Functional Brain Connectivity Studies

Neueste Paper

• CAMOS: Coupled Oscillatory State-Space Model for Multimodal Clinical Time-Series

  Maxx Richard Rahman, Mostafa Hammouda, Wolfgang Maass · 1. Oktober 2026

  Longitudinal clinical cohorts are multimodal, irregularly sampled and pervasively incomplete: in ADNI, positron emission tomography and cerebrospinal fluid assays are absent from roughly half of all visits. Linear state-space models handle irregular sampling gracefully but treat a missing modality b…

• BrainNet Studio: A Unified Toolkit for Brain Network Construction, Intelligent Analysis, and Visualization

  Xiwei Zeng, Shengrong Li, Yiheng Liu, Chunwei Tian, Daoqiang Zhang, Qi Zhu · 30. September 2026

  Brain networks characterize structural and functional relationships among brain regions and support research on cognition, brain disorders, and brain-computer interfaces. Their time-varying topology and higher-order spatiotemporal dependencies are not adequately represented by conventional static ne…

• GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling

  Haohui Jia, Zheng Chen, Jathurshan Pradeepkumar, Xu Cao, Yasuko Matsubara, Yasushi Sakurai, Takashi Matsubara · 30. September 2026

  Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channels. Conditional flow matching provides a framework for distributional forecasting, yet it remains unclear whether graph-informed …

• Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders

  Giovanni Marraffini (UNITO), Victoria Shevchenko (UNITO), Carlo Alberto Barbano (UNITO), Demian Wassermann (MIND) · 30. September 2026

  Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learned from large unlabelled corpora should capture individual functional dynamics and generalise across cohorts. However, kernel ridge regression (KRR) …

• Information Bottleneck-Guided Adaptive Hypergraph Transformer for Brain Disease Diagnosis

  Jingxi Feng, Xudong Chen, Yifan Zhang, Heming Xu, Hongcheng Han, Xijing Wang, Dong Zhang, Shaoyi Du · 30. September 2026

  Exploring high-order correlations and long-range dependencies in brain networks holds significant value for both neuroscience research and clinical diagnosis. However, previous studies have lacked a unified integration of high-order and long-range dependency information in brain networks, and there …

• Explainable Deep Learning of Resting-State Functional Connectomes Reveals Network Biomarkers of Adolescent Intelligence

  Md. Tanvir Rahman, Nabil Anan Orka, Asaduzzaman Khan, Mohammad Ali Moni · 29. September 2026

  Mapping resting-state brain organization to individual differences in cognitive ability remains a major challenge in population neuroinformatics. Although deep learning enables flexible modeling of brain connectivity, limited interpretability restricts its scientific and clinical utility. To address…

• MAC-Net: A Multi-Task Deep Learning Framework for Modeling Cognitive Function From Task-Based fMRI

  Md. Tanvir Rahman, Nabil Anan Orka, Asaduzzaman Khan, Mohammad Ali Moni · 29. September 2026

  Objective cognitive assessment from neural signals supports neurorehabilitation, but individual-level prediction from task-based fMRI (tfMRI) remains difficult because neural features coexist with substantial demographic and scanner-related variation. We present the Multi-task Activation and Contras…

• Bayesian Uncertainty Quantification for fMRI Functional Connectivity via Simulation-Based Inference

  Simon Carter, Zeming Kuang, Lilianne R. Mujica-Parodi, Helmut H. Strey · 28. September 2026

  Optimizing fMRI scan duration and spatial resolution is critical for experimental design, yet traditional correlation-based approaches cannot quantify uncertainty or disentangle scanner measurement noise from true neural variability across subjects. Without principled uncertainty bounds, researchers…

• Geometric Feature Learning for Functional Data Valued on the Symmetric Positive Definite Manifold

