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# Representation learning for more reproducible fMRI data analyses

Elodie Germani, Elisa Fromont, Camille Maumet

► To cite this version:

Elodie Germani, Elisa Fromont, Camille Maumet. Representation learning for more reproducible fMRI data analyses. IABM 2023 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédical, Mar 2023, Paris, France. pp.1. hal-04068797

HAL Id: hal-04068797

https://inria.hal.science/hal-04068797

Submitted on 14 Apr 2023

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# UNDERSTANDING VARIABILITY

## How does analytical variability vary with sample size ?

(Germani et al., 2022)

<figure><img src="image_2.png" /><figcaption>Fig. 1 - Maximum statistical value inside ROI depending on sample size (right) and ratio of largest/smallest maximum statistical value depending on sample sizes (left)</figcaption></figure>

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In psychology, (Klau et al., 2020) showed that the vibration of effects decreases and stabilizes as sample size increases.

<figure><img src="image_4.png" /></figure>

* ✓ Reproduction of 8 pipelines used in a many-analyst study
* → **NARPS Open Pipelines:** github.com/Inria-Empenn/narps_open_pipelines

* ✓ Selection of subsets with different numbers of participants;

* ✓ Replication of the results with the subsets.

<figure><img src="image_5.png" /></figure>

* → With growing sample sizes, maximum statistical values seemed to converge

* → Confirmed by the stabilization and convergence to 1.3 of the ratio between the largest and the smallest maximum statistical values among teams with sample size.

* → But even for large sample sizes, analytical variability remains non neglectable.

Code available at [gitlab.inria.fr/egermani/analytic_variability_fmri/](https://gitlab.inria.fr/egermani/analytic_variability_fmri/)

## Exploring variability patterns in the analytical space

(Germani et al., 2023b)

<figure><img src="image_6.png" /><figcaption>Fig. 2 - Variability of group statistic maps (n=50) of contrast right hand. (A) K-mean clustering on PCA components of statistic maps obtained with different pipelines (Analysis software, Smoothing kernel, Motion parameters, HRF derivatives). (B) Mean statistic maps for each cluster.</figcaption></figure>

<figure><img src="image_7.png" /></figure>

The fMRI pipeline-space is especially large and challenging to investigate due the pipeline's sensitivity to particular properties of the data under study.

<figure><img src="image_8.png" /></figure>

* ✓ Use of 24 different fMRI pipelines with varying parameters (software, smoothing kernel, etc.) on data from the Human Connectome Project (n=1080 participants)

* ✓ Dimension reduction using PCA or AutoEncoders and clustering methods

<figure><img src="image_9.png" /></figure>

* → Pipelines have different behaviors depending on contrasts

* → Some pipelines give closer results than others, especially those sharing specific parameters.

Code available at [gitlab.inria.fr/egermani/hcp_pipelines/](https://gitlab.inria.fr/egermani/hcp_pipelines/)

# Representation learning
## for more reproducible
## fMRI data analyses

Elodie Germani<fnref n="1" />, Elisa Fromont<fnref n="2" />*, Camille Maumet<fnref n="1" />*

<figure><img src="image_10.png" /></figure>

## SAME RAW DATA, DIFFERENT ANALYTIC PIPELINES

* Neuroimaging workflows are highly flexible with many possible choices at each step of an analysis (Carp 2012).

* However, different analytical choices can cause variation in the results (Botvinik-Nezer et al., 2020).

* This puts into question the validity of published results and calls for dedicated methodologies to handle this new level of variability (Dafflon et al. 2020)

→ **A better understanding of the relationships between results coming from different pipelines is essential in order to develop new methods to tackle this variability.**

# METHODS TO TACKLE THIS VARIABILITY

## Benefitting from data variability to build more generalizable

## brain decoding models (Germani et al., 2023a)

<figure><img src="image_11.png" /></figure>

Training effective Deep Learning models on fMRI data comes with challenges, e.g. lack of training data and large number of sources of variability in the data.

<figure><img src="image_12.png" /></figure>

* ✓ Use of the NeuroVault (Gorgolewski et al., 2015) database: N=28,532 maps with unconstrained annotations.

* ✓ Pre-training of an unsupervised model (AutoEncoder) using NeuroVault and fine-tuning for classification tasks on smaller datasets.

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 3 - Mean accuracies for models initialized with default algorithm (blue) and pre-trained CAE (orange)</figcaption></figure>

## Transferring pipelines patterns between contrast maps

<figure><img src="image_15.png" /></figure>

Re-using fMRI maps processed with different pipelines can be difficult and augment the risk of having false positive findings (Rolland et al., 2022)

<figure><img src="image_16.png" /></figure>

* ✓ We hypothesize that different pipelines can be seen as different styles applied to an fMRI map.

* ✓ Use of style transfer methods (Isola et al, 2018) to convert contrast maps between pipelines.

<figure><img src="image_17.png" /></figure>

* → Some patterns of a pipeline can easily be transferred between contrast maps (e.g. smoothing)

* → Other parameters may display different patterns in contrast maps depending on specific properties of the data, making style transfer difficult to learn

Code available at [gitlab.inria.fr/egermani/pipeline_transition/](https://gitlab.inria.fr/egermani/pipeline_transition/)

<figure><img src="image_18.png" /><figcaption>Fig. 4 - Comparison of contrast maps obtained with a source pipeline, a target pipeline and the map generated using style transfer algorithm.</figcaption></figure>

<figure><img src="image_19.png" /></figure>

## References

Botvinik-Nezer, R. et al. 2020. « Variability in the analysis of a single neuroimaging dataset by many teams ». Nature.

Carp, J. 2012. « On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of FMRI Experiments ». Frontiers in Neuroscience.

Dafflon, J. et al. 2020. « Neuroimaging: into the Multiverse ». bioRxiv.

Germani, E. et al. 2022. « FMRI data analysis: How does analytical variability vary with sample size? ». OHBM 2022 - 28th Annual Meeting of the Organization for Human Brain Mapping.

Germani E. et al. 2023a. « On the benefits of self-taught learning for brain decoding. » arXiv. (In minor revision at GigaScience)

Germani E. et al. 2023b. « Exploring variability patterns in the task-fMRI analytical space » (submitted at OHBM 2023)

Gorgolewski, K.J. et al. 2015. « NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain ». Frontiers in Neuroinformatics.

Klau, S. et al. 2020. « Comparing the vibration of effects due to model, data pre-processing and sampling uncertainty on a large data set in personality psychology ». Department of Statistics: Technical Reports.

Isola, P. et al. 2018. « Image-to-Image Translation with Conditional Adversarial Networks ». arXiv.

Rolland, X. et al. 2022., « Towards efficient fmri data re-use: can we run between-group analyses with datasets processed differently with spm ? ». ISBI 2022.

Inria

LACODAM

Large scale Collaborative Data Mining

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EMPENN

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Université
de Rennes

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UMR IRISA

<footnote n="1">¹ Univ Rennes, Inria, CNRS, Inserm, Rennes, France</footnote>

<footnote n="2">² Univ Rennes, IUF, Inria, CNRS, IRISA, Rennes, France</footnote>

<footnote>* Joint senior authorship</footnote>