1. Bishop SL, Lord C. Commentary: Best practices and processes for assessment of autism spectrum disorder – the intended role of standardized diagnostic instruments. J Child Psychol Psychiatry;2023 (Apr 2)

Development of standardized diagnostic instruments has facilitated the systematic characterization of individuals with autism spectrum disorders (ASD) in clinical and research settings. However, overemphasis on scores from specific instruments has significantly detracted from the original purpose of these tools. Rather than provide a definitive « answer, » or even a confirmation of diagnosis, standardized diagnostic instruments were designed to aid clinicians in the process of gathering information about social communication, play, and repetitive and sensory behaviors relevant to diagnosis and treatment planning. Importantly, many autism diagnostic instruments are not validated for certain patient populations, including those with severe vision, hearing, motor, and/or cognitive impairments, and they cannot be administered via a translator. In addition, certain circumstances, such as the need to wear personal protective equipment (PPE), or behavioral factors (e.g., selective mutism) may interfere with standardized administration or scoring procedures, rendering scores invalid. Thus, understanding the uses and limitations of specific tools within specific clinical or research populations, as well as similarities or differences between these populations and the instrument validation samples, is paramount. Accordingly, payers and other systems must not mandate the use of specific tools in cases when their use would be inappropriate. To ensure equitable access to appropriate assessment and treatment services, it is imperative that diagnosticians be trained in best practice methods for the assessment of autism, including if, how, and when to appropriately employ standardized diagnostic instruments.

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2. Veenstra-VanderWeele J, O’Reilly KC, Dennis MY, Uribe-Salazar JM, Amaral DG. Translational Neuroscience Approaches to Understanding Autism. Am J Psychiatry;2023 (Apr 1);180(4):265-276.

While autism spectrum disorder affects nearly 2% of children in the United States, little is known with certainty concerning the etiologies and brain systems involved. This is due, in part, to the substantial heterogeneity in the presentation of the core symptoms of autism as well as the great number of co-occurring conditions that are common in autistic individuals. Understanding the neurobiology of autism is further hampered by the limited availability of postmortem brain tissue to determine the cellular and molecular alterations that take place in the autistic brain. Animal models therefore provide great translational value in helping to define the neural systems that constitute the social brain and mediate repetitive behaviors or interests. If they are based on genetic or environmental factors that contribute to autism, organisms from flies to nonhuman primates may serve as models of the neural structure or function of the autistic brain. Ultimately, successful models can also be employed to test the safety and effectiveness of potential therapeutics. This is an overview of the major animal species that are currently used as models of autism, including an appraisal of the advantages and limitations of each.

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3. Zheng J, Shao L, Yan Z, Lai X, Duan F. Study subnetwork developing pattern of autism children by non-negative matrix factorization. Comput Biol