Confocal Laser Induced Fluorescence with Comparable Spatial Localization to the Conventional Method

We present measurements of ion velocity distributions obtained by laser induced fluorescence (LIF) using a single viewport in an argon plasma. A patent pending design, which we refer to as the confocal fluorescence telescope, combines large objective lenses with a large central obscuration and a spatial filter to achieve high spatial localization along the laser injection direction. Models of the injection and collection optics of the two assemblies are used to provide a theoretical estimate of the spatial localization of the confocal arrangement, which is taken to be the full width at half maximum of the spatial optical response. The new design achieves approximately 1.4 mm localization at a focal length of 148.7 mm, improving on previously published designs by an order of magnitude and approaching the localization achieved by the conventional method. The confocal method, however, does so without requiring a pair of separated, perpendicular optical paths. The confocal technique therefore eases the two window access requirement of the conventional method, extending the application of LIF to experiments where conventional LIF measurements have been impossible or difficult, or where multiple viewports are scarce

Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations

A longitudinal analysis of diet quality scores and the risk of incident depression in the SUN project

Background: Some studies have pointed out that several dietary patterns could be associated with a reduced risk of depression among adults. This association seems to be consistent across countries, cultures and populations. The objective of the study was to compare and to establish the type of relationship between three diet quality scores and depression in the SUN (Seguimiento Universidad de Navarra) Cohort study. Methods: We performed a dynamic cohort study based on Spanish university graduates free of depression at baseline. Dietary intake was repeatedly assessed at baseline and after 10 years of follow-up with a validated semi-quantitative foodfrequency questionnaire. Three previously described diet quality scores: Mediterranean Diet Score (MDS), Pro-vegetarian Dietary Pattern (PDP) and Alternative Healthy Eating Index-2010 (AHEI-2010) were built. Participants were classified as having depression if they reported a new clinical diagnosis of depression by a physician or initiated the use of an antidepressant drug during follow-up. Time-dependent Cox regression models with cumulative averages of diet and restricted cubic splines were used to estimate hazard ratios of depression according to quintiles of adherence to the MDS, PDP and AHEI-2010. Results: One thousand and fifty one incident cases of depression were observed among 15,093 participants from the SUN Cohort after a median follow-up of 8.5 years. Inverse and significant associations were observed between the three diet quality scores and depression risk. The hazard ratios and 95 % confidence intervals for extreme quintiles (fifth versus first) of updated adherence to MDS, PDP and AHEI-2010 were 0.84 (0.69–1.02), 0.74 (0.61–0.89) and 0.60 (0.49–0.72), respectively. The dose–response analyses showed non-linear associations, suggesting that suboptimal adherence to these dietary patterns may partially be responsible for increased depression risk. Conclusions: Better adherence to the MDS, PDP and AHEI-2010 was associated with a reduced risk of depression among Spanish adults. However, our data suggested a threshold effect so that although the risk of depression was reduced when comparing moderate versus lower adherence, there was not much extra benefit for the comparison between moderate and high or very high adherence

ADGRL3 (LPHN3) variants predict substance use disorder

Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, and substance use disorder (SUD). Variants in the ADGRL3 (LPHN3) gene predispose to ADHD and predict ADHD severity, disruptive behaviors comorbidity, long-term outcome, and response to treatment. In this study, we investigated whether variants within ADGRL3 are associated with SUD, a disorder that is frequently co-morbid with ADHD. Using family-based, case-control, and longitudinal samples from disparate regions of the world (n = 2698), recruited either for clinical, genetic epidemiological or pharmacogenomic studies of ADHD, we assembled recursive-partitioning frameworks (classification tree analyses) with clinical, demographic, and ADGRL3 genetic information to predict SUD susceptibility. Our results indicate that SUD can be efficiently and robustly predicted in ADHD participants. The genetic models used remained highly efficient in predicting SUD in a large sample of individuals with severe SUD from a psychiatric institution that were not ascertained on the basis of ADHD diagnosis, thus identifying ADGRL3 as a risk gene for SUD. Recursive-partitioning analyses revealed that rs4860437 was the predominant predictive variant. This new methodological approach offers novel insights into higher order predictive interactions and offers a unique opportunity for translational application in the clinical assessment of patients at high risk for SUD

