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# Variability in brain imaging studies across different analysis pipelines

Camille Maumet

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## To cite this version:

Camille Maumet. Variability in brain imaging studies across different analysis pipelines. VAMOS 2025 - 19th International Working Conference on Variability Modelling of Software-Intensive Systems, Feb 2025, Rennes, France. 2025. [https://hal-04926453v2](https://hal-04926453v2)

HAL Id: hal-04926453

https://inria.hal.science/hal-04926453v2

Submitted on 4 Feb 2025

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# Variability in brain imaging studies across different analysis pipelines

19th International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS)

Camille Maumet

Inria, Univ Rennes, Inserm, CNRS, IRISA
# A multiplicity of analysis pipelines

<figure><img src="image_6.png" /></figure>

Input data

Analysis pipeline

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

Results
# A multiplicity of analysis pipelines

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

Input data

Analysis pipeline

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

Results

<figure><img src="image_10.png" /><figcaption>Input: Brain images (MRI)</figcaption></figure>
# A multiplicity of analysis pipelines

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

Analysis pipeline

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

Results

Results: Activation images
# A multiplicity of analysis pipelines

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

Analysis pipeline

<figure><img src="image_14.png" /></figure>

Results

Results: Activation images

<figure><img src="image_15.png" /></figure>
# A multiplicity of analysis pipelines

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

Analysis pipeline

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

Results

## Results: Activation images

<figure><img src="image_18.png" /></figure>

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

Music
# A multiplicity of analysis pipelines

<figure><img src="image_20.png" /></figure>

Analysis pipeline

<figure><img src="image_21.png" /></figure>

Results

## Results: Activation images

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /><figcaption>Music</figcaption></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /><figcaption>Visual</figcaption></figure>
# A multiplicity of analysis pipelines

Input data

<figure><img src="image_26.png" /></figure>

Analysis pipeline

<figure><img src="image_27.png" /></figure>

Results

<figure><img src="image_28.png" /></figure>

Results: Activation images

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /><figcaption>Music</figcaption></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /><figcaption>Visual</figcaption></figure>

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /><figcaption>Hand</figcaption></figure>
# A multiplicity of analysis pipelines

<figure><img src="image_35.png" /></figure>

Input data

Analysis pipeline

<figure><img src="image_36.png" /></figure>

Results
# A multiplicity of analysis pipelines

<figure><img src="image_37.png" /></figure>

Data preparation

<figure><img src="image_38.png" /></figure>

Derived
data

Statistics

<figure><img src="image_39.png" /></figure>

Results
# A multiplicity of analysis pipelines

<figure><img src="image_40.png" /></figure>

Input data

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

Results
# A multiplicity of analysis pipelines

<figure><img src="image_43.png" /></figure>

Input data

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

Results
# A multiplicity of analysis pipelines

<figure><img src="image_46.png" /></figure>

Input data

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

Results

<figure><img src="image_49.png" /></figure>

≠ algorithm
# A multiplicity of analysis pipelines

<figure><img src="image_50.png" /></figure>

Input data

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

Results

<figure><img src="image_53.png" /></figure>

≠ algorithm

<figure><img src="image_54.png" /></figure>

≠ software
# A multiplicity of analysis pipelines

<figure><img src="image_55.png" /></figure>

Input data

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>

Results

<figure><img src="image_58.png" /></figure>

≠ algorithm

<figure><img src="image_59.png" /></figure>

≠ software

<figure><img src="image_60.png" /></figure>

≠ software version
# A multiplicity of analysis pipelines

<figure><img src="image_61.png" /></figure>

Input data

<figure><img src="image_62.png" /></figure>

Results

<figure><img src="image_63.png" /></figure>

≠ algorithm

<figure><img src="image_64.png" /></figure>

≠ parameters

<figure><img src="image_65.png" /></figure>

≠ software

<figure><img src="image_66.png" /></figure>

≠ software version
# A multiplicity of analysis pipelines

<figure><img src="image_67.png" /></figure>

Input data

<figure><img src="image_68.png" /></figure>

Results

<figure><img src="image_69.png" /></figure>

≠ algorithm

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

≠ parameters

<figure><img src="image_72.png" /></figure>

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≠ software

<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /></figure>

≠ environment

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>

≠ software version
# A multiplicity of analysis pipelines

<figure><img src="image_78.png" /></figure>

Input data

<figure><img src="image_79.png" /></figure>

<figure><img src="image_80.png" /></figure>

Results

... and many more...

