Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...

Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

https://cfmriweb.ucsd.edu/tliu/pdf/behzadi07_compcor.pdf

A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

Dynamic functional connectivity: Promise, issues, and interpretations

Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

/pdf/dpabi-data-processing-analysis-for-resting-state-brain-23x2xbv688.pdf

DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

/pdf/the-autism-brain-imaging-data-exchange-towards-a-large-scale-32vwazsr5n.pdf

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

http://cfmriweb.ucsd.edu/tliu/pdf/liu04_caf.pdf

Caffeine alters the temporal dynamics of the visual BOLD response

http://fmri.ucsd.edu/tliu/pdf/restom06_physnoise.pdf

Physiological noise reduction for arterial spin labeling functional MRI

https://cfmriweb.ucsd.edu/tliu/pdf/behzadi05_compliance.pdf

An arteriolar compliance model of the cerebral blood flow response to neural stimulus.

http://fmri.ucsd.edu/tliu/pdf/behzadi06_initdip.pdf

Yashar Behzadi

Papers

A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

Caffeine alters the temporal dynamics of the visual BOLD response

Physiological noise reduction for arterial spin labeling functional MRI

An arteriolar compliance model of the cerebral blood flow response to neural stimulus.

Caffeine reduces the initial dip in the visual BOLD response at 3 T.