Intrinsic network abnormalities in children with autism spectrum disorder: an independent component analysis.

Aberrant intrinsic brain networks are consistently observed in individuals with autism spectrum disorder. However, studies examining the strength of functional connectivity across brain regions have yielded conflicting results. Therefore, this study aimed to investigate the functional connectivity of the resting brain in children with low-functioning autism, including during the early developmental stages. We explored the functional connectivity of 43 children with autism spectrum disorder and 54 children with typical development aged 2 to 12 years using resting-state functional magnetic resonance imaging data. We used independent component analysis to classify the brain regions into six intrinsic networks and analyzed the functional connectivity within each network. Moreover, we analyzed the relationship between functional connectivity and clinical scores. In children with autism, the under-connectivities were observed within several brain networks, including the cognitive control, default mode, visual, and somatomotor networks. In contrast, we found over-connectivities between the subcortical, visual, and somatomotor networks in children with autism compared with children with typical development. Moderate effect sizes were observed in entire networks (Cohen's d = 0.43-0.77). These network alterations were significantly correlated with clinical scores such as the communication sub-score (r = - 0.442, p = 0.045) and the calibrated severity score (r = - 0.435, p = 0.049) of the Autism Diagnostic Observation Schedule. These opposing results observed based on the brain areas suggest that aberrant neurodevelopment proceeds in various ways depending on the functional brain regions in individuals with autism spectrum disorder.

Erratum: A Study of the Reliability and Validity of the Korean Version of DSM-5 Symptom Measure-Inattention and Anger for Parent and Guardian of Child Age 6 to 17.

[This corrects the article on p. 71 in vol. 32, PMID: 33828406.].

Alterations in Social Brain Network Topology at Rest in Children With Autism Spectrum Disorder.

OBJECTIVE: Underconnectivity in the resting brain is not consistent in autism spectrum disorder (ASD). However, it is known that the functional connectivity of the default mode network is mainly decreased in childhood ASD. This study investigated the brain network topology as the changes in the connection strength and network efficiency in childhood ASD, including the early developmental stages. METHODS: In this study, 31 ASD children aged 2-11 years were compared with 31 age and sex-matched children showing typical development. We explored the functional connectivity based on graph filtration by assessing the single linkage distance and global and nodal efficiencies using resting-state functional magnetic resonance imaging. The relationship between functional connectivity and clinical scores was also analyzed. RESULTS: Underconnectivities within the posterior default mode network subregions and between the inferior parietal lobule and inferior frontal/superior temporal regions were observed in the ASD group. These areas significantly correlated with the clinical phenotypes. The global, local, and nodal network efficiencies were lower in children with ASD than in those with typical development. In the preschool-age children (2-6 years) with ASD, the anterior-posterior connectivity of the default mode network and cerebellar connectivity were reduced. CONCLUSION: The observed topological reorganization, underconnectivity, and disrupted efficiency in the default mode network subregions and social function-related regions could be significant biomarkers of childhood ASD.

A Study of the Reliability and Validity of the Korean Version of DSM-5 Symptom Measure-Inattention and Anger for Parent and Guardian of Child Age 6 to 17.

OBJECTIVES: This study was conducted to investigate the reliability and validity of the Korean version of the DSM-5 Level 2 Cross-Cutting Symptom Measure-inattention [Swanson, Nolan and Pelham, version IV (SNAP-IV)] and anger [Patient-Reported Outcome Measurement Information System (PROMIS) Anger] for parents and guardians of children aged 6-17 years. METHODS: We included 104 children and adolescents diagnosed with attention-deficit/hyperactivity disorder (ADHD), ADHD with anxiety and depression, depressive disorder, anxiety disorder, and tic disorder with somatic symptoms (ADHD=41, depression=9, anxiety=14, ADHD+anxious depression=11, tic+somatic symptoms=29). Their ages ranged from 8 years to 15 years. The participants' mothers completed the SNAP-IV, PROMIS Anger scale, Korean version of the IOWA Conners Rating Scale (K-IOWA), and Korean ADHD Rating Scale (K-ARS) so that the reliability and validity of the SNAP-IV and PROMIS Anger scales, which are DSM-5 scales for assessing inattention and anger of children and adolescents, could be examined. RESULTS: The reliability coefficient of SNAP-IV (Cronbach's α) was 0.94. The correlation coefficients between SNAP-IV, K-IOWA inattention, and K-ARS inattention scores ranged from 0.73 to 0.86. The mean SNAP-IV scores of the ADHD and the ADHD+anxious depression groups were significantly higher than those of the anxiety and the tic+somatic symptoms groups. The reliability coefficient of the PROMIS Anger was 0.91. The correlation coefficient between PROMIS Anger and K-IOWA oppositional/defiant scores was 0.75. The PROMIS Anger mean score of the ADHD+anxious depression group tended to be higher than that of the other groups. CONCLUSION: These results suggest that the Korean version of the DSM-5 Level 2 Cross-Cutting Symptom Measure-inattention and anger for parent and guardian of child age 6-17 might be a reliable and valid test and may be useful for screening children and adolescents with ADHD.

Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.

Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.

Au-Polypyrrole Framework Nanostructures for Improved Localized Surface Plasmon Resonance Volatile Organic Compounds Gas Sensing.

In this paper, we propose an Au-polypyrrole (Ppy) nanorod gas sensor for the detection of volatile organic compound (VOC) gases. This gas sensor operates on the principle of localized surface plasmon resonance (LSPR). The Au-Ppy nanorods used in this experiment were synthesized using an anodic aluminum oxide template by the electrochemical deposition method. Using field emission scanning electron microscopy, we confirmed that the Au-Ppy nanorod arrays were successfully fabricated with a uniform size. By depositing gold, the Au-Ppy nanorods exhibited both optical and LSPR interference. The gas sensing properties of the fabricated nanorods were tested for VOCs such as acetic acid, benzene, and toluene with a short response time (~1 min). Moreover, the proposed VOC gas sensing system was tested with three types of VOC gases over a wide concentration range from 10 to 100 ppm. Highest sensitivity was observed for acetic acid gas, which had a linear relation with the gas concentration, indicating that the system can be used as a gas sensor.

Response characterization of a fiber optic sensor array with dye-coated planar waveguide for detection of volatile organic compounds.

We have developed a multi-array side-polished optical-fiber gas sensor for the detection of volatile organic compound (VOC) gases. The side-polished optical-fiber coupled with a polymer planar waveguide (PWG) provides high sensitivity to alterations in refractive index. The PWG was fabricated by coating a solvatochromic dye with poly(vinylpyrrolidone). To confirm the effectiveness of the sensor, five different sensing membranes were fabricated by coating the side-polished optical-fiber using the solvatochromic dyes Reinhardt's dye, Nile red, 4-aminophthalimide, 4-amino-N-methylphthalimide, and 4-(dimethylamino)cinnamaldehyde, which have different polarities that cause changes in the effective refractive index of the sensing membrane owing to evanescent field coupling. The fabricated gas detection system was tested with five types of VOC gases, namely acetic acid, benzene, dimethylamine, ethanol, and toluene at concentrations of 1, 2,…,10 ppb. Second-regression and principal component analyses showed that the response properties of the proposed VOC gas sensor were linearly shifted bathochromically, and each gas showed different response characteristics.

Impacts of sensory attributes and emotional responses on the hedonic ratings of odors in dairy products.

Consumers often decide to purchase certain items as a result of prompt emotional responses evoked by the products; therefore, it is important to investigate the emotional responses stimulated by food samples, as well as the sensory attributes of the products. The aim of this study was to examine influences of sensory attribute and emotional response on olfactory hedonic ratings of dairy products. Additionally, we compared this effect between women and men subjects. Sensory attributes of six natural odors produced by commercial dairy products were evaluated by 9 trained panelists. In addition, 100 untrained panelists (50 women and 50 men) rated the emotional response stimulated by the 6 odors using 25 paired semantic differential scales and the hedonicity using a 9 cm line scale, respectively. Untrained panelists more liked "sweet aromatics," "sour aromatics," "fermented aromatics," and "rich aromatics" over the other sensory attributes of dairy products. The olfactory hedonic ratings were increased when the odor was more characterized as "fragrant," "attractive," "comfortable," "familiar," "faint," "natural," or "modern." Specific sensory attributes were found to be related to specific adjective pairs representing the emotional response. Moreover, the ratings of adjective pairs representing emotional response of odors and their influences on hedonic ratings differed significantly between sexes. In conclusion, our findings suggest that the emotional responses as well as the sensory attributes affect the hedonic ratings of odors in dairy products.