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

Chaddad, Ahmad; Desrosiers, Christian; Hassan, Lama; Tanougast, Camel

doi:10.1186/s12868-017-0373-0

Cited by 81 publications

(61 citation statements)

References 56 publications

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“…Neocortical studies on autism have found a 67% increase in the number of neurons in prefrontal cortex [68]. Similarly, a connection has been reported between ASD and neuronal size abnormalities in medial temporal lobe structures including the hippocampus [69,70].…”

Section: Discussionmentioning

confidence: 75%

Effects of PM2.5 and gases exposure during prenatal and early-life on autism–like phenotypes in male rat offspring

et al. 2020

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Background: Epidemiological studies have reported associations between elevated air pollution and autism spectrum disorders (ASD). However, we hypothesized that exposure to air pollution that mimics real world scenarios, is a potential contributor to ASD. The exact etiology and molecular mechanisms underlying ASD are not well understood. Thus, we assessed whether changes in OXTR levels may be part of the mechanism linking PM 2.5 /gaseous pollutant exposure and ASD. The current in-vivo study investigated the effect of exposure to fine particulate matter (PM 2.5) and gaseous pollutants on ASD using behavioral and molecular experiments. Four exposure groups of Wistar rats were included in this study: 1) particulate matter and gaseous pollutants exposed (PGE), 2) gaseous pollutants only exposed (GE), 3) autism-like model (ALM) with VPA induction, and 4) clean air exposed (CAE) as the control. Pregnant dams and male pups were exposed to air pollutants from embryonic day (E0) to postnatal day (PND21). Results: The average ± SD concentrations of air pollutants were: PM 2.5 : 43.8 ± 21.1 μg/m 3 , CO: 13.5 ± 2.5 ppm, NO 2 : 0.341 ± 0.100 ppm, SO 2 : 0.275 ± 0.07 ppm, and O 3 : 0.135 ± 0.01 ppm. The OXTR protein level, catalase activity (CAT), and GSH concentrations in the ALM, PGE, and GE rats were lower than those in control group (CAE). However, the decrements in the GE rats were smaller than other groups. Also in behavioral assessments, the ALM, PGE, and GE rats demonstrated a repetitive /restricted behavior and poor social interaction, but the GE rats had weaker responses compared to other groups of rats. The PGE and GE rats showed similar trends in these tests compared to the VPA rats. Conclusions: This study suggested that exposure to ambient air pollution contributed to ASD and that OXTR protein may serve as part of the mechanism linking them.

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Section: Discussionmentioning

confidence: 75%

Effects of PM2.5 and gases exposure during prenatal and early-life on autism–like phenotypes in male rat offspring

et al. 2020

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“…Of the studies that perform classification, SVMs were the most prevalent classification method (Almeida et al, 2017; Chaddad et al, 2017; Demirhan, 2018; Ecker et al, 2010b; Hoeft et al, 2011; Katuwal et al, 2015; Qureshi et al, 2017; Sabuncu et al, 2015; Subbaraju et al, 2015; Vidhusha and Anandhan, 2015; Vigneshwaran et al, 2013; Xiao et al, 2017), which achieved an accuracy ranging from 53 to 97%, on varying ASD datasets. Of all classification approaches, SVM obtained the highest accuracy in ASD classification (accuracy = 97.8%), albeit on a small dataset (n = 30) (Vidhusha and Anandhan, 2015).…”

Section: Classification Of Asd Diagnosis and Prediction Of Outcomesmentioning

confidence: 99%

“…Another common finding across the reviewed studies is a lack of a thorough validation of the classification and regression findings. Many of the studies outlined in Supplementary Table 8 employ no validation approach, while Leave One Out Cross Validation (LOOCV) was used frequently (Chaddad et al, 2017), however as this approach tests on only one data point it is an unreliable estimate of model generalisability. Although splitting datasets into exclusive model training and testing partitions is the most rigorous test of a models generalisability, as was done in a few of the reviewed studies all with n > 100 datasets (Almeida et al, 2017; Ghiassian et al, 2016; Qureshi et al, 2017; Vigneshwaran et al, 2013), limited data in neuroimaging studies necessitates a more efficient usage of data for the purpose of model training and testing.…”

Section: Classification Of Asd Diagnosis and Prediction Of Outcomesmentioning

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

107

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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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“…In autism spectrum disorder patients, a radiomics study using hippocampus and amygdala biomarkers has provided an unprecedented opportunity to improve the individualized diagnosis and treatment by means of biologically based measurement for mental disorders. 20 In this study, on the basis of the dysconnectivity theory of SZ and by means of radiomics approaches, we aimed to develop and validate a method of disease definition for SZ by resting-state functional connectivity using radiomics strategy in first-episode untreated patients, improving objective SZ individualized diagnosis using quantitative and specific biomarker in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Disease Definition for Schizophrenia by Functional Connectivity Using Radiomics Strategy

Cui

Liu

Wang

et al. 2018

Schizophrenia Bulletin

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Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence from neuroimaging has implicated disrupted functional connectivity in the pathophysiology. We aimed to develop and validate a method of disease definition for SZ by resting-state functional connectivity using radiomics strategy. This study included 2 data sets collected with different scanners. A total of 108 first-episode SZ patients and 121 healthy controls (HCs) participated in the current study, among which 80% patients and HCs (n = 183) and 20% (n = 46) were selected for training and testing in intra-data set validation and 1 of the 2 data sets was selected for training and the other for testing in inter-data set validation, respectively. Functional connectivity was calculated for both groups, features were selected by Least Absolute Shrinkage and Selection Operator (LASSO) method, and the clinical utility of its features and the generalizability of effects across samples were assessed using machine learning by training and validating multivariate classifiers in the independent samples. We found that the accuracy of intra-data set training was 87.09% for diagnosing SZ patients by applying functional connectivity features, with a validation in the independent replication data set (accuracy = 82.61%). The inter-data set validation further confirmed the disease definition by functional connectivity features (accuracy = 83.15% for training and 80.07% for testing). Our findings demonstrate a valid radiomics approach by functional connectivity to diagnose SZ, which is helpful to facilitate objective SZ individualized diagnosis using quantitative and specific functional connectivity biomarker.

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

Cited by 81 publications

References 56 publications

Effects of PM2.5 and gases exposure during prenatal and early-life on autism–like phenotypes in male rat offspring

Effects of PM2.5 and gases exposure during prenatal and early-life on autism–like phenotypes in male rat offspring

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

Disease Definition for Schizophrenia by Functional Connectivity Using Radiomics Strategy

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