Automatic speaker diarization for natural conversation analysis in autism clinical trials.

Challenges in social communication is one of the core symptom domains in autism spectrum disorder (ASD). Novel therapies are under development to help individuals with these challenges, however the ability to show a benefit is dependent on a sensitive and reliable measure of treatment effect. Currently, measuring these deficits requires the use of time-consuming and subjective techniques. Objective measures extracted from natural conversations could be more ecologically relevant, and administered more frequently-perhaps giving them added sensitivity to change. While several studies have used automated analysis methods to study autistic speech, they require manual transcriptions. In order to bypass this time-consuming process, an automated speaker diarization algorithm must first be applied. In this paper, we are testing whether a speaker diarization algorithm can be applied to natural conversations between autistic individuals and their conversational partner in a natural setting at home over the course of a clinical trial. We calculated the average duration that a participant would speak for within their turn. We found a significant correlation between this feature and the Vineland Adaptive Behaviour Scales (VABS) expressive communication score (r = 0.51, p = 7 × 10-5). Our results show that natural conversations can be used to obtain measures of talkativeness, and that this measure can be derived automatically, thus showing the promise of objectively evaluating communication challenges in ASD.

Prediction of cognitive decline in Parkinson's disease (PD) patients with electroencephalography (EEG) connectivity characterized by time-between-phase-crossing (TBPC).

The aim of the study is to identify the dynamic change pattern of EEG to predict cognitive decline in patients with Parkinson's disease. Here we demonstrate that the quantification of synchrony-pattern changes across the scalp, measured using electroencephalography (EEG), offers an alternative approach of observing an individual's functional brain organization. This method, called "Time-Between-Phase-Crossing" (TBPC), is based on the same phenomenon as the phase-lag-index (PLI); it also considers intermittent changes in the signals of phase differences between pairs of EEG signals, but additionally analyzes dynamic connectivity changes. We used data from 75 non-demented Parkinson's disease patients and 72 healthy controls, who were followed over a period of 3 years. Statistics were calculated using connectome-based modeling (CPM) and receiver operating characteristic (ROC). We show that TBPC profiles, via the use of intermittent changes in signals of analytic phase differences of pairs of EEG signals, can be used to predict cognitive decline in Parkinson's disease (p < 0.05).

Simulating the impact of noise on gait features extracted from smartphone sensor-data for the remote assessment of movement disorders.

Signs and symptoms of movement disorders can be remotely measured at home through sensor-based assessment of gait. However, sensor noise may impact the robustness of such assessments, in particular in a Bring-Your-Own-Device setting where the quality of sensors might vary. Here, we propose a framework to study the impact of inertial measurement unit noise on sensor-based gait features. This framework includes synthesizing realistic acceleration signals from the lower back during a gait cycle in OpenSim, estimating the magnitude of sensor noise from five smartphone models, perturbing the synthesized acceleration signal with the estimated noise in a Monte Carlo simulation, and computing gait features. In addition, we show that realistic levels of sensor noise have only a negligible impact on step power, a measure of gait.

Dynamic Functional Connectivity of EEG: From Identifying Fingerprints to Gender Differences to a General Blueprint for the Brain's Functional Organization.

An individual's brain functional organization is unique and can reliably be observed using modalities such as functional magnetic resonance imaging (fMRI). Here we demonstrate that a quantification of the dynamics of functional connectivity (FC) as measured using electroencephalography (EEG) offers an alternative means of observing an individual's brain functional organization. Using data from both healthy individuals as well as from patients with Parkinson's disease (PD) (n = 103 healthy individuals, n = 57 PD patients), we show that "dynamic FC" (DFC) profiles can be used to identify individuals in a large group. Furthermore, we show that DFC profiles predict gender and exhibit characteristics shared both among individuals as well as between both hemispheres. Furthermore, DFC profile characteristics are frequency band specific, indicating that they reflect distinct processes in the brain. Our empirically derived method of DFC demonstrates the potential of studying the dynamics of the functional organization of the brain using EEG.

Estimation of free-living walking cadence from wrist-worn sensor accelerometry data and its association with SF-36 quality of life scores.

