# A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes

arXiv: https://arxiv.org/abs/2105.14409 · HTML: https://mlsanity.com/paper/2105.14409/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 30 May 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [A Riemannian Network for SPD Matrix Learning](https://mlsanity.com/paper/1608.04233/) (2016)
- [Deep Representation Learning For Multimodal Brain Networks](https://mlsanity.com/paper/2007.09777/) (2020)
- [Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks](https://mlsanity.com/paper/1911.03583/) (2019)
- [Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph](https://mlsanity.com/paper/2009.11058/) (2020)
- [Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data](https://mlsanity.com/paper/2007.01930/) (2020)

## Built on by (most cited first, 5 of 5)

- [Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome](https://mlsanity.com/paper/2211.07363/) (2022)
- [MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction](https://mlsanity.com/paper/2307.07093/) (2023)
- [mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds](https://mlsanity.com/paper/2303.14986/) (2023)
- [Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound](https://mlsanity.com/paper/2408.04826/) (2024)
- [Bayesian Models of Functional Connectomics and Behavior](https://mlsanity.com/paper/2301.06182/) (2023)

## Abstract (arXiv)

We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures. Our specialized autoencoder infers a low dimensional manifold embedding for the rs-fMRI correlation matrices that mimics a canonical outer-product decomposition. The embedding is simultaneously used to reconstruct DTI tractography matrices via a second manifold alignment decoder and to predict inter-subject phenotypic variability via an artificial neural network. We validate our framework on a dataset of 275 healthy individuals from the Human Connectome Project database and on a second clinical dataset consisting of 57 subjects with Autism Spectrum Disorder. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive and interpretable brain biomarkers in a cross-validated setting. Finally, our framework outperforms several baselines at predicting behavioral phenotypes in both real-world datasets.