29 citations · 40 across the 28 of their papers we have counts for
13 papers · 1 filter
Redundancy as a Structural Information Principle for Learning and Generalization
Yuda Bi, Ying Zhu, Vince D Calhoun
We present a theoretical framework that extends classical information theory to finite and structured systems by redefining redundancy as a fundamental property of information orga…
Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
Yi-Ting Hung, Li-Hsiang Lin, Vince D. Calhoun
Recent advances in deep learning highlight the need for personalized models that can learn from small samples, handle high-dimensional features, and remain interpretable. To addres…
Scaling Laws are Redundancy Laws
Yuda Bi, Vince D Calhoun
Scaling laws, a defining feature of deep learning, reveal a striking power-law improvement in model performance with increasing dataset and model size. Yet, their mathematical orig…
A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis
Reihaneh Hassanzadeh, Anees Abrol, Hamid Reza Hassanzadeh +1
Multimodal data analysis can lead to more accurate diagnoses of brain disorders due to the complementary information that each modality adds. However, a major challenge of using mu…
NeuroKoop: Neural Koopman Fusion of Structural-Functional Connectomes for Identifying Prenatal Drug Exposure in Adolescents
Badhan Mazumder, Aline Kotoski, Vince D. Calhoun +1
Understanding how prenatal exposure to psychoactive substances such as cannabis shapes adolescent brain organization remains a critical challenge, complicated by the complexity of…
The Financial Connectome: A Brain-Inspired Framework for Modeling Latent Market Dynamics
Yuda Bi, Vince D Calhoun
We propose the Financial Connectome, a new scientific discipline that models financial markets through the lens of brain functional architecture. Inspired by the foundational work…
Algebraic Connectivity Reveals Modulated High-Order Functional Networks in Alzheimer's Disease
Giorgio Dolci, Silvia Saglia, Lorenza Brusini +3
Functional MRI is a neuroimaging technique that analyzes the functional activity of the brain by measuring blood-oxygen-level-dependent signals throughout the brain. The derived fu…
MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
Bi Yuda, Jia Sihan, Gao Yutong +3
Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enhance deep learning predictions a…
GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis
Hu Xu, Yang Jingling, Jia Sihan +2
Generative models based on deep learning have shown significant potential in medical imaging, particularly for modality transformation and multimodal fusion in MRI-based brain imag…
Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis
Badhan Mazumder, Lei Wu, Vince D. Calhoun +1
Gaining insights into the structural and functional mechanisms of the brain has been a longstanding focus in neuroscience research, particularly in the context of understanding and…
Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling
Badhan Mazumder, Ayush Kanyal, Lei Wu +2
Clinical studies reveal disruptions in brain structural connectivity (SC) and functional connectivity (FC) in neuropsychiatric disorders such as schizophrenia (SZ). Traditional app…
4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis
Yuxiang Wei, Yanteng Zhang, Xi Xiao +3
Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanc…
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis
Yipu Zhang, Likai Wang, Kuan-Jui Su +7
Resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity networks (FCNs) have become critical for understanding neurological disorders.…