Poster No:
294
Submission Type:
Abstract Submission
Authors:
Wei-ting Ko1, Chih-Min Liu2, Susan Shur-Fen Gau3
Institutions:
1Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan, 2Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan, 3National Taiwan University Hospital, Taipei, Taiwan
First Author:
Wei-ting Ko
Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University
Taipei, Taiwan
Co-Author(s):
Chih-Min Liu
Department of Psychiatry, National Taiwan University Hospital
Taipei, Taiwan
Introduction:
The associations between regional surface areas(rSA) and healthy populations' intelligence varied during development. Children with larger rSA performed better intelligence, but the rSA decreased rapidly after adolescence(Schnack, 2015). Thus, the positive correlation between intelligence and rSA was weaker after youth in the healthy population. Autism (ASD) and schizophrenia (SCZ) were neurodevelopmental disorders related to significant brain alterations (Langenbach,2022; Ohta, 2016; Yang, 2016). A lower intelligence was a common feature in patients with ASD and SCZ, and the shared genetic variants in these two disorders were highly associated with general intelligence (Moreau, 2021; Zhao, 2018). Previous evidence indicated SCZ has smaller rSA than healthy controls (van Erp, 2018), and ASD has reversed rSA alterations (Mensen, 2016). We hypothesized that the rSA alterations between ASD and SSD, which interacted with age, are associated with general intelligence.
Methods:
This study recruited 36 SCZ, 39 ASD, and 76 healthy controls (HC). The distribution of age and sex were not significantly statistically different. The mean age (standard deviation) of SCZ, ASD, and HC were 22.77(1.77), 21.98(2.68), and 21.98(2.68). We measured the intelligence using the subtests on the Wechsler Adult Intelligence Scale (WAIS-III). These subtests were block design, information, arithmetic, and digit span-forward and -backward. This study acquired high-resolution T1 weighted images on a 3 Tesla Magnetic Resonance Imaging (MRI; Trio, Siemens, Erlangen, Germany) system with a 32-channel phased-array head coil. A three-dimensional magnetization-prepared rapid gradient-echo (3D MPRAGE) sequence with the following parameters: repetition time (TR) = 2,000 ms, echo time (TE) = 3ms, flip angle = 9°, acquisition matrix size = 256×192×208, the field of view (FOV) = 256×192×208 mm³ and spatial resolution = 1×1×1 mm³. Freesurfer (https://surfer.nmr.mgh.harvard.edu/) calculates the rSAs. SAS 9.4 proceeded with all statistical methods in this study. The homogeneity of scores of intelligent subtests was examined by analysis of covariance, and the generalized linear model examined the interaction effect of age and group. Canonical correlation analysis (CCA) estimated the correlation between rSA and general intelligence.
Results:
The group and age interaction statistically significantly influenced 16 rSA. These ROIs were the left anterior cingulate gyrus and sulcus (G_and_S_cingul_Ant), the bilateral anterior segment of the circular sulcus of the insula (S_Cir_ins), the right inferior segment of the S_Cir_ins, the left superior segment of S_Cir_ins, the right long insular gyrus and central sulcus of the insula, the right central sulcus (S_Centeral), the anterior segment of the lateral sulcus, the left inferior temporal sulcus (S_temporal_inf), the left temporal plane of the superior temporal gyrus, the left parahippocampal part of the medial occipito-temporal gyrus (G_oc_temp_med_Parahip), the left interparietal and transverse parietal sulci, the left medial occipito-temporal sulcus and lingual sulcus, the bilateral pericallosal sulcus (S_pericallosal), the middle frontal sulcus, the right postcentral and subparietal sulci, and the left horizontal ramus of the anterior segment of the lateral sulcus. We calculated the canonical correlation coefficient between rSA and the scores of subtests, which were similar between ASD and SCZ. ASD and SCZ have no significant difference in the information and the digit span-backward. The canonical correlation coefficient between 16 rSA and information in ASD and SCZ were -0.57 and -0.35, and the canonical correlation coefficient between 16 rSA and the digit span-backward in ASD and SCZ were 0.22 and -0.85.
Conclusions:
The results of this study support our hypothesis, and it suggested that neurodevelopmental disorders such as ASD and SCZ performed rSA alterations, which have moderate to high negative associations with general intelligence.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Higher Cognitive Functions:
Higher Cognitive Functions Other
Keywords:
ADULTS
Autism
Cognition
MRI
Schizophrenia
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Were any animal research approved by the relevant IACUC or other animal research panel?
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Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Free Surfer
Provide references using APA citation style.
1. Langenbach, B. P. (2022). Cortical changes in patients with schizophrenia across two ethnic backgrounds. Scientific reports, 12(1), 10810. https://doi.org/10.1038/s41598-022-14914-3
2. Mensen, V. T. (2016). Development of cortical thickness and surface area in autism spectrum disorder. NeuroImage. Clinical, 13, 215–222. https://doi.org/10.1016/j.nicl.2016.12.003
3. Moreau, C. A. (2021). Dissecting autism and schizophrenia through neuroimaging genomics. Brain : a journal of neurology, 144(7), 1943–1957. https://doi.org/10.1093/brain/awab096
4. Ohta, H. (2016). Increased Surface Area, but not Cortical Thickness, in a Subset of Young Boys With Autism Spectrum Disorder. Autism research : official journal of the International Society for Autism Research, 9(2), 232–248. https://doi.org/10.1002/aur.1520
5. Schnack, H. G. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral cortex (New York, N.Y. : 1991), 25(6), 1608–1617. https://doi.org/10.1093/cercor/bht357
6. van Erp, T. G. M. (2018). Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biological psychiatry, 84(9), 644–654. https://doi.org/10.1016/j.biopsych.2018.04.023
7. Yang, D. Y. (2016). Cortical morphological markers in children with autism: a structural magnetic resonance imaging study of thickness, area, volume, and gyrification. Molecular autism, 7, 11. https://doi.org/10.1186/s13229-016-0076-x
8. Zhao, Y. (2018). Variance of IQ is partially dependent on deletion type among 1,427 22q11.2 deletion syndrome subjects. American journal of medical genetics. Part A, 176(10), 2172–2181. https://doi.org/10.1002/ajmg.a.40359
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