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Our social relationships determine our health and well-being. In rodent models, there is now strong support for the rewarding properties of aggressive or assertive behaviors to be critical for the expression and development of adaptive social relationships, buffering from stress and protecting from the development of psychiatric disorders such as depression. However, due to the false belief that aggression is not a part of the normal repertoire of social behaviors displayed by females, almost nothing is known about the neural mechanisms mediating the rewarding properties of aggression in half the population. In the following study, using Syrian hamsters as a well-validated and translational model of female aggression, we investigated the effects of aggressive experience on the expression of markers of postsynaptic structure (PSD-95, Caskin I) and excitatory synaptic transmission (GluA1, GluA2, GluA4, NR2A, NR2B, mGluR1a, and mGluR5) in the nucleus accumbens (NAc), caudate putamen and prefrontal cortex. Aggressive experience resulted in an increase in PSD-95, GluA1 and the dimer form of mGluR5 specifically in the NAc 24 h following aggressive experience. There was also an increase in the dimer form of mGluR1a 1 week following aggressive experience. Aggressive experience also resulted in an increase in the strength of the association between these postsynaptic proteins and glutamate receptors, supporting a common mechanism of action. In addition, 1 week following aggressive experience there was a positive correlation between the monomer of mGluR5 and multiple AMPAR and NMDAR subunits. In conclusion, we provide evidence that aggressive experience in females results in an increase in the expression of postsynaptic density, AMPARs and group I metabotropic glutamate receptors, and an increase in the strength of the association between postsynaptic proteins and glutamate receptors. This suggests that aggressive experience may result in an increase in excitatory synaptic transmission in the NAc, potentially encoding the rewarding and behavioral effects of aggressive interactions. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age. 

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.