user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learning

Paper content:
The purpose of an encoding model is to predict brain activity given a stimulus.
In this contribution, we attempt at estimating a whole brain encoding model of auditory perception in a naturalistic stimulation setting.
We analyze data from an open dataset, in which 16 subjects watched a short movie while their brain activity was being measured using functional MRI.
We extracted feature vectors aligned with the timing of the audio from the movie, at different layers of a Deep Neural Network pretrained on the classification of auditory scenes.
fMRI data was parcellated using hierarchical clustering on 500 parcels, and encoding models were estimated using a fully connected neural network with one hidden layer, trained to predict the signals for each parcel from the DNN features.
Individual encoding models were successfully trained and predicted brain activity on unseen data, in parcels located in the superior temporal lobe, as well as dorsolateral prefrontal regions, which are usually considered as areas involved in auditory and language processing.
Taken together, this contribution extends previous attempts on estimating encoding models, by showing the ability to model brain activity using a generic DNN (ie not specifically trained for this purpose) to extract auditory features, suggesting a degree of similarity between internal DNN representations and brain activity in naturalistic settings.
One important motivation for incorporating machine learning in neuroscientific discovery is the establishment of predictive models, as opposed to models based on statistical inference [1] .
While the latter are unable to generalize to a new dataset, the former aim at sucessful generalization.
In particular, encoding models aim at predicting brain activity given a model of the stimulus presented to the subject.
A successful model should enable generalization to unseen data, enabling a better understanding of the underlying brain functions.
Furthermore, an accurate encoding model could potentially be used to enhance machine learning, by providing an auxiliary source of training data, as recent evidence suggest that actual brain activity can guide machine learning [2] .
In this study, we tested whether a pretrained network could be used to estimate encoding models, in the case of naturalistic auditory perception.
We were able to train encoding models on individual subjects to predict brain activity using the deepest layers of SoundNet, using less than 20 minutes of fMRI data.
The obtained models best predicted the activity in brain areas that are part of a language-related network.
However, the current study has the following limitations.
First, we extracted features from the auditory part of the stimuli, while the modeled brain activity involves many other brain functions, namely visual perception, as well as higher level cognitive functions such as memory and emotional responses.
This probably explains why we obtain R 2 = 0.5 in the best case.
Providing a richer stimuli representation using more general purpose feature extractors would probably enable a more complete model of brain activity.
Second, we estimated brain parcellations on single subject data using only 20 minutes of MRI, which might not be enough to obtain a reliable set of ROIs [6] .
Further studies should use either more repetitions on each subject, or attempt at learning parcellations across subjects, after having spatially normalized each individual to a template.
Third, we didn't find a clear relationship between spatial extent of our encoding models as a function of the SoundNet layer.
This could be due to the fact that SoundNet was trained independently of the brain data, and was never optimized for encoding models.
One possible avenue would be to perform fine tuning, or retrain from scratch, in order to optimize the estimation of encoding models.
Finally, in our approach we ignored the temporal dynamics of both the feature vectors and the fMRI data, as well as the dependencies between ROIs implied by brain connectivity.
In future studies, we will consider the use of recurrent neural networks, as well as graph representation learning [7] , in order to tackle those issues.

assistant: text: Feature vectors from SoundNet can predict brain activity of subjects watching a movie in auditory and language related brain regions.