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.

Giovannetti, A., Susi, G., Casti, P., Mencattini, A., Pusil, S., Lopez, M.E., et al. (2021). Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography. NEURAL COMPUTING & APPLICATIONS, 33(21), 14651-14667 [10.1007/s00521-021-06105-4].

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Susi G.;Casti P.;Mencattini A.;Di Natale C.;Martinelli E.
2021

Abstract

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.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/07
eng
Alzheimer’s disease
Deep CNN feature transfer
Deep learning
Ensemble classification
Functional connectivity
Magnetoencephalography
Giovannetti, A., Susi, G., Casti, P., Mencattini, A., Pusil, S., Lopez, M.E., et al. (2021). Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography. NEURAL COMPUTING & APPLICATIONS, 33(21), 14651-14667 [10.1007/s00521-021-06105-4].
Giovannetti, A; Susi, G; Casti, P; Mencattini, A; Pusil, S; Lopez, Me; Di Natale, C; Martinelli, E
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/289495
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