67 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Emory UniversityUS12 papers
- Georgia State UniversityUS12 papers
- Georgia Institute of TechnologyUS11 papers
- Tulane UniversityUS5 papers
- Mind Research NetworkUS3 papers
- Universitat de ValènciaES3 papers
- Vrije Universiteit AmsterdamNL3 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- Child Mind InstituteUS2 papers
- Copenhagen University HospitalDK2 papers
- Harvard University PressUS2 papers
- University of Maryland, Baltimore CountyUS2 papers
23 papers
MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis
Kunyu Zhang, Qiang Li, Shujian Yu
Recent evidence suggests that modeling higher-order interactions (HOIs) in functional magnetic resonance imaging (fMRI) data can enhance the diagnostic accuracy of machine learning…
MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
Armina Fani, Mike Doan, Isabelle Le +4
Deployment complexity and specialized hardware requirements hinder the adoption of deep learning models in neuroimaging. We present MindGrab, a lightweight, fully convolutional mod…
Functional Correspondences in the Human and Marmoset Visual Cortex During Movie Watching: Insights from Correlation, Redundancy, and Synergy
Qiang Li, Ting Xu, Vince D. Calhoun
The world of beauty is deeply connected to the visual cortex, as perception often begins with vision in both humans and marmosets. In this study, to investigate their functional co…
Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help
Keqi Han, Yao Su, Lifang He +4
Graph deep learning models, a class of AI-driven approaches employing a message aggregation mechanism, have gained popularity for analyzing the functional brain connectome in neuro…
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…
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…
Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks
Gang Qu, Ziyu Zhou, Vince D. Calhoun +2
Multimodal neuroimaging modeling has becomes a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales,…
Multimodal MRI Accurately Identifies Amyloid Status in Unbalanced Cohorts in Alzheimer's Disease Continuum
Giorgio Dolci, Charles A. Ellis, Federica Cruciani +5
Amyloid- (A) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's diseas…
An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease
Giorgio Dolci, Federica Cruciani, Md Abdur Rahaman +6
\textbf{Objective:} Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI), where patien…
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks
Bishal Thapaliya, Robyn Miller, Jiayu Chen +8
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs…
Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data
Bishal Thapaliya, Esra Akbas, Jiayu Chen +5
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for t…
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff +111
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is mean…
Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy
Qiang Li, Shujian Yu, Kristoffer H Madsen +2
Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to…
Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion
Ricardo Augusto Borsoi, Isabell Lehmann, Mohammad Abu Baker Siddique Akhonda +4
Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tas…
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
Anton Orlichenko, Gang Qu, Gemeng Zhang +5
Objective: Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often enc…
Functional Connectivity via Total Correlation: Analytical results in Visual Areas
Qiang Li, Greg Ver Steeg, Jesus Malo
Recent studies invoke the superiority of the multivariate Total Correlation concept over the conventional pairwise measures of functional connectivity in biological networks. Those…
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
Usman Mahmood, Zening Fu, Vince Calhoun +1
Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classificat…
A deep learning model for data-driven discovery of functional connectivity
Usman Mahmood, Zening Fu, Vince Calhoun +1
Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matri…
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Gang Qu, Li Xiao, Wenxing Hu +3
Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity net…
A Psychophysically Oriented Saliency Map Prediction Model
Qiang Li
Visual attention is one of the most significant characteristics for selecting and understanding the outside redundancy world. The human vision system cannot process all information…
A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
Aiying Zhang, Gemeng Zhang, Biao Cai +4
Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. The ability of emotion identification begins in infancy and contin…
Meta-modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia
Haleh Falakshahi, Victor M. Vergara, Jingyu Liu +13
Objective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on…
Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
Rogers F. Silva, Sergey M. Plis, Tulay Adali +2
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produ…