Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer’s Disease Diagnosis

Frontiers in Psychiatry

卷:11

Naoki Furutani¹  Yoshio Minabe¹  Masafumi Kameya¹  Tetsu Hirosawa¹  Yuta Nariya³  Sarah Noto⁴  Albert C. Yang⁵  Tetsuya Takahashi⁶  Mitsuru Kikuchi⁶ 

[1] Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan;

[2] Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States;

[3] Faculty of Medicine, The University of Tokyo, Tokyo, Japan;

[4] Faculty of Nursing, National College of Nursing, Tokyo, Japan;

[5] Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan;

[6] Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan;

关键词: alpha oscillation;    Alzheimer’s disease (AD);    amplitude complexity;    ensemble empirical mode decomposition (EEMD);    magnetoencephalography (MEG);    multiscale entropy (MSE);   

DOI  :  10.3389/fpsyt.2020.531801

来源: DOAJ

【 摘 要 】

Despite growing evidence of aberrant neuronal complexity in Alzheimer’s disease (AD), it remains unclear how this variation arises. Neural oscillations reportedly comprise different functions depending on their own properties. Therefore, in this study, we investigated details of the complexity of neural oscillations by decomposing the oscillations into frequency, amplitude, and phase for AD patients. We applied resting-state magnetoencephalography (MEG) to 17 AD patients and 21 healthy control subjects. We first decomposed the source time series of the MEG signal into five intrinsic mode functions using ensemble empirical mode decomposition. We then analyzed the temporal complexities of these time series using multiscale entropy. Results demonstrated that AD patients had lower complexity on short time scales and higher complexity on long time scales in the alpha band in temporal regions of the brain. We evaluated the alpha band complexity further by decomposing it into amplitude and phase using Hilbert spectral analysis. Consequently, we found lower amplitude complexity and higher phase complexity in AD patients. Correlation analyses between spectral complexity and decomposed complexities revealed scale-dependency. Specifically, amplitude complexity was positively correlated with spectral complexity on short time scales, whereas phase complexity was positively correlated with spectral complexity on long time scales. Regarding the relevance of cognitive function to the complexity measures, the phase complexity on the long time scale was found to be correlated significantly with the Mini-Mental State Examination score. Additionally, we examined the diagnostic utility of the complexity characteristics using machine learning (ML) methods. We prepared a feature pool using multiple sparse autoencoders (SAEs), chose some discriminating features, and applied them to a support vector machine (SVM). Compared to the simple SVM and the SVM after feature selection (FS + SVM), the SVM with multiple SAEs (SAE + FS + SVM) had improved diagnostic accuracy. Through this study, we 1) advanced the understanding of neuronal complexity in AD patients using decomposed temporal complexity analysis and 2) demonstrated the effectiveness of combining ML methods with information about signal complexity for the diagnosis of AD.

【 授权许可】

Unknown