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Tulay Adali
University of Maryland, Baltimore County
400Publications
56H-index
13.5kCitations
Publications 404
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#1Armin Iraji (Georgia Institute of Technology)H-Index: 7
#2Robyn Miller (Georgia Institute of Technology)
Last.Vince D. Calhoun (Georgia Institute of Technology)H-Index: 93
view all 4 authors...
There has been growing interest in studying the temporal reconfiguration of brain functional connectivity to understand the role of dynamic interaction (e.g., integration and segregation) among neuronal populations in cognitive functions. However, it is crucial to differentiate between various dynamic properties because nearly all existing dynamic connectivity studies are presented as spatiotemporally dynamic, even though they fall into different categories. As a result, variation in the spatial...
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#1Rami Mowakeaa (UMBC: University of Maryland, Baltimore County)H-Index: 1
#2Zois Boukouvalas (UW: University of Washington)H-Index: 1
Last.Tulay Adali (UMBC: University of Maryland, Baltimore County)H-Index: 56
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Examples of complex-valued random phenomena in science and engineering are abound, and joint blind source separation (JBSS) provides an effective way to analyze multiset data. Thus there is a need for flexible JBSS algorithms for efficient data-driven feature extraction in the complex domain. Independent vector analysis (IVA) is a prominent recent extension of independent component analysis to multivariate sources, i.e., to perform JBSS, but its effectiveness is determined by how well the source...
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#1Alexander von Lühmann (BU: Boston University)H-Index: 4
#2Zois Boukouvalas (UMD: University of Maryland, College Park)H-Index: 5
Last.Tulay Adali (UMBC: University of Maryland, Baltimore County)H-Index: 56
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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...
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#1Xiaowei Song (UMBC: University of Maryland, Baltimore County)H-Index: 3
#2Suchita Bhinge (UMBC: University of Maryland, Baltimore County)H-Index: 3
Last.Tulay Adali (UMBC: University of Maryland, Baltimore County)H-Index: 56
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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...
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#1Suchita Bhinge (UMBC: University of Maryland, Baltimore County)H-Index: 3
#2Qunfang Long (UMBC: University of Maryland, Baltimore County)H-Index: 2
Last.Tulay Adali (UMBC: University of Maryland, Baltimore County)H-Index: 56
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Dynamic functional network connectivity (dFNC) analysis is a widely-used to study associations between dynamic functional correlations and cognitive abilities. Traditional methods analyze time-varying association of different spatial networks while assuming that the spatial network itself is stationary. However, there has been very little work focused on voxelwise spatial variability. Exploiting the variability across both the temporal and spatial domains provide a more promising direction to ob...
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#1Suchita Bhinge (UMB: University of Maryland, Baltimore)H-Index: 3
#2Rami Mowakeaa (UMB: University of Maryland, Baltimore)H-Index: 1
Last.Tulay Adali (UMB: University of Maryland, Baltimore)H-Index: 56
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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...
1 CitationsSource
#1Evrim AcarH-Index: 22
#2Carla SchenkerH-Index: 1
Last.Tulay Adali (UMBC: University of Maryland, Baltimore County)H-Index: 56
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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...
1 CitationsSource
#1Wei Du (UMD: University of Maryland, College Park)H-Index: 6
#2Ronald Phlypo (UMD: University of Maryland, College Park)H-Index: 11
Last.Tulay Adali (UMD: University of Maryland, College Park)H-Index: 56
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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. ...
1 CitationsSource
#1Shile Qi (The Mind Research Network)H-Index: 5
#2Jing Sui (The Mind Research Network)H-Index: 31
Last.Vince D. Calhoun (The Mind Research Network)H-Index: 93
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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 ...
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#1Chunying Jia (UMBC: University of Maryland, Baltimore County)H-Index: 1
#2A B S Akhonda Mohammad (UMBC: University of Maryland, Baltimore County)
Last.Tulay Adali (UMBC: University of Maryland, Baltimore County)H-Index: 56
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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...
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