Shared Independent Component Analysis for Multi-Subject Neuroimaging
Authors: Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvarinen
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show via simulations that Sh ICA-J leads to improved results while being very fast to fit. While Sh ICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, Sh ICA-ML, that is both more accurate and more costly. Further, Sh ICA comes with a principled method for shared components estimation. Finally, we provide empirical evidence on f MRI and MEG datasets that Sh ICA yields more accurate estimation of the components than alternatives. |
| Researcher Affiliation | Academia | Hugo Richard Inria Université Paris-Saclay Palaiseau, France Pierre Ablin DMA CNRS and ENS Paris, France Bertrand Thirion Inria Université Paris-Saclay Palaiseau, France Alexandre Gramfort Inria Université Paris-Saclay Palaiseau, France Aapo Hyvärinen Department of Computer Science University of Helsinki Helsinki, Finland |
| Pseudocode | Yes | Algorithm 1 Sh ICA-J Input : Covariances Cij = E[xix j ] ( Wi)i Multiset CCA(( Cij)ij) Q Joint Diag(( Wi Cii W i )i) Γij Q Wi Cij W j Q (Φi)i Scaling((Γij)ij) Return : Unmixing matrices (Φi Q Wi)i. |
| Open Source Code | Yes | Our code is available at https://github.com/hugorichard/Sh ICA. |
| Open Datasets | Yes | f MRI experiments used the following datasets: sherlock [15], forrest [28] , raiders [49] and gallant [49]. In the following experiments we consider the Cam-CAN dataset [56]. |
| Dataset Splits | No | The paper describes a training and testing split strategy ('The unmixing operators are learned using all subjects and 80% of the runs. Then they are applied on the remaining 20% of the runs using 80% of the subjects...'), but it does not explicitly mention a separate validation set for hyperparameter tuning. |
| Hardware Specification | No | Computations were run on a large server using up to 100 GB of RAM and 20 CPUs in parallel. This is not specific enough to identify hardware (e.g., no CPU model, no GPU mentioned). |
| Software Dependencies | No | Experiments used Nilearn [3] and MNE [26] for f MRI and MEG data processing respectively, as well as the scientific Python ecosystem: Matplotlib [31], Scikit-learn [45], Numpy [29] and Scipy [61]. We use the Picard algorithm for non-Gaussian ICA [2], and mvlearn for multi-view ICA [47]. No version numbers are provided for these software dependencies. |
| Experiment Setup | Yes | In the following synthetic experiments, data are generated according to model (1) with p = 4 components and m = 5 views and mixing matrices are generated by sampling coefficients from a standardized Gaussian. Gaussian components are generated from a standardized Gaussian and their noise has standard deviation Σ 1 2 i (obtained by sampling from a uniform density between 0 and 1) while non-Gaussian components are generated from a Laplace distribution and their noise standard deviations are equal. Data are first reduced using a subject-specific PCA with p = 10 components. The unmixing operators are learned using all subjects and 80% of the runs. |