The structural connectivity provides the underlying configuration for multiple disorders
Poster No:
572
Submission Type:
Abstract Submission
Authors:
Weiyang Shi1,2, Zhenwei Dong1,3, Ming Song1, Yu Zhang2, Tianzi Jiang1,2,3
Institutions:
1Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 2Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou, China, 3School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
First Author:
Weiyang Shi
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Research Center for Augmented Intelligence, Zhejiang Lab
Beijing, China|Hangzhou, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Research Center for Augmented Intelligence, Zhejiang Lab
Beijing, China|Hangzhou, China
Co-Author(s):
Zhenwei Dong
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Tianzi Jiang
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Research Center for Augmented Intelligence, Zhejiang Lab|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Hangzhou, China|Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Research Center for Augmented Intelligence, Zhejiang Lab|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Hangzhou, China|Beijing, China
Introduction:
The structural connectivity (SC) has been thought to have intricate relationships with abnormal patterns of multiple brain disorders (Hansen et al., 2022a). Describing these relationships to uncover underlying principles and utilizing it to gain new insights into these diseases has been a focal point of research in this field. In this study, we introduce the spectral energy to valid the intrinsic coupling between the abnormal cortical patterns of multiple disorders and SC. Furthermore, by employing the network control theory (Parkes et al., 2023), we emphasize the SC informed relationships between neurotransmitter receptors/transporters and disorders.
Methods:
The group average SC and the statistical pathological maps (effect sizes for case-control differences, Cohen's d) of cortical thickness for eight disorders were obtained from ENIGMA Toolbox (Larivière et al., 2021) which are mapped to DK atlas with 68 cortical ROIs. The density maps of 19 neurotransmitter receptors/transporters, estimated using PET images collected across several studies, were extracted from neuromaps (Markello et al., 2022; Hansen et al., 2022b).
To quantify the relationship between specific signal pattern x (representing an abnormal map associated with a disorder) and SC, we introduced the concept of SC-spectral energy. A smaller spectral energy value indicates a stronger coupling between the signal x and the SC architecture (Fig. 1A). To further investigate the SC-informed relationships between receptors and disorders, we considered the density maps of receptors as a priori variables and utilized the network control theory (Parkes et al., 2023; Luppi et al., 2023) to calculate the transition energy from the healthy state to the disorder patterns (Fig. 1B). The spin tests (Váša et al., 2018) were used to performed statistical tests cross this study.
To quantify the relationship between specific signal pattern x (representing an abnormal map associated with a disorder) and SC, we introduced the concept of SC-spectral energy. A smaller spectral energy value indicates a stronger coupling between the signal x and the SC architecture (Fig. 1A). To further investigate the SC-informed relationships between receptors and disorders, we considered the density maps of receptors as a priori variables and utilized the network control theory (Parkes et al., 2023; Luppi et al., 2023) to calculate the transition energy from the healthy state to the disorder patterns (Fig. 1B). The spin tests (Váša et al., 2018) were used to performed statistical tests cross this study.
·Figure 1. The framework for investigating the relationships between disorder-associated cortical thickness abnormal patterns and SC.
Results:
To evaluate the coupling between SC and the abnormal patterns of cortical thickness for eight brain disorders (see Fig. 2A for details), we calculated the SC-spectral energy of these patterns (Fig. 2B), respectively. The spectral energy of the diseases suggests a significant coupling between these abnormal spatial patterns and the white matter topological network of brain (p_spin< 0.05, FDR corrected), except for epilepsy, which demonstrated a trend with p=0.051. Based on network control theory, we further estimated the network control energy (NCE) required for transitions from the healthy state to the 8 disorder patterns, respectively. The NCE showed a significant correlation with the SC- spectral energy across disorders, with a Spearman's correlation coefficient of ρ=1.0 (Fig. 2B).
By considering the 19 neurotransmitter receptors/transporters as a priori distributions for the control set, we evaluated the NCE required by each disorder when using empirical receptor maps (Fig. 2D) and compared these values to their respective null surrogates (Fig. 2E). The NCE required for a specific disorder under the setting of control set with specific receptor was observed to be lower than its corresponding null spins, suggesting that this receptor contributes to the emergence of the abnormal pattern through SC, such as D1 to MDD, D2 to SZ, and so on (blue pairs in Fig. 2E with p_spin< 0.05, uncorrected). Conversely, higher NCE requirements indicate that the corresponding receptor may potentially counteract specific disease patterns through SC (red pairs in Fig. 2E with p_spin< 0.05, uncorrected).
By considering the 19 neurotransmitter receptors/transporters as a priori distributions for the control set, we evaluated the NCE required by each disorder when using empirical receptor maps (Fig. 2D) and compared these values to their respective null surrogates (Fig. 2E). The NCE required for a specific disorder under the setting of control set with specific receptor was observed to be lower than its corresponding null spins, suggesting that this receptor contributes to the emergence of the abnormal pattern through SC, such as D1 to MDD, D2 to SZ, and so on (blue pairs in Fig. 2E with p_spin< 0.05, uncorrected). Conversely, higher NCE requirements indicate that the corresponding receptor may potentially counteract specific disease patterns through SC (red pairs in Fig. 2E with p_spin< 0.05, uncorrected).
·Figure 2. SC provides the substrate for various disorders.
Conclusions:
This study provides a novel perspective on characterizing the coupling between disease-related brain abnormal patterns and SC using a novel spectral energy approach. By emphasizing the relationship between diseases and receptors bridged by SC, rather than relying solely on spatial correlations, the study also highlights the significance of utilizing brain's structural topological organization to better understand brain diseases and develop effective therapeutic interventions.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Methods Development
Keywords:
Data analysis
DISORDERS
Psychiatric Disorders
1|2Indicates the priority used for review
Provide references using author date format
Hansen, J.Y. (2022b), ‘Mapping neurotransmitter systems to the structural and functional organization of the human neocortex’, Nature Neuroscience, 25(11), pp. 1569–1581.
Larivière, S. (2021), ‘The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets’, Nature Methods, 18(7), pp. 698–700.
Luppi, A.I. (2023), ‘Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease’. bioRxiv, p. 2023.03.16.532981.
Markello, R.D. (2022), ‘Neuromaps: structural and functional interpretation of brain maps’, Nature Methods, 19(11), pp. 1472–1479.
Parkes, L. (2023), ‘Using network control theory to study the dynamics of the structural connectome’, bioRxiv: The Preprint Server for Biology, p. 2023.08.23.554519.
Váša, F. (2018), ‘Adolescent Tuning of Association Cortex in Human Structural Brain Networks’, Cerebral Cortex, 28(1), pp. 281–294.