<figure><img src="image_81.png" /></figure>

≠ algorithm

<figure><img src="image_82.png" /></figure>

≠ software

<figure><img src="image_83.png" /></figure>

≠ software version

<figure><img src="image_84.png" /></figure>

≠ parameters

<figure><img src="image_85.png" /></figure>

≠ environment

**A family of acceptable pipelines**
over $10^{30}$ combinations...
# A multiplicity of analysis pipelines

<figure><img src="image_86.png" /></figure>

Input data

<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /></figure>

Results

... and many more...

<figure><img src="image_89.png" /></figure>

≠ algorithm

<figure><img src="image_90.png" /></figure>

≠ software

<figure><img src="image_91.png" /></figure>

≠ software version

<figure><img src="image_92.png" /></figure>

≠ parameters

<figure><img src="image_93.png" /></figure>

≠ environment

A family of acceptable pipelines
over $10^{30}$ combinations...

Software
Variants
<figure><img src="image_94.png" /></figure>

# Variability in pipelines...

## Variability in results?
**Many-analyst** project : **NARPS**

<figure><img src="image_95.png" /></figure>

1 dataset

9 'yes/no'
questions

[Botvinik-Nezer et. al, Nature 2020]
**Many-analyst** project : **NARPS**

<figure><img src="image_96.png" /></figure>

1 dataset

9 'yes/no'
questions

**Question 1:** Is there a
**parametric effect of gain:**
Positive effect in
**ventromedial prefrontal cortex** - for the equal
indifference group?

[Botvinik-Nezer et. al, Nature 2020]
**Many-analyst** project : **NARPS**

<figure><img src="image_97.png" /></figure>

1 dataset

<figure><img src="image_98.png" /></figure>

9 'yes/no'
questions

<figure><img src="image_99.png" /></figure>

70 analysis pipelines
by different teams

[Botvinik-Nezer et. al, Nature 2020]
**Many-analyst** project : **NARPS**

Results over the **70 post-processing pipelines**:

<figure><img src="image_100.png" /></figure>

9 'yes/no' questions

[Botvinik-Nezer et. al, Nature 2020]
**Many-analyst project : NARPS**

Results over the **70 post-processing pipelines**:

<figure><img src="image_101.png" /></figure>

9 'yes/no' questions

[Botvinik-Nezer et. al, Nature 2020]
**Many-analyst project : NARPS**

Results over the **70 post-processing pipelines**:

<figure><img src="image_102.png" /></figure>

**9 'yes/no' questions**

[Botvinik-Nezer et. al, Nature 2020]
<figure><img src="image_103.png" /></figure>

# Understanding
analytical variability
# Understanding analytical variability...

?

<figure><img src="image_104.png" /></figure>
# Understanding analytical variability...

?

<figure><img src="image_105.png" /></figure>

Numerical
uncertainty
(e.g. ≠ OS)

Different
implen.

Unwanted
variability
# Understanding analytical variability...

?

<figure><img src="image_106.png" /></figure>

Numerical
uncertainty
(e.g. ≠ OS)

Assumption
violations

Unwanted
variability

Different
implen.

Unreliable
results

Data
induced
variability
# Understanding analytical variability...

?

Better fit to the data

Variability of interest

?

Assumption violations

<figure><img src="image_107.png" /></figure>

Numerical uncertainty (e.g. ≠ OS)

Data induced variability

Different implem.

Unreliable results

Unwanted variability
# Variability across neuroimaging software

<figure><img src="image_108.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_109.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

<figure><img src="image_110.png" /></figure>

**Goal 1:** Investigate the variability observed in brain imaging results when using different domain software packages
# Variability across neuroimaging software

<figure><img src="image_111.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_112.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

<figure><img src="image_113.png" /></figure>

**Goal 1:** Investigate the variability observed in brain imaging results when using different domain software packages

* Reproduced 3 studies from the literature

* Using 3 brain imaging software: AFNI, FSL, SPM
  (used in 80% literature [Carp, 2012])

* Pipelines aligned using available configurations.
# Variability across neuroimaging software

* Variability in the activation maps

<figure><img src="image_114.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_115.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

<table><thead><tr><th></th><th>study ds001</th><th>study ds109</th><th>study ds120</th></tr></thead><tbody><tr><td>AFNI software</td><td></td><td></td><td></td></tr><tr><td>FSL software</td><td></td><td></td><td></td></tr><tr><td>SPM software</td><td></td><td></td><td></td></tr><tr><td>Original paper</td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_116.png" /></figure>

