A longitudinal study found that loneliness is closely linked to lapses in immediate and delayed recall. Neuroscientists know that there is a link between loneliness and cognitive decline in older adults, although it is still difficult to understand the exact magnitude of the link. A new longitudinal study provides evidence that a proportion of people who feel lonely end up having more memory impairment, though this doesn't necessarily mean that their brains age faster. The report, published in Aging & Mental Health, shows that older adults with higher levels of loneliness scored lower on tests of immediate and delayed recall. Even so, the rate at which their memory declined over six years was virtually identical to those who were not lonely.

Grandparents who play with, read to, and look after their grandkids score better on cognitive tests. Breakthroughs, discoveries, and DIY tips sent six days a week. From physical fitness to doing puzzles to going out with friends, there's a laundry list of advice out there to help protect our brains from cognitive decline as we age . Taking care of grandchildren may also help brain health, according to new research from the American Psychological Association published today in the journal . "Many grandparents provide regular care for their grandchildren--care that supports families and society more broadly," Flavia Chereches, a study co-author and Ph.D. candidate at Tilburg University in the Netherlands, said in a statement.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

Blood Tests for Alzheimer's Are Here New diagnostic kits aim to revolutionize early screening of the disease, potentially allowing patients to receive treatments--such as monoclonal antibodies--sooner. Last month, The US Food and Drug Administration approved a new blood test for assisting the diagnosis of Alzheimer's disease. Tau is one of two proteins, the other being amyloid, that become malformed and accumulate in the brains of patients with certain types of dementia. It is believed that the buildup of these proteins interferes with the communication of brain cells, leading to these patients' symptoms. The test had already received authorization in July for marketing in Europe and is thus the first early screening system for Alzheimer's for use in primary care settings approved in the planet's two major pharmaceutical markets.

Early detection of Alzheimer's disease (AD) is crucial because its neurodegenerative effects are irreversible, and neuropathologic and social-behavioral risk factors accumulate years before diagnosis. Identifying higher-risk individuals earlier enables prevention, timely care, and equitable resource allocation. We predict cognitive performance from social determinants of health (SDOH) using the NIH NIA-supported PREPARE Challenge Phase 2 dataset derived from the nationally representative Mex-Cog cohort of the 2003 and 2012 Mexican Health and Aging Study (MHAS). Data: The target is a validated composite cognitive score across seven domains-orientation, memory, attention, language, constructional praxis, and executive function-derived from the 2016 and 2021 MHAS waves. Predictors span demographic, socioeconomic, health, lifestyle, psychosocial, and healthcare access factors. Methodology: Missingness was addressed with a singular value decomposition (SVD)-based imputation pipeline treating continuous and categorical variables separately. This approach leverages latent feature correlations to recover missing values while balancing reliability and scalability. After evaluating multiple methods, XGBoost was chosen for its superior predictive performance. Results and Discussion: The framework outperformed existing methods and the data challenge leaderboard, demonstrating high accuracy, robustness, and interpretability. SHAP-based post hoc analysis identified top contributing SDOH factors and age-specific feature patterns. Notably, flooring material emerged as a strong predictor, reflecting socioeconomic and environmental disparities. Other influential factors, age, SES, lifestyle, social interaction, sleep, stress, and BMI, underscore the multifactorial nature of cognitive aging and the value of interpretable, data-driven SDOH modeling.

What's my Alzheimer's risk, and can I really do anything to change it? Can you escape your genetic inheritance, and do lifestyle changes actually make a difference? Daniel Cossins set out to understand what the evidence on Alzheimer's really means for him A few years ago, my dad was diagnosed with Alzheimer's disease, just like his older brother and his mum before him. Slowly, his personality began to ebb away. Now, at the age of 75, his cognitive decline is accelerating: he no longer recognises his granddaughters, for instance, and he lives in a near-constant state of confusion, which means he is losing his independence, too. As I process this loss and try to support my parents, I have become increasingly curious about what my family history means for me.

Neural Information Processing Systems http://nips.cc/

Deep learning systems often struggle with processing long sequences, where computational complexity can become a bottleneck. Current methods for automated dementia detection using speech frequently rely on static, time-agnostic features or aggregated linguistic content, lacking the flexibility to model the subtle, progressive deterioration inherent in speech production. These approaches often miss the dynamic temporal patterns that are critical early indicators of cognitive decline. In this paper, we introduce TAI-Speech, a Temporal Aware Iterative framework that dynamically models spontaneous speech for dementia detection. The flexibility of our method is demonstrated through two key innovations: 1) Optical Flow-inspired Iterative Refinement: By treating spectrograms as sequential frames, this component uses a convolutional GRU to capture the fine-grained, frame-to-frame evolution of acoustic features. 2) Cross-Attention Based Prosodic Alignment: This component dynamically aligns spectral features with prosodic patterns, such as pitch and pauses, to create a richer representation of speech production deficits linked to functional decline (IADL). TAI-Speech adaptively models the temporal evolution of each utterance, enhancing the detection of cognitive markers. Experimental results on the DementiaBank dataset show that TAI-Speech achieves a strong AUC of 0.839 and 80.6\% accuracy, outperforming text-based baselines without relying on ASR. Our work provides a more flexible and robust solution for automated cognitive assessment, operating directly on the dynamics of raw audio.

Synthetic clinical data are increasingly important for advancing AI in healthcare, given strict privacy constraints on real-world EHRs, limited availability of annotated rare-condition data, and systemic biases in observational datasets. While large language models (LLMs) can generate fluent clinical text, producing synthetic data that is both realistic and clinically meaningful remains challenging. We introduce DualAlign, a framework that enhances statistical fidelity and clinical plausibility through dual alignment: (1) statistical alignment, which conditions generation on patient demographics and risk factors; and (2) semantic alignment, which incorporates real-world symptom trajectories to guide content generation. Using Alzheimer's disease (AD) as a case study, DualAlign produces context-grounded symptom-level sentences that better reflect real-world clinical documentation. Fine-tuning an LLaMA 3.1-8B model with a combination of DualAlign-generated and human-annotated data yields substantial performance gains over models trained on gold data alone or unguided synthetic baselines. While DualAlign does not fully capture longitudinal complexity, it offers a practical approach for generating clinically grounded, privacy-preserving synthetic data to support low-resource clinical text analysis.