Hasil untuk "Neurophysiology and neuropsychology"

Fábio Köpp, César H. Lenzi, César V. Flores et al.

In this work, we explore how astronomical observations (specifically measurements of masses, radii, and tidal deformabilities) can constrain the presence of quark matter inside neutron stars, namely the phase transition from nuclear matter to deconfined quark matter. Our approach employs Bayesian analysis to study this phenomenon. Hadronic matter is modeled using the relativistic mean-field (RMF) approximation, for which we have selected two parameter sets: \(NL3^{*}ωρ\), representing hadronic matter with nucleons only, and $EL3ωρ$ with nucleons only and $EL3ωρY$, which includes hyperons. On the other hand deconfined quark matter is modeled using the vector-MIT bag model. For our purpose, the phase transition is implemented using the Maxwell construction. Bayesian inference is performed by tuning three parameters: the bag constant (i.e. $B^{1/4}$), the vector coupling constant \(\left(G_{v}\right)\), and the Dirac sea contribution ($b_{4}$). We found that a phase transition could exist at densities below \(2.0\,n_{0}\) for both the $EL3ωρ- EL3ωρY $ and $NL3^{*}ωρ$ parametrizations. As a consequence, our results also indicate that a hybrid neutron star could have a large quark core that comprises more than \(80\%\) of its size.

Pakwan Chanprakhon, Michael F. Reid, and Jon-Paul R. Wells

We report on the synthesis and spectroscopic characterization of Sm$^{3+}$-doped K$_2$YF$_5$ microparticles. The particles were synthesized via the hydrothermal technique, yielding a particle size of approximately 20 $μ$m in length. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses confirmed their orthorhombic crystal structure. A combination of absorption and laser excited fluorescence performed on samples cooled to 10~K, allow for the determination of {56} experimental crystal-field levels. A parametrised crystal-field analysis for Sm$^{3+}$ in the C$_{\rm s}$ point group symmetry centres of K$_2$YF$_5$ yields good approximation to the data.

Schreier DR, Fecchio M, Guay CS et al.

David R Schreier,1– 4 Matteo Fecchio,3 Christian S Guay,1,2,5 Reid G Kovacs,1,6– 8 Mark Olchanyi,2,3,6 Ariel L Mueller,1 Timothy T Houle,1 Brian L Edlow,3,9 Emery N Brown,1,2,6,7 Ken Solt1 1Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Department of Anaesthesia, Harvard Medical School, Boston, MA, USA; 2Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; 3Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; 4Department of Neurology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland; 5Department of Anesthesiology, Perioperative and Pain Medicine, The University of Utah, Salt Lake City, UT, USA; 6Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; 7Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; 8Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; 9Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USACorrespondence: David R Schreier, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA, Email david.r.schreier@gmail.comPurpose: Dexmedetomidine (DEX) is a well-tolerated sedative drug that induces a sleep-like state. DEX sedation offers a model to study transitions between different states of consciousness (indicated by, eg, behavior, the electroencephalogram (EEG), or transcranial magnetic stimulation (TMS) evoked EEG responses). However, the effects of repeated DEX exposure on recovery are poorly understood and will be investigated in this pilot study.Participants and Methods: We aim to enroll 12 healthy volunteers (6 females, 6 males). Although we do not expect TMS-EEG to interfere with DEX sedation, due to the paucity of evidence this study uses a parallel arm design (TMS-EEG, non-TMS-EEG). Participants will be sedated twice, one week apart, and responsiveness monitored by a click-task to auditory beeps. A 64-channel EEG and additional physiological signals will be recorded. Cognition and vigilance tests will be performed before sedation (baseline), after return of responsiveness (ROR), and before discharge. TMS-EEG will be performed at baseline, during sedation, and during recovery. Using a smartwatch and questionnaires, we will assess sleep quality, sleepiness, and experiences during sedation and TMS-EEG.Results: We will report the difference of time to ROR between the first and second study visit, and explore potential differences across sex and study arms. We will evaluate state transitions by comparing responsiveness, traditional EEG signatures, TMS-evoked EEG responses (ie, perturbational complexity index), and report on cognition and vigilance test performance.Conclusion: This pilot trial will report on the effect of repeated DEX exposure on the recovery period, and the investigation of state transitions will advance our scientific understanding of altered states of consciousness. REDEX will provide valuable insights and data for designing future DEX sedation studies. Moreover, we will report on the potential of biological sex as a confounding factor and the feasibility of TMS-EEG under DEX.Keywords: consciousness, sedation, sleep, electroencephalography, transcranial magnetic stimulation, target-controlled infusion

