Discriminating mild traumatic brain injury using sparse dictionary learning of functional network dynamics.

Mild traumatic brain injury (mTBI) is usually caused by a bump, blow, or jolt to the head or penetrating head injury, and carries the risk of inducing cognitive disorders. However, identifying the biomarkers for the diagnosis of mTBI is challenging as evident abnormalities in brain anatomy are rarely found in patients with mTBI. In this study, we tested whether the alteration of functional network dynamics could be used as potential biomarkers to better diagnose mTBI. We propose a sparse dictionary learning framework to delineate spontaneous fluctuation of functional connectivity into the subject-specific time-varying evolution of a set of overlapping group-level sparse connectivity components (SCCs) based on the resting-state functional magnetic resonance imaging (fMRI) data from 31 mTBI patients in the early acute phase (<3 days postinjury) and 31 healthy controls (HCs). The identified SCCs were consistently distributed in the cohort of subjects without significant inter-group differences in connectivity patterns. Nevertheless, subject-specific temporal expression of these SCCs could be used to discriminate patients with mTBI from HCs with a classification accuracy of 74.2% (specificity 64.5% and sensitivity 83.9%) using leave-one-out cross-validation. Taken together, our findings indicate neuroimaging biomarkers for mTBI individual diagnosis based on the temporal expression of SCCs underlying time-resolved functional connectivity.

Functional parcellation of the hippocampus from resting-state dynamic functional connectivity.

The hippocampus consists of functionally and structurally heterogeneous regions that are involved in multiple functions such as learning and memory. Previous studies on connectivity-based functional subdivisions of the hippocampus, however, overlooked the dynamic nature of resting-state functional connectivity (FC). In this study, we selected 50 subjects with the lowest head motion from the Human Connectome Project dataset and performed a two-stage spectral clustering technique to windowed FC correlations for identifying the potential covariant structures during the spontaneous fluctuation of hippocampal-cortical FC. The obtained covariant structures were believed to be functionally homogeneous by coupling with whole-brain regions in all transient connectivity states and consequently subdivided the left and right hippocampus into six and five functional subregions, respectively. Further, we demonstrated that this dynamic-FC-derived hippocampal parcellation exhibited significantly improved reproducibility of segmented subregions across subjects compared with static FC analysis. The findings extend our understanding to the functional organization within the hippocampus and provide a more comprehensive view of the functional flexibility of the hippocampus over time.

Functional connectivity changes in the entorhinal cortex of taxi drivers.

INTRODUCTION: As a major interface between the hippocampus and the neocortex, the entorhinal cortex (EC) is widely known to play a pivotal role in spatial memory and navigation. Previous studies have suggested that the EC can be divided into the anterior-lateral (alEC) and the posterior-medial subregions (pmEC), with the former receiving object-related information from the perirhinal cortex and the latter receiving scene-related information from the parahippocampal cortex. However, the functional connectivity maps of the EC subregions in the context of extensive navigation experience remain elusive. In this study, we analyzed the functional connectivity of the EC in subjects with long-term navigation experience and aimed to find the navigation-related change in the functional properties of the human EC. METHODS: We investigated the resting-state functional connectivity changes in the EC subregions by comparing the EC functional connectivity maps of 20 taxi drivers with those of 20 nondriver controls. Furthermore, we examined whether the functional connectivity changes of the EC were related to the number of taxi driving years. RESULTS: Significantly reduced functional connectivity was found in the taxi drivers between the left pmEC and the right anterior cingulate cortex (ACC), right angular gyrus, and bilateral precuneus as well as some temporal regions, and between the right pmEC and the left inferior temporal gyrus. Notably, the strength of the functional connectivity between the left pmEC and the left precuneus, as well as the right ACC, was negatively correlated with the years of taxi driving. CONCLUSION: This is the first study to explore the impact of long-term navigation experience on the connectivity patterns of the EC, the results of which may shed new light on the potential influence of extensive navigational training on the functional organization of the EC in healthy human brains.

Impact of global signal regression on characterizing dynamic functional connectivity and brain states.

