Functional connectivity estimation in fMRI data: Influence of preprocessing and time course selection

Abstract

A number of techniques have been used to provide functional connectivity estimates for a given fMRI data set. In this study we compared two methods: a 'rest-like' method where the functional connectivity was estimated for the whitened residuals after regressing out the task-induced effects, and a within-condition method where the functional connectivity was estimated separately for each experimental condition. In both cases four pre-processing strategies were used: 1) time courses extracted from standard pre-processed data (standard); 2) adjusted time courses extracted using the volume of interest routines in SPM2 from standard pre-processed data (spm); 3) time courses extracted from ICA denoised data (standard denoised); and 4) adjusted time courses extracted from ICA denoised data (spm denoised). The temporal correlation between time series extracted from two cortical regions were statistically compared with the temporal correlation between a time series extracted from a cortical region and a time series extracted form a region placed in CSF. Since the later correlation is due to physiological noise and other artifacts, we used this comparison to investigate whether rest-like and task modulated connectivity could be estimated from the same data set. The pre-processing strategy had a significant effect on the connectivity estimates with the standard time courses providing larger connectivity values than the spm time courses for both estimation methods. The CSF comparison indicated that for our data set only rest-like connectivity could be estimated. The rest-like connectivity values were similar with connectivity estimated from resting state data.

Consistency and functional specialization in the default mode brain network

“…Similar to conventional activation mapping approaches, this type of modulated covariance in functional imaging studies may represent brain regional activities that are truly covariant (i.e., ''functionally connected'') during the specific performance of a task, or those regions whose activities are more simply coactivated, but not functionally coupled, as a result of task performance (34). However, if these activity fluctuations are characterized within a clearly and consistently defined functional network, as suggested by our results, then this type of modulated covariance may be best interpreted as a pattern of task-related functional connectivity among implicated regions.…”

Atypical network connectivity for imitation in autism spectrum disorder

“…In studies of clinical disorders such as ASD, these confounds may be aggravated by systematic differences in the response to the scanning environment (lying constrained inside a noisy magnet bore), which may prompt participants to engage in unknown and uncontrolled mental activities. As an alternative approach, data acquired during task performance can also be used for fcMRI, since network-specific spontaneous BOLD fluctuations are simultaneously present in time series acquired during task performance and can be separated statistically from task-related responses (Arfanakis et al, 2000; Fair et al, 2007; Fox, Snyder, Zacks, & Raichle, 2006; Gavrilescu et al, 2008). Therefore, isolating low-frequency BOLD fluctuations in data sets acquired during performance of a task unrelated to the network of interest provides a reasonable approach to examining intrinsic functional connectivity.…”

Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity

“…Numerous studies and reviews have explored the implications of various pre-processing steps (e.g., [6][7][8][9][10][11]); however, only a few to date have broadly addressed postprocessing techniques (for a recent review of functional connectivity methodologies with emphasis on the computational aspects, see: [12,13]; or for emphasis on clinical applications, see: [14,15]). In the following review, we will address the diverse array of post-procesing techniques available, with a focus on the theoretical presuppositions of each for exploring brain organization and function (see Fig.…”