Mina Mohammadkhani, Mostafa Jarah, Dariush Gholami et al.

Abstract Background There is growing evidence of the contribution of microtubule dynamics to dendritic spine changes, synaptic plasticity, axonal transportation, and cell polarity. Besides, one of the well-studied effects of Cannabis on human behavior is memory disability. As Δ9-tetrahydrocannabinol (Δ9-THC) is the most pivotal chemical of Cannabis, we investigated the effect of Δ9-THC on microtubule dynamicity and the structural study of tubulin (microtubule monomer). Results Our results show that Δ9-THC changes microtubule dynamicity compared to the control group. The turbidity assay results demonstrated that Δ9-THC reduces microtubule polymerization in a concentration-dependent manner. Circular Dichroism spectroscopy also studied the structural changes of the purified tubulin, which revealed significant changes in the secondary structure of the tubulin. Furthermore, Silico studies predicted one binding site for Δ9-THC on β-tubulin. Conclusions We concluded that Δ9-THC could reduce the microtubule’s stability, which may conversely affect brain function by microtubule dynamic changes caused by secondary structural changes of tubulin and preventing tubulin-tubulin interaction.

S.J. van Norden, K.H. Kho, A.M. Meppelink et al.

Recently, a subcutaneous electroencephalography (sqEEG) electrode became available, enabling ultra-long-term EEG recordings. In our PREDYct study, we aim to predict the efficacy of vagus nerve stimulation (VNS) in epilepsy patients. The study records sqEEG for up to fifteen months, including a 2–3 month period before VNS implantation. Here we report on initial patient experiences, compliance and recordings of epileptiform discharges (EDs). We included the first 10 participants from our ongoing PREDYct study. We calculated recording compliance for each patient. Patients rated their pain during electrode implantation, wearing and explantation using the visual analogue scale (VAS). Approximately two weeks of data from each patient were visually assessed and all EDs were annotated. We used these annotations to evaluate circadian rhythms in ED rate and duration and to compare seizure diaries with annotated ictal patterns. Electrode implantation and explantation were well-tolerated with a median VAS of 4 and 2, respectively. Wearing the electrode was also well-tolerated with median VAS scores of 0 and 0.5 at two and fifteen months, respectively. On average, patients wore the electrode for 441 days with an overall compliance of 27 %, resulting in 28,331 h of EEG. A circadian rhythm in ED rate was observed in three patients, while one patient exhibited a circadian rhythm in ED duration. A comparison of reported seizures and annotated ictal patterns revealed discrepancies. Ultra-long-term sqEEG is well-tolerated and has the potential to be a valuable tool for monitoring treatment. Nevertheless, it remains a challenge but crucial to attain high recording compliance.

Ping Wang, Chong Chen, Hao Liao et al.

Test of {macroscopic realism} (MR) is key to understanding the foundation of quantum mechanics. Due to the existence of the {non-invasive measurability} loophole and other interpretation loopholes, however, such test remains an open question. Here we propose a general inequality based on high-order correlations of measurements for a loophole-free test of MR at the weak signal limit. Importantly, the inequality is established using the statistics of \textit{raw data} recorded by classical devices, without requiring a specific model for the measurement process, so its violation would falsify MR without the interpretation loophole. The non-invasive measurability loophole is also closed, since the weak signal limit can be verified solely by measurement data (using the relative scaling behaviors of different orders of correlations). We demonstrate that the inequality can be broken by a quantum spin model. The inequality proposed here provides an unambiguous test of the MR principle and is also useful to characterizing {quantum coherence}.

