Objectively measured physical activity using wrist-worn accelerometers as a predictor of incident Alzheimer's Disease in the UK Biobank.

Background: Alzheimer's disease (AD) affects over 6 million people and is the seventh-leading cause of death in the US. This study compares wrist-worn accelerometry-derived PA measures against traditional risk factors for incident AD in the UK Biobank.

Methods: Of 42,157 UK Biobank participants 65 years and older who had accelerometry data and no prior AD diagnosis, 157 developed AD by April 1, 2021 (264,988 person-years or on average 6.

Comparing automated subcortical volume estimation methods; amygdala volumes estimated by FSL and FreeSurfer have poor consistency.

Subcortical volumes are a promising source of biomarkers and features in biosignatures, and automated methods facilitate extracting them in large, phenotypically rich datasets. However, while extensive research has verified that the automated methods produce volumes that are similar to those generated by expert annotation; the consistency of methods with each other is understudied. Using data from the UK Biobank, we compare the estimates of subcortical volumes produced by two popular software suites: FSL and FreeSurfer.

A tensor based varying-coefficient model for multi-modal neuroimaging data analysis.

All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimaging data is increasingly being combined with other non-imaging modalities, such as behavioral and genetic data.

Expectation generation and its effect on subsequent pain and visual perception.

Bayesian accounts of perception, such as predictive processing, suggest that perceptions integrate expectations and sensory experience, and thus assimilate to expected values. Furthermore, more precise expectations should have stronger influences on perception. We tested these hypotheses in a paradigm that manipulates both the mean value and the precision of cues within-person.

Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale.

Recent gains in functional magnetic resonance imaging (fMRI) studies have been driven by increasingly sophisticated statistical and computational techniques and the ability to capture brain data at finer spatial and temporal resolution. These advances allow researchers to develop population-level models of the functional brain representations underlying behavior, performance, clinical status, and prognosis. However, even following conventional preprocessing pipelines, considerable inter-individual disparities in functional localization persist, posing a hurdle to performing compelling population-level inference.