  Samuel V. Singh, Mimi Zhang · 28. September 2026

  We here develop a functional neural network, termed MatFAE, for learning trajectories on the Riemannian manifold of symmetric positive definite (SPD) matrices. MatFAE features intrinsic layers that map manifold-valued functions to Euclidean vector-valued functions, followed by a functional layer tha…

• FlatClip: A Geometry-Aware Surface-Level Baseline for fMRI Representation Learning

  Mo Wang, Wenhao Ye, Zihan Ning, Jiayu Zuo, Junfeng Xia, Hongkai Wen, Quanying Liu · 28. September 2026

  Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models are efficient but coarse, whereas voxel-level models preserve fine-grained spatial structure but require specialized 3D/4D architectures and costly fMRI-…

• Beyond Feature Reliability: Repeat-Informed Multifractal Curve Regression for Brain-Age Prediction

  Yu Chang, Anzhe Cheng, Jiahao Chen, Heng Ping, Peiyu Zhang, Puquan Pan, Tamoghna Chattopadhyay, Sophia Thomopoulos, Shahin Nazarian, Paul Thompson, Paul Bogdan · 25. September 2026

  Brain-age prediction from resting-state fMRI provides a quantitative framework for characterizing age-related changes in spontaneous brain dynamics and for identifying functional signatures. Existing studies have linked fractal and multifractal scaling to age and examined the reliability of individu…

• It's the Geometry, Not the Model: Effective Rank and Subspace Alignment in Functional Connectivity Classification

  Xiao Fan, Jingyuan Li, Yubo Han, Hongbin Guo, Guanya Li, Yang Hu, Wenchao Zhang, Weibin Ji, Yi Zhang · 25. September 2026

  Resting-state functional connectivity (FC) is widely used to classify brain phenotypes and disorders. Most pipelines use the full connectome and seek gains through model design. We instead examine how FC geometry constrains classification and cross-site transfer. Across-subject FC variation concentr…

• A Scaling Study for fMRI Foundation Models

  Wenhao Ye, Xuanye Pan, Junfeng Xia, Junxiang Zhang, Mo Wang, Quanying Liu · 24. September 2026

  Scaling laws have guided large-model development in computer vision and natural language processing, but the relationships among data, model size, and compute remain unclear for functional magnetic resonance imaging (fMRI) foundation models. Here, we conduct a controlled empirical study using pretra…

• A generalizable structural brain MRI foundation model built through dual-priority federated pretraining

  Zhen Yu, Yang Liu, Xiahai Zhuang, Qingchao Chen · 24. September 2026

  Foundation models hold promise for generalizable analysis of structural brain magnetic resonance imaging (MRI) across development, aging and disease. However, existing models are typically built through centralized pretraining on pooled data, despite privacy and governance constraints. Such pooling …

• The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis

  Aakash Patel, Panos Ketonis, Shreya Saxena, Smita Krishnaswamy, David van Dijk · 23. September 2026

  Analyzing neuroimaging data requires specialized coding and statistical expertise, which limits accessibility for researchers without computational backgrounds. We present the AI Neuroscientist, a language agent for interactive data exploration. The system integrates a large language model (LLM) wit…

• HyperAMS-Net: Adaptive Multi-Scale Spatial Hypergraph Network for Brain Disorder Classification

  Proloy Kumar Mondal, Md Kamran Hussin Chowdhury, Hoi Leong Lee · 18. September 2026

  Accurate classification of brain disorders from neuroimaging data remains challenging because of substantial inter-subject heterogeneity and the complex multi-scale patterns present in functional connectivity and morphological representations. To address these challenges, we propose HyperAMS-Net, a …

• Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge

  Yitong Li, Alexandra Samoylova, Fabian Bongratz, Timo Grimmer, Dennis M. Hedderich, Igor Yakushev, Christian Wachinger · 18. September 2026

  Cortical hypometabolism measured by Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a highly sensitive biomarker for dementia diagnosis. However, high costs, radiation exposure, and limited accessibility constrain its clinical utility. While cross-modal synthesis from Magnetic Resonance…

