Abnormal Brain Structure in Consciousness Disorders: A Normative Modeling Approach
Poster No:
2015
Submission Type:
Abstract Submission
Authors:
Zhizhong Jiang1, Yihe Zhang1, Zhongyin Liang1, Huihui Niu1, Wenzhao Deng1, Tiantian Liu1, Hongyimei Liu1, Qiuyou Xie2, Ruiwang Huang1
Institutions:
1School of Psychology, Key Laboratory of Brain, South China Normal University, Guangzhou, Guangdong, 2Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong
First Author:
Zhizhong Jiang
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Co-Author(s):
Yihe Zhang
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Zhongyin Liang
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Huihui Niu
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Wenzhao Deng
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Tiantian Liu
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Hongyimei Liu
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Ruiwang Huang
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Introduction:
Disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), are characterized by severe impairments in awareness and cognition (Giacino et al., 2014). Although previous studies analyzed abnormal structure and functions of DOC by using conventional imaging methods, it remains unclear about the neural substrates involved, In addition, individual heterogeneity among DOC patients due to brain injuries is often overlooked (Fischer et al., 2022; Morozova et al., 2018). The normative modeling, which utilizes deviation scores from a large-scale centile model, may offer a model-based analytic approach to uncovering individual variations and detecting subtle brain abnormalities that conventional imaging methods may overlook (Rutherford et al., 2023). By elucidating the alterations of brain structural features, we aim to identify potential biomarkers for diagnosing and predicting outcomes in DOC, and explore potential clinical interventions for patients with heterogeneity.
Methods:
Subjects
We recruited 86 DOC patients and 32 healthy adults as the controls (HCs) in the Guangzhou Liuhuaqiao Hospital (GLH). For each patient, the severity of the condition was assessed by the Coma Recovery Scale-Revised (CRS-R). The study was approved by the Institutional Review Board (IRB) of the GLH. Written informed consent was obtained from each patient's guardian and healthy volunteers.
Data acquisition
The brain structural MRI (sMRI) data were obtained on a GE 3T MR scanner at the GLH. Fig. 1 shows that the image quality was examined using MRIQC. We exclude the data from the patients with severe brain atrophy, brain edema, or focal brain damage (Esteban et al., 2017). Finally, we included 34 DOC patients (20M/14F, aged 45.5 ± 14.2 years) and 30 healthy subjects (18M/12F, aged 35.0 ± 10.6 years) for the following analysis.
Data analysis
The sMRI data were preprocessed using FreeSurfer and the brain were parcellated into 144 regions according to the Destrieux atlas. We derived brain structural features, including cortical thickness and subcortical volume for each subject. The imaging data were divided into two subsets, the training and test subsets. We used the PCNtoolkit (Marquand et al., 2019), a Python package for clinical neuroimaging tasks, to transfer pre-established normative models (Rutherford et al., 2022) with the features of HCs in the training subset. Subsequently, we mapped the structural features of DOCs in the test subset onto the transferred models to assess individual neuroimaging metrics against population-based norms. Deviation scores were obtained from the normative models and were further analyzed.
We recruited 86 DOC patients and 32 healthy adults as the controls (HCs) in the Guangzhou Liuhuaqiao Hospital (GLH). For each patient, the severity of the condition was assessed by the Coma Recovery Scale-Revised (CRS-R). The study was approved by the Institutional Review Board (IRB) of the GLH. Written informed consent was obtained from each patient's guardian and healthy volunteers.
Data acquisition
The brain structural MRI (sMRI) data were obtained on a GE 3T MR scanner at the GLH. Fig. 1 shows that the image quality was examined using MRIQC. We exclude the data from the patients with severe brain atrophy, brain edema, or focal brain damage (Esteban et al., 2017). Finally, we included 34 DOC patients (20M/14F, aged 45.5 ± 14.2 years) and 30 healthy subjects (18M/12F, aged 35.0 ± 10.6 years) for the following analysis.