Himanshu Yadav, Dario Paape, Garrett Smith et al.

Zhiyi Chen, Ali Yadollahpour

Abstract Translating artificial intelligence techniques into the realm of cognitive neuroscience holds promise for significant breakthroughs in our ability to probe the intrinsic mechanisms of the brain. The recent unprecedented development of robust AI models is changing how and what we understand about the brain. In this Editorial, we invite contributions for a BMC Neuroscience Collection on “AI and Cognitive Neuroscience”.

Salah Ghamizi, Maxime Cordy, Yuejun Guo et al.

Much research on Machine Learning testing relies on empirical studies that evaluate and show their potential. However, in this context empirical results are sensitive to a number of parameters that can adversely impact the results of the experiments and potentially lead to wrong conclusions (Type I errors, i.e., incorrectly rejecting the Null Hypothesis). To this end, we survey the related literature and identify 10 commonly adopted empirical evaluation hazards that may significantly impact experimental results. We then perform a sensitivity analysis on 30 influential studies that were published in top-tier SE venues, against our hazard set and demonstrate their criticality. Our findings indicate that all 10 hazards we identify have the potential to invalidate experimental findings, such as those made by the related literature, and should be handled properly. Going a step further, we propose a point set of 10 good empirical practices that has the potential to mitigate the impact of the hazards. We believe our work forms the first step towards raising awareness of the common pitfalls and good practices within the software engineering community and hopefully contribute towards setting particular expectations for empirical research in the field of deep learning testing.

Jonathan C Balloch, Julia Kim, and Jessica L Inman et al.

The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the study of exploration methods is usually only considered in the context of learning the optimal policy for a single learning task. However, in the context of online task transfer, where there is a change to the task during online operation, we hypothesize that exploration strategies that anticipate the need to adapt to future tasks can have a pronounced impact on the efficiency of transfer. As such, we re-examine the exploration--exploitation trade-off in the context of transfer learning. In this work, we review reinforcement learning exploration methods, define a taxonomy with which to organize them, analyze these methods' differences in the context of task transfer, and suggest avenues for future investigation.

Kyriakos Stylianopoulos, George Alexandropoulos, Chongwen Huang et al.

The emergent technology of Reconfigurable Intelligent Surfaces (RISs) has the potential to transform wireless environments into controllable systems, through programmable propagation of information-bearing signals. Techniques stemming from the field of Deep Reinforcement Learning (DRL) have recently gained popularity in maximizing the sum-rate performance in multi-user communication systems empowered by RISs. Such approaches are commonly based on Markov Decision Processes (MDPs). In this paper, we instead investigate the sum-rate design problem under the scope of the Multi-Armed Bandits (MAB) setting, which is a relaxation of the MDP framework. Nevertheless, in many cases, the MAB formulation is more appropriate to the channel and system models under the assumptions typically made in the RIS literature. To this end, we propose a simpler DRL approach for orchestrating multiple metasurfaces in RIS-empowered multi-user Multiple-Input Single-Output (MISO) systems, which we numerically show to perform equally well with a state-of-the-art MDP-based approach, while being less demanding computationally.

Xiangrong Guo, Shiwei Jiang, Jian Xu et al.

