Association Between the Probability of Autism Spectrum Disorder and Normative Sex-Related Phenotypic Diversity in Brain Structure

Ecker, Christine; Andrews, Derek Sayre; Gudbrandsen, Maria; Marquand, André F.; Ginestet, Cedric E.; Daly, Eileen; Murphy, Clodagh; Lai, Meng‐Chuan; Lombardo, Michael; Ruigrok, Amber N. V.; Bullmore, Edward T.; Suckling, John; Williams, Steven; Baron‐Cohen, Simon; Craig, Michael C.; Murphy, Declan

doi:10.1001/jamapsychiatry.2016.3990

Cited by 59 publications

(67 citation statements)

References 47 publications

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“…Except for acrossgender age differences in group B, no statistically significant differences in age were found between male and female subjects or between ASD and DC subjects. The age bias in group B could be related to the fact that girls are less likely than boys to meet diagnostic criteria for ASD [47,48]. The inter-rater reliability of manual segmentation labels is reported in Fig.…”

Section: Resultsmentioning

confidence: 99%

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

et al. 2017

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Background: Emerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences. However, only a limited number of studies have investigated the link between image texture and ASD. This paper proposes the study of texture features based on grey level co-occurrence matrix (GLCM) as a means for characterizing differences between ASD and development control (DC) subjects. Our study uses 64 T1-weighted MRI scans acquired from two groups of subjects: 28 typical age range subjects 4-15 years old (14 ASD and 14 DC, age-matched), and 36 non-typical age range subjects 10-24 years old (20 ASD and 16 DC). GLCM matrices are computed from manually labeled hippocampus and amygdala regions, and then encoded as texture features by applying 11 standard Haralick quantifier functions. Significance tests are performed to identify texture differences between ASD and DC subjects. An analysis using SVM and random forest classifiers is then carried out to find the most discriminative features, and use these features for classifying ASD from DC subjects. Results:Preliminary results show that all 11 features derived from the hippocampus (typical and non-typical age) and 4 features extracted from the amygdala (non-typical age) have significantly different distributions in ASD subjects compared to DC subjects, with a significance of p < 0.05 following Holm-Bonferroni correction. Features derived from hippocampal regions also demonstrate high discriminative power for differentiating between ASD and DC subjects, with classifier accuracy of 67.85%, sensitivity of 62.50%, specificity of 71.42%, and the area under the ROC curve (AUC) of 76.80% for age-matched subjects with typical age range. Conclusions:Results demonstrate the potential of hippocampal texture features as a biomarker for the diagnosis and characterization of ASD.

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Section: Resultsmentioning

confidence: 99%

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

et al. 2017

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“…This study was cross‐sectional, specific to high functioning males with ASD, lacked pubertal developmental measures [Urosevic et al, ] and did not include a semi‐structured clinical interview of possible psychiatric comorbidity such as the SCID‐5‐RV. Hence, our findings describe age‐related differences that may not generalize to others within the autism spectrum, including females [Craig et al, ; Ecker et al, ; Wilson et al, ]. However, study strengths include the relatively large sample of physically healthy, medication naive, clearly diagnosed males with ASD without confounding psychiatric diagnoses and matched controls, and the 25 year age span from childhood to adulthood.…”

Section: Limitationsmentioning

confidence: 86%

Abnormal functional activation and maturation of ventromedial prefrontal cortex and cerebellum during temporal discounting in autism spectrum disorder

