Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction

• URL: http://arxiv.org/abs/2603.07524v1
• Date: Sun, 08 Mar 2026 08:28:14 GMT
• Title: Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
• Abstract summary: A neural dynamics-informed pre-trained framework is proposed for personalized brain functional network construction.<n>The proposed framework extracts personalized representations of neural activity patterns in heterogeneous scenarios.
• Score: 10.450067698585485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain activity is intrinsically a neural dynamic process constrained by anatomical space. This leads to significant variations in spatial distribution patterns and correlation patterns of neural activity across variable and heterogeneous scenarios. However, dominant brain functional network construction methods, which relies on pre-defined brain atlases and linear assumptions, fails to precisely capture varying neural activity patterns in heterogeneous scenarios. This limits the consistency and generalizability of the brain functional networks constructed by dominant methods. Here, a neural dynamics-informed pre-trained framework is proposed for personalized brain functional network construction. The proposed framework extracts personalized representations of neural activity patterns in heterogeneous scenarios. Personalized brain functional networks are obtained by utilizing these representations to guide brain parcellation and neural activity correlation estimation. Systematic evaluations were employed on 18 datasets across tasks, such as virtual neural modulation and abnormal neural circuit identification. Experimental results demonstrate that the proposed framework attains superior performance in heterogeneous scenarios. Overall, the proposed framework challenges the dominant brain functional network construction method.

Related papers

• Discovering Chunks in Neural Embeddings for Interpretability [53.80157905839065]
  We propose leveraging the principle of chunking to interpret artificial neural population activities.<n>We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities.<n>We identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts.
  arXiv  Detail & Related papers  (2025-02-03T20:30:46Z)
• IP$^{2}$-RSNN: Bi-level Intrinsic Plasticity Enables Learning-to-learn in Recurrent Spiking Neural Networks [20.88195975299024]
  We develop a recurrent spiking neural network with bi-level intrinsic plasticity (IP$2$-RSNN)<n>Our results indicate that the proposed bi-level intrinsic plasticity plays a critical role in enabling L2L in RSNNs.
  arXiv  Detail & Related papers  (2025-01-24T14:45:03Z)
• Retinal Vessel Segmentation via Neuron Programming [17.609169389489633]
  This paper introduces a novel approach to neural network design, termed neuron programming'', to enhance a network's representation ability at the neuronal level. Comprehensive experiments validate that neuron programming can achieve competitive performance in retinal blood segmentation.
  arXiv  Detail & Related papers  (2024-11-17T16:03:30Z)
• Brain-like Functional Organization within Large Language Models [58.93629121400745]
  The human brain has long inspired the pursuit of artificial intelligence (AI) Recent neuroimaging studies provide compelling evidence of alignment between the computational representation of artificial neural networks (ANNs) and the neural responses of the human brain to stimuli. In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs) This framework links the AN sub-groups to FBNs, enabling the delineation of brain-like functional organization within large language models (LLMs)
  arXiv  Detail & Related papers  (2024-10-25T13:15:17Z)
• Graph-Based Representation Learning of Neuronal Dynamics and Behavior [2.3859858429583665]
  We introduce the Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a novel framework that models time-varying neuronal connectivity.<n>TAVRNN learns latent dynamics at the single-unit level while maintaining interpretable population-level representations.<n>We validate TAVRNN on three diverse datasets: (1) electrophysiological data from a freely behaving rat, (2) primate somatosensory cortex recordings during a reaching task, and (3) biological neurons in the DishBrain platform interacting with a virtual game environment.
  arXiv  Detail & Related papers  (2024-10-01T13:19:51Z)
• 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)
• Automated Natural Language Explanation of Deep Visual Neurons with Large Models [43.178568768100305]
  This paper proposes a novel post-hoc framework for generating semantic explanations of neurons with large foundation models. Our framework is designed to be compatible with various model architectures and datasets, automated and scalable neuron interpretation.
  arXiv  Detail & Related papers  (2023-10-16T17:04:51Z)
• Learning to Act through Evolution of Neural Diversity in Random Neural Networks [9.387749254963595]
  In most artificial neural networks (ANNs), neural computation is abstracted to an activation function that is usually shared between all neurons. We propose the optimization of neuro-centric parameters to attain a set of diverse neurons that can perform complex computations.
  arXiv  Detail & Related papers  (2023-05-25T11:33:04Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution [51.333918985340425]
  We develop a novel attention mesh convolution model to predict cortical gyro-sulcal segmentation maps on individual brains. Experiments show that the prediction performance via our model outperforms other state-of-the-art models.
  arXiv  Detail & Related papers  (2022-05-21T14:08:53Z)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• A Neural Dynamic Model based on Activation Diffusion and a Micro-Explanation for Cognitive Operations [4.416484585765028]
  The neural mechanism of memory has a very close relation with the problem of representation in artificial intelligence. A computational model was proposed to simulate the network of neurons in brain and how they process information.
  arXiv  Detail & Related papers  (2020-11-27T01:34:08Z)

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.