Quantitative analysis of MRI-visible perivascular spaces in schizophrenia
Poster No:
599
Submission Type:
Abstract Submission
Authors:
Hagyeong Yu1, Changmin Ryu1, Junghwa Kang1, Yoonho Nam1, Tae Young Lee2
Institutions:
1Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, South Korea, 2Department of Neuropsychiatry, Pusan National University Yangsan Hospital, Yangsan, South Korea
First Author:
Hagyeong Yu
Division of Biomedical Engineering, Hankuk University of Foreign Studies
Yongin, South Korea
Division of Biomedical Engineering, Hankuk University of Foreign Studies
Yongin, South Korea
Co-Author(s):
Changmin Ryu
Division of Biomedical Engineering, Hankuk University of Foreign Studies
Yongin, South Korea
Division of Biomedical Engineering, Hankuk University of Foreign Studies
Yongin, South Korea
Junghwa Kang
Division of Biomedical Engineering, Hankuk University of Foreign Studies
Yongin, South Korea
Division of Biomedical Engineering, Hankuk University of Foreign Studies
Yongin, South Korea
Yoonho Nam
Division of Biomedical Engineering, Hankuk University of Foreign Studies
Yongin, South Korea
Division of Biomedical Engineering, Hankuk University of Foreign Studies
Yongin, South Korea
Tae Young Lee
Department of Neuropsychiatry, Pusan National University Yangsan Hospital
Yangsan, South Korea
Department of Neuropsychiatry, Pusan National University Yangsan Hospital
Yangsan, South Korea
Introduction:
Schizophrenia is a complex neuropsychiatric disorder characterized by diverse symptoms affecting cognitive, emotional, and social functioning. The glymphatic system, a network of dilated PVS (dPVS) and vessels in the brain responsible for clearing waste products and facilitating cerebrospinal fluid flow, has gained increasing attention in neuroimaging research.
However, there are not many studies focused on dPVS when it comes to schizophrenia. By using dPVS imaging as a tool for assessing the glymphatic system in the brain, valuable insights into its functionality and potential implications for neurological conditions like schizophrenia may be expected. In this study, we investigated dPVS in schizophrenia subgroups by a visual and volumetric assessment using our automatic pipeline.
However, there are not many studies focused on dPVS when it comes to schizophrenia. By using dPVS imaging as a tool for assessing the glymphatic system in the brain, valuable insights into its functionality and potential implications for neurological conditions like schizophrenia may be expected. In this study, we investigated dPVS in schizophrenia subgroups by a visual and volumetric assessment using our automatic pipeline.
Methods:
We collected 3D T1-weighted images of subjects categorized into schizophrenia subgroups. The subgroups include 66 patients with first-episode psychosis (FEP), 31 patients with treatment-resistant schizophrenia (TRS), 48 patients classified as clinical high risk for psychosis (CHR), 25 patients with major depressive disorder (MD), and 90 healthy control subjects (HC).
For volumetric assessment, we segmented the dPVS included in the regions of interest (ROI), specifically basal ganglia (BG) and white matter (WM), using deep learning-based automatic pipeline. Then, the volumes and numbers of dPVS were calculated for each subject and subgroup. To provide the dPVS volume fraction, the dPVS volumes were divided by the individual brain volumes of each patient. In addition, to compare the dPVS distributions between subgroups, we performed a nonlinear fully deformable registration to MNI space with FSL Anat. For statistical analysis, Student's t-test was used to compare healthy control group with each schizophrenia subgroup. A p-value ≤0.05 was considered as statistically significant.
For volumetric assessment, we segmented the dPVS included in the regions of interest (ROI), specifically basal ganglia (BG) and white matter (WM), using deep learning-based automatic pipeline. Then, the volumes and numbers of dPVS were calculated for each subject and subgroup. To provide the dPVS volume fraction, the dPVS volumes were divided by the individual brain volumes of each patient. In addition, to compare the dPVS distributions between subgroups, we performed a nonlinear fully deformable registration to MNI space with FSL Anat. For statistical analysis, Student's t-test was used to compare healthy control group with each schizophrenia subgroup. A p-value ≤0.05 was considered as statistically significant.
Results:
Our findings reveal differences in dPVS numbers and volumes among schizophrenia subgroups, especially in treatment-resistant schizophrenia(TRS) which showed smaller dPVS volumes compared to other groups in both WM and BG.
Figure 2a shows the quantitative differences in dPVS volumes among the various subgroups. Notably, TRS showed relatively small dPVS volumes and numbers compared to other groups for both WM (p ≤ 0.05) and BG (p ≤ 0.05).
Figure 2b shows the group averaged dPVS maps, offering a visual representation of the distribution of dPVS of each subgroup.
Figure 2a shows the quantitative differences in dPVS volumes among the various subgroups. Notably, TRS showed relatively small dPVS volumes and numbers compared to other groups for both WM (p ≤ 0.05) and BG (p ≤ 0.05).
Figure 2b shows the group averaged dPVS maps, offering a visual representation of the distribution of dPVS of each subgroup.
Conclusions:
We have investigated the volumes and numbers of dPVS in schizophrenia subgroups. While dPVS volumes in TRS have been observed to be significantly smaller than other subgroups, further research is needed to fully explain the underlying mechanisms driving these volume differences.
Our results indicate that the quantification of dPVS may hold promise in distinguishing different schizophrenia subgroups. While this study contributes to our understanding of PVS in schizophrenia, ongoing research is essential to clarify the mechanisms underpinning the observed differences and to determine the clinical relevance of dPVS quantification as a diagnostic tool for schizophrenia.
Our results indicate that the quantification of dPVS may hold promise in distinguishing different schizophrenia subgroups. While this study contributes to our understanding of PVS in schizophrenia, ongoing research is essential to clarify the mechanisms underpinning the observed differences and to determine the clinical relevance of dPVS quantification as a diagnostic tool for schizophrenia.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Other Methods 2
Keywords:
Basal Ganglia
Data Registration
Dopamine
Machine Learning
Neurological
Psychiatric
Psychiatric Disorders
Schizophrenia
Segmentation
White Matter
1|2Indicates the priority used for review
·Figure 1. Overview workflow of the processing pipeline for dPVS quantification.
·Figure 2. a) Boxplots showing counts and differences in normalized volumes of dPVS in the BG and WM. b) Group-averaged dPVS maps displaying the distribution of dPVS in the BG and WM.
Provide references using author date format
2. Sotgiu MA, Lo Jacono A, Barisano G, Saderi L, Cavassa V, Montella A, Crivelli P, Carta A and Sotgiu S (2023) Brain perivascular spaces and autism: clinical and pathogenic implications from an innovative volumetric MRI study. Front. Neurosci. 17:1205489. doi: 10.3389/fnins.2023.1205489