The Long and Winding Road to Real-Life Experiments: Remote Assessment of Executive Functions with Computerized Games-Results from 8 Years of Naturalistic Interventions.

Mate Marote is an open-access cognitive training software aimed at children between 4 and 8 years old. It consists of a set of computerized games specifically tailored to train and evaluate Executive Functions (EF), a class of processes critical for purposeful, goal-directed behavior, including working memory, planning, flexibility, and inhibitory control. Since 2008, several studies were performed with this software at children's own schools in interventions supervised in-person by cognitive scientists. After 2015, we incorporated naturalistic, yet controlled, interventions with children's own teachers' help. The platform includes a battery of standardized tests, disguised as games, to assess children's EF. The main question that emerges is whether the results, obtained with these traditional tasks but conducted without the presence of researchers, are comparable to those widely reported in the literature, that were obtained in more supervised settings. In this study, we were able to replicate the expected difficulty and age effects in at least one of the analyzed dependent variables of each employed test. We also report important discrepancies between the expected and the observed response time patterns, specifically for time-constrained tasks. We hereby discuss the benefits and setbacks of a new possible strategy for this type of assessment in naturalistic settings. We conclude that this battery of established EF tasks adapted for its remote usage is appropriate to measure the expected mental processes in naturalistic settings, enriching opportunities to upscale cognitive training interventions at schools. These types of tools can constitute a concerted strategy to bring together educational neuroscience research and real-life practice.

Processing of the same narrative stimuli elicits common functional connectivity dynamics between individuals.

It has been suggested that conscious experience is linked to the richness of brain state repertories, which change in response to environmental and internal stimuli. High-level sensory stimulation has been shown to alter local brain activity and induce neural synchrony across participants. However, the dynamic interplay of cognitive processes underlying moment-to-moment information processing remains poorly understood. Using naturalistic movies as an ecological laboratory model of the real world, here we investigate how the processing of complex naturalistic stimuli alters the dynamics of brain network interactions and how these in turn support information processing. Participants underwent fMRI recordings during movie watching, scrambled movie watching, and resting. By measuring the phase-synchrony between different brain networks, we analyzed whole-brain connectivity patterns. Our finding revealed distinct connectivity patterns associated with each experimental condition. We found higher synchronization of brain patterns across participants during movie watching compared to rest and scrambled movie conditions. Furthermore, synchronization levels increased during the most engaging parts of the movie. The synchronization dynamics among participants were associated with suspense; scenes with higher levels of suspense induced greater synchronization. These results suggest that processing the same high-level information elicits common neural dynamics across individuals, and that whole-brain functional connectivity tracks variations in processed information and subjective experience.

Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration.

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here:  https://demo.sci.tellapp.org/ .

A Machine Learning Approach to Personalize Computerized Cognitive Training Interventions.

Executive functions are a class of cognitive processes critical for purposeful goal-directed behavior. Cognitive training is the adequate stimulation of executive functions and has been extensively studied and applied for more than 20 years. However, there is still a lack of solid consensus in the scientific community about its potential to elicit consistent improvements in untrained domains. Individual differences are considered one of the most important factors of inconsistent reports on cognitive training benefits, as differences in cognitive functioning are both genetic and context-dependent, and might be affected by age and socioeconomic status. We here present a proof of concept based on the hypothesis that baseline individual differences among subjects would provide valuable information to predict the individual effectiveness of a cognitive training intervention. With a dataset from an investigation in which 73 6-year-olds trained their executive functions using an online software with a fixed protocol, freely available at www.matemarote.org.ar, we trained a support vector classifier that successfully predicted (average accuracy = 0.67, AUC = 0.707) whether a child would improve, or not, after the cognitive stimulation, using baseline individual differences as features. We also performed a permutation feature importance analysis that suggested that all features contribute equally to the model's performance. In the long term, this results might allow us to design better training strategies for those players who are less likely to benefit from the current training protocols in order to maximize the stimulation for each child.