Objective. We evaluate the stride segmentation performance of the Adaptive Empirical Pattern Transformation (ADEPT) for subsecond-level accelerometry data collected in the free-living environment using a wrist-worn sensor.Approach. We substantially expand the scope of the existing ADEPT pattern-matching algorithm. Methods are applied to subsecond-level accelerometry data collected continuously for 4 weeks in 45 participants, including 30 arthritis and 15 control patients. We estimate the daily walking cadence for each participant and quantify its association with SF-36 quality of life measures.Main results. We provide free, open-source software to segment individual walking strides in subsecond-level accelerometry data. Walking cadence is significantly associated with the role physical score reported via SF-36 after adjusting for age, gender, weight and height.Significance. Methods provide automatic, precise walking stride segmentation, which allows estimation of walking cadence from free-living wrist-worn accelerometry data. Results provide new evidence of associations between free-living walking parameters and health outcomes.

Developmental function and state transitions of a gene expression oscillator in Caenorhabditis elegans.

Gene expression oscillators can structure biological events temporally and spatially. Different biological functions benefit from distinct oscillator properties. Thus, finite developmental processes rely on oscillators that start and stop at specific times, a poorly understood behavior. Here, we have characterized a massive gene expression oscillator comprising > 3,700 genes in Caenorhabditis elegans larvae. We report that oscillations initiate in embryos, arrest transiently after hatching and in response to perturbation, and cease in adults. Experimental observation of the transitions between oscillatory and non-oscillatory states at high temporal resolution reveals an oscillator operating near a Saddle Node on Invariant Cycle (SNIC) bifurcation. These findings constrain the architecture and mathematical models that can represent this oscillator. They also reveal that oscillator arrests occur reproducibly in a specific phase. Since we find oscillations to be coupled to developmental processes, including molting, this characteristic of SNIC bifurcations endows the oscillator with the potential to halt larval development at defined intervals, and thereby execute a developmental checkpoint function.

An improved qEEG index for asymmetry detection during the Wada test.

The Wada test is commonly used to evaluate language and memory lateralization in candidates for epilepsy surgery. The spatial Brain Symmetry Index (BSI) quantifies inter-hemispheric differences in the EEG. Its application has been shown to be feasible during Wada testing. We developed a method for the quantification of EEG asymmetry that matches visual assessments of the EEG better than BSI. Fifty-three patients' EEG data, with a total of 85 injections were analyzed. In a step-wise, data-driven manner, multiple electrode and frequency band combinations were evaluated. Eventually, BSI, calculated using only the frontal electrodes F3 and F4, was combined with a temporal measure of delta power in the central electrodes, C3 and C4, into a new measure: cBSI. Using the area under the ROC curve (AUC), we showed that cBSI performs significantly better relative to BSI (median AUC 0.98 versus 0.96, p=0.0015, Wilcoxon signed rank test). Our results showed that asymmetry detection was significantly improved by combining temporal with spatial qEEG measures. In the future, our combined qEEG measure could allow for a more objective way of monitoring EEG asymmetry, thereby increasing the feasibility of using EEG as a monitoring tool during the Wada test. Future studies should, however, validate our cBSI method in real time in the operating room or radiology suite.

Visually evoked blood flow responses and interaction with dynamic cerebral autoregulation: correction for blood pressure variation.

Visually evoked flow responses recorded using transcranial Doppler ultrasonography are often quantified using a dynamic model of neurovascular coupling. The evoked flow response is seen as the model's response to a visual step input stimulus. However, the continuously active process of dynamic cerebral autoregulation (dCA) compensating cerebral blood flow for blood pressure fluctuations may induce changes of cerebral blood flow velocity (CBFV) as well. The effect of blood pressure variability on the flow response is evaluated by separately modeling the dCA-induced effects of beat-to-beat measured blood pressure related CBFV changes. Parameters of 71 subjects are estimated using an existing, well-known second order dynamic neurovascular coupling model proposed by Rosengarten et al., and a new model extending the existing model with a CBFV contributing component as the output of a dCA model driven by blood pressure as input. Both models were evaluated for mean and systolic CBFV responses. The model-to-data fit errors of mean and systolic blood pressure for the new model were significantly lower compared to the existing model: mean: 0.8%±0.6 vs. 2.4%±2.8, p<0.001; systolic: 1.5%±1.2 vs. 2.2%±2.6, p<0.001. The confidence bounds of all estimated neurovascular coupling model parameters were significantly (p<0.005) narrowed for the new model. In conclusion, blood pressure correction of visual evoked flow responses by including cerebral autoregulation in model fitting of averaged responses results in significantly lower fit errors and by that in more reliable model parameter estimation. Blood pressure correction is more effective when mean instead of systolic CBFV responses are used. Measurement and quantification of neurovascular coupling should include beat-to-beat blood pressure measurement.