Young children’s linguistic and communicative abilities are foundational for their academic achievement and overall well-being. We present the positive outcomes of a brief tablet-based intervention aimed at teaching toddlers and preschoolers new word-object and letter-sound associations. We conducted two experiments, one involving toddlers ( ~ 24 months old, n = 101) and the other with preschoolers ( ~ 42 months old, n = 152). Using a pre-post equivalent group design, we measured the children’s improvements in language and communication skills resulting from the intervention. Our results showed that the intervention benefited toddlers’ verbal communication and preschoolers’ speech comprehension. Additionally, it encouraged vocalizations in preschoolers and enhanced long-term memory for the associations taught in the study for all participants. In summary, our study demonstrates that the use of a ludic tablet-based intervention for teaching new vocabulary and pre-reading skills can improve young children’s linguistic and communicative abilities, which are essential for future development.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social communication and behavior. Early diagnosis is crucial to enhance the patient’s quality of life through treatments and therapies. In this research, two white matter (WM) fiber bundle segmentation methods are analyzed and compared in terms of their performance and impact on the results obtained from the analyzes applied to a database comprising 37 adolescents, 19 subjects with autism and 18 controls. To achieve this, we conducted the segmentation of deep white matter tracts, and computed average diffusion-based indices for each tract, such as Apparent Diffusion Coefficient (ADC), Fractional Anisotropy (FA), and Generalized Fractional Anisotropy (GFA). We applied statistical tests to identify features with significant differences between groups based on the results of two segmentation methods. Significant differences in diffusion-based indices were found in certain cingulate, thalamic, corticospinal, and corpus callosum fascicles. Furthermore, we performed classification between patients and controls using each fascicle feature independently with the Support Vector Machine (SVM) and Decision Trees (DT) algorithms. Finally, we applied the classifiers to the most relevant features for each segmentation method. Overall, even with the limitations of our small database, we demonstrated that the segmentation algorithm has a high impact on WM tract-based analyzes and prediction, with the autocencoder-based algorithm showing better results than a distance-based method.

There is ongoing interest in the dynamics of resting state brain networks (RSNs) as potential predictors of cognitive and behavioural states. Multivariate Autoregressors (MAR) are used to model regional brain activity as a linear combination of past activity in other regions. The coefficients of the MAR are taken as estimates of effective brain connectivity. However, assumption of stationarity, and the large number of coefficients renders the MAR impractical for estimating brain networks from standard neuroimaging time-series of limited durations. We propose HsMM-MAR-AC, a novel sparse hybrid discrete-continuous model for the efficient estimation of time-dependent effective brain networks from non-stationary brain activity time-series. Discrete quasi-stationary Brain States, and the fast switching between them, are modelled by a Hidden semi-Markov Model whose continuous emissions are drawn from a sparse MAR. The coefficients of the MAR are restricted by Anatomical Brain Connectivity information in two ways: 1) Effective direct connectivity between two brain regions is only considered if the corresponding anatomical connection exists; and 2) the autoregressors lag associated with each connection is based on the fiber length between the two regions, such that only one lag per connection is estimated. We test the accuracy of HsMM-MAR-AC in recovering simulated resting state networks of various durations, and at different thresholds of anatomical restrictions. We demonstrate that HsMM-MAR-AC recovers the RSNs more accurately than the benchmark method of the sliding window, with as little as 4 minutes of data. We also show that when the anatomical restrictions are relaxed, longer time-series are needed to estimate the networks, and became computationally unfeasible without anatomical restrictions. HsMM-MAR-AC offers an efficient model for estimating time-dependent Effective Connectivity from neuroimaging data that exploits the advantages of Hidden Markov and MAR models without identifiability problems, excessive demand on data collection, or unnecessary computational effort.

Brain neuronal networks of structural and func-tional connections have a hierarchical organization and a complex relationship between them. To study brain dynamics, it is important to identify the cortical level of parcellation of greater metastability. This paper presents a new multiscale cortical parcellation method based on the geodesic distance between vertices of the cortical surface and agglomerative hierarchical clustering, starting from an anatomical parcellation. First, the centroids of each region are efficiently calculated using the geodesic distance between the region’s vertices. Then, an affinity graph is constructed between the region centroids, based on the geodesic distance, from which a dendrogram is constructed using hierarchical clustering. Finally, an adaptive tree partitioning method is employed to obtain parcellations at various granularity levels, producing a multiscale parcellation. Furthermore, we propose an optimized method for the calculation of structural connectomes for each parcellation level. This framework will be made available and can be applied to different fine-grained parcellations. Additional information, such as structural connectivity information can be easily added to the framework. In future work this multiscale cortical parcellation will allow for simulations of cerebral dynamics at different levels.