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>

<figure><img src="image_121.png" /></figure>

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<figure><img src="image_123.png" /></figure>

<figure><img src="image_124.png" /></figure>

<figure><img src="image_125.png" /></figure>

<figure><img src="image_126.png" /></figure>

<figure><img src="image_127.png" /></figure>

[Bowring Maumet* Nichols*, HBM 2019]
# Variability across neuroimaging software

* Variability in the activation maps

<figure><img src="image_128.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_129.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

<table><thead><tr><th></th><th>study ds001</th><th>study ds109</th><th>study ds120</th></tr></thead><tbody><tr><td>AFNI software</td><td></td><td></td><td></td></tr><tr><td>FSL software</td><td></td><td></td><td></td></tr><tr><td>SPM software</td><td></td><td></td><td></td></tr><tr><td>Original paper</td><td></td><td></td><td></td></tr></tbody></table>

[Bowring Maumet* Nichols*, HBM 2019]
# Variability across neuroimaging software

* Variability in the activation maps

<figure><img src="image_130.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_131.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

AFNI software

<figure><img src="image_132.png" /></figure>

z = -32

<figure><img src="image_133.png" /></figure>

z = -18

<figure><img src="image_134.png" /></figure>

z = 0

<figure><img src="image_135.png" /></figure>

z = 12

<figure><img src="image_136.png" /></figure>

z = 24

FSL software

<figure><img src="image_137.png" /></figure>

z = -32

<figure><img src="image_138.png" /></figure>

z = -18

<figure><img src="image_139.png" /></figure>

z = 0

<figure><img src="image_140.png" /></figure>

z = 12

<figure><img src="image_141.png" /></figure>

z = 24

SPM software

<figure><img src="image_142.png" /></figure>

z = -32

<figure><img src="image_143.png" /></figure>

z = -18

<figure><img src="image_144.png" /></figure>

z = 0

<figure><img src="image_145.png" /></figure>

z = 12

<figure><img src="image_146.png" /></figure>

z = 24

study ds001

<figure><img src="image_147.png" /></figure>

AFNI
software

FSL
software

SPM
software

Original
paper

[Bowring Maumet\* Nichols\*, HBM 2019]
# Variability across neuroimaging software

* Variability in the activation maps

<figure><img src="image_148.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_149.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

### AFNI software

<figure><img src="image_150.png" /></figure>

<figure><img src="image_151.png" /></figure>

<figure><img src="image_152.png" /></figure>

<figure><img src="image_153.png" /></figure>

<figure><img src="image_154.png" /></figure>

### FSL software

<figure><img src="image_155.png" /></figure>

<figure><img src="image_156.png" /></figure>

<figure><img src="image_157.png" /></figure>

<figure><img src="image_158.png" /></figure>

<figure><img src="image_159.png" /></figure>

### SPM software

<figure><img src="image_160.png" /></figure>

<figure><img src="image_161.png" /></figure>

<figure><img src="image_162.png" /></figure>

<figure><img src="image_163.png" /></figure>

<figure><img src="image_164.png" /></figure>

study ds001

study ds001

study ds109

study ds120

AFNI
software

FSL
software

SPM
software

Original
paper

<figure><img src="image_165.png" /></figure>

<figure><img src="image_166.png" /></figure>

<figure><img src="image_167.png" /></figure>

<figure><img src="image_168.png" /></figure>

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<figure><img src="image_170.png" /></figure>

<figure><img src="image_171.png" /></figure>

<figure><img src="image_172.png" /></figure>

<figure><img src="image_173.png" /></figure>

<figure><img src="image_174.png" /></figure>

<figure><img src="image_175.png" /></figure>

<figure><img src="image_176.png" /></figure>

### Dice coefficient

<figure><img src="image_177.png" /></figure>

The choice of
software
impacts the
activations
# Variability across neuroimaging software

* Variability in the unthresholded statistic maps

<figure><img src="image_178.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_179.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

<table><thead><tr><th>study ds001</th><th>study ds109</th><th>study ds120</th></tr></thead><tbody><tr><td>AFNI software</td><td>AFNI software</td><td>AFNI software</td></tr><tr><td>FSL software</td><td>FSL software</td><td>FSL software</td></tr><tr><td>SPM software</td><td>SPM software</td><td>SPM software</td></tr></tbody></table>

