标题： BrainNetMLP：一种用于功能脑网络分类的高效有效基线 显示英文标题

标题： BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classification

摘要： 近年来，通过将大脑建模为感兴趣区域（ROIs）的网络，并利用它们的连接来理解大脑功能和诊断精神障碍，功能性脑网络分类取得了重大进展。 各种深度学习架构，包括卷积神经网络、图神经网络以及最近的Transformer，已被开发出来。 然而，尽管这些模型的复杂性不断增加，性能提升并不显著。 这引发了一个问题：增加模型复杂性是否必然导致更高的分类准确性？ 在本文中，我们重新审视最简单的深度学习架构——多层感知机（MLP），并提出了一种纯MLP方法，称为BrainNetMLP，用于功能性脑网络分类，该方法充分利用了MLP的优势，包括高效的计算和更少的参数。 此外，BrainNetMLP结合了双分支结构，以联合捕捉空间连通性和频谱信息，实现精确的时空特征融合。 我们在两个公开且流行的脑网络分类数据集上评估了我们提出的BrainNetMLP，即人类连接组计划（HCP）和自闭症脑成像数据交换（ABIDE）。 实验结果表明，纯MLP方法可以达到最先进的性能，揭示了基于MLP的模型在功能性脑网络分类中作为更高效且有效的替代方案的潜力。 代码将在 https://github.com/JayceonHo/BrainNetMLP 上提供。 显示英文摘要

摘要： Recent studies have made great progress in functional brain network classification by modeling the brain as a network of Regions of Interest (ROIs) and leveraging their connections to understand brain functionality and diagnose mental disorders. Various deep learning architectures, including Convolutional Neural Networks, Graph Neural Networks, and the recent Transformer, have been developed. However, despite the increasing complexity of these models, the performance gain has not been as salient. This raises a question: Does increasing model complexity necessarily lead to higher classification accuracy? In this paper, we revisit the simplest deep learning architecture, the Multi-Layer Perceptron (MLP), and propose a pure MLP-based method, named BrainNetMLP, for functional brain network classification, which capitalizes on the advantages of MLP, including efficient computation and fewer parameters. Moreover, BrainNetMLP incorporates a dual-branch structure to jointly capture both spatial connectivity and spectral information, enabling precise spatiotemporal feature fusion. We evaluate our proposed BrainNetMLP on two public and popular brain network classification datasets, the Human Connectome Project (HCP) and the Autism Brain Imaging Data Exchange (ABIDE). Experimental results demonstrate pure MLP-based methods can achieve state-of-the-art performance, revealing the potential of MLP-based models as more efficient yet effective alternatives in functional brain network classification. The code will be available at https://github.com/JayceonHo/BrainNetMLP.