Development of MRI Techniques for Applications in Substance Use Disorders

National Institute On Drug Abuse

Investigators

Linked publications & trials

Abstract

1. Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents Cognitive function and general psychopathology are two important classes of human behavior dimensions, individually relate to mental disorders across diagnostic categories. However, whether the two transdiagnostic dimensions link to common or distinct brain networks that convey resilience or risk for the development of psychiatric disorders remains unclear. The current study is a longitudinal investigation with 11,875 youths aged 9- to 10-years-old at study onset, from the Adolescent Brain Cognitive Development study. A machine-learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multi-domain behavioral assessments including cognitive functions and psychopathological measures. For the latent rsFC factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using two-year follow-up data and its genetic association was evaluated using twin data from the same cohort. A latent functional connectome pattern was identified that showed a strong and generalizable association with the multi-domain behavioral assessments (5-fold cross validation: = 0.680.73, for the training set (N = 5096); = 0.56 0.58, for the test set (N = 1476)). This functional connectome pattern was highly heritable (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with cumulative number of psychiatric disorders assessed concurrently and 2-years post-MRI-scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period. (Manuscript under review) 2. Structural fingerprinting of the frontal aslant tract: predicting cognitive control capacity and obsessive-compulsive symptoms White matter of the human brain is influenced by common genetic variations and shaped by neural activity-dependent experiences. Variations in microstructure of cerebral white matter across individuals and even across fiber tracts might underlie differences in cognitive capacity and vulnerabilities to mental disorders. The frontoparietal and cingulo-opercular networks of the brain constitute the central system supporting cognitive functions, and functional connectivity of these networks has been used to distinguish individuals known as functional fingerprinting. The frontal aslant tract (FAT) that passes through the two networks has been implicated in executive functions. However, whether FAT can be used as a structural fingerprint to distinguish individuals and predict individuals cognitive function and dysfunction is unknown. Here we investigated the fingerprinting property of FAT microstructural profiles using three independent diffusion MRI datasets with repeated scans. We found that diffusion and geometric profiles of FAT can be used to distinguish individuals with a high accuracy. Next, we demonstrated that fractional anisotropy, a widely used index of white matter integrity, in different FAT segments predicted distinct cognitive functions, including working memory, inhibitory control, and relational reasoning. Finally, we assessed the contribution of altered FAT microstructural profiles to cognitive dysfunction in unmedicated patients with obsessive-compulsive disorders. We found that the altered microstructure in FAT was associated with the severity of obsessive-compulsive symptoms. Collectively, our findings suggest that the microstructural profiles of FAT can identify individuals with a high accuracy and may serve as an imaging marker for predicting individuals cognitive capacity and disease severity. (Wang et al., Journal of Neuroscience, in press) 3. Targeting the pathological network: feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one schizophrenia cohort with auditory verbal hallucinations. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects. (Cao et al., Frontiers in Neuroscience, 2023) 4. The association between default-mode network functional connectivity and childhood trauma on the symptom load in male adults with methamphetamine use disorder. The relationship between adverse childhood experiences and methamphetamine use disorder (MUD) has been shown in previous studies; nevertheless, the underlying neural mechanisms remain elusive. Childhood trauma is associated with aberrant functional connectivity (FC) within the default-mode network (DMN). Furthermore, within the DMN, FC may contribute to impaired self-awareness in addiction, while cross-network FC is critical for relapse. We aimed to investigate whether childhood trauma was associated with DMN-related resting-state FC among healthy controls and patients with MUD and to examine whether DMN-related FC affected the effect of childhood trauma on the symptom load of MUD diagnosis. Twenty-seven male patients with MUD and 27 male healthy controls were enrolled and completed the Childhood Trauma Questionnaire (CTQ). DMN-related resting-state FC was examined using functional magnetic resonance imaging. There were 47.1% healthy controls and 66.7% MUD patients in this study with adverse childhood experiences. Negative correlations between adverse childhood experiences and within-DMN FC were observed in both healthy controls and MUD patients, while within-DMN FC was significantly altered in MUD patients. The detrimental effects of adverse childhood experiences on MUD patients may be attenuated through DMN-executive control networks (ECN) FC. In conclusion, adverse childhood experiences were negatively associated with within-DMN FC in MUD patients and healthy controls. However, DMN-ECN FC may attenuate the effects of childhood trauma on symptoms load of MUD. (Wei et al., Clinical Psychopharmacology and Neuroscience, in press)