Normative modeling reveals brain structural differences in post-stroke aphasia
Poster No:
1866
Submission Type:
Abstract Submission
Authors:
Zhongyin Liang1, Xinrui Li1, Tiantian Liu1, Hongyimei Liu1, Zhizhong Jiang1, Huihui Niu1, Wenzhao Deng1, Liting Chen2, Ruiwang Huang1
Institutions:
1School of Psychology, Key Laboratory of Brain, South China Normal University, Guangzhou, Guangdong, 2Department of Radiology, the First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, P.R. China
First Author:
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
Co-Author(s):
Xinrui Li
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
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
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
Liting Chen
Department of Radiology, the First Affiliated Hospital, Jinan University
Guangzhou, Guangdong, P.R. China
Department of Radiology, the First Affiliated Hospital, Jinan University
Guangzhou, Guangdong, P.R. China
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:
Aphasia is a language disorder that is caused by damage to the brain's regions responsible for language, typically resulting in the loss of abilities in speaking, listening, reading, and writing. The types and affected brain regions vary among patients. Normative modeling is a statistical method based on healthy population data to assess individual deviations in brain structure (Marquand et al., 2019). Given the variability of lesion locations in aphasia, personalized analysis is crucial for optimizing treatment strategies. The current study applied normative modeling to calculate deviation Z-scores for cortical thickness and subcortical volume in post-stroke aphasia (PSA) patients. We aimed to identify significantly deviated brain regions and to examine correlations between Z-scores and aphasia severity.
Methods:
Subjects. We recruited 18 PSA patients and 38 healthy adults (18 as the control group and 20 as the adapting set) from the Speech Disorder Diagnosis and Treatment Center, the First Affiliated Hospital of Jinan University (FAHJU). For each patient, the aphasia severity of PSA was assessed using the Chinese version of the Western Aphasia Battery (WAB) (Shewan & Kertesz, 1980), which includes measures of speech fluency, spontaneous speech, auditory comprehension, repetition, and naming, as well as the Aphasia Quotient (AQ). Non-language cognitive abilities (NLCA) (Wu et al., 2017) were also evaluated in 17 of the PSA patients. The study was approved by the Institutional Review Board (IRB) of FAHJU. All subjects provided written informed consent before the study.
Data acquisition. The MRI data were acquired on a GE 3T MR scanner in FAHJU. High resolution brain structural images were acquired using T1-weighted 3D-BRAVO sequence.
Data preprocessing. The data were preprocessed using FreeSurfer. The preprocessing steps mainly included removal of non-brain tissue, segmentation of cortical and subcortical structures, calculation of cortical thickness, and assessment of tissue volumes.
Statistical analysis. Using the adapting set to adjust a pre-existing model (Rutherford et al., 2022a), we applied PCN-toolkit (Rutherford et al., 2022b) for normative modeling in PSA and healthy controls (HC). Z-scores were calculated to assess deviations from the "norm", and independent t-tests was used to identify significant differences in Z-scores between PSA and HC.
Data acquisition. The MRI data were acquired on a GE 3T MR scanner in FAHJU. High resolution brain structural images were acquired using T1-weighted 3D-BRAVO sequence.
Data preprocessing. The data were preprocessed using FreeSurfer. The preprocessing steps mainly included removal of non-brain tissue, segmentation of cortical and subcortical structures, calculation of cortical thickness, and assessment of tissue volumes.
Statistical analysis. Using the adapting set to adjust a pre-existing model (Rutherford et al., 2022a), we applied PCN-toolkit (Rutherford et al., 2022b) for normative modeling in PSA and healthy controls (HC). Z-scores were calculated to assess deviations from the "norm", and independent t-tests was used to identify significant differences in Z-scores between PSA and HC.
Results:
Fig. 1 shows significant negative deviations in 30 brain regions obtained from the normative modeling. We found significant atrophy in the left-amygdala in patients compared to HC group (t = -2.505, p = 0.017). Correlation analysis showed that Z-scores of the left amygdala were significantly positively correlated with AQ (r = 0.524, p = 0.026), Auditory Comprehension (r = 0.541, p = 0.002), and Repetition (r = 0.510, p = 0.031). The left amygdala Z-scores were significantly positively correlated with the total consciousness score (r = 0.535, p = 0.027).
Conclusions:
PSA exhibited significant reductions in cortical thickness and significant volume loss in subcortical regions, primarily in the temporal and frontal lobes, insula and amygdala. In the frontal lobe, the PSA had significant differences in cortical thickness compared with the controls, mainly in the left superior frontal gyrus, left inferior frontal sulcus, frontomarginal gyrus and sulcus. In the temporal lobe, the abnormalities in patients were primarily cortical atrophy in the left middle temporal gyrus and the left inferior temporal sulcus. The frontal lobe is primarily involved in language production, and the temporal lobe is mainly responsible for language comprehension and recognition (Stefaniak et al., 2021). Although the amygdala is not directly involved in language control, its influence on emotion and cognition may indirectly affect language use and comprehension (Ortiz-Mantilla et al., 2010). The findings may provide a reference for the neuroanatomical biomarkers at the individual level for PSA.
Language:
Language Comprehension and Semantics 2
Reading and Writing
Novel Imaging Acquisition Methods:
Anatomical MRI 1
Keywords:
Aphasia
Cortex
STRUCTURAL MRI
Sub-Cortical
1|2Indicates the priority used for review
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Provide references using APA citation style.
Rutherford, S., Fraza, C., Dinga, R., Kia, S. M., Wolfers, T., Zabihi, M., ... & Marquand, A. F. (2022). Charting brain growth and aging at high spatial precision. elife, 11, e72904.
Rutherford, S., Kia, S. M., Wolfers, T., Fraza, C., Zabihi, M., Dinga, R., ... & Marquand, A. F. (2022). The normative modeling framework for computational psychiatry. Nature protocols, 17(7), 1711-1734.
Shewan, C. M., & Kertesz, A. (1980). Reliability and validity characteristics of the Western Aphasia Battery (WAB). Journal of Speech and Hearing Disorders, 45(3), 308-324.
Wu, J. B., Lyu, Z. H., Liu, X. J., Li, H. P., & Wang, Q. (2017). Development and standardization of a new cognitive assessment test battery for Chinese aphasic patients: a preliminary study. Chinese Medical Journal, 130(19), 2283-2290.
Stefaniak, J. D., Alyahya, R. S., & Ralph, M. A. L. (2021). Language networks in aphasia and health: A 1000 participant activation likelihood estimation meta-analysis. Neuroimage, 233, 117960.
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