Using machine learning to understand cognition from brain images
Brain imaging provides increasingly rich and complex measures of neural
activity. Making full use of this data within the framework of
conventional statistics is challenging as it requires formulating clear
and simple hypotheses to test. Progress in machine learning and data
mining enables fitting detailed models. However these are geared towards
prediction and our inference framework must be revisited to use them to
draw conclusions on brain mechanisms [Varoquaux & Thirion 2014]. I will
present a few progresses in this endeavor.
First, I will show how "encoding" models, that predict brain activity
from the perception and action of the subject can uncover the features
of our world that are naturally reflected in the brain. I will explore a
detailed example of such encoding models that outline a high-resolved
decomposition of the human visual system from natural stimuli
[Eickenberg, submitted].
What is the cognitive function of brain regions outlined in such an
encoding model, or in any standard cognitive neuroimaging experiment?
These brain regions are defined as activating during a certain task or
for certain features of the stimuli. Cognitive studies would like draw
the converse conclusion: that activity in the brain regions implies the
behavior. Without careful analysis, such conclusion is a fallacy from a
logical standpoint: a reverse inference.
A first methodological tool the ground reverse inferences is decoding
models: predictive models deducing the task from the brain observed
brain images. Here, machine learning is not only used for its
prediction, but also in an inverse problem settings, to recover
discriminant regions. Given the scarcity of data and the dimensionality
of the brain images, the problem is very ill-posed and requires
injecting various priors. I will summarize five years of research in
developing and validating regularizations that encode efficiently priors
well suited for neuroimaging, in particular to segment and outline brain
regions. Specifically, these draw ideas from total-variation [Michel TMI
2011] or randomized-clustering [Varoquaux ICML 2012] approaches.
To come to general conclusions on the function of a brain region, it is
also necessary to explore a vast repertoire of cognitive function. Doing
so requires joint analysis of many studies. Such an analysis is very
valuable, as it enables going beyond the idiosyncrasies of a study, but
it requires describing a huge variety tasks with a common vocabulary. On
30 different fMRI studies, we describe experiment conditions with 20
different experimental-psychology notions [Schwartz NIPS 2013]. Using a
predictive model that leverages contrasts between conditions with
similar stimuli, we can describe a completely new experiment with these
notions. Importantly, prediction is perform on unseen studies, thus
validating that the decoder has learned maps specific to the notion of
interest, and not to the experimental paradigm. A precious outcome is a
"reverse inference" atlas of the predictive regions for these notions.
[Varoquaux & Thirion 2014]: How machine learning is shaping cognitive
neuroimaging http://www.gigasciencejournal.com/content/3/1/28
[Michel TMI 2011] Total variation regularization for fMRI-based
prediction of behavior https://hal.inria.fr/inria-00563468
[Varoquaux ICML 2012] Small-sample brain mapping: sparse recovery on
spatially correlated designs with randomization and clustering
https://hal.inria.fr/hal-00705192
[Schwartz NIPS 2013] Mapping cognitive ontologies to and from the brain
https://hal.inria.fr/hal-00904763v2
