The potential of convolutional neural networks for identifying neural states based on electrophysiological signals: experiments on synthetic and real patient data

Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonical frequency bands or various time-domain features, and then training machine learning systems that use those predefined features as inputs and infer what the underlying brain state is at each given time point. However, whether this algorithmic approach is best suited to extract all available information contained within the neural waveforms remains an open question. Here, we aim to explore different algorithmic approaches in terms of their potential to yield improvements in decoding performance based on neural activity such as measured through local field potentials (LFPs) recordings or electroencephalography (EEG). In particular, we aim to explore the potential of end-to-end convolutional neural networks, and compare this approach with other machine learning methods that are based on extracting predefined feature sets. To this end, we implement and train a number of machine learning models, based either on manually constructed features or, in the case of deep learning-based models, on features directly learnt from the data. We benchmark these models on the task of identifying neural states using simulated data, which incorporates waveform features previously linked to physiological and pathological functions. We then assess the performance of these models in decoding movements based on local field potentials recorded from the motor thalamus of patients with essential tremor. Our findings, derived from both simulated and real patient data, suggest that end-to-end deep learning-based methods may surpass feature-based approaches, particularly when the relevant patterns within the waveform data are either unknown, difficult to quantify, or when there may be, from the point of view of the predefined feature extraction pipeline, unidentified features that could contribute to decoding performance. The methodologies proposed in this study might hold potential for application in adaptive deep brain stimulation (aDBS) and other brain-computer interface systems

Molecular Dynamics Simulation of Strong Shock Waves Propagating in Dense Deuterium With the Effect of Excited Electrons

We present a molecular dynamics simulation of shock waves propagating in dense deuterium with the electron force field method [J. T. Su and W. A. Goddard, Phys. Rev. Lett. 99, 185003 (2007)], which explicitly takes the excitation of electrons into consideration. Non-equilibrium features associated with the excitation of electrons are systematically investigated. We show that chemical bonds in D$_2$ molecules lead to a more complicated shock wave structure near the shock front, compared with the results of classical molecular dynamics simulation. Charge separation can bring about accumulation of net charges on the large scale, instead of the formation of a localized dipole layer, which might cause extra energy for the shock wave to propagate. In addition, the simulations also display that molecular dissociation at the shock front is the major factor corresponding to the "bump" structure in the principal Hugoniot. These results could help to build a more realistic picture of shock wave propagation in fuel materials commonly used in the inertial confinement fusion

Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

Introduction: Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience. Methods: This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson’s disease patients. Results: Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models. Discussion: Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study’s findings can inform future design iterations of neural decoders for adaptive DBS and BCI

Average power and burst analysis revealed complementary information on drug-related changes of motor performance in Parkinson's disease

In patients with Parkinson's disease (PD), suppression of beta and increase in gamma oscillations in the subthalamic nucleus (STN) have been associated with both levodopa treatment and motor functions. Recent results suggest that modulation of the temporal dynamics of theses oscillations (bursting activity) might contain more information about pathological states and behaviour than their average power. Here we directly compared the information provided by power and burst analyses about the drug-related changes in STN activities and their impact on motor performance within PD patients. STN local field potential (LFP) signals were recorded from externalized patients performing self-paced movements ON and OFF levodopa. When normalised across medication states, both power and burst analyses showed an increase in low-beta oscillations in the dopamine-depleted state during rest. When normalised within-medication state, both analyses revealed that levodopa increased movement-related modulation in the alpha and low-gamma bands, with higher gamma activity around movement predicting faster reaches. Finally, burst analyses helped to reveal opposite drug-related changes in low- and high-beta frequency bands, and identified additional within-patient relationships between high-beta bursting and movement performance. Our findings suggest that although power and burst analyses share a lot in common they also provide complementary information on how STN-LFP activity is associated with motor performance, and how levodopa treatment may modify these relationships in a way that helps explain drug-related changes in motor performance. Different ways of normalisation in the power analysis can reveal different information. Similarly, the burst analysis is sensitive to how the threshold is defined - either for separate medication conditions separately, or across pooled conditions. In addition, the burst interpretation has far-reaching implications about the nature of neural oscillations - whether the oscillations happen as isolated burst-events or are they sustained phenomena with dynamic amplitude variations? This can be different for different frequency bands, and different for different medication states even for the same frequency band

