EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants

Neuroimaging markers associated with maintenance of optimal memory performance in late-life

The use of computerized devices for neuropsychological assessment (CNADs) as an effective alternative to the traditional pencil-and-paper modality has recently increased exponentially, both in clinical practice and research, especially due to the pandemic. However, several authors underline that the computerized modality requires the same psychometric validity as "in-presence" tests. The current study aimed at building and validating a computerized version of the verbal and non-verbal recognition memory test (RMT) for words, unknown faces and buildings. Seventy-two healthy Italian participants, with medium-high education and ability to proficiently use computerized systems, were enrolled. The sample was subdivided into six groups, one for each age decade. Twelve neurological patients with mixed aetiology, age and educational level were also recruited. Both the computerized and the paper-and-pencil versions of the RMT were administered in two separate sessions. In healthy participants, the computerized and the paper-and-pencil versions of the RMT showed statistical equivalence for words, unknown faces and buildings. In the neurological patients, no statistical difference was found between the performance at the two versions of the RMT. A moderate-to-good inter-rater reliability between the two versions was also found in both samples. Finally, the computerized version of the RMT was perceived as acceptable by both healthy participants and neurological patients at System Usability Scale (SUS). The computerized version of the RMT can be used as a reliable alternative to the traditional version.

Abnormalities in the integration of emotion and cognition have long been considered hallmark characteristics of schizophrenia. Study authors used a well-established emotional memory model from the neuroscience literature to assess the facilitative impact of emotional valence of information on long-term memory consolidation in schizophrenia. Participants with schizophrenia (n=33) indicated somewhat higher levels of emotional intensity in response to emotional images than did healthy (n=28) participants. However, when recognition memory was tested 24 hr later, schizophrenia participants did not show enhancement of memory for positive images as was found in healthy participants. Their memory enhancement for negative images did not differ from that of healthy participants. Correlations between self-reported physical and social anhedonia were significantly inversely correlated with intensity ratings of positive stimuli during the encoding phase for healthy participants but were negligible for schizophrenia participants. These results suggest a failure to adequately integrate positive emotional experience in memory consolidation processes in schizophrenia participants, despite appropriate initial response to positive stimuli, which may contribute to symptoms such as anhedonia by reducing the long-term impact of positive experiences in motivating hedonic behavior in day-to-day life.

Constructing gene circuits that satisfy quantitative performance criteria has been a long‐standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three‐gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high‐throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model‐guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.

A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS). Previous studies have identified that the peak power spectral density (PSD) value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 minutes of tACS at the participant-specific, peak µ frequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-state µ SMRs. After tACS, we observed significant improvements in event-related desynchronizations (ERDs) of µ sensorimotor rhythms (SMRs), and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N=10) -but not in an active control-stimulation control group (N=10). Lastly, we showed a significant correlation between the resting-state µ SMRs and µ ERD, offering a mechanistic interpretation behind the observed changes in online BCI performances. Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.

Patients with obsessive-compulsive disorder (OCD) exhibit recall deficits on word learning tasks, mediated by their failure to detect semantic connections among the words. In the present experiment, using methods devised by Bransford & Franks (1971), we tested whether this encoding problem impairs their extraction of gist from complex linguistic material. Consistent with our hypothesis, OCD patients extracted less gist from related sentences than did healthy participants. The groups did not differ in recognition memory for individual sentences or in criterion for affirming previously encountered sentences as 'old', as evinced by signal detection indices of memory sensitivity (d') and response bias (beta), or in recognition memory confidence. These data provide further evidence that OCD patients exhibit less reliance on organizational strategies than do healthy control participants. These data are consistent with neuropsychological research suggestive of prefrontal executive problems in OCD.

The problem of choosing the firing angles for accomplishing optimal performance in current-controlled switched reluctance generators (SRGs) is examined. The optimal performance is reached with the correct balance between the criteria of high efficiency and low torque ripple. The concept of the method is based on the optimal control of turn-on and turn-off angles according to electrical load requirements and depending on rotor speed and dc-link voltage. A simple controller is proposed that on-line determines the optimal firing angles. The suggested controller does not affect the complexity of the drive and the knowledge of the magnetization curves is not required for its implementation. Simulation and experimental results are presented to validate the resulting improvements of the proposed control scheme.

Context Strategies for achieving greater ruminant livestock productivity are essential to meet the food demands of growing populations, but sustainable changes are difficult to identify given the inherent complexity of such systems. Systems models can address this issue by allowing the impact of potential changes to be explored. Aims To develop a holistic systems model for goat production in an extensive Mediterranean environment which could allow changes in key management factors influencing the system to be investigated. Methods Initially, a conceptual comprehensive stock-and-flow model of a representative Mediterranean goat production system was constructed. This was used to identify informative indicators that would represent the overall technical and economic performance of the system. Sub-models were then assembled to build the full systems model. The model was parameterised with data collected over 3 years for goat holdings in northern Morocco. Scenario analysis techniques are used to explore the strategies that optimise performance under climate and feed price challenges. Key results Meat production is particularly important during periods of drought when increased meat yields can counteract the expected reduction in milk yields, to protect human food security, prevent excessive rangeland degradation and preserve natural nutritional resources. Feed price shocks during drought can have significant negative impacts on the system and zero feed input is shown to be a more sustainable strategy than reliance on high price feed during drought. Any alternative feed sources need to have a high forage component to reduce grazing periods significantly and promote rangeland preservation. Implications A diverse management strategy with a mixed meat and dairy semi-intensive production is more stable than specialised dairy systems and allows goat production and financial viability of intensification to be maintained under climatic stress; maintaining meat production was necessary to optimise performance. Conclusions The model allows improved insight into management strategies which could optimise animal husbandry performance in goat subsistence systems. However, the work also demonstrates the difficulty of constructing a truly holistic model since, to be practical, such constructs must necessarily be bounded; parameter selection and the limits to the boundaries imposed are inevitably critical.

