DySCo: a general framework for dynamic Functional Connectivity DOI Creative Commons
Giuseppe de Alteriis,

Oliver Sherwood,

Alessandro Ciaramella

et al.

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

Published: June 13, 2024

A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across areas change with time. However, the main approaches have been developed and applied mostly empirically, lacking unifying theoretical framework, general interpretation, common set measures quantify matrices properties. Moreover, field has ad-hoc algorithms compute process efficiently. This prevented show its full potential datasets and/or real time applications. With paper, we introduce Symmetric Matrix framework (DySCo), associated repository. DySCo approach allows study signals at different spatio-temporal scales, down voxel level, computationally ultrafast. unifies single most employed matrices, which share mathematical structure. Doing so it allows: 1) new interpretation further justifies use capture spatiotemporal patterns data form easily translatable imaging modalities. 2) The introduction Recurrence EVD store eigenvectors eigenvalues all types efficent manner orders magnitude faster than naive algorithms, without loss information. 3) To simply define quantities interest for dynamic analyses such as: amount connectivity (norm similarity between their informational complexity. methodology here validated on both synthetic dataset rest/N-back task experimental - fMRI Human Connectome Project dataset. We demonstrate proposed are highly sensitive changes configurations. illustrate computational efficiency toolbox, perform voxel-level, very demanding afforded by RMEVD algorithm.

Language: Английский

DySCo: A general framework for dynamic functional connectivity DOI Creative Commons
Giuseppe de Alteriis,

Oliver Sherwood,

Alessandro Ciaramella

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(3), P. e1012795 - e1012795

Published: March 7, 2025

A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across areas change over time. However, the main approaches have been developed and applied mostly empirically, lacking common theoretical framework clear view on interpretation results derived matrices. Moreover, community has not using most efficient algorithms compute process matrices efficiently, which prevented showing its full potential with datasets and/or real-time applications. In paper, we introduce Symmetric Matrix (DySCo), associated repository. DySCo presents commonly used measures language implements them computationally way. This allows study activity at different spatio-temporal scales, down voxel level. provides single to: (1) Use as tool capture interaction patterns data form easily translatable imaging modalities. (2) Provide comprehensive set quantify properties evolution time: amount connectivity, similarity between matrices, their informational complexity. By combining it possible perform analysis. (3) Leverage Temporal Covariance EVD algorithm (TCEVD) store eigenvectors values then also EVD. Developing eigenvector space orders magnitude faster more memory than naïve space, without loss information. The methodology here validated both synthetic dataset rest/N-back task experimental fMRI Human Connectome Project dataset. We show all proposed are sensitive changes configurations consistent time subjects. To illustrate computational efficiency toolbox, performed level, demanding but afforded by TCEVD.

Language: Английский

Citations

1
Metastability demystified — the foundational past, the pragmatic present and the promising future DOI
Fran Hancock, Fernando Rosas, Andrea I. Luppi

et al.

Nature reviews. Neuroscience, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 11, 2024

Language: Английский

Citations

4
Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularity DOI Creative Commons

Enrico Caprioglio,

Luc Berthouze

Frontiers in Network Physiology, Journal Year: 2024, Volume and Issue: 4

Published: Aug. 21, 2024

Oscillatory complex networks in the metastable regime have been used to study emergence of integrated and segregated activity brain, which are hypothesised be fundamental for cognition. Yet, parameters underlying mechanisms necessary achieve hard identify, often relying on maximising correlation with empirical functional connectivity dynamics. Here, we propose show that brain’s hierarchically modular mesoscale structure alone can give rise robust dynamics (metastable) chimera states presence phase frustration. We construct unweighted 3-layer hierarchical identical Kuramoto-Sakaguchi oscillators, parameterized by average degree network a structural parameter determining ratio connections between within blocks upper two layers. Together, these affect characteristic timescales system. Away from critical synchronization point, detect lowest layer coexisting Using Laplacian renormalization group flow approach, uncover distinct pathways towards achieving regimes detected In layers, how symmetry-breaking depend slow eigenmodes instead, achieved as separation layers reaches threshold. Our results an explicit relationship metastability, states, system, bridging gap harmonic based studies data oscillatory models.

Language: Английский

Citations

2
DySCo: a general framework for dynamic Functional Connectivity DOI Creative Commons
Giuseppe de Alteriis,

Oliver Sherwood,

Alessandro Ciaramella

et al.

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

Published: June 13, 2024

A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across areas change with time. However, the main approaches have been developed and applied mostly empirically, lacking unifying theoretical framework, general interpretation, common set measures quantify matrices properties. Moreover, field has ad-hoc algorithms compute process efficiently. This prevented show its full potential datasets and/or real time applications. With paper, we introduce Symmetric Matrix framework (DySCo), associated repository. DySCo approach allows study signals at different spatio-temporal scales, down voxel level, computationally ultrafast. unifies single most employed matrices, which share mathematical structure. Doing so it allows: 1) new interpretation further justifies use capture spatiotemporal patterns data form easily translatable imaging modalities. 2) The introduction Recurrence EVD store eigenvectors eigenvalues all types efficent manner orders magnitude faster than naive algorithms, without loss information. 3) To simply define quantities interest for dynamic analyses such as: amount connectivity (norm similarity between their informational complexity. methodology here validated on both synthetic dataset rest/N-back task experimental - fMRI Human Connectome Project dataset. We demonstrate proposed are highly sensitive changes configurations. illustrate computational efficiency toolbox, perform voxel-level, very demanding afforded by RMEVD algorithm.

Language: Английский

Citations

0