[HTML][HTML] Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
Cognitive performance can be predicted from an individual's functional brain connectivity
with modest accuracy using machine learning approaches. As yet, however, predictive …
with modest accuracy using machine learning approaches. As yet, however, predictive …
fMRI functional connectivity is a better predictor of general intelligence than cortical morphometric features and ICA parcellation order affects predictive performance
Intelligence, as a general cognitive ability, shows a substantial inter-subject variation.
Because of its impact on our lives, there is great interest in explaining the neural substrates …
Because of its impact on our lives, there is great interest in explaining the neural substrates …
Brain covariance selection: better individual functional connectivity models using population prior
Spontaneous brain activity, as observed in functional neuroimaging, has been shown to
display reproducible structure that expresses brain architecture and carries markers of brain …
display reproducible structure that expresses brain architecture and carries markers of brain …
Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity
Although functional connectivity and associated graph theory measures (eg, centrality; how
centrally important to the network a region is) are widely used in brain research, the full …
centrally important to the network a region is) are widely used in brain research, the full …
[HTML][HTML] Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics
There is significant interest in the development and application of deep neural networks
(DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their …
(DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their …
[HTML][HTML] ConnSearch: A framework for functional connectivity analysis designed for interpretability and effectiveness at limited sample sizes
Functional connectivity studies increasingly turn to machine learning methods, which
typically involve fitting a connectome-wide classifier, then conducting post hoc interpretation …
typically involve fitting a connectome-wide classifier, then conducting post hoc interpretation …
Combining multiple connectomes via canonical correlation analysis improves predictive models
Generating models from functional connectivity data that predict behavioral measures holds
great clinical potential. While the majority of the literature has focused on using only …
great clinical potential. While the majority of the literature has focused on using only …
Using connectome-based predictive modeling to predict individual behavior from brain connectivity
Neuroimaging is a fast-developing research area in which anatomical and functional images
of human brains are collected using techniques such as functional magnetic resonance …
of human brains are collected using techniques such as functional magnetic resonance …
A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients
Multivariate prediction of human behavior from resting state data is gaining increasing
popularity in the neuroimaging community, with far-reaching translational implications in …
popularity in the neuroimaging community, with far-reaching translational implications in …
Predicting individualized intelligence quotient scores using brainnetome-atlas based functional connectivity
Variation in several brain regions and neural parameters is associated with intelligence. In
this study, we adopted functional connectivity (FC) based on Brainnetome-atlas to predict …
this study, we adopted functional connectivity (FC) based on Brainnetome-atlas to predict …