Alzheimer’s Disease & Dementia

Biography

Biography: Matthias L Schroeter

Abstract

Recently, new diagnostic criteria including imaging biomarkers have been proposed for frontotemporal lobar degeneration (FTLD) in particular for its behavioural variant and language subtypes. Th ese imaging criteria shall enable individual diagnosis. New imaging criteria were validated by conducting quantitative anatomical likelihood estimate meta-analyses according to the QUOROM/PRISMA statement across studies published in the literature. Th ese meta-analyses identify the neural correlates for each of the FTLD subtypes and underline disease-specifi city of the imaging criteria. Analyses were conducted separately for atrophy measured with magnetic resonance imaging (MRI) and glucose metabolism measured with [F18] fl uorodeoxyglucose positron emission tomography (FDG-PET). Both imaging methods revealed specifi c regional patterns. Results might open the road to method-specifi c imaging criteria as already suggested for Alzheimer’s disease. If new imaging criteria are valid they shall enable early individual diagnosis in single patients. To prove the potential for individual diagnosis we investigated whether FTLD might be diagnosed with cutting edge pattern classifi cation algorithms in multimodal imaging data. Support vector machine classifi cation (SVM) with multimodal imaging data (MRI & FDG-PET) enabled early individual detection of FTLD and discrimination between FTLD and Alzheimer’s disease. Limiting SVM classifi cation regionally to meta-analytically identifi ed disease networks even improved discrimination accuracy. Analyses were also reliable in multi-centric data. In conclusion, our results support and refi ne the application of imaging criteria and suggest that pattern classifi cation algorithms enable early individual diagnosis and diff erential diagnosis of FTLD sub types, a precondition for early intervention strategies.