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A baseline for the multivariate comparison of resting-state networks.

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12-71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

Pubmed ID: 21442040 RIS Download

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Associated grants

  • Agency: NIMH NIH HHS, Id: R01 MH072681
  • Agency: NIBIB NIH HHS, Id: R01 EB005846
  • Agency: NIBIB NIH HHS, Id: R01 EB006841
  • Agency: NIAAA NIH HHS, Id: P20 AA017068
  • Agency: NIBIB NIH HHS, Id: R01 EB000840
  • Agency: NCRR NIH HHS, Id: P20 RR021938
  • Agency: NINDS NIH HHS, Id: R21 NS064464
  • Agency: NIDA NIH HHS, Id: K01 DA021632
  • Agency: NIDA NIH HHS, Id: R03 DA022435
  • Agency: NIDA NIH HHS, Id: R03 DA024212

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Chronux

Open-source software package for the analysis of neural data. Chronux routines may be employed in the analysis of both point process and continuous data, ranging from preprocessing, exploratory and confirmatory analysis. The current release is implemented as a MATLAB library. Chronux offers several routines for computing spectra and coherences for both point and continuous processes. In addition, it also offers several general purpose routines that were found useful such as a routine for extracting specified segments from data, or binning spike time data with bins of a specified size. Since the data can be continuous valued, point process times, or point processes that are binned, methods that apply to all these data types are given in routines whose names end with ''''c'''' for continuous, ''''pb'''' for binned point processes, and ''''pt'''' for point process times. Thus, mtspectrumc computes the spectrum of continuous data, mtspectrumpb computes a spectrum for binned point processes, and mtspectrumpt compute spectra for data consisting of point process times. Hybrid routines are also available and similarly named - for instance coherencycpb computes the coherency between continuous and binned point process data.

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Analysis of Functional NeuroImages

A set of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries. Since AFNI is free, all users are beta testers for life. The code is updated frequently in response to bug reports and feature creep. There is no support guarantee, but in practice you can usually get a quick response by posting problems/questions on the AFNI Message Board. Sample datasets and extensive educational material are also available at the AFNI Web site.

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Group ICA of fMRI Toolbox

A MATLAB toolbox which implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) functional magnetic resonance imaging data. GIFT works on MATLAB 6.5 and higher. Many ICA algorithms were generously contributed by Dr. Andrzej Cichocki.

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