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20232026
most citedAberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks

7 citations · 12 across the 16 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CV2024

Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks

Oktay Agcaoglu, Rogers F. Silva, Deniz Alacam +3

Different brain imaging modalities offer unique insights into brain function and structure. Combining them enhances our understanding of neural mechanisms. Prior multimodal studies…

q-bio.NC2024★ 1 cited

Correlation of Correlation Networks: High-Order Interactions in the Topology of Brain Networks

Qiang Li, Jingyu Liu, Vince D. Calhoun

To understand collective network behavior in the complex human brain, pairwise correlation networks alone are insufficient for capturing the high-order interactions that extend bey…

q-bio.NC2024★ 2 cited

The Dynamics of Triple Interactions in Resting fMRI: Insights into Psychotic Disorders

Qiang Li, Vince D. Calhoun, Armin Iraji

The human brain dynamically integrated and configured information to adapt to the environment. To capture these changes over time, dynamic second-order functional connectivity was…

cs.LG2024

Generalized Dynamic Brain Functional Connectivity Based on Random Convolutions

Yongjie Duan, Vince D. Calhoun, Zhiying Long

Dynamic functional connectivity (DFC) analysis has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal time-varying dynamic changes of brain states.…

cs.LG2024

Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment

Bradley T. Baker, Mustafa S. Salman, Zening Fu +4

In the clinical treatment of mood disorders, the complex behavioral symptoms presented by patients and variability of patient response to particular medication classes can create d…

cs.LG2024

Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse

Bradley T. Baker, Barak A. Pearlmutter, Robyn Miller +2

Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks…