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Molecular Autism
Published in: Molecular Autism 1/2022

Open Access 01-12-2022 | Magnetic Resonance Imaging | Research

Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder

Authors: Xiaonan Guo, Guangjin Zhai, Junfeng Liu, Yabo Cao, Xia Zhang, Dong Cui, Le Gao

Published in: Molecular Autism | Issue 1/2022

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Abstract

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks.

Methods

Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model.

Results

Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively.

Limitations

Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity.

Conclusions

These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.
Appendix
Available only for authorised users
Literature
1.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). Arlington: American Psychiatric Publishing. 2013.
2.
Huerta M, Lord C. Diagnostic evaluation of autism spectrum disorders. Pediatr Clin. 2012;59(1):103–11.
3.
Lombardo MV, Lai MC, Baron-Cohen S. Big data approaches to decomposing heterogeneity across the autism spectrum. Mol Psychiatry. 2019;24(10):1435–50.CrossRef
4.
Lombardo MV, Pramparo T, Gazestani V, Warrier V, Bethlehem RAI, Carter Barnes C, et al. Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes. Nat Neurosci. 2018;21(12):1680–8.CrossRef
5.
Wing L, Potter D. The epidemiology of autistic spectrum disorders: is the prevalence rising? Ment Retard Dev Disabil Res Rev. 2002;8(3):151–61.CrossRef
6.
Rane P, Cochran D, Hodge SM, Haselgrove C, Kennedy DN, Frazier JA. Connectivity in autism: a review of MRI connectivity studies. Harv Rev Psychiatry. 2015;23(4):223–44.CrossRef
7.
Muller RA, Shih P, Keehn B, Deyoe JR, Leyden KM, Shukla DK. Underconnected, but how? a survey of functional connectivity MRI studies in autism spectrum disorders. Cereb Cortex. 2011;21(10):2233–43.CrossRef
8.
Guo X, Duan X, Long Z, Chen H, Wang Y, Zheng J, et al. Decreased amygdala functional connectivity in adolescents with autism: a resting-state fMRI study. Psychiatry Res Neuroimaging. 2016;257:47–56.CrossRef
9.
McKinnon CJ, Eggebrecht AT, Todorov A, Wolff JJ, Elison JT, Adams CM, et al. Restricted and repetitive behavior and brain functional connectivity in infants at risk for developing autism spectrum disorder. Biol Psychiatry-Cognit Neurosci Neuroimaging. 2019;4(1):50–61.
10.
Lynch CJ, Uddin LQ, Supekar K, Khouzam A, Phillips J, Menon V. Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits. Biol Psychiatry. 2013;74(3):212–9.CrossRef
11.
Just MA, Cherkassky VL, Keller TA, Minshew NJ. Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain. 2004;127(8):1811–21.CrossRef
12.
Belmonte MK, Allen G, Beckel-Mitchener A, Boulanger LM, Carper RA, Webb SJ. Autism and abnormal development of brain connectivity. J Neurosci. 2004;24(42):9228–31.CrossRef
13.
Supekar K, Uddin Lucina Q, Khouzam A, Phillips J, Gaillard William D, Kenworthy Lauren E, et al. Brain hyperconnectivity in children with autism and its links to social deficits. Cell Rep. 2013;5(3):738–47.CrossRef
14.
Padmanabhan A, Lynn A, Foran W, Luna B, O’Hearn K. Age related changes in striatal resting state functional connectivity in autism. Front Hum Neurosci. 2013;7:814.CrossRef
15.
Hull JV, Dokovna LB, Jacokes ZJ, Torgerson CM, Irimia A, Van Horn JD. Resting-state functional connectivity in autism spectrum disorders: a review. Front Psych. 2017;7:205.CrossRef
16.
Agelink van Rentergem JA, Deserno MK, Geurts HM. Validation strategies for subtypes in psychiatry: a systematic review of research on autism spectrum disorder. Clin Psychol Rev. 2021;87:102033.CrossRef
