1. Functional circuits from a dorsolateral prefrontal cortex locus predict cocaine relapse: Implications for neuromodulation treatment The dorsolateral prefrontal cortex (DLPFC) has been successfully targeted with transcranial magnetic stimulation (TMS) to treat depression and shows promise in addiction. However, optimal DLFPC TMS targeting for cocaine dependence (CD) is unknown. The mechanism through which DLFPC is involved in drug relapse also remains unclear. In this study, we assess the efficacy of functional connectivity from common DLFPC TMS targets in predicting cocaine relapse. We also identify DLPFC circuits predictive of cocaine relapse. Forty-five CD participants underwent resting-state fMRI scanning at the end of standard inpatient treatment and were followed for 168 days or until relapse. Using functional connectivity from seven common DLPFC TMS targets, voxel-wise Cox regression analyses were conducted, and composite indices were generated. Predictive models built based on these indices were compared among these seven DLFPC targets. Eight participants remained abstinent through the follow-up. The seven DLPFC targets yielded Area-Under-Curve (prediction power) ranging from 0.411 to 0.882 with only the Rusjan Target -50, 30, 36 being a significant predictor (p<0001). DLPFC-based protective and risk circuits were identified from this target. We identified one DLPFC locus from which resting state circuits strongly and significantly predicted cocaine relapse, suggesting it may be an optimal target for neuromodulation treatment of CD. We further identified two DLPFC based circuits that confer risk of relapse or protective against relapse and may lead to an objective assessment of the adequacy of a course of treatment. (Zhai et al., manuscript under review) 2. Understanding the acute modulation of brain activity by transcranial magnetic stimulation The goal of this study is to investigate acute modulations of brain activity by transcranial magnetic stimulation (TMS). Using simultaneous TMS and functional magnetic resonance imaging (fMRI), we aim to evaluate TMS induced changes in brain activity, including regional brain activation and inter-regional functional connectivity. Repetitive TMS will be applied over the dorsolateral prefrontal cortex (DLPFC) of about 50 participants, with different frequencies and interleaved with fMRI acquisition to provide online monitoring of brain activity. Furthermore, we will assess the relationship between the TMS induced brain activity and the anatomical connection obtained from diffusion tensor imaging (DTI), using individual variations in these imaging measures. Results from this study will help to understand the underling mechanism of TMS and will provide insights for interpretation of TMS and fMRI data. 3. Factors affecting detection power in resting-state fMRI using high resolution Echo-Planar Imaging Latest development in magnetic resonance imaging (MRI) hardware and software has significantly improved image acquisition for functional MRI (fMRI) techniques including resting-state fMRI (rsfMRI). Specifically, with improvements in gradient and radio-frequency coils and advances in pulse sequence designs, functional images with higher spatial and temporal resolution can be achieved. However, while smaller voxel size has the benefit to resolve finer brain structures, it also decreases voxel-wise signa-to-noise ratio (SNR) and subsequently temporal SNR (tSNR), which is critical for the sensitivity of fMRI. Although the improved temporal resolution allows more image frames to be collected per unit time, the overall power in detecting brain activity using the high spatiotemporal fMRI remains to be evaluated. Here, we aimed to evaluate the effects of spatial smoothing, scan length, sample size, seed size and location on resting-state functional connectivity (rsFC) and tSNR using data from the human connectome project (HCP). Results from this analysis show an important effect of smoothing on the rsFC-strength as well as on the tSNR. In contrast, while rsFC-strength is not affected by sample size, the variance decreases with the increasing number of participants, therefore improving the detection power for larger samples. Besides, scan length and seed size seem to have a moderate effect on rsFC-strength. Finally, seed location has an important impact on rsFC-maps, since rsFC-strength from cortical seeds seems higher than from sub-cortical seeds. In summary, our findings show that the choice of an ideal set of parameters can be critical for the success of a rsfMRI study. (Caparelli et al., manuscript in preparation) 4. Down-regulation of the superficial amygdala - insula circuitry predicts level of nicotine dependence in sated smokers The amygdala is a critical brain structure involved in nicotine addiction. Down regulation of the amygdala functioning following cigarette smoke has been already previously observed. However, the amygdala is not a sole structure, but it is composed of a set of distinct nuclei with unique functional pathways, and little is known about the effects of cigarette smoking on the functional circuits of theses nuclei. In this study, we use resting-state fMRI (rsfMRI) to evaluate the impact of smoking on the circuitry of amygdala subdivisions, obtained from modularity results, and assess their relationship with the level of nicotine addiction severity and harm avoidance behavior in sated smokers. Our findings reveal a decrease in connectivity on the superficial-amygdala insula circuitry for sated smokers compared to non-smokers, which is related with cigarette use and severity in nicotine addiction. These results suggest an impairment of the superficial-amygdala due to cigarette use that increase their harm avoidance behavior leading the smokers to seek for more drug use to avoid abstinence effects, supporting a critical role for the superficial-amygdala in nicotine addiction. (Caparelli et al., manuscript in preparation) 5. Regional GABA Concentrations Modulate Inter-network Resting-state Functional Connectivity Coordinated activity within and differential activity between large-scale neuronal networks such as the default mode network (DMN) and the control network (CN) is a critical feature of brain organization. The CN usually exhibits activations in response to cognitive tasks while the DMN shows deactivations; in addition, activity between the two networks is anti-correlated at rest. To address this issue, we used functional MRI to measure whole-brain BOLD signal during resting-state and task-evoked conditions, and MR spectroscopy (MRS) to quantify GABA and glutamate concentrations, in nodes within the DMN and CN (MPFC and DLPFC, respectively) in 19 healthy individuals at 3 Tesla. We found that GABA concentrations in the MPFC were significantly associated with DMN deactivation during a working memory task and with anti-correlation between DMN and CN at rest and during task performance, while GABA concentrations in the DLPFC weakly modulated DMN-CN anti-correlation in the opposite direction. Highlighting specificity, glutamate played a less significant role related to brain activity. These findings indicate that GABA in the MPFC is potentially involved in orchestrating between-network brain activity at rest and during task performance. (Xi et al., Cerebral Cortex)
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