Cholinergic Modulation Supports Dynamic Switching of Resting State Networks Through Selective DMN Suppression DOI
Pavel Šanda, Jaroslav Hlinka, Monica van den Berg

et al.

Published: Jan. 1, 2023

Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it known that neuromodulation of alertness impact resting-state activity, neural mechanisms behind such modulation are unknown. In this work, we a computational model demonstrate cholinergic input influences its functional connectivity through cellular synaptic modulation. The results from match closely with experimental work on direct Default Mode Network (DMN) rodents. We further extended our study human connectome derived diffusion-weighted MRI. simulations, an increase resulted brain-wide reduction connectivity. Furthermore, selective DMN captured experimentally observed transitions between baseline states suppressed fluctuations associated attention external tasks. Our thus provides insight into potential for effects dynamics.

Language: Английский

Brain networks and intelligence: A graph neural network based approach to resting state fMRI data DOI Creative Commons

Bishal Thapaliya,

Esra Akbaş, Jiayu Chen

et al.

Medical Image Analysis, Journal Year: 2024, Volume and Issue: 101, P. 103433 - 103433

Published: Dec. 16, 2024

Language: Английский

Citations

6
Neural dynamics of semantic control underlying generative storytelling DOI Creative Commons
Clara Rastelli, Antonino Greco, Chiara Finocchiaro

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 18, 2024

Storytelling has been pivotal for the transmission of knowledge and cultural norms across human history. A crucial process underlying generation narratives is exertion cognitive control on semantic representations stored in memory, a phenomenon referred as control. Despite extensive literature investigating neural mechanisms generative language tasks, little effort done towards storytelling under naturalistic conditions. Here, we probed participants to generate stories response set instructions which triggered narrative that was either appropriate (ordinary), novel (random), or balanced (creative), while recording functional magnetic resonance imaging (fMRI) signal. By leveraging deep models, demonstrated how ideally level during story generation. At level, creative were differentiated by multivariate pattern activity frontal cortices compared ordinary ones fronto- temporo-parietal with respect randomly generated stories. Crucially, similar brain regions also encoding features distinguished behaviourally. Moreover, decomposed dynamics into connectome harmonic modes found specific spatial frequency patterns modulation Finally, different coupling within between default mode, salience networks when contrasting their controls. Together, our findings highlight regulation exploration ideation contribute deeper understanding underpinning role storytelling.

Language: Английский

Citations

4
Extreme events at the onset of epileptic-like intermittent activity of FitzHugh–Nagumo oscillators on small-world networks DOI
Javier Cubillos Cornejo, Miguel Escobar Mendoza, Ignacio Bordeu

et al.

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 192, P. 116000 - 116000

Published: Jan. 16, 2025

Language: Английский

Citations

0
Identification of recurrent dynamics in distributed neural populations DOI Creative Commons
Rodrigo Osuna-Orozco, Edward Castillo, Kameron Decker Harris

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(2), P. e1012816 - e1012816

Published: Feb. 6, 2025

Large-scale recordings of neural activity over broad anatomical areas with high spatial and temporal resolution are increasingly common in modern experimental neuroscience. Recently, recurrent switching dynamical systems have been used to tackle the scale complexity these data. However, an important challenge remains providing insights into existence structure linear dynamics time series Here we test a scalable approach time-varying autoregression low-rank tensors recover stochastic mass models multiple stable attractors. We demonstrate that parsimonious representation system matrices terms modes can attractor simple via clustering. then consider simulations based on human brain connectivity matrix low global connection strength regimes, reveal hierarchical clustering dynamics. Finally, explain impact forecast delay estimation underlying rank variability This study illustrates prediction error minimization is not sufficient meaningful dynamic it crucial account for three key timescales arising from dynamics, noise processes, switching.

Language: Английский

Citations

0
GRAM: Graph Regularizable Assessment Metric DOI
Mariem Touihri, Ahmed Nebli

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 256 - 265

Published: Jan. 1, 2025

Language: Английский

Citations

0
Computing Approximate Global Symmetry of Complex Networks with Application to Brain Lateral Symmetry DOI Creative Commons
Anna Pidnebesna, David Hartman, Aneta Pokorná

et al.

Information Systems Frontiers, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Language: Английский

Citations

0
From MRI to 3D-SNN brain models DOI

Simona Nedelcheva,

Petia Koprinkova‐Hristova

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3274, P. 020005 - 020005

Published: Jan. 1, 2025

Language: Английский

Citations

0
Neural dynamics of semantic control underlying generative storytelling DOI Creative Commons
Clara Rastelli, Antonino Greco, Chiara Finocchiaro

et al.

Communications Biology, Journal Year: 2025, Volume and Issue: 8(1)

Published: March 28, 2025

Language: Английский

Citations

0
\({O({k})}\)-Equivariant Dimensionality Reduction on Stiefel Manifolds DOI Open Access
Andrew Lee, Harlin Lee, José A. Perea

et al.

SIAM Journal on Mathematics of Data Science, Journal Year: 2025, Volume and Issue: 7(2), P. 410 - 437

Published: April 9, 2025

Language: Английский

Citations

0
Amplitude entropy captures chimera resembling behavior in the altered brain dynamics during seizures DOI Creative Commons
Saptarshi Ghosh, Isa Dallmer‐Zerbe, Barbora Bučková

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 23, 2025

Abstract Epilepsy is a neurological disease characterized by epileptic seizures, which commonly manifest with pronounced frequency and amplitude changes in the EEG signal. In case of focal initially localized pathological activity spreads from so-called “onset zone” to wider network brain areas. Chimeras, defined as states simultaneously occurring coherent incoherent dynamics symmetrically coupled networks are increasingly invoked for characterization seizures. particular, chimera-like have been observed during transition normal (asynchronous) seizure (synchronous) state. However, chimeras epilepsy only investigated respect varying phases oscillators. We propose novel method capture characteristic recorded seizures estimating directly signals frequency- time-resolved manner. test on publicly available intracranial dataset 16 patients epilepsy. show that proposed measure, titled Amplitude Entropy, sensitive altered seizure, demonstrating its significant increases compared before after seizure. This finding robust across patients, their different bands. future, Entropy could serve not feature detection, but also help characterizing other networked systems dynamics.

Language: Английский

Citations

0