The potential of convolutional neural networks for identifying neural states based on electrophysiological signals: experiments on synthetic and real patient data

Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonical frequency bands or various time-domain features, and then training machine learning systems that use those predefined features as inputs and infer what the underlying brain state is at each given time point. However, whether this algorithmic approach is best suited to extract all available information contained within the neural waveforms remains an open question. Here, we aim to explore different algorithmic approaches in terms of their potential to yield improvements in decoding performance based on neural activity such as measured through local field potentials (LFPs) recordings or electroencephalography (EEG). In particular, we aim to explore the potential of end-to-end convolutional neural networks, and compare this approach with other machine learning methods that are based on extracting predefined feature sets. To this end, we implement and train a number of machine learning models, based either on manually constructed features or, in the case of deep learning-based models, on features directly learnt from the data. We benchmark these models on the task of identifying neural states using simulated data, which incorporates waveform features previously linked to physiological and pathological functions. We then assess the performance of these models in decoding movements based on local field potentials recorded from the motor thalamus of patients with essential tremor. Our findings, derived from both simulated and real patient data, suggest that end-to-end deep learning-based methods may surpass feature-based approaches, particularly when the relevant patterns within the waveform data are either unknown, difficult to quantify, or when there may be, from the point of view of the predefined feature extraction pipeline, unidentified features that could contribute to decoding performance. The methodologies proposed in this study might hold potential for application in adaptive deep brain stimulation (aDBS) and other brain-computer interface systems

Recognition and binding of mismatch repair proteins at an oncogenic hot spot

BACKGROUND: The current investigation was undertaken to determine key steps differentiating G:T and G:A repair at the H-ras oncogenic hot spot within the nuclear environment because of the large difference in repair efficiency of these two mismatches. RESULTS: Electrophoretic mobility shift (gel shift) experiments demonstrate that DNA containing mismatched bases are recognized and bound equally efficiently by hMutSα in both MMR proficient and MMR deficient (hMLH1-/-) nuclear extracts. Competition experiments demonstrate that while hMutSα predictably binds the G:T mismatch to a much greater extent than G:A, hMutSα demonstrates a surprisingly equal ratio of competitive inhibition for both G:T and G:A mismatch binding reactions at the H-ras hot spot of mutation. Further, mismatch repair assays reveal almost 2-fold higher efficiency of overall G:A repair (5'-nick directed correct MMR to G:C and incorrect repair to T:A), as compared to G:T overall repair. Conversely, correct MMR of G:T → G:C is significantly higher (96%) than that of G:A → G:C (60%). CONCLUSION: Combined, these results suggest that initiation of correct MMR requires the contribution of two separate steps; initial recognition by hMutSα followed by subsequent binding. The 'avidity' of the binding step determines the extent of MMR pathway activation, or the activation of a different cellular pathway. Thus, initial recognition by hMutSα in combination with subsequent decreased binding to the G:A mismatch (as compared to G:T) may contribute to the observed increased frequency of incorrect repair of G:A, resulting in the predominant GGC → GTC (Gly → Val) ras-activating mutation found in a high percentage of human tumors

Molecular identification and toxin analysis of Alexandrium spp. in the Beibu Gulf: first report of toxic A. tamiyavanichii in Chinese coastal waters

