# Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture

arXiv: https://arxiv.org/abs/1707.06682 · HTML: https://mlsanity.com/paper/1707.06682/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 11 of 11)

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- [MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://mlsanity.com/paper/2309.13363/) (2023)
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- [Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review](https://mlsanity.com/paper/2405.00577/) (2024)
- [Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States](https://mlsanity.com/paper/2303.14391/) (2023)
- [Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networks](https://mlsanity.com/paper/2404.10031/) (2024)
- [Deconvolving Complex Neuronal Networks into Interpretable Task-Specific Connectomes](https://mlsanity.com/paper/2407.00201/) (2024)
- [Cognitive Networks and Performance Drive fMRI-Based State Classification Using DNN Models](https://mlsanity.com/paper/2409.00003/) (2024)
- [Graph Attention Network-Based Detection of Autism Spectrum Disorder](https://mlsanity.com/paper/2603.26971/) (2026)
- [Rhamba: Region-Aware Hybrid Attention-Mamba Framework for Self-Supervised Learning in Resting-State fMRI](https://mlsanity.com/paper/2605.01240/) (2026)
- [BrainNet Studio: A Unified Toolkit for Brain Network Construction, Intelligent Analysis, and Visualization](https://mlsanity.com/paper/2609.37956/) (2026)

## Abstract (arXiv)

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.