Long-Term Functional Changes in Multiple Cortical Areas

<div><p>(A) Color-coded (red shows high values; blue, low values) representation of individual contributions measured as the correlation coefficient (<i>R</i>) of neurons to linear model predictions of hand position for 42 training sessions. The average contribution steadily increased with training. The bar on the left indicates cortical location of the neurons.</p> <p>(B–E) Average contribution of neurons located in different cortical areas (PMd, M1, S1, and SMA, respectively) to hand position prediction during 42 recording sessions.</p> <p>(F) Average contribution for the whole ensemble to hand position prediction versus hand velocity predictions. A linear increase in contribution was observed only for predictions of hand position.</p></div

Performance of Linear Models in Predicting Multiple Parameters of Arm Movements, Gripping Force, and EMG from the Activity Frontoparietal Neuronal Ensembles Recorded in Pole Control

<div><p>(A) Motor parameters (blue) and their prediction using linear models (red). From top to bottom: Hand position (HPx, HPy) and velocity (HVx, HVy) during execution of task 1 and gripping force (GF) during execution of tasks 2 and 1.</p> <p>(B) EMGs (blue) recorded in task 1 and their prediction (red).</p> <p>(C) Contribution of neurons from the same ensemble to predictions of hand position (top), velocity (middle), and gripping force (bottom). Contributions were measured as correlation coefficients (<i>R</i>) between the recorded motor parameters and their values predicted by the linear model. The color bar at the bottom indicates cortical areas where the neurons were located. Each neuron contributed to prediction of multiple parameters of movements, and each area contained information about all parameters.</p> <p>(D–F) Contribution of different cortical areas to model predictions of hand position, velocity (task 1), and gripping force (task 2). For each area, ND curves represent the average prediction accuracy (<i>R²</i>) as a function of number of neurons needed to attain it. Contributions of each cortical area vary for different parameters. Typically, more than 30 randomly sampled neurons were required for an acceptable level of prediction.</p> <p>(G–I) Comparison of the contribution of single units (blue) and multiunits (red) to predictions of hand position, velocity, and gripping force. Single units and multiunits were taken from all cortical areas. Single units' contribution exceeded that of multiunits by approximately 20%.</p></div

Experimental Setup, Behavioral Tasks, Changes in Performance with Training, EMG Records during Pole and Brain Control, and Stability of Model Predictions

<div><p>(A) Behavioral setup and control loops, consisting of the data acquisition system, the computer running multiple linear models in real time, the robot arm equipped with a gripper, and the visual display. The pole was equipped with a gripping force transducer. Robot position was translated into cursor position on the screen, and feedback of the gripping force was provided by changing the cursor size.</p> <p>(B) Schematics of three behavioral tasks. In task 1, the monkey's goal was to move the cursor to a visual target (green) that appeared at random locations on the screen. In task 2, the pole was stationary, and the monkey had to grasp a virtual object by developing a particular gripping force instructed by two red circles displayed on the screen. Task 3 was a combination of tasks 1 and 2. The monkey had to move the cursor to the target and then develop a gripping force necessary to grasp a virtual object.</p> <p>(C–E) Behavioral performance for two monkeys in tasks 1–3. The percentage of correctly completed trials increased, while the time to conclude a trial decreased with training. This was true for both pole (blue) and brain (red) control. Horizontal (green) lines indicate chance performance obtained from the random walk model. The introduction of the robot arm into the BMIc control loop resulted in a drop in behavioral performance. In approximately seven training sessions, the animal's behavioral performance gradually returned to the initial values. This effect took place during both pole and brain control.</p> <p>(F) Stability of model predictions of hand velocity during long pole-control sessions (more than 50 min) for two monkeys performing task 1. The first 10 min of performance were used to train the model, and then its coefficients were frozen. Model predictions remained highly accurate for tens of minutes.</p> <p>(G) Surface EMGs of arm muscles recorded in task 1 for pole control (left) and brain control without arm movements (right). Top plots show the X-coordinate of the cursor; plots below display EMGs of wrist flexors, wrist extensors, and biceps. EMG modulations were absent in brain control.</p></div