Progression modelling of cognitive decline and associated FDG‐PET imaging features in Alzheimer’s disease

Background Prognosis for Alzheimer’s disease is difficult, with rates of disease progression varying widely. Despite the extensive use of clinical dementia severity assessment scales to determine dementia diagnosis and to monitor progression, there is no consensus on which imaging factors may accurately predict future decline trajectories on these measures. Determination of baseline imaging patterns that are related to slower, or faster, decline rates could be used to identify those at highest risk of worse outcomes and inform care planning. Method Decline trajectories were estimated from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset which contains clinical, imaging, and biological assessment records for 1,736 patients followed‐up over 8 years at 3, 6, or 12 month intervals. Clusters of similar decline trajectories were identified for patients with relevant baseline fluorodeoxyglucose positron emission tomography (FDG‐PET) imaging data (N=530) using mini‐mental state examination (MMSE), clinical dementia rating sum of boxes (CDR‐SB), Alzheimer’s disease assessment scale (ADAS‐13), and functional activities questionnaire (FAQ) assessment scores. Decline trajectories allocated to slow, intermediate, and fast clusters were further analysed to find significant associations with PET imaging features using statistical parametric mapping (SPM12). Result When compared to cognitively normal non‐decliners, slow, intermediate and fast cluster groups showed similar topographic patterns of hypometabolism on FDG‐PET for all four clinical assessments (p