MAPs: Methods And Primers for Computational Psychiatry and Neuroeconomics
Dr. Alan Anticevic , Associate Professor, Department of Psychiatry, Division of Neurocognition, Neurocomputation, and Neurogenetics (N3) , Yale School of Medicine
A common challenge that research projects have to resolve is to integrate available neuroimaging pipelines for multi-modal (i.e. structural, functional, and diffusion) imaging and for publicly available datasets. Quantitative Neuroimaging Environment & ToolboX (QuNex) integrates multiple neuroimaging processing and analytic pipelines into a single, flexible and powerful platform that can be used for multi-modal neuroimaging.
Despite growing need for platforms that can unite neuroimaging approaches and data analysis across modalities (structural, functional, and diffusion imaging data), to our knowledge, there is no single platform that is capable to allow researchers to have easy access to these resources and flexibility in analyzing data.
Supported by several grants, QuNex is built to be user friendly (we aim to help a new user to start working with the environment within 30 min), flexible for use with different types of data, and expandable (through plug-ins and code libraries). Several papers are published that describe functionalities of QuNex. It is open source software and deliberately integrates only software that is open source at this time.
Knowledge of Linux and script execution is helpful. Most important is the researcher’s ability to know what they are looking for with respect to neuroimaging processing and analytic approaches.
This is open source software and deliberately integrates only software that is open source at this time.
Can be used with all modes of neuroimaging, as well as most large-scale neuroimaging data. Many large-scale datasets have been used with QuNex, including PNC, BSNIP, HCP and others.