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Xu, Y., He, X., Li, H., Zhang, T., Lei, F., Gu, H., & Anderson, D. M. Molecular identification and toxin analysis of Alexandrium spp. in the Beibu Gulf: first report of toxic A. tamiyavanichii in Chinese coastal waters. Toxins, 13(2), (2021): 161, https://doi.org/10.3390/toxins13020161.The frequency of harmful algal blooms (HABs) has increased in China in recent years. Information about harmful dinoflagellates and paralytic shellfish toxins (PSTs) is still limited in China, especially in the Beibu Gulf, where PSTs in shellfish have exceeded food safety guidelines on multiple occasions. To explore the nature of the threat from PSTs in the region, eight Alexandrium strains were isolated from waters of the Beibu Gulf and examined using phylogenetic analyses of large subunit (LSU) rDNA, small subunit (SSU) rDNA, and internal transcribed spacer (ITS) sequences. Their toxin composition profiles were also determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS). All eight strains clustered in the phylogenetic tree with A. pseudogonyaulax, A. affine, and A. tamiyavanichii from other locations, forming three well-resolved groups. The intraspecific genetic distances of the three Alexandrium species were significantly smaller than interspecific genetic distances for Alexandrium species. Beibu Gulf isolates were therefore classified as A. pseudogonyaulax, A. affine, and A. tamiyavanichii. No PSTs were identified in A. pseudogonyaulax, but low levels of gonyautoxins (GTXs) 1 to 5, and saxitoxin (STX) were detected in A. tamiyavanichii (a total of 4.60 fmol/cell). The extremely low level of toxicity is inconsistent with PST detection above regulatory levels on multiple occasions within the Beibu Gulf, suggesting that higher toxicity strains may occur in those waters, but were unsampled. Other explanations including biotransformation of PSTs in shellfish and the presence of other PST-producing algae are also suggested. Understanding the toxicity and phylogeny of Alexandrium species provides foundational data for the protection of public health in the Beibu Gulf region and the mitigation of HAB events.This research was funded by the National Natural Science Foundation of China (41976155, 41506137), the Natural Science Foundation of Guangxi Province (2020GXNSFDA297001, 2016GXNSFBA380037), the Woods Hole Center for Oceans and Human Health (National Science Foundation grant OCE-1840381 and National Institutes of Health grants NIEHS-1P01-ES028938-01), the Opening Project of Guangxi Laboratory on the Study of Coral Reefs in the South China Sea (GXLSCRSCS2019002), the Opening Foundation of Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education (Nanning Normal University), and the Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation (Nanning Normal University) (GTEU-KLOP-K1803)

Dynamics of bond breaking and formation in polyethylene near shock front

In a systematic study of shock wave propagating in crystalline polyethylenes using molecular dynamics method and the electron force field (eFF) potential, we show that microscopic structure of shock front is significantly affected by the anisotropy of long carbon chain and the bond breaking and recombination dynamics. However, macroscopic properties measured in Hugoniot experiments, such as compression ratio and shock velocity, are not sensitive to carbon chain anisotropy and bond dynamics. Our work also display that hydrogen molecules are formed when the piston speed is in the region between 10 km/s and 30 km/s. However, carbon-hydrogen pair distribution function does not display an indication of carbon-hydrogen phase segregation

Over-expression of eukaryotic translation initiation factor 4 gamma 1 correlates with tumor progression and poor prognosis in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>The aim of the present study was to analyze the expression of eukaryotic translation initiation factor 4 gamma 1 (<it>EIF4G1</it>) in nasopharyngeal carcinoma (NPC) and its correlation with clinicopathologic features, including patients' survival time.</p> <p>Methods</p> <p>Using real-time PCR, we detected the expression of <it>EIF4G1 </it>in normal nasopharyngeal tissues, immortalized nasopharyngeal epithelial cell lines NP69, NPC tissues and cell lines. <it>EIF4G1 </it>protein expression in NPC tissues was examined using immunohistochemistry. Survival analysis was performed using Kaplan-Meier method. The effect of <it>EIF4G1 </it>on cell invasion and tumorigenesis were investigated.</p> <p>Results</p> <p>The expression levels of <it>EIF4G1 </it>mRNA were significantly greater in NPC tissues and cell lines than those in the normal nasopharyngeal tissues and NP69 cells (<it>P </it>< 0.001). Immunohistochemical analysis revealed that the expression of <it>EIF4G1 </it>protein was higher in NPC tissues than that in the nasopharyngeal tissues (<it>P </it>< 0.001). In addition, the levels of <it>EIF4G1 </it>protein in tumors were positively correlated with tumor T classification (<it>P </it>= 0.039), lymph node involvement (N classification, <it>P </it>= 0.008), and the clinical stages (<it>P </it>= 0.003) of NPC patients. Patients with higher <it>EIF4G</it>1 expression had shorter overall survival time (<it>P </it>= 0.019). Multivariate analysis showed that <it>EIF4G1 </it>expression was an independent prognostic indicator for the overall survival of NPC patients. Using shRNA to knock down the expression of <it>EIF4G1 </it>not only markedly inhibited cell cycle progression, proliferation, migration, invasion, and colony formation, but also dramatically suppressed <it>in vivo </it>xenograft tumor growth.</p> <p>Conclusion</p> <p>Our data suggest that <it>EIF4G1 </it>can serve as a biomarker for the prognosis of NPC patients.</p