Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation

Search Page

My NCBI Filters
Results by year

Table representation of search results timeline featuring number of search results per year.

Year Number of Results
2018 1
2019 1
2020 1
Text availability
Article attribute
Article type
Publication date

Search Results

2 results
Results by year

Citations

1 article found by citation matching

Search results

Filters applied: . Clear all
Page 1
Capturing Dynamic Connectivity from Resting State fMRI using Time-Varying Graphical Lasso.
Cai B, Zhang G, Zhang A, Stephen JM, Wilson TW, Calhoun VD, Wang Y. Cai B, et al. IEEE Trans Biomed Eng. 2018 Nov 9:10.1109/TBME.2018.2880428. doi: 10.1109/TBME.2018.2880428. Online ahead of print. IEEE Trans Biomed Eng. 2018. PMID: 30418876 Free PMC article.
Recently, the fMRI community has realized the limitation of assuming static connectivity and dynamic approaches are more prominent in the resting state fMRI (rs-fMRI) analysis. ...In this study, we apply a time-varying
Recently, the fMRI community has realized the limitation of assuming static connectivity and dynamic approaches are mor …
A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity.
Cai B, Zhang G, Zhang A, Hu W, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Cai B, et al. J Neurosci Methods. 2020 Feb 15;332:108531. doi: 10.1016/j.jneumeth.2019.108531. Epub 2019 Dec 10. J Neurosci Methods. 2020. PMID: 31830544
BACKGROUND: Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In particular, time-varying connectivity analysis has emerged as an important measure to uncover essential kno …
BACKGROUND: Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectiv
Feedback