The neural signature of trait impulsivity in methamphetamine use disorder

589 

Abstract Submission 

Gangliang Zhong¹, Tianzhen Chen¹, Hang Su¹, Jiang Du¹, Min Zhao^1,2,3

¹Shanghai Jiao Tong University, Shanghai, China, ²Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai, China, ³CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, China

Gangliang Zhong  
Shanghai Jiao Tong University
Shanghai, China

Tianzhen Chen  
Shanghai Jiao Tong University
Shanghai, China

Hang Su  
Shanghai Jiao Tong University
Shanghai, China

Jiang Du  
Shanghai Jiao Tong University
Shanghai, China

Min Zhao  
Shanghai Jiao Tong University|Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center|CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences
Shanghai, China|Shanghai, China|Shanghai, China

Methamphetamine (MA) is a widely abused illicit drug with significant global prevalence 1. The detrimental impact of drug abuse on mortality rates necessitates improved prevention efforts based on known biological mechanisms. Excessive impulsivity has been consistently identified as a key psychological factor in addiction disorders, as well as in other neuropsychiatric and neurological conditions 2. While research has shown that high trait impulsivity is associated with addictive disorders, the search for reliable brain-based predictors of impulsivity for future prevention remains ongoing.

Understanding the neural mechanisms underlying impulsivity can aid in the development of personalized and innovative treatment approaches. Connectome-based predictive modeling (CPM), a recently developed whole-brain approach, was employed to identify impulsive connections associated with MA addiction. Impulsive behavior can stem from both heightened motivation and reduced motivation (apathy), representing failures in information processing or response control. The Barratt Impulsiveness Scale (BIS-11) 3, a widely used self-report scale, captures this heterogeneity through three subscales. CPM has been previously utilized to identify neural markers of impulsivity in opioid and cocaine addiction using functional connectivity data acquired during neurocognitive tasks 4,5. However, its application in predicting impulsivity in addiction has not been explored.

A group of MA-using individuals (n = 44) underwent resting-state functional magnetic resonance imaging (rsfMRI) scans. Trait impulsivity during abstinence was assessed using the BIS-11, which measures non-planning impulsiveness, motor impulsiveness, and cognitive impulsiveness. CPM with leave-one-out cross-validation was performed to identify neural networks predictive of trait impulsivity. This approach utilizes group connectivity matrices and behavioral data (in this case, trait impulsivity) to generate a predictive model of the behavioral data 6,7. Regression analyses, such as Pearson's correlation or partial correlation, are employed to correlate edges and behavioral data from the training dataset, revealing positive and negative predictive networks.

Follow-up analyses were conducted to assess the specificity of the identified impulsivity connections. To determine clinical relevance, the strength of the MA impulsivity network was compared with that of healthy subjects (n = 35). The stability of these networks over time and in relation to pre- and post-treatment was tested in an independent brain intervention dataset (n = 25).

CPM identified an MA non-impulsivity network characterized by stronger within-network connectivity between the prefrontal cortex, dorsal and ventral striatum, hippocampus, and nucleus accumbens. The overall CPM model successfully predicted non-planning impulsivity, as evidenced by a significant correspondence between predicted and actual non-impulsivity values (r = -0.59, df = 43, p < 0.001) (Figure 1).

This MA impulsivity network was anatomically distinct from the identified healthy impulsivity network. Connectivity strength in the independent sample remained unchanged with rTMS treatment, and strength at pretreatment and posttreatment assessments significantly predicted impulsivity (p < 0.001).

The identification of brain-based predictors of impulsivity enhances our understanding of addiction and can improve interventions and clinical practice. By tailoring therapies to individual neural function or neuromarkers, these findings can directly impact treatment approaches. The results indicate that changes in established neural networks contribute to variations in treatment outcomes for substance use disorder. Thus, understanding the neural mechanisms of impulsivity in addiction can aid in developing personalized treatment approaches.

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ¹

Executive Function, Cognitive Control and Decision Making ²

Addictions

MRI