{"forum": "SyxENQtL8H", "submission_url": "https://openreview.net/forum?id=SyxENQtL8H", "submission_content": {"TL;DR": "Feature vectors from SoundNet can predict brain activity of subjects watching a movie in auditory and language related brain regions.", "keywords": ["neuroimaging", "deep learning", "transfer learning", "audio", "encoding models"], "authors": ["Nicolas Farrugia", "Victor Nepveu", "Deycy Camila Arias Villamil"], "title": "Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learning", "abstract": "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. ", "authorids": ["nicolas.farrugia@imt-atlantique.fr", "victor.nepveu@imt-atlantique.net", "deycy-camila.arias-villamil@imt-atlantique.net"], "pdf": "/pdf/567a27c5920b3fb483ef29aa98bcc2aea62ab57b.pdf", "paperhash": "farrugia|estimating_encoding_models_of_cortical_auditory_processing_using_naturalistic_stimuli_and_transfer_learning"}, "submission_cdate": 1568211756369, "submission_tcdate": 1568211756369, "submission_tmdate": 1571682634945, "submission_ddate": null, "review_id": ["HyxFxLKlwr", "S1e7s36wDr", "rye5apbiPr"], "review_url": ["https://openreview.net/forum?id=SyxENQtL8H&noteId=HyxFxLKlwr", "https://openreview.net/forum?id=SyxENQtL8H&noteId=S1e7s36wDr", "https://openreview.net/forum?id=SyxENQtL8H&noteId=rye5apbiPr"], "review_cdate": [1568867824596, 1569344667400, 1569557954229], "review_tcdate": [1568867824596, 1569344667400, 1569557954229], "review_tmdate": [1570047569310, 1570047557995, 1570047537164], "review_readers": [["everyone"], ["everyone"], ["everyone"]], "review_writers": [["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper38/AnonReviewer1"], ["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper38/AnonReviewer3"], ["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper38/AnonReviewer2"]], "review_reply_count": [{"replyCount": 0}, {"replyCount": 0}, {"replyCount": 0}], "review_replyto": ["SyxENQtL8H", "SyxENQtL8H", "SyxENQtL8H"], "review_content": [{"title": "Straightforward application of DNN-based encoding models in the auditory domain", "category": "AI->Neuro", "intersection_comment": "Use of DNNs to explain observed brain responses falls right at the intersection.", "evaluation": "2: Poor", "importance_comment": "This paper provides a straightforward application of SoundNet to predict fMRI responses to an audio stream. The question remains to what extent the presented results provide new results beyond those of https://www.cell.com/neuron/abstract/S0896-6273(18)30250-2 and https://arxiv.org/abs/1606.02627.", "rigor_comment": "Standard statistics are performed. Thresholding for threshold map seems quite arbitrarily chosen. It remains unclear which stimulus features are driving the response predictions.", "importance": "2: Marginally important", "clarity_comment": "Some typos. E.g. improvising => improving; this results; ...\n\n", "intersection": "4: High", "technical_rigor": "2: Marginally convincing", "comment": "+ Use of audio-based DNNs can provide interesting insights into which stimulus properties drive neural responses\n+ DLPFC results could potentially point to novel properties that are predictive of responses\n\n- Novelty compared to existing work unclear\n- Insights about what stimulus properties are driving the predicted responses will strengthen the paper", "clarity": "3: Average readability"}, {"evaluation": "3: Good", "intersection": "4: High", "importance_comment": "Not sure how much we can gain from this sort of study. The authors show that deeper layers of a pre-trained auditory network can be used to predict fMRI responses, albeit not very well. But what can we conclude from this? Would the same be true of a different auditory network with very different properties? How much does it depend on the specific structure of that network? Could it just be the case that higher level features appear deeper in the network and correspond to areas recorded by fMRI?", "clarity": "4: Well-written", "technical_rigor": "3: Convincing", "intersection_comment": "Definitely relevant, a similar approach to what has been tried with much success in vision.", "rigor_comment": "All seemed reasonable, but I would have liked to have seen controls against different architectures.", "comment": "I've given this the \"good\" evaluation because even though I personally am not convinced by this type of approach, it seems to be reasonably well done and I know that a number of people do find it useful and convincing.", "importance": "2: Marginally important", "title": "Deeper layers of a pre-trained auditory model can be used to partly predict fMRI responses, but not sure what we can conclude from that", "category": "AI->Neuro", "clarity_comment": "No problem understanding this work."}, {"title": "Good start but some details unclear ", "importance": "3: Important", "importance_comment": "Understanding cortical acoustic processing is an important neuroscience goal. This paper doesn't however motivate the specific model being chosen, or what different layers mean. It is stated more like a prediction task instead of an understanding the brain task.", "rigor_comment": "From what I understood, the R2 is being computed as the maximum over an ROI, at least in one part of the paper if not all. the maximum is a very noisy statistic and is not very reliable as a metric for model fitting and improvement. \n\nFrom the paper, it seems that the authors picked the best fold to interpret (after looking at the results). This is effectively double dipping and negatively affects reproducibility.\n\nR2 values in fMRI single trials are typically low and that is ok. The solution is not to use the maximum (if I understood correctly the motivation). The authors should be reporting single voxel metrics (over the brain) or should be computing some mean statistics. ", "clarity_comment": "The paper is understandable but some methods crucial methods details are left out (3.1 and 3.2)", "clarity": "3: Average readability", "evaluation": "2: Poor", "intersection_comment": "Using an AI algorithm as a model of what the brain is doing. Although here how the model (SoundNet) could be an analogy of the brain (e.g. what could the different layers correspond to) could be more elaborated on.", "intersection": "4: High", "technical_rigor": "2: Marginally convincing", "category": "AI->Neuro"}], "comment_id": [], "comment_cdate": [], "comment_tcdate": [], "comment_tmdate": [], "comment_readers": [], "comment_writers": [], "comment_reply_content": [], "comment_content": [], "comment_replyto": [], "comment_url": [], "meta_review_cdate": null, "meta_review_tcdate": null, "meta_review_tmdate": null, "meta_review_ddate ": null, "meta_review_title": null, "meta_review_metareview": null, "meta_review_confidence": null, "meta_review_readers": null, "meta_review_writers": null, "meta_review_reply_count": null, "meta_review_url": null, "decision": "Accept (Poster)"}