Graph-Based Network Analysis of Resting-State Functional MRI

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future

Connecting Openness and the Resting-State Brain Network: A Discover-Validate Approach

In personality neuroscience, the openness-brain association has been a topic of interest. Previous studies usually started from difference in openness trait and used it to infer brain functional activity characteristics, but no study has used a “brain-first” research strategy to explore that association based on more objective brain imaging data. In this study, we used a fully data-driven approach to discover and validate the association between openness and the resting-state brain network. We collected data of 120 subjects as a discovery sample and 56 subjects as a validation sample. The Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) was used to measure the personality characteristics of all the subjects. Using an exploratory approach based on independent component analysis of resting-state functional magnetic resonance imaging (fMRI) data, we identified a parietal network that consisted of the precuneus and inferior parietal lobe. The inter-subject similarity of the parietal memory network exhibited significant associations with openness trait, and this association was validated using the 56-subject independent sample. This finding connects the openness trait to the characteristics of a neural network and helps to understand the underlying biology of the openness trait

Spontaneous Brain Activity in the Default Mode Network Is Sensitive to Different Resting-State Conditions with Limited Cognitive Load

BACKGROUND: Recent functional MRI (fMRI) studies have demonstrated that there is an intrinsically organized default mode network (DMN) in the resting brain, primarily made up of the posterior cingulate cortex (PCC) and the medial prefrontal cortex (MPFC). Several previous studies have found that the DMN is minimally disturbed during different resting-state conditions with limited cognitive demand. However, this conclusion was drawn from the visual inspection of the functional connectivity patterns within the DMN and no statistical comparison was performed. METHODOLOGY/PRINCIPAL FINDINGS: Four resting-state fMRI sessions were acquired: 1) eyes-closed (EC) (used to generate the DMN mask); 2) EC; 3) eyes-open with no fixation (EO); and 4) eyes-open with a fixation (EO-F). The 2-4 sessions were counterbalanced across participants (n = 20, 10 males). We examined the statistical differences in both functional connectivity and regional amplitude of low frequency fluctuation (ALFF) within the DMN among the 2-4 resting-state conditions (i.e., EC, EO, and EO-F). Although the connectivity patterns of the DMN were visually similar across these three different conditions, we observed significantly higher functional connectivity and ALFF in both the EO and the EO-F conditions as compared to the EC condition. In addition, the first and second resting EC conditions showed significant differences within the DMN, suggesting an order effect on the DMN activity. CONCLUSIONS/SIGNIFICANCE: Our findings of the higher DMN connectivity and regional spontaneous activities in the resting state with the eyes open suggest that the participants might have more non-specific or non-goal-directed visual information gathering and evaluation, and mind wandering or daydreaming during the resting state with the eyes open as compared to that with the eyes closed, thus providing insights into the understanding of unconstrained mental activity within the DMN. Our results also suggest that it should be cautious when choosing the type of a resting condition and designating the order of the resting condition in multiple scanning sessions in experimental design

Mapping the human brain function in vivo

Mapping of the human brain function in vivo is among the most promising means of uncovering the relationship between brain and behavior. Both the 1000 Functional Connectome Project ~1 and Human Connectome Project~2 have made advancements in the collection, management, and sharing of massive neuroimaging datasets. In China, the government plans to announce the China Brain Project (CBP), a national brain project aimed at understanding neural mechanisms underlying human cognition with applications of brain disease and brain-inspired computation. Methods for in vivo human brain mapping must be included in the CBP, as they make possible direct assessment of brain structure and activity and contribute directly to translational research

The anatomy of reliability: a must read for future human brain mapping

Human brain mapping (HBM) is increasingly becoming a multi-disciplinary field where some scientific issues are fundamental for all scientists and applications of using the technology to investigate individual differences. Reliability represents a significant issue for all scientific fields and has particularly been overlooked for decades by the HBM field 1. Meanwhile, recent advances in open science have offered the field big data for developing novel methodological frameworks as well as performing large-scale investigations of the brain-mind associations based upon the individual differences assessed with HBM 2. A systematic investigation of reliability seems still far behind these HBM developments. It is critical that reliability is evaluated ahead of these applications, motivating the current commentary on delineation of the anatomy of reliability for future HBM

Functional brain network mapping with dual regression

Functional magnetic resonance imaging (fMRI) is an in-vivo non-invasive technique for measuring brain activity with excellent spatial and good temporal resolution. Without performing explicit tasks, resting-state fMRI (rfMRI) is widely used to map the functional connectivity network (FCN), which refers to a large-scale network of interdependent or functionally connected brain regions and it could be detected by using different algorithms (Zuo and Xing, 2014). Seed-based correlation is deemed as one of the most widely used method to identify FCN. This approach needs to extract a representative time series by averaging time series of all voxels within a small region of interest (ROI). While the selection of the ROI appears simple and straightforward, it becomes very challenging to determine an ROI for mapping representative activities of FCN including multiple distant areas

Concordance among indices of intrinsic brain function: Insights from inter-individual variation and temporal dynamics

Various resting-state fMRI (R-fMRI) measures have been developed to characterize intrinsic brain activity. While each of these measures has gained a growing presence in the literature, questions remain regarding the common and unique aspects these indices capture. The present work provided a comprehensive examination of inter-individual variation and intra-individual temporal variation for commonly used measures, including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, network centrality and global signal correlation. Regardless of whether examining intra-individual or inter-individual variation, we found that these definitionally distinct R-fMRI indices tend to exhibit a relatively high degree of covariation, which doesn't exist in phase randomized surrogate data. As a measure of intrinsic brain function, concordance for R-fMRI indices was negatively correlated with age across individuals (i.e., concordance among functional indices decreased with age). To understand the functional significance of concordance, we noted that higher concordance was generally associated with higher strengths of R-fMRI indices, regardless of whether looking through the lens of inter-individual (i.e., high vs. low concordance participants) or intra-individual (i.e., high vs. low concordance states identified via temporal dynamic analyses) differences. We also noted a linear increase in functional concordance together with the R-fMRI indices through the scan, which may suggest a decrease in arousal. The current study demonstrated an enriched picture regarding the relationship among the R-fMRI indices, as well as provided new insights in examining dynamic states within and between individuals. (C) 2017 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved