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Tülay Adali
University of Maryland, Baltimore
404Publications
52H-index
12kCitations
Publications 404
Newest
Published on Jun 1, 2019in NeuroImage 5.81
Alexander von Lühmann (BU: Boston University), Zois Boukouvalas3
Estimated H-index: 3
(UMD: University of Maryland, College Park)
+ 1 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Abstract In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currently there exists no gold-standard. Although a plenitude of methodological approaches implicitly assume the presence of latent processes in the signals, elaborate Blind-Source-Separation methods have rarely been applied. A reason are challenging characteristics such as Non-instantaneous and non-constant coupling, correlated noise and statisti...
Published on Jul 1, 2019in Journal of Neuroscience Methods 2.79
Xiaowei Song (UMBC: University of Maryland, Baltimore County), Suchita Bhinge2
Estimated H-index: 2
(UMBC: University of Maryland, Baltimore County)
+ 1 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Abstract Background : Data driven analysis methods such as independent component analysis (ICA) offer the advantage of estimating subject contributions when used in a second-level analysis. With the traditionally used regression-based methods this is achieved with a design matrix that has to be specified a priori. New method : We show that the ability of ICA to estimate subject contributions can be effectively used to perform steady-state as well as transient analysis of task functional magnetic...
Published on Jan 1, 2019in IEEE Transactions on Medical Imaging 7.82
Suchita Bhinge2
Estimated H-index: 2
(UMBC: University of Maryland, Baltimore County),
Rami Mowakeaa1
Estimated H-index: 1
(UMBC: University of Maryland, Baltimore County)
+ 1 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Dynamic functional connectivity analysis is an effective way to capture the networks that are functionally associated and continuously changing over the scanning period. However, these methods mostly analyze the dynamic associations across the activation patterns of the spatial networks while assuming that the spatial networks are stationary. Hence, a model that allows for the variability in both domains and reduces the assumptions imposed on the data provides an effective way for extracting spa...
Published in Frontiers in Neuroscience 3.65
Evrim Acar20
Estimated H-index: 20
,
Carla Schenker + -3 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with m...
Published on May 1, 2019in Journal of Signal Processing Systems
Wei Du (UMD: University of Maryland, College Park), Ronald Phlypo1
Estimated H-index: 1
(UMD: University of Maryland, College Park),
Tülay Adali52
Estimated H-index: 52
(UMD: University of Maryland, College Park)
Labeling of data is often difficult, expensive, and time consuming since efforts of experienced human annotators are required, and often we have large number of samples and noisy data. Co-training is a practical and powerful semi-supervised learning method as it yields high classification accuracy with a training data set containing only a small set of labeled data. For successful co-training performance, two important conditions need to be satisfied for the features: diversity and sufficiency. ...
Published in bioRxiv
Shile Qi3
Estimated H-index: 3
(The Mind Research Network),
Jing Sui28
Estimated H-index: 28
(CAS: Chinese Academy of Sciences)
+ -3 AuthorsDongdong Lin (The Mind Research Network)
ABSTRACT There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized ...
Published on Mar 1, 2019
Chunying Jia (UMBC: University of Maryland, Baltimore County), A B S Akhonda Mohammad (UMBC: University of Maryland, Baltimore County)+ 3 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Fusing datasets from different brain signal modalities improves accuracy in finding biomarkers of neuropsychiatric diseases. Several approaches, such as joint independent component analysis (ICA) and independent vector analysis (IVA), are useful but fall short of exploring multiple associations between different modalities, especially for the case where one underlying component in one modality might have multiple associations with others in another modality. This relationship is possible since o...
Published on Mar 1, 2019
Mohammad A.B.S. Akhonda (UMB: University of Maryland, Baltimore), Mohammad A. B. S. Akhonda (UMB: University of Maryland, Baltimore)+ 2 AuthorsTülay Adali52
Estimated H-index: 52
(UMB: University of Maryland, Baltimore)
Data-driven methods based on independent component analysis (ICA) and its extensions, have been attractive for data fusion as they minimize the assumptions placed on the data. Two widely used extensions of ICA, joint ICA (jICA) and multiset canonical correlation analysis prior to joint ICA (MCCA-jICA) fuse data from different datasets by assuming identical mixing matrices. However, these methods typically only take the common features into account within the linked datasets by disregarding the a...
Published on Feb 1, 2019in Human Brain Mapping 4.55
Qunfang Long1
Estimated H-index: 1
(UMBC: University of Maryland, Baltimore County),
Suchita Bhinge2
Estimated H-index: 2
(UMBC: University of Maryland, Baltimore County)
+ 3 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Published on Jan 1, 2019in Journal of Neuroscience Methods 2.79
Yuri Levin-Schwartz5
Estimated H-index: 5
(UMBC: University of Maryland, Baltimore County),
Vince Daniel Calhoun87
Estimated H-index: 87
(UNM: University of New Mexico),
Tülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Abstract Background The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupted by psychiatric disorders such as schizophrenia. While the increasing number of these methods motivates a comparison of their relative performance, such a comparison is difficult to perform on real fMRI data, since the ground truth is, relatively, unkno...
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