Atypical brain hierarchy in autism spectrum disorder: insights from neuronal oscillation pattern
Poster No:
357
Submission Type:
Late-Breaking Abstract Submission
Authors:
Yunge Zhang1, Fengyu Cong1, Aiguo Chen2, Guanyu Gong3, Dongyue Zhou4, Yang Song1, Lin Lin1, Siyu Zheng1, Zhou Deng1, Raju Bapi5, Jin Sun6, Hunajie Li1
Institutions:
1Central Hospital of Dalian University of Technology, Dalian University of Technology, Dalian, China, 2Nanjing Sport Institute, Nanjing, China, 3The Institute for Translational Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian,China, 4Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud, Nijmegen, the Netherlands, 5Cognitive Science Lab, International Institute of Information Technology, Hyderabad, India, 6Center of Women and Children's Health Research Faculty of Medicine, Dalian University of Technology, Dalian, China
First Author:
Yunge Zhang
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Co-Author(s):
Fengyu Cong
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Guanyu Gong
The Institute for Translational Medicine, Affiliated Zhongshan Hospital of Dalian University
Dalian,China
The Institute for Translational Medicine, Affiliated Zhongshan Hospital of Dalian University
Dalian,China
Dongyue Zhou
Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud
Nijmegen, the Netherlands
Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud
Nijmegen, the Netherlands
Yang Song
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Lin Lin
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Siyu Zheng
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Zhou Deng
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Jin Sun
Center of Women and Children's Health Research Faculty of Medicine, Dalian University of Technology
Dalian, China
Center of Women and Children's Health Research Faculty of Medicine, Dalian University of Technology
Dalian, China
Hunajie Li
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Central Hospital of Dalian University of Technology, Dalian University of Technology
Dalian, China
Introduction:
The human brain's hierarchical structures are built upon anatomical hierarchy and evolve into functional hierarchy with more complexity. This hierarchy is evident in the segregation between the unimodal (primary sensory and motor regions) and transmodal regions (high-order functional areas), as well as between the somatomotor network (SM) and visual network (Vis)) (Margulies et al., 2016). The balance of this hierarchy supports normal brain functions, enabling it to process information efficiently. Further, disruptions in this hierarchy are linked to psychiatric conditions like autism spectrum disorder (ASD) (Hong et al., 2019). However, most studies used function connectivity (FC) to evaluate brain hierarchy and study the system level changes in FC in ASD, but how the neuron activity systemically changes in ASD remains unclear. Here, we proposed an approach that evaluates brain hierarchy based on neuronal oscillation patterns and explored the atypical patterns in ASD at the system level.
Methods:
Data: We analyzed resting-state fMRI data of 264 male subjects (132 ASD, 132 healthy controls (CON)) from 7 sites of the ABIDE II dataset (Di Martino et al., 2017), with full IQ higher than 80 and age/IQ matching within each site. There were 184 subjects (92 ASD, 92 CON) having records of Social Responsiveness Scale (SRS) T scores.
Energy gradient: Neuronal oscillation patterns were assessed using energy distribution across frequency bands. After extracting time courses using a 400-node cortical atlas (Schaefer et al., 2018), the multivariate empirical mode decomposition (MEMD) (Rehman & Mandic, 2010) was used to decompose time courses into five intrinsic mode functions (IMFs), each of which represents a specific frequency band. Energy of each IMF was calculated using the Hilbert Transform and normalized by dividing by the sum energy of five IMFs. For each subject, the energy similarity matrix was constructed using Pearson correlation between the energy distribution of any two nodes. To quantify the hierarchy embodied in the energy distribution, the gradient analysis was applied to mean energy similarity matrix first to generate the group-level references and then applied to each subject and aligned to the references (Vos De Wael et al., 2020). To evaluate the segregation between networks, the 400 nodes were divided into 15 regions and the network median distance (NMD) was calculated, which was the difference between the median gradient value of two regions. The FC gradients were also calculated for comparison to the energy gradients, and the Spearman correlation was calculated between corresponding axes.
Energy gradient: Neuronal oscillation patterns were assessed using energy distribution across frequency bands. After extracting time courses using a 400-node cortical atlas (Schaefer et al., 2018), the multivariate empirical mode decomposition (MEMD) (Rehman & Mandic, 2010) was used to decompose time courses into five intrinsic mode functions (IMFs), each of which represents a specific frequency band. Energy of each IMF was calculated using the Hilbert Transform and normalized by dividing by the sum energy of five IMFs. For each subject, the energy similarity matrix was constructed using Pearson correlation between the energy distribution of any two nodes. To quantify the hierarchy embodied in the energy distribution, the gradient analysis was applied to mean energy similarity matrix first to generate the group-level references and then applied to each subject and aligned to the references (Vos De Wael et al., 2020). To evaluate the segregation between networks, the 400 nodes were divided into 15 regions and the network median distance (NMD) was calculated, which was the difference between the median gradient value of two regions. The FC gradients were also calculated for comparison to the energy gradients, and the Spearman correlation was calculated between corresponding axes.
Results:
Through gradient analysis, the function segregation between transmodal and unimodal regions, and between the SM and Vis was observed in the energy distribution like in the FC (Fig. 1), indicating that brain networks had distinct oscillation patterns which might contribute to the hierarchical organization of the brain. Compared to the CON, the NMD between transmodal and unimodal regions was reduced in ASD on the energy gradient, which was related to their social deficits (Fig. 2). A similar trend was observed on the FC gradient but no difference survived after FDR correction. These results implied that the function segregation between transmodal and unimodal regions was deficient, and this imbalance of hierarchy led to their social difficulties. Further, the energy gradient was more sensitive to identifying abnormalities in ASD, highlighting the potential of this approach for research in psychiatric disorders.
·Fig.1 Correlation between energy gradients and function connectivity gradients
·Fig.2 Group differences on network median distance of unimodal-transmodal axis on the energy gradient and FC gradient
Conclusions:
We proposed an approach based on neural oscillation patterns to assess brain hierarchy, providing a novel perspective on functional segregation between networks. Additionally, we observed suppression of the transmodal-unimodal gradient in ASD, contributing new insights into the abnormalities in system-level neural oscillation patterns in this population.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Other Methods 2
Keywords:
Autism
FUNCTIONAL MRI
Other - functional hierachy
1|2Indicates the priority used for review
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Rehman, N., & Mandic, D. P. (2010). Multivariate empirical mode decomposition. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 466(2117), 1291–1302. https://doi.org/10.1098/rspa.2009.0502
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex, 28(9), 3095–3114. https://doi.org/10.1093/cercor/bhx179
Vos De Wael, R., Benkarim, O., Paquola, C., Lariviere, S., Royer, J., Tavakol, S., Xu, T., Hong, S.-J., Langs, G., Valk, S., Misic, B., Milham, M., Margulies, D., Smallwood, J., & Bernhardt, B. C. (2020). BrainSpace: A toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Communications Biology, 3(1), 103. https://doi.org/10.1038/s42003-020-0794-7