arXiv Machine Learning
Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing
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arXiv Machine Learning
Jul 23
Geometry-Guided Generative Representation for Functional Brain Graphs
arXiv:2511. 04539v2 Announce Type: replace-cross Abstract: In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties covary and partially overlap across individuals and conditions.
arXiv Machine Learning
Oct 9
BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
BrainATCL is an unsupervised, nonparametric framework that learns adaptive temporal brain connectivity from resting‑state fMRI data. It dynamically adjusts the lookback window for each snapshot based on newly added edges and encodes graph sequences with a GINE‑Mamba2 backbone, incorporating brain‑structure and function‑informed edge attributes. The method is evaluated on functional link prediction and age estimation, showing superior performance and strong generalization, even across sessions.
arXiv AI
Jul 9
Latent graph encoding of multimodal neuroimaging features with generative AI architectures
arXiv:2607. 07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encoding and latent space processes is crucial for studying structural and functional properties of the brain.
arXiv Machine Learning
Sep 7
Reservoir-Based Graph Convolutional Networks
The paper introduces RGC‑Net, a Reservoir‑Based Graph Convolutional Network that combines fixed‑random reservoir dynamics with a structured convolutional framework for graph learning. It addresses limitations of existing reservoir‑based GNNs by adding a leaky integrator for better feature retention and a robust, adaptable architecture for graph classification and generation. Experiments demonstrate state‑of‑the‑art performance on classification and generative tasks, including dynamic brain connectivity, with faster convergence and reduced over‑smoothing.
arXiv Machine Learning
Oct 5
Structural-Functional Brain Connectivity Generation via Multimodal Hypergraph-based Flow Matching
The paper introduces Multimodal Hypergraph Flow Matching (MHG‑FM), a framework that jointly generates structural connectivity (SC) and functional connectivity (FC) by constructing modality‑specific hypergraphs and learning higher‑order representations with Hypergraph Neural Network encoders. It employs Dual Cross‑Attention for bidirectional cross‑modal fusion, a variational autoencoder to map fused representations into a latent space, and conditional flow matching to synthesize connectivity and perform multimodal translation. Experiments on the Human Connectome Project Young Adult dataset demonstrate that MHG‑FM outperforms state‑of‑the‑art baselines in reconstruction quality, topology preservation, distributional similarity, and SC‑FC coupling, while achieving roughly eight times faster sampling than a comparable diffusion backbone.
Hugging Face Trending Papers
Oct 6
ReGraph: A Computational Account of Emergent Generalization in the "what" and "where" Dual Visual Streams
Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neuroscience. The foundation for this capacity lies up...