A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data (2023)

Zhang, C., Lin, Q., Niu, Y., Li, W., Gong, X., Cong, F., Wang, Y., & Calhoun, V. D. (2023). Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data. Human Brain Mapping, 44(17), 5712-5728. https://doi.org/10.1002/hbm.26471

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Zhang, Chao‐Ying; Lin, Qiu‐Hua; Niu, Yan‐Wei; Li, Wei‐Xing; Gong, Xiao‐Feng; Cong, Fengyu; Wang, Yu‐Ping; Calhoun, Vince D.

Lehti tai sarja: Human Brain Mapping

ISSN: 1065-9471

eISSN: 1097-0193

Julkaisuvuosi: 2023

Ilmestymispäivä: 30.08.2023

Volyymi: 44

Lehden numero: 17

Artikkelin sivunumerot: 5712-5728

Kustantaja: Wiley

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1002/hbm.26471

Linkki tutkimusaineistoon: https://www.humanconnectome.org/

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/89050

Tiivistelmä

Brain networks extracted by independent component analysis (ICA) from magnitude-only fMRI data are usually denoised using various amplitude-based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex-valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude-only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex-valued data. The main idea is to generate a mathematical SSP map for a magnitude map using a mapping framework, and the mapping framework is built using complex-valued fMRI data with a known SSP map. Here we leverage the fact that the phase map derived from phase fMRI data has similar phase information to the SSP map. After verifying the use of the magnitude data of complex-valued fMRI, this framework is generalized to work with magnitude-only data, allowing use of our approach even without the availability of the corresponding phase fMRI datasets. We test the proposed method using both simulated and experimental fMRI data including complex-valued data from University of New Mexico and magnitude-only data from Human Connectome Project. The results provide evidence that the mathematical SSP denoising with a fixed phase change is effective for denoising spatial maps from magnitude-only fMRI data in terms of retaining more BOLD-related activity and fewer unwanted voxels, compared with amplitude-based thresholding. The proposed method provides a unified and efficient SSP approach to denoise ICA brain networks in fMRI data.

YSO-asiasanat: riippumattomien komponenttien analyysi; toiminnallinen magneettikuvaus; aivot

Vapaat asiasanat: fMRI; independent component analysis; denoising; mathematical spatial source phase; mapping framework; fixed phase change

Liittyvät organisaatiot

OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2023

JUFO-taso: 2