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.
Papers
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A method for making group inferences from functional MRI data using independent component analysis
TL;DR: A novel approach for drawing group inferences using ICA of fMRI data is introduced, and its application to a simple visual paradigm that alternately stimulates the left or right visual field is presented.
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The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery
TL;DR: This Perspective uses the term "chronnectome" to describe metrics that allow a dynamic view of coupling and focuses on multivariate approaches developed in the group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis.
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A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.
TL;DR: An overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software and an overview of the use of I CA to combine or fuse multimodal data are provided.
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Estimating the number of independent components for functional magnetic resonance imaging data.
TL;DR: This work uses the software package ICASSO to analyze the independent component estimates at different orders and shows that, when ICA is performed at overestimated orders, the stability of the IC estimates decreases and the estimation of task related brain activations show degradation.
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Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms.
TL;DR: A careful examination of some of the assumptions behind ICA methodologies is reported, examples of when applying ICA would provide difficult‐to‐interpret results, and suggestions for applying I CA to fMRI data especially when more than one task‐related component is present in the data are offered.