Graph neural networks in network neuroscience
A Bessadok, MA Mahjoub… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Noninvasive medical neuroimaging has yielded many discoveries about the brain
connectivity. Several substantial techniques mapping morphological, structural and …
connectivity. Several substantial techniques mapping morphological, structural and …
Deep learning for brain disorder diagnosis based on fMRI images
In modern neuroscience and clinical study, neuroscientists and clinicians often use non-
invasive imaging techniques to validate theories and computational models, observe brain …
invasive imaging techniques to validate theories and computational models, observe brain …
A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes
Early diagnosis remains a significant challenge for many neurological disorders, especially
for rare disorders where studying large cohorts is not possible. A novel solution that …
for rare disorders where studying large cohorts is not possible. A novel solution that …
Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data
Autism spectrum disorder (ASD) is a developmental disorder, affecting about 1% of the
global population. Currently, the only clinical method for diagnosing ASD are standardized …
global population. Currently, the only clinical method for diagnosing ASD are standardized …
Bedside functional monitoring of the dynamic brain connectivity in human neonates
J Baranger, C Demene, A Frerot, F Faure… - Nature …, 2021 - nature.com
Clinicians have long been interested in functional brain monitoring, as reversible functional
losses often precedes observable irreversible structural insults. By characterizing neonatal …
losses often precedes observable irreversible structural insults. By characterizing neonatal …
[HTML][HTML] SSPNet: An interpretable 3D-CNN for classification of schizophrenia using phase maps of resting-state complex-valued fMRI data
QH Lin, YW Niu, J Sui, WD Zhao, C Zhuo… - Medical Image …, 2022 - Elsevier
Convolutional neural networks (CNNs) have shown promising results in classifying
individuals with mental disorders such as schizophrenia using resting-state fMRI data …
individuals with mental disorders such as schizophrenia using resting-state fMRI data …
Fetal brain abnormality classification from MRI images of different gestational age
O Attallah, MA Sharkas, H Gadelkarim - Brain sciences, 2019 - mdpi.com
Magnetic resonance imaging (MRI) is a common imaging technique used extensively to
study human brain activities. Recently, it has been used for scanning the fetal brain …
study human brain activities. Recently, it has been used for scanning the fetal brain …
Brain connectivity based graph convolutional networks and its application to infant age prediction
Infancy is a critical period for the human brain development, and brain age is one of the
indices for the brain development status associated with neuroimaging data. The difference …
indices for the brain development status associated with neuroimaging data. The difference …
A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants
Survivors following very premature birth (ie,≤ 32 weeks gestational age) remain at high risk
for neurodevelopmental impairments. Recent advances in deep learning techniques have …
for neurodevelopmental impairments. Recent advances in deep learning techniques have …
Acute histologic chorioamnionitis independently and directly increases the risk for brain abnormalities seen on magnetic resonance imaging in very preterm infants
Background The independent risk for neurodevelopmental impairments attributed to
chorioamnionitis in premature infants remains controversial. Delayed brain maturation or …
chorioamnionitis in premature infants remains controversial. Delayed brain maturation or …