Psychiatry Investig Search


Psychiatry Investig > Volume 20(4); 2023 > Article
Lu, Chen, Wang, Huang, Jiang, Nguchu, Chen, Qiu, and Wang: Cerebellar Structural Abnormality in Autism Spectrum Disorder: A Magnetic Resonance Imaging Study



This study uses structural magnetic resonance imaging to explore changes in the cerebellar lobules in patients with autism spectrum disorder (ASD) and further analyze the correlation between cerebellar structural changes and clinical symptoms of ASD.


A total of 75 patients with ASD and 97 typically developing (TD) subjects from Autism Brain Imaging Data Exchange dataset were recruited. We adopted an advanced automatic cerebellar lobule segmentation technique called CEREbellum Segmentation to segment each cerebellar hemisphere into 12 lobules. Normalized cortical thickness of each lobule was recorded, and group differences in the cortical measures were evaluated. Correlation analysis was also performed between the normalized cortical thickness and the score of Autism Diagnostic Interview-Revised.


Results from analysis of variance showed that the normalized cortical thickness of the ASD group differed significantly from that of the TD group; specifically, the ASD group had lower normalized cortical thickness than the TD group. Post-hoc analysis revealed that the differences were more predominant in the left lobule VI, left lobule Crus I and left lobule X, and in the right lobule VI and right lobule Crus I. Lowered normalized cortical thickness in the left lobule Crus I in the ASD patients correlated positively with the abnormality of development evident at or before 36 months subscore.


These results suggest abnormal development of cerebellar lobule structures in ASD patients, and such abnormality might significantly influence the pathogenesis of ASD. These findings provide new insights into the neural mechanisms of ASD, which may be clinically relevant to ASD diagnosis.


Autism spectrum disorder (ASD), is a complex neurodevelopmental disorder characterized by difficulties in social interaction, repetitive, stereotyped behaviors, and a certain degree of cognitive deficits. Recent statistics have shown a growing prevalence of ASD in 0.7% of children aged 6-12 years [1]. The pathogenesis of ASD remains unclear, with some reports proposing a relationship with genetics [2], environment, neurotransmitters, and neural pathways. Due to this, ASD is associated with different levels of severity and performance in cognition, behavior, and language, which makes the diagnosis and treatment of ASD rather difficult.
In recent years, magnetic resonance imaging (MRI) is a good non-invasive imaging method and is widely used in research. Analyzing structural, functional, and metabolic MRI information would help identify objective, biological markers of ASD. And the correlation analyses would provide bases for neuropathology and render therapeutic directions for clinical treatment. Previous studies of ASD have reported alterations in focal brain regions, especially the frontal and temporal lobes, hippocampus, amygdala, and cerebellum, but the results were inconsistent [3,4], which may also be due to the heterogeneity of ASD.
Over the last 30 years, the study of functions of the cerebellum has been challenging. Available literature proposes that higher-order functions such as emotion regulation, attention control, cognition, and memory requires involvement of the cerebellum. Because of its involvement in a wide range of functions, the impaired cerebellum is associated with several psychiatric and neurological disorders [5-8]. Earlier studies have already found structural and functional anomalies in patients with ASD, which involved the cerebellum areas [9-11]. Even though the cerebellar structure is small, it is still rich in neurons, and so it has been shown to have functional topography across its subregions. These subregions have a crucial role to play in higher-order cognitive functions [12]. Even so, ASD studies on cerebellar subregions are few, which limits our understanding of neural bases of ASD.
Therefore, we aimed to explore the morphological changes related to ASD in the cerebellar lobules by including additional cerebellar lobules which were not involved in the previous studies. We surmise that additional lobules would help uncover structural changes associated with ASD, thus providing additional neural mechanisms to support the diagnosis of clinical ASD.



