Inter-subject pattern analysis

A powerful scheme for group-level MVPA

Sylvain Takerkart

 

with Qi Wang, Bastien Cagna, Thierry Chaminade

 

http://int.univ-amu.fr

http://meca-brain.org

http://neuralbasesofcommunication.eu

Outline

  • Group-level MVPA
  • Benchmark on real data
  • Insights from artificial data
  • Conclusions

Multivariate pattern analysis

predictive model: f(X) = y

assessing neural coding principles

[Varoquaux 2014]

[Haynes 2015]

X: the brain pattern, y: the target variable

Multivariate pattern analysis

"traditionally" performed within-subject

"Here, we show that the topographic arrangement of the full pattern

of response is consistent within subjects,

...but we are not able to

perform a similar correlational analysis across subjects"

[Haxby 2001]

Inter-individual variability

Methods to overcome variability

[Haxby 2011] [Grosenick 2013] [Takerkart 2014] [Yamada 2016] [Hoyos-Idrobo 2018] etc.

...at the group level

This tests...

...is consistent across subjects

...how much decoding...

...how coding...

Benchmarking G-MVPA and ISPA

Everything is identical, except the cross-validation

Benchmarking G-MVPA and ISPA

  • binary classification task
  • logistic regression
  • permutation test on accuracies at the second level
  • same number of observations because of LOSO

Everything identical, except the cross-validation

Outline

  • Group-level MVPA
  • Benchmark on real data
  • Insights from artificial data
  • Conclusions

Searchlight decoding

[Kahnt 2011]

Mapping local multivariate effects using sliding window decoding

Dataset 1

  • decoding right hand vs left hand tapping
  • 15 subjects
  • 360 trials / subject

Dataset 2

  • decoding vocal vs. non-vocal audio stimuli
  • 39 subjects
  • 144 trials / subject

Dataset 1

Dataset 2

Dataset 1

Dataset 2

Dataset 1

Dataset 2

Conclusion (real data)

  • results are consistent, but with differences!
  • ISPA "detects more" on Dataset 1
  • G-MVPA "detects more" on Dataset 2

Why?

Outline

  • Group-level MVPA
  • Benchmark on real data
  • Insights from artificial data
  • Conclusions

Designing artificial data

  • 21 subjects
  • 2 conditions
  • 100 points (trials) per condition per subject
  • total = 4200 points per dataset

 

  • 2D patterns
  • parametrically controlled characteristics

 

Effect size

Inter-individual variability

Some example datasets...

Experiments

  • 2 parameters : effect size, variability
  • 100 data sets for each value of the 2 parameters
  • evaluation criterion: the number of data sets  which offer significant group decoding ?

G-MVPA results

Number of datasets with significant decoding

Number of datasets with significant decoding

ISPA results

Number of datasets with significant decoding

Number of datasets with significant decoding

Comparing G-MVPA and ISPA

Number of datasets with significant decoding

Outline

  • Group-level MVPA
  • Benchmark on real data
  • Insights from artificial data
  • Conclusions

Take-home message #1

Machine learning for neuroimaging...

...small sample size regime

Training set size matters!

Designing experiments with more trials...

Take-home message #2

Be careful when interpreting G-WSPA results!

Sources of these differences?

[Todd 2013] [Allefeld 2016] [Gilron 2017]

  • bad registration?
  • functional variability?
  • idiosyncrasies?

Take-home message #3

Interpretation is unambiguous with ISPA

...at no extra computational cost...

ISPA can be more powerful

Q. Wang, B. Cagna, T. Chaminade, and S. Takerkart, ‘Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA’, NeuroImage, vol. 204, Jan. 2020.


paper:

https://hal.archives-ouvertes.fr/hal-02313840/

 

nilearn-based code:

https://github.com/SylvainTakerkart/inter_subject_pattern_analysis

 

data:

https://openneuro.org/datasets/ds001771/

https://zenodo.org/record/2591038

 

slides:

https://slides.com/takerkart/

Go see Qi Wang's poster!

Extras...

Dataset 1

Dataset 2

20191121_Journée_IRMf_ISPA

By Sylvain Takerkart

20191121_Journée_IRMf_ISPA

  • 1,102