A Novel Approach Analysing the Dynamic Brain Functional Connectivity for Improved MCI Detection.

OBJECTIVE: Dynamic functional connectivity (dFC) of the brain has been explored for the detection of mild cognitive impairment (MCI), preventing potential development of Alzheimer's disease. Deep learning is widely used method for dFC analysis but is unfortunately computationally expensive and unexplainable. Root mean square value (RMS) of the pairwise Pearson's correlation of the dFC is also proposed but is insufficient for accurate MCI detection. The present study aims at exploring the feasibility of several novel features for dFC analysis, and thus, reliable MCI detection. METHODS: A public resting-state functional magnetic resonance imaging dataset containing healthy controls (HC), early MCI (eMCI), and late MCI (lMCI) patients was used. In addition to RMS, nine features were extracted from the pairwise Pearson's correlation of the dFC, inducing amplitude-, spectral-, entropy-, and autocorrelation-related features, and time reversibility. A Student's t-test and a least absolute shrinkage and selection operator (LASSO) regression were employed for feature dimension reduction. A SVM was then adopted for two classification objectives: HC vs. lMCI and HC vs. eMCI. Accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve were calculated as performance metrics. RESULTS: 6109 out of 66700 features are significantly different between HC and lMCI and 5905 between HC and eMCI. Besides, the proposed features produce excellent classification results for both tasks, outperforming most of the existing methods. SIGNIFICANCE: This study proposes a novel and general framework for dFC analysis, providing a promising tool for the detection of many neurological brain diseases using different brain signals.

Functional force stimulation alters motor neuron discharge patterns.

Introduction: Beneficial effects have been observed for mechanical vibration stimulation (MVS), which are mainly attributed to tonic vibration reflex (TVR). TVR is reported to elicit synchronized motor unit activation during locally applied vibration. Similar effects are also observed in a novel vibration system referred to as functional force stimulation (FFS). However, the manifestation of TVR in FFS is doubted due to the use of global electromyography (EMG) features in previous analysis. Our study aims to investigate the effects of FFS on motor unit discharge patterns of the human biceps brachii by analyzing the motor unit spike trains decoded from the high-density surface EMG. Methods: Eighteen healthy subjects volunteered in FFS training with different amplitudes and frequencies. One hundred and twenty-eight channel surface EMG was recorded from the biceps brachii and then decoded after motion-artifact removal. The discharge timings were extracted and the coherence between different motor unit spike trains was calculated to quantify synchronized activation. Results and discussion: Significant synchronization within the vibration cycle and/or its integer multiples is observed for all FFS trials, which increases with increased FFS amplitude. Our results reveal the basic physiological mechanism involved in FFS, providing a theoretical foundation for analyzing and introducing FFS into clinical rehabilitation programs.

A Capacitive Electrocardiography System With Dedicated Noise-Cancellation Algorithms for Morphological Analysis.

OBJECTIVE: Capacitive electrocardiography (cECG) has been proposed for ambulatory cardiac health monitoring. However, due to the high sensitivity of capacitive electrodes to various noise sources, particularly the power-line interference (PLI) and motion artifacts (MA), the existing capacitive systems are only verified in terms of RR interval. The aim of the present study is to explore the feasibility of using cECG for morphological analysis, and thus to extract clinical meaningful parameters. METHODS: A capacitive electrode with active guarding is realized. A phase-locked-loop (PLL) based adaptive canceller is employed to remove PLI from the cECG. Wavelet analysis is adopted to cancel other noises. The developed capacitive system and algorithms are evaluated by real ECG measurements on 7 volunteers using 3-lead configuration. The correlation coefficient (CC) between the processed cECG and the wet ECG is calculated in two different conditions: with and without the QRS complex. Several frequently used diagnostic parameters, i.e., RR interval, QRS interval, P segment, T segment, ST segment, are extracted and compared with that obtained from the wet ECG. RESULTS: High CCs are observed between the cECG and the wet ECG in both conditions, i.e., 0.97±0.03 with the QRS complex and 0.92±0.07 without the QRS complex. Besides, RR interval extracted from the two different ECG signals are identical for each subject. Other diagnostic parameters are quite similar. SIGNIFICANCE: Our results suggest cECG to be reliable for ambulatory heart rate monitoring. The results also indicate the feasibility of using cECG for clinical diagnosis.