The experimental data used in this paper were from the Autism Brain Imaging Data Exchange (ABIDE), a large, international, publicly available dataset ( The subject inclusion criteria were: 1) the amount of data at the subject’s site is greater than 30; 2) Wechsler Abbreviated Scale of Intelligence assessment of intelligence with a full scale intelligence quotient (FIQ) score >75 (if FIQ was missing, the validated Performance IQ and Verbal IQ scores were used to estimate); 3) Subject age should be 6-18 years; and 4) no chronic systemic diseases, nor use of antipsychotic medications.
A total of 172 subjects from three independent sample sets (NYU Langone Medical Center, Stanford University, and University of Leuven) from the ABIDE I dataset were included in the study. A total of 75 individuals had ASD (64 males and 11 females, aged 7-18 years) and 97 individuals were typically development (TD, 72 males and 25 females, aged 6-18 years). To allow a reliable comparison and joint analysis in multi-site studies, we applied a robust data harmonization method, Com-Bat, which adopts statistical or mathematical concepts to reduce unwanted site variability while maintaining the biological content, such as cortical thickness and age information [13]. Table 1 shows the demographic information of the participants, which includes age, gender, FIQ, and the scores of four items in the Autism Diagnostic Interview-Revised (ADI-R). The ADI-R score statistics in Table 1 are for the 54 individuals in the ASD group for whom scores were recorded. Statistical data of age, gender, or IQ did not differ significantly between ASD and TD individuals.

Clinical diagnosis

The inclusion criteria that ASD subjects met include the requirements for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision. The diagnosis involved one or more scores from either ADI-R, the Autism Diagnostic Observation Schedule, or the Social Responsiveness Scale. Of the 75 patients, 54 had their ADI-R score recorded and the remaining 21 had to be excluded in a follow-up behavioral correlation analysis.
The ADI-R, known as the “ground truth,” is the widely used diagnostic scale for autism. The ADI-R assesses four specific domains: three functional domains, which include language or communication, reciprocal social interactions, and repetitive, restricted and stereotyped behavior and interest; and one diagnostic domain, which measures the presence of abnormality of development evident at or before 36 months.

Image processing

Both MRI structural and resting-state functional images were acquired for each subject. Considering the purpose of this study, we selected data with T1-weighted images in three orientations: cross-sectional, coronal, and sagittal, while the quality of the selected images was examined jointly by two imaging physicians with intermediate or higher titles. The highresolution structural images were acquired with a standard fast spoiled gradient-echo T1-weighted sequence (TR: 11.08 ms; TE: 4.3 ms; flip angle: 45°; FOV: 256 mm; 256×256 matrix; 180 slices; 1 mm3 resolution). Data processing was performed using Volbrain, an online MR brain volume measurement system ( that uses CERES (CEREbellum Segmentation) technology to segment and estimate structural measurements. CERES is a patch-based multiatlas segmentation tool that automatically segments the cerebellar lobes [14]. Each cerebellar hemisphere was segmented automatically into 12 lobules (I-II, III, IV, V, VI, Crus I, Crus II, VIIB, VIIIA, VIIIB, IX, and X). Measurements of total volume, grey matter volume, cortical thickness, and normalized cortical thickness were recorded. Figure 1 shows the cerebellar segmentation maps in Montreal Neurological Institute space as reported by Volbrain.

Statistical analyses

Statistical analyses were performed on IBM SPSS 25 statistical software (IBM Corp., Armonk, NY, USA), and data normality was tested using the Shapiro-Wilk test. Analysis of variance (ANOVA) tests were used to analyze the differences across the groups, and independent samples t-test was used to assess the differences between groups in the 24 lobule structures. A chi-square test was applied to test the count data. The relationships between clinical symptoms and cerebellar lobule structures were evaluated using Pearson correlation analysis. All tests were statistically significant at p<0.05.


Comparison of normalized cortical thickness measurements of the cerebellar lobules in the ASD and TD groups

In ANOVA tests, we found significant differences in normalized cortical thickness between the ASD and TD groups (F=5.436, p=0.021). Of 24 lobules (12 from each cerebellar hemisphere), five lobules (right lobule VI, left lobule VI, right lobule Crus I, left lobule Crus I, and left lobule X) differed significantly between ASD and TD in measurements of normalized cortical thickness as tested by independent sample t-test. Specifically, the normalized cortical thickness appeared to be smaller in the ASD group than the TD group in these lobules, with p-values of 0.006, 0.017, 0.016, 0.008, and 0.01, respectively. While no other cerebellar lobules presented significant differences in normalized cortical thickness. These results were summarized in Table 2 and Figure 2.

