Assessing functional connectivity in the human brain by fMRI

Abstract

Functional magnetic resonance imaging is widely used to detect and delineate regions of the brain that change their level of activation in response to specific stimuli and tasks. Simple activation maps depict only the average level of engagement of different regions within distributed systems. FMRI potentially can reveal additional information about the degree by which components of large-scale neural systems are functionally coupled together to achieve specific tasks. In order to better understand how brain regions contribute to functionally connected circuits, it is necessary to record activation maps either as a function of different conditions, at different times or in different subjects. Data obtained under different conditions may then be analyzed by a variety of techniques to infer correlations and couplings between nodes in networks. Several different multivariate statistical methods have been adapted and applied to analyze the variations within such data. An approach of particular interest that is suited to studies of connectivity within single subjects makes use of acquisitions of runs of MRI images obtained while the brain is in a so-called steady state, either at rest (i.e. without any specific stimulus or task) or in a condition of continuous activation. The interregional correlations between fluctuations of MRI signal potentially reveal functional connectivity. Recent studies have established that interregional correlations between different components of circuits in each of the visual, language, motor and working memory systems can be detected in the resting state. The correlations at baseline are changed during the performance of a continuous task. In this review the various methods available for assessing connectivity are described and evaluated.

Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors

“…As we demonstrated here, the attractors are encoded in the connectivity matrix and are presumably associated with the computation of a specific brain function, which is evidenced by the fact that the resting state networks resemble intermittent activations of spatial network patterns that are consistently known to be activated under cognitive and behavioral task conditions (Fox et al, 2005). There have been several other studies, which demonstrated that in the absence of an overt task, spontaneous fluctuations in the BOLD signal correlate across functionally related brain regions (Lowe et al, 1998;Gusnard and Raichle, 2001;Greicius et al, 2003;Rogers et al, 2007). Some of these regions are also part of the Default Mode Network (Greicius et al, 2003), which identifies regions showing the greatest deactivation during externally imposed cognitive challenges.…”

“…Nevertheless, the equilibrium state of the brain, i.e., the spontaneous, not stimuli-or task-evoked brain activity during rest, does not reflect just a trivial random activity as one may naively expect. During the last decade, numerous experimental investigations have shown that spontaneous brain activity during rest is highly structured into characteristic spatiotemporal patterns, the socalled resting-state networks (RSNs) (Biswal et al, 1995;Greicius et al, 2003;Fox et al, 2005Fox et al, , 2007Fransson, 2005;Raichle and Mintun, 2006;Rogers et al, 2007;Vincent et al, 2007).…”

Connectivity-Informed fMRI Activation Detection

“…To generate task data, samples of A were drawn from (2) with V 2 set to Ω i . To simulate activation, the means of the first 20 regions in (2) were set to a small positive number, δ, that depended on the SNR, δ 2 /σ 2 . Gaussian noise was added to δ to further introduce inter-subject variability.…”

“…However, accumulating evidence suggests that brain function is also mediated through the interactions between brain regions in what is referred to as functional connectivity [2]. Although incorporation of functional connectivity may provide activation models that better reflect the nature of brain activity, few existing methods have been designed for such purposes [3].…”

Fiber Connectivity Integrated Brain Activation Detection

“…To map brain regions to function, standard analysis models the fMRI observations at each voxel as a linear combination of expected temporal responses using the general linear model (GLM) [1]. This univariate approach does not model the integrative property of the brain, which is known to facilitate brain function [2]. To ameliorate this serious limitation, the use of local neighbourhood information has been proposed to regularize activation detection [3,4].…”