Dynamic brain connectivity is a better predictor of PTSD than static connectivity.

Using resting-state functional magnetic resonance imaging, we test the hypothesis that subjects with post-traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero-lag correlation) and static effective connectivity (EC; directional time-lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479-4496, 2017. © 2017 Wiley Periodicals, Inc.

Trauma-specific Grey Matter Alterations in PTSD.

Previous studies have demonstrated that patients with posttraumatic stress disorder (PTSD) caused by different types of trauma may show divergence in epidemiology, clinical manifestation and treatment outcome. However, it is still unclear whether this divergence has neuroanatomic correlates in PTSD brains. To elucidate the general and trauma-specific cortical morphometric alterations, we performed a meta-analysis of grey matter (GM) changes in PTSD (N = 246) with different traumas and trauma-exposed controls (TECs, N = 347) using anisotropic effect-size signed differential mapping and its subgroup analysis. Our results revealed general GM reduction (GMR) foci in the prefrontal-limbic-striatal system of PTSD brains when compared with those of TECs. Notably, the GMR patterns were trauma-specific. For PTSD by single-incident traumas, GMR foci were found in bilateral medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), insula, striatum, left hippocampus and amygdala; and for PTSD by prolonged traumas in the left insula, striatum, amygdala and middle temporal gyrus. Moreover, Clinician-Administered PTSD Scale scores were found to be negatively associated with the GM changes in bilateral ACC and mPFC. Our study indicates that the GMR patterns of PTSD are associated with specific traumas, suggesting a stratified diagnosis and treatment for PTSD patients.

Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models.

Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain's functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain's functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84% of PTSD patients and 86% of NC subjects are successfully classified via multiple HMMs using majority voting.

Identifying and characterizing resting state networks in temporally dynamic functional connectomes.

An important application of resting state fMRI data has been to identify resting state networks (RSN). The conventional RSN studies attempted to discover consistent networks through functional connectivity analysis over the whole scan time, which implicitly assumes that RSNs are static. However, the brain undergoes dynamic functional state changes and the functional connectome patterns vary along with time, even in resting state. Hence, this study aims to characterize temporal brain dynamics in resting state. It utilizes the temporally dynamic functional connectome patterns to extract a set of resting state clusters and their corresponding RSNs based on the large-scale consistent, reproducible and predictable whole-brain reference system of dense individualized and common connectivity-based cortical landmarks (DICCCOL). Especially, an effective multi-view spectral clustering method was performed by treating each dynamic functional connectome pattern as one view, and this procedure was also applied on static multi-subject functional connectomes to obtain the static clusters for comparison. It turns out that some dynamic clusters exhibit high similarity with static clusters, suggesting the stability of those RSNs including the visual network and the default mode network. Moreover, two motor-related dynamic clusters show correspondence with one static cluster, which implies substantially more temporal variability of the motor resting network. Particularly, four dynamic clusters exhibited large differences in comparison with their corresponding static networks. Thus it is suggested that these four networks might play critically important roles in functional brain dynamics and interactions during resting state, offering novel insights into the brain function and its dynamics.

Inferring functional interaction and transition patterns via dynamic Bayesian variable partition models.

Multivariate connectivity and functional dynamics have been of wide interest in the neuroimaging field, and a variety of methods have been developed to study functional interactions and dynamics. In contrast, the temporal dynamic transitions of multivariate functional interactions among brain networks, in particular, in resting state, have been much less explored. This article presents a novel dynamic Bayesian variable partition model (DBVPM) that simultaneously considers and models multivariate functional interactions and their dynamics via a unified Bayesian framework. The basic idea is to detect the temporal boundaries of piecewise quasi-stable functional interaction patterns, which are then modeled by representative signature patterns and whose temporal transitions are characterized by finite-state transition machines. Results on both simulated and experimental datasets demonstrated the effectiveness and accuracy of the DBVPM in dividing temporally transiting functional interaction patterns. The application of DBVPM on a post-traumatic stress disorder (PTSD) dataset revealed substantially different multivariate functional interaction signatures and temporal transitions in the default mode and emotion networks of PTSD patients, in comparison with those in healthy controls. This result demonstrated the utility of DBVPM in elucidating salient features that cannot be revealed by static pair-wise functional connectivity analysis.

Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients.