Correlation between the normalized cortical thickness of cerebellar lobules and clinical symptoms in autistic patients

The correlation analyses between cerebellar lobule structures and clinical symptoms were performed with data from only 54 out of 75 patients since only these patients had ADI-R scores recorded. In combination with the results of Section 3.1, Pearson correlation analysis was used to correlate the normalized cortical thickness of the right lobule VI, left lobule VI, right lobule Crus I, left lobule Crus I, and left lobule X of the cerebellum with the scores of four items in the ADI-R (reciprocal social interaction subscore; abnormalities in communication subscore; rsetricted, repetitive, and stereotyped patterns of behavior subscore; and abnormalities of developmental evident at or before 36 months subscore). There was a positive correlation between the normalized cortical thickness of the left lobule Crus I and abnormalities of developmental evident at or before 36 months subscore (r=0.3, p=0.028), as shown in Figure 3. Nonetheless, the remaining behavioral scores did not show a significant correlation with normalized cortical thickness.


The cerebellum, located in the posterior cranial fossa, on the back of the pons and medulla oblongata, accounts for about 10% of the brain’s total volume but contains more neurons than any other part of the brain [15]. MRI, as a multiparametric, multimodal and non-invasive examination technique, is more frequently used in the study of cerebellar structure and function [16-19]. Buckner et al. [20] mapped cerebellar function by analyzing magnetic resonance image data from 1,000 subjects, refined to the cerebellar lobules. That is, each lateral cerebellar hemisphere is divided into three parts: the anterior cerebellar lobe (including lobules I-V), the posterior cerebellar lobe (including lobules VI, Crus I, Crus II, VIIB, VIIIA, VIIIB, and IX), and the choroidal lobule (mainly lobule X).
Based on extensive fiber connections between brain regions, the anterior cerebellar lobe and the choroidal lobule are involved in motor control and regulation of somatic balance, the posterior cerebellar lobe may be involved in memory, attention, emotional control, and even involved in social processes [21]. Thus, cerebellar damage can result in dysfunctions including language, spatial and executive functions as well as emotional dysregulation.
Several studies have reported that the cerebellum is closely related to psychiatric disorders, especially autism [22,23], attention deficit hyperactivity disorder [24], obsessive-compulsive disorder [25], and schizophrenia [26]. However, previous studies have mostly used the cerebellum or lobes as the object, suggesting that there is an increase or decrease in cerebellar or lobar volume in the patient group compared to the normal control group [27,28]. This is because early depictions of the cerebellar contours were manually segmented by experts, which is undoubtedly a tedious, time-consuming process, and large-scale, fine-grained segmentation is impractical. Subsequently, a spatially unbiased atlas template of the cerebellum and brainstem (SUIT) is an automated algorithm based on spatially unbiased mapping templates of the cerebellum and brainstem developed specifically for cerebellar segmentation that has been widely used [29,30]. In SUIT, cerebellar lobules I, II, III, and IV cannot be separated and are collectively referred to as lobules I-IV, making it impossible to study these lobules separately [31,32].
Although smaller in size, lobules I-III are still relevant for separate analysis [33]. In 2017, a patch-based multi-atlas segmentation tool, CERES, was developed to differentiate and measure the 24 lobules of the cerebellum automatically and rapidly by simply providing standard resolution MRI T1-weighted images. Its advanced results have also been shown to be superior to at least five widely used automated cerebellar segmentation methods14 and have been applied in studies of disease cerebellar alterations with good evaluation [7,33]. This is the key reason for choosing this algorithm in this study.
