Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding

• URL: http://arxiv.org/abs/2510.07342v1
• Date: Tue, 07 Oct 2025 18:26:53 GMT
• Title: Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding
• Abstract summary: fMRI data is acquired as a 3D volume of voxels, where each voxel has a defined spatial location in the brain.<n> conventional encoding models often flatten this volume into a 1D vector and treat voxel responses as independent outputs.<n>We introduce the Neural Response Function (NRF), a framework that models fMRI activity as a continuous function over anatomical space.
• Score: 4.586830141349792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural encoding models aim to predict fMRI-measured brain responses to natural images. fMRI data is acquired as a 3D volume of voxels, where each voxel has a defined spatial location in the brain. However, conventional encoding models often flatten this volume into a 1D vector and treat voxel responses as independent outputs. This removes spatial context, discards anatomical information, and ties each model to a subject-specific voxel grid. We introduce the Neural Response Function (NRF), a framework that models fMRI activity as a continuous function over anatomical space rather than a flat vector of voxels. NRF represents brain activity as a continuous implicit function: given an image and a spatial coordinate (x, y, z) in standardized MNI space, the model predicts the response at that location. This formulation decouples predictions from the training grid, supports querying at arbitrary spatial resolutions, and enables resolution-agnostic analyses. By grounding the model in anatomical space, NRF exploits two key properties of brain responses: (1) local smoothness -- neighboring voxels exhibit similar response patterns; modeling responses continuously captures these correlations and improves data efficiency, and (2) cross-subject alignment -- MNI coordinates unify data across individuals, allowing a model pretrained on one subject to be fine-tuned on new subjects. In experiments, NRF outperformed baseline models in both intrasubject encoding and cross-subject adaptation, achieving high performance while reducing the data size needed by orders of magnitude. To our knowledge, NRF is the first anatomically aware encoding model to move beyond flattened voxels, learning a continuous mapping from images to brain responses in 3D space.

Related papers

• Simple Models, Rich Representations: Visual Decoding from Primate Intracortical Neural Signals [0.0]
  We address the problem of decoding visual information from high-density intracortical recordings in primates.<n>We develop a modular generative decoding pipeline that combines low-resolution latent reconstruction with semantically conditioned diffusion.<n>This framework provides principles for brain-computer interfaces and semantic neural decoding.
  arXiv  Detail & Related papers  (2026-01-16T09:10:31Z)
• 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)
• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• Rest2Visual: Predicting Visually Evoked fMRI from Resting-State Scans [30.743554598059692]
  We introduce Rest2Visual, a conditional generative model that predicts visually evoked fMRI (ve-fMRI) from resting-state input and 2D visual stimuli.<n>Our results provide compelling evidence that individualized spontaneous neural activity can be transformed into stimulus-aligned representations.
  arXiv  Detail & Related papers  (2025-09-17T01:08:03Z)
• From Flat to Round: Redefining Brain Decoding with Surface-Based fMRI and Cortex Structure [11.760848227175591]
  Reconstructing visual stimuli from human brain activity (e.g., fMRI) bridges neuroscience and computer vision.<n>We propose a novel sphere tokenizer that explicitly models fMRI signals as spatially coherent 2D spherical data on the cortical surface.<n>We also propose integration of structural MRI (sMRI) data, enabling personalized encoding of individual anatomical variations.
  arXiv  Detail & Related papers  (2025-07-22T09:34:39Z)
• Voxel-Level Brain States Prediction Using Swin Transformer [65.9194533414066]
  We propose a novel architecture which employs a 4D Shifted Window (Swin) Transformer as encoder to efficiently learn-temporal information and a convolutional decoder to enable brain state prediction at the same spatial and temporal resolution as the input fMRI data.<n>Our model has shown high accuracy when predicting 7.2s resting-state brain activities based on the prior 23.04s fMRI time series.<n>This shows promising evidence that thetemporal organization of the human brain can be learned by a Swin Transformer model, at high resolution, which provides a potential for reducing fMRI scan time and the development of brain-computer interfaces
  arXiv  Detail & Related papers  (2025-06-13T04:14:38Z)
• Brain3D: Generating 3D Objects from fMRI [76.41771117405973]
  We design a novel 3D object representation learning method, Brain3D, that takes as input the fMRI data of a subject.<n>We show that our model captures the distinct functionalities of each region of human vision system.<n>Preliminary evaluations indicate that Brain3D can successfully identify the disordered brain regions in simulated scenarios.
  arXiv  Detail & Related papers  (2024-05-24T06:06:11Z)
• 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)
• BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations [67.79256149583108]
  We propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals. By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point.
  arXiv  Detail & Related papers  (2024-04-30T10:53:30Z)
• MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
  Brain decoding aims to reconstruct stimuli from acquired brain signals. Currently, brain decoding is confined to a per-subject-per-model paradigm. We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
  arXiv  Detail & Related papers  (2024-04-11T15:46:42Z)
• Data-driven modelling of brain activity using neural networks, Diffusion Maps, and the Koopman operator [0.0]
  We propose a machine-learning approach to model long-term out-of-sample dynamics of brain activity from task-dependent fMRI data. We use Diffusion maps (DMs) to discover a set of variables that parametrize the low-dimensional manifold on which the emergent high-dimensional fMRI time series evolve. We construct reduced-order-models (ROMs) on the embedded manifold via two techniques: Feedforward Neural Networks (FNNs) and the Koopman operator.
  arXiv  Detail & Related papers  (2023-04-24T09:08:12Z)
• Deep Representations for Time-varying Brain Datasets [4.129225533930966]
  This paper builds an efficient graph neural network model that incorporates both region-mapped fMRI sequences and structural connectivities as inputs. We find good representations of the latent brain dynamics through learning sample-level adaptive adjacency matrices. These modules can be easily adapted to and are potentially useful for other applications outside the neuroscience domain.
  arXiv  Detail & Related papers  (2022-05-23T21:57:31Z)
• A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study [0.0]
  We introduce an end-to-end algorithm capable of extracting features from full 4-D data using 3-D CNNs and 3-D Magnetical LSTMs. Our results show that the proposed model achieves state of the art results on single sites with F1-scores of 0.78 and 0.7 on NYU and UM sites, respectively.
  arXiv  Detail & Related papers  (2020-02-14T11:52:00Z)

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.