Functional connectomes (FCs) have been recently shown to be powerful in characterizing brain conditions. However, many previous studies assumed temporal stationarity of FCs, while their temporal dynamics are rarely explored. Here, based on the structural connectomes constructed from diffusion tensor imaging data, FCs are derived from resting-state fMRI (R-fMRI) data and are then temporally divided into quasi-stable segments via a sliding time window approach. After integrating and pooling over a large number of those temporally quasi-stable FC segments from 44 post-traumatic stress disorder (PTSD) patients and 51 healthy controls, common FC (CFC) patterns are derived via effective dictionary learning and sparse coding algorithms. It is found that there are 16 CFC patterns that are reproducible across healthy controls, and interestingly, two additional CFC patterns with altered connectivity patterns [termed signature FC (SFC) here] exist dominantly in PTSD subjects. These two SFC patterns alone can successfully differentiate 80% of PTSD subjects from healthy controls with only 2% false positive. Furthermore, the temporal transition dynamics of CFC patterns in PTSD subjects are substantially different from those in healthy controls. These results have been replicated in separate testing datasets, suggesting that dynamic functional connectomics signatures can effectively characterize and differentiate PTSD patients.

A comparative study of theoretical graph models for characterizing structural networks of human brain.

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

Mechanism of the Inhibitory Effects of Eucommia ulmoides Oliv. Cortex Extracts (EUCE) in the CCl 4 -Induced Acute Liver Lipid Accumulation in Rats.

Eucommia ulmoides Oliv. (EU) has been used for treatment of liver diseases. The protective effects of Eucommia Ulmoides Oliv. cortex extracts (EUCE) on the carbon tetrachloride- (CCl4-) induced hepatic lipid accumulation were examined in this study. Rats were orally treated with EUCE in different doses prior to an intraperitoneal injection of 1 mg/kg CCl4. Acute injection of CCl4 decreased plasma triglyceride but increased hepatic triglyceride and cholesterol as compared to control rats. On the other hand, the pretreatment with EUCE diminished these effects at a dose-dependent manner. CCl4 treatment decreased glutathione (GSH) and increased malondialdehyde (MDA) accompanied by activated P450 2E1. The pretreatment with EUCE significantly improved these deleterious effects of CCl4. CCl4 treatment increased P450 2E1 activation and ApoB accumulation. Pretreatment with EUCE reversed these effects. ER stress response was significantly increased by CCl4, which was inhibited by EUCE. One of the possible ER stress regulatory mechanisms, lysosomal activity, was examined. CCl4 reduced lysosomal enzymes that were reversed with the EUCE. The results indicate that oral pretreatment with EUCE may protect liver against CCl4-induced hepatic lipid accumulation. ER stress and its related ROS regulation are suggested as a possible mechanism in the antidyslipidemic effect of EUCE.

Characterization of task-free and task-performance brain states via functional connectome patterns.

Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during R-fMRI/T-fMRI scans, it has been challenging to ascertain whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states during task-free/task-performance periods. This paper presents a novel computational approach to characterizing and differentiating the brain's functional status into task-free or task-performance states, by which the functional brain activities can be effectively understood and differentiated. Briefly, the brain's functional state is represented by a whole-brain quasi-stable connectome pattern (WQCP) of R-fMRI or T-fMRI data based on 358 consistent cortical landmarks across individuals, and then an effective sparse representation method was applied to learn the atomic connectome patterns (ACPs) of both task-free and task-performance states. Experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. A certain portion of ACPs from R-fMRI and T-fMRI data were overlapped, suggesting some subjects with overlapping ACPs were not in the expected task-free/task-performance brain states. Besides, potential outliers in the T-fMRI dataset were further investigated via functional activation detections in different groups, and our results revealed unexpected task-performances of some subjects. This work offers novel insights into the functional architectures of the brain.

DICCCOL: dense individualized and common connectivity-based cortical landmarks.

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work.

Modeling dynamic functional information flows on large-scale brain networks.

Growing evidence from the functional neuroimaging field suggests that human brain functions are realized via dynamic functional interactions on large-scale structural networks. Even in resting state, functional brain networks exhibit remarkable temporal dynamics. However, it has been rarely explored to computationally model such dynamic functional information flows on large-scale brain networks. In this paper, we present a novel computational framework to explore this problem using multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. Basically, recent literature reports including our own studies have demonstrated that the resting state brain networks dynamically undergo a set of distinct brain states. Within each quasi-stable state, functional information flows from one set of structural brain nodes to other sets of nodes, which is analogous to the message package routing on the Internet from the source node to the destination. Therefore, based on the large-scale structural brain networks constructed from DTI data, we employ a dynamic programming strategy to infer functional information transition routines on structural networks, based on which hub routers that most frequently participate in these routines are identified. It is interesting that a majority of those hub routers are located within the default mode network (DMN), revealing a possible mechanism of the critical functional hub roles played by the DMN in resting state. Also, application of this framework on a post trauma stress disorder (PTSD) dataset demonstrated interesting difference in hub router distributions between PTSD patients and healthy controls.

Altered regional homogeneity in post-traumatic stress disorder: a resting-state functional magnetic resonance imaging study.

