Time series modeling characterizes stride time variability to identify individuals with neurodegenerative disorders

The progressive death and dysfunction of neurons causes altered stride-to-stride variability in individuals with Amyotrophic Lateral Sclerosis (ALS) and Huntington's Disease (HD). Yet these altered gait dynamics can manifest differently in these populations based on how and where these neurodegenerative disorders attack the central nervous system. Time series analyses can quantify differences in stride time variability which can help contribute to the detection and identification of these disorders. Here, autoregressive modeling time series analysis was utilized to quantify differences in stride time variability amongst the Controls, the individuals with ALS, and the individuals with HD. For this study, fifteen Controls, 12 individuals with ALS and 15 individuals with HD walked up and down a hallway continuously for 5-min. Participants wore force sensitive resistors in their shoes to collect stride time data. A second order autoregressive (AR) model was fit to the time series created from the stride time data. The mean stride time and two AR model coefficients served as metrics to identify differences in stride time variability amongst the three groups. The individuals with HD walked with significantly greater stride time variability indicating a more chaotic gait while the individuals with ALS adopted more ordered, less variable stride time dynamics (p < 0.001). A plot of the stride time metrics illustrated how each group exhibited significantly different stride time dynamics. The stride time metrics successfully quantified differences in stride time variability amongst individuals with neurodegenerative disorders. This work provided valuable insight about how these neuromuscular disorders disrupt motor coordination leading to the adoption of new gait dynamics.

Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands

We aimed to map and analyze LAI by using Landsat 8 and Sentinel-2 time series and the corresponding ground measurements collected in pure Anatolian black pine [Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe] stands within seven-month (from June to December) period. A total of 30 sample plots were selected and seven-month changes of LAI values were determined through hemispherical photography for each sample plot. Remote sensing (reflectance values and vegetation indices obtained from Landsat-8 and Sentinel-2) and topographic (elevation, aspect, and slope) data were used to model the LAI for each month using multiple linear regression (MLR) method. Additionally, the data for all months were combined and modeled. In this case, autoregressive modeling techniques were used to solve the temporal autocorrelation problem. Our study indicated that the models developed from Sentinel-2 give more successful results than Landsat 8 on monthly LAI models. The most successful models were obtained in June by using the reflectance values (R adj 2  = 0.39, RMSE = 0.3138 m2 m-2), reflectance values-topographic data (R adj 2  = 0.59, RMSE = 0.3174 m2 m-2), vegetation indices-topographic data (R adj 2  = 0.82, RMSE = 0.2126 m2 m-2), and reflectance values-vegetation indices-topographic data (R adj 2  = 0.93, RMSE = 0.1060 m2 m-2). Among the autoregressive modeling techniques, the highest success was obtained with the Landsat 8 OLI using the moving average (2) procedure (R2 = 0.56). This study is significant that it is the first to analyze the monthly effect on LAI modeling and mapping in pure Anatolian black pine stands using both reflectance values, vegetation indices, and topographic data.

Use of Composites in Analysis of Individual Time Series: Implications for Person-Specific Dynamic Parameters

Psychology has seen an increase in the study of intra-individual processes through time series. Although timely and relevant, modeling variance from unique sources in time series may lead to model non-convergence, making it tempting to reduce the number of parameters in the model. The purpose of this paper was to investigate use of composite scores in analysis of individual time series. To meet this goal, we compared the dynamic factor analysis (DFA) model using multiple indicators with the autoregressive (AR) and autoregressive + white noise (AR + WN) models using composites. We conducted a Monte Carlo study in which a DFA(1,1) model was used to generate the data with varying conditions of size of factor loadings, number of indicators, size of AR parameters, and time series length. We also conducted analysis of empirical data from six individuals' daily self-reports of mood. Findings indicated that the DFA(1,1) and AR(1) + WN models performed comparably in their recovery of AR parameters, while the AR(1) model underestimated the parameter under nearly all conditions. However, variability of estimates may make the AR(1) + WN model less viable for researchers conducting individual-level analyses when the true data-generating mechanism is the DFA(1, 1) model.

