scholarly journals Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder

2019 ◽  
Vol 73 (7) ◽  
pp. 409-415 ◽  
Author(s):  
Bun Yamagata ◽  
Takashi Itahashi ◽  
Junya Fujino ◽  
Haruhisa Ohta ◽  
Osamu Takashio ◽  
...  
2018 ◽  
Vol 13 (7) ◽  
pp. 765-773 ◽  
Author(s):  
Bun Yamagata ◽  
Takashi Itahashi ◽  
Motoaki Nakamura ◽  
Masaru Mimura ◽  
Ryu-Ichiro Hashimoto ◽  
...  

Autism ◽  
2020 ◽  
pp. 136236132095005
Author(s):  
Trenesha L Hill ◽  
Tiffany C White ◽  
Bruno J Anthony ◽  
Judy Reaven ◽  
Bryn Harris ◽  
...  

There is often a large time gap between caregivers’ initial concerns and the diagnosis of autism spectrum disorder. The current study aimed to identify factors associated with missed or delayed autism spectrum disorder diagnoses among children in Colorado. In a surveillance-based sample of 8-year-old children with autism spectrum disorder ( N = 572), we examined differences between children who were identified with autism spectrum disorder by a community provider and/or were eligible for special education services under an autism eligibility (documented diagnosis) and children who were first identified with autism spectrum disorder through a systematic record review (newly identified). Compared to documented diagnosis children, newly identified children were more likely to be female, aggressive, and argumentative. They were less likely to have had a developmental regression, sleep abnormalities, or an autism screener or diagnostic measure in their records. Newly identified children also had a poorer quality of information in their records. Furthermore, among documented diagnosis children, variations in clinical presentations were associated with significantly different mean ages at autism spectrum disorder diagnosis; children who showed early delays, motor abnormalities, hyperactivity and attention deficits, and odd responses to sensory stimuli received a diagnosis much earlier than documented diagnosis children with other clinical presentations. Lay abstract Although autism can be reliably diagnosed as early as 2 years of age, many children are not diagnosed with autism until much later. We analyzed data to determine why many of the 8-year-old children who resided in Colorado and were identified as having autism through a review of their health and/or educational records did not have a documented clinical diagnosis of autism and were not eligible for special education services under an autism eligibility. We found that children who did not have a documented clinical diagnosis of autism and were not eligible for special education services under an autism eligibility were more likely to be female, aggressive, and argumentative. They had a poorer quality of information in their records and were less likely to have had a developmental regression, sleep problems, or an autism screener or diagnostic measure in their records. These results suggest that the symptoms characteristic of autism among this group of children may have been attributed to another disorder and that clinicians may be able to recognize autism more readily in children with more functional impairment and those who experience a developmental regression. We also discovered that differences in symptom presentations among children who had a documented clinical diagnosis of autism and/or were eligible for special education services under an autism eligibility were associated with different ages at autism diagnosis.


Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Ke Niu ◽  
Jiayang Guo ◽  
Yijie Pan ◽  
Xin Gao ◽  
Xueping Peng ◽  
...  

Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.


2019 ◽  
Author(s):  
Bun Yamagata ◽  
Takashi Itahashi ◽  
Junya Fujino ◽  
Haruhisa Ohta ◽  
Osamu Takashio ◽  
...  

AbstractAimPrior structural MRI studies demonstrated atypical gray matter characteristics in siblings of individuals with autism spectrum disorder (ASD). However, they did not clarify which aspect of gray matter presents the endophenotype. Further, because they did not enroll siblings of TD people, they underestimated the difference between individuals with ASD and their unaffected siblings. The current study aimed to solve these questions.MethodsWe recruited 30 pairs of adult male siblings (15 of them have an ASD endophenotype, other 15 pairs not) and focused on four gray matter parameters: cortical volume and three surface-based parameters (cortical thickness, fractal dimension, and sulcal depth [SD]). First, we sought to identify a pattern of an ASD endophenotype, comparing the four parameters. Then, we compared individuals with ASD and their unaffected siblings in the cortical parameters to identify neural correlates for the clinical diagnosis accounting for the difference between TD siblings.ResultsA sparse logistic regression with a leave-one-pair-out cross-validation showed the highest accuracy for the identification of an ASD endophenotype (73.3%) with the SD compared with the other three parameters. A bootstrapping analysis accounting for the difference in the SD between TD siblings showed a significantly large difference between individuals with ASD and their unaffected siblings in six out of 68 regions-of-interest accounting for multiple comparisons.ConclusionsThis proof-of-concept study suggests that an ASD endophenotype emerges in SD and that neural correlates for the clinical diagnosis can be dissociated from the endophenotype when we accounted for the difference between TD siblings. (248/250 words)


2020 ◽  
Vol 29 (4) ◽  
pp. 1783-1797
Author(s):  
Kelly L. Coburn ◽  
Diane L. Williams

