Mattson Ogg

Pre-Training for Large-Scale Functional Connectome Fingerprinting Supports Generalization and Transfer Learning in Functional Neuroimaging

The paper pre-trains a neural network on functional connectome data to enable generalization and transfer learning in fMRI, achieving high recognition accuracy across datasets and scan durations.

Estimation of neuronal tuning for word meaning from passively recorded naturalistic speech

The paper develops a pipeline to estimate neuronal tuning for word meaning from naturalistic speech and validates it using patient data and ground-truth controls.

Optimizing Artificial Neural Network Models to Predict Brain-Age from Functional MRI

This study optimizes deep learning models to predict brain-age from resting state fMRI data, achieving accurate predictions but showing mixed results for clinical populations.

Behavioral and neural sound classification: Insights from natural and synthetic sounds

The paper investigates how natural and synthetic sounds are classified behaviorally and neurally, identifying key acoustic features that support sound category recognition.

Laying the Foundation: Modern Transformers for Gold-Standard Sleep Analysis and Beyond

The paper evaluates transformer-based models for sleep stage classification, finding they match state-of-the-art techniques and provide a foundation for future data-driven sleep research.

Self-Supervised Transformer Model Training for a Sleep-EEG Foundation Model

This paper introduces a self-supervised transformer model for sleep-EEG data that outperforms supervised methods and can be fine-tuned for new tasks like individual identification and brain age estimation.