Xiaoyan Ke

White matter impairments in autism, evidence from voxel-based morphometry and diffusion tensor imaging.

This study explored white matter abnormalities in a group of Chinese children with high functioning autism (HFA). Twelve male children with HFA and ten matched typically developing children underwent diffusion tensor imaging (DTI) as well three-dimensional T1-weighted MRI for voxel-based morphometry (VBM). We found a significant decrease of the white matter density in the right frontal lobe, left parietal lobe and right anterior cingulate and a significant increase in the right frontal lobe, left parietal lobe and left cingulate gyrus in the HFA group compared with the control group. The HFA group also had decreased FA in the frontal lobe and left temporal lobe. By combining DT-MRI FA and MRI volumetric analyses based on the VBM model, the results showed consistent white matter abnormalities in a group of Chinese children with HFA.

Voxel-based morphometry study on brain structure in children with high-functioning autism.

Earlier studies have suggested abnormal brain volumes in autism, but inconsistencies exist. Using voxel-based morphometry, we compared global and regional brain volumes in 17 high-functioning autistic children with 15 matched controls. We identified significant reduction in left white matter volume and white/gray matter ratio in autism. Regional brain volume reductions were detected for right anterior cingulate, left superior parietal lobule white matter volumes, and right parahippocampal gyrus gray matter volume, whereas enlargements in bilateral supramarginal gyrus, right postcentral gyrus, right medial frontal gyrus, and right posterior lobe of cerebellum gray matter in autism. Our findings showed global and regional brain volumes abnormality in high-functioning autism.

Detecting abnormalities of corpus callosum connectivity in autism using magnetic resonance imaging and diffusion tensor tractography

The corpus callosum (CC) has emerged as one of the primary targets of autism research. To detect aberrant CC interhemispheric connectivity in autism, we performed T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI)-based tractography in 18 children with high functioning autism (HFA) and 16 well-matched typically developing (TD) children. We compared global and regional T1 measures (CC volume, and CC density), and the DTI measures [fractional anisotropy (FA), apparent diffusion coefficient (ADC), average fiber length (AFL), and fiber number (FN)] of transcallosal fibers, between the two groups. We also evaluated the relationships between scores on the Childhood Autism Rating Scale (CARS) and CC T1 or DTI measurements. Significantly less white matter density in the anterior third of the CC, and higher ADC and lower FN values of the anterior third transcallosal fiber tracts were found in HFA patients compared to TD children. These results suggested that the anterior third CC density and transcallosal fiber connectivity were affected in HFA children.

Response Inhibition Impairment in High Functioning Autism and Attention Deficit Hyperactivity Disorder: Evidence from Near-Infrared Spectroscopy Data

Background Response inhibition, an important domain of executive function (EF), involves the ability to suppress irrelevant or interfering information and impulses. Previous studies have shown impairment of response inhibition in high functioning autism (HFA) and attention deficit hyperactivity disorder (ADHD), but more recent findings have been inconsistent. To date, almost no studies have been conducted using functional imaging techniques to directly compare inhibitory control between children with HFA and those with ADHD. Method Nineteen children with HFA, 16 age- and intelligence quotient (IQ)-matched children with ADHD, and 16 typically developing (TD) children were imaged using functional near-infrared spectroscopy (NIRS) while performing Go/No-go and Stroop tasks. Results Compared with the TD group, children in both the HFA and ADHD groups took more time to respond during the No-go blocks, with reaction time longest for HFA and shortest for TD. Children in the HFA and ADHD groups also made a greater number of reaction errors in the No-go blocks than those in the TD group. During the Stroop task, there were no significant differences between these three groups in reaction time and omission errors. Both the HFA and ADHD groups showed a higher level of inactivation in the right prefrontal cortex (PFC) during the No-go blocks, relative to the TD group. However, no significant differences were found between groups in the levels of oxyhemoglobin concentration in the PFC during the Stroop task. Conclusion Functional brain imaging using NIRS showed reduced activation in the right PFC in children with HFA or ADHD during an inhibition task, indicating that inhibitory dysfunction is a shared feature of both HFA and ADHD.

Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder mainly showed atypical social interaction, communication, and restricted, repetitive patterns of behavior, interests and activities. Now clinic diagnosis of ASD is mostly based on psychological evaluation, clinical observation and medical history. All these behavioral indexes could not avoid defects such as subjectivity and reporter‐dependency. Therefore researchers devoted themselves to seek relatively stable biomarkers of ASD as supplementary diagnostic evidence. The goal of present study is to generate relatively stable predictive model based on anatomical brain features by using machine learning technique. Forty‐six ASD children and thirty‐nine development delay children aged from 18 to 37 months were evolved in. As a result, the predictive model generated by regional average cortical thickness of regions with top 20 highest importance of random forest classifier showed best diagnostic performance. And random forest was proved to be the optimal approach for neuroimaging data mining in small size set and thickness‐based classification outperformed volume‐based classification and surface area‐based classification in ASD. The brain regions selected by the models might attract attention and the idea of considering biomarkers as a supplementary evidence of ASD diagnosis worth exploring. Autism Res 2017, 0: 000–000.

Exploring the relationship between fairness and ‘brain types’ in children with high-functioning autism spectrum disorder

Background Existing research typically focuses on only one domain of cognition with regard to fairness—theory of mind or executive function. However, children with High‐functioning autism spectrum disorder (HF‐ASD) are cognitively impaired in both domains. Moreover, little is known about fairness characteristics in children with HF‐ASD in relation to both domains of cognition. Methods Thirty children with HF‐ASD as well as 39 children with typical development (TD) were evaluated in this study. We investigated the development of childrens fairness characteristics as a responder in a mini ultimatum game (UG). The different ‘brain types,’ i.e., with or without HF‐ASD, were evaluated using the Empathy Questionnaire‐Systemizing Questionnaire (E/SC‐Q). Furthermore, we explored the relationship between fairness and brain types using Pearson correlation analyses. Results Children in the HF‐ASD group were more likely to accept unfair offers than were children in the TD group (χ2 = 17.513, p = .025). In the HF‐ASD group, the acceptance rate of unfair offers was correlated with the discrepancy score (r = 0.363, p = .048), while there were no significant correlations in the TD group. In HF‐ASD group, compared with Type S, acceptance rate of unfair offer was significant higher in Extreme Type S ‘brain type’ (F = 28.584, p < .001). While dividing TD participants by ‘brain type’, there was no significant difference in acceptance rate of unfair offer among five difference ‘brain types’ (F = 1.131, p = .358). Stepwise regression revealed that Extreme Type S positively predicted acceptance of unfair offers (F [1, 68] = 8.695, p < .001). Discussion Our findings show that children with HF‐ASD were more likely to accept an unfair offer; in particular, the more unbalanced the development of empathy and systemizing was, the more significant the unfairness preference observed. Extreme Type S positively predicted the acceptance of unfair offers by children with HF‐ASD. Registration of clinical trials World Health Organization class I registered international clinical trial platform, ChiCTR‐ROC‐17012877. HighlightsWe examined the advanced cognitive function of ASD.We enrolled children and adolescent as research participants.We exploring the relationship between ‘brain type’ and fairness in children and adolescent with ASD.We found fairness was related with the balance between empathy and systematizing in children and adolescent with ASD.

Alterations in hub organization in the white matter structural network in toddlers with autism spectrum disorder: A 2-year follow-up study: Qian et al.\Hub organization in ASD: A follow-up study

Little is currently known about the longitudinal developmental patterns of hubs in the whole‐brain white matter (WM) structural networks among toddlers with autism spectrum disorder (ASD). This study utilized diffusion tensor imaging (DTI) and deterministic tractography to map the WM structural networks in 37 ASD toddlers and 27 age‐, gender‐ and developmental quotient‐matched controls with developmental delay (DD) toddlers aged 2–3 years old at baseline (Time 1) and at 2‐year follow‐up (Time 2). Furthermore, graph‐theoretical methods were applied to investigate alterations in the network hubs in these patients at the two time points. The results showed that after 2 years, 17 hubs were identified in the ASD subjects compared to the controls, including 13 hubs that had not changed from baseline and 4 hubs that were newly identified. In addition, alterations in the properties of the hubs of the right middle frontal gyrus, right insula, left median cingulate gyri, and bilateral precuneus were significantly correlated with alterations in the behavioral data for ASD patients. These results indicated that at the stage of 2–5 years of age, ASD children showed distributions of network hubs that were relatively stable, with minor differences. Abnormal developmental patterns in the five areas mentioned above in ASD may contribute to abnormalities in the social and nonsocial characteristics of this disorder. Autism Res 2018, 11: 1218–1228.