Finding Kinematics-Driven Latent Neural States From Neuronal Population Activity for Motor Decoding

While intracortical brain-machine interfaces (BMIs) demonstrate feasibility to restore mobility to people with paralysis, it is still challenging to maintain high-performance decoding in clinical BMIs. One of the main obstacles for high-performance BMI is the noise-prone nature of traditional decoding methods that connect neural response explicitly with physical quantity, such as velocity. In contrast, the recent development of latent neural state model enables a robust readout of large-scale neuronal population activity contents. However, these latent neural states do not necessarily contain kinematic information useful for decoding. Therefore, this study proposes a new approach to finding kinematics-dependent latent factors by extracting latent factors' kinematics-dependent components using linear regression. We estimated these components from the population activity through nonlinear mapping. The proposed kinematics-dependent latent factors generate neural trajectories that discriminate latent neural states before and after the motion onset. We compared the decoding performance of the proposed analysis model with the results from other popular models. They are factor analysis (FA), Gaussian process factor analysis (GPFA), latent factor analysis via dynamical systems (LFADS), preferential subspace identification (PSID), and neuronal population firing rates. The proposed analysis model results in higher decoding accuracy than do the others (>17% improvement on average). Our approach may pave a new way to extract latent neural states specific to kinematic information from motor cortices, potentially improving decoding performance for online intracortical BMIs

Decoding Kinematic Information From Primary Motor Cortex Ensemble Activities Using a Deep Canonical Correlation Analysis

The control of arm movements through intracortical brain-machine interfaces (BMIs) mainly relies on the activities of the primary motor cortex (M1) neurons and mathematical models that decode their activities. Recent research on decoding process attempts to not only improve the performance but also simultaneously understand neural and behavioral relationships. In this study, we propose an efficient decoding algorithm using a deep canonical correlation analysis (DCCA), which maximizes correlations between canonical variables with the non-linear approximation of mappings from neuronal to canonical variables via deep learning. We investigate the effectiveness of using DCCA for finding a relationship between M1 activities and kinematic information when non-human primates performed a reaching task with one arm. Then, we examine whether using neural activity representations from DCCA improves the decoding performance through linear and non-linear decoders: a linear Kalman filter (LKF) and a long short-term memory in recurrent neural networks (LSTM-RNN). We found that neural representations of M1 activities estimated by DCCA resulted in more accurate decoding of velocity than those estimated by linear canonical correlation analysis, principal component analysis, factor analysis, and linear dynamical system. Decoding with DCCA yielded better performance than decoding the original FRs using LSTM-RNN (6.6 and 16.0% improvement on average for each velocity and position, respectively; Wilcoxon rank sum test, p < 0.05). Thus, DCCA can identify the kinematics-related canonical variables of M1 activities, thus improving the decoding performance. Our results may help advance the design of decoding models for intracortical BMIs

Extracting single-trial neural interaction using latent dynamical systems model

In systems neuroscience, advances in simultaneous recording technology have helped reveal the population dynamics that underlie the complex neural correlates of animal behavior and cognitive processes. To investigate these correlates, neural interactions are typically abstracted from spike trains of pairs of neurons accumulated over the course of many trials. However, the resultant averaged values do not lead to understanding of neural computation in which the responses of populations are highly variable even under identical external conditions. Accordingly, neural interactions within the population also show strong fluctuations. In the present study, we introduce an analysis method reflecting the temporal variation of neural interactions, in which cross-correlograms on rate estimates are applied via a latent dynamical systems model. Using this method, we were able to predict time-varying neural interactions within a single trial. In addition, the pairwise connections estimated in our analysis increased along behavioral epochs among neurons categorized within similar functional groups. Thus, our analysis method revealed that neurons in the same groups communicate more as the population gets involved in the assigned task. We also showed that the characteristics of neural interaction from our model differ from the results of a typical model employing cross-correlation coefficients. This suggests that our model can extract nonoverlapping information about network topology, unlike the typical model

Could Fractional Exhaled Nitric Oxide Test be Useful in Predicting Inhaled Corticosteroid Responsiveness in Chronic Cough? A Systematic Review

