91ÌÒÉ«

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. These 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. These meta-analyses identify the neural correlates for each of the FTLD subtypes and underline disease-specificity of the imaging criteria. Analyses were conducted separately for atrophy measured with magnetic resonance imaging (MRI) and glucose metabolism measured with [F18] fluorodeoxyglucose positron emission tomography (FDG-PET). Both imaging methods revealed specific regional patterns. Results might open the road to method-specific 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 classification algorithms in multimodal imaging data. Support vector machine classification (SVM) with multimodal imaging data (MRI & FDG-PET) enabled early individual detection of FTLD and discrimination between FTLD and Alzheimer�s disease. Limiting SVM classification regionally to meta-analytically identified disease networks even improved discrimination accuracy. Analyses were also reliable in multi-centric data. In conclusion, our results support and refine the application of imaging criteria and suggest that pattern classification algorithms enable early individual diagnosis and differential diagnosis of FTLD sub types, a precondition for early intervention strategies.

Biography

Email: schroet@cbs.mpg.de