Physical activity is a modifiable lifestyle factor with potential to support cognitive resilience. However, the association of moderate-to-vigorous physical activity (MVPA) intensity, and timing, with cognitive function and region-specific brain structure remain poorly understood. We analyzed data from 45,892 UK Biobank participants aged 60 years and older with valid wrist-worn accelerometer data, cognitive testing, and structural brain MRI. MVPA was measured both continuously (mins per week) and categorically (thresholded using >=150 min/week based on WHO guidelines). Associations with cognitive performance and regional brain volumes were evaluated using multivariable linear models adjusted for demographic, socioeconomic, and health-related covariates. We conducted secondary analyses on MVPA timing and subgroup effects. Higher MVPA was associated with better performance across cognitive domains, including reasoning, memory, executive function, and processing speed. These associations persisted in fully adjusted models and were higher among participants meeting WHO guidelines. Greater MVPA was also associated with subcortical brain regions (caudate, putamen, pallidum, thalamus), as well as regional gray matter volumes involved in emotion, working memory, and perceptual processing. Secondary analyses showed that MVPA at any time of day was associated with cognitive functions and brain volume particularly in the midday-afternoon and evening. Sensitivity analysis shows consistent findings across subgroups, with evidence of dose-response relationships. Higher MVPA is associated with preserved brain structure and enhanced cognitive function in later life. Public health strategies to increase MVPA may support healthy cognitive aging and generate substantial economic benefits, with global gains projected to reach USD 760 billion annually by 2050.

In brain neural networks, Local Field Potential (LFP) signals represent the dynamic flow of information. Analyzing LFP clinical data plays a critical role in improving our understanding of brain mechanisms. One way to enhance our understanding of these mechanisms is to identify a global model to predict brain signals in different situations. This paper identifies a global data-driven based on LFP recordings of the Nucleus Accumbens and Hippocampus regions in freely moving rats. The LFP is recorded from each rat in two different situations: before and after the process of getting a reward which can be either a drug (Morphine) or natural food (like popcorn or biscuit). A comparison of five machine learning methods including Long Short Term Memory (LSTM), Echo State Network (ESN), Deep Echo State Network (DeepESN), Radial Basis Function (RBF), and Local Linear Model Tree (LLM) is conducted to develop this model. LoLiMoT was chosen with the best performance among all methods. This model can predict the future states of these regions with one pre-trained model. Identifying this model showed that Morphine and natural rewards do not change the dynamic features of neurons in these regions.

Innovative technologies such as artificial intelligence (AI) machine learning and optogenetics are accelerating discoveries in life sciences, especially in the field of neuroscience. A new breakthrough study published in Current Biology by pioneering brain researchers at Vanderbilt University used optogenetics and AI machine learning to reveal that dopamine is not just a "pleasure molecule" -- a revolutionary finding that may impact how addiction and psychiatric diseases are treated in the future. "Dopamine deficits are seen in patients suffering from substance use disorder," said Erin Calipari, an assistant professor of pharmacology at Vanderbilt University, and faculty member of both the Vanderbilt Brain Institute and the Vanderbilt Center for Addiction Research. "These individuals have reduced dopamine as well as deficits in decision-making that would be explained by our data and new model. These deficits in decision-making are highly correlated with the severity of addiction as well as predicting treatment outcomes. These data are really key to understanding the relationship between dopamine this disease and figuring out how to treat it."

Listening to music often evokes intense emotions [ 1 Brain correlates of music-evoked emotions. Recent research suggests that musical pleasure comes from positive reward prediction errors, which arise when what is heard proves to be better than expected [ 3 Predictions and the brain: how musical sounds become rewarding. Central to this view is the engagement of the nucleus accumbens--a brain region that processes reward expectations--to pleasurable music and surprising musical events [ 4 Interactions between the nucleus accumbens and auditory cortices predict music reward value. However, expectancy violations along multiple musical dimensions (e.g., harmony and melody) have failed to implicate the nucleus accumbens [ 9 Adults and children processing music: an fMRI study. Whether changes in musical expectancy elicit pleasure has thus remained elusive [ 11 Musical pleasure and musical emotions.