Biology subjects

Vallesi, A.

Publications and source records attributed to Vallesi, A..

6 recordsLinked to original sources

bioRxiv2026

Towards a scalable effective connectivity estimation in fMRI

Characterizing directed connections between brain regions is fundamental for understanding the brains network organization and how it supports cognitive processes. However, a reliable methodology allowing assessment of effective connectivity (EC) at the whole-brain level and across high-resolution parcellations has remained elusive. This limitation reflects a trade-off between model detail and scalability. Mechanistic approaches such as Dynamic Causal Modeling (DCM) provide a principled description of directed interactions but become impractical for large parcellations, whereas scalable methods based on linear-system inversion often fail to reliably recover key features of the underlying effective connectivity. Here, using data from the Human Connectome Project S1200 release, we show that the multivariate Ornstein-Uhlenbeck (mOU) approach for EC estimation can be adapted to yield EC estimates that are in broad agreement with those of an advanced DCM, at least when the spatial variability of the hemodynamic response falls within the physiological range characteristic of the healthy adult brain. Furthermore, thanks to its low computational cost, mOU can be easily scaled up to large neuroimaging cohorts and high-resolution parcellations. High-resolution, mOU-based EC outperforms functional connectivity for single-subject connectome fingerprinting. In addition, it enables a high-resolution characterization of the brains hierarchical organization and modular architecture by identifying directed interactions within and between communities and revealing driver and receiver regions. Overall, our methodology provides a reliable and scalable EC estimation pipeline of wide applicability to cognitive and network neuroscience.

neuroscience↗

bioRxiv2026

Cracking the predictive stability code: dual trial-by-trial spatio-temporo-spectral dynamics of task representation directional tuning by Bayesian conflict expectations

Cognitive stability, or task focus, enables goal-directed behavior through representational dynamics: by prioritizing task-relevant representations over competing task-irrelevant ones. Beyond corrective reactive mechanisms operating after conflict arises, stability can be deployed predictively through the statistical learning of conflict regularities, either proactively, guided by global conflict expectations, or through a predictive reactive mode guided by item-specific ones. Yet, the representational mechanisms enabling such predictive adjustments remain underspecified. We addressed this by re-analyzing EEG data from a spatial Stroop task concurrently manipulating list-wide (LWPC) and item-specific (ISPC) proportion congruency. Moving beyond existing analytical approaches, we developed a framework combining trial-by-trial Bayesian conflict-expectancy modeling with a novel spatio-temporo-spectral searchlight Representational Similarity Analysis, and leveraged directional representational modeling complemented by ridge-regression decoding. We first identified the necessary computational substrate for stability predictive reconfigurations: the spatio-temporo-spectral patterns encoding task (Target, Distractor) and conflict-expectancy (LWPC, ISPC) representations, the latter representations encoded already in the pre-stimulus windows. Crucially, conflict expectations modulated Target and Distractor encoding strength with distinct spatio-temporal and spectral dynamics, revealing a functional dissociation. LWPC-induced proactive stability follows a "Target-first" policy, primarily strengthening the Target early post-stimulus via Alpha and lower-Beta patterns, weakening Distractor encoding only later on; conversely, ISPC-induced predictive reactive stability follows a "Distractor-first" policy, rapidly weakening Distractor encoding via response-locked posterior Beta patterns, strengthening the Target only downstream. Our study refines theoretical cognitive control models, incorporating a mechanistic explanation for predictive stability, while also establishing a multidimensional methodological template for interrogating the underlying substrates of adaptive behavior and beyond.

neuroscience↗

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↗