Considering that changes in brain structure inevitably lead to abnormalities in certain brain functions and the greater accessibility of structural MRI data, demonstrating the existence of variability between patients with ASD and healthy populations with the help of morphological alterations in the cerebellum has been the main method of research. Previous studies have found lobular hypoplasia in the cerebellum of patients with ASD. This is mainly manifested by a reduction in the volume of lobules VI and VII [34] and a reduction in the gray matter of lobules Crus I, Crus II, VIII, and IX [17,35,36].
Cortical thickness is a morphological indicators that is related to neurobiological processes [8,37,38]. The normalized cortical thickness is the result of its normalization with the cube root of the intracranial volume [14]. The present study found a significant difference between the ASD and TD groups in the overall mean values of normalized cortical thickness of the 24 cerebellar lobules, suggesting the presence of altered cerebellar structure in patients with ASD compared to typically developing individuals. Then specific to each lobule, it was found that the normalized cortical thickness of the right lobule VI, left lobule VI, right lobule Crus I, left lobule Crus I, and left lobule X of the cerebellum in the ASD group was smaller than that in the TD group, leading to the conclusion that there was cortical thinning in localized areas of the cerebellum in ASD patients. This is consistent with the findings of Hadjikhani et al. [39] who found a thinning of the cerebral cortex belonging to the region of the mirror neuron system. Wang et al. [40] compared the developmental changes in the thickness of the cerebellar lobules in 19 patients with ASD and 14 TD and found thinning of the right Crus II cortical thickness in patients with ASD. These findings are entirely inconsistent with the present study; this may be due to the heterogeneity in ASD or sample population.
There is still an ongoing debate about whether ASD is associated with thinning or thickening of cerebral cortical thickness [40-42]. In a study of cortical thickness in 266 patients with ASD between the ages of 6 and 35 years, it was found that there was a general increase in cortical thickness (mainly on the left side) from the age of 6 years in patients with ASD, but this difference became smaller in adulthood [41,43]. This led to the conjecture of delayed cortical maturation in ASD patients and validated the dynamic nature of morphological changes in the ASD brain. This also explains the complex course of ASD and the high individual heterogeneity of the disease.
Structural differences within the cerebellum are also associated with the core features of ASD deficits, and the reduction in Crus II lobule thickness was accompanied by asymmetry, both of which were associated with the severity of stereotypic behavioral symptoms [39]. This study also found a positive correlation between the normalized cortical thickness of the left lobule Crus I and abnormalities of developmental evident at or before 36 months subscore in patients with ASD, suggesting a role of the cerebellum in the development of ASD in preschool-aged children. This may suggest that a variety of interventions for a child’s preschool years can help him learn critical social, communication, functional, and behavioral skills. The behavioral correlates of abnormalities in normalized cortical thickness in specific lobules of the cerebellum may provide additional ideas for the development of biomarkers and psychiatric therapies that provide potential precise targets for clinical treatment, diagnosis, prediction, and prognostic development of ASD. However, no correlation was found between the thinned cortical thickness and clinical symptoms such as social skills, communication abnormalities, and repetitive specific behaviors in ASD patients. Considering the differences in sample sources, data collection and analysis methods among the groups made the findings not completely consistent.
In summary, the cerebellum has become one of the key brain regions affected by ASD and is gradually becoming a hot spot for research. Using an advanced, automated cerebellar lobule segmentation technique to study morphological differences in the cerebellar lobules, we found that patients with ASD have abnormal structural development of the cerebellar lobules, and correlates positively with clinical symptoms. This further reveals the role of the cerebellum in the pathogenesis of autism and provides additional neural mechanism support for the diagnosis of clinical autism.