17.
18.
Katuwal GJ, Baum SA, Cahill ND, Michael AM. Divide and conquer: sub-grouping of ASD improves ASD detection based on brain morphometry. PLoS ONE. 2016;11(4):e0153331.CrossRef
19.
Hong S-J, Valk SL, Di Martino A, Milham MP, Bernhardt BC. Multidimensional neuroanatomical subtyping of autism spectrum disorder. Cereb Cortex. 2018;28(10):3578–88.CrossRef
20.
Easson AK, Fatima Z, McIntosh AR. Functional connectivity-based subtypes of individuals with and without autism spectrum disorder. Netw Neurosci. 2019;3(2):344–62.CrossRef
21.
Hahamy A, Behrmann M, Malach R. The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder. Nat Neurosci. 2015;18(2):302–9.CrossRef
22.
Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014;19(6):659–67.CrossRef
23.
Guo X, Duan X, Chen H, He C, Xiao J, Han S, et al. Altered inter- and intrahemispheric functional connectivity dynamics in autistic children. Hum Brain Mapp. 2020;41(2):419–28.CrossRef
24.
25.
Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R. Movement-related effects in fMRI time-series. Magn Reson Med. 1996;35(3):346–55.CrossRef
26.
Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, et al. Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage. 2012;60(1):623–32.CrossRef
27.
Yan C-G, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage. 2013;76:183–201.CrossRef
28.
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59(3):2142–54.CrossRef
29.
Chen H, Uddin LQ, Guo X, Wang J, Wang R, Wang X, et al. Parsing brain structural heterogeneity in males with autism spectrum disorder reveals distinct clinical subtypes. Hum Brain Mapp. 2019;40(2):628–37.CrossRef
30.
Chen H, Nomi JS, Uddin LQ, Duan X, Chen H. Intrinsic functional connectivity variance and state-specific under-connectivity in autism. Hum Brain Mapp. 2017;38(11):5740–55.CrossRef
31.
Guo X, Duan X, Suckling J, Chen H, Liao W, Cui Q, et al. Partially impaired functional connectivity states between right anterior insula and default mode network in autism spectrum disorder. Hum Brain Mapp. 2019;40(4):1264–75.CrossRef
32.
Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional network organization of the human brain. Neuron. 2011;72(4):665–78.CrossRef
33.
Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci. 2013;16(9):1348–55.CrossRef
34.
Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage. 2009;44(3):893–905.CrossRef
35.
Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci. 2005;102(27):9673–8.CrossRef
36.
Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, et al. The autism diagnostic observation schedule—generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. 2000;30(3):205–23.CrossRef
37.
Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V. Support vector regression machines. Adv Neural Inf Process Syst. 1997;9:155-61.
38.
Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J. LIBLINEAR: A library for large linear classification. J Mach Learn Res. 2008;9:1871–4.
39.
Schölkopf B, Smola AJ, Bach F. Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT Press; 2002.
40.
Golland P, Fischl B, editors. Permutation tests for classification: towards statistical significance in image-based studies. In: Biennial international conference on information processing in medical imaging. Springer; 2003.
41.
Werner E, Dawson G. Validation of the phenomenon of autistic regression using home videotapes. Arch Gen Psychiatry. 2005;62(8):889–95.CrossRef
42.
Chen C, Bailey B, Muller R, editors. Towards autism subtypes? Unsupervised machine learning using fcMRI features. In: Oral presentation at the organization for human brain mapping annual meeting, Honolulu, HI, USA; 2015.
