29 citations · 40 across the 28 of their papers we have counts for
31 papers
Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis
Zijiao Chen, Nicholas Lu, Xinhui Li +14
AI agents can execute scientific analyses, but an analytic output becomes a defensible claim only after alternatives are weighed and the claim is limited to what the evidence suppo…
AdaptICA: Data-Adaptive Transformation Learning for Independent Component Analysis
Lida Jalili, Jingyu Liu, Vince D. Calhoun +1
Independent component analysis (ICA) is widely used to recover latent structure from signal and imaging data, but standard ICA assumes that the observed measurement scale preserves…
Latent graph encoding of multimodal neuroimaging features with generative AI architectures
Ishaan Batta, Meenu Ajith, Vince Calhoun
While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encodin…
ReMAP-PET: Beyond Visual Understanding -- Learning Region-Guided Metabolic Alignment Semantics from Brain PET
Dasen Dai, Yanteng Zhang, Shuoqi Li +6
Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain foundation models treat PET as…
Timescale Coalescence Makes Hidden Persistent Forcing Spectrally Dark
Yuda Bi, Chenyu Zhang, Vince D Calhoun
Under coarse observation, unresolved slow forcing can remain dynamically active yet locally invisible to reduced spectral inference. For a solvable driven AR benchmark, the lo…
A 1/R Law for Kurtosis Contrast in Balanced Mixtures
Yuda Bi, Wenjun Xiao, Linhao Bai +1
Kurtosis-based Independent Component Analysis (ICA) weakens in wide, balanced mixtures. We prove a sharp redundancy law: for a standardized projection with effective width $R_{\mat…
KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure
Badhan Mazumder, Lei Wu, Sir-Lord Wiafe +2
Exposure to psychoactive substances during pregnancy, such as cannabis, can disrupt neurodevelopment and alter large-scale brain networks, yet identifying their neural signatures r…
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.…
Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis
Yuxiang Wei, Anees Abrol, Vince Calhoun
Recent advances in deep learning structured state space models, especially the Mamba architecture, have demonstrated remarkable performance improvements while maintaining linear co…
A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals
Yuxiang Wei, Anees Abrol, James Lah +2
Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, emphasizing the importance of early detection for proper treatment. Functional magnetic resonanc…
Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification
Nagur Shareef Shaik, Teja Krishna Cherukuri, Vince D. Calhoun +1
Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and ove…
Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks for Neuroimaging
Bradley T. Baker, Vince D. Calhoun, Sergey M. Plis
Neural networks, whice have had a profound effect on how researchers study complex phenomena, do so through a complex, nonlinear mathematical structure which can be difficult for h…