Availability of Data and Material

Data is available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Data curation: Qifang Lu, Yanming Wang, Li Huang, Xiaoxiao Wang. Formal analysis: Qifang Lu, Jin Chen, Zhoufan Jiang. Project administration: Qifang Lu, Xiaoxiao Wang. Visualization: Qifang Lu, Jin Chen, Zhoufan Jiang. Writing—original draft: Qifang Lu, Xiaoxiao Wang. Writing—review & editing: Jin Chen, Benedictor Alexander Nguchu, Shishuo Chen, Bensheng Qiu, Xiaoxiao Wang.

Funding Statement

This work was supported by the National Natural Science Foundation of China (grant nos. 81701665, 21876041), the Fundamental Research Funds for the Central Universities (WK5290000002), the University Synergy Innovation Program of Anhui Province (GXXT-2021-003), the scientific research project of Health Commission of Anhui Province (AHWJ2021a034), the provincial education research project of Department of Education Anhui Province (2020jyxm0945), the innovation team project of Anhui Medical College (YZ2020TD005).

Figure 1.
CERES cerebellar marker segmentation map. Resulting images are located in MNI space from CERES volumetry reports. The 12 lobules (I-II, III, IV, V, VI, Crus I, Crus II, VIIB, VIIIA, VIIIB, IX, and X) of the cerebellar hemisphere are labeled on the lobules segmentation maps. CERES, CEREbellum Segmentation; MNI, Montreal Neurological Institute.
Figure 2.
Comparative results of normalized cortical thickness of cerebellar lobules in ASD and TD groups. Among the 24 lobules of the cerebellum, the normalized cortical thickness of right lobule VI (p=0.006), left lobule VI (p=0.017), right lobule Crus I (p=0.016), left lobule Crus I (p=0.008), and left lobule X (p=0.01) in ASD were significantly reduced compared with the TD group. Error bars indicate interquartile range. *indicate an exception value. ASD, autism spectrum disorder; TD, typically developing.
Figure 3.
Scatter plot of normalized cortical thickness of the left lobule Crus I and ADI-R score in autism patients. This findings illustrated, in ASD a positive correlation between the normalized cortical thickness of the left lobule Crus I and abnormalities of developmental evident at or before 36 months subscore (r=0.3, p=0.028). ADI-R, Autism Diagnostic Interview-Revised; ASD, autism spectrum disorder.
Table 1.
Sample data statistical information
ASD (N=75) TD (N=97) p
Age (yr) 11.86±2.78 12.53±2.99 0.129
Gender (male:female) 1900-01-02 16:11 1900-01-03 0:25 0.076
FIQ* 107.02±17.13 111.94±13.76 0.065
ADI-R_ Social total 19.94±5.72 N/A N/A
ADI-R_Verbal total 16.2±4.60 N/A N/A
ADI-R_RRB total 5.96±2.75 N/A N/A
ADI-R_Onset total 3.35±1.47 N/A N/A

Values are presented as mean±standard deviation. The ADI-R score statistics are for the 54 individuals in the ASD group for whom scores were recorded. ADI-R_ Social total: reciprocal social interaction subscore total for ADI-R; ADI-R_Verbal total: abnormalities in communication subscore total for ADI-R; ADI-R_RRB total: restricted, repetitive, and stereotyped patterns of behavior subscore total for ADI-R; ADI-R_Onset total: abnormality of development evident at or before 36 months subscore total for ADIR.

* FIQ is only a group comparison of the other two sample sets because there is no FIQ value for the sample from the University of Leuven sample;

independent sample t-test;

chi-square test.

ASD, autism spectrum disorder; TD, typically development; FIQ, full scale intelligence quotient; ADI-R, Autism Diagnostic Interview-Revised; N/A, not applicable

Table 2.
Comparison of normalized cortical thickness measurements of the cerebellar lobules in the ASD and TD groups
Normalized ortical thickness Group Mean SD SEM p
VI right ASD 4.145 0.228 0.026 0.006*
TD 4.234 0.194 0.020
VI left ASD 4.257 0.208 0.024 0.017*
TD 4.328 0.178 0.018
Crus I right ASD 4.029 0.226 0.026 0.016*
TD 4.106 0.192 0.020
Crus I left ASD 4.018 0.227 0.026 0.008*
TD 4.111 0.222 0.023
X left ASD 2.063 0.338 0.039 0.01*
TD 2.201 0.349 0.035

* p<0.05.

ASD, autism spectrum disorder; TD, typically developing; SD, standard deviation; SEM, standard error mean


