Abstract
Background
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized
by substantial clinical and biological heterogeneity. Quantitative and individualized
metrics for delineating the heterogeneity of brain structure in ASD are still lacking.
Likewise, the extent to which brain structural metrics of ASD deviate from typical
development (TD) and whether deviations can be used for parsing brain structural phenotypes
of ASD is unclear.
Methods
T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Data Exchange
(ABIDE) II (nTD = 564) were used to generate a normative model to map brain structure deviations
of ABIDE I subjects (nTD = 560, nASD = 496). Voxel-based morphometry was used to compute gray matter volume. Non-negative
matrix factorization was employed to decompose the gray matter matrix into 6 factors
and weights. These weights were used for normative modeling to estimate the factor
deviations. Then, clustering analysis was used to identify ASD subtypes.
Results
Compared with TD, ASD showed increased weights and deviations in 5 factors. Three
subtypes with distinct neuroanatomical deviation patterns were identified. ASD subtype
1 and subtype 3 showed positive deviations, whereas ASD subtype 2 showed negative
deviations. Distinct clinical manifestations in social communication deficits were
identified among the three subtypes.
Conclusions
Our findings suggest that individuals with ASD have heterogeneous deviation patterns
in brain structure. The results highlight the need to test for subtypes in neuroimaging
studies of ASD. This study also presents a framework for understanding neuroanatomical
heterogeneity in this increasingly prevalent neurodevelopmental disorder.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Biological PsychiatryAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Autism.Lancet. 2014; 383: 896-910
- Gene hunting in autism spectrum disorder: On the path to precision medicine.Lancet Neurol. 2015; 14: 1109-1120
- Diagnosis and management of autism spectrum disorder in the era of genomics: Rare disorders can pave the way for targeted treatments.Pediatr Clin North Am. 2015; 62: 607-618
- Reconciling dimensional and categorical models of autism heterogeneity: A brain connectomics and behavioral study.Biol Psychiatry. 2020; 87: 1071-1082
- Diagnostic evaluation of autism spectrum disorders.Pediatr Clin North Am. 2012; 59: 103-111
- Six developmental trajectories characterize children with autism.Pediatrics. 2012; 129: e1112-e1120
- Evidence for latent classes of IQ in young children with autism spectrum disorder.Am J Ment Retard. 2008; 113: 439-452
- Exploring developmental and behavioral heterogeneity among preschoolers with ASD: A cluster analysis on principal components.Autism Res. 2020; 13: 796-809
- Identification of distinct psychosis biotypes using brain-based biomarkers.Am J Psychiatry. 2016; 173: 373-384
- Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD Working Group.Am J Psychiatry. 2018; 175: 359-369
- Reduced gray matter volume of pars opercularis is associated with impaired social communication in high-functioning autism spectrum disorders.Biol Psychiatry. 2010; 68: 1141-1147
- Mapping progressive gray matter alterations in early childhood autistic brain.Cereb Cortex. 2021; 31: 1500-1510
- Toward neurosubtypes in autism.Biol Psychiatry. 2020; 88: 111-128
- Subtypes of autism by cluster analysis based on structural MRI data.Eur Child Adolesc Psychiatry. 2005; 14: 138-144
- Multidimensional neuroanatomical subtyping of autism spectrum disorder.Cereb Cortex. 2018; 28: 3578-3588
- Parsing brain structural heterogeneity in males with autism spectrum disorder reveals distinct clinical subtypes.Hum Brain Mapp. 2019; 40: 628-637
- Learning the parts of objects by non-negative matrix factorization.Nature. 1999; 401: 788-791
- Individual differences in intrinsic brain networks predict symptom severity in autism spectrum disorders.Cereb Cortex. 2021; 31: 681-693
- Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies.Biol Psychiatry. 2016; 80: 552-561
- Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.Neuroimage. 2014; 97: 333-348
- Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models.JAMA Psychiatry. 2018; 75: 1146-1155
- Dissecting the heterogeneous cortical anatomy of autism spectrum disorder using normative models.Biol Psychiatry Cogn Neurosci Neuroimaging. 2019; 4: 567-578
- The development of growth references and growth charts.Ann Hum Biol. 2012; 39: 382-394
- Large-scale automated synthesis of human functional neuroimaging data.Nat Methods. 2011; 8: 665-670
- The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism.Mol Psychiatry. 2014; 19: 659-667
- Enhancing studies of the connectome in autism using the Autism Brain Imaging Data Exchange II.Sci Data. 2017; 4: 170010
- Subcortical structural covariance in young children with autism spectrum disorder.Prog Neuropsychopharmacol Biol Psychiatry. 2020; 99: 109874
- Partially impaired functional connectivity states between right anterior insula and default mode network in autism spectrum disorder.Hum Brain Mapp. 2019; 40: 1264-1275
- Model election and adaptation of hyperparameters.in: Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA2006: 105-128
- A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder.Commun Biol. 2020; 3: 486
