Publisher: NPJ Science of Learning  Link>

ABSTRACT

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.


[:es]Publisher: eNeuro Link>

ABSTRACT

Variations in human behavior correspond to the adaptation of the nervous system to different internal and environmental demands. Attention, a cognitive process for weighing environmental demands, changes over time. Pupillary activity, which is affected by fluctuating levels of cognitive processing, appears to identify neural dynamics that relate to different states of attention. In mice, for example, pupil dynamics directly correlate with brain state fluctuations. Although, in humans, alpha-band activity is associated with inhibitory processes in cortical networks during visual processing, and its amplitude is modulated by attention, conclusive evidence linking this narrowband activity to pupil changes in time remains sparse. We hypothesize that, as alpha activity and pupil diameter indicate attentional variations over time, these two measures should be comodulated. In this work, we recorded the electroencephalographic (EEG) and pupillary activity of 16 human subjects who had their eyes fixed on a gray screen for 1 min. Our study revealed that the alpha-band amplitude and the high-frequency component of the pupil diameter covariate spontaneously. Specifically, the maximum alpha-band amplitude was observed to occur ∼300 ms before the peak of the pupil diameter. In contrast, the minimum alpha-band amplitude was noted to occur ∼350 ms before the trough of the pupil diameter. The consistent temporal coincidence of these two measurements strongly suggests that the subject’s state of attention, as indicated by the EEG alpha amplitude, is changing moment to moment and can be monitored by measuring EEG together with the diameter pupil.

[:]

Publisher: Frontiers in Neural Circuits Link>

ABSTRACT

While external stimulation can reliably trigger neuronal activity, cerebral processes can operate independently from the environment. In this study, we conceptualize autogenous cerebral processes (ACPs) as intrinsic operations of the brain that exist on multiple scales and can influence or shape stimulus responses, behavior, homeostasis, and the physiological state of an organism. We further propose that the field should consider exploring to what extent perception, arousal, behavior, or movement, as well as other cognitive functions previously investigated mainly regarding their stimulus–response dynamics, are ACP-driven.

Publisher: Cognition, Link>

ABSTRACT

The importance of proportional reasoning has long been recognized by psychologists and educators, yet we still do not have a good understanding of how humans mentally represent proportions. In this paper we present a psychophysical model of proportion estimation, extending previous approaches. We assumed that proportion representations are formed by representing each magnitude of a proportion stimuli (the part and its complement) as Gaussian activations in the mind, which are then mentally combined in the form of a proportion. We next derived the internal representation of proportions, including bias and internal noise parameters -capturing respectively how our estimations depart from true values and how variable estimations are. Methodologically, we introduced a mixture of components to account for contaminating behaviors (guessing and reversal of responses) and framed the model in a hierarchical way. We found empirical support for the model by testing a group of 4th grade children in a spatial proportion estimation task. In particular, the internal density reproduced the asymmetries (skewedness) seen in this and in previous reports of estimation tasks, and the model accurately described wide variations between subjects in behavior. Bias estimates were in general smaller than by using previous approaches, due to the model's capacity to absorb contaminating behaviors. This property of the model can be of especial relevance for studies aimed at linking psychophysical measures with broader cognitive abilities. We also recovered higher levels of noise than those reported in discrimination of spatial magnitudes and discuss possible explanations for it. We conclude by illustrating a concrete application of our model to study the effects of scaling in proportional reasoning, highlighting the value of quantitative models in this field of research.


Publisher:  Alzheimer's Association Link>

ABSTRACT

INTRODUCTION

Age-related hearing loss is an important risk factor for cognitive decline. However, audiogram thresholds are not good estimators of dementia risk in subjects with normal hearing or mild hearing loss. Here we propose to use distortion product otoacoustic emissions (DPOAEs) as an objective and sensitive tool to estimate the risk of cognitive decline in older adults with normal hearing or mild hearing loss.

METHODS

We assessed neuropsychological, brain magnetic resonance imaging, and auditory analyses on 94 subjects > 64 years of age.

