INVESTIGADORES
GARCIA adolfo Martin
congresos y reuniones científicas
Título:
Towards affordable biomarkers of frontotemporal dementia: A classification study via network information sharing
Autor/es:
DOTTORI, MARTÍN; SEDEÑO, LUCAS; MARTORELL CARO, MIGUEL; ALIFANO, FLORENCIA; HESSE, EUGENIA; MIKULAN, EZEQUIEL; GARCÍA, ADOLFO M.; LILLO, PATRICIA; SLACHEVSKY, ANDREA; SERRANO, CECILIA; FRAIMAN, DANIEL; IBÁÑEZ, AGUSTÍN
Lugar:
Buenos Aires
Reunión:
Encuentro; 2nd Latin American Brain Mapping Network Meeting; 2017
Resumen:
Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis through clinical assessment. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost and portability. Here, we relied on EEG signals to study resting-state functional connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls using a novel information-sharing method. To evaluate the specificity of our results, we also tested Alzheimer?s disease (AD) patients. The classification power of the ensuing connectivity patterns was tested through a supervised classification algorithm (support vector machine, SVM). In addition, we compared the classification power yielded by (i) functional connectivity measures, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Moreover, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. This is the first study reporting better discrimination rates for bvFTD through a combination of EEG connectivity and neuropsychological measures. Thus, our findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.