Recently, resting-state functional magnetic resonance imaging (fMRI) studies have been extended to explore fluctuations in correlations over shorter timescales, referred to as dynamic functional connectivity (dFC). However, the impact of global signal regression (GSR) on dFC is not well established, despite the intensive investigations of the influence of GSR on static functional connectivity (sFC). This study aimed to examine the effect of GSR on the performance of the sliding-window correlation, a commonly used method for capturing functional connectivity (FC) dynamics based on resting-state fMRI and simultaneous electroencephalograph (EEG)-fMRI data. The results revealed that the impact of GSR on dFC was spatially heterogeneous, with some susceptible regions including the occipital cortex, sensorimotor area, precuneus, posterior insula and superior temporal gyrus, and that the impact was temporally modulated by the mean global signal (GS) magnitude across windows. Furthermore, GSR substantially changed the connectivity structures of the FC states responding to a high GS magnitude, as well as their temporal features, and even led to the emergence of new FC states. Conversely, those FC states marked by obvious anti-correlation structures associated with the default model network (DMN) were largely unaffected by GSR. Finally, we reported an association between the fluctuations in the windowed magnitude of GS and the time-varying EEG power within subjects, which implied changes in mental states underlying GS dynamics. Overall, this study suggested a potential neuropsychological basis, in addition to nuisance sources, for GS dynamics and highlighted the need for caution in applying GSR to sliding-window correlation analyses. At a minimum, the mental fluctuations of an individual subject, possibly related to ongoing vigilance, should be evaluated during the entire scan when the dynamics of FC is estimated.

Impact of 36 h of total sleep deprivation on resting-state dynamic functional connectivity.

Resting-state functional magnetic resonance imaging (fMRI) studies using static functional connectivity (sFC) measures have shown that the brain function is severely disrupted after long-term sleep deprivation (SD). However, increasing evidence has suggested that resting-state functional connectivity (FC) is dynamic and exhibits spontaneous fluctuation on a smaller timescale. The process by which long-term SD can influence dynamic functional connectivity (dFC) remains unclear. In this study, 37 healthy subjects participated in the SD experiment, and they were scanned both during rested wakefulness (RW) and after 36 h of SD. A sliding-window based approach and a spectral clustering algorithm were used to evaluate the effects of SD on dFC based on the 26 qualified subjects' data. The outcomes showed that time-averaging FC across specific regions as well as temporal properties of the FC states, such as the dwell time and transition probability, was strongly influenced after SD in contrast to the RW condition. Based on the occurrences of FC states, we further identified some RW-dominant states characterized by anti-correlation between the default mode network (DMN) and other cortices, and some SD-dominant states marked by significantly decreased thalamocortical connectivity. In particular, the temporal features of these FC states were negatively correlated with the correlation coefficients between the DMN and dorsal attention network (dATN) and demonstrated high potential in classification of sleep state (with 10-fold cross-validation accuracy of 88.6% for dwell time and 88.1% for transition probability). Collectively, our results suggested that the temporal properties of the FC states greatly account for changes in the resting-state brain networks following SD, which provides new insights into the impact of SD on the resting-state functional organization in the human brain.

Making group inferences using sparse representation of resting-state functional mRI data with application to sleep deprivation.

Past studies on drawing group inferences for functional magnetic resonance imaging (fMRI) data usually assume that a brain region is involved in only one functional brain network. However, recent evidence has demonstrated that some brain regions might simultaneously participate in multiple functional networks. Here, we presented a novel approach for making group inferences using sparse representation of resting-state fMRI data and its application to the identification of changes in functional networks in the brains of 37 healthy young adult participants after 36 h of sleep deprivation (SD) in contrast to the rested wakefulness (RW) stage. Our analysis based on group-level sparse representation revealed that multiple functional networks involved in memory, emotion, attention, and vigilance processing were impaired by SD. Of particular interest, the thalamus was observed to contribute to multiple functional networks in which differentiated response patterns were exhibited. These results not only further elucidate the impact of SD on brain function but also demonstrate the ability of the proposed approach to provide new insights into the functional organization of the resting-state brain by permitting spatial overlap between networks and facilitating the description of the varied relationships of the overlapping regions with other regions of the brain in the context of different functional systems. Hum Brain Mapp 38:4671-4689, 2017. © 2017 Wiley Periodicals, Inc.