T
Tulay Adali
Researcher at University of Maryland, Baltimore County
Publications - 466
Citations - 22805
Tulay Adali is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Independent component analysis & Blind signal separation. The author has an hindex of 64, co-authored 429 publications receiving 20040 citations. Previous affiliations of Tulay Adali include Johns Hopkins University & University of Baltimore.
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Journal ArticleDOI
Constrained source-based morphometry identifies structural networks associated with default mode network.
Li Luo,Lai Xu,Lai Xu,Rex E. Jung,Rex E. Jung,Godfrey D. Pearlson,Tulay Adali,Vince D. Calhoun +7 more
TL;DR: The findings suggest that the functional DMN is underpinned by a corresponding brain-wide structural network that is additionally applicable to a wide variety of problems identifying structural networks from seed regions.
Proceedings ArticleDOI
ICA of fMRI data: Performance of three ICA algorithms and the importance of taking correlation information into account
TL;DR: By taking the correlation information into account, the default mode network (DMN) component, an important one in the study of brain function, is more consistently estimated using FBSS, the most widely used algorithm for fMRI analysis.
Proceedings ArticleDOI
Surface reconstruction and visualization of the surgical prostate model
Jianhua Xuan,Isabell A. Sesterhenn,Wendelin S. Hayes,Yue Joseph Wang,Tulay Adali,Yukako Yagi,Matthew T. Freedman,Seong Ki Mun +7 more
TL;DR: An advanced image analysis and graphics software is developed to reconstruct and visualize previously images prostate specimens to define tumor volume and distribution and pathways of needle biopsies, thus allowing improved understanding of prostate cancer behavior and current diagnosis-staging methodology.
Journal ArticleDOI
Modeling nuclear reactor core dynamics with recurrent neural networks
TL;DR: The test results presented exhibit the capability of the recurrent neural network model to capture the complex dynamics of the system, yielding accurate predictions of the System Response, in a non-linear, complex dynamic system characterized by a large number of state variables.
Proceedings ArticleDOI
IVA for multi-subject FMRI analysis: A comparative study using a new simulation toolbox
TL;DR: A new fMRI simulation toolbox (SimTB) is used to simulate multi-subject realistic fMRI datasets that include inter-subject variability and shows that in addition to offering an effective solution for making group inferences, IVA algorithms provide superior performance in terms of capturing spatial inter- subject variability.