Electron-impact excitation of X 1Sigma_g⁺(v[double-prime]=0) to the a[double-prime] 1Sigma_g⁺, b 1Piu, c3 1Piu, o3 1Piu, b[prime] 1Sigma_u⁺, c₄^[prime] 1Sigma_u⁺, G 3Piu, and F 3Piu states of molecular nitrogen

Measurements of differential cross sections (DCSs) for electron-impact excitation of the a[double-prime] 1Sigmag+, b 1Piu, c3 1Piu, o3 1Piu, b[prime] 1Sigmau+, c4[prime] 1Sigmau+, G 3Piu, and F 3Piu states in N2 from the X 1Sigmag+(v[double-prime]=0) ground level are presented. The DCSs were obtained from energy-loss spectra in the region of 12 to 13.82 eV measured at incident energies of 17.5, 20, 30, 50, and 100 eV and for scattering angles ranging from 2° to 130°. The analysis of the spectra follows a different algorithm from that employed in a previous study of N2 for the valence states [Khakoo et al. Phys. Rev. A 71, 062703 (2005)], since the 1Piu and 1Sigmau+ states form strongly interacting Rydberg-valence series. The results are compared with existing data

Design and validation of a real-time spiking-neural-network decoder for brain–machine interfaces

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain–machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF

Refining determinants of associations of visit-to-visit blood pressure variability with cardiovascular risk: results from the Action to Control Cardiovascular Risk in Diabetes Trial

OBJECTIVES: As there is uncertainty about the extent to which baseline blood pressure level or cardiovascular risk modifies the relationship between blood pressure variability (BPv) and cardiovascular disease, we comprehensively examined the role of BPv in cardiovascular disease risk in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Trial. METHODS: Using data from ACCORD, we examined the relationship of BPv with development of the primary CVD outcome, major coronary heart disease (CHD), and total stroke using time-dependent Cox proportional hazards models. RESULTS: BPv was associated with the primary CVD outcome and major CHD but not stroke. The positive association with the primary CVD outcome and major CHD was more pronounced in low and high strata of baseline SBP (140 mmHg) and DBP (80 mmHg). The effect of BPv on CVD and CHD was more pronounced in those with both prior CVD history and low blood pressure. Dips, not elevations, in blood pressure appeared to drive these associations. The relationships were generally not attenuated by adjustment for mean blood pressure, medication adherence, or baseline comorbidities. A sensitivity analysis using CVD events from the long-term posttrial follow-up (ACCORDION) was consistent with the results from ACCORD. CONCLUSION: In ACCORD, the effect of BPv on adverse cardiovascular (but not cerebrovascular) outcomes is modified by baseline blood pressure and prior CVD. Recognizing these more nuanced relationships may help improve risk stratification and blood pressure management decisions as well as provide insight into potential underlying mechanisms.12 month embargo; published 01 November 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Blood Pressure Variability and Risk of Heart Failure in ACCORD and the VADT

In ACCORD, CV and ARV of SBP and DBP were associated with increased risk of HF, even after adjusting for other risk factors and mean blood pressure (e.g., CV-SBP: hazard ratio [HR] 1.15, P = 0.01; CV-DBP: HR 1.18, P = 0.003). In the VADT, DBP variability was associated with increased risk of HF (ARV-DBP: HR 1.16, P = 0.001; CV-DBP: HR 1.09, P = 0.04). Further, in ACCORD, those with progressively lower baseline blood pressure demonstrated a stepwise increase in risk of HF with higher CV-SBP, ARV-SBP, and CV-DBP. Effects of blood pressure variability were related to dips, not elevations, in blood pressure.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes.

ObjectiveBrain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI.ApproachSpikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together.Main resultsLMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor.SignificanceThese findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs

Neural Population Dynamics Underlying Motor Learning Transfer.

Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal

Electron impact excitation of X 1Σg+(v′′=0)X\,{^1\Sigma_g^+}(v''=0) to the a′′ 1Σg+a''\,{^1\Sigma_g^+}, b 1Πub\,{^1\Pi_u}, c3 1Πuc_3\,{^1\Pi_u}, o3 1Πuo_3\,{^1\Pi_u}, b′ 1Σu+b'\,{^1\Sigma_u^+}, c4′ 1Σu+c_4'\,{^1\Sigma_u^+}, G 3ΠuG\,{^3\Pi_u}, and F 3ΠuF\,{^3\Pi_u} states of molecular nitrogen

Measurements of differential cross sections (DCSs) for electron-impact excitation of the a″ Σg+1, b Πu1, c3 Πu1, o3 Πu1, b′ Σu+1, c4′ Σu+1, G Πu3, and F Πu3 states in N2 from the X Σg+1 (v″ =0) ground level are presented. The DCSs were o

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

OBJECTIVE: Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. APPROACH: Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. MAIN RESULTS: LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. SIGNIFICANCE: These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs

Publisher Correction: Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper