Braingnn: Interpretable brain graph neural network for fmri analysis
Understanding which brain regions are related to a specific neurological disorder or
cognitive stimuli has been an important area of neuroimaging research. We propose …
cognitive stimuli has been an important area of neuroimaging research. We propose …
A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI
DA Di Giovanni, DL Collins - Neurosurgical Review, 2023 - Springer
Deep learning algorithms have greatly improved our ability to estimate eloquent cortex
regions from resting-state brain scans for patients about to undergo neurosurgery. The use …
regions from resting-state brain scans for patients about to undergo neurosurgery. The use …
Certified robustness of graph convolution networks for graph classification under topological attacks
Graph convolution networks (GCNs) have become effective models for graph classification.
Similar to many deep networks, GCNs are vulnerable to adversarial attacks on graph …
Similar to many deep networks, GCNs are vulnerable to adversarial attacks on graph …
Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights
J Anbarasi, R Kumari, M Ganesh, R Agrawal - BMC neurology, 2024 - Springer
Connectomics is a neuroscience paradigm focused on noninvasively mapping highly
intricate and organized networks of neurons. The advent of neuroimaging has led to …
intricate and organized networks of neurons. The advent of neuroimaging has led to …
Deep representation learning: Fundamentals, technologies, applications, and open challenges
Machine learning algorithms have had a profound impact on the field of computer science
over the past few decades. The performance of these algorithms heavily depends on the …
over the past few decades. The performance of these algorithms heavily depends on the …
[HTML][HTML] Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks
Localizing the eloquent cortex is a crucial part of presurgical planning. While invasive
mapping is the gold standard, there is increasing interest in using noninvasive fMRI to …
mapping is the gold standard, there is increasing interest in using noninvasive fMRI to …
DeepEZ: a graph convolutional network for automated epileptogenic zone localization from resting-state fMRI connectivity
N Nandakumar, D Hsu, R Ahmed… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Objective: Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up
and therapeutic planning in medication refractory epilepsy. In this paper, we present the first …
and therapeutic planning in medication refractory epilepsy. In this paper, we present the first …
Deep representation learning: Fundamentals, perspectives, applications, and open challenges
Machine Learning algorithms have had a profound impact on the field of computer science
over the past few decades. These algorithms performance is greatly influenced by the …
over the past few decades. These algorithms performance is greatly influenced by the …
Refinenet: An automated framework to generate task and subject-specific brain parcellations for resting-state fmri analysis
Parcellations used in resting-state fMRI (rs-fMRI) analyses are derived from group-level
information, and thus ignore both subject-level functional differences and the downstream …
information, and thus ignore both subject-level functional differences and the downstream …
A multi-scale spatial and temporal attention network on dynamic connectivity to localize the eloquent cortex in brain tumor patients
We present a deep neural network architecture that combines multi-scale spatial attention
with temporal attention to simultaneously localize the language and motor areas of the …
with temporal attention to simultaneously localize the language and motor areas of the …