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Qiu et al. present a novel deep learning strategy to generate high-resolution visualizations of Alzheimer’s disease risk in humans that are highly interpretable and can accurately predict Alzheimer’s disease status. They then test the model by comparing its performance to that of neurologists and neuropathological data., Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Introduction Despite the availability of age- and education-adjusted standardized scores for most neuropsychological tests, there is a lack of objective rules in how to interpret multiple concurrent neuropsychological test scores that characterize the heterogeneity of Alzheimer's disease. Methods Using neuropsychological test scores of 2091 participants from the Framingham Heart Study, we devised an automated algorithm that follows general diagnostic criteria and explores the heterogeneity of Alzheimer's disease. Results We developed a series of stepwise diagnosis rules that evaluate information from multiple neuropsychological tests to produce an intuitive and objective Alzheimer's disease dementia diagnosis with more than 80% accuracy. Discussion A data-driven stepwise diagnosis system is useful for diagnosis of Alzheimer's disease from neuropsychological tests. It demonstrated better performance than the traditional dichotomization of individuals' performance into satisfactory and unsatisfactory outcomes, making it more reflective of dementia as a spectrum disorder. This algorithm can be applied to both within clinic and outside-of-clinic settings., Highlights • Stepwise diagnosis rules for Alzheimer's disease (AD) dementia diagnosis. • Heterogeneous AD profiles based on specific AD risk factors identified. • Selection of important neuropsychological tests aid differential diagnoses. • Grading system more reflective of dementia as a spectrum disorder. • Automated diagnosis rules applicable to both within clinic and outside-of-clinic settings.

Introduction Subtle cognitive alterations that precede clinical evidence of cognitive impairment may help predict the progression to Alzheimer’s disease (AD). Neuropsychological (NP) testing is an attractive modality for screening early evidence of AD. Methods Longitudinal NP and demographic data from the Framingham Heart Study (FHS; N = 1696) and the National Alzheimer's Coordinating Center (NACC; N = 689) were analyzed using an unsupervised machine learning framework. Features, including age, logical memory-immediate and delayed recall, visual reproduction-immediate and delayed recall, the Boston naming tests, and Trails B, were identified using feature selection, and processed further to predict the risk of development of AD. Results Our model yielded 83.07 ± 3.52% accuracy in FHS and 87.57 ± 1.19% accuracy in NACC, 80.52 ± 3.93%, 86.74 ± 1.63% sensitivity in FHS and NACC respectively, and 85.63 ± 4.71%, 88.41 ± 1.38% specificity in FHS and NACC, respectively. Discussion Our results suggest that a subset of NP tests, when analyzed using unsupervised machine learning, may help distinguish between high- and low-risk individuals in the context of subsequent development of AD within 5 years. This approach could be a viable option for early AD screening in clinical practice and clinical trials.

The Framingham Heart Study (FHS) was established in 1948 and is the longest, ongoing prospective cohort study studying cardiovascular diseases and stroke. Initially, the FHS was primarily focused on cardiovascular diseases; however, over recent decades data from the FHS have found that lifetime exposure to major cardiovascular risk factors such as hypertension, diabetes mellitus, arterial stiffness, and pressure pulsatility adversely impact neuropsychological functioning particularly in older individuals contributing to the onset of dementia including Alzheimer’s disease. Research from the FHS suggest that appropriate management of cardiovascular risk factors as well as healthy lifestyle practices that include regular blood pressure monitoring, not smoking, and eating a choline-rich diet appear to minimize neuropsychological decline as well as dementia onset.

The relationship between body mass index (BMI) and health outcomes of older adults, including dementia, remains controversial. Many studies find inverse associations between BMI and dementia among older adults, while in other studies high BMI in midlife is associated with increased dementia risk. In this issue, Li et al. (Am J Epidemiol. 2021;190(12):2503–2510) examine BMI from mid- to late life and risk of dementia using the extensive follow-up of the Framingham Offspring Study. They found changing trends in the association between BMI and dementia from a positive association for midlife (ages 40–49) to an inverse trend in late life. Their work demonstrates the importance of studying dementia risk factors across the life course. Midlife obesity might be an important modifiable risk factor for dementia. However, because incipient dementia can lead to weight loss, reverse causation remains a key source of bias that could explain an inverse trend between BMI and dementia in older ages. The extent of other biases, including unmeasured confounding, inaccuracy of BMI as a measure for adiposity, or selective survival, are also unclear. Triangulating evidence on body composition and dementia risk could lead to better targets for dementia intervention, but future work will need to evaluate specific pathways.

Alzheimer’s disease (AD) is the primary cause of dementia worldwide (1), with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and magnetic resonance imaging (MRI) to identify likely cases, yet effective practices remain variably-applied and lacking in sensitivity and specificity (2). Here we report an explainable deep learning strategy that delineates unique AD signatures from multimodal inputs of MRI, age, gender, and mini-mental state examination (MMSE) score. Our framework linked a fully convolutional network (FCN) to a multilayer perceptron (MLP) to construct high resolution maps of disease probability from local brain structure. This enabled precise, intuitive visualization of individual AD risk en route to accurate diagnosis. The model was trained using clinically-diagnosed AD and cognitively normal (NC) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n=417) (3), and validated on three independent cohorts: the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n=382) (4), the Framingham Heart Study (FHS, n=102) (5), and the National Alzheimer’s Coordinating Center (NACC, n=582) (6). Model performance was consistent across datasets, with mean accuracy values of 0.966, 0.948, 0.815, and 0.916 for ADNI, AIBL, FHS and NACC, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n=11), and high-risk cerebral regions predicted by the model closely tracked postmortem histopathological findings. This framework provides a clinically-adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for AD diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.