Development and evaluation of a prototype in-flight instrument flight rules (IFR) procedures trainer

An in-flight instrument flight rules (IFR) procedures trainer capable of providing simulated indications of instrument flight in a typical general aviation aircraft independent of ground based navigation aids was developed. The IFR navaid related instruments and circuits from an ATC 610J table top simulator were installed in a Cessna 172 aircraft and connected to its electrical power and pitot static systems. The benefits expected from this hybridization concept include increased safety by reducing the number of general aviation aircraft conducting IFR training flights in congested terminal areas, and reduced fuel use and instruction costs by lessening the need to fly to and from navaid equipped airports and by increased efficiency of the required in-flight training. Technical feasibility was demonstrated and the operational feasibility of the concept was evaluated. Results indicated that the in-flight simulator is an effective training device for teaching IFR procedural skills

Exploiting Multiple Sensory Modalities in Brain-Machine Interfaces

Recent improvements in cortically-controlled brain-machine interfaces (BMIs) have raised hopes that such technologies may improve the quality of life of severely motor-disabled patients. However, current generation BMIs do not perform up to their potential due to the neglect of the full range of sensory feedback in their strategies for training and control. Here we confirm that neurons in the primary motor cortex (MI) encode sensory information and demonstrate a significant heterogeneity in their responses with respect to the type of sensory modality available to the subject about a reaching task. We further show using mutual information and directional tuning analyses that the presence of multi-sensory feedback (i.e. vision and proprioception) during replay of movements evokes neural responses in MI that are almost indistinguishable from those responses measured during overt movement. Finally, we suggest how these playback-evoked responses may be used to improve BMI performance

Temporal Evolution of Both Premotor and Motor Cortical Tuning Properties Reflect Changes in Limb Biomechanics

A prevailing theory in the cortical control of limb movement posits that premotor cortex initiates a high-level motor plan that is transformed by the primary motor cortex (MI) into a low-level motor command to be executed. This theory implies that the premotor cortex is shielded from the motor periphery and therefore its activity should not represent the low-level features of movement. Contrary to this theory, we show that both dorsal (PMd) and ventral premotor (PMv) cortices exhibit population-level tuning properties that reflect the biomechanical properties of the periphery similar to those observed in M1. We recorded single-unit activity from M1, PMd, and PMv and characterized their tuning properties while six rhesus macaques performed a reaching task in the horizontal plane. Each area exhibited a bimodal distribution of preferred directions during execution consistent with the known biomechanical anisotropies of the muscles and limb segments. Moreover, these distributions varied in orientation or shape from planning to execution. A network model shows that such population dynamics are linked to a change in biomechanics of the limb as the monkey begins to move, specifically to the state-dependent properties of muscles. We suggest that, like M1, neural populations in PMd and PMv are more directly linked with the motor periphery than previously thought

Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain–machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey\u27s arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback

Simulating multiple merger pathways to the central kinematics of early-type galaxies

Two-dimensional integral field surveys such as ATLAS^3D are producing rich observational data sets yielding insights into galaxy formation. These new kinematic observations have highlighted the need to understand the evolutionary mechanisms leading to a spectrum of fast-rotators and slow-rotators in early-type galaxies. We address the formation of slow and fast rotators through a series of controlled, comprehensive hydrodynamical simulations sampling idealized galaxy merger scenarios constructed from model spiral galaxies. Idealized and controlled simulations of this sort complement the more 'realistic' cosmological simulations by isolating and analyzing the effects of specific parameters, as we do in this paper. We recreate minor and major binary mergers, binary merger trees with multiple progenitors, and multiple sequential mergers. Within each of these categories of formation history, we correlate progenitor gas fraction, mass ratio, orbital pericenter, orbital ellipticity, and spin with remnant kinematic properties. We create kinematic profiles of these 95 simulations comparable to ATLAS^3D data. By constructing remnant profiles of the projected specific angular momentum (lambda_R = / , triaxiality, and measuring the incidences of kinematic twists and kinematically decoupled cores, we distinguish between varying formation scenarios. We find that binary mergers nearly always form fast rotators. Slow rotators can be formed from zero initial angular momentum configurations and gas-poor mergers, but are not as round as the ATLAS^3D galaxies. Remnants of binary merger trees are triaxial slow rotators. Sequential mergers form round slow rotators that most resemble the ATLAS^3D rotators.Comment: MNRAS, in press, 12 pages, 15 figure

Improving Brain–Machine Interface Performance by Decoding Intended Future Movements

Objective. A brain–machine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject\u27s intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user\u27s intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user\u27s future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user\u27s future intent can compensate for the negative effect of control delay on BMI performance

Spatio-Temporal Patterning in Primary Motor Cortex at Movement Onset

Voluntary movement initiation involves the engagement of large populations of motor cortical neurons around movement onset. Despite knowledge of the temporal dynamics that lead to movement, the spatial structure of these dynamics across the cortical surface remains unknown. In data from 4 rhesus macaques, we show that the timing of attenuation of beta frequency local field potential oscillations, a correlate of locally activated cortex, forms a spatial gradient across primary motor cortex (MI). We show that these spatio-temporal dynamics are recapitulated in the engagement order of ensembles of MI neurons. We demonstrate that these patterns are unique to movement onset and suggest that movement initiation requires a precise spatio-temporal sequential activation of neurons in MI

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip