Spatiotemporal complexity profiles of brain functional activity in diverse psychiatric disorders
Poster No:
555
Submission Type:
Abstract Submission
Authors:
Yu Du1, Chen Ran1, Jiahui Shi1, Ziyan Deng1, Ting Ma2, Chenfei Ye1
Institutions:
1Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), ShenZhen, GuangDong, 2School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), ShenZhen, GuangDong
First Author:
Yu Du
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Co-Author(s):
Chen Ran
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Jiahui Shi
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Ziyan Deng
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Ting Ma
School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Chenfei Ye
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)
ShenZhen, GuangDong
Introduction:
Variability of neural activity fluctuations is an important symbol of neural activity, determining cognitive behavior to a certain extent (Leonhard Waschke, 2021). According to latest research, spontaneous complexity drops of rs-fMRI could describe the propagation of neural activity patterns, indicating the crucial characteristics of brain dynamic activities (Stephan Krohn, 2023). Given that brain functional activity of psychiatric disorders has been found to have abnormal dynamics (Luheng Zhang, 2022; Javaheripour, 2023), we hypothesize that complexity of brain activity of psychiatric disorders also exhibits pattern abnormality which may serve as a biomarker. In this study, we applied weight permutation entropy (WPE) method (Fadlallah B, 2013) on resting-state fMRI data across groups of psychiatric disorders, aiming to characterize the variability profiles of neural activity fluctuations in psychosis.
Methods:
We leveraged rs-fMRI data from 202 participants, comprising 112 individuals with a spectrum of disorders like Schizophrenia (SZ), Major Depressive Disorder (MDD), Bipolar Disorder (BP), Post-Traumatic Stress Disorder (PTSD), Dysthymia, and Generalized Anxiety Disorder (GAD), and 90 healthy controls (HC). Forty-two distinct behavioral measurements were leveraged, categorized into five groups: clinical surveys, MRI-based surveys, supplemental surveys, clinician-administered scales, and test-my-brain online assessments. These data were sourced from the Transdiagnostic Connectomes Project (TCP) (Sidhant Chopra, 2024). Signal complexity of BOLD activity was calculated as WPE through symbolic encoding of the time series vectors. We employed a sliding window technique to analyze BOLD time series, yielding a time-resolved signal complexity vector. We defined the threshold of complexity drop as the first percentile of total WPE distribution. When a region's signal complexity reached the threshold, it was considered as exhibiting a complexity drop (see Fig. 1A). The sensitivity analysis for selection of window parameters was also conducted (see Fig. 1B). Complexity drop affinity, representing the average level of activity, was calculated for each brain vertex. To investigate the differences in drop affinity in brain region vertices and their correlation with behavioral measurements, we employed the Kruskal-Wallis test with Bonferroni correction. We conducted post-hoc tests with Bonferroni correction to explore the variations in drop affinity among individuals with disorders and HC. For brain vertices where drop affinity pattern and measurements exhibiting significant (p <= 0.05 after correction) difference between groups, Pearson correlation coefficient was calculated between with correction.
·Figure 1
Results:
Significant differences in drop affinity were exhibited by 13 regions of interest (ROIs), particularly with a higher level of activity in the somatomotor network (SOM) in HC compared to other subject groups (Fig. 2A). In the visual network, 7 ROIs showed lower drop affinity in MDD groups compared with HC, while 3 ROIs exhibited higher in PTSD than HC (Fig. 2B). Higher level of activity in the SOM was observed in 16 ROIs in HC compared to PTSD (Fig. 2B). In the default mode network (DMN), lower drop affinity was observed in 6 ROIs in the MDD than HC, and the function of 4 ROIs tended to be more activated in the dysthymia group than HC (Fig. 2B). Several behavioral measurement were observed to show significant association with ROI-wise drop affinity within subjects of mental illness (Fig. 2C).
·Figure 2
Conclusions:
Common functional disruption of neural spatiotemporal complexity is highlighted by our results. A more general observation of dynamic activity differences is indicated by a lower complexity drop affinity in the DMN, Visual, SOM, and VAN, suggesting potential impairment, consistent with prior evidence (Luheng Zhang, 2022) (Javaheripour, 2023).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
FUNCTIONAL MRI
Psychiatric
Psychiatric Disorders
1|2Indicates the priority used for review
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2. Stephan Krohn et al. ,A spatiotemporal complexity architecture of human brain activity. Sci. Adv.9,eabq3851(2023).DOI:10.1126/sciadv.abq3851.
3. Luheng Zhang, Ran Zhang, Shaoqiang Han, Fay Y. Womer, Yange Wei, Jia Duan, Miao Chang, Chao Li, Ruiqi Feng, Juan Liu, Pengfei Zhao, Xiaowei Jiang, Shengnan Wei, Zhiyang Yin, Yifan Zhang, Yanbo Zhang, Xizhe Zhang, Yanqing Tang, Fei Wang, Three major psychiatric disorders share specific dynamic alterations of intrinsic brain activity, Schizophrenia Research, Volume 243, 2022, Pages 322-329, ISSN 0920-9964, https://doi.org/10.1016/j.schres.2021.06.014.
4. Javaheripour, N., Colic, L., Opel, N. et al. Altered brain dynamic in major depressive disorder: state and trait features. Transl Psychiatry 13, 261 (2023). https://doi.org/10.1038/s41398-023-02540-0.
5. Fadlallah B, Chen B, Keil A, Príncipe J. Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information. Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Feb;87(2):022911. doi: 10.1103/PhysRevE.87.022911. Epub 2013 Feb 20. PMID: 23496595.
6. Sidhant Chopra et al. , The Transdiagnostic Connectome Project: a richly phenotyped open dataset for advancing the study of brain-behavior relationships in psychiatry. DOI: https://doi.org/10.1101/2024.06.18.24309054