# A hypothesis-driven method based on machine learning for neuroimaging data analysis

arXiv: https://arxiv.org/abs/2202.04397 · HTML: https://mlsanity.com/paper/2202.04397/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Cross-validation failure: small sample sizes lead to large error bars](https://mlsanity.com/paper/1706.07581/) (2017)
- [Classification accuracy as a proxy for two sample testing](https://mlsanity.com/paper/1602.02210/) (2016)
- [Can parametric statistical methods be trusted for fMRI based group studies?](https://mlsanity.com/paper/1511.01863/) (2015)
- [Better-Than-Chance Classification for Signal Detection](https://mlsanity.com/paper/1608.08873/) (2016)
- [Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks](https://mlsanity.com/paper/2002.07874/) (2020)
- [Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities](https://mlsanity.com/paper/1912.12274/) (2019)
- [The Same Analysis Approach: Practical protection against the pitfalls of novel neuroimaging analysis methods](https://mlsanity.com/paper/1703.06670/) (2017)
- [Classical Statistics and Statistical Learning in Imaging Neuroscience](https://mlsanity.com/paper/1603.01857/) (2016)
- [A connection between the pattern classification problem and the General Linear Model for statistical inference](https://mlsanity.com/paper/2012.08903/) (2020)

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

- [Is K-fold cross validation the best model selection method for Machine Learning?](https://mlsanity.com/paper/2401.16407/) (2024)
- [Statistical Agnostic Regression: a machine learning method to validate regression models](https://mlsanity.com/paper/2402.15213/) (2024)

## Abstract (arXiv)

There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is assessed by randomly permuting image labels or by the use of random effect models that consider between-subject variability. These multivariate MLE-based statistical pipelines, whilst potentially more effective for detecting activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation, and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of the conventional GLM has been demonstrated to be connected to an univariate classification task when the design matrix is expressed as a binary indicator matrix. In this paper we explore the complete connection between the univariate GLM and MLE regressions. To this purpose we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support Vector Regression (SVR) in the inverse problem (SVR-iGLM). Subsequently, random field theory (RFT) is employed for assessing statistical significance following a conventional GLM benchmark. Experimental results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result in different experimental design estimates that are significantly related to the predefined functional task. Moreover, using real data from a multisite initiative the proposed MLE-based inference demonstrates statistical power and the control of false positives, outperforming the regular GLM.