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.

OBJECTIVE:To elucidate the underlying mechanisms between C-reactive protein (CRP) and cardiovascular disease, we examined the association of circulating CRP in healthy reference range (r1.0 mg/dl) measured by high-sensitive CRP assay with the metabolic syndrome (MS). DESIGN: Cross-sectional study of circulating CRP in adult men. SUBJECTS: A total of 3692 Japanese men aged 34-69 y. MEASUREMENTS: Serum CRP, total cholesterol, triglycerides, LDL-cholesterol, fasting glucose, fasting insulin, uric acid, systolic blood pressure, diastolic blood pressure, and body mass index (BMI). RESULTS: There was a statistically significant positive correlation between CRP and BMI (r ¼ 0.25), total cholesterol (r ¼ 0.096), triglycerides (r ¼ 0.22), LDL-cholesterol (r ¼ 0.12), fasting glucose (r ¼ 0.088), fasting insulin (r ¼ 0.17), uric acid (r ¼ 0.13), systolic blood pressure (r ¼ 0.12), and diastolic blood pressure (r ¼ 0.11), and a significant negative correlation of CRP with HDLcholesterol (r ¼ 0.24). After adjusting for age, smoking, and all other components of MS, obesity, hypertriglyceridemia, hyper-LDL-cholesterolemia, diabetes, hyperinsulinemia, and hyperuricemia were significantly associated with both mildly (Z0.06 mg/ dl) and moderately (Z0.11 mg/dl) elevated CRP. Compared with men who had no such components of the MS, those who had one, two, three, four, and five or more components were, respectively, 1.48, 1.84, 1.92, 3.42, and 4.17 times more likely to have mildly elevated CRP levels (trend Po0.001). As for moderately elevated CRP, the same association was observed. CONCLUSIONS: These results indicate that a variety of components of the MS are associated with elevated CRP levels in a systemic low-grade inflammatory state.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer’s disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

No abstract