A Computationally-Efficient, Online-Learning Algorithm for Detecting High-Voltage Spindles in the Parkinsonian Rats

Abnormally-synchronized, high-voltage spindles (HVSs) are associated with motor deficits in 6-hydroxydopamine-lesioned parkinsonian rats. The non-stationary, spike-and-wave HVSs (5-13 Hz) represent the cardinal parkinsonian state in the local field potentials (LFPs). Although deep brain stimulation (DBS) is an effective treatment for the Parkinson's disease, continuous stimulation results in cognitive and neuropsychiatric side effects. Therefore, an adaptive stimulator able to stimulate the brain only upon the occurrence of HVSs is demanded. This paper proposes an algorithm not only able to detect the HVSs with low latency but also friendly for hardware realization of an adaptive stimulator. The algorithm is based on autoregressive modeling at interval, whose parameters are learnt online by an adaptive Kalman filter. In the LFPs containing 1131 HVS episodes from different brain regions of four parkinsonian rats, the algorithm detects all HVSs with 100% sensitivity. The algorithm also achieves higher precision (96%) and lower latency (61 ms), while requiring less computation time than the continuous wavelet transform method. As the latency is much shorter than the mean duration of an HVS episode (4.3 s), the proposed algorithm is suitable for realization of a smart neuromodulator for mitigating HVSs effectively by closed-loop DBS.

A procedure to increase the power of Granger-causal analysis through temporal smoothing

BACKGROUND

How the human brain coordinates network activity to support cognition and behavior remains poorly understood. New high-resolution recording modalities facilitate a more detailed understanding of the human brain network. Several approaches have been proposed to infer functional networks, indicating the transient coordination of activity between brain regions, from neural time series. One category of approach is based on statistical modeling of time series recorded from multiple sensors (e.g., multivariate Granger causality). However, fitting such models remains computationally challenging as the history structure may be long in neural activity, requiring many model parameters to fully capture the dynamics.

NEW METHOD

We develop a method based on Granger causality that makes the assumption that the history dependence varies smoothly. We fit multivariate autoregressive models such that the coefficients of the lagged history terms are smooth functions. We do so by modelling the history terms with a lower dimensional spline basis, which requires many fewer parameters than the standard approach and increases the statistical power of the model.

RESULTS

We show that this procedure allows accurate estimation of brain dynamics and functional networks in simulations and examples of brain voltage activity recorded from a patient with pharmacoresistant epilepsy.

COMPARISON WITH EXISTING METHOD

The proposed method has more statistical power than the Granger method for networks of signals that exhibit extended and smooth history dependencies.

CONCLUSIONS

The proposed tool permits conditional inference of functional networks from many brain regions with extended history dependence, furthering the applicability of Granger causality to brain network science.

Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation

A normal cardiac activation starts in the sinoatrial node and then spreads throughout the atrial myocardium, thus defining the P-wave of the electrocardiogram. However, when the onset of paroxysmal atrial fibrillation (PAF) approximates, a highly disturbed electrical activity occurs within the atria, thus provoking fragmented and eventually longer P-waves. Although this altered atrial conduction has been successfully quantified just before PAF onset from the signal-averaged P-wave spectral analysis, its evolution during the hours preceding the arrhythmia has not been assessed yet. This work focuses on quantifying the P-wave spectral content variability over the 2h preceding PAF onset with the aim of anticipating as much as possible the arrhythmic episode envision. For that purpose, the time course of several metrics estimating absolute energy and ratios of high- to low-frequency power in different bands between 20 and 200Hz has been computed from the P-wave autoregressive spectral estimation. All the analyzed metrics showed an increasing variability trend as PAF onset approximated, providing the P-wave high-frequency energy (between 80 and 150Hz) a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF and patients less than 1h close to a PAF episode. This discriminant power was similar to that provided by the most classical time-domain approach, i.e., the P-wave duration. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 88.07%, thus constituting a reliable noninvasive harbinger of PAF onset with a reasonable anticipation. The information provided by this methodology could be very useful in clinical practice either to optimize the antiarrhythmic treatment in patients at high-risk of PAF onset and to limit drug administration in low risk patients.

Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies among Observations

In DAE (DNA After Enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments ("enriched regions") versus all the other regions ("background"). However, many confounding factors may influence DAE-seq signals. In addition, the signals in adjacent genomic regions may exhibit strong correlations, which invalidate the independence assumption employed by many existing methods. To mitigate these issues, we develop a novel Autoregressive Hidden Markov Model (AR-HMM) to account for covariates effects and violations of the independence assumption. We demonstrate that our AR-HMM leads to improved performance in identifying enriched regions in both simulated and real datasets, especially in those in epigenetic datasets with broader regions of DAE-seq signal enrichment. We also introduce a variable selection procedure in the context of the HMM/AR-HMM where the observations are not independent and the mean value of each state-specific emission distribution is modeled by some covariates. We study the theoretical properties of this variable selection procedure and demonstrate its efficacy in simulated and real DAE-seq data. In summary, we develop several practical approaches for DAE-seq data analysis that are also applicable to more general problems in statistics.