© 2016 Background Fractional exhaled nitric oxide (FENO) is a safe and convenient test for assessing T H 2 airway inflammation, which is potentially useful in the management of patients with chronic cough. Objective To summarize the current evidence on the diagnostic usefulness of FENO for predicting inhaled corticosteroid (ICS) responsiveness in patients with chronic cough. Methods A systematic literature review was conducted to identify articles published in peer-reviewed journals up to February 2015, without language restriction. We included studies that reported the usefulness of FENO (index test) for predicting ICS responsiveness (reference standard) in patients with chronic cough (target condition). The data were extracted to construct a 2 × 2 accuracy table. Study quality was assessed with Quality Assessment of Diagnostic Accuracy Studies 2. Results We identified 5 original studies (2 prospective and 3 retrospective studies). We identified considerable heterogeneities in study design and outcome definitions, and thus were unable to perform a meta-analysis. The proportion of ICS responders ranged from 44% to 59%. Sensitivity and specificity ranged from 53% to 90%, and from 63% to 97%, respectively. The reported area under the curve ranged from abou t 0.60 to 0.87; however, studies with a prospective design and a lower prevalence of asthma had lower area under the curve values. None measured placebo effects or objective cough frequency. Conclusions We did not find strong evidence to support the use of FENO tests for predicting ICS responsiveness in chronic cough. Further studies need to have a randomized, placebo-controlled design, and should use validated measurement tools for cough. Standardization would facilitate the development of clinical evidence

Pulsed Laser Beam Welding of Pd\u3csub\u3e43\u3c/sub\u3eCu\u3csub\u3e27\u3c/sub\u3eNi\u3csub\u3e10\u3c/sub\u3eP\u3csub\u3e20\u3c/sub\u3e Bulk Metallic Glass

We used pulsed laser beam welding method to join Pd43Cu27Ni10P20 (at.%) bulk metallic glass and characterized the properties of the joint. Fusion zone and heat-affected zone in the weld joint can be maintained completely amorphous as confirmed by X-ray diffraction and differential scanning calorimetry. No visible defects were observed in the weld joint. Nanoindentation and bend tests were carried out to determine the mechanical properties of the weld joint. Fusion zone and heat-affected zone exhibit very similar elastic moduli and hardness when compared to the base material, and the weld joint shows high ductility in bending which is accomplished through the operation of multiple shear bands. Our results reveal that pulsed laser beam welding under appropriate processing parameters provides a practical viable method to join bulk metallic glasses

Impact of Chronic Cough on Health-Related Quality of Life in the Korean Adult General Population: The Korean National Health and Nutrition Examination Survey 2010–2016

PurposeChronic cough is a prevalent condition in the community and may pose considerable impairment to quality of life (QoL). However, its disease burden remains largely undefined in the general population. The present study investigated the relationship between chronic cough and health-related QoL in a Korean nationwide population database, with an emphasis on clinical conditions which may confound the impact of cough.MethodsThis study analyzed cross-sectional datasets of adults (aged ≥ 40 years) in the Korean National Health and Nutrition Examination Survey 2010–2016. Health-related QoL was assessed using the 3-level EuroQoL 5-dimension component (EQ-5D-3L) index score. The presence of chronic cough and other conditions were defined using structured questionnaires.ResultsThe prevalence of chronic cough was 3.48% ± 0.17% among adults aged ≥ 40 years. The overall EQ-5D-3L index score was significantly lower in subjects with than without chronic cough (0.79 ± 0.01 vs. 0.86 ± 0.00, P < 0.001). In subgroup analyses by age and sex, chronic cough had a notably large impact on QoL in women aged ≥ 65 years (vs. those without chronic cough: 0.55 ± 0.04 vs. 0.70 ± 0.01, P < 0.001), although the mean difference in the scores exceeded the minimally important difference score of 0.05 in all subgroups. In multivariate analyses, chronic cough was significantly associated with QoL, independent of confounders including depression, arthritis, asthma, and chronic obstructive pulmonary disease. In dimension analyses, chronic cough was more associated with anxiety/depression, pain/discomfort, and usual activities than with self-care or mobility in the EQ-5D.ConclusionsThe present study demonstrated significant associations between chronic cough and health-related QoL in a nationwide large general adult population aged ≥ 40 years, which were independent of clinical confounders. The impact of chronic cough was greater in women aged ≥ 65 years. These findings indicate a considerable burden of chronic cough in the general population and warrant further investigations to assess the disease burden of chronic cough in a global scale