Biology subjects

Ambrosini, E.

Publications and source records attributed to Ambrosini, E..

3 recordsLinked to original sources

bioRxiv2025

Altered mechanical properties of astrocytes lacking MLC1; implications for the leukodystrophy MLC

Loss of function of the astrocyte protein MLC1 causes Megalencephalic Leukoencephalopathy with subcortical Cysts (MLC), a leukodystrophy characterized by white matter edema and slow neurological deterioration. MLC1 dysfunction leads to swelling of perivascular astrocyte endfeet and an impaired attachment of endfeet to blood vessels. In isolated primary astrocytes, loss of MLC1 hinders recovery of astrocytes from cell swelling, but the cellular function of MLC1 is not completely understood. MLC1 modulates gating of mechanosensitive ion channels involved in volume regulation. The cytoskeleton plays a crucial role in cell volume regulation, and interactions between the cytoskeleton and cell membrane affect the properties of mechanosensitive ion channels. Therefore, we investigated whether primary Mlc1-null mouse astrocytes show a disruption in their mechanical properties. We measured mechanical properties of cultured primary astrocytes with an indentation technique and demonstrated that Mlc1-null astrocytes are softer than wild-type astrocytes. Proteomic analysis confirmed dysregulation of several cytoskeleton-related pathways in Mlc1-null astrocytes. Confocal imaging revealed that organization of the actin cytoskeleton is unaffected. Instead, we observed alterations in focal adhesions, which aid in relaying mechanical forces between the cytoskeleton, cell membrane, and the extracellular matrix (ECM). Together, our findings reveal that the mechanical properties of Mlc1-null astrocytes are altered, and that disrupted cytoskeleton-membrane-ECM interactions potentially play a role in the disease. Modulators of astrocyte mechanobiology might therefore hold promise for MLC therapy development.

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

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↗