Murphy

Christakou

Giampietro

et al. 2017

Human Brain Mapping

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People with autism spectrum disorder (ASD) have poor decision-making and temporal foresight. This may adversely impact on their everyday life, mental health, and productivity. However, the neural substrates underlying poor choice behavior in people with ASD, or its' neurofunctional development from childhood to adulthood, are unknown. Despite evidence of atypical structural brain development in ASD, investigation of functional brain maturation in people with ASD is lacking. This cross-sectional developmental fMRI study investigated the neural substrates underlying performance on a temporal discounting (TD) task in 38 healthy (11-35 years old) male adolescents and adults with ASD and 40 age, sex, and IQ-matched typically developing healthy controls. Most importantly, we assessed group differences in the neurofunctional maturation of TD across childhood and adulthood. Males with ASD had significantly poorer task performance and significantly lower brain activation in typical regions that mediate TD for delayed choices, in predominantly right hemispheric regions of ventrolateral/dorsolateral prefrontal cortices, ventromedial prefrontal cortex, striatolimbic regions, and cerebellum. Importantly, differential activation in ventromedial frontal cortex and cerebellum was associated with abnormal functional brain maturation; controls, in contrast to people with ASD, showed progressively increasing activation with increasing age in these regions; which furthermore was associated with performance measures and clinical ASD measures (stereotyped/restricted interests). Findings provide first cross-sectional evidence that reduced activation of TD mediating brain regions in people with ASD during TD is associated with abnormal functional brain development in these regions between childhood and adulthood, and this is related to poor task performance and clinical measures of ASD. Hum Brain Mapp 38:5343-5355, 2017. © 2017 Wiley Periodicals, Inc.

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“…The result of fewer female participants is that the observed findings of brain structure associated with ASD may not generalise to females (Lai et al, 2015). In fact, studies have demonstrated differences in brain structure based on gender, including altered GMV (Subbaraju et al, 2015), cortical thickness (Ecker et al, 2017), increased temporal WMV/GMV and reduced cerebellar volume (Bloss and Courchesne, 2007), increased amygdala volume (Schumann et al, 2009), increased WMV/GMV volume (Beacher et al, 2012) and reduced gyrification (Schaer et al, 2013) in girls with autism relative to boys. This will necessitate collecting sufficient data for females with ASD, in order to achieve accurate estimates of gender differences in brain structures.…”

Section: Discussionmentioning

confidence: 99%

A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective

Pagnozzi

Conti

Calderoni

et al. 2018

Intl J of Devlp Neuroscience

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101

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Autism Spectrum Disorder (ASD) affects approximately 1% of the population and leads to impairments in social interaction, communication and restricted, repetitive behaviours. Establishing robust neuroimaging biomarkers of ASD using structural magnetic resonance imaging (MRI) is an important step for diagnosing and tailoring treatment, particularly early in life when interventions can have the greatest effect. However currently, there is mixed findings on the structural brain changes associated with autism. Therefore in this systematic review, recent (post-2007), high-resolution (3 T) MRI studies investigating brain morphology associated with ASD have been collated to identify robust neuroimaging biomarkers of ASD. A systematic search was conducted on three databases; PubMed, Web of Science and Scopus, resulting in 123 reviewed articles. Patients with ASD were observed to have increased whole brain volume, particularly under 6 years of age. Other consistent changes observed in ASD patients include increased volume in the frontal and temporal lobes, increased cortical thickness in the frontal lobe, increased surface area and cortical gyrification, and increased cerebrospinal fluid volume, as well as reduced cerebellum volume and reduced corpus callosum volume, compared to typically developing controls. Findings were inconsistent regarding the developmental trajectory of brain volume and cortical thinning with age in ASD, as well as potential volume differences in the white matter, hippocampus, amygdala, thalamus and basal ganglia. To elucidate these inconsistencies, future studies should look towards aggregating MRI data from multiple sites or available repositories to avoid underpowered studies, as well as utilising methods which quantify larger-scale image features to reduce the number of statistical tests performed, and hence risk of false positive findings. Additionally, studies should look to perform a thorough validation strategy, to ensure generalisability of study findings, as well as look to leverage the improved image resolution of 3 T scanning to identify subtle brain changes related to ASD.

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Association Between the Probability of Autism Spectrum Disorder and Normative Sex-Related Phenotypic Diversity in Brain Structure

Abstract: These findings highlight the need for considering normative sex-related phenotypic diversity when determining an individual's risk for ASD and provide important novel insights into the neurobiological mechanisms mediating sex differences in ASD prevalence.

Cited by 59 publications

References 47 publications

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

Abnormal functional activation and maturation of ventromedial prefrontal cortex and cerebellum during temporal discounting in autism spectrum disorder

A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective

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