# Machine Learning for Neuroimaging with Scikit-Learn

arXiv: https://arxiv.org/abs/1412.3919 · HTML: https://mlsanity.com/paper/1412.3919/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2014.

## Built on by (most cited first, 30 of 70)

- [Toward a realistic model of speech processing in the brain with self-supervised learning](https://mlsanity.com/paper/2206.01685/) (2022)
- [Py-Feat: Python Facial Expression Analysis Toolbox](https://mlsanity.com/paper/2104.03509/) (2021)
- [Aligning individual brains with Fused Unbalanced Gromov-Wasserstein](https://mlsanity.com/paper/2206.09398/) (2022)
- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech](https://mlsanity.com/paper/2103.01032/) (2021)
- [Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification](https://mlsanity.com/paper/2409.10944/) (2024)
- [Data-Driven Network Neuroscience: On Data Collection and Benchmark](https://mlsanity.com/paper/2211.12421/) (2022)
- [Modeling structure-building in the brain with CCG parsing and large language models](https://mlsanity.com/paper/2210.16147/) (2022)
- [Spatially Covariant Lesion Segmentation](https://mlsanity.com/paper/2301.07895/) (2023)
- [NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties](https://mlsanity.com/paper/2402.01590/) (2024)
- [Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention](https://mlsanity.com/paper/2205.09576/) (2022)
- [BrainOOD: Out-of-distribution Generalizable Brain Network Analysis](https://mlsanity.com/paper/2502.01688/) (2025)
- [Shared Independent Component Analysis for Multi-Subject Neuroimaging](https://mlsanity.com/paper/2110.13502/) (2021)
- [Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion](https://mlsanity.com/paper/2410.08228/) (2024)
- [SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges](https://mlsanity.com/paper/2504.18882/) (2025)
- [Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems](https://mlsanity.com/paper/2104.02545/) (2021)
- [Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality](https://mlsanity.com/paper/2101.10471/) (2021)
- [Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans](https://mlsanity.com/paper/2206.15445/) (2022)
- [Finding emergence in data by maximizing effective information](https://mlsanity.com/paper/2308.09952/) (2023)
- [Evaluating deep transfer learning for whole-brain cognitive decoding](https://mlsanity.com/paper/2111.01562/) (2021)
- [A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries](https://mlsanity.com/paper/2208.00840/) (2022)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds](https://mlsanity.com/paper/2405.07977/) (2024)
- [Across-subject ensemble-learning alleviates the need for large samples for fMRI decoding](https://mlsanity.com/paper/2407.12056/) (2024)
- [LITcoder: A General-Purpose Library for Building and Comparing Encoding Models](https://mlsanity.com/paper/2509.09152/) (2025)
- [Comparing interpretation methods in mental state decoding analyses with deep learning models](https://mlsanity.com/paper/2205.15581/) (2022)
- [PAVI: Plate-Amortized Variational Inference](https://mlsanity.com/paper/2206.05111/) (2022)
- [Mind-to-Image: Projecting Visual Mental Imagination of the Brain from fMRI](https://mlsanity.com/paper/2404.05468/) (2024)
- [R&B -- Rhythm and Brain: Cross-subject Decoding of Music from Human Brain Activity](https://mlsanity.com/paper/2406.15537/) (2024)
- [Multimodal LLM With Hierarchical Mixture-of-Experts for VQA on 3D Brain MRI](https://mlsanity.com/paper/2509.25889/) (2025)

## Abstract (arXiv)

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.