Background The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/fβ. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline. New Method This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. Results The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. Conclusions The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series.

Cervical auscultation (CA) is an affordable, non-invasive technique used to observe sounds occurring during swallowing. CA involves swallowing characterization via stethoscopes or microphones, while accelerometers can detect other vibratory signals. While the effects of fluid viscosity on swallowing accelerometry signals is well understood, there are still open questions about these effects on swallowing sounds. Therefore, this study investigated the influence of fluids with increasing thickness on swallowing sound characteristics. We collected swallowing sounds and swallowing accelerometry signals from 56 healthy participants. Each participant completed five water swallows, five swallows of nectar-thick apple juice, and five swallows of honey-thick apple juice. These swallows were completed in neutral head and chin-tuck head positions. After pre-processing of collected signals, a number of features in time, frequency and time-frequency domains were extracted. Our numerical analysis demonstrated that significant influence of viscosity was found in most of the features. In general, features extracted from swallows in the neutral head position were affected more than swallows from the chin-tuck position. Furthermore, most of the differences were found between water and fluids with higher viscosity. Almost no significant differences were found between swallows involving nectar-thick and honey-thick apple juices. Our results also showed that thicker fluids had higher acoustic regularity and predictability as demonstrated by the information-theoretic features, and a lower frequency content as demonstrated by features in the frequency domain. According to these results, we can conclude that viscosity of fluids should be considered in future investigations involving swallowing sounds.

Cervical auscultation (CA) is an affordable, non-invasive technique used to observe sounds occurring during swallowing. CA involves swallowing characterization via stethoscopes or microphones, while accelerometers can detect other vibratory signals. While the effects of fluid viscosity on swallowing accelerometry signals is well understood, there are still open questions about these effects on swallowing sounds. Therefore, this study investigated the influence of fluids with increasing thickness on swallowing sound characteristics. We collected swallowing sounds and swallowing accelerometry signals from 56 healthy participants. Each participant completed five water swallows, five swallows of nectar-thick apple juice, and five swallows of honey-thick apple juice. These swallows were completed in neutral head and chin-tuck head positions. After pre-processing of collected signals, a number of features in time, frequency and time-frequency domains were extracted. Our numerical analysis demonstrated that significant influence of viscosity was found in most of the features. In general, features extracted from swallows in the neutral head position were affected more than swallows from the chin-tuck position. Furthermore, most of the differences were found between water and fluids with higher viscosity. Almost no significant differences were found between swallows involving nectar-thick and honey-thick apple juices. Our results also showed that thicker fluids had higher acoustic regularity and predictability as demonstrated by the information-theoretic features, and a lower frequency content as demonstrated by features in the frequency domain. According to these results, we can conclude that viscosity of fluids should be considered in future investigations involving swallowing sounds.

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia.

Gait accelerometry is a promising tool to assess human walking and reveal deteriorating gait characteristics in patients and can be a rich source of clinically relevant information about functional declines in older adults. Therefore, in this paper, we present a comprehensive set of signal features that may be used to extract clinically valuable information from gait accelerometry signals. To achieve our goal, we collected tri-axial gait accelerometry signals from 35 adults 65 years of age and older. Fourteen subjects were healthy controls, 10 participants were diagnosed with Parkinson's disease, and 11 participants were diagnosed with peripheral neuropathy. The data were collected while the participants walked on a treadmill at a preferred walking speed. Accelerometer signal features in time, frequency and time-frequency domains were extracted. The results of our analysis showed that some of the extracted features were able to differentiate between healthy and clinical populations. Signal features in all three domains were able to emphasize variability among different groups, and also revealed valuable information about variability of the signals between anterior-posterior, mediolateral, and vertical directions within subjects. The current results imply that the proposed signal features can be valuable tools for the analysis of gait accelerometry data and should be utilized in future studies.

The overall goal of this project is to develop a humane non-human primate model of traumatic spinal cord injury that will facilitate the development and evaluation of therapeutic interventions. The model utilizes neurophysiological techniques to identify the location of the upper motor neuron axons that innervate the lower motor neurons that control tail musculature. This facilitates the placement of a selective lesion that partially disconnects the upper and lower motor neuron supply to the musculature of the tail. An implanted transmitter quantitatively measures electromyography data from the tail. The preliminary data indicates that this model is feasible. The subject was able to tolerate the implantation of the transmitter, without adverse effects. As well, there was no limb impairment, bowel dysfunction or bladder dysfunction. The histopathologic and electromyographic features of the selective experimental lesion were similar to human spinal cord injury.