A deep CNN‐based approach for predicting MCI to AD conversion

• Published in: Alzheimer's & dementia Vol. 16
• Main Authors: Casti, Paola, Giovannetti, Antonio, Susi, Gianluca, Mencattini, Arianna, Pusil, Sandra Angelica, García, María Eugenia López, Natale, Corrado Di, Martinelli, Eugenio
• Format: Journal Article
• Language: English
• Published: 01.12.2020
• ISSN: 1552-5260; 1552-5279
• DOI: 10.1002/alz.047570

Background Deep convolutional neural networks (CNNs) have demonstrated a considerable ability to decode various biomedical signals, including 2D and 3D neuroimaging data. Magnetoencephalography‐based functional connectivity (MEG‐FC) is proven to be a useful tool in predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD), since early signs of AD may be hidden in the way neuronal activity is spatially distributed and coordinated among different brain regions. In the present work, we propose a deep learning approach for the early diagnosis of AD, able to capture the electrophysiological anomalies occurring before the conversion by exploit an image‐based coding of MEG‐FC data. The proposed approach has been tested on a set of resting‐state MEG recordings, from a longitudinal study (24 months between 2 MEG scans for each subject) involving 54 participants: 27 'progressive' patients (i.e., MCI at the time of the 1st scan converted to AD at the time of the 2nd scan), and 27 'static' patients (i.e., MCI at the time of the 1st scan remained MCI at the time of the 2nd scan). Method MEG and MRI data have been gathered from the 54 patients using an Elekta Vectorview system and a General Electric 1.5T MRI scanner, respectively. Source reconstruction was performed with LCMV beamformer. The obtained neural MEG sources were anatomically parcellated into 90 ROIs according to the AAL atlas. Considering the signals stemming from the 90 ROIs we extracted the MEG‐FC measures (PLV and COR), for different frequency bands [Hz]: θ(4‐8), α(8‐12), β(12‐30), and γ(30‐55). MEG‐FC has been then coded into image‐based representations and hierarchically decomposed by means of the CNN layers of 'AlexNet'. Finally, different classification techniques have been tested as predictive modules. Result With the proposed approach we obtained an accuracy of 0.9 in discriminating ‘static’ from ‘progressive’ patients using MEG‐FC data from the first scan. The best classification results have been obtained using the Linear Discriminant Analysis and Support Vector Machine techniques. Conclusion The classification of FC profiles mediated by hierarchical CNNs decomposition is a promising method for detecting the early neurophysiological anomalies characterizing the progression to AD.