1. Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain Human brain functional networks contain a few densely connected hubs that play a vital role in transferring information across regions during resting and task states. However, the relationship of these functional hubs to measures of brain physiology, such as regional cerebral blood flow (rCBF), remains incompletely understood. Here, we used functional MRI data of blood-oxygenation-level-dependent and arterial-spin-labeling perfusion contrasts to investigate the relationship between functional connectivity strength (FCS) and rCBF during resting and an N-back working-memory task. During resting state, functional brain hubs with higher FCS were identified, primarily in the default-mode, insula, and visual regions. The FCS showed a striking spatial correlation with rCBF, and the correlation was stronger in the default-mode network (DMN;including medial frontal-parietal cortices) and executive control network (ECN;including lateral frontal-parietal cortices) compared with visual and sensorimotor networks. Moreover, the relationship was connection-distance dependent;i.e., rCBF correlated stronger with long-range hubs than short-range ones. It is notable that several DMN and ECN regions exhibited higher rCBF per unit connectivity strength (rCBF/FCS ratio);whereas, this index was lower in posterior visual areas. During the working-memory experiment, both FCS-rCBF coupling and rCBF/FCS ratio were modulated by task load in the ECN and/or DMN regions. Finally, task-induced changes of FCS and rCBF in the lateral-parietal lobe positively correlated with behavioral performance. Together, our results indicate a tight coupling between blood supply and brain functional topology during rest and its modulation in response to task demands, which may shed light on the physiological basis of human brain functional connectome. (Published in Proc Natl Acad Sci U S A. 110:1929-1934, 2013). 2. Relationship between GABA/Glutamate concentrations and task-induced deactivation Deactivation of the human brains default mode network (DMN) is regarded as suppression of endogenous activity to support exogenous task-related processes. This phenomenon has important functional relevance, and insufficient DMN deactivation has been implicated in several neuropsychiatric disorders. However, the neurochemical mechanism of the DMNs deactivation remains largely unknown. In the present study, we test the hypothesis that the major excitatory and inhibitory neurotransmitters, glutamate and gamma-Aminobutyric acid (GABA), respectively, modulate DMN deactivation. We employed magnetic resonance spectroscopy to measure neurotransmitter concentrations in the posterior cingulate cortex/precuneus (PCC/PCu), a key component of the DMN, and functional magnetic resonance imaging (fMRI) to evaluate DMN deactivation induced by an n-back working memory task. Our results demonstrate significant associations of glutamate and GABA with DMN deactivation. Specifically, high regional GABA concentration in the PCC/PCu area is associated with greater deactivation induced by the task in the same region, while high glutamate concentration is associated with reduced deactivation. Furthermore, the association of GABA with DMN deactivation increases with the cognitive loads. These neurochemical characteristics of DMN deactivation may provide novel insights towards better understanding of the DMNs functions under normal physiological conditions and dysfunctions in neuropsychiatric disorders. (Manuscript under revision) 3. Cerebral blood volume (CBV) based fMRI to detect resting-state functional connectivity Resting-state brain activity has been investigated extensively using BOLD contrast. However, BOLD signal represents the combined effects of multiple physiological processes and its spatial localization is less accurate than that of cerebral blood flow and volume (CBF and CBV). In this study, whole-brain gradient and spin echo (GRASE) based vascular space occupancy (VASO) imaging technique was employed to detect the resting-state brain activity using CBV-weighted signal. Specifically, using independent component analysis, intrinsic brain networks, including default mode, salience, executive control, visual, auditory, and sensorimotor networks were revealed robustly by the VASO technique. Compared to the BOLD technique, we have demonstrated that VASO images have reduced susceptibility-induced signal loss in frontal and temporal regions. Consequently VASO-based functional connectivity signals were well preserved in these brain regions that tend to suffer from signal loss and geometric distortion in BOLD. Our study suggests that 3D-GRASE VASO imaging, with its improved spatial specificity and less sensitivity to susceptibility artifacts, may have advantages in resting-state fMRI studies. (Manuscript under revision) 4. Brain network organization is disrupted in chronic cocaine dependents as revealed by modular analysis of resting-state functional MRI Using resting-state functional magnetic resonance imaging and modularity network analysis, we aimed to explore alterations within and between brain modules related to reward, affective and cognitive functions following chronic cocaine use. We first identified brain modules of interest, the default-mode network, salience network, executive control network, medial temporal lobe and striatum by modularity analysis. Group comparisons showed that compared with healthy controls, there was a significantly decreased inter-module connectivity between the default-mode and salience modules, and between the default-mode and medial temporal modules in cocaine users. Topological roles were assigned to each voxel in each module according to their intra- and inter-module connectivity. The connector hubs in the bilateral insula and rostral anterior cingulate cortex showed decreased connectivity with the other modules. Furthermore, we observed negative correlations between alexithymia and intra-module connectivity and local and global efficiency within the salience module. Our results indicate that cocaine addiction is associated with disruptions in the interactions within and/or between modules that have been implicated in self-referential thinking (default-mode network), emotion (medial temporal lobe), and coordinating of internal and external stimuli (salience network), which provide the evidence for disrupted integrity in large-scale brain networks underlying drug addiction. (Presented in OHBM 2013) 5. Genetic modulation of brain networks The goal of this study is to investigate specific genetic variants that influence brain structure and function. Recent advances in neuroimaging and genetics allow acquisition of both highly detailed structural and functional brain scans and genome-wide genotype information, which offers a new opportunity to find the genes influencing brain structure and function. So far, we have collected resting-state fMRI, anatomical and diffusion tensor imaging date, as well as blood samples on 457 subjects. We have applied several image analysis methods, such as independent component analysis (ICA) and graph-theory based network analysis, to extract important characteristics in the resting state fMRI data. Using group ICA, we identified important brain networks responsible for brain functions, including default mode network (DMN), salience network (SN), left and right executive control network (ECN). For each individual, within-network and between-network indices were derived. Network properties such as local and global efficiency were also computed for each subject. These imaging metrics, which are believed to reflect brain functions, are used to for genome-wide association studies.

Project Start
Project End
Budget Start
Budget End
Support Year
10
Fiscal Year
2013
Total Cost
$913,059
Indirect Cost
Name
National Institute on Drug Abuse
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Type
DUNS #
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Miao, Xinyuan; Gu, Hong; Yan, Lirong et al. (2014) Detecting resting-state brain activity by spontaneous cerebral blood volume fluctuations using whole brain vascular space occupancy imaging. Neuroimage 84:575-84
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