1. Zhou H, Xu X, Yan W, Zou X, Wu L, Luo X, et al. Prevalence of autism spectrum disorder in China: a nationwide multi-center populationbased study among children aged 6 to 12 years. Neuroscience Bulletin 2020;36:961-971.
crossref pmid pmc pdf
2. Yenkoyan K, Grigoryan A, Fereshetyan K, Yepremyan D. Advances in understanding the pathophysiology of autism spectrum disorders. Behav Brain Res 2017;331:92-101.
crossref pmid
3. Postema MC, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, et al. Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets. Nat Commun 2019;10:4958
crossref pmid pmc pdf
4. Pappaianni E, Siugzdaite R, Vettori S, Venuti P, Job R, Grecucci A. Three shades of grey: detecting brain abnormalities in children with autism using source-, voxel- and surface-based morphometry. Eur J Neurosci 2018;47:690-700.
crossref pmid pdf
5. Lupo M, Olivito G, Siciliano L, Masciullo M, Bozzali M, Molinari M, et al. Development of a psychiatric disorder linked to cerebellar lesions. Cerebellum 2018;17:438-446.
crossref pmid pdf
6. Stoodley CJ. The cerebellum and neurodevelopmental disorders. Cerebellum 2016;15:34-37.
crossref pmid pmc pdf
7. de Borba FC, Querin G, França MC Jr, Pradat PF. Cerebellar degeneration in adult spinal muscular atrophy patients. J Neurol 2020;267:2625-2631.
crossref pmid pdf
8. Tikoo S, Suppa A, Tommasin S, Gianni C, Conte G, Mirabella G, et al. The cerebellum in drug-naive children with Tourette syndrome and obsessive-compulsive disorder. Cerebellum 2022;21:867-878.
crossref pmid pdf
9. Peter S, De Zeeuw CI, Boeckers TM, Schmeisser MJ. Cerebellar and striatal pathologies in mouse models of autism spectrum disorder. Adv Anat Embryol Cell Biol 2017;224:103-119.
crossref pmid
10. Kelly E, Escamilla CO, Tsai PT. Cerebellar dysfunction in autism spectrum disorders: deriving mechanistic insights from an internal model framework. Neuroscience 2021;462:274-287.
crossref pmid
11. Bruchhage MMK, Bucci MP, Becker EBE. Cerebellar involvement in autism and ADHD. Handb Clin Neurol 2018;155:61-72.
crossref pmid
12. Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, et al. Distinct cerebellar contributions to intrinsic connectivity networks. J Neurosci 2009;29:8586-8594.
crossref pmid pmc
13. Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 2018;167:104-120.
crossref pmid
14. Romero JE, Coupé P, Giraud R, Ta VT, Fonov V, Park MTM, et al. CERES: a new cerebellum lobule segmentation method. Neuroimage 2017;147:916-924.
crossref pmid
15. Herculano-Houzel S. Coordinated scaling of cortical and cerebellar numbers of neurons. Front Neuroanat 2010;4:12
crossref pmid pmc
16. Li Y, Yang L, Li L, Xie Y, Fang P. The resting-state cerebro-cerebellar function connectivity and associations with verbal working memory performance. Behav Brain Res 2022;417:113586
crossref pmid
17. Clausi S, Olivito G, Siciliano L, Lupo M, Laghi F, Baiocco R, et al. The cerebellum is linked to theory of mind alterations in autism. A direct clinical and MRI comparison between individuals with autism and cerebellar neurodegenerative pathologies. Autism Res 2021;14:2300-2313.
pmid pmc
18. Alves CAPF, Fragoso DC, Gonçalves FG, Marussi VH, Amaral LLFD. Cerebellar ataxia in children: a clinical and MRI approach to the differential diagnosis. Top Magn Reson Imaging 2018;27:275-302.
19. Cocozza S, Costabile T, Pontillo G, Lieto M, Russo C, Radice L, et al. Cerebellum and cognition in Friedreich ataxia: a voxel-based morphometry and volumetric MRI study. J Neurol 2020;267:350-358.
crossref pmid pdf
20. Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol 2011;106:2322-2345.
crossref pmid pmc
21. Igelström KM, Webb TW, Graziano MSA. Functional connectivity between the temporoparietal cortex and cerebellum in autism spectrum disorder. Cereb Cortex 2017;27:2617-2627.
22. Cardon GJ, Hepburn S, Rojas DC. Structural covariance of sensory networks, the cerebellum, and amygdala in autism spectrum disorder. Front Neurol 2017;8:615
crossref pmid pmc
23. D’Mello AM, Moore DM, Crocetti D, Mostofsky SH, Stoodley CJ. Cerebellar gray matter differentiates children with early language delay in autism. Autism Res 2016;9:1191-1204.
crossref pmid pdf
