A neurotransmitter-related and age-stable modular integration abnormality of reward circuit in ASD

Poster No:

1426 

Submission Type:

Abstract Submission 

Authors:

Chen Yang1, Zhi Ma1, Qiang Dong1, Rong Zhang1, Yu Chen1, Dan Chen2, Yu-Feng Zang3, Li-Xia Yuan4

Institutions:

1Hangzhou Normal University, Hangzhou, Zhejiang, 2Zhejiang University School of Medicine, Hangzhou, Zhejiang, 3Hangzhou normal university, Hangzhou, China, 4Hangzhou Normal University, Hangzhou, Zhejiang Province

First Author:

Chen Yang  
Hangzhou Normal University
Hangzhou, Zhejiang

Co-Author(s):

Zhi Ma  
Hangzhou Normal University
Hangzhou, Zhejiang
Qiang Dong  
Hangzhou Normal University
Hangzhou, Zhejiang
Rong Zhang  
Hangzhou Normal University
Hangzhou, Zhejiang
Yu Chen  
Hangzhou Normal University
Hangzhou, Zhejiang
Dan Chen  
Zhejiang University School of Medicine
Hangzhou, Zhejiang
Yu-Feng Zang  
Hangzhou normal university
Hangzhou, China
Li-Xia Yuan  
Hangzhou Normal University
Hangzhou, Zhejiang Province

Introduction:

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with social communication impairments as a core symptom(Pelphrey, Shultz, Hudac, & Vander Wyk, 2011). Social dysfunction in ASD is associated with the social brain(Gotts et al., 2012; Muller & Fishman, 2018), which encompasses four specific subnetworks, i.e., reward-related system (RRS) (Bickart, Hollenbeck, Barrett, & Dickerson, 2012; Chevallier, Kohls, Troiani, Brodkin, & Schultz, 2012), theory of mind (ToM) network(Mar, 2011; Schurz, Radua, Aichhorn, Richlan, & Perner, 2014), mirror neuron system (MNS) (Babiloni et al., 2017; Molenberghs, Cunnington, & Mattingley, 2012), and face perception network (FPN) (Cohen Kadosh, Cohen Kadosh, Dick, & Johnson, 2011; Duchaine & Yovel, 2015). However, the relationship between neural mechanisms of social dysfunction and modular integration of these subnetworks within the social brain remains unclear in ASD.

Methods:

To define the four subnetworks in the social brain, related meta-analytic activation maps were obtained from Neurosynth(Yarkoni, Poldrack, Nichols, Van Essen, & Wager, 2011). The participation coefficient (PC) (Guimerà & Nunes Amaral, 2005) was applied to explore the abnormal modular integration of the four subnetworks in 298 ASD participants and 348 typically developing (TD) controls from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. Then, its association with clinical symptoms was investigated with Pearson's or Spearman's partial correlation. Furthermore, to explore the molecular underpinnings of the dysfunctional modular integration in ASD, its associations with neurotransmitter densities were obtained with spatial correlation(Dukart et al., 2021). Additionally, age effect on aberrant modular integration was estimated with linear regression models. Finally, we assess the reproducibility of our results from a meta-perspective using the ABIDE II dataset.

Results:

ASD patients exhibited increased integration of the RRS (p = 7.10×10-6, Cohen's d = 0.36, Bonferroni corrected) relative to TDs, which were attributed to increased inter‐modular connections between the RRS and FPN as well as MNS. The increased integration of the RRS was positively correlated with Social Responsiveness Scale (SRS) total scores (r = 0.18, p = 0.003, FDR corrected). Furthermore, increased modular integration of the RRS was negatively associated with the neurotransmitters such as 5HT1a (r = -0.64, p = 0.005, FDR corrected) and GABAa (r = -0.67, p = 0.005, FDR corrected). Additionally, a significant main effect of age and diagnosis on the modular integration of RRS were revealed. Finally, the replicated meta-analysis demonstrated similar modular integration disruption of RRS in ASD.
Supporting Image: 1.jpg
   ·Modular participation coefficient alterations in ASD
Supporting Image: 2.jpg
   ·A symptom-related, neurotransmitter-associated, and age-stable modular integration abnormality of reward circuit in ASD
 

Conclusions:

We revealed a symptom-related, neurotransmitter-associated, age-stable, and reproducible modular integration abnormality of reward circuit in ASD. Our study supported the excitatory-inhibitory balance theory of ASD and may shed light on the development of new intervention strategies for social impairments of ASD.

Disorders of the Nervous System:

Neurodevelopmental/ Early Life (eg. ADHD, autism)

Emotion, Motivation and Social Neuroscience:

Social Cognition
Social Neuroscience Other 2

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis

Keywords:

Autism
FUNCTIONAL MRI
Neurotransmitter
Social Interactions

1|2Indicates the priority used for review

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Functional MRI

For human MRI, what field strength scanner do you use?

3.0T

Provide references using APA citation style.

Babiloni, C. (2017). Frontal Functional Connectivity of Electrocorticographic Delta and Theta Rhythms during Action Execution Versus Action Observation in Humans. Front Behav Neurosci, 11, 20.
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Duchaine, B. (2015). A Revised Neural Framework for Face Processing. Annu Rev Vis Sci, 1, 393-416.
Dukart, J. (2021). JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum Brain Mapp, 42(3), 555-566.
Gotts, S. J. (2012). Fractionation of social brain circuits in autism spectrum disorders. Brain, 135(Pt 9), 2711-2725.
Guimerà, R. (2005). Functional cartography of complex metabolic networks. Nature, 433(7028), 895-900.
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Molenberghs, P. (2012). Brain regions with mirror properties: a meta-analysis of 125 human fMRI studies. Neurosci Biobehav Rev, 36(1), 341-349.
Muller, R. A. (2018). Brain Connectivity and Neuroimaging of Social Networks in Autism. Trends Cogn Sci, 22(12), 1103-1116.
Pelphrey, K. A. (2011). Research review: Constraining heterogeneity: the social brain and its development in autism spectrum disorder. J Child Psychol Psychiatry, 52(6), 631-644.
Schurz, M. (2014). Fractionating theory of mind: a meta-analysis of functional brain imaging studies. Neurosci Biobehav Rev, 42, 9-34.
Yarkoni, T. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nat Methods, 8(8), 665-670.

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