Purpose Neurodevelopmental processes that begin during gestation and continue throughout childhood typically support language development. Understanding these processes can help us to understand the disruptions to language that occur in neurodevelopmental conditions, such as autism spectrum disorder (ASD). Method For this tutorial, we conducted a focused literature review on typical postnatal brain development and structural and functional magnetic resonance imaging, diffusion tensor imaging, magnetoencephalography, and electroencephalography studies of the neurodevelopmental differences that occur in ASD. We then integrated this knowledge with the literature on evidence-based speech-language intervention practices for autistic children. Results In ASD, structural differences include altered patterns of cortical growth and myelination. Functional differences occur at all brain levels, from lateralization of cortical functions to the rhythmic activations of single neurons. Neuronal oscillations, in particular, could help explain disrupted language development by elucidating the timing differences that contribute to altered functional connectivity, complex information processing, and speech parsing. Findings related to implicit statistical learning, explicit task learning, multisensory integration, and reinforcement in ASD are also discussed. Conclusions Consideration of the neural differences in autistic children provides additional scientific support for current recommended language intervention practices. Recommendations consistent with these neurological findings include the use of short, simple utterances; repetition of syntactic structures using varied vocabulary; pause time; visual supports; and individualized sensory modifications.


2020 ◽  
Vol 29 (2) ◽  
pp. 890-902
Author(s):  
Lynn Kern Koegel ◽  
Katherine M. Bryan ◽  
Pumpki Lei Su ◽  
Mohini Vaidya ◽  
Stephen Camarata

Purpose The purpose of this systematic review was to identify parent education procedures implemented in intervention studies focused on expressive verbal communication for nonverbal (NV) or minimally verbal (MV) children with autism spectrum disorder (ASD). Parent education has been shown to be an essential component in the habilitation of individuals with ASD. Parents of individuals with ASD who are NV or MV may particularly benefit from parent education in order to provide opportunities for communication and to support their children across the life span. Method ProQuest databases were searched between the years of 1960 and 2018 to identify articles that targeted verbal communication in MV and NV individuals with ASD. A total of 1,231 were evaluated to assess whether parent education was implemented. We found 36 studies that included a parent education component. These were reviewed with regard to (a) the number of participants and participants' ages, (b) the parent education program provided, (c) the format of the parent education, (d) the duration of the parent education, (e) the measurement of parent education, and (f) the parent fidelity of implementation scores. Results The results of this analysis showed that very few studies have included a parent education component, descriptions of the parent education programs are unclear in most studies, and few studies have scored the parents' implementation of the intervention. Conclusions Currently, there is great variability in parent education programs in regard to participant age, hours provided, fidelity of implementation, format of parent education, and type of treatment used. Suggestions are made to provide both a more comprehensive description and consistent measurement of parent education programs.


2020 ◽  
Vol 29 (1) ◽  
pp. 327-334 ◽  
Author(s):  
Allison Gladfelter ◽  
Cassidy VanZuiden

Purpose Although repetitive speech is a hallmark characteristic of autism spectrum disorder (ASD), the contributing factors that influence repetitive speech use remain unknown. The purpose of this exploratory study was to determine if the language context impacts the amount and type of repetitive speech produced by children with ASD. Method As part of a broader word-learning study, 11 school-age children with ASD participated in two different language contexts: storytelling and play. Previously collected language samples were transcribed and coded for four types of repetitive speech: immediate echolalia, delayed echolalia, verbal stereotypy, and vocal stereotypy. The rates and proportions of repetitive speech were compared across the two language contexts using Wilcoxon signed-ranks tests. Individual characteristics were further explored using Spearman correlations. Results The children produced lower rates of repetitive speech during the storytelling context than the play-based context. Only immediate echolalia differed between the two contexts based on rate and approached significance based on proportion, with more immediate echolalia produced in the play-based context than in the storytelling context. There were no significant correlations between repetitive speech and measures of social responsiveness, expressive or receptive vocabulary, or nonverbal intelligence. Conclusions The children with ASD produced less immediate echolalia in the storytelling context than in the play-based context. Immediate echolalia use was not related to social skills, vocabulary, or nonverbal IQ scores. These findings offer valuable insights into better understanding repetitive speech use in children with ASD.


2020 ◽  
Vol 29 (2) ◽  
pp. 586-596 ◽  
Author(s):  
Kaitlyn A. Clarke ◽  
Diane L. Williams

Purpose The aim of this research study was to examine common practices of speech-language pathologists (SLPs) who work with children with autism spectrum disorder (ASD) with respect to whether or not SLPs consider processing differences in ASD or the effects of input during their instruction. Method Following a qualitative research method, how SLPs instruct and present augmentative and alternative communication systems to individuals with ASD, their rationale for method selection, and their perception of the efficacy of selected interventions were probed. Semistructured interviews were conducted as part of an in-depth case report with content analysis. Results Based on completed interviews, 4 primary themes were identified: (a) instructional method , (b) input provided , (c) decision-making process , and (d) perceived efficacy of treatment . Additionally, one secondary theme, training and education received , was identified . Conclusions Clinicians reported making decisions based on the needs of the child; however, they also reported making decisions based on the diagnostic category that characterized the child (i.e., ASD). The use of modeling when teaching augmentative and alternative communication to individuals with ASD emerged as a theme, but variations in the method of modeling were noted. SLPs did not report regularly considering processing differences in ASD, nor did they consider the effects of input during instruction.


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