“More Is Different” in Functional Magnetic Resonance Imaging: A Review of Recent Data Analysis Techniques

Abstract

Two aspects play a key role in recently developed strategies for functional magnetic resonance imaging (fMRI) data analysis: first, it is now recognized that the human brain is a complex adaptive system and exhibits the hallmarks of complexity such as emergence of patterns arising out of a multitude of interactions between its many constituents. Second, the field of fMRI has evolved into a data-intensive, big data endeavor with large databases and masses of data being shared around the world. At the same time, ultra-high field MRI scanners are now available producing data at previously unobtainable quality and quantity. Both aspects have led to shifts in the way in which we view fMRI data. Here, we review recent developments in fMRI data analysis methodology that resulted from these shifts in paradigm.

Functional network centrality in obesity: A resting-state and task fMRI study

“…In this article, we apply graph-theoretic measures of functional connectivity to both resting-state and task functional magnetic resonance imaging (fMRI) data to address this issue. Functional brain connectivity describes the relations between distinct brain areas based on the correlation between fMRI time series (e.g., Lohmann et al, 2013). In obesity research, several groups have examined functional connectivity by applying seedbased analysis or independent component analysis (ICA).…”

Deficient approaches to human neuroimaging

“…Network modeling may come to be a key strategy for identifying relevant functional structures in the human brain (Sporns, 2013). Effectively, such network-based approaches may help to characterize the brain on its own terms as a complex dynamic system (Lohmann et al, 2013b). Simulation of brain dynamics using biophysically realistic simulations (Deco et al, 2008; Markram et al, 2011; Gerstner et al, 2012) offers promise for the identification and understanding of brain mechanisms, in particular by bridging all spatial scales.…”

“…In particular, such massively univariate methods consider neuronal communication processes only indirectly as they mainly evaluate whether significant activity occurs at any specified voxel. Since the major functional role of neurons is to transmit co-ordinated activity to separate places in the brain, more sophisticated analysis methods which consider simultaneously the BOLD signal at multiple voxels may thus be better suited to analyze human brain function (Lohmann et al, 2013b). It is vital to recognize, however, that not all multivariate models considering neuronal communication (e.g., in terms of effective connectivity) are free of problems.…”

Connectome Networks: From Cells to Systems

“…Once brain network data have been rendered in matrix form, they are amenable to an extremely wide range of statistical and modeling tools coming from network science, especially the mathematical framework of graph theory (Bullmore and Sporns 2009). A comprehensive overview of the application and interpretation of graph-theoretical approaches to brain networks is beyond the scope of this chapter [for reviews, see Rubinov and Sporns (2010), Stam (2010), and Lohmann et al (2013)]. Briefly, descriptive measures of brain network connectivity fall into at least three different categories, reporting on different aspects of network organization.…”