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IEEE Transactions on Medical Imaging|March 24, 2022
Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain AgingMatthias Wilms, Jordan J Bannister, Pauline Mouches, et al.
Neuroimage. Clinical|April 20, 2023
Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasetsMilton Camacho, Matthias Wilms, Pauline Mouches, et al.
Journal of Alzheimer'S Disease : JAD|April 7, 2020
Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging DataSascha Gill, Pauline Mouches, Sophie Hu, et al.
Scientific Data|February 19, 2020
High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderlyDeepthi Rajashekar, Matthias Wilms, M Ethan MacDonald, et al.
Frontiers in Pediatrics|October 15, 2025
The future is in the background: background EEG patterns, not acute seizures, predict epilepsy and neurodevelopmental outcomes in neonatal HIEKristine E Woodward, Pauline de Jesus, Kimberly Amador, et al.
Neuroimage. Clinical|January 5, 2021
Structural and functional connectivity of motor circuits after perinatal stroke: A machine learning studyHelen L Carlson, Brandon T Craig, Alicia J Hilderley, et al.
International Journal of Geriatric Psychiatry|March 29, 2021
Neural correlates of the impulse dyscontrol domain of mild behavioral impairmentSascha Gill, Meng Wang, Pauline Mouches, et al.
Frontiers in Neurology|October 25, 2024
Analysis and visualization of the effect of multiple sclerosis on biological brain ageCatharina J A Romme, Emma A M Stanley, Pauline Mouches, et al.
The World Journal of Biological Psychiatry : the Official Journal of the World Federation of Societies of Biological Psychiatry|January 8, 2024
Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approachRajamannar Ramasubbu, Elliot C Brown, Pauline Mouches, et al.
Epilepsia|July 17, 2023
Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot studyGuillermo Delgado-García, Jordan D T Engbers, Samuel Wiebe, et al.
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