function and other cognitive abilities in mild to moderate AD and finally severe AD (Figure 1). The spread can be seen in patterns of changes in biomarkers of β-amyloid deposition (Figure 2), glucose metabolism (Figure 3) and in structural changes in the brain measured by MRI (Figure 4). AD is characterized by the progressive disruption of the brain connectome. As the disease progresses there are fewer connections between essential brain regions (Figure 5). Many genes in addition to APOE ε4 underlie late onset AD. ADNI data has helped to identify or confirm 10 of the approximately 20 risk genes currently identified Cerebrovascular disease can accelerate disease progression in AD. Both the cognitively normal and MCI groups are pathologically heterogeneous. Some people show no signs of AD, some show signs of progressing to AD quickly, and others show signs of progressing to dementias other than AD. Diagnosis of AD and the prediction of future AD ADNI data has been used to test many diagnostic and prognostic machine learning algorithms. The most successful to date have used deep learning approaches that combine longitudinal data chronicling changes in biomarkers over time from more than one imaging, genetic, or biological modality. Diagnosis One example of a combination of biomarkers that can accurately diagnose AD is: Changes in brain atrophy patterns over time (measured by MRI) Levels of β-amyloid and tau (measured in CSF) A second approach to diagnosis is to extract the most pertinent information from MRI scans alone. Deep learning algorithms can diagnose AD with greater than 95% accuracy, and can diagnose MCI due to AD with greater than 82% accuracy. As imaging scans are expensive and sometimes unavailable, and the analysis of CSF requires an invasive lumbar puncture procedure, ADNI blood samples are being used to develop diagnostic blood tests for clinical use. These are currently not as accurate as other methods. Prediction Deep learning algorithms which extract the most pertinent information from MRI scans can also predict the progression of MCI patients to AD several years in advance with accuracies of greater than 90%. Development of biomarkers for clinical trials The major aim of ADNI is to develop biomarkers to enable successful clinical trials. AD clinical trials are now focusing on preventing the disease rather than curing it. Because AD pathology develops many years before outward signs of the disease such as memory loss, preventive therapies are targeted to cognitively normal