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

arXiv: https://arxiv.org/abs/2505.11538 · HTML: https://mlsanity.com/paper/2505.11538/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 17 of 17)

- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Rethinking the Value of Network Pruning](https://mlsanity.com/paper/1810.05270/) (2018)
- [TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis](https://mlsanity.com/paper/2410.16032/) (2024)
- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [Multimodal Transformer with Multi-View Visual Representation for Image Captioning](https://mlsanity.com/paper/1905.07841/) (2019)
- [Graph-MLP: Node Classification without Message Passing in Graph](https://mlsanity.com/paper/2106.04051/) (2021)
- [Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2110.00685/) (2021)
- [FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation](https://mlsanity.com/paper/2205.12465/) (2022)
- [Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis](https://mlsanity.com/paper/2003.10613/) (2020)
- [DynaMixer: A Vision MLP Architecture with Dynamic Mixing](https://mlsanity.com/paper/2201.12083/) (2022)
- [Community-Aware Transformer for Autism Prediction in fMRI Connectome](https://mlsanity.com/paper/2307.10181/) (2023)
- [Analyzing complex functional brain networks: fusing statistics and network science to understand the brain](https://mlsanity.com/paper/1302.5721/) (2013)
- [BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals](https://mlsanity.com/paper/2406.17086/) (2024)
- [Raformer: Redundancy-Aware Transformer for Video Wire Inpainting](https://mlsanity.com/paper/2404.15802/) (2024)
- [Signal Processing over Time-Varying Graphs: A Systematic Review](https://mlsanity.com/paper/2412.00462/) (2024)

## Built on by (most cited first, 2 of 2)

- [ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis](https://mlsanity.com/paper/2601.00416/) (2026)
- [UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning](https://mlsanity.com/paper/2609.31298/) (2026)

## Abstract (arXiv)

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.