[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 …
The Semi-constrained Network-Based Statistic (scNBS): Integrating Local and Global Information for Brain Network Inference
Functional connectomics has become a popular topic over the last two decades.
Researchers often conduct inference at the level of groups of edges, or “components", with …
Researchers often conduct inference at the level of groups of edges, or “components", with …
[HTML][HTML] Disambiguating brain functional connectivity
Functional connectivity (FC) analyses of correlations of neural activity are used extensively
in neuroimaging and electrophysiology to gain insights into neural interactions. However …
in neuroimaging and electrophysiology to gain insights into neural interactions. However …
[HTML][HTML] Leveraging edge-centric networks complements existing network-level inference for functional connectomes
The human connectome is modular with distinct brain regions clustering together to form
large-scale communities, or networks. This concept has recently been leveraged in novel …
large-scale communities, or networks. This concept has recently been leveraged in novel …
Network level enrichment provides a framework for biological interpretation of machine learning results
Abstract Machine learning algorithms are increasingly being utilized to identify brain
connectivity biomarkers linked to behavioral and clinical outcomes. However, research often …
connectivity biomarkers linked to behavioral and clinical outcomes. However, research often …
[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 …
Disentangling brain graphs: A note on the conflation of network and connectivity analyses
SL Simpson, PJ Laurienti - Brain connectivity, 2016 - liebertpub.com
Understanding the human brain remains the holy grail in biomedical science, and arguably
in all of the sciences. Our brains represent the most complex systems in the world (and some …
in all of the sciences. Our brains represent the most complex systems in the world (and some …
Can structure predict function at individual level in the human connectome?
Abstract Several studies predicting Functional Connectivity (FC) from Structural Connectivity
(SC) at individual level have been published in recent years, each promising increased …
(SC) at individual level have been published in recent years, each promising increased …
[HTML][HTML] MULAN: Evaluation and ensemble statistical inference for functional connectivity
Many analysis methods exist to extract graphs of functional connectivity from neuronal
networks. Confidence in the results is limited because,(i) different methods give different …
networks. Confidence in the results is limited because,(i) different methods give different …
GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures
Abstract Background We previously presented GraphVar as a user-friendly MATLAB toolbox
for comprehensive graph analyses of functional brain connectivity. Here we introduce a …
for comprehensive graph analyses of functional brain connectivity. Here we introduce a …