Biology subjects

Vallesi, A.

Publications and source records attributed to Vallesi, A..

4 recordsLinked to original sources

bioRxiv2023

EEG microstate transition cost correlates with task demands

The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the frameworks effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain. Author SummaryIn our daily lives, our brains manage various tasks with different mental demands. Yet, quantifying how much mental effort each task demands is not always straightforward. To tackle this challenge, we developed a way to measure how much cognitive effort our brains use during tasks directly from electroencephalography (EEG) data, which is one of the most used tools to non-invasively measure brain activity. Our approach involved the identification of distinct patterns of synchronized neural activity across the brain, named EEG microstates. By employing optimal transport theory, we established a framework to quantify the cost associated with cognitive transitions based on modifications in EEG microstates. This allowed us to link changes in brain activity patterns to the cognitive effort required for task performance. To validate our framework, we applied it to EEG data collected during a commonly employed cognitive task known as the Stroop task. This task is recognized for challenging us with varying levels of cognitive demand. Our analysis revealed that as the task became more demanding, there were discernible shifts in the EEG microstates. Importantly, these shifts in neural activity patterns corresponded to higher costs associated with cognitive transitions. Our approach offers a promising methodology to assess cognitive effort using neural data, contributing to our comprehension of how the brain manages and adapts to varying cognitive challenges.

neuroscience↗

bioRxiv2023

Understanding the link between functional profiles and intelligence through dimensionality reduction and graph analysis

There is a growing interest in neuroscience for how individual-specific structural and functional features of the cortex relate to cognitive traits. This work builds on previous research which, using classical high-dimensional approaches, has proven that the interindividual variability of functional connectivity profiles reflects differences in fluid intelligence. To provide an additional perspective into this relationship, the present study uses a recent framework for investigating cortical organization: functional gradients. This approach places local connectivity profiles within a common low-dimensional space whose axes are functionally interretable dimensions. Specifically, this study uses a data-driven approach focussing on areas where FC variability is highest across individuals to model different facets of intelligence. For one of these loci, in the right ventral-lateral prefrontal cortex (vlPFC), we describe an association between fluid intelligence and relative functional distance from sensory and high-cognition systems. Furthermore, the topological properties of this region indicate that with decreasing functional affinity with the latter, its functional connections are more evenly distributed across all networks. Participating in multiple functional networks may reflect a better ability to coordinate sensory and high-order cognitive systems. Significant StatementThe human brain is highly variable. In particular, the way brain regions communicate to one another - that is, how they are functionally connected - constitutes a neural fingerprint of the individual. In this study, we make use of a recent methodological approach to characterize the connectivity patterns of transmodal (closely linked to abstract processing) and unimodal (closely linked to sensory processing) brain regions in an attempt to explain how this balance affects intelligence. We show that the more the functional profile of executive control regions is distant to that of abstract processing, the better they are at integrating information coming from widespread neural systems, ultimately leading to better cognitive performance.

neuroscience↗

bioRxiv2023

lmeEEG: Mass linear mixed-effects modeling of EEG data with crossed random effects

BackgroundMixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models. New methodHere, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG. ResultslmeEEG showed excellent performance properties in terms of power and false positive rate. Comparison with existing methodslmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster). ConclusionslmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.

neuroscience↗

bioRxiv2022

Non-triplet genetic code in ciliate Euplotes ciliates is a result of neutral evolution

Although several variants of the standard genetic code are known, its triplet character is universal with an exception in ciliates Euplotes, where stop codons at internal mRNA positions specify ribosomal frameshifting. How did Euplotes spp. evolved and maintained such an unusual genetic code remains a mystery. To investigate these questions, we explored the evolution of frameshifting occurrence in Euplotes genes. We sequenced and analyzed several transcriptomes from different Euplotes spp to characterize the gain-and-loss dynamics of frameshift sites. Surprisingly, we found a sharp asymmetry between frameshift gain and frameshift loss events with the former exceeding the latter by about 10 folds. Further analysis of mutation rates in protein-coding and non-coding regions revealed that this asymmetry is expected based on single nucleotide mutation rates and does not require positive selection for frameshifting. We found that the number of frameshift sites in Euplotes spp is increasing and is far from the steady state. The steady equilibrium state is expected in about 0.1 to 1 billion years leading to about a 10 fold increase in the number of frameshift sites in Euplotes genes.

evolutionary biology↗