  A popular approach to decompose the neural bases of language consists in
correlating, across individuals, the brain responses to different stimuli (e.g.
regular speech versus scrambled words, sentences, or paragraphs). Although
successful, this `model-free' approach necessitates the acquisition of a large
and costly set of neuroimaging data. Here, we show that a model-based approach
can reach equivalent results within subjects exposed to natural stimuli. We
capitalize on the recently-discovered similarities between deep language models
and the human brain to compute the mapping between i) the brain responses to
regular speech and ii) the activations of deep language models elicited by
modified stimuli (e.g. scrambled words, sentences, or paragraphs). Our
model-based approach successfully replicates the seminal study of Lerner et al.
(2011), which revealed the hierarchy of language areas by comparing the
functional-magnetic resonance imaging (fMRI) of seven subjects listening to
7min of both regular and scrambled narratives. We further extend and precise
these results to the brain signals of 305 individuals listening to 4.1 hours of
narrated stories. Overall, this study paves the way for efficient and flexible
analyses of the brain bases of language.
