New perspectives in autism, Part I: The clinical spectrum of autism

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

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.

Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age

We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroanatomical regions. Significance tests are performed to identify regional texture differences between ASD vs. TDC and male vs. female groups, as well as correlations with age (corrected p < 0.05). The open-access brain imaging data exchange (ABIDE) brain MRI dataset is used to evaluate texture features derived from 31 brain regions from 1112 subjects including 573 typically developing control (TDC, 99 females, 474 males) and 539 Autism spectrum disorder (ASD, 65 female and 474 male) subjects. Statistically significant texture differences between ASD vs. TDC groups are identified asymmetrically in the right hippocampus, left choroid-plexus and corpus callosum (CC), and symmetrically in the cerebellar white matter. Sex-related texture differences in TDC subjects are found in primarily in the left amygdala, left cerebellar white matter, and brain stem. Correlations between age and texture in TDC subjects are found in the thalamus-proper, caudate and pallidum, most exhibiting bilateral symmetry.

Correlates of Social Functioning in Autism Spectrum Disorder: The Role of Social Cognition

BACKGROUND

Individuals with autism spectrum disorder (ASD) experience marked challenges with social function by definition, but few modifiable predictors of social functioning in ASD have been identified in extant research. This study hypothesized that deficits in social cognition and motor function may help to explain poor social functioning in individuals with ASD.

METHOD

Cross-sectional data from 108 individuals with ASD and without intellectual disability ages 9 through 27.5 were used to assess the relationship between social cognition and motor function, and social functioning.

RESULTS

Results of hierarchical multiple regression analyses revealed that greater social cognition, but not motor function, was significantly associated with better social functioning when controlling for sex, age, and intelligence quotient. Post-hoc analyses revealed that, better performance on second-order false belief tasks was associated with higher levels of socially adaptive behavior and lower levels of social problems.

CONCLUSIONS

Our findings support the development and testing of interventions that target social cognition in order to improve social functioning in individuals with ASD. Interventions that teach generalizable skills to help people with ASD better understand social situations and develop competency in advanced perspective taking have the potential to create more durable change because their effects can be applied to a wide and varied set of situations and not simply a prescribed set of rehearsed situations.

Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey

Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employed to investigate facets of ASD in order to better understand this complex syndrome. This paper reviews recent applications of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI), to study autism spectrum disorder (ASD). Main reported findings are sometimes contradictory due to different age ranges, hardware protocols, population types, numbers of participants, and image analysis parameters. The primary anatomical structures, such as amygdalae, cerebrum, and cerebellum, associated with clinical-pathological correlates of ASD are highlighted through successive life stages, from infancy to adulthood. This survey demonstrates the absence of consistent pathology in the brains of autistic children and lack of research investigations in patients under 2 years of age in the literature. The known publications also emphasize advances in data acquisition and analysis, as well as significance of multimodal approaches that combine resting-state, task-evoked, and sMRI measures. Initial results obtained with the sMRI and DTI show good promise toward the early and non-invasive ASD diagnostics.

Child Behavior Checklist Scores for School-Aged Children with Autism: Preliminary Evidence of Patterns Suggesting the Need for Referral

The Child Behavior Checklist (CBCL) is a widely used questionnaire to assess behavioral and emotional problems. It is often used as a diagnostic screener, but autism spectrum disorders (ASD) are not included in the CBCL for school-aged children. This study investigated patterns of CBCL scores in 108 children with high-functioning ASD from two independent samples, and 67 IQ- and age-matched controls. Scores on the CBCL Thought and Social Problems scales significantly differentiated children with ASD from controls. Both independent ASD samples had the same pattern of elevations, with mean scores over two standard deviations above the mean for Social, Thought, and Attention Problems. The Withdrawn/Depressed scale was elevated to at least the borderline clinical range for half of the ASD sample. This pattern of elevations is consistent with two prior studies of the CBCL with school-aged children with ASD, and therefore may warrant follow-up assessment to rule out an ASD.

Neuropsychologic functioning in children with autism: further evidence for disordered complex information-processing

A wide range of abilities was assessed in 56 high-functioning children with autism and 56 age- and IQ-matched controls. Stepwise discriminant analyses produced good group discrimination for sensory-perceptual, motor, complex language, and complex memory domains but lower agreement for the reasoning domain than previously obtained for adults. Group discrimination did not occur for attention, simple language, simple memory, and visuospatial domains. Findings provide additional support for a complex information-processing model for autism, previously based on adult data, demonstrating a pattern across domains of selective impairments on measures with high demands for integration of information and sparing when demands were low. Children as compared to adults with autism exhibited more prominent sensory-perceptual symptoms and less pronounced reasoning deficits reflecting brain maturation.

The profile of memory function in children with autism

A clinical memory test was administered to 38 high-functioning children with autism and 38 individually matched normal controls, 8-16 years of age. The resulting profile of memory abilities in the children with autism was characterized by relatively poor memory for complex visual and verbal information and spatial working memory with relatively intact associative learning ability, verbal working memory, and recognition memory. A stepwise discriminant function analysis of the subtests found that the Finger Windows subtest, a measure of spatial working memory, discriminated most accurately between the autism and normal control groups. A principal components analysis indicated that the factor structure of the subtests differed substantially between the children with autism and controls, suggesting differing organizations of memory ability.

Autistic regression and disintegrative disorder: how important the role of epilepsy?

At least a third of autistic toddlers regress in language, sociability, play, and often cognition. Many fewer children undergo a similar, unexplained regression after language is fully developed (disintegrative disorder [DD]). Epilepsy or a paroxysmal electroencephalogram (EEG) with/without clinical seizures, including electrical status epilepticus in slow wave sleep (ESES), may be associated, in occasional children, with either selective loss of language (Landau-Kleffner syndrome [LKS]) or with pervasive autistic regression. Fluctuation in language and behavior deficits should raise the suspicion of epilepsy. Review of the literature and of the author's experience suggests that epilepsy probably plays a relatively minor, although non-negligible, pathogenetic role in autistic regression. Multidisciplinary, possibly multi-institutional, longitudinal studies that encompass the regression are needed to sharpen diagnostic criteria to devise more effective therapies.

Evaluation for suspected language disorders in preschool children

Pediatricians can identify a child with a developmental language disorder by expanding a traditional developmental assessment with special screening tests of language skills. They can direct parents to specialists who can define the nature of the language disturbance in more detail and predict its natural history. They must then support the parents as they learn to use the community resources available for remediation of their child. As more is learned about the biologic bases of normal and abnormal language development in children, pediatricians will undoubtedly have an even more central role in identification of patients with language disorders and education of their families.