• Geometric-to-Semantic Spherical Transfer Learning for Cortical Sulci Labeling

  Saeb Tounsi, Jo\"el Chavas, Pietro Gori, Vincent Frouin, Denis Rivi\`ere, Jean-Fran\c{c}ois Mangin · 14. September 2026

  Deep learning on cortical surfaces faces a dilemma: capturing the complex topology of over 60 nomenclature-dependent sulci per hemisphere requires high-capacity models, yet the extreme scarcity of expert annotations ($N=62$ subjects) inevitably causes overfitting. Standard supervised approaches fail…

• Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures

  Julien Laval, Robin Guiavarch, Antoine Dufournet, Racim Menasria, Barth\'el\'emy Drabczuk, Cristobal Mendoza, Saeb Tounsi, Chikh Abdelghani Baroud, Merieme Bourenane, Vanessa Troiani, William Snyder, Marisa A Patti, Myl\`ene Moyal, Marion Plaze, Arnaud Cachia, Federica Santacroce, Giorgia Committeri, Claire Cury, Kevin De Matos, Olivier Colliot, Zhong Yi Sun, Clara Fischer, Vincent Frouin, Pietro Gori, Denis Rivi\`ere, Jo\"el Chavas, Jean-Fran\c{c}ois Mangin · 11. September 2026

  The human brain folds in utero, primarily during late gestation. Shortly after birth, cortical folding patterns are established and remain stable thereafter, making them promising early neurodevelopmental markers. Yet it is unclear whether the representations given by current neuroimaging foundation…

• Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs

  Saurabh Sihag, Andrea Cavallo, Elvin Isufi, Gonzalo Mateos, Alejandro Ribeiro · 10. September 2026

  This feature article provides an overview of the theoretical foundations for coVariance neural networks (VNNs), i.e., graph neural networks (GNNs) operating on covariance matrices as graphs. Covariance matrices are ubiquitous across domains, and hence, the deployment of GNNs often leverages graphs o…

• BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models

  Junfeng Xia, Wenhao Ye, Junxiang Zhang, Jiayu Zuo, Mo Wang, Quanying Liu · 10. September 2026

  fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study whether measured learning relations can organize both stage…

• M2LG-DG: A Multi-modal Local-Global Domain Generalization Framework for Cross-site Major Depressive Disorder Classification

  Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin, Alan Wee-Chung Liew · 10. September 2026

  Classification models based on resting-state functional magnetic resonance imaging (rs-fMRI) often show lower performance at imaging sites not included during model development, which can limit their use in clinical settings. Domain generalization (DG) addresses this issue by learning representation…

• Tensor-based Brain Surface Modeling and Analysis

  Moo K. Chung, Keith J. Worsley, Steve Robbins, Alan C. Evans · 4. September 2026

  We present a unified computational approach to tensor-based morphometry in detecting the brain surface shape differences between two clinical groups based on magnetic resonance images. Our approach is novel in a sense that we combined surface modeling, surface data smoothing and statistical analysis…

• Multiscale Community-Based Fingerprinting of Signed Functional Networks

  Sema Athamnah, Selin Aviyente · 31. August 2026

  Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or \textit{fingerprints}, that can identify individuals across repeated sessions and tasks. Existing methods mostly rely on edge-level features that are sensitive to noise, difficult to interpret, …

• MSR-IVA: Masked Structural Residual Independent Vector Analysis for State-Aware Fusion of Structural MRI and Dynamic Functional Network Connectivity

  Victor Solomon, Zening Fu, Rafal Angryk, Vince D. Calhoun, Jingyu Liu · 27. August 2026

  Multimodal fusion of structural MRI (sMRI) and dynamic functional network connectivity (dFNC) can reveal how brain structure relates to changing functional states. When the same structural latent representation is coupled with multiple states, applying independent vector analysis (IVA) separately to…