior to either the first MRI or neuropathological examinations. In both cohorts, various markers related to inflammation, lipid metabolism, and cell adhesion were associated with CAA proxy measures. Specifically, both increased (Fas ligand receptor, Receptor for Advanced Glycosylation End-Products, Osteopontin, and Vascular Cell Adhesion Molecule-1) and decreased (Vitronectin, Endothelial Growth Factor) biomarker levels were associated with lobar microbleeds, while increased apolipoproteins (ApoAII, ApoCI, ApoCIII, ApoE, and clusterin) and decreased AXL were associated with CAA severity in neuropathology. Ratios between inversely associated markers enhanced correlation strength and differed between CAA and non-CAA. Given the small sample sizes in our exploratory analyses, larger studies are required to evaluate the discriminatory potential and clinical translatability of the identified biomarkers for CAA.

The past decade of multi-omics studies revealed perturbations in genetic, epigenetic, transcriptomic, proteomic, and metabolic networks in Alzheimer's disease (AD) detected in brain, cerebrospinal fluid (CSF), and blood biospecimens. Interactions among these networks and environmental factors are thought to contribute to risk and progression of this neurodegenerative dementia. Understanding the molecular and environmental risk in AD across all populations is essential in the development of cures and biomarkers for this complex disease. While most molecular studies to date have focused on populations of European ancestry, emerging multi-ancestry and multi-omics studies are revealing both shared and ancestry-specific biological signatures associated with disease susceptibility, biomarker profiles, and clinical presentation. Genomic studies show that established AD risk loci such as APOE, ABCA7, and TREM2 exhibit ancestry-dependent effects, while trans-ethnic genome-wide association studies identified novel disease risk loci (e.g., LRRC4C, LHX5-AS1) and protective haplotypes unique to African American (AA) and admixed populations. Epigenomic and transcriptomic studies reveal ancestry-linked variation in chromatin accessibility, DNA methylation, and gene expression, particularly in immune, lipid metabolism, and synaptic pathways. Proteomic analyses demonstrate differences in CSF and brain protein networks, including extracellular matrix and synaptic modules enriched or reduced in AA AD brains. Metabolomic and lipidomic data further highlight differential abundance in non-European cohorts. Integrating these multi-omics layers across ancestries provides a framework for understanding how genetic background and environmental context interact to drive AD heterogeneity. Such integrative, ancestry-aware approaches will refine biomarker interpretation, improve diagnostic accuracy, and guide development of therapeutics for AD.

Polyphenols and regular physical activity are increasingly recognized as complementary lifestyle interventions that influence the gut-brain axis and contribute to neuroprotection. Emerging evidence highlights the central role of the gut microbiota in mediating these effects by transforming dietary and host-derived substrates into bioactive metabolites. These metabolites can activate the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway, a key regulator of cellular antioxidant defenses, mitochondrial function, and anti-inflammatory responses processes that are critically impaired in neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. This review synthesizes current mechanistic insights into how polyphenol-derived metabolites and exercise-induced alterations in gut microbial composition converge to modulate Nrf2 signaling. We discuss the roles of key microbiota-derived metabolites, including short-chain fatty acids, urolithins, and indole derivatives, in regulating oxidative stress, neuroinflammation, and synaptic function. Furthermore, we examine evidence from preclinical models supporting the synergistic effects of dietary polyphenols and physical activity on gut microbiota-mediated neuroprotection. Finally, we address translational challenges and highlight the potential of integrating dietary and exercise-based strategies to harness microbiota-dependent Nrf2 activation. This integrative framework provides a basis for developing personalized, microbiome-informed interventions aimed at delaying or mitigating neurodegeneration.

BACKGROUND: Agitation in older adults presents unique challenges for emergency medical services (EMS) clinicians. To safely assess, treat, and transport these patients, EMS providers must manage this agitation, which is often caused by delirium or behavioral symptoms of dementia. Strategies routinely used in younger patients, such as physical restraints and chemical sedatives, have a much higher risk of causing harm in older adults. EMS provider practices in this area are understudied. Our objective was to examine U.S. state protocols for guidance on agitation management in older adults. METHODS: We reviewed publicly available statewide EMS protocols and the National EMS Model Guidelines to identify agitation management guidance and whether modifications for older adults were included. We assessed each protocol for the presence of 26 elements using a standardized approach. RESULTS: We analyzed 34 state protocols and the National Model Guidelines. While 33 protocols (97%) included guidance for managing agitation and 19 protocols (58%) included pediatric-specific considerations, only 11 (33%) addressed older adults. Among these, the most common modification was sedative medication dose reduction (9 protocols, 27%). No protocols included modifications to guidance/criteria for physical restraint use, prioritization/escalation between physical restraint and chemical sedation, or sedative medication selection for older adults. Guidance on medication selection varied: 29 protocols (88%) included standing orders for sedative medications, most commonly midazolam (27 protocols, 82%), ketamine (23 protocols, 70%), haloperidol (17 protocols, 52%). Only 7 protocols (21%) included specific dose adjustments for older adults, typically a 50% reduction. Benzodiazepines were most commonly recommended, while antipsychotics were less common. CONCLUSIONS: Most state EMS protocols provide guidance on management of agitated patients, including use of physical restraints and chemical sedatives, yet modifications or specific guidance for older adults are uncommon. Recognizing and addressing this gap represents an important opportunity to improve quality of prehospital care for older adults.

Background and ObjectivesHearing impairment (HI) is one of the most common disabilities in older adults. This cross-sectional study examined the descriptive epidemiology of HI in 15,179 older adults in the 2016-2018 wave of the Health and Retirement Study.Research Design and MethodsHearing was assessed at two pitches (1000, 3000 Hz) and 3 loudness levels (35, 55, 75 dB HL). HI was defined as hearing <50% of tones in one or both ears. Demographics (age, race, ethnicity, sex, education), noisy occupation, smoking status, and medical comorbidities (diabetes, CVD, hypertension, cancer, stroke, dementia) were assessed via survey. Associations between HI and these factors were evaluated using Poisson regression.ResultsHI was highly prevalent (74% overall), and varied by sex (76.2% men, 72.1% women), ethnicity (78.7% Hispanic, 72.8% non-Hispanic) and age (21-65 years-old 64.3%, 65-80 years-old 81.3%, 80+ years-old 93.5%). After multivariable adjustment, HI was less common in women compared to men (IRR: 0.92; CI: 0.87-0.98) and was more common in those with Hispanic/Latino ethnicity compared to White (IRR:1.13, CI: 1.03-1.23). HI was more common in those with less than a college degree (less than high school: IRR = 1.47, CI = 1.34-1.61; high school graduate: IRR = 1.24, CI = 1.13-1.35; some college: IRR = 1.15, CI = 1.06-1.24). Compared to non-smokers, current smokers had higher risk of HI (IRR = 1.19, CI = 1.11-1.28). Lastly, history of stroke was associated with a greater risk of HI (IRR = 1.06, CI = 1.02-1.11).Discussion and ImplicationsHI is highly prevalent in the HRS sample and is correlated with chronic diseases and demographic factors. This study underscores the need for regular hearing screening and intervention among older US adults.

Aberrant activation of the classical complement pathway in the brain is implicated in contributing to synapse loss and neurodegeneration in various neurodegenerative conditions. Given that C3aR is a druggable target in the complement pathway, we evaluated the potential of C3aR knockout (KO) to rescue neurodegeneration in a tauopathy model and neuroinflammatory responses in an acute endotoxemia model. We found that C3aR KO did not rescue Tau pathology, microglia activation markers, neurodegeneration, or behavioral abnormalities in tauopathy model mice. While we found that endotoxemia resulted in numerous transcriptional changes, including distinct alterations in subpopulations of microglia, astrocytes, and oligodendrocytes, C3aR KO did not impact these alterations. Together, our results suggest that the beneficial effects of blocking the complement classical pathway in neurodegeneration models are likely independent of C3aR activation and raise questions about the rationale for therapeutically targeting C3aR for neurodegenerative disease.

Here the current and emerging roles of brain positron emission tomography (PET) in Alzheimer's disease (AD) in the era of anti-amyloid-β antibody therapy, with a focus on clinical applications, methodological considerations, and future perspectives were reviewed. A narrative review of the literature on PET imaging in AD, including FDG-PET, amyloid PET, and tau PET, was conducted with particular emphasis on their clinical utility in diagnosis, and disease monitoring. Relevant guidelines, including appropriate use criteria and Japanese clinical guidelines, were also reviewed. FDG-PET provides valuable information for the differential diagnosis of neurodegenerative dementias based on characteristic hypometabolic patterns, although its role remains supportive due to the lack of direct assessment of molecular pathology. Amyloid PET enables noninvasive visualization of cerebral amyloid-β deposition and has become essential for confirming eligibility for anti-amyloid therapies. Standardized use criteria and interpretation guidelines are critical for appropriate clinical implementation. Quantitative approaches, such as standardized uptake value ratios (SUVRs) and the Centiloid scale, improve comparability across studies and institutions. Tau PET reflects neurofibrillary pathology and correlates with disease severity and progression, with increasing relevance for patient stratification. In addition, recent advances in high-resolution dedicated brain PET systems and artificial intelligence-based image analysis are expected to enhance diagnostic performance and workflow efficiency. In the era of disease-modifying therapy, brain PET imaging has become integral to the clinical management of AD. Amyloid PET is indispensable for treatment eligibility, while tau PET provides complementary information on disease stage and prognosis. Ongoing technological and methodological advancements will further expand the role of PET imaging in precision medicine for dementia.

OBJECTIVES: The purpose of this study was to investigate the effect of group Cognitive Stimulation Therapy (CST) for people living with Parkinson's disease and dementia (PDD) and their care partners. METHOD: This study used a within-subjects pre-test post-test design. Participants were volunteers living with PDD and their care partners ( = 20 each). Participants living with PDD participated in 7 wk of bi-weekly 1-h long CST group classes. They completed pre and post cognition, health-related quality of life, depressive symptoms, and relationship quality assessments, care partners completed pre and post relationship quality and caregiver burden assessments, and both groups completed a satisfaction questionnaire at post. RESULTS: Among participants living with PDD, cognition improved from pre to post ( = 0.002). Care partners reported needing to provide assistance in more tasks from pre to post intervention ( = 0.04). Both groups reported 'good' to 'excellent' satisfaction on average at post. CONCLUSION: Participation in a group CST course resulted in improved cognition in this small sample of people living with PDD. In addition, participants living with PDD and their care partners were satisfied with the program, although supporting attendance may have increased burden on care partners. Although preliminary, these findings suggest that group CST may be beneficial for people living with PDD.