# Deep learning for neuroimaging: a validation study

arXiv: https://arxiv.org/abs/1312.5847 · HTML: https://mlsanity.com/paper/1312.5847/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2013.

## Built on by (most cited first, 21 of 21)

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- [Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes](https://mlsanity.com/paper/2209.02876/) (2022)
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- [SENDER: SEmi-Nonlinear Deep Efficient Reconstructor for Extraction Canonical, Meta, and Sub Functional Connectivity in the Human Brain](https://mlsanity.com/paper/2209.05627/) (2022)
- [AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers -- A narrative review of a growing field](https://mlsanity.com/paper/2406.17822/) (2024)
- [Defining and Benchmarking a Data-Centric Design Space for Brain Graph Construction](https://mlsanity.com/paper/2508.12533/) (2025)
- [Transformers for Multimodal Brain State Decoding: Integrating Functional Magnetic Resonance Imaging Data and Medical Metadata](https://mlsanity.com/paper/2512.08462/) (2025)

## Abstract (arXiv)

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.