[Bowring Maumet* Nichols*, HBM 2019]
# Variability across neuroimaging software

* Variability in the unthresholded statistic maps

<figure><img src="image_180.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_181.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

<table><thead><tr><th></th><th>study ds001</th><th>study ds109</th><th>study ds120</th></tr></thead><tbody><tr><th>AFNI software</th><td></td><td></td><td></td></tr><tr><th>FSL software</th><td></td><td></td><td></td></tr><tr><th>SPM software</th><td></td><td></td><td></td></tr></tbody></table>

## Correlations

AFNI vs. FSL 0.62

AFNI vs. SPM 0.61

FSL vs. SPM 0.68

[Bowring Maumet* Nichols*, HBM 2019]
# Variability across neuroimaging software

* Variability in the unthresholded statistic maps

<figure><img src="image_182.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_183.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

<table><thead><tr><th></th><th>study ds001</th><th>study ds109</th><th>study ds120</th></tr></thead><tbody><tr><td>AFNI software</td><td></td><td></td><td></td></tr><tr><td>FSL software</td><td></td><td></td><td></td></tr><tr><td>SPM software</td><td></td><td></td><td></td></tr></tbody></table>

## Correlations

AFNI vs. FSL 0.62

AFNI vs. SPM 0.61

FSL vs. SPM 0.68

The choice of software impacts both the final activation and statistic maps
# Variability across neuroimaging software

<figure><img src="image_184.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_185.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

**Goal 2:** Investigate how remaining differences in software implementations can explain the observed differences

## study ds001

1. Preprocessing

2. Subject-level Signal

3. Subject-level Drift

4. Subject-level Noise

5. Group-level Stat

<figure><img src="image_186.png" /></figure>

<figure><img src="image_187.png" /></figure>

<figure><img src="image_188.png" /></figure>

<figure><img src="image_189.png" /></figure>

<figure><img src="image_190.png" /></figure>

AFNI
software

<figure><img src="image_191.png" /></figure>

<figure><img src="image_192.png" /></figure>

<figure><img src="image_193.png" /></figure>

FSL
software
# Variability across neuroimaging software

<figure><img src="image_194.png" /><figcaption>Alex Bowring</figcaption></figure>

<figure><img src="image_195.png" /><figcaption>Tom Nichols</figcaption></figure>

Oxford Uni. (UK)

**Goal 2:** Investigate how remaining differences in software implementations can explain the observed differences

study ds001

1. Preprocessing

2. Subject-level Signal

3. Subject-level Drift

4. Subject-level Noise

5. Group-level Stat

<figure><img src="image_196.png" /></figure>

<figure><img src="image_197.png" /></figure>

<figure><img src="image_198.png" /></figure>

<figure><img src="image_199.png" /></figure>

AFNI
software

<figure><img src="image_200.png" /></figure>

<figure><img src="image_201.png" /></figure>

<figure><img src="image_202.png" /></figure>

<figure><img src="image_203.png" /></figure>

FSL
software

1 main contributing factor was found but different for each study

[Bowring Nichols\* Maumet\*, HBM 2021]
<figure><img src="image_204.png" /></figure>

# Towards large-scale exploration of the pipeline space
Open questions
Open questions

<figure><img src="image_205.png" /></figure>

How many variants in
the brain imaging
pipeline space?
Open questions

<figure><img src="image_206.png" /></figure>

How many variants in
the brain imaging
pipeline space?

<figure><img src="image_207.png" /></figure>

Which proportion of the
pipeline space is **used**
**by the community?**
# Open questions

<figure><img src="image_208.png" /></figure>

How many variants in the brain imaging pipeline space?

<figure><img src="image_209.png" /></figure>

Which proportion of the pipeline space is **used by the community?**

<figure><img src="image_210.png" /></figure>

Are there **variants** that always lead to **large impact** in the results?
Open questions

<figure><img src="image_211.png" /></figure>

How many variants in
the brain imaging
pipeline space?

<figure><img src="image_212.png" /></figure>

Which proportion of the
pipeline space is **used**
**by the community?**

<figure><img src="image_213.png" /></figure>

Are there **variants** that
always lead to **large**
**impact** in the results?

<figure><img src="image_214.png" /></figure>

Are there **variants** that
have a very **small**
**impact** on the results?
Open questions

<figure><img src="image_215.png" /></figure>

How many variants in
the brain imaging
pipeline space?