24. Goetz M, Schwabova JP, Hlavka Z, Ptacek R, Surman CB. Dynamic balance in children with attention-deficit hyperactivity disorder and its relationship with cognitive functions and cerebellum. Neuropsychiatr Dis Treat 2017;13:873-880.
crossref pmid pmc pdf
25. Narayanaswamy JC, Jose D, Kalmady SV, Agarwal SM, Venkatasubramanian G, Janardhan Reddy YC. Cerebellar volume deficits in medication-naïve obsessive compulsive disorder. Psychiatry Res Neuroimaging 2016;254:164-168.
crossref pmid
26. Hirjak D, Wolf RC, Kubera KM, Stieltjes B, Maier-Hein KH, Thomann PA. Neurological soft signs in recent-onset schizophrenia: focus on the cerebellum. Prog Neuropsychopharmacol Biol Psychiatry 2015;60:18-25.
crossref pmid
27. Laidi C, Boisgontier J, Chakravarty MM, Hotier S, d’Albis MA, Mangin JF, et al. Cerebellar anatomical alterations and attention to eyes in autism. Sci Rep 2017;7:12008
crossref pmid pmc pdf
28. Traut N, Beggiato A, Bourgeron T, Delorme R, Rondi-Reig L, Paradis AL, et al. Cerebellar volume in autism: literature meta-analysis and analysis of the Autism Brain Imaging Data Exchange cohort. Biol Psychiatry 2018;83:579-588.
crossref pmid
29. Diedrichsen J. A spatially unbiased atlas template of the human cerebellum. Neuroimage 2006;33:127-138.
crossref pmid
30. Lindig T, Bender B, Kumar VJ, Hauser TK, Grodd W, Brendel B, et al. Pattern of cerebellar atrophy in Friedreich’s ataxia-using the SUIT template. Cerebellum 2019;18:435-447.
crossref pmid pdf
31. Bernard JA, Peltier SJ, Wiggins JL, Jaeggi SM, Buschkuehl M, Fling BW, et al. Disrupted cortico-cerebellar connectivity in older adults. Neuroimage 2013;83:103-119.
crossref pmid
32. Wang H, Li R, Zhou Y, Wang Y, Cui J, Nguchu BA, et al. Altered cerebro-cerebellum resting-state functional connectivity in HIV-infected male patients. J Neurovirol 2018;24:587-596.
crossref pmid pdf
33. Uwisengeyimana JD, Nguchu BA, Wang Y, Zhang D, Liu Y, Qiu B, et al. Cognitive function and cerebellar morphometric changes relate to abnormal intra-cerebellar and cerebro-cerebellum functional connectivity in old adults. Exp Gerontol 2020;140:111060
crossref pmid
34. Crucitti J, Hyde C, Enticott PG, Stokes MA. Are vermal lobules VI-VII smaller in autism spectrum disorder? Cerebellum 2020;19:617-628.
crossref pmid pdf
35. Becker EB, Stoodley CJ. Autism spectrum disorder and the cerebellum. Int Rev Neurobiol 2013;113:1-34.
crossref pmid
36. D’Mello AM, Crocetti D, Mostofsky SH, Stoodley CJ. Cerebellar gray matter and lobular volumes correlate with core autism symptoms. Neuroimage Clin 2015;7:631-639.
crossref pmid pmc
37. Huttenlocher PR. Morphometric study of human cerebral cortex development. Neuropsychologia 1990;28:517-527.
crossref pmid
38. Lenroot RK, Schmitt JE, Ordaz SJ, Wallace GL, Neale MC, Lerch JP, et al. Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Hum Brain Mapp 2009;30:163-174.
crossref pmid
39. Hadjikhani N, Joseph RM, Snyder J, Tager-Flusberg H. Anatomical differences in the mirror neuron system and social cognition network in autism. Cereb Cortex 2006;16:1276-1282.
crossref pmid
40. Wang Y, Xu Q, Zuo C, Zhao L, Hao L. Longitudinal changes of cerebellar thickness in autism spectrum disorder. Neurosci Lett 2020;728:134949
crossref pmid
41. Wallace GL, Eisenberg IW, Robustelli B, Dankner N, Kenworthy L, Giedd JN, et al. Longitudinal cortical development during adolescence and young adulthood in autism spectrum disorder: increased cortical thinning but comparable surface area changes. J Am Acad Child Adolesc Psychiatry 2015;54:464-469.
crossref pmid pmc
42. Khundrakpam BS, Lewis JD, Kostopoulos P, Carbonell F, Evans AC. Cortical thickness abnormalities in autism spectrum disorders through late childhood, adolescence, and adulthood: a large-scale MRI study. Cereb Cortex 2017;27:1721-1731.
crossref pmid
43. Prigge MBD, Bigler ED, Travers BG, Froehlich A, Abildskov T, Anderson JS, et al. Social Responsiveness Scale (SRS) in relation to longitudinal cortical thickness changes in autism spectrum disorder. J Autism Dev Disord 2018;48:3319-3329.
crossref pmid pmc pdf


Browse all articles >

Editorial Office
#522, 27, Seochojungang-ro 24-gil, Seocho-gu, Seoul 06601, Korea
Tel: +82-2-717-0892    E-mail:                

Copyright © 2024 by Korean Neuropsychiatric Association.

Developed in M2PI

Close layer
prev next