43.
Aylward EH, Minshew NJ, Field K, Sparks B, Singh N. Effects of age on brain volume and head circumference in autism. Neurology. 2002;59(2):175–83.CrossRef
44.
Lin HY, Ni HC, Lai MC, Tseng WYI, Gau SSF. Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Mol Autism. 2015;6(1):1–18.CrossRef
45.
He C, Chen H, Uddin LQ, Erramuzpe A, Bonifazi P, Guo X, et al. Structure-function connectomics reveals aberrant developmental trajectory occurring at preadolescence in the autistic brain. Cereb Cortex. 2020;30(9):5028–37.CrossRef
46.
Salmond CH, Vargha-Khadem F, Gadian DG, de Haan M, Baldeweg T. Heterogeneity in the patterns of neural abnormality in autistic spectrum disorders: evidence from ERP and MRI. Cortex. 2007;43(6):686–99.CrossRef
47.
Lai M-C, Lombardo MV, Suckling J, Ruigrok AN, Chakrabarti B, Ecker C, et al. Biological sex affects the neurobiology of autism. Brain. 2013;136(9):2799–815.CrossRef
48.
Reardon AM, Li K, Langley J, Hu XP. Subtyping autism spectrum disorder via joint modeling of clinical and connectomic profiles. Brain Connect. 2022;12(2):193–205.
49.
Maximo JO, Kana RK. Aberrant “deep connectivity” in autism: a cortico–subcortical functional connectivity magnetic resonance imaging study. Autism Res. 2019;12(3):384–400.CrossRef
50.
Gotts SJ, Simmons WK, Milbury LA, Wallace GL, Cox RW, Martin A. Fractionation of social brain circuits in autism spectrum disorders. Brain J Neurol. 2012;135(Pt 9):2711–25.CrossRef
51.
Qi S, Morris R, Turner JA, Fu Z, Jiang R, Deramus TP, et al. Common and unique multimodal covarying patterns in autism spectrum disorder subtypes. Mol Autism. 2020;11(1):1–15.CrossRef
52.
Tang S, Sun N, Floris DL, Zhang X, Di Martino A, Yeo BT. Reconciling dimensional and categorical models of autism heterogeneity: a brain connectomics and behavioral study. Biol Psychiat. 2020;87(12):1071–82.CrossRef
53.
Goch CJ, Stieltjes B, Henze R, Hering J, Poustka L, Meinzer HP, et al. Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis. Int J Comput Assist Radiol Surg. 2014;9(3):357–65.CrossRef
54.
Uddin LQ, Supekar K, Lynch CJ, Khouzam A, Phillips J, Feinstein C, et al. Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiat. 2013;70(8):869–79.CrossRef
55.
Halladay AK, Bishop S, Constantino JN, Daniels AM, Koenig K, Palmer K, et al. Sex and gender differences in autism spectrum disorder: summarizing evidence gaps and identifying emerging areas of priority. Mol Autism. 2015;6(1):1–5.CrossRef
56.
Guo X, Chen H, Long Z, Duan X, Zhang Y, Chen H. Atypical developmental trajectory of local spontaneous brain activity in autism spectrum disorder. Sci Rep. 2017;7:39822.CrossRef
57.
Henry TR, Dichter GS, Gates K. Age and gender effects on intrinsic connectivity in autism using functional integration and segregation. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(5):414–22.
58.
Li Y, Zhu YY, Nguchu BA, Wang YM, Wang HJ, Qiu BS, et al. Dynamic functional connectivity reveals abnormal variability and hyper-connected pattern in autism spectrum disorder. Autism Res. 2020;13(2):230–43.CrossRef
59.
Gao Y, Sun J, Cheng L, Yang Q, Li J, Hao Z, et al. Altered resting state dynamic functional connectivity of amygdala subregions in patients with autism spectrum disorder: a multi-site fMRI study. J Affect Disord. 2022;312:69–77.CrossRef
Metadata
Title
Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder
Authors
Xiaonan Guo
Guangjin Zhai
Junfeng Liu
Yabo Cao
Xia Zhang
Dong Cui
Le Gao
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Molecular Autism / Issue 1/2022
Electronic ISSN: 2040-2392
DOI
https://doi.org/10.1186/s13229-022-00535-0

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