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For the scope of predicting the early signs of Alzheimer\u2019s 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\u2013temporal connectivity profiles and in their spatial relationships.<\/jats:p>","DOI":"10.1007\/s00521-021-06105-4","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T18:14:23Z","timestamp":1621448063000},"page":"14651-14667","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer\u2019s disease with magnetoencephalography"],"prefix":"10.1007","volume":"33","author":[{"given":"Antonio","family":"Giovannetti","sequence":"first","affiliation":[]},{"given":"Gianluca","family":"Susi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7489-3022","authenticated-orcid":false,"given":"Paola","family":"Casti","sequence":"additional","affiliation":[]},{"given":"Arianna","family":"Mencattini","sequence":"additional","affiliation":[]},{"given":"Sandra","family":"Pusil","sequence":"additional","affiliation":[]},{"given":"Mar\u00eda Eugenia","family":"L\u00f3pez","sequence":"additional","affiliation":[]},{"given":"Corrado","family":"Di Natale","sequence":"additional","affiliation":[]},{"given":"Eugenio","family":"Martinelli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"6105_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05332-5","author":"R Rajasree","year":"2020","unstructured":"Rajasree R, Columbus CC, Shilaja C (2020) Multiscale-based multimodal image classification of brain tumor using deep learning method. 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