Gait is a complex process involving both cognitive and sensory ability and is strongly impacted by the environment. In this paper, we propose to study of the impact of a cognitive task during gait on the cerebral blood flow velocity, the blood flow signal features and the correlation of gait and blood flow features through a dual task methodology. Both cerebral blood flow velocity and gait characteristics of eleven participants with no history of brain or gait conditions were recorded using transcranial Doppler on mid-cerebral artery while on a treadmill. The cognitive task was induced by a backward counting starting from 10,000 with decrement of 7. Central blood flow velocity raw and envelope features were extracted in both time, frequency and time-scale domain; information-theoretic metrics were also extracted and statistical significances were inspected. A similar feature extraction was performed on the stride interval signal. Statistical differences between the cognitive and baseline trials, between the left and right mid-cerebral arteries signals and the impact of the antropometric variables where studied using linear mixed models. No statistical differences were found between the left and right mid-cerebral arteries flows or the baseline and cognitive state gait features, while statistical differences for specific features were measured between cognitive and baseline states. These statistical differences found between the baseline and cognitive states show that cognitive process has an impact on the cerebral activity during walking. The state was found to have an impact on the correlation between the gait and blood flow features.

Gait function is traditionally assessed using well-lit, unobstructed walkways with minimal distractions. In patients with subclinical physiological abnormalities, these conditions may not provide enough stress on their ability to adapt to walking. The introduction of challenging walking conditions in gait can induce responses in physiological systems in addition to the locomotor system. There is a need for a device that is capable of monitoring multiple physiological systems in various walking conditions. To address this need, an Android-based gait-monitoring device was developed that enabled the recording of a patient's physiological systems during walking. The gait-monitoring device was tested during self-regulated overground walking sessions of fifteen healthy subjects that included 6 females and 9 males aged 18 to 35 years. The gait-monitoring device measures the patient's stride interval, acceleration, electrocardiogram, skin conductance and respiratory rate. The data is stored on an Android phone and is analyzed offline through the extraction of features in the time, frequency and time-frequency domains. The analysis of the data depicted multisystem physiological interactions during overground walking in healthy subjects. These interactions included locomotion-electrodermal, locomotion-respiratory and cardiolocomotion couplings. The current results depicting strong interactions between the locomotion system and the other considered systems (i.e., electrodermal, respiratory and cardivascular systems) warrant further investigation into multisystem interactions during walking, particularly in challenging walking conditions with older adults.

Swallowing and swallowing disorders have garnered continuing interest over the past several decades. Electroencephalography (EEG) is an inexpensive and non-invasive procedure with very high temporal resolution which enables analysis of short and fast swallowing events, as well as an analysis of the organizational and behavioral aspects of cortical motor preparation, swallowing execution and swallowing regulation. EEG is a powerful technique which can be used alone or in combination with other techniques for monitoring swallowing, detection of swallowing motor imagery for diagnostic or biofeedback purposes, or to modulate and measure the effects of swallowing rehabilitation. This paper provides a review of the existing literature which has deployed EEG in the investigation of oropharyngeal swallowing, smell, taste and texture related to swallowing, cortical pre-motor activation in swallowing, and swallowing motor imagery detection. Furthermore, this paper provides a brief review of the different modalities of brain imaging techniques used to study swallowing brain activities, as well as the EEG components of interest for studies on swallowing and on swallowing motor imagery. Lastly, this paper provides directions for future swallowing investigations using EEG.

The anatomical rationale and efficacy of the chin tuck in improving airway protection for some people with swallowing disorders have been well researched and established. However, there are still open questions regarding whether brain activity for swallowing control is altered while performing this chin-tuck maneuver. In this study, we collected EEG signals from 55 healthy adults while swallowing in the neutral and chin-tuck head positions. The time-frequency based synchrony measure was used to form brain networks. We investigated both the small-world properties of these brain networks and differences among the constructed brain networks for the two head positions during swallowing tasks. We showed that brain networks for swallowing in both head positions exhibit small-world properties. Furthermore, we showed that swallowing in the chin-tuck head position affects brain networks in the Alpha and Gamma frequency bands. According to these results, we can tell that the parameter of head position should be considered in future investigations which utilize EEG signals during swallowing activity.

Objective To test whether more physiologically assessed hot flashes were associated with greater connectivity in the default mode network (DMN), the network of brain regions active during rest. We particularly focus on DMN networks supporting the hippocampus as this region is rich in estrogen receptors and has previously been linked to hot flashes. Design Women underwent 24 hours of physiologic and diary hot flash monitoring, functional magnetic resonance imaging, 72 hours of sleep actigraphy monitoring, a blood draw, questionnaires, and physical measures. Setting Community Participants Twenty midlife women ages 40–60 with their uterus and both ovaries and not taking hormone therapy Interventions None Main outcome measures DMN functional connectivity Results Controlling for age, race, education, a greater number of physiologically-monitored hot flashes were associated with greater DMN connectivity [beta, B (standard error, SE)=.004 (.002), p<.05], particularly hippocampal DMN connectivity [B(SE)=.005 (.002), p<.05]. Findings were most pronounced for sleep physiologic hot flashes [with hippocampal DMN, B(SE)= .02 (.007), p<0.01]. Associations persisted additionally controlling for sleep, depressive symptoms, and serum estradiol concentrations. Conclusions More physiologically-monitored hot flashes were associated with greater DMN connectivity, particularly networks supporting the hippocampus. Findings were most pronounced for sleep hot flashes. Findings underscore the importance of continued investigation of the central nervous system in efforts to understand this classic menopausal phenomenon.

Cervical auscultation is the recording of sounds and vibrations caused by the human body from the throat during swallowing. While traditionally done by a trained clinician with a stethoscope, much work has been put toward developing more sensitive and clinically useful methods to characterize the data obtained with this technique. The eventual goal of the field is to improve the effectiveness of screening algorithms designed to predict the risk that swallowing disorders pose to individual patients' health and safety. This paper provides an overview of these signal-processing techniques and summarizes recent advances made with digital transducers in hopes of organizing the highly varied research on cervical auscultation. It investigates where on the body these transducers are placed in order to record a signal as well as the collection of analog and digital filtering techniques used to further improve the signal quality. It also presents the wide array of methods and features used to characterize these signals, ranging from simply counting the number of swallows that occur over a period of time to calculating various descriptive features in the time, frequency, and phase space domains. Finally, this paper presents the algorithms that have been used to classify these data into “normal” and “abnormal” categories. Both linear as well as nonlinear techniques are presented in this regard.