Search Results for author: Eloy Geenjaar

Found 8 papers, 3 papers with code

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data

1 code implementation29 Mar 2021 Alex Fedorov, Eloy Geenjaar, Lei Wu, Thomas P. DeRamus, Vince D. Calhoun, Sergey M. Plis

We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout.

Out-of-Distribution Generalization Self-Supervised Learning

Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes

1 code implementation7 Sep 2022 Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P. DeRamus, Margaux Luck, Maria Misiura, R Devon Hjelm, Sergey M. Plis, Vince D. Calhoun

Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings.

Self-Supervised Learning

Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures

no code implementations29 Aug 2021 Eloy Geenjaar, Tonya White, Vince Calhoun

The VAE is trained on voxelwise rs-fMRI data and performs non-linear dimensionality reduction that retains meaningful information.

Dimensionality Reduction regression +2

Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data

no code implementations26 May 2022 Eloy Geenjaar, Amrit Kashyap, Noah Lewis, Robyn Miller, Vince Calhoun

Our approach is evaluated on data with multiple motor sub-tasks to assess whether the model captures disentangled latent factors that correspond to each sub-task.

CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs

no code implementations7 Oct 2022 Eloy Geenjaar, Noah Lewis, Amrit Kashyap, Robyn Miller, Vince Calhoun

To analyze communication, the brain is often split up into anatomical regions that each perform certain computations.

Specificity

Learning low-dimensional dynamics from whole-brain data improves task capture

no code implementations18 May 2023 Eloy Geenjaar, Donghyun Kim, Riyasat Ohib, Marlena Duda, Amrit Kashyap, Sergey Plis, Vince Calhoun

We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks, and demonstrate that it outperforms widely used dimensionality reduction techniques in how well the latent timeseries relates to behavioral sub-tasks, such as left-hand or right-hand tapping.

Dimensionality Reduction

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