RESULTS

We found that cochlear dysfunction, measured by DPOAEs—and not by conventional audiometry—was associated with Clinical Dementia Rating Sum of Boxes (CDR-SoB) classification and brain atrophy in the group with mild hearing loss (25 to 40 dB) and normal hearing (<25 dB).

DISCUSSION

Our findings suggest that DPOAEs may be a non-invasive tool for detecting neurodegeneration and cognitive decline in the older adults, potentially allowing for early intervention.

Publisher:  IEEE Explore  Link>

ABSTRACT

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.

[:es]Publisher: Frontiers in Neuroscience, Link>

ABSTRACT

Hippocampal-dependent memories emerge late during postnatal development, aligning with hippocampal maturation. During sleep, the two-stage memory formation model states that through hippocampal-neocortical interactions, cortical slow-oscillations (SO), thalamocortical Spindles, and hippocampal sharp-wave ripples (SWR) are synchronized, allowing for the consolidation of hippocampal-dependent memories. However, evidence supporting this hypothesis during development is still lacking. Therefore, we performed successive object-in-place tests during a window of memory emergence and recorded in vivo the occurrence of SO, Spindles, and SWR during sleep, immediately after the memory encoding stage of the task. We found that hippocampal-dependent memory emerges at the end of the 4th postnatal week independently of task overtraining. Furthermore, we observed that those animals with better performance in the memory task had increased Spindle density and duration and lower density of SWR. Moreover, we observed changes in the SO-Spindle and Spindle-SWR temporal-coupling during this developmental period. Our results provide new evidence for the onset of hippocampal-dependent memory and its relationship to the oscillatory phenomenon occurring during sleep that helps us understand how memory consolidation models fit into the early stages of postnatal development.

[:]

Publisher:  IEEE Explora Link>

ABSTRACT

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.

Publisher: Cognitive Systems Research, Elsevier  Link>

ABSTRACT

This article presents a transdisciplinary analysis of the challenges in fusing neuroscience concepts with artificial intelligence (AI) to create AI systems inspired by biological cognition. We explore the structural and functional disparities between the neocortex’s canonical microcircuits and existing AI models, focusing on architectural differences, learning mechanisms, and energy efficiency. The discussion extends to adapting non-goal-oriented learning and dynamic neuronal connections from biological brains to enhance AI’s flexibility and efficiency. This work underscores the potential of neuroscientific insights to revolutionize AI development, advocating for a paradigm shift towards more adaptable and brain-like AI systems. We conclude that there is major room for bioinspiration by focusing on developing architecture, objective functions, and learning rules using a local instead of a global approach.

[:es]Publisher: The European journal of neuroscience Link>

ABSTRACT

It is widely accepted that the brain, like any other physical system, is subjected to physical constraints that restrict its operation. The brain's metabolic demands are particularly critical for proper neuronal function, but the impact of these constraints continues to remain poorly understood. Detailed single-neuron models are recently integrating metabolic constraints, but these models’ computational resources make it challenging to explore the dynamics of extended neural networks, which are governed by such constraints. Thus, there is a need for a simplified neuron model that incorporates metabolic activity and allows us to explore the dynamics of neural networks. This work introduces an energy-dependent leaky integrate-and-fire (EDLIF) neuronal model extension to account for the effects of metabolic constraints on the single-neuron behavior. This simple, energy-dependent model could describe the relationship between the average firing rate and the Adenosine triphosphate (ATP) cost as well as replicate a neuron's behavior under a clinical setting such as amyotrophic lateral sclerosis (ALS). Additionally, EDLIF model showed better performance in predicting real spike trains – in the sense of spike coincidence measure – than the classical leaky integrate-and-fire (LIF) model. The simplicity of the energy-dependent model presented here makes it computationally efficient and, thus, suitable for studying the dynamics of large neural networks.

[:]

agencia nacional de investigación y desarrollo
Edificio de Innovación UC, Piso 2
Vicuña Mackenna 4860
Macul, Chile