Static and dynamic functional connectivity alterations in adolescent major depressive disorder
Poster No:
389
Submission Type:
Abstract Submission
Authors:
Weijie Bao1, Hailong Li2, Yingxue Gao2, Ruohan Feng3, Lingxiao Cao4, Zilin Zhou2, Mengyue Tang2, Yingying Wang2, Xue Li2, Xiaoqi Huang2
Institutions:
1Sichuan University West China Hospital Department of Radiology, Chengdu, Sichuan, 2Sichuan University West China Hospital Department of Radiology, Chengdu, SiChuan, 3The Third Hospital of Mianyang, Mianyang, China, 4Sichuan University West China Hospital Department of Radiology, Chengdu, China
First Author:
Co-Author(s):
Introduction:
Major depressive disorder (MDD) is the most common psychiatric disorder in the world, and adolescents show a lifetime prevalence of MDD about 11% (Miller, 2021) which accompanied by high recurrence rate and risk of suicide (Chi, 2021). Previous studies had demonstrated that adult MDD would characterize by both abnormal static and dynamic functional networks in brain (Mulders, 2015; Zhi, 2018). Specifically, the static FNC abnormality of adult MDD mainly located in the default mode network (DMN) and the dynamic FNC abnormality mainly in the frontoparietal (FPN), visual (VIS) and sensorimotor networks (SMN). This evidence supported a concept that depression is a network-based disorder. However, few study focus on the static and dynamic FNC in aMDD, which render our understanding of aMDD brain network abnormality patterns lagged behind that of adults MDD, and prevent us figuring out the neural mechanism of early-stage depression.
Methods:
83 first episode and drug-naïve aMDD patients and 59 age/gender- matched HCs were recruited in our study. The Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD) were used to assess the severity of anxiety and depression symptoms in subjects separately. And the demographic and clinical information can be seen in Table 1.
We applied spatial group independent component analysis (GICA) using GIFT software to identify brain intrinsic network. A relatively high model order ICA (component number=100) was chosen to acquire independent components (ICs). Peak coordinates of spatial maps and IC spatial correlation with Yeo 7 network template, subcortical and cerebellum template were used to selected 44 meaningful ICs. These ICs was sorted into nine networks, including DMN, FPN, SMN, limbic, VIS, dorsal attention (DAN), ventral attention (VAN), subcortical (SC) and cerebellum networks (Ce). All ICs and networks can be acquired in Figure 1A. Sliding window approach was conducted to acquire FNC matrix in each time window with 30 TRs window length and 1 TR step. And K-means algorithm was performed to divide the dynamic FNC (dFNC) windows into separate clusters. Lastly, group comparison of static and dynamic FNC was conducted based on two-sample t-test. The threshold for the sFNC and dFNC results was P < 0.005.
We applied spatial group independent component analysis (GICA) using GIFT software to identify brain intrinsic network. A relatively high model order ICA (component number=100) was chosen to acquire independent components (ICs). Peak coordinates of spatial maps and IC spatial correlation with Yeo 7 network template, subcortical and cerebellum template were used to selected 44 meaningful ICs. These ICs was sorted into nine networks, including DMN, FPN, SMN, limbic, VIS, dorsal attention (DAN), ventral attention (VAN), subcortical (SC) and cerebellum networks (Ce). All ICs and networks can be acquired in Figure 1A. Sliding window approach was conducted to acquire FNC matrix in each time window with 30 TRs window length and 1 TR step. And K-means algorithm was performed to divide the dynamic FNC (dFNC) windows into separate clusters. Lastly, group comparison of static and dynamic FNC was conducted based on two-sample t-test. The threshold for the sFNC and dFNC results was P < 0.005.
Results:
Compared to HC group, aMDD showed increased static FNC (sFNC) between SMN and FPN, as well as between VIS and VAN. The aMDD group also exhibited decreased sFNC between VIS and Ce, between FPN and SC. And sFNC results can be acquired in Figure 1B.
Dynamic FNC analysis showed the FNC matrix among nine networks could be clustered into two configuration states, among which state 1 mainly exhibited sparse connections, whereas state 2 mainly showed dense connection. The state 1 was characterized by positive FNC within DMN, SMN and VIS, as well as between VIS and SMN. The state 2 was characterized by negative FNC between DMN and VAN. The aMDD exhibited increased FC between the VIS and VAN, decreased FC among DAN, VAN, VIS and DMN and within DAN in state 1. In state 2, aMDD patients mainly showed decreased FC within SMN and among the FPN, SC and Ce. And dFNC results can be acquired in Figure 2.
Dynamic FNC analysis showed the FNC matrix among nine networks could be clustered into two configuration states, among which state 1 mainly exhibited sparse connections, whereas state 2 mainly showed dense connection. The state 1 was characterized by positive FNC within DMN, SMN and VIS, as well as between VIS and SMN. The state 2 was characterized by negative FNC between DMN and VAN. The aMDD exhibited increased FC between the VIS and VAN, decreased FC among DAN, VAN, VIS and DMN and within DAN in state 1. In state 2, aMDD patients mainly showed decreased FC within SMN and among the FPN, SC and Ce. And dFNC results can be acquired in Figure 2.
Conclusions:
The abnormal sFNC and dFNC in state 1 between VIS and VAN may be associated with the impaired response to relevant stimuli in aMDD patients (Passarotti, 2009; Seeley, 2007). We also discovered stable decreased sFNC and dFNC between FPN and SC in aMDD in state 2. The FPN mainly involved in higher order modulation of cognitive and emotional processes (Marek, 2018) and FPN-subcortical network was associated with executive functions (Zhi, 2018). The aMDD patients in state 2 displayed FNC changes between FPN and SC, which may be associated with impaired cognitive and executive function in patients. Our study found the different brain network changes patterns in two states configure, which may help us better understand the neural mechanism of adolescent MDD and provide a potential target network to classify adolescent MDD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Lifespan Development:
Early life, Adolescence, Aging 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Psychiatric Disorders
Other - Adolescent depression, static, dynamic, functional network connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Passarotti, A. M., Sweeney, J. A., & Pavuluri, M. N. (2009). Neural correlates of incidental and directed facial emotion processing in adolescents and adults. Social Cognitive and Affective Neuroscience, 4(4), 387-398.
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