<figure><img src="image_216.png" /></figure>

Which proportion of the
pipeline space is **used**
**by the community?**

<figure><img src="image_217.png" /></figure>

Are there **variants** that
always lead to **large**
**impact** in the results?

<figure><img src="image_218.png" /></figure>

Are there **variants** that
have a very **small**
**impact** on the results?

<figure><img src="image_219.png" /><figcaption>Youenn
Merel</figcaption></figure>

<figure><img src="image_220.png" /><figcaption>Mathieu
Acher</figcaption></figure>
# Open questions

<figure><img src="image_221.png" /></figure>

How many variants in the brain imaging pipeline space?

<figure><img src="image_222.png" /></figure>

Which proportion of the pipeline space is **used by the community?**

<figure><img src="image_223.png" /></figure>

Are there **variants** that always lead to **large impact** in the results?

<figure><img src="image_224.png" /></figure>

Are there **variants** that have a very **small impact** on the results?

<figure><img src="image_225.png" /><figcaption>Youenn
Merel</figcaption></figure>

<figure><img src="image_226.png" /><figcaption>Mathieu
Acher</figcaption></figure>

<figure><img src="image_227.png" /></figure>

How well do **intuitions from expert brain imagers** map with the actual observed variability?
# Open code and data to investigate analytical variability

<figure><img src="image_228.png" /><figcaption>Open data
HCP many pipeline</figcaption></figure>

<figure><img src="image_229.png" /></figure>
# Open code and data to investigate analytical variability

<figure><img src="image_230.png" /><figcaption>Open data
**HCP many pipeline**</figcaption></figure>

<figure><img src="image_231.png" /></figure>
# NARPS open pipelines

<figure><img src="image_232.png" /><figcaption>Boris
Clenet</figcaption></figure>

<figure><img src="image_233.png" /><figcaption>Elodie
Germani</figcaption></figure>

<figure><img src="image_234.png" /><figcaption>Jérémy
Lefort-Besnard</figcaption></figure>

<figure><img src="image_235.png" /></figure>

1 dataset

<figure><img src="image_236.png" /></figure>

9 'yes/no'
questions

<figure><img src="image_237.png" /></figure>

70 analysis pipelines
by different teams
# NARPS open pipelines

<figure><img src="image_238.png" /><figcaption>Boris
Clenet</figcaption></figure>

<figure><img src="image_239.png" /><figcaption>Elodie
Germani</figcaption></figure>

<figure><img src="image_240.png" /><figcaption>Jérémy
Lefort-Besnard</figcaption></figure>

<figure><img src="image_241.png" /></figure>

1 dataset

<figure><img src="image_242.png" /></figure>

9 'yes/no'
questions

<figure><img src="image_243.png" /></figure>

70 analysis pipelines
by different teams
# NARPS open pipelines

<figure><img src="image_244.png" /><figcaption>Boris
Clenet</figcaption></figure>

<figure><img src="image_245.png" /><figcaption>Elodie
Germani</figcaption></figure>

<figure><img src="image_246.png" /><figcaption>Jérémy
Lefort-Besnard</figcaption></figure>

<figure><img src="image_247.png" /></figure>

1 dataset

<figure><img src="image_248.png" /></figure>

9 'yes/no'
questions

<figure><img src="image_249.png" /></figure>

70 analysis pipelines
by different teams

**Yes/No** answer +
confidence level

**Brain images**

<figure><img src="image_250.png" /></figure>

<figure><img src="image_251.png" /></figure>

**Pipeline** COBIDAS
compliant description
(textual)
# NARPS open pipelines

<figure><img src="image_252.png" /><figcaption>Boris
Clenet</figcaption></figure>

<figure><img src="image_253.png" /><figcaption>Elodie
Germani</figcaption></figure>

<figure><img src="image_254.png" /><figcaption>Jérémy
Lefort-Besnard</figcaption></figure>

<figure><img src="image_255.png" /></figure>

1 dataset

<figure><img src="image_256.png" /></figure>

9 'yes/no'
questions

<figure><img src="image_257.png" /></figure>

70 analysis pipelines
by different teams

**Yes/No** answer +
confidence level

**Brain images**

<figure><img src="image_258.png" /></figure>

<figure><img src="image_259.png" /></figure>

**Pipeline** COBIDAS
compliant description
(textual)

x9 research
questions
# NARPS open pipelines

<figure><img src="image_260.png" /><figcaption>Boris
Clenet</figcaption></figure>

