A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Snowball ICA : A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data (2020)


Hu, Guoqiang; Waters, Abigail B.; Aslan, Serdar; Frederick, Blaise; Cong, Fengyu; Nickerson, Lisa D. (2020). Snowball ICA : A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data. Frontiers in Neuroscience, 14, 569657. DOI: 10.3389/fnins.2020.569657


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Hu, Guoqiang; Waters, Abigail B.; Aslan, Serdar; Frederick, Blaise; Cong, Fengyu; Nickerson, Lisa D.

Lehti tai sarja: Frontiers in Neuroscience

ISSN: 1662-4548

eISSN: 1662-453X

Julkaisuvuosi: 2020

Volyymi: 14

Artikkelinumero: 569657

Kustantaja: Frontiers Media

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

DOI: https://doi.org/10.3389/fnins.2020.569657

Avoin saatavuus: Open access -julkaisukanavassa ilmestynyt julkaisu

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/72271


Tiivistelmä

In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because large-scale networks are widely spatially distributed and thus have increased mutual information with noise. As such, conventional ICA algorithms with high model orders may not extract these components at all. This conflict makes the selection of model order a problem. We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues. The algorithm collects all information for each network from fMRI data without the limitations of network scale. Using simulations and in vivo resting-state fMRI data, our results show that component estimation using Snowball ICA is more accurate than traditional ICA. The Snowball ICA software is available at https://github.com/GHu-DUT/Snowball-ICA.


YSO-asiasanat: toiminnallinen magneettikuvaus; signaalinkäsittely; signaalianalyysi; riippumattomien komponenttien analyysi

Vapaat asiasanat: independent component analysis; functional magnetic resonance imaging; model order; dimension reduction; mutual information


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Alustava JUFO-taso: 1


Viimeisin päivitys 2020-20-10 klo 13:38