Probing age-related changes in cardio-respiratory dynamics by multimodal coupling assessment.

Quantifying respiratory sinus arrhythmia (RSA) can provide an index of parasympathetic function. Fourier spectral analysis, the most widely used approach, estimates the power of the heart rate variability in the frequency band of breathing. However, it neglects the time-varying characteristics of the transitions as well as the nonlinear properties of the cardio-respiratory coupling. Here, we propose a novel approach based on Hilbert-Huang transform, called the multimodal coupling analysis (MMCA) method, to assess cardio-respiratory dynamics by examining the instantaneous nonlinear phase interactions between two interconnected signals (i.e., heart rate and respiration) and compare with the counterparts derived from the wavelet-based method. We used an online database. The corresponding RSA components of the 90-min ECG and respiratory signals of 20 young and 20 elderly healthy subjects were extracted and quantified. A cycle-based analysis and a synchro-squeezed wavelet transform were also introduced to assess the amplitude or phase changes of each respiratory cycle. Our results demonstrated that the diminished mean and standard deviation of the derived dynamical RSA activities can better discriminate between elderly and young subjects. Moreover, the degree of nonlinearity of the cycle-by-cycle RSA waveform derived from the differences between the instantaneous frequency and the mean frequency of each respiratory cycle was significantly decreased in the elderly subjects by the MMCA method. The MMCA method in combination with the cycle-based analysis can potentially be a useful tool to depict the aging changes of the parasympathetic function as well as the waveform nonlinearity of RSA compared to the Fourier-based high-frequency power and the wavelet-based method.

Hydrostable and Nitryl/Methyl-Functionalized Metal-Organic Framework for Drug Delivery and Highly Selective CO2 Adsorption.

By using a strategy of introducing hydrophobic groups to the linkers, a hydrostable MOF was constructed based on 5-nitroisophthalate and 2,2'-dimethyl-4,4'-bipyridine coligands, revealing a 3D dia topology structure with a 1D channel parallel to the c axis. TGA, PXRD, and water vapor sorption results show high thermal and water stability for the framework. The framework is very porous and possesses not only high busulfan payloads with an encapsulation efficiency up to 21.5% (17.2 wt %) but also very high CO2 selective capture compared with that of other small gases (i.e., CH4, N2, O2, CO, and H2) at 298 K based on molecular simulations due to the pore surface being populated by methyl and nitryl groups. Furthermore, in vitro MTT assays were conducted on four different cells lines with increasing concentrations of the framework, and the results showed that the framework was nontoxic (cell viability >80%) in spite of the concentrations up to 500 µg/mL.

Correlations between the signal complexity of cerebral and cardiac electrical activity: a multiscale entropy analysis.

The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG) and a 19-channel eye-closed routine electroencephalography (EEG). Multiscale entropy (MSE) analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS), and photic stimulation of slow frequencies (slow-PS)) of EEG in the 1-58 Hz frequency range, and three RR interval (RRI) time series (awake-state, sleep and that concomitant with the EEG) for each subject. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a) the summed MSE value on coarse scales of the awake RRI (scales 11-20, RRI-MSE-coarse) were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6-20, EEG-MSE-coarse) at Fp2, C4, T6 and T4; (b) the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c) the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d) the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e) the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f) the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g) fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration.

Empirical mode decomposition based detrended sample entropy in electroencephalography for Alzheimer's disease.

Quantitative electroencephalographs (qEEG) provide a potential method to objectively quantify the cortical activations in Alzheimer's disease (AD), but they are too insensitive to probe the alteration of EEG in the early AD. The sample entropy (SaEn) attempts to quantify the complex information embedded in EEG non-linearly, which fits in that EEG originates from non-linear interactions. However, a technical issue which has been ignored by most researchers is that the signal should be stationary. In order to resolve the non-stationarity of SaEn in EEG to improve the sensitivity, an empirical mode decomposition (EMD) was applied for detrending in this study. Twenty-seven AD patients (9M/18F; mean age 74.0±1.5 years) were included. Their initial Minimal Mental Status Examination was 19.3±0.7. They received the first resting-awake 30-mine EEG before the therapy. Five of them received a follow-up examination within 6 months after the therapy. The 30-s EEG data without artifacts were selected and analyzed with a new proposed method, "EMD-based detrended-SaEn" to attenuate the influence of intrinsic non-stationarity. The correlation factors in 27 AD patients showed a moderate correlation (0.361-0.523, p<0.05) between MMSE and EMD-based detrended SaEn in Fp1, Fp2, F4 and T3. There was a high correlation (Correlation coefficient=0.975, p<0.05) between the changes of MMSE and the changes of EMD-based detrended-SaEn in F7 in 5 follow-up patients. The dynamic complexity of EEG fluctuations is degraded by pathological degeneration, and EMD-based detrended SaEn provides an objective, non-invasive and non-expensive tool for evaluating and following AD patients.