Associations between Drinking, Smoking with Psychotic, Depressive and Developmental Disorders
Poster No:
1962
Submission Type:
Abstract Submission
Authors:
Chuang Liang1, Ling Qiu1, Peter Kochunov2, Kent E. Hutchison3, Jing Sui4, Rongtao Jiang5, Dongmei Zhi4, Victor M. Vergara6, Xiao Yang7, Daoqiang Zhang1, Zening Fu6, Juan R. Bustillo8, Shile Qi1, Vince Calhoun6
Institutions:
1Nanjing University of Aeronautics and Astronautics, Nanjing, China, 2University of Maryland School of Medicine, Baltimore, MD, 3University of Colorado Boulder, Boulder, CO, 4Beijing Normal University, Beijing, China, 5Yale University, New Haven, CT, 6Georgia State University, Atlanta, GA, 7West China Hospital of Sichuan University, Chengdu, China, 8University of New Mexico, Albuquerque, NM
First Author:
Co-Author(s):
Introduction:
Substance use is an important confounder of brain imaging findings in various diseases. Drinking (DRN) and smoking (SMK) are the two representative and prevailing addictive related disorders worldwide[1-3]. Individuals affected by psychotic, depressive and developmental disorders are at a higher risk for DRN and SMK. However, little work has been done to evaluate the effects of these substances on brain structure and function, which further contribute to symptoms and cognition in individuals with these disorders. Further understanding of these relationships may assist clinicians in the development of future approaches to improve symptoms and cognition among psychotic, depressive and developmental disorders.
Methods:
Multimodal brain imaging data of DRN (n=707), SMK (n=281), psychotic disorders (including schizophrenia: SZ, n=178, schizoaffective disorder: SAD, n=134 and bipolar: BP, n=143), depressive disorder (including major depressive disorder: MDD, n=260) and developmental disorders (including autism spectrum disorder: ASD, n=421 and attention-deficit/hyperactivity disorders: ADHD, n=346) were collected across multiple consortiums[4-9]. Alcohol use disorder identification test (AUDIT) and fagerström test for nicotine dependence (FTND) scores were used as references to guide functional MRI (fractional amplitude of low frequency fluctuations, fALFF) and structural MRI (gray matter volume, GMV) fusion to identify the multimodal brain patterns related to DRN and SMK, respectively (Fig. 1a). Then the DRN/SMK/DRN+SMK-associated brain patterns were used as regions of interest (ROIs) to extract fALFF and GMV features from psychotic, depressive and developmental disorders, respectively (Fig. 1b). Finally, correlation analyses between the extracted brain features with symptoms and cognition were performed to evaluate the relationship of these brain regions with symptoms and cognition across 6 brain disorders (Fig. 1c).
Results:
(1) The default mode network (DMN) and salience network (SN) were the identified multimodal brain patterns associated with DRN, whereas the DMN and fronto-limbic network (FLN) were the identified multimodal brain patterns associated with SMK (Fig. 2-I); (2) the DMN related to DRN and SMK was associated with symptom (Schizo-Bipolar Scale, SBS), whereas the fronto-basal ganglia (FBG) was correlated with cognition (Wide Range Achievement Test-IV: WRAT and Brief Assessment of Cognition in Schizophrenia: BACS) in psychosis (Fig. 2-II); (3) the middle temporal cortex (MTC) related to DRN and SMK was associated with cognition (digit symbol and Ruminative Response Scale: RRS rumination) in depression (Fig. 2-III); (4) the DMN related to DRN and SMK was associated with symptom (Social Responsiveness Scale: SRS mannerisms), whereas the SN and limbic system (LB) were associated with cognition (verbal IQ) in developmental disorders (Fig. 2-IV).
Conclusions:
This is the first attempt to identify DRN and SMK-related brain patterns and further investigate the associations between the identified brain patterns and different clinical subdomains (symptoms and cognition) in psychotic, depressive and developmental disorders. Results suggest that DRN and SMK were related with structural abnormalities and dysfunction in DMN, SN and FLN and had significant impacts on cognition and symptoms in psychotic, depressive and developmental disorders likely via different brain networks. There are two broad implications from our results. Methodologically, co-morbid substance use has to be accounted for in neuroimaging studies of psychotic, depressive developmental populations. In clinical terms, alcohol and tobacco use disorders are extremely common and especially difficult to treat amongst mentally-ill populations. Understanding the brain pathophysiology in these co-morbid conditions may assist clinical scientists in the development of better substance cessation approaches.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Multivariate Approaches 1
Keywords:
Addictions
Other - multimodal fusion; psychotic disorders; depressive disorder; developmental disorders
1|2Indicates the priority used for review
·Figure 1. Flowchart of this study design.
·Figure 2. The identified AUDIT/FTND-associated multimodal joint components (I) and their associations with psychotic (II), depressive (III) and developmental (IV) disorders.
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