Density profiles of Alzheimer disease regional brain pathology for the Huddinge brain bank: pattern recognition emulates and expands upon Braak staging

• Published in: Experimental gerontology 2000-09, Vol.35 (6), p.851-864
• Main Authors: Corder, E.H, Woodbury, M.A, Volkmann, I, Madsen, D.K, Bogdanovic, N, Winblad, B
• Format: Article
• Language: English
• Subjects: Alzheimer Disease - pathology; Alzheimer's disease; Brain - pathology; Dementia; Diffuse plaque; Fuzzy logic; Grade-of membership analysis; Humans; Latent class analysis; Medicin och hälsovetenskap; Neurites - pathology; Neuritic plaque; Neurofibrillary tangles; Neurofibrillary Tangles - pathology; Organ Size; Pattern recognition
• ISSN: 0531-5565; 1873-6815
• DOI: 10.1016/S0531-5565(00)00147-9

Density profiles of Alzheimer's disease (AD) regional brain pathology were constructed for 249 subjects in the Huddinge Brain Bank. Counts per square millimeter for neurofibrillary tangles (NFT), diffuse plaques (DP), and neuritic plaques (NP) in 38 areas were investigated using a pattern recognition technique called GoM. The seven distributional profiles of AD neuropathology emulated and expanded upon Braak staging illustrating induction (Groups 1–3) and clinical progression (Groups 4–7). Normal aging represented limited AD changes, few NFT in the entorhinal cortex and hippocampal CA1 (Group 1). The threshold for possible AD was NFT in the subiculum (Group 2), found with DP in the neocortex. Temporal medial NFT was the threshold for probable AD (Group 4). The ‘oldest-old’, often demented without brain atrophy, had extensive entorhinal/CA1 NFT and cortical DP, but few cortical NFT or NP (Group 5). A second subtype ‘disconnection’ (Group 6) lacked AD pathology for a specific set of subcortical and cortical areas. Accumulation of NFT in first-affected areas continued through end-stage disease (Group 7), with apparent rapid transition of DP to NP in the cortex during clinical progression. The evolution of AD is a highly ordered sequential process. Pattern recognition approaches such as GoM may be useful in better defining the process.