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

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

Encouraging job crafting in the workplace for newcomers: A two-year multi-wave study

It is important to identify the antecedents of newcomers’ job crafting as it assists with their adjustment in the workplace. This study made use of transformational leadership and newcomers’ calling as organizational and personal resources that predict job crafting. We hypothesized that transformational leadership would have an indirect relationship with newcomers’ job crafting after 2 years through their occupational self-efficacy and that their calling would moderate this mediational path. A multi-wave approach was employed wherein data from 280 new employees were collected three times during the first 2 years of their careers. The survey was completed by 150 participants. The results illustrated that transformational leadership was positively related to newcomers’ job crafting after 2 years of entry through their occupational self-efficacy. Additionally, newcomers’ calling moderated the mediating effect of occupational self-efficacy between transformational leadership and job crafting. The theoretical and practical implications of this study are discussed

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

First results from the HAYSTAC axion search

The axion is a well-motivated cold dark matter (CDM) candidate first postulated to explain the absence of $CP$ violation in the strong interactions. CDM axions may be detected via their resonant conversion into photons in a "haloscope" detector: a tunable high-$Q$ microwave cavity maintained at cryogenic temperature, immersed a strong magnetic field, and coupled to a low-noise receiver. This dissertation reports on the design, commissioning, and first operation of the Haloscope at Yale Sensitive to Axion CDM (HAYSTAC), a new detector designed to search for CDM axions with masses above $20$ $\mu\mathrm{eV}$. I also describe the analysis procedure developed to derive limits on axion CDM from the first HAYSTAC data run, which excluded axion models with two-photon coupling $g_{a\gamma\gamma} \gtrsim 2\times10^{-14}$ $\mathrm{GeV}^{-1}$, a factor of 2.3 above the benchmark KSVZ model, over the mass range $23.55 < m_a < 24.0$ $\mu\mathrm{eV}$. This result represents two important achievements. First, it demonstrates cosmologically relevant sensitivity an order of magnitude higher in mass than any existing direct limits. Second, by incorporating a dilution refrigerator and Josephson parametric amplifier, HAYSTAC has demonstrated total noise approaching the standard quantum limit for the first time in a haloscope axion search.Comment: Ph.D. thesis. 346 pages, 58 figures. A few typos corrected relative to the version submitted to ProQues

The Effects of Acupuncture Stimulation for Brain Activation and Alcohol Abstinence Self-Efficacy: Functional MRI Study

We attempted to investigate whether acupuncture stimulation at HT7 can have an effect on brain activation patterns and alcohol abstinence self-efficacy. Thirty-four right-handed healthy subjects were recruited for this study. They were randomly assigned into two groups: the HT7 (Shenmen) group and the LI5 (Yangxi) group. Acupuncture stimulation was performed using a block paradigm during fMRI scanning. Additionally, the Korean version of Alcohol Abstinence Self-Efficacy Scale (AASES) was used to determine the effect of acupuncture stimulation on self-efficacy to abstain from alcohol use. According to the result of fMRI group analysis, the activation induced by HT7 stimulation was found on the bilateral postcentral gyrus, inferior parietal lobule, inferior frontal gyrus, claustrum, insula, and anterior lobe of the cerebellum, as well as on the left posterior lobe of the cerebellum (p<0.001, uncorrected). According to the AASES analysis, the interaction effect for gender and treatment was marginally significant (F(1,30)=4.152, p=0.050). For female group, the simple main effect of treatment was significant (F(1,11)=8.040, p=0.016), indicating that the mean change score was higher in the HT7 stimulation than in the LI5 stimulation. Therefore, our study has provided evidence to support that HT7 stimulation has a positive therapeutic effect on the alcohol-related diseases

Strain-Mediated Interlayer Coupling Effects on the Excitonic Behaviors in an Epitaxially Grown MoS2/WS2 van der Waals Heterobilayer.

van der Waals heterostructures composed of two different monolayer crystals have recently attracted attention as a powerful and versatile platform for studying fundamental physics, as well as having great potential in future functional devices because of the diversity in the band alignments and the unique interlayer coupling that occurs at the heterojunction interface. However, despite these attractive features, a fundamental understanding of the underlying physics accounting for the effect of interlayer coupling on the interactions between electrons, photons, and phonons in the stacked heterobilayer is still lacking. Here, we demonstrate a detailed analysis of the strain-dependent excitonic behavior of an epitaxially grown MoS2/WS2 vertical heterostructure under uniaxial tensile and compressive strain that enables the interlayer interactions to be modulated along with the electronic band structure. We find that the strain-modulated interlayer coupling directly affects the characteristic combined vibrational and excitonic properties of each monolayer in the heterobilayer. It is further revealed that the relative photoluminescence intensity ratio of WS2 to MoS2 in our heterobilayer increases monotonically with tensile strain and decreases with compressive strain. We attribute the strain-dependent emission behavior of the heterobilayer to the modulation of the band structure for each monolayer, which is dictated by the alterations in the band gap transitions. These findings present an important pathway toward designing heterostructures and flexible devices

Development and Clinical Evaluation of a Rapid Serodiagnostic Test for Toxoplasmosis of Cats Using Recombinant SAG1 Antigen

Rapid serodiagnostic methods for Toxoplasma gondii infection in cats are urgently needed for effective control of transmission routes toward human infections. In this work, 4 recombinant T. gondii antigens (SAG1, SAG2, GRA3, and GRA6) were produced and tested for the development of rapid diagnostic test (RDT). The proteins were expressed in Escherichia coli, affinity-purified, and applied onto the nitrocellulose membrane of the test strip. The recombinant SAG1 (rSAG1) showed the strongest antigenic activity and highest specificity among them. We also performed clinical evaluation of the rSAG1-loaded RDT in 182 cat sera (55 household and 127 stray cats). The kit showed 0.88 of kappa value comparing with a commercialized ELISA kit, which indicated a significant correlation between rSAG1-loaded RDT and the ELISA kit. The overall sensitivity and specificity of the RDT were 100% (23/23) and 99.4% (158/159), respectively. The rSAG1-loaded RDT is rapid, easy to use, and highly accurate. Thus, it would be a suitable diagnostic tool for rapid detection of antibodies in T. gondii-infected cats under field conditions