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ABSTRACT ObjectiveNeighborhood perceptions are associated with physical and mental health outcomes; however, the biological associates of this relationship remain to be fully understood. Here, we evaluate the relationship between neighborhood perceptions and amygdala activity and connectivity with salience network (i.e., insula, anterior cingulate, thalamus) nodes. MethodsForty-eight older adults (mean age = 68 [7] years, 52% female, 47% non-Hispanic Black, 2% Hispanic) without dementia or depression completed the Perceptions of Neighborhood Environment Scale. Lower scores indicated less favorable perceptions of aesthetic quality, walking environment, availability of healthy food, safety, violence (i.e., more perceived violence), social cohesion, and participation in activities with neighbors. Participants separately underwent resting-state functional magnetic resonance imaging. ResultsLess favorable perceived safety (β= −0.33,p_FDR= .04) and participation in activities with neighbors (β= −0.35,p_FDR= .02) were associated with higher left amygdala activity, independent of covariates including psychosocial factors. Less favorable safety perceptions were also associated with enhanced left amygdala functional connectivity with the bilateral insular cortices and the left anterior insula (β= −0.34,p_FDR= .04). Less favorable perceived social cohesion was associated with enhanced left amygdala functional connectivity with the right thalamus (β =−0.42,p_FDR= .04), and less favorable perceptions about healthy food availability were associated with enhanced left amygdala functional connectivity with the bilateral anterior insula (right:β= −0.39,p_FDR= .04; left:β= −0.42,p_FDR= .02) and anterior cingulate gyrus (β= −0.37,p_FDR= .04). ConclusionsTaken together, our findings document relationships between select neighborhood perceptions and amygdala activity as well as connectivity with salience network nodes; if confirmed, targeted community-level interventions and existing community strengths may promote brain-behavior relationships. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.