On Network Derivation, Classification, and Visualization: A Response to Habeck and Moeller
Year: 2011
Type: article
Abstract: In the decade and a half since Biswal's fortuitous discovery of spontaneous correlations in functional imaging data, the field of functional connectivity (FC) has seen exponential growth resulting in the identification of widely-replicated intrinsic networks and the innovation of novel analytic methods with the promise of diagnostic application. As such a young field undergoing rapid change, we have yet to converge upon a desired and needed set of standards. In this issue, Habeck and Moeller begin a dialogue for developing best practices by providing four criticisms with respect to FC estimation methods, interpretation of FC networks, assessment of FC network features in classifying sub-populations, and network visualization. Here, we respond to Habeck and Moeller and provide our own perspective on the concerns raised in the hope that the neuroimaging field will benefit from this discussion.
Source: Brain Connectivity
Institutions: Mind Research Network, University of New Mexico
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: A Unified Framework for Flexible Brain Image Analysis, Unified multivariate data-driven solutions for static and dynamic brain connectivity, Neural Mechanism of Schizophrenia: Use of Multiple Neuroimaging Tools to Examine, G222768804, Informed Data-Driven Fusion of Behavior, Brain Function, and Genes, RESEARCH AND DEVELOPMENT OF BETTER DIAGNOSIC TOOLS FOR MENTAL ILLNESS, NEUROLOGICAL DISORDERS, TRAUMATIC BRAIN INJURY AND POST TRAUMATIC STRESS DISOR, MRN: MISSION OF DISCOVERY AND ADVANCEMENT OF CLINICAL SOLUTIONS FOR THE PREVENTION, DIAGNOSIS, AND TREATMENT OF MENTAL ILLNESS AND OTHER BRAIN DISOR, Multivariate methods for identifying multitask/multimodal brain imaging biomarkers
OpenAlex ID: W2091106132