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Alzheimer’s disease cortical tau pathology initiates in the layer II cell clusters of entorhinal cortex, but it is not known why these specific neurons are so vulnerable. Aging macaques exhibit the same qualitative pattern of tau pathology as humans, including initial pathology in layer II entorhinal cortex clusters, and thus can inform etiological factors driving selective vulnerability. Macaque data have already shown that susceptible neurons in dorsolateral prefrontal cortex express a “signature of flexibility” near glutamate synapses on spines, where cAMP-PKA magnification of calcium signaling opens nearby potassium and hyperpolarization-activated cyclic nucleotide-gated channels to dynamically alter synapse strength. This process is regulated by PDE4A/D, mGluR3, and calbindin, to prevent toxic calcium actions; regulatory actions that are lost with age/inflammation, leading to tau phosphorylation. The current study examined whether a similar “signature of flexibility” expresses in layer II entorhinal cortex, investigating the localization of PDE4D, mGluR3, and HCN1 channels. Results showed a similar pattern to dorsolateral prefrontal cortex, with PDE4D and mGluR3 positioned to regulate internal calcium release near glutamate synapses, and HCN1 channels concentrated on spines. As layer II entorhinal cortex stellate cells do not express calbindin, even when young, they may be particularly vulnerable to magnified calcium actions and ensuing tau pathology.

 

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. 

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.