3 citations · 5 across the 11 of their papers we have counts for
11 papers
Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study
Elodie Germani, Nikhil Baghwat, Mathieu Dugré +7
Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for pre…
Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data
Son Nguyen, Adam Wang, Albert Montillo
Traditional deep learning (DL) models have two ubiquitous limitations. First, they assume training samples are independent and identically distributed (i.i.d), an assumption often…
UQ-ARMED: Uncertainty quantification of adversarially-regularized mixed effects deep learning for clustered non-iid data
Alex Treacher, Kevin Nguyen, Dylan Owens +2
This work demonstrates the ability to produce readily interpretable statistical metrics for model fit, fixed effects covariance coefficients, and prediction confidence. Importantly…
Longitudinal Prognosis of Parkinsons Outcomes using Causal Connectivity
Cooper J. Mellema, Kevin P. Nguyen, Alex Treacher +2
Parkinsons disease (PD) is a movement disorder and the second most common neurodengerative disease but despite its relative abundance, there are no clinically accepted neuroimaging…
DC and SA: Robust and Efficient Hyperparameter Optimization of Multi-subnetwork Deep Learning Models
Alex H. Treacher, Albert Montillo
We present two novel hyperparameter optimization strategies for optimization of deep learning models with a modular architecture constructed of multiple subnetworks. As complex net…
Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data
Kevin P. Nguyen, Albert Montillo
Natural science datasets frequently violate assumptions of independence. Samples may be clustered (e.g. by study site, subject, or experimental batch), leading to spurious associat…
Novel Machine Learning Approaches for Improving the Reproducibility and Reliability of Functional and Effective Connectivity from Functional MRI
Cooper J. Mellema, Albert Montillo
Objective: New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify…
Prediction of individual progression rate in Parkinson's disease using clinical measures and biomechanical measures of gait and postural stability
Vyom Raval, Kevin P. Nguyen, Ashley Gerald +2
Parkinson's disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted…
Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder
Cooper J. Mellema, Alex Treacher, Kevin P. Nguyen +1
Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functi…
Improved motion correction for functional MRI using an omnibus regression model
Vyom Raval, Kevin P. Nguyen, Albert Montillo
Head motion during functional Magnetic Resonance Imaging acquisition can significantly contaminate the neural signal and introduce spurious, distance-dependent changes in signal co…
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning
Kevin P. Nguyen, Cherise Chin Fatt, Alex Treacher +3
The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help…