742 citations
- Johns Hopkins UniversityUS7 papers
- Duke UniversityUS4 papers
- Centre Inria de SaclayFR3 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR3 papers
- Johns Hopkins MedicineUS3 papers
- CEA Paris-SaclayFR2 papers
- Center for Translational Research in Neuroimaging and Data ScienceUS2 papers
- Concordia UniversityCA2 papers
- Georgia Institute of TechnologyUS2 papers
- Harvard UniversityUS2 papers
- InsermFR2 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
16 papers
Breaking Negative Cycles: A Reflection-To-Action System For Adaptive Change
Minsol Michelle Kim, Daniel M. Low, David Lafond +8
Breaking negative mental health cycles, including rumination and recurring regrets, requires reflection that translates awareness into behavioral change. Grounded in the Transtheor…
Functional Correspondences in the Human and Marmoset Visual Cortex During Movie Watching: Insights from Correlation, Redundancy, and Synergy
Qiang Li, Ting Xu, Vince D. Calhoun
The world of beauty is deeply connected to the visual cortex, as perception often begins with vision in both humans and marmosets. In this study, to investigate their functional co…
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff +111
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is mean…
Numerical Stability of DeepGOPlus Inference
Inés Gonzalez Pepe, Yohan Chatelain, Gregory Kiar +1
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many pr…
Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic
Ali Salari, Yohan Chatelain, Gregory Kiar +1
Operating system (OS) updates introduce numerical perturbations that impact the reproducibility of computational pipelines. In neuroimaging, this has important practical implicatio…
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features
Dung Truong, Michael Milham, Scott Makeig +1
The success of deep learning in computer vision has inspired the scientific community to explore new analysis methods. Within the field of neuroscience, specifically in electrophys…