ICN_Atlas

ICN_Atlas is an SPM compatible toolbox that is aimed at facilitating the interpretation of neuroimaging data in the context of intrinsic connectivity networks (ICNs, a.k.a. resting-state networks or RSNs) by describing fMRI maps or other statistical maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of ‘engagement’ of ICNs for any given statistical map of interest. Besides the functionally-derived atlasing capabilities (based on the BrainMap ICN data) the toolbox provides anatomy-based atlasing (based on the JHU and Brainnetome Atlas data), as well.

Specifications

Category:

Atlas Data, Atlas Application, Overlap Metrics, Volumetric Analysis, Statistical Operation, Visualization

License:

Attribution Non-Commercial Share Alike

Development Status:

4 - Beta

Domain:

Domain Independent, MR, PET/SPECT

Environment:

Other Environment

Intended Audience:

Developers, End Users

Operating System:

OS Independent

Programming Language:

MATLAB

Supported Data Format:

ANALYZE, NIfTI-1

Associations

build requires:

SPM

is a plugin for:

SPM

Recent Activity - Documents

Technical Publications documentation

ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks. posted by Lajos Rudolf KozÃ¡k on Dec 15, 2017

Recent Activity - Forums

help forum

individual ICs: collapsing or using global metric posted by Salma Bent Ridha on Apr 17, 2018

help forum

Welcome to Help posted by Lajos Rudolf KozÃ¡k on Dec 15, 2017

Recent Activity - News

Tool & Resource news

ICN_Atlas first public release available posted by Lajos Rudolf KozÃ¡k on Mar 12, 2018

Recent Activity - Files

icn_atlas: v1.0 (r20180306) release

ICN_atlas_public_r20180306.zip posted by Lajos Rudolf KozÃ¡k on Mar 6, 2018

Statistics more information

Home Page

RRID: SCR_015890

DOI:10.1016/j.neuroimage.2017.09.014

Funding: This work was partly undertaken at UCLH/UCL who received a proportion
of funding from the UK Department of Health's NIHR Biomedical
Research Centres funding scheme and at Semmelweis University who
was partly supported by the National Development Agency, Hungary
[grant number TAMOP 4.2.1.B-09/1/KMR-2010-0001]. We are grateful
to the Wolfson Foundation and the Epilepsy Society for supporting the
Epilepsy Society MRI scanner. This work was partly funded through
grants and bursaries from the Bolyai Research Fellowship Program of the
Hungarian Academy of Sciences; the Hungarian National Brain Research
Program [grant number KTIA_NAP_13-1-2013-0001]; the Medical
Research Council UK [MRC grant number G0301067]; and the Action
Medical Research [grant number SP4646].