https://www.selleckchem.com/products/z-ietd-fmk.html Alzheimer's continuum biological profiles (A+T-N-, A+T+N-, A+T-N+, and A+T+N+) were established in the 2018 National Institute on Aging and Alzheimer's Association research framework for Alzheimer's disease (AD). We aim to assess the relation between AT(N) biomarker profiles and brain functional connectivity (FC) and assess the neural correlates of anosognosia. We assessed local functional coupling and between-network connectivity through between-group intrinsic local correlation and independent component analyses. The neural correlates of anosognosia were assessed via voxel-wise linear regression analysis in prodromal AD. Statistical significance for the FC analysis was set at p ≤ 0.05 false discovery rate (FDR)-corrected for cluster size. One hundred and twenty-one and 73 participants were included in the FC and the anosognosia analysis, respectively. The FC in the default mode network is greater in prodromal AD than AD with dementia (i.e., local correlation T = 8.26, p-FDR less then 0.001, k = 1179; independent component analysis cerebellar network, T = 4.01, p-FDR = 0.0012, k = 493). The default mode network is persistently affected in the early stages of Alzheimer's biological continuum. The anterior cingulate cortex (T = 2.52, p-FDR = 0.043, k = 704) is associated with anosognosia in prodromal AD.Older adults with anxiety have lower gray matter brain volume-a component of accelerated aging. We have previously validated a machine learning model to predict brain age, an estimate of an individual's age based on voxel-wise gray matter images. We investigated associations between brain age and anxiety, depression, stress, and emotion regulation. We recruited 78 participants (≥50 years) along a wide range of worry severity. We collected imaging data and computed voxel-wise gray matter images, which were input into an existing machine learning model to estimate brain age. We conducted a multivariable linear regress