Development of MRI Techniques for Drug-Abuse Applications

1. 默认模式网络中神经递质水平、任务诱发失活和静息状态功能连通性的关系

1. The relationship among neurotransmitter levels, task-induced deactivation, and resting-state functional connectivity in the default mode network Default mode network (DMN) has been identified as a fundamental brain network and its activity is suppressed in response to task-demanding external attention. Declined DMN deactivation has been found in various neuropsychiatric disorders, but he neural mechanism underlying this observation, however, is still largely unknown. In the present study, we used task-based fMRI (n-back working memory task), resting-state fMRI and magnetic resonance spectroscopy to measure task-induced deactivation, inter-regional intrinsic functional connectivity and regional neurotransmitter (glutamate and GABA) concentrations in the posterior cingulate cortex/precuneus (PCC/PCu) region of the DMN, respectively, and further examined the triple-relationship among these measures. The triple-relationship discovered in the present study has the potential to bridge DMN-deactivation related findings from various neuroimaging modalities, and may provide new insights into the neural mechanism of DMN deactivation. Moreover, this finding may have significant implications for neuropsychiatric disorders related to the DMN dysfunction and suggests an integrated application of pharmacological and neuromodulation-based strategies for rescuing DMN deactivation deficits. (Manuscript in preparation) 2. Multimodal imaging analysis revealed that salience and default mode network dysregulation in chronic cocaine users predict treatment outcome In this study, we investigated consequences of structural differences on resting-state functional connectivity (rsFC) in cocaine addiction and tested whether rsFC of the identified circuits can predict relapse in an independent cohort. Subjects included 64 non-treatment-seeking cocaine users (NTSCUs) and 67 healthy controls (HCs) and an independent treated cohort (n=45) of cocaine dependent individuals scanned at the end of a 30-day residential treatment program. Differences in cortical thickness and related rsFC between NTSCUs and HCs were identified. Survival analysis, applying rsFC of the identified circuits and clinical characteristics to the treatment cohort, was used to predict relapse. Lower cortical thickness in bilateral insula and higher thickness in bilateral temporal pole (TP) were found in NTSCUs versus HCs. Whole brain rsFC analyses with these four altered regions as seeds revealed 17 weaker circuits including within the salience network and between TP and elements of the default mode network. Applying these circuits and clinical characteristics to the treated cocaine dependent cohort, functional connectivity between right TP and medial prefrontal cortex (mPFC), combined with years of education, predicted relapse status at 150 days with 88% accuracy. The involvement of the TP-mPFC circuit in a model highly predictive of relapse may highlight the importance of social-emotional functions in cocaine dependence, and provide a potential underlying neural target for therapeutic interventions, and for identifying those at high risk of relapse. (Submitted for publication) 3. Modularity of the amygdala reveals subnetworks that correspond to its main anatomical subdivisions Similarities in the cellular and neurochemical composition of the amygdaloid subnuclei suggests their clustering into subunits that may exhibit unique functional organization. The topological principal of community structure, on the other hand, has been used to identify functional subnetworks, in neuroimaging data, that reflects the brain effective organization. Here we used the principle of modularity to investigate the modular organization of the amygdala using resting state functional magnetic resonance imaging (rsfMRI) data. We found that modularity analysis identified subnetworks consistent with the main anatomical subdivisions of the amygdala that embody relevant functional and structural properties. Additionally, functional connectivity pathways of these subunits, and their correlation with task-induced amygdala activation, revealed distinct functional profiles consistent with the neurobiology of the amygdala nuclei. These modularity findings corroborate the structurefunction relationship between amygdala anatomical substructures, supporting the use of network analysis techniques to generate biologically meaningful partitions of brain structures. (Submitted for publication) 4. Nicotine abstinence induced connectivity changes in amygdala and insular circuits predict relapse to smoking. Amygdala and insula are involved in affective functions that play critical roles in developing, maintaining and relapsing to drug addiction. While lesions to insula are associated with a diminished craving for smoking, nicotine withdrawal is associated with an elevated amygdala-insula resting-state functional connectivity (rsFC) circuit, which is pharmacologically down-regulated. However, whether and how the acute abstinence induced changes in insular and amygdala circuits contribute to smoking cessation is largely unknown. In this study, we examined the effects of acute nicotine abstinence on the amygdalar and insular rsFC circuits and the relationship between the resultant circuits changes and nicotine withdrawal symptoms and treatment outcomes. Testing on treatment-seeking smokers who participated two, smoking and 24-hr abstinence, imaging sessions, we found rsFC increase between posterior insula (PI) and sensorimotor cortex (SMC), as well as increase between amygdala and PI due to abstinence. The increase between PI and SMC is positively correlated with abstinence-induced craving and positively associated with relapse to drug use, suggesting this circuit may underlie nicotine sensitization and interoceptive perception of the sensory representation of drug craving. In contrast, the increased connectivity between amygdala and PI, which is negatively correlated with withdrawal symptoms and positively associated with successful abstinence, may suggest a neural mechanism of copying with abstinence-induced withdrawal. Furthermore, a combination of these two circuits, not each one individually, was able to better predict smoking relapse. (Presented in the OHBM 2016). 5. CYP2A6 genetic variation alters striatal-cingulate circuits, network hubs and executive processing in smokers. Variation in the CYP2A6 gene alters the rate of nicotine metabolic inactivation and is associated with smoking behaviors and cessation success rates. However, the underlying neurobiological mechanisms of this genetic influence remain unknown. In this study, intrinsic functional connectivity strength (FCS) was applied to resting state fMRI data in 66 smokers and 92 nonsmokers. A subset of subjects (n=23/20; smokers/nonsmokers) performed the Monetary Incentive Delay (MID) task and a Go/NoGo task on two occasions, in the presence and absence of a nicotine patch. A significant CYP2A6 Smoking effect was found in the dorsal anterior cingulate cortex (dACC) and ventral striatum (VS), such that the normal (vs. slow) genotype individuals showed greater FC strength in smokers but not nonsmokers. FCS was negatively associated with nicotine dependence severity in slow genotype individuals. Both hubs were biased by inputs from the insula identified from seed-based connectivity. Similar Gene Environment interactions were seen in VS during smoking abstinence when subjects performed the MID task and in dACC when they performed the Go/NoGo task; both reductions were normalized in smokers (and increased in nonsmokers) following acute nicotine administration. Since the CYP2A6 effect was seen only in smokers, these data suggest that the rate of nicotine metabolism shapes brain circuits that compute reward and impulsivity processes. (Submitted for publication)