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Found 13 papers, 5 papers with code
An image-computable model of speeded decision-making
Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
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.
REFORMS: Reporting Standards for Machine Learning Based Science
Machine learning (ML) methods are proliferating in scientific research.
A Comparison of Neuroelectrophysiology Databases
As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration.
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data
At their core, these frameworks learn the dynamics of brain activity by modeling sequences of activity akin to how sequences of text are modeled in NLP.
Differentiable programming for functional connectomics
We introduce a new analytic paradigm and software toolbox that implements common operations used in functional connectomics as fully differentiable processing blocks.
DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation
In this paper, we introduce DeepDefacer, an application of deep learning to MRI anonymization that uses a streamlined 3D U-Net network to mask facial regions in MRI images with a significant increase in speed over traditional de-identification software.
Comparing interpretation methods in mental state decoding analyses with deep learning models
Deep learning (DL) models find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (e. g., perceiving fear or joy) and brain activity by identifying those brain regions (and networks) whose activity allows to accurately identify (i. e., decode) these states.
NEMAR: An open access data, tools, and compute resource operating on NeuroElectroMagnetic data
To take advantage of recent and ongoing advances in large-scale computational methods, and to preserve the scientific data created by publicly funded research projects, data archives must be created as well as standards for specifying, identifying, and annotating deposited data.
Challenges for cognitive decoding using deep learning methods
In cognitive decoding, researchers aim to characterize a brain region's representations by identifying the cognitive states (e. g., accepting/rejecting a gamble) that can be identified from the region's activity.
Computational and informatics advances for reproducible data analysis in neuroimaging
We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community.
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Function
Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time.
Neurons and Cognition
False discovery rate smoothing
We also apply the method to a data set from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods.
Methodology Applications Computation
