# Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations

arXiv: https://arxiv.org/abs/2409.11377 · HTML: https://mlsanity.com/paper/2409.11377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 80 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness](https://mlsanity.com/paper/2110.03753/) (2021)
- [Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data](https://mlsanity.com/paper/2206.11417/) (2022)
- [NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics](https://mlsanity.com/paper/2306.06202/) (2023)
- [AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2011.11004/) (2020)
- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)
- [Data-Driven Network Neuroscience: On Data Collection and Benchmark](https://mlsanity.com/paper/2211.12421/) (2022)
- [Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism](https://mlsanity.com/paper/1907.01288/) (2019)
- [Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals](https://mlsanity.com/paper/2307.00222/) (2023)
- [Learning to Approximate Adaptive Kernel Convolution on Graphs](https://mlsanity.com/paper/2401.11840/) (2024)
- [Benchmarking Graph Neural Networks for FMRI analysis](https://mlsanity.com/paper/2211.08927/) (2022)
- [DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics](https://mlsanity.com/paper/2306.03088/) (2023)
- [Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders](https://mlsanity.com/paper/1912.01181/) (2019)

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

- [Large Connectome Model: An fMRI Foundation Model of Brain Connectomes Empowered by Brain-Environment Interaction in Multitask Learning Landscape](https://mlsanity.com/paper/2510.18910/) (2025)
- [GeoDynamics: A Geometric State-Space Neural Network for Understanding Brain Dynamics on Riemannian Manifolds](https://mlsanity.com/paper/2601.13570/) (2026)

## Abstract (arXiv)

An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand the relationship between functional fluctuation and human cognition/behavior using a data-driven approach. To that end, tremendous efforts have been made in machine learning to predict cognitive states from evolving volumetric images of blood-oxygen-level-dependent (BOLD) signals. Due to the complex nature of brain function, however, the evaluation on learning performance and discoveries are not often consistent across current state-of-the-arts (SOTA). By capitalizing on large-scale existing neuroimaging data (34,887 data samples from six public databases), we seek to establish a well-founded empirical guideline for designing deep models for functional neuroimages by linking the methodology underpinning with knowledge from the neuroscience domain. Specifically, we put the spotlight on (1) What is the current SOTA performance in cognitive task recognition and disease diagnosis using fMRI? (2) What are the limitations of current deep models? and (3) What is the general guideline for selecting the suitable machine learning backbone for new neuroimaging applications? We have conducted a comprehensive evaluation and statistical analysis, in various settings, to answer the above outstanding questions.