RT Journal Article
T1 Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography
A1 Giovannetti, Antonio
A1 Susi, Gianluca
A1 Casti, Paola
A1 Mencattini, Arianna
A1 Pusil Arce, Sandra Angélica
A1 López García, María Eugenia
A1 Di Natale, Corrado
A1 Martinelli, Eugenio
AB In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.
PB Springer
SN 0941-0643
YR 2021
FD 2021-05-19
LK https://hdl.handle.net/20.500.14352/116651
UL https://hdl.handle.net/20.500.14352/116651
LA eng
NO Giovannetti, A., Susi, G., Casti, P., Mencattini, A., Pusil, S., López, M. E., ... & Martinelli, E. (2021). Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography. Neural Computing and Applications, 33(21), 14651-14667.
NO "Open access funding provided by Universita` degli Studi di Roma Tor Vergata within the CRUI-CARE Agreement"
NO Ministerio de Economía y Competitividad (España)
NO Universita` degli Studi di Roma Tor Vergata
DS Docta Complutense
RD 13 abr 2025