Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

Distinguishing Alzheimer's disease (AD) and frontotemporal dementia (FTD) currently relies on a clinical history and examination, but positron emission tomography with [(18)F] fluorodeoxyglucose (FDG-PET) shows different patterns of hypometabolism in these disorders that might aid differential diagnosis. Six dementia experts with variable FDG-PET experience made independent, forced choice, diagnostic decisions in 45 patients with pathologically confirmed AD (n = 31) or FTD (n = 14) using five separate methods: (1) review of clinical summaries, (2) a diagnostic checklist alone, (3) summary and checklist, (4) transaxial FDG-PET scans and (5) FDG-PET stereotactic surface projection (SSP) metabolic and statistical maps. In addition, we evaluated the effect of the sequential review of a clinical summary followed by SSP. Visual interpretation of SSP images was superior to clinical assessment and had the best inter-rater reliability (mean kappa = 0.78) and diagnostic accuracy (89.6%). It also had the highest specificity (97.6%) and sensitivity (86%), and positive likelihood ratio for FTD (36.5). The addition of FDG-PET to clinical summaries increased diagnostic accuracy and confidence for both AD and FTD. It was particularly helpful when raters were uncertain in their clinical diagnosis. Visual interpretation of FDG-PET after brief training is more reliable and accurate in distinguishing FTD from AD than clinical methods alone. FDG-PET adds important information that appropriately increases diagnostic confidence, even among experienced dementia specialists.