10 citations · 24 across the 14 of their papers we have counts for
5 papers · 1 filter
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
Yutong Gao, Charles A. Ellis, Vince D. Calhoun +1
The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field i…
Predictive Sparse Manifold Transform
Yujia Xie, Xinhui Li, Vince D. Calhoun
We present Predictive Sparse Manifold Transform (PSMT), a minimalistic, interpretable and biologically plausible framework for learning and predicting natural dynamics. PSMT incorp…
Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey
Md. Mahfuzur Rahman, Vince D. Calhoun, Sergey M. Plis
Deep learning (DL) models have been popular due to their ability to learn directly from the raw data in an end-to-end paradigm, alleviating the concern of a separate error-prone fe…
Learning low-dimensional dynamics from whole-brain data improves task capture
Eloy Geenjaar, Donghyun Kim, Riyasat Ohib +4
The neural dynamics underlying brain activity are critical to understanding cognitive processes and mental disorders. However, current voxel-based whole-brain dimensionality reduct…
Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy
Qiang Li, Shujian Yu, Kristoffer H Madsen +2
Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to…