OBJECTIVE: Little is known about the brain systems that contribute to vulnerability to post-traumatic stress disorder (PTSD). Comparison of the resting-state patterns of intrinsic functional synchronization, as measured by functional magnetic resonance imaging (fMRI), between groups with and without PTSD following a traumatic event can help identify the neural mechanisms of the disorder and targets for intervention. METHODS: Fifty-four PTSD patients and 72 matched traumatized subjects who experienced the 2008 Sichuan earthquake were imaged with blood oxygen level-dependent (BOLD) fMRI and analyzed using the measure of regional homogeneity (ReHo) during the resting state. RESULTS: PTSD patients presented enhanced ReHo in the left inferior parietal lobule and right superior frontal gyrus, and reduced ReHo in the right middle temporal gyrus and lingual gyrus, relative to traumatized individuals without PTSD. CONCLUSION: Our findings showed that abnormal brain activity exists under resting conditions in PTSD patients who had been exposed to a major earthquake. Alterations in the local functional connectivity of cortical regions are likely to contribute to the neural mechanisms underlying PTSD.

Characterization of task-free/task-performance brain states.

Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during fMRI scans, it has been very challenging to tell whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states in task-free/task-performance. This paper presents a novel approach to characterizing the brain's functional status into task-free or task-performance states. The basic idea here is that the brain's functional state is represented by a whole-brain quasi-stable connectivity pattern (WQCP), and an effective sparse coding procedure was then applied to learn the atomic connectivity patterns (ACP) of both task-free and task-performance states based on training R-fMRI and T-fMRI data. Our experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. However, a certain portion of ACPs from R-fMRI and T-fMRI datasets are overlapping, suggesting that those subjects with overlapping ACPs were not in the expected task-free/task-performance states during R-fMRI/T-fMRI scans.

Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering.

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI)/functional MRI (fMRI) data has received extensive interest recently. However, the regularity of these structural or functional brain networks across multiple neuroimaging modalities and across individuals is largely unknown. This paper presents a novel approach to infer group-wise consistent brain sub-networks from multimodal DTI/fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed on our recently developed and extensively validated large-scale cortical landmarks. We applied the proposed algorithm on 80 multimodal structural and functional brain networks of 40 healthy subjects, and obtained consistent multimodal brain sub-networks within the group. Our experiments demonstrated that the derived brain sub-networks have improved inter-modality and inter-subject consistency.

Altered resting-state functional connectivity of thalamus in earthquake-induced posttraumatic stress disorder: a functional magnetic resonance imaging study.

BACKGROUND: Thalamic dysfunction has been found in patients with posttraumatic stress disorder (PTSD), suggesting that the thalamus may be implicated in the etiology of PTSD. However, no studies have explored the functional connectivity between the thalamus and other brain regions during resting-state. The objective of the present study was to investigate the resting-state functional connectivity of the thalamus in recent onset medication-naive PTSD sufferers who went through an earthquake in the Sichuan province of China. METHODS: Fifty-four participants with PTSD and seventy-two age and gender matched traumatized controls without PTSD recruited from the 2008 Sichuan earthquake were scanned by 3T functional magnetic resonance imaging (fMRI) in resting state. Region of interest (ROI)-based functional connectivity analysis was employed to identify the potential differences in the functional connectivity of the thalamus between the two groups. RESULTS: In the PTSD group, the thalamus-ROIs showed decreased positive functional connectivity to particular brain regions including right medial frontal gyrus and left anterior cingulate cortex. Importantly, we also found increased positive functional connectivity of thalamus-ROIs with bilateral inferior frontal and left middle frontal gyri, left inferior parietal lobule as well as right precuneus in the PTSD participants when compared to traumatized controls without PTSD. CONCLUSION: The results provide evidence that abnormal resting state functional connections linking the thalamus to cortical regions may be involved in the underlying pathology in PTSD.

Abnormal baseline brain activity in posttraumatic stress disorder: a resting-state functional magnetic resonance imaging study.

Little is known about how spontaneous brain activity during the resting state may be altered in posttraumatic stress disorder (PTSD) compared to traumatized individuals. In the current study, we used a measure of amplitude of low-frequency (0.01-0.08 Hz) fluctuation (ALFF) to investigate the regional baseline brain function of this disorder. Fifty-four medication-naive PTSD patients and seventy-two matched traumatized comparison subjects who experienced the Sichuan major earthquake participated in a functional magnetic resonance imaging (fMRI) scan. We analyzed the difference between the PTSD and comparison groups during a resting state using ALFF. PTSD patients showed decreased ALFF values in right lingual gyrus, cuneus, middle occipital gyrus, insula, and cerebellum, and increased ALFF values in right medial and middle frontal gyri, relative to traumatized individuals without PTSD. The ALFF value in the right medial frontal gyrus was positively correlated with severity of the disorder. Our findings show that abnormality of intrinsic brain activity exists under resting conditions in PTSD patients exposed to a major earthquake. Altered ALFF in predominantly right hemisphere cortical and subcortical regions and in cerebellum potentially contribute to the neural mechanisms underlying traumatic memory and symptoms in PTSD.