<figure><img src="image_261.png" /><figcaption>Elodie
Germani</figcaption></figure>

<figure><img src="image_262.png" /><figcaption>Jérémy
Lefort-Besnard</figcaption></figure>

<figure><img src="image_263.png" /></figure>

1 dataset

<figure><img src="image_264.png" /></figure>

9 'yes/no'
questions

<figure><img src="image_265.png" /></figure>

70 analysis pipelines
by different teams

**Yes/No** answer +
confidence level

**Brain images**

<figure><img src="image_266.png" /></figure>

<figure><img src="image_267.png" /></figure>

**Pipeline** COBIDAS
compliant description
(textual)

X Code

x9 research
questions
# NARPS open pipelines

<figure><img src="image_268.png" /><figcaption>Boris
Clenet</figcaption></figure>

<figure><img src="image_269.png" /><figcaption>Elodie
Germani</figcaption></figure>

<figure><img src="image_270.png" /><figcaption>Jérémy
Lefort-Besnard</figcaption></figure>

<figure><img src="image_271.png" /></figure>

1 dataset

<figure><img src="image_272.png" /></figure>

9 'yes/no'
questions

<figure><img src="image_273.png" /></figure>

70 analysis pipelines
by different teams

**Yes/No** answer +
confidence level

**Brain images**

<figure><img src="image_274.png" /></figure>

<figure><img src="image_275.png" /></figure>

**Pipeline** COBIDAS
compliant description
(textual)

✓ **Code** → NARPS
open

x9 research
questions
# NARPS open pipelines

<figure><img src="image_276.png" /><figcaption>Boris
Clenet</figcaption></figure>

<figure><img src="image_277.png" /><figcaption>Elodie
Germani</figcaption></figure>

<figure><img src="image_278.png" /><figcaption>Jérémy
Lefort-Besnard</figcaption></figure>

<figure><img src="image_279.png" /></figure>

brainhack.org

**Credits** - This project was presented and received contributions during the following events:

* Empenn hackathon (February 2024)

* [Brainhack Marseille 2023](December 2023)

* [ORIGAMI lab](September 2023) hackathon

* [OHBM Brainhack 2023](July 2023): Arshitha Basavaraj, Boris Clénet, Rémi Gau, Élodie Germani, Yaroslav Halchenko, Camille Maumet, Paul Taylor.

* [e-ReproNim FENS NENS Cluster Brainhack](June 2023) : Liz Bushby, Boris Clénet, Michael Dayan, Aimee Westbrook.

* [OHBM Brainhack 2022](June 2022): Elodie Germani, Arshitha Basavaraj, Trang Cao, Rémi Gau, Anna Menacher, Camille Maumet.

**Open code**

<figure><img src="image_280.png" /></figure>

https://github.com/Inria-Empenn/narps_open_pipelines/
# Open code and data to investigate analytical variability

<figure><img src="image_281.png" /><figcaption>Open data
HCP many pipeline</figcaption></figure>

<figure><img src="image_282.png" /></figure>
# HCP many pipeline

<figure><img src="image_283.png" /><figcaption>Elodie
Germani</figcaption></figure>

<figure><img src="image_284.png" /><figcaption>Pierre
Maurel</figcaption></figure>

<figure><img src="image_285.png" /><figcaption>Elisa
Fromont</figcaption></figure>

* **1 dataset**

* Human Connectome Project (HCP) Young Adult

<figure><img src="image_286.png" /></figure>

* **24 pipelines**

* FSL, SPM / 2 HRF / 2 smoothing / 3 motion regressors

* **1080 participants and 1000 groups** (50 subjects)

**Shared data** (in compliance with GDPR)

<figure><img src="image_287.png" /></figure>

HCP multi pipeline on Public-nEUro

[Germani et al, preprint hal:inserm-04356768]
<figure><img src="image_288.png" /></figure>

<figure><img src="image_289.png" /></figure>

# Variability in brain imaging studies across different analysis pipelines

Camille Maumet

Thank you!

## Acknowledgments

<figure><img src="image_290.png" /></figure>

<figure><img src="image_291.png" /></figure>

<figure><img src="image_292.png" /></figure>

PROGRAMME
DE RECHERCHE

SANTÉ
NUMÉRIQUE

<figure><img src="image_293.png" /></figure>

@cmaumet.bsky.social

<figure><img src="image_294.png" /></figure>

@cmaumet@fediscience.org