Magnetoencephalography dimensionality reduction informed by dynamic brain states
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| Název: | Magnetoencephalography dimensionality reduction informed by dynamic brain states |
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| Autoři: | Annie E Cathignol, Lionel Kusch, Marianna Angiolelli, Emahnuel Troisi Lopez, Arianna Polverino, Antonella Romano, Giuseppe Sorrentino, Viktor Jirsa, Giovanni Rabuffo, Pierpaolo Sorrentino |
| Zdroj: | Eur J Neurosci The European journal of neuroscience, vol. 61, no. 9, pp. e70128 |
| Informace o vydavateli: | Cold Spring Harbor Laboratory, 2024. |
| Rok vydání: | 2024 |
| Témata: | Research Report, Adult, Male, Young Adult, Humans, Magnetoencephalography/methods, Brain/physiology, Algorithms, Female, Dimensionality Reduction, PHATE algorithm, brain dynamics, dimensionality reduction, magnetoencephalography, neuronal avalanches, resting state, Magnetoencephalography, Brain |
| Popis: | Complex spontaneous brain dynamics mirror the large number of interactions taking place among regions, supporting higher functions. Such complexity is manifested in the inter-regional dependencies among signals derived from different brain areas, as observed utilising neuroimaging techniques, like magnetoencephalography. The dynamics of this data produce numerous subsets of active regions at any moment as they evolve. Notably, converging evidence shows that these states can be understood in terms of transient coordinated events that spread across the brain over multiple spatial and temporal scales. Those can be used as a proxy of the “effectiveness” of the dynamics, as they become stereotyped or disorganised in neurological diseases. However, given the high dimensional nature of the data, representing them has been challenging thus far. Dimensionality reduction techniques are typically deployed to describe complex interdependencies and improve their interpretability. However, many dimensionality reduction techniques lose information about the sequence of configurations that took place. Here, we leverage a newly described algorithm, PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding), specifically designed to preserve the dynamics of the system in the low-dimensional embedding space. We analysed source-reconstructed resting-state magnetoencephalography from 18 healthy subjects to represent the dynamics of the configuration in low-dimensional space. After reduction with PHATE, unsupervised clustering via K-means is applied to identify distinct clusters. The topography of the states is described, and the dynamics are represented as a transition matrix. All the results have been checked against null models, providing a parsimonious account of the large-scale, fast, aperiodic dynamics during resting-state. |
| Druh dokumentu: | Article Other literature type |
| Popis souboru: | application/pdf |
| ISSN: | 1460-9568 0953-816X |
| DOI: | 10.1101/2024.08.08.607151 |
| DOI: | 10.1111/ejn.70128 |
| Přístupová URL adresa: | https://pubmed.ncbi.nlm.nih.gov/40353396 https://serval.unil.ch/resource/serval:BIB_CCD61E072D1F.P001/REF.pdf http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_CCD61E072D1F2 https://serval.unil.ch/notice/serval:BIB_CCD61E072D1F |
| Rights: | CC BY NC ND CC BY |
| Přístupové číslo: | edsair.doi.dedup.....8bd4c921bf74c0562920ae283d5f1ed7 |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Complex spontaneous brain dynamics mirror the large number of interactions taking place among regions, supporting higher functions. Such complexity is manifested in the inter-regional dependencies among signals derived from different brain areas, as observed utilising neuroimaging techniques, like magnetoencephalography. The dynamics of this data produce numerous subsets of active regions at any moment as they evolve. Notably, converging evidence shows that these states can be understood in terms of transient coordinated events that spread across the brain over multiple spatial and temporal scales. Those can be used as a proxy of the “effectiveness” of the dynamics, as they become stereotyped or disorganised in neurological diseases. However, given the high dimensional nature of the data, representing them has been challenging thus far. Dimensionality reduction techniques are typically deployed to describe complex interdependencies and improve their interpretability. However, many dimensionality reduction techniques lose information about the sequence of configurations that took place. Here, we leverage a newly described algorithm, PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding), specifically designed to preserve the dynamics of the system in the low-dimensional embedding space. We analysed source-reconstructed resting-state magnetoencephalography from 18 healthy subjects to represent the dynamics of the configuration in low-dimensional space. After reduction with PHATE, unsupervised clustering via K-means is applied to identify distinct clusters. The topography of the states is described, and the dynamics are represented as a transition matrix. All the results have been checked against null models, providing a parsimonious account of the large-scale, fast, aperiodic dynamics during resting-state. |
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| ISSN: | 14609568 0953816X |
| DOI: | 10.1101/2024.08.08.607151 |