Objective: To assess associations of single and combined exposures to lead and stress during different stages of pregnancy with offspring neurodevelopment. Methods: We measured prenatal lead (maternal blood-lead in early-pregnancy and umbilical-cord-blood-lead) and maternal stress levels in Shanghai-Birth-Cohort from 2013 to 2016. Maternal stress was assessed using Center-for-Epidemiological-Studies-Depression-Scale and Self-Rating-Anxiety-Scale during mid-pregnancy. The Ages-Stages-Questionnaires-3 (at 6/12-months-of-age) and Bayley-III (at 24-months-of-age) were both used to assess neurodevelopment. Results: A total of 2132 mother-child pairs with both prenatal lead and stress measurements were included. The geometric-means of blood-lead in early-pregnancy and cord-blood-lead were 1.46 μg/dL and 1.33 μg/dL, respectively. Among the study women, 1.89 % and 0.14 % were screened positive for depression and anxiety. Adjusting for related confounders, the combined exposures had stronger adverse associations with offspring social-emotional skills than single exposures; and the combined exposure in early-pregnancy was associated with greater neurodevelopmental differences than combined exposure around-birth, especially in social-emotion at 24 months-of-age [β (95 %CI): − 10.48(−17.42, −3.54) vs. − 5.95(−11.53, −0.36)]. Conclusions: Both single and combined prenatal exposures to lead/stress impaired infant neuro-development, and the effects of combined exposure may be more profound than single exposures. Combined exposure in early-pregnancy may be associated with worse neurodevelopmental outcomes than combined exposure around-birth, especially in social-emotional development.

Mochamad Bahrudin, Probo Yudha Pratama Putra, Dinda Amalia Eka Putri

Introduction: Rapid diagnosis is crucial for stroke patients since it is an emergency that may result in morbidity and mortality. The gold standard, which is a CT scan of the brain is not always feasible, hence, Siriraj and Bahrudin Score, as well as Gajah Mada Algorithm are likely to be alternatives. Objectives: This study aims to determine the specificity and sensitivity of the Siriraj and Bahrudin Score as well as Gajah Mada Algorithm. Method: A cross-sectional study was undertaken at five network hospitals of the Medical Faculty University in Muhammadiyah Malang. It involves a sample of 304 medical records used to determine the sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve with output area under the curve (AUC). Result: The result showed that the sensitivity and specificity of Bahrudin and Siriraj Acore, as well as Gajah Mada Algorithm for determining infarct stroke was 91.3% vs 89.7% vs 61.2% and 67.7% vs 69.4% vs 77.4%, respectively. For determining hemorrhagic stroke the values were 67.7% vs 69.4% vs 77.4% and 91.3% vs 89.7% vs 61.2%, respectively. Furthermore, the area under the curve of Bahrudin and Siriraj Scores was better than Gajah Mada Algorithm. Conclusion: In conclusion, Bahrudin and Siriraj Scores have good accuracy, sensitivity, and specificity in diagnosing stroke than Gajah Mada Algorithm.

Julie M. Schneider, Alyson D. Abel, Jacob Momsen et al.

AbstractBuilding a robust vocabulary in grade school is essential for academic success. Children from lower socioeconomic status (SES) households on average perform below their higher SES peers on word learning tasks, negatively impacting their vocabulary; however, significant variability exists within this group. Many children from low SES homes perform as well as, or better than, their higher SES peers on measures of word learning. The current study addresses what processes underlie this variability, by comparing the neural oscillations of 44 better versus worse word learners (ages 8–15 years) from lower SES households as they infer the meaning of unknown words. Better word learners demonstrated increases in theta and beta power as a word was learned, whereas worse word learners exhibited decreases in alpha power. These group differences in neural oscillatory engagement during word learning indicate there may be different strategies employed based on differences in children’s skills. Notably, children with greater vocabulary knowledge are more likely to exhibit larger beta increases, a strategy that is associated with better word learning. This sheds new light on the mechanisms that support word learning in children from low SES households.

Thanh Long Vu, Sayak Mukherjee, Tim Yin et al.

The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid's security and resilience. In particular, the increased uncertainties and rapidly changing operational conditions in power systems have revealed outstanding issues in terms of either speed, adaptiveness, or scalability of the existing control methods for power systems. On the other hand, the availability of massive real-time data can provide a clearer picture of what is happening in the grid. Recently, deep reinforcement learning(RL) has been regarded and adopted as a promising approach leveraging massive data for fast and adaptive grid control. However, like most existing machine learning (ML)-basedcontrol techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel method for safe RL-based load shedding of power systems that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

Xin Wang

In this essay, we explore a point of intersection between deep learning and neuroscience, through the lens of large language models, transfer learning and network compression. Just like perceptual and cognitive neurophysiology has inspired effective deep neural network architectures which in turn make a useful model for understanding the brain, here we explore how biological neural development might inspire efficient and robust optimization procedures which in turn serve as a useful model for the maturation and aging of the brain.