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences.3rd ed. Routledge, New York2002
- Pushing the boundaries of psychiatric neuroimaging to ground diagnosis in biology.eNeuro. 2019; 6 (ENEURO.0384-19.2019)
- Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD.Neuroimage. 2014; 102: 207-219
- Reduced gray matter volume in the social brain network in adults with autism spectrum disorder.Front Hum Neurosci. 2017; 11: 395
- Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism.Biol Psychiatry. 2011; 70: 833-841
- Gray matter abnormalities in pediatric autism spectrum disorder: A meta-analysis with signed differential mapping.Eur Child Adolesc Psychiatry. 2017; 26: 933-945
- Brain growth across the life span in autism: Age-specific changes in anatomical pathology.Brain Res. 2011; 1380: 138-145
- Mapping early brain development in autism.Neuron. 2007; 56: 399-413
- Total brain volume and corpus callosum size in medication-naive adolescents and young adults with autism spectrum disorder.Biol Psychiatry. 2009; 66: 316-319
- Longitudinal volumetric brain changes in autism spectrum disorder ages 6-35 years.Autism Res. 2015; 8: 82-93
- Gray matter characteristics in mid and old aged adults with ASD.J Autism Dev Disord. 2016; 46: 2666-2678
- A comprehensive volumetric analysis of the cerebellum in children and adolescents with autism spectrum disorder.Autism Res. 2009; 2: 246-257
- Magnetic resonance imaging and head circumference study of brain size in autism: Birth through age 2 years.Arch Gen Psychiatry. 2005; 62: 1366-1376
- Unusual brain growth patterns in early life in patients with autistic disorder. An MRI study.Neurology. 2001; 57: 245-254
- Neuroanatomy of autism.Trends Neurosci. 2008; 31: 137-145
- Autism spectrum disorder causes, mechanisms, and treatments: Focus on neuronal synapses.Front Mol Neurosci. 2013; 6: 19
- Big data approaches to decomposing heterogeneity across the autism spectrum.Mol Psychiatry. 2019; 24: 1435-1450
- Reconceptualizing functional brain connectivity in autism from a developmental perspective.Front Hum Neurosci. 2013; 7: 458
- Migration abnormality in the left cingulate gyrus presenting with autistic disorder.J Child Neurol. 2006; 21: 600-604
- The cingulate cortex and limbic systems for emotion, action, and memory.Brain Struct Funct. 2019; 224: 3001-3018
- Abnormal regional cerebral blood flow in childhood autism.Brain. 2000; 123: 1838-1844
- Abnormal brain activity in social reward learning in children with autism spectrum disorder: An fMRI study.Yonsei Med J. 2015; 56: 705-711
- Altered cerebellar connectivity in autism and cerebellar-mediated rescue of autism-related behaviors in mice.Nat Neurosci. 2017; 20: 1744-1751
- Cerebellar modulation of the reward circuitry and social behavior.Science. 2019; 363eaav0581
- Meeting of minds: The medial frontal cortex and social cognition.Nat Rev Neurosci. 2006; 7: 268-277
- Altered functional connectivity in frontal lobe circuits is associated with variation in the autism risk gene CNTNAP2.Sci Transl Med. 2010; 2: 56ra80
- The limbic system: An anatomic, phylogenetic, and clinical perspective.J Neuropsychiatry Clin Neurosci. 1997; 9: 315-330
- A revised limbic system model for memory, emotion and behaviour.Neurosci Biobehav Rev. 2013; 37: 1724-1737
- Altered white matter connectivity as a neural substrate for social impairment in autism spectrum disorder.Cortex. 2015; 62: 158-181
- Neuroimaging of autism.Neuroradiology. 2010; 52: 3-14
- Brain mechanisms supporting flexible cognition and behavior in adolescents with autism spectrum disorder.Biol Psychiatry. 2021; 89: 172-183
- The pathophysiology of restricted repetitive behavior.J Neurodev Disord. 2009; 1: 114-132
- Impaired inhibitory control is associated with higher-order repetitive behaviors in autism spectrum disorders.Psychol Med. 2009; 39: 1559-1566
- The heterogeneity problem: Approaches to identify psychiatric subtypes.Trends Cogn Sci. 2019; 23: 584-601
- Normative analysis of individual brain differences based on a population MRI-based atlas of cynomolgus macaques.Cereb Cortex. 2021; 31: 341-355
- Individual differences v. the average patient: Mapping the heterogeneity in ADHD using normative models.Psychol Med. 2020; 50: 314-323
- Decomposing heterogeneity in autism spectrum disorder through neurosubtyping.Biol Psychiatry. 2020; 87: e37-e38
- Clustering autism: Using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity.Mol Psychiatry. 2015; 20: 118-125
- The promise and the pitfalls of autism research: An introductory note for new autism researchers.Brain Res. 2011; 1380: 3-9
- Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder.Mol Psychiatry. 2020; 25: 614-628
- Heterogeneity in the patterns of neural abnormality in autistic spectrum disorders: Evidence from ERP and MRI.Cortex. 2007; 43: 686-699
- Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons.PLoS Comput Biol. 2011; 7e1002038
- Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data.PLoS Comput Biol. 2018; 14e1006376
- Brain disorders? Precisely.Science. 2015; 348: 499-500
- Resting-state connectivity biomarkers define neurophysiological subtypes of depression.Nat Med. 2017; 23: 28-38
Article Info
Publication History
Published online: January 27, 2022
Accepted:
January 14,
2022
Received in revised form:
December 28,
2021
Received:
August 11,
2021
Identification
Copyright
© 2022 Society of Biological Psychiatry.
