Functional Network Connectivity

Functional network connectivity (FNC) research investigates the relationships between different brain regions' activity, aiming to understand how these interactions contribute to cognitive functions and neurological disorders. Current research heavily utilizes machine learning, particularly deep learning models like GANs and Bayesian networks, to analyze FNC data from neuroimaging (fMRI, sMRI) and genomic data, often focusing on identifying biomarkers for diseases like Alzheimer's and depression and predicting treatment response. These studies leverage multi-modal data and multi-scale features to improve diagnostic accuracy and personalize treatment strategies. The ultimate goal is to translate these findings into improved clinical diagnostics and more effective, targeted interventions for neurological and psychiatric conditions.

Papers

June 19, 2024

An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease
Giorgio Dolci (1, 2), Federica Cruciani (1), Md Abdur Rahaman (2), Anees Abrol (2), Jiayu Chen (2), Zening Fu (2), Ilaria Boscolo Galazzo (1), Gloria Menegaz (1), Vince D. Calhoun (2) ((1) Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy, (2) Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA)
Alzheimer'S Disease Functional Magnetic Resonance Imaging Common Disease Mild Cognitive Impairment Gray Matter Generative Multimodal Model Functional Network Connectivity

April 25, 2024

DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks
Matthew Squires, Xiaohui Tao, Soman Elangovan, Raj Gururajan, Haoran Xie, Xujuan Zhou, Yuefeng Li, U Rajendra Acharya
Conditional Generative Adversarial Network Generative Diversity Transcranial Magnetic Stimulation Functional Network Connectivity

February 12, 2024

Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment
Bradley T. Baker, Mustafa S. Salman, Zening Fu, Armin Iraji, Elizabeth Osuch, Jeremy Bockholt, Vince D. Calhoun
Person Identification Multi Scale Feature Complex Behavior Drug Classification Functional Network Inconsistent Response Functional Network Connectivity

September 15, 2023

Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs
Yuda Bi, Anees Abrol, Jing Sui, Vince Calhoun
C Gan Functional Connectivity Structural MRI Cross Modality Synthesis Functional Network Connectivity

January 6, 2023

Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks
Shuai Liu, Xiao Guo, Shun Qi, Huaning Wang, Xiangyu Chang
Functional Magnetic Resonance Imaging Multi Site Joint Estimation Major Depressive Disorder Multi Site Data Functional Network Connectivity

Functional Network Connectivity

Papers

An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease

DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks

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

Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs

Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks