Recommendations to address key recruitment challenges of Alzheimer's disease clinical trials.

Clinical trials for Alzheimer's disease (AD) are slower to enroll study participants, take longer to complete, and are more expensive than trials in most other therapeutic areas. The recruitment and retention of a large number of qualified, diverse volunteers to participate in clinical research studies remain among the key barriers to the successful completion of AD clinical trials. An advisory panel of experts from academia, patient-advocacy organizations, philanthropy, non-profit, government, and industry convened in 2020 to assess the critical challenges facing recruitment in Alzheimer's clinical trials and develop a set of recommendations to overcome them. This paper briefly reviews existing challenges in AD clinical research and discusses the feasibility and implications of the panel's recommendations for actionable and inclusive solutions to accelerate the development of novel therapies for AD.

Potential downside of high initial visual acuity.

Children who are treated for congenital cataracts later exhibit impairments in configural face analysis. This has been explained in terms of a critical period for the acquisition of normal face processing. Here, we consider a more parsimonious account according to which deficits in configural analysis result from the abnormally high initial retinal acuity that children treated for cataracts experience, relative to typical newborns. According to this proposal, the initial period of low retinal acuity characteristic of normal visual development induces extended spatial processing in the cortex that is important for configural face judgments. As a computational test of this hypothesis, we examined the effects of training with high-resolution or blurred images, and staged combinations, on the receptive fields and performance of a convolutional neural network. The results show that commencing training with blurred images creates receptive fields that integrate information across larger image areas and leads to improved performance and better generalization across a range of resolutions. These findings offer an explanation for the observed face recognition impairments after late treatment of congenital blindness, suggest an adaptive function for the acuity trajectory in normal development, and provide a scheme for improving the performance of computational face recognition systems.

Towards perception awareness: Perceptual event detection for Brain computer interfaces.

Brain computer interface (BCI) technology is becoming increasingly popular in many domains such as entertainment, mental state analysis, and rehabilitation. For robust performance in these domains, detecting perceptual events would be a vital ability, enabling adaptation to and act on the basis of user's perception of the environment. Here we present a framework to automatically mine spatiotemporal characteristics of a given perceptual event. As this "signature" is derived directly from subject's neural behavior, it can serve as a representation of the subject's perception of the targeted scenario, which in turn allows a BCI system to gain a new level of context awareness: perception awareness. As a proof of concept, we show the application of the proposed framework on MEG signal recordings from a face perception study, and the resulting temporal and spatial characteristics of the derived neural signature, as well as it's compatibility with the neuroscientific literature on face perception.