Data analysis
The sMRI data were preprocessed using FreeSurfer and the brain were parcellated into 144 regions according to the Destrieux atlas. We derived brain structural features, including cortical thickness and subcortical volume for each subject. The imaging data were divided into two subsets, the training and test subsets. We used the PCNtoolkit (Marquand et al., 2019), a Python package for clinical neuroimaging tasks, to transfer pre-established normative models (Rutherford et al., 2022) with the features of HCs in the training subset. Subsequently, we mapped the structural features of DOCs in the test subset onto the transferred models to assess individual neuroimaging metrics against population-based norms. Deviation scores were obtained from the normative models and were further analyzed.
Results:
Fig. 2 shows the individual deviations from normative brain trajectories. Significant negative deviations were observed in the patients in the bilateral superior circular insula, lateral orbital sulci, and inferior frontal gyrus, indicating notable volume reductions in these regions in the patients. We also detected significant volume expansions in the insular cortex and central sulcus. Fig. 3a shows the significant lateralization of the lateral orbital sulci in cortical thickness. A two-sample t-test was used to identify the regions with significantly different cortical surface areas between the DOCs and HCs, calculated separately based on extreme deviation and cortical thickness (Fig. 3b). We calculated the total number of extreme deviations for each patient and estimated their correlations with the CRS-R scores (Fig. 3c).
Conclusions:
The current study found significant structural abnormalities in DOCs. The observed structural asymmetry in the lateral orbital gyrus may reveal the lateralized brain dysfunction in DOCs. These results, along with the use of normative modeling, may provide a comprehensive insight into the cerebral changes in DOC. The findings have implications for advancing diagnostic and therapeutic approaches in the future.
Modeling and Analysis Methods:
Bayesian Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Cortical Anatomy and Brain Mapping
Novel Imaging Acquisition Methods:
Anatomical MRI
Perception, Attention and Motor Behavior:
Consciousness and Awareness 1
Keywords:
Consciousness
DISORDERS
MRI
Structures
Trauma
Other - Normative Modeling
1|2Indicates the priority used for review
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Provide references using APA citation style.
Fischer, D., Newcombe, V., Fernandez-Espejo, D., & Snider, S. B. (2022). Applications of Advanced MRI to Disorders of Consciousness. Seminars in Neurology, 42(03), 325–334.
Giacino, J. T., Fins, J. J., Laureys, S., & Schiff, N. D. (2014). Disorders of consciousness after acquired brain injury: The state of the science. Nature Reviews Neurology, 10(2), 99–114.
Marquand, A. F., Kia, S. M., Zabihi, M., Wolfers, T., Buitelaar, J. K., & Beckmann, C. F. (2019). Conceptualizing mental disorders as deviations from normative functioning. Molecular Psychiatry, 24(10), 1415–1424.
Morozova, S., Kremneva, E., Sergeev, D., Sinitsyn, D., Legostaeva, L., Iazeva, E., Krotenkova, M., Ryabinkina, Y., Suponeva, N., & Piradov, M. (2018). Conventional Structural Magnetic Resonance Imaging in Differentiating Chronic Disorders of Consciousness. Brain Sciences, 8(8), 144.
Rutherford, S., Barkema, P., Tso, I. F., Sripada, C., Beckmann, C. F., Ruhe, H. G., & Marquand, A. F. (2023). Evidence for embracing normative modeling. Elife, 12, e85082.
Rutherford, S., Fraza, C., Dinga, R., Kia, S. M., Wolfers, T., Zabihi, M., Berthet, P., Worker, A., Verdi, S., Andrews, D., Han, L. K., Bayer, J. M., Dazzan, P., McGuire, P., Mocking, R. T., Schene, A., Sripada, C., Tso, I. F., Duval, E. R., … Marquand, A. F. (2022). Charting brain growth and aging at high spatial precision. eLife, 11, e72904.