Functional Magnetic Resonance Imaging (fMRI) enables the non-invasive mapping of brain activity during various cognitive processes, and understanding how specific cognitive tasks activate distinct brain regions is crucial for both neuroscience and clinical applications. However, acquiring task-based fMRI (tfMRI) is costly, time-consuming, and often infeasible for individuals unable to perform tasks. Resting-state fMRI (rsfMRI), which is more widely available and does not require task compliance, can instead be leveraged to infer task-related activations. In this work, we propose BrainSparseCNN, a sparse 3D convolutional neural network that exploits the brain’s spatial structure to predict tfMRI contrasts from rsfMRI while efficiently processing high-dimensional neuroimaging data. BrainSparseCNN achieves up to 7% higher Pearson correlation than surface-based and dense volumetric competitors, with statistically significant gains (p < 0.01) across all tasks, furthermore improving spatial alignment, subject identification accuracy, and saliency interpretability while maintaining computational efficiency. Overall, BrainSparseCNN provides a more precise, interpretable, and scalable framework for inferring individual functional activation maps from resting-state data, with code publicly available at https://github.com/univanxx/brainsparsecnn.

Alexandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov, Airat Valiev, Vladimir Makharev, Petr Anokhin, Galina Zubkova, Elena Tutubalina. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Though Large Vision-Language Models (LVLMs) are being actively explored in medicine, their ability to conduct complex realworld telemedicine consultations combining accurate diagnosis with professional dialogue remains underexplored.This paper presents 3MDBench (Medical Multimodal Multi-agent Dialogue Benchmark), an open-source framework for simulating and evaluating LVLM-driven telemedical consultations.3MDBench simulates patient variability through temperament-based Patient Agent and evaluates diagnostic accuracy and dialogue quality via Assessor Agent.It includes 2996 cases across 34 diagnoses from real-world telemedicine interactions, combining textual and image-based data.The experimental study compares diagnostic strategies for widely used open and closed-source LVLMs.We demonstrate that multimodal dialogue with internal reasoning improves F1 score by 6.5% over non-dialogue settings, highlighting the importance of context-aware, informationseeking questioning.Moreover, injecting predictions from a diagnostic convolutional neural network into the LVLM's context boosts F1 by up to 20%.