Biology subjects

Ponce-Alvarez, A.

Publications and source records attributed to Ponce-Alvarez, A..

3 recordsLinked to original sources

bioRxiv2018

Distinct Functional Connectivity Mode during Viewing Natural Scenes Revealed by Principal Component Analysis

A fundamental question in systems neuroscience is how spontaneous activity at rest is reorganized during task performance. Recent studies suggest a strong relationship between resting and task FC. Furthermore, the relationship between resting and task FC has been shown to reflect individual differences. Particularly, various studies have demonstrated that the FC has higher reliability and provides enhanced detection of individual differences while viewing natural scenes. Although the large-scale organization of FC during rest and movie-viewing conditions have been well studied in relation to individual variations, the re-organization of FC during viewing natural scenes have not been studied in depth. In this study, we used principal component analysis on FC during rest and movie-viewing condition to characterize the dimensionality of FC patterns across conditions and subjects. We found that the variations in FC patterns related to viewing natural scenes can be explained by a single component, which enables identification of the task over subjects with 100% accuracy. We showed that the FC mode associated to viewing natural scenes better reflects individual variations. Furthermore, we investigated the signatures of movie-viewing-specific functional modes in dynamic FC based on phase-locking values between brain regions. We found that the movie-specific functional mode is persistent across time; suggesting the emergence of a stable processing mode. To explain the reorganization of whole-brain FC through the changes in local dynamics, we appeal to a large-scale computational model. This modelling suggested that the reorganization of whole-brain FC is associated to the interaction between frontal-parietal and frontal-temporal activation patterns.

neuroscience↗

bioRxiv2017

Stereotypical Modulations In Dynamic Functional Connectivity Explained By Changes In BOLD Variance

Spontaneous activity measured in human subject under the absence of any task exhibits complex patterns of correlation that largely correspond to large-scale functional topographies obtained with a wide variety of cognitive and perceptual tasks. These \"resting state networks\" (RSNs) fluctuate over time, forming and dissolving on the scale of seconds to minutes. While these fluctuations, most prominently those of the default mode network, have been linked to cognitive function, it remains unclear whether they result from random noise or whether they index a non-stationary process which could be described as state switching.\n\nIn this study, we use a sliding windows-approach to relate temporal dynamics of RSNs to global modulations in correlation and BOLD variance. We compare empirical data, phase-randomized surrogate data, and data simulated with a stationary model. We find that RSN time courses exhibit a large amount of coactivation in all three cases, and that the modulations in their activity are closely linked to global dynamics of the underlying BOLD signal.\n\nWe find that many properties of the observed fluctuations in FC and BOLD, including their ranges and their correlations amongst each other, are explained by fluctuations around the average FC structure. However, we also encounter interesting characteristics that are not explained in this way. In particular, we find that the brain spends more time in the troughs of modulations than can be expected from stationary dynamics.

neuroscience↗

bioRxiv2016

Robust extraction of spatio-temporal patterns from resting state fMRI

It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions (\"communities\") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.

neuroscience↗