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 …
[HTML][HTML] Artificial intelligence for brain diseases: A systematic review
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for
analyzing complex medical data and extracting meaningful relationships in datasets, for …
analyzing complex medical data and extracting meaningful relationships in datasets, for …
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 …
Quantifying deviations of brain structure and function in major depressive disorder across neuroimaging modalities
Importance Identifying neurobiological differences between patients with major depressive
disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for …
disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for …
Braingb: a benchmark for brain network analysis with graph neural networks
Mapping the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder
Importance Biological psychiatry aims to understand mental disorders in terms of altered
neurobiological pathways. However, for one of the most prevalent and disabling mental …
neurobiological pathways. However, for one of the most prevalent and disabling mental …
[HTML][HTML] A deep learning approach to predict autism spectrum disorder using multisite resting-state fMRI
Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental
disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) …
disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) …
[HTML][HTML] Graph theory approach for the structural-functional brain connectome of depression
JY Yun, YK Kim - Progress in Neuro-Psychopharmacology and …, 2021 - Elsevier
To decipher the organizational styles of neural underpinning in major depressive disorder
(MDD), the current article reviewed recent neuroimaging studies (published during 2015 …
(MDD), the current article reviewed recent neuroimaging studies (published during 2015 …
[HTML][HTML] Tourism research after the COVID-19 outbreak: Insights for more sustainable, local and smart cities
LA Casado-Aranda, J Sánchez-Fernández… - Sustainable Cities and …, 2021 - Elsevier
This paper presents the results of a bibliometric analysis of academic research dealing with
COVID-19 in the area of city destination development from 1 December 2019 to 31 March …
COVID-19 in the area of city destination development from 1 December 2019 to 31 March …
A graph theory-based modeling of functional brain connectivity based on EEG: a systematic review in the context of neuroergonomics
LE Ismail, W Karwowski - Ieee Access, 2020 - ieeexplore.ieee.org
Graph theory analysis, a mathematical approach, has been applied in brain connectivity
studies to explore the organization of network patterns. The computation of graph theory …
studies to explore the organization of network patterns. The computation of graph theory …