Rogić Vidaković M, Šoda J, Jerković A et al.

Maja Rogić Vidaković,1 Joško Šoda,2 Ana Jerković,1 Benjamin Benzon,1 Karla Bakrač,1 Silvia Dužević,1 Igor Vujović,2 Mario Mihalj,3 Renata Pecotić,1,4 Maja Valić,1,4 Angela Mastelić,5 Maximilian Vincent Hagelien,1 Marina Zmajević Schőnwald,6 Zoran Đogaš1,4 1University of Split, School of Medicine, Department of Neuroscience, Laboratory for Human and Experimental Neurophysiology (LAHEN), Split, Croatia; 2University of Split, Faculty of Maritime Studies, Signal Processing, Analysis and Advanced Diagnostics Research and Education Laboratory (SPAADREL), Split, Croatia; 3University Hospital Split, Department of Neurology, Laboratory of Electromyoneurography, Split, Croatia; 4University of Split, Split Sleep Medical Center, Split 21000, Croatia; 5University of Split, School of Medicine, Department of Medical Chemistry and Biochemistry, Split, Croatia; 6Clinical Medical Centre “Sisters Of Mercy”, Department of Neurosurgery, Clinical Unit for Intraoperative Neurophysiologic Monitoring, Zagreb, CroatiaCorrespondence: Maja Rogić VidakovićUniversity of Split, School of Medicine, Department of Neuroscience, Laboratory for Human and Experimental Neurophysiology (LAHEN), Split, CroatiaTel +385 21 557 876Fax +358 21 557 955Email maja.rogic@mefst.hrPurpose: An increase in resting motor threshold (RMT), prolonged cortical silent period duration (CSP), and reduced short-latency afferent inhibition (SAI), confirmed with previous transcranial magnetic stimulation (TMS), suggest decreased cortical excitability in obstructive sleep apnea syndrome (OSAS). The present study included MRI of OSAS patients for navigated TMS assessment of the RMT, as an index of the threshold for corticospinal activation at rest, and SAI as an index of cholinergic neurotransmission. We hypothesize to confirm findings on SAI and RMT with adding precision in the targeting of motor cortex in OSAS.Subjects and Methods: After acquiring head MRIs for 17 severe right-handed OSAS and 12 healthy subjects, the motor cortex was mapped with nTMS to assess the RMT and SAI, with motor evoked potentials (MEPs) recorded from the abductor-pollicis brevis (APB) muscle. The 120%RMT intensity was used for the SAI by a paired-pulse paradigm in which the electrical stimulation to the median nerve is followed by magnetic stimulation of the motor cortex at inter-stimulus intervals (ISIs) of 18– 28 ms (ISIs18-28). The SAI control condition included a recording of MEPs without peripheral stimulation. Latency and amplitude of MEP at RMT at 120%RMT for eleven different at ISIs18-28 were analyzed.Results: The study showed a significantly lower percentage deviation of MEP amplitude at ISIs(18-28ms) from the control condition between OSAS and healthy subjects (U=44.0, p=0.01). The intensity of stimulation at RMT was significantly higher in OSAS subjects (U=55.0, p=0.04*). Correlation analysis showed that BMI significantly negatively correlated (ρ=− 0.47) with MEP amplitude percentage deviation in OSAS patients.Conclusion: The nTMS study results in increased RMT, and reduced cortical afferent inhibition in OSAS patients for SAI at ISIs18-28, confirming previous findings of impaired cortical afferent inhibition in OSAS. Future nTMS studies are desirable to elucidate the role of RMT and SAI in diagnostics and treatment of OSAS, and to elucidate the usefulness of nTMS in OSAS research.Keywords: short-latency afferent inhibition, obstructive sleep apnea, transcranial magnetic stimulation, motor evoked potentials, primary motor cortex, transcutaneous electrical nerve stimulation

Sarah L. Master, Maria K. Eckstein, Neta Gotlieb et al.