BackgroundIt is unclear whether and how elite athletes process physiological or psychological challenges differently than healthy comparison subjects. In general, individuals optimize exercise level as it relates to differences between expected and experienced exertion, which can be conceptualized as a body prediction error. The process of computing a body prediction error involves the insular cortex, which is important for interoception, i.e. the sense of the physiological condition of the body. Thus, optimal performance may be related to efficient minimization of the body prediction error. We examined the hypothesis that elite athletes, compared to control subjects, show attenuated insular cortex activation during an aversive interoceptive challenge.Methodology/Principal FindingsElite adventure racers (n = 10) and healthy volunteers (n = 11) performed a continuous performance task with varying degrees of a non-hypercapnic breathing load while undergoing functional magnetic resonance imaging. The results indicate that (1) non-hypercapnic inspiratory breathing load is an aversive experience associated with a profound activation of a distributed set of brain areas including bilateral insula, dorsolateral prefrontal cortex and anterior cingulated; (2) adventure racers relative to comparison subjects show greater accuracy on the continuous performance task during the aversive interoceptive condition; and (3) adventure racers show an attenuated right insula cortex response during and following the aversive interoceptive condition of non-hypercapnic inspiratory breathing load.Conclusions/SignificanceThese findings support the hypothesis that elite athletes during an aversive interoceptive condition show better performance and an attenuated insular cortex activation during the aversive experience. Interestingly, differential modulation of the right insular cortex has been found previously in elite military personnel and appears to be emerging as an important brain system for optimal performance in extreme environments.

In control of vibrations, diffusion and many other problems governed by partial differential equations, there is freedom in the choice of actuator location. The actuator location should be chosen to optimize performance objectives. In this paper, we consider ℋ ∞ performance with state-feedback. It is shown that the corresponding optimal actuator problem is well-posed. In practice, approximations are used to determine the optimal actuator location. The optimal performance and the corresponding actuator location of the approximating sequence should converge to the exact optimal performance and location. Conditions for this convergence in the case of ℋ ∞ -control are provided. The results are illustrated with an example.

It is widely documented that athletes should consume carbohydrates prior to, during and after exercise. Ingestion of carbohydrates at these times will optimise performance and recovery. In spite of this knowledge, there is a paucity of information available to athletes concerning the types of carbohydrate foods to select. Therefore, it is suggested that the glycaemic index may be an important resource when selecting an ideal carbohydrate. The glycaemic index categories foods containing carbohydrates according to the blood glucose response that they elicit. Carbohydrate foods evoking the greatest responses are considered to be high glycaemic index foods, while those producing a relatively smaller response are categorised as low glycaemic index foods. Athletes wishing to consume carbohydrates 30 to 60 minutes before exercise should be encouraged to ingest low glycaemic index foods. Consuming these types of foods will decrease the likelihood of creating hyperglycaemia and hyperinsulinaemia at the onset of exercise, while providing exogenous carbohydrate throughout exercise. It is recommended that high glycaemic index foods be consumed during exercise. These foods will ensure rapid digestion and absorption, which will lead to elevated blood glucose levels during exercise. Post-exercise meals should consist of high glycaemic index carbohydrates. Low glycaemic foods do not induce adequate muscle glycogen resynthesis compared with high glycaemic index foods.

Competitive swimmers commonly focus upon optimising performance at a single competition. A period where training volume is incrementally reduced or "tapered" often precedes such a competition. The use of taper is justified as increases in muscular power, and the restoration of plasma haematocrit, haemoglobin and creatine kinase are evident with this training reduction. A consistent performance improvement of approximately 3% has also been reported with taper in competitive swimmers. However, there are limitations in terms of what comprises a successful taper schedule. It appears that a taper which improves performance involves a substantial (60 to 90%) graded reduction in training volume, and daily high intensity interval work over a 7- to 21-day period. Training frequency should be reduced by no more than 50%; a more conservative estimate would be to reduce frequency by approximately 20%. Optimal performance is likely when the reduction in training frequency is combined with the qualitative knowledge of the coach and/or athlete during taper.

This paper presents a comprehensive timing optimization methodology for power-efficient high-resolution image sensors with column-parallel single-slope analog-to-digital converters (ADCs). The aim of the method is to optimize the read-out timing for each period in the image sensor's operation, while considering various factors such as ADC decision time, slew rate, and settling time. By adjusting the ramp reference offset and optimizing the amplifier bandwidth of the comparator, the proposed methodology minimizes the power consumption of the amplifier array, which is one of the most power-hungry circuits in the system, while maintaining a small color linearity error and ensuring optimal performance. To demonstrate the effectiveness of the proposed method, a power-efficient 108 MP 3-D stacked CMOS image sensor with a 10-bit column-parallel single-slope ADC array was implemented and verified. The image sensor achieved a random noise of 1.4 e-rms, a column fixed-pattern noise of 66 ppm at an analog gain of 16, and a remarkable figure-of-merit (FoM) of 0.71 e-·nJ. The sensor utilized a one-row read-out time of 6.9 µs, an amplifier bandwidth of 1.1 MHz, and a reference digital-to-analog converter (DAC) offset of 512 LSB. This timing optimization methodology enhances energy efficiency in high-resolution image sensors, enabling higher frame rates and improved system performance. It could be adapted for various imaging applications requiring optimized performance and reduced power consumption, making it a valuable tool for designers aiming to achieve optimal performance in power-sensitive applications.

Performance investigation of a novel composite channel considering tapered-3D wavy structure

Particle loading as a design parameter for composite radiation shielding