Search Results for author:
Found 10 papers, 3 papers with code
Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data
To test this, we apply the framework to temporally unstructured resting-state fMRI data from individuals with schizophrenia and healthy controls.
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning
In this paper, we propose a new convolution-transformer hybrid model architecture with masked auto-encoding for low-data bio-signal transfer learning, introduce a frequency-based masked auto-encoding task, employ a more comprehensive evaluation framework, and evaluate how much and when (multimodal) pre-training improves fine-tuning performance.
Learning low-dimensional dynamics from whole-brain data improves task capture
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.
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
To analyze communication, the brain is often split up into anatomical regions that each perform certain computations.
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
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.
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
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.
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures
The VAE is trained on voxelwise rs-fMRI data and performs non-linear dimensionality reduction that retains meaningful information.
Fusing multimodal neuroimaging data with a variational autoencoder
Neuroimaging studies often involve the collection of multiple data modalities.
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data
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.
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Sensory input from multiple sources is crucial for robust and coherent human perception.
