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The nonlinear dynamic characteristics of human brain cognition and music stimulation are introduced based on the negative emotions of college students to explore the influence of music stimulation on human brain cognition activities and analyze the nonlinear dynamic characteristics of electroencephalogram (EEG) signals. First, the correlation between EEG signals and human brain cognition, as well as the nonlinear dynamics of cognition, are explained. The average correlation dimension and the maximum value of the Lyapunov exponent are selected to characterize the nonlinear dynamic characteristics of EEG signals. Second, the power spectrum and EEG coherence are considered, and an emotion assessment algorithm is proposed. Finally, music stimulation is introduced, and its role in adjusting college students’ negative emotions is discussed. Results demonstrate significant differences in the average correlation dimension and the maximum value of the Lyapunov component of the participants in different cognition states under normal physiological conditions. The cognition function state shows chaotic behaviors, explaining the nonlinear dynamic characteristics of EEG signals. After music stimulation, a significant increase in EEG relative power is mostly located in the frontal and temporal regions of the brain. The EEG coherence in the same brain region shows growth changes. After the intervention of music stimulation, the emotion assessment scores of the self-rating anxiety scale, the positive and negative affect schedule, and the self-rating depression scale are reduced, and the entire changing process presents statistical significance. In conclusion, music stimulation intervention can affect human brain cognition activity, playing a positive role in emotion regulation.

Late-onset Alzheimer’s disease (LOAD) is a polygenic disorder with a long prodromal phase, making early diagnosis challenging. Twin studies estimate LOAD as 60-80% heritable, and while common genetic variants can account for 30% of this heritability, nearly 70% remains “missing”. Polygenic risk scores (PRS) leverage combined effects of many loci to predict LOAD risk, but often lack sensitivity to preclinical disease changes, limiting clinical utility. Our group has built and published on a resilience phenotype to model better-than-expected cognition give amyloid pathology burden and hypothesized it may assist in preclinical polygenic risk prediction. Thus, we built a LOAD PRS and a resilience PRS and evaluated both in predicting cognition in a dementia-free cohort (N=254). The LOAD PRS had a significant main effect on baseline memory (β=-0.18, P=1.68E-03). Both the LOAD PRS (β=-0.03, P=1.19E-03) and the resilience PRS (β=0.02, P=0.03) had significant main effects on annual memory decline. The resilience PRS interacted with CSF Aβ on baseline memory (β=-6.04E-04, P=0.02), whereby it predicted baseline memory among Aβ+ individuals (β=0.44, P=0.01) but not among Aβ- individuals (β=0.06, P=0.46). Excluding APOE from PRS resulted in mainly LOAD PRS associations attenuating, but notably the resilience PRS interaction with CSF Aβ and selective prediction among Aβ+ individuals was consistent. Although the resilience PRS is currently somewhat limited in scope from the phenotype’s cross-sectional nature, our results suggest that the resilience PRS may be a promising tool in assisting in preclinical disease risk prediction among dementia-free and Aβ+ individuals, though replication and fine-tuning are needed.

The greatest known risk factor for Alzheimer’s disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FA_FWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10^-32; T1: r=0.61, p=1.45x10^-26, FW+T1: r=0.77, p=6.48x10^-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32x10^-7; T1: β=-1.331, p=6.52x10^-7; FW+T1: β=-1.476, p=2.53x10^-10; executive function, FW: β=-1.276, p=1.46x10^-9; T1: β=-1.337, p=2.52x10^-7; FW+T1: β=-1.850, p=3.85x10^-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62x10^-11; T1: β=-0.097, p=1.40x10^-8; FW+T1: β=-0.101, p=1.35x10^-11; executive function, FW: β=-0.125, p=1.20x10^-10; T1: β=-0.163, p=4.25x10^-12; FW+T1: β=-0.158, p=1.65x10^-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

This paper includes the themes and descriptions for TSG-20 Learning and Cognition, the session topics developed in response to the themes, a report of the review process and results of this, the TSG-20 program including the research focus for each invited speaker, profiles of the invited researchers, the content of their submitted papers, and the authors and titles of other papers. It also includes participant reflections about sessions that provide indicators of future intended research directions.

Alzheimer’s disease (AD) is a polygenic disorder with a prolonged prodromal phase, complicating early diagnosis. Recent research indicates that increased astrocyte reactivity is associated with a higher risk of pathogenic tau accumulation, particularly in amyloid-positive individuals. However, few clinical tools are available to predict which individuals are likely to exhibit elevated astrocyte activation and, consequently, be susceptible to hyperphosphorylated tau-induced neurodegeneration. Polygenic risk scores (PRS) aggregate the effects of multiple genetic loci to provide a single, continuous metric representing an individual’s genetic risk for a specific phenotype. We hypothesized that an astrocyte activation PRS could aid in the early detection of faster clinical decline. Therefore, we constructed an astrocyte activation PRS and assessed its predictive value for cognitive decline and AD biomarkers (i.e., cerebrospinal fluid [CSF] levels of Aβ1-42, total tau, and p-tau181) in a cohort of 791 elderly individuals. The astrocyte activation PRS showed significant main effects on cross-sectional memory (β = -0.07, p = 0.03) and longitudinal executive function (β = -0.01, p = 0.03). Additionally, the PRS interacted with amyloid positivity (p.intx = 0.02), whereby indicating that amyloid burden modifies the association between the PRS and annual rate of language decline. Furthermore, the PRS was negatively associated with CSF Aβ1-42 levels (β = -3.4, p = 0.07) and interacted with amyloid status, such that amyloid burden modifies the association between the PRS and CSF phosphorylated tau levels (p.intx = 0.08). These findings suggest that an astrocyte activation PRS could be a valuable tool for early disease risk prediction, potentially enabling intervention during the interval between pathogenic amyloid and tau accumulation.