Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline
- URL: http://arxiv.org/abs/2303.06410v1
- Date: Sat, 11 Mar 2023 14:04:58 GMT
- Title: Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline
- Authors: Xuhang Chen, Baiying Lei, Chi-Man Pun, Shuqiang Wang
- Abstract summary: Brain diffuser is a diffusion based end-to-end brain network generative model.
It exploits more structural connectivity features and disease-related information by analyzing disparities in structural brain networks across subjects.
For the case of Alzheimer's disease, the proposed model performs better than the results from existing toolkits on the Alzheimer's Disease Neuroimaging Initiative database.
- Score: 54.93591298333767
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Brain network analysis is essential for diagnosing and intervention for
Alzheimer's disease (AD). However, previous research relied primarily on
specific time-consuming and subjective toolkits. Only few tools can obtain the
structural brain networks from brain diffusion tensor images (DTI). In this
paper, we propose a diffusion based end-to-end brain network generative model
Brain Diffuser that directly shapes the structural brain networks from DTI.
Compared to existing toolkits, Brain Diffuser exploits more structural
connectivity features and disease-related information by analyzing disparities
in structural brain networks across subjects. For the case of Alzheimer's
disease, the proposed model performs better than the results from existing
toolkits on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Related papers
- MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data [64.92867794764247]
MindAligner is a framework for cross-subject brain decoding from limited fMRI data.
Brain Transfer Matrix (BTM) projects the brain signals of an arbitrary new subject to one of the known subjects.
Brain Functional Alignment module is proposed to perform soft cross-subject brain alignment under different visual stimuli.
arXiv Detail & Related papers (2025-02-07T16:01:59Z) - BrainOOD: Out-of-distribution Generalizable Brain Network Analysis [19.986844377608247]
Graph Neural Networks (GNNs) have shown promising in analyzing brain networks.
BrainOOD is a novel framework tailored for brain networks that enhances GNNs' Out-of-Distribution generalization and interpretability.
Our approach outperforms 16 existing methods and improves generalization to OOD subjects by up to 8.5%.
arXiv Detail & Related papers (2025-02-02T14:26:09Z) - BrainMAP: Learning Multiple Activation Pathways in Brain Networks [77.15180533984947]
We introduce a novel framework BrainMAP to learn Multiple Activation Pathways in Brain networks.
Our framework enables explanatory analyses of crucial brain regions involved in tasks.
arXiv Detail & Related papers (2024-12-23T09:13:35Z) - 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) - BrainNetDiff: Generative AI Empowers Brain Network Generation via
Multimodal Diffusion Model [7.894526238189559]
We introduce BrainNetDiff, which combines a multi-head Transformer encoder to extract relevant features from fMRI time series.
We validate applicability of this framework in the construction of brain network across healthy and neurologically impaired cohorts.
arXiv Detail & Related papers (2023-11-09T08:27:12Z) - Transformer-Based Hierarchical Clustering for Brain Network Analysis [13.239896897835191]
We propose a novel interpretable transformer-based model for joint hierarchical cluster identification and brain network classification.
With the help of hierarchical clustering, the model achieves increased accuracy and reduced runtime complexity while providing plausible insight into the functional organization of brain regions.
arXiv Detail & Related papers (2023-05-06T22:14:13Z) - Adversarial Learning Based Structural Brain-network Generative Model for
Analyzing Mild Cognitive Impairment [7.403660531145136]
Mild cognitive impairment(MCI) is a precursor of Alzheimer's disease(AD)
In this work, an adversarial learning-based structural brain-network generative model(SBGM) is proposed to directly learn the structural connections from brain diffusion tensor images.
Our proposed model tri-classifies EMCI, LMCI, and NC subjects, achieving a classification accuracy of 83.33% on the Alzheimer's Disease Neuroimaging Initiative(ADNI) database.
arXiv Detail & Related papers (2022-08-09T02:45:53Z) - 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) - BrainNetGAN: Data augmentation of brain connectivity using generative
adversarial network for dementia classification [9.312868504719193]
Alzheimer's disease is the most common age-related dementia.
Brain MRI offers a noninvasive biomarker to detect brain aging.
Alzheimer's disease is the most common age-related dementia.
arXiv Detail & Related papers (2021-03-10T23:44:53Z) - Towards a Neural Model for Serial Order in Frontal Cortex: a Brain
Theory from Memory Development to Higher-Level Cognition [53.816853325427424]
We propose that the immature prefrontal cortex (PFC) use its primary functionality of detecting hierarchical patterns in temporal signals.
Our hypothesis is that the PFC detects the hierarchical structure in temporal sequences in the form of ordinal patterns and use them to index information hierarchically in different parts of the brain.
By doing so, it gives the tools to the language-ready brain for manipulating abstract knowledge and planning temporally ordered information.
arXiv Detail & Related papers (2020-05-22T14:29:51Z)
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.