Assessing functional connectivity in the human brain by fMRI

Abstract

Functional magnetic resonance imaging (fMRI) 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 to 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 multivariate statistical methods have been adapted and applied to analyze 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. 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. Correlations at baseline are changed during the performance of a continuous task. In this review, various methods available for assessing connectivity are described and evaluated.

Figure 1

Several methods of mapping connectivity in the whole brain using fMRI time series data. Partial least squares is not shown; while it is also correlation or covariance-based, it typically uses a different and often more complex data matrix.

Figure 2

Modeling effective connectivity between several regions identified on the basis of prior analysis or hypotheses. Conceptually, the procedure is similar for structural equation modeling (SEM) and multivariate autoregressive modeling (MAR). The most important difference is that directionality of the influences is an assumption of the model for SEM, but can potentially be determined from the data with MAR because of MAR's use of temporal information.

Figure 3

Regions of interest in the working memory system exhibit intrinsic connectivity that depends on memory load. In this example, regions were defined from working memory activation maps (top) and applied to data from continuous performance of the N-back task at different loads. Steady-state correlations with left prefrontal cortex (bottom) increased with load for anterior cingulate (ACC); decreased with load for a region in the medial frontal gyrus (MFG); and were unaffected by load in a control region. The MFG represents a region commonly appearing in deactivation maps and hypothesized to form part of a “default mode” network.