rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis
Abstract Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria
through a lengthy and time-consuming process. Much effort is being made to identify brain …
through a lengthy and time-consuming process. Much effort is being made to identify brain …
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment
We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict
clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local …
clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local …
Benchmarking functional connectome-based predictive models for resting-state fMRI
Functional connectomes reveal biomarkers of individual psychological or clinical traits.
However, there is great variability in the analytic pipelines typically used to derive them from …
However, there is great variability in the analytic pipelines typically used to derive them from …
A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity
Brain connectivity alterations associated with mental disorders have been widely reported in
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
Geomstats: a Python package for Riemannian geometry in machine learning
We introduce Geomstats, an open-source Python package for computations and statistics on
nonlinear manifolds such as hyperbolic spaces, spaces of symmetric positive definite …
nonlinear manifolds such as hyperbolic spaces, spaces of symmetric positive definite …
R-mixup: Riemannian mixup for biological networks
Biological networks are commonly used in biomedical and healthcare domains to effectively
model the structure of complex biological systems with interactions linking biological entities …
model the structure of complex biological systems with interactions linking biological entities …
Machine learning on human connectome data from MRI
CJ Brown, G Hamarneh - arXiv preprint arXiv:1611.08699, 2016 - arxiv.org
Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that
allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these …
allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these …
[HTML][HTML] Machine Learning and Graph Signal Processing Applied to Healthcare: A Review
Signal processing is a very useful field of study in the interpretation of signals in many
everyday applications. In the case of applications with time-varying signals, one possibility is …
everyday applications. In the case of applications with time-varying signals, one possibility is …
Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging
A Brahim, N Farrugia - Artificial Intelligence in Medicine, 2020 - Elsevier
Graph signal processing (GSP) is a framework that enables the generalization of signal
processing to multivariate signals described on graphs. In this paper, we present an …
processing to multivariate signals described on graphs. In this paper, we present an …
Predicting cognitive scores with graph neural networks through sample selection learning
Analyzing the relation between intelligence and neural activity is of the utmost importance in
understanding the working principles of the human brain in health and disease. In existing …
understanding the working principles of the human brain in health and disease. In existing …