Huang Y, Hennig S, Fietze I et al.

Ying Huang,1 Steve Hennig,1 Ingo Fietze,1 Thomas Penzel,1,2 Christian Veauthier1 1Interdisciplinary Center of Sleep Medicine, Charité University Medicine Berlin, Berlin 10117, Germany; 2Department of Biology, Saratov State University, Saratow, RussiaCorrespondence: Christian VeauthierInterdisziplinäres Schlafmedizinisches Zentrum, Charité Universitätsmedizin Berlin, Luisenstraße 13, Berlin 10117 Tel +49 30 513 232Fax +49 30 513 959Email christian.veauthier@charite.deBackground: Sleepiness at the wheel affects 10% to 15% of drivers and is one major cause of death on highways with one-third of fatal accidents. Obstructive sleep apnea (OSA) is one of the most common sleep disorders leading to sleepiness at the wheel. The aim of this study was to compare the psychomotor vigilance test reaction time (PVT RT) in OSA patients and controls (morning and afternoon) with the results of a divided attention steering simulator (DASS). A second purpose was to compare these results with the mean sleep latencies in the multiple sleep latency test (MSLT), the Epworth Sleepiness Scale (ESS) values and a neurocognitive test (test of attentional performance, TAP).Patients and Methods: Thirty eight OSA patients and 16 age and sex matched healthy controls were investigated by ESS, PVT, TAP, MSLT, and DASS (response time, failed responses, lane deviation, and off-road-events).Results: With increasing age, the performance in the DASS decreased. There was no correlation between the DASS and the results of the MSLT and ESS. The controls showed a significantly faster DASS response time in the morning compared to OSA patients (median 2.1 versus 3.0; p=0.044) and fewer off-road events (9 versus 37; p=0.042). We found a moderate correlation between the PVT RT and all parameters of the DASS, as well as the TAP “alertness” subtest.Conclusion: The increase of PVT RT as well as the decreased tonic alertness in the TAP in untreated OSA patients correlated with an impairment of simulated driving performance. The PVT and the TAP are both suitable diagnostic tools for measuring impaired driving ability in OSA patients. The MSLT did not correlate with the simulated driving performance. We recommend investigation of a longer version of the PVT in order to increase its sensitivity.Keywords: polysomnography, sustained attention, tonic alertness, vigilance, MSLT, simulated driving performance

Zahra Erisian, Hajar Torkan, Fariborz Mokarian

Aim and Background: Breast cancer is the most common cancer among women around the world that from the very beginning of diagnosis can cause fear, despair and psychological harm, and challenge all aspects of life (including physical, psychological, social). The aim of this study was determination of the role of emotional regulation, pain - efficacy and disease perception in predicting the negative mood of women with breast cancer. Methods and Materials: The research design was descriptive correlational and the statistical population of the study included all women with breast cancer referring to Isfahan health centers in spring of 2018. Accordingly, 160 women with breast cancer were selected by available sampling method, and based on the criteria for entry and exit. Data collection tools consisted of Gross and John's emotion regulation questionnaires (2003), Nicholas pain (1995) self-efficacy inventory, Brodbent & et.al (2006) illness perception questionnaire, Lovibond, and Lovibond's negative mood scale (1995). To analyze the data, Pearson correlation coefficient and multiple linear regression were used. Findings: The results showed that the perception of the disease (β= 0/46, p= 0/001), self-efficacy of pain (β= 0/15, p= 0/021), negatively and meaningfully and the suppression strategy (β= 0/21, p= 0/001) positive and meaningful can predict changes related to the negative mood of women with breast cancer. Conclusions: It is concluded that the type of emotion regulation strategy, the degree of self-efficacy of pain and the level of perception of the disease have a significant effect on mood disorders in women with breast cancer;  therefore, it can be understood by a deeper understanding of psychological characteristics and beliefs The disease in women with breast cancer takes steps to increase their mental health and well-being with new conditions.