Biology subjects

Pereda, E.

Publications and source records attributed to Pereda, E..

5 recordsLinked to original sources

bioRxiv2025

Scale-dependent brain age with higher-order statisticsfrom structural magnetic resonance imaging

Inferring chronological age from magnetic resonance imaging (MRI) brain data has become a valuable tool for the early detection of neurodegenerative diseases. We present a method inspired by cosmological techniques for analyzing galaxy surveys, utilizing higher-order summary statistics with multivariate two- and three-point analyses in 3D Fourier space. This method offers physiological interpretability during the inference, allowing the detection of scales where brain anatomy differs across age groups, providing insights into brain aging processes. Similarly to the evolution of cosmic structures, the brain structure also evolves naturally but displays contrasting behaviors at different scales. On larger scales, structure loss occurs with age, possibly due to ventricular expansion, while smaller scales show increased structure, likely related to decreased cortical thickness and gray/white matter volume. Using MRI data from the OASIS-3 database of 869 sessions, our method predicts chronological age with a Mean Absolute Error (MAE) of 3.1 years, while providing information as a function of scale. A posterior density estimation shows that the 1-{sigma} uncertainty for each individual varies between [~] 2 and 8 years, suggesting that, beyond sample variance, complex genetic or lifestyle-related factors may influence brain aging. We perform a twofold validation of the method. First, we apply the method to the Cam-CAN dataset, yielding a MAE of [~] 5.9 years for the age range from 18 to 88 years. Second, we apply the method to thousands of simulated MRI images generated with a state-of-the-art Latent Diffusion model. This work demonstrates the utility of interdisciplinary research, bridging cosmological methods and neuroscience. PACS87.19.lf, 87.61.Jc, 02.50.Tt, 87.19.lo

neuroscience↗

bioRxiv2023

The more, the merrier: multivariate phase synchronization methods excel pairwise ones in estimating brain connectivity from reconstructed neural sources

The estimation of functional connectivity (FC) from electro-(EEG) or magnetoencephalographic (MEG) recordings suffers from low spatial resolution, being one of the reasons for the reduced number of sensors compared to the number of reconstructed sources of activity. This problem can be avoided by estimating FC between larger regions instead of individual sources. However, combining all the sources in each area to produce a single time series per region is far from trivial. We have used simultaneous EEG/MEG data from 11 participants and compared the FC estimates from both techniques by using different multivariate approaches. Since the underlying generators are identical for EEG and MEG, the more similar the FC estimation from both techniques is, the more accurate it is likely to be. The results show that using either the average or the root-mean-square of the bivariate source-to-source FC estimates consistently outperforms the use of a representative time series from each area. We concluded that the reconstructed activity in each brain region is too complex to be reduced to a single representative time series and that full multivariate approaches must be used to describe between-area FC from electrophysiological recordings accurately. Moreover, the high correlation between the FC values estimated from EEG and MEG suggests that the results found in the high-sensitivity, low-noise MEG can be transferable to the more affordable EEG, at least when high-quality source reconstruction is used.

bioengineering↗

bioRxiv2019

Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment

It is evident the need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI). MCI patients have a high risk of developing Alzheimers disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings about the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys)function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings.\n\nThe activity of different brain rhythms {{delta}, {theta}, 1, 2, {beta}1, {beta}2, {gamma}1, {gamma}2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine ninety anatomical regions of interest (ROIs). A dynamic functional connectivity graph (DFCG) was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and also cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 Mild Cognitive Impairment (MCI) patients and 20 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments that further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. Estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (nstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation.\n\nWe succeeded a high classification accuracy on the blind dataset (85 %) which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could manipulate properly the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.

neuroscience↗

bioRxiv2018

Gamma oscillations coordinate different theta rhythms in the hippocampus

Hippocampal firing is organized in theta sequences controlled by internal memory-related processing and by external sensory cues. How these computations are segregated or integrated, depending on the cognitive needs, is not fully understood. Although theta activity in the hippocampus is most commonly studied as a unique coherent oscillation, it is the result of a complex interaction between different rhythm generators. Here we investigated the coordination between theta generators as a possible mechanism to couple or decouple internally and externally driven computations. We separated and quantified three different theta current generators from the hippocampus of freely behaving rats, one originating in CA3 with current sinks in CA1 str. radiatum and two with current sinks in CA1 str. lacunosum-moleculare and dentate molecular layer, mainly driven by entorhinal cortex (EC) layers 3 and 2, respectively. These theta generators followed non fully coherent dynamics and presented epochs of higher and lower phase coupling, suggesting a flexible interaction between them. Selective optogenetic inhibition in CA3 depressed the str. radiatum generator without affecting the EC-driven theta oscillations, indicating that theta rhythm generators can be modulated independently. In addition, band-specific gamma interactions with theta oscillations selectively occurred with the corresponding pathway-specific theta current generator, supporting the existence of different theta-gamma coding frameworks to organize neuronal firing in the hippocampus. Importantly, we found that epochs of highly synchronized theta rhythmicity across generators preferentially occurred during memory-guided exploration and mismatch novelty detection in familiar environments, two conditions in which internally generated memory representations need to be coordinated with the incoming sensory information about external cues. We propose a mechanism for segregating and integrating hippocampal computations based on the coexistence of different theta-gamma frameworks that flexibly couple or decouple accommodating the cognitive needs.

neuroscience↗

bioRxiv2018

Heart evoked brain oscillatory networks and its interruption in early stages of Alzheimer’s disease

Understanding how the heart influences brain dynamics will suppose a deep change for the neuroscience, psychology and medicine. A mainstay questions is the heart modulation of resting state brain networks and its relation with both the cardiac dynamics and the cognitive status. We evaluated the heart evoked basal networks for controls and two groups of mild cognitive impairment patients, stable and progressive to Alzheimers disease without cardiovascular alteration symptoms. Our results in controls show that a healthy cognitive performance correlates with the heart modulation of brain dynamics in areas of the default mode network, and that the heart influence on brain networks varies along the cardiac cycle and the spectral band. However, the cognitive deficit produced by dementia correlates with the lack of heart modulation on brain activity. The heart influence on brain networks is disrupted in patients by producing hypersynchronization, accompanied by decreased cardiac complexity. We designed a surrogate and predictive procedure based on machine learning to compare the heart evoked results with the neural activity no locked to heartbeats. Based on our longitudinal data, we conclude that the prediction to progression to Alzheimers disease is higher when considering the heart - brain interaction than when taking into account only the brain dynamics. We can conclude that brain networks in control subjects were more responsive to the heart cycle, allowing a wealthier, more complex pattern of oscillations. Our results highlight the role of heart in cognitive neuroscience by showing that basal brain networks are modulated by the cardiac dynamics.

neuroscience↗