Modeling Shared responses in Neuroimaging Studies through MultiView ICA
Authors: Hugo Richard, Luigi Gresele, Aapo Hyvarinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the usefulness of our approach first on f MRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects. Moreover, the sources recovered by our model exhibit lower between-session variability than other methods. On magnetoencephalography (MEG) data, our method yields more accurate source localization on phantom data. Applied on 200 subjects from the Cam-CAN dataset it reveals a clear sequence of evoked activity in sensor and source space. 4 Experiments |
| Researcher Affiliation | Academia | Hugo Richard Inria, Université Paris-Saclay Saclay, France hugo.richard@inria.fr Luigi Gresele MPI for Intelligent Systems, MPI for Biological Cybernetics, Tübingen, Germany luigi.gresele@tuebingen.mpg.de Aapo Hyvärinen Inria, Université Paris-Saclay, Saclay, France Department of Computer Science HIIT, University of Helsinki, Finland aapo.hyvarinen@helsinki.fi Bertrand Thirion Inria, Université Paris-Saclay Saclay, France bertrand.thirion@inria.fr Alexandre Gramfort Inria, Université Paris-Saclay Saclay, France alexandre.gramfort@inria.fr Pierre Ablin Département de Mathématiques et Applications Ecole Normale Supérieure Paris, France pierre.ablin@ens.fr |
| Pseudocode | Yes | Algorithm 1: Alternate quasi-Newton method for Multi View ICA |
| Open Source Code | Yes | The code for Multi View ICA is available online at https://github.com/hugorichard/multiviewica. |
| Open Datasets | Yes | The sherlock dataset [19] contains recordings of 16 subjects watching an episode of the BBC TV show "Sherlock" (50 mins). The forrest dataset [35] was collected while 19 subjects were listening to an auditory version of the film "Forrest Gump" (110 mins). The clips dataset [59] was collected while 12 participants were exposed to short video clips (130 mins). The raiders dataset [59] was collected while 11 participants were watching the movie "Raiders of the Lost Ark" (110 mins). The raiders-full dataset [59] is an extension of the raiders dataset where the first two scenes of the movie are shown twice (130 mins). Finally, we apply Multi View ICA on the Cam-CAN dataset [66]. |
| Dataset Splits | Yes | We split the data into three groups. First, we randomly choose 80% of all runs from all subjects to form the training set. Then, we randomly choose 80% of subjects and take the remaining 20% runs as testing set. The left-out runs of the remaining 20% subjects form the validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used to run the experiments. |
| Software Dependencies | No | We use Matplotlib for plotting [37] , scikit-learn for machine-learning pipelines [55], MNE for MEG processing [30], Nilearn for f MRI processing and for its Can ICA implementation [2], Brainiak [45] for its SRM implementation. |
| Experiment Setup | Yes | In the following, the noise parameter in Multiview ICA is always fixed to σ = 1. We use the function f( ) = log cosh( ), giving the non-linearity f ( ) = tanh( ). We use the Infomax cost function [8] with the same non-linearity to perform standard ICA, with the Picard algorithm [1] for fast and robust minimization of the cost function. Picard is applied with the default hyper-parameters. |