Details

Autor(en) / Beteiligte

• Colloby, Sean J
• Cromarty, Ruth A
• Peraza, Luis R
• Johnsen, Kristinn
• Jóhannesson, Gísli
• Bonanni, Laura
• Onofrj, Marco
• Barber, Robert
• O’Brien, John T
• Taylor, John-Paul

Titel

Multimodal EEG-MRI in the differential diagnosis of Alzheimer’s disease and dementia with Lewy bodies

Ist Teil von

• Journal of psychiatric research, 2016-07, Vol.78, p.48-55

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2016

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• Abstract Differential diagnosis of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) remains challenging; currently the best discriminator is striatal dopaminergic imaging. However this modality fails to identify 15-20% of DLB cases and thus other biomarkers may be useful. It is recognised electroencephalography (EEG) slowing and relative medial temporal lobe preservation are supportive features of DLB, although individually they lack diagnostic accuracy. Therefore, we investigated whether combined EEG and MRI indices could assist in the differential diagnosis of AD and DLB. Seventy two participants (21 Controls, 30 AD, 21 DLB) underwent resting EEG and 3 Tesla MR imaging. Six EEG classifiers previously generated using support vector machine algorithms were applied to the present dataset. MRI index was derived from medial temporal atrophy (MTA) ratings. Logistic regression analysis identified EEG predictors of AD and DLB. A combined EEG-MRI model was then generated to examine whether there was an improvement in classification compared to individual modalities. For EEG, two classifiers predicted AD and DLB (model: χ2 =22.1, df=2, p<0.001, Nagelkerke R2 = 0.47, classification = 77% (AD 87%, DLB 62%)). For MRI, MTA also predicted AD and DLB (model: χ2 =6.5, df=1, p=0.01, Nagelkerke R2 = 0.16, classification = 67% (77% AD, 52% DLB). However, a combined EEG-MRI model showed greater prediction in AD and DLB (model: χ2 =31.1, df=3, p<0.001, Nagelkerke R2 = 0.62, classification = 90% (93% AD, 86% DLB)). While suggestive and requiring validation, diagnostic performance could be improved by combining EEG and MRI, and may represent an alternative to dopaminergic imaging.