Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic

• URL: http://arxiv.org/abs/2603.24176v2
• Date: Tue, 31 Mar 2026 09:59:29 GMT
• Title: Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic
• Abstract summary: High-resolution fMRI limits large-scale applications.<n>EEG offers millisecond-level temporal cues that complement fMRI.<n>EEG-based reconstruction of dynamic fMRI improves sequence continuity and practical applicability.
• Score: 31.029079653041336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain mechanisms. Functional magnetic resonance imaging (fMRI) provides high-resolution cortical representations that form a strong basis for characterizing fine-grained brain activity patterns. The high acquisition cost of fMRI limits large-scale applications, therefore making high-quality fMRI reconstruction a crucial task. Electroencephalography (EEG) offers millisecond-level temporal cues that complement fMRI. Leveraging this complementarity, we present an EEG-conditioned framework for reconstructing dynamic fMRI as continuous neural sequences with high spatial fidelity and strong temporal coherence at the cortical-vertex level. To address sampling irregularities common in real fMRI acquisitions, we incorporate a null-space intermediate-frame reconstruction, enabling measurement-consistent completion of arbitrary intermediate frames and improving sequence continuity and practical applicability. Experiments on the CineBrain dataset demonstrate superior voxel-wise reconstruction quality and robust temporal consistency across whole-brain and functionally specific regions. The reconstructed fMRI also preserves essential functional information, supporting downstream visual decoding tasks. This work provides a new pathway for estimating high-resolution fMRI dynamics from EEG and advances multimodal neuroimaging toward more dynamic brain activity modeling.

Related papers

• DD-INR: Dynamics-Driven Implicit Neural Representation for Accelerated Whole-Brain Functional MRI Reconstruction [0.9867739577559261]
  fMRI enables enhanced detection of neurovascular (BOLD) activity in the brain.<n>Traditional MRI reconstruction methods fail to recover with high k-space under: Task-kedevo signals are small in magnitude.<n>We present DD-INR, a framework tailored for accelerated fMRI reconstruction.
  arXiv  Detail & Related papers  (2026-06-09T12:09:34Z)
• Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis [5.338598022966635]
  We present a framework that integrates MRI and fMRI for classification of Alzheimer Disease (AD), Mild Cognitive Impairment, and Normal Cognitive State.<n> structural features are extracted from MRI using 3D convolutional neural networks, while temporal features are learned from fMRI sequences.<n>Results show that data augmentation substantially improves classification stability and generalization, particularly for the multimodal 3DCNN-LSTM model.
  arXiv  Detail & Related papers  (2026-03-09T12:58:45Z)
• Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction [65.67001243986981]
  We propose MindHier, a coarse-to-fine fMRI-to-image reconstruction framework built on scale-wise autoregressive modeling.<n>MindHier achieves superior semantic fidelity, 4.67x faster inference, and more deterministic results than the diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-10-25T15:40:07Z)
• NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping [15.811159139388971]
  We introduce NeuroBOLT, i.e., Neuro-to-BOLD Transformer, to translate raw EEG data to fMRI activity signals across the brain.<n>Our experiments demonstrate that NeuroBOLT effectively reconstructs unseen resting-state fMRI signals from primary sensory, high-level cognitive areas, and deep subcortical brain regions.
  arXiv  Detail & Related papers  (2024-10-07T02:47:55Z)
• BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals [11.030708270737964]
  We propose Brain Masked Auto-Encoder (BrainMAE) for learning representations directly from fMRI time-series data. BrainMAE consistently outperforms established baseline methods by significant margins in four distinct downstream tasks.
  arXiv  Detail & Related papers  (2024-06-24T19:16:24Z)
• MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding [50.55024115943266]
  We introduce a novel semantic alignment method of multi-subject fMRI signals using so-called MindFormer. This model is specifically designed to generate fMRI-conditioned feature vectors that can be used for conditioning Stable Diffusion model for fMRI- to-image generation or large language model (LLM) for fMRI-to-text generation. Our experimental results demonstrate that MindFormer generates semantically consistent images and text across different subjects.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• Leveraging sinusoidal representation networks to predict fMRI signals from EEG [3.3121941932506473]
  We propose a novel architecture that can predict fMRI signals directly from multi-channel EEG without explicit feature engineering. Our model achieves this by implementing a Sinusoidal Representation Network (SIREN) to learn frequency information in brain dynamics. We evaluate our model using a simultaneous EEG-fMRI dataset with 8 subjects and investigate its potential for predicting subcortical fMRI signals.
  arXiv  Detail & Related papers  (2023-11-06T03:16:18Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• Joint fMRI Decoding and Encoding with Latent Embedding Alignment [77.66508125297754]
  We introduce a unified framework that addresses both fMRI decoding and encoding. Our model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework.
  arXiv  Detail & Related papers  (2023-03-26T14:14:58Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
  We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
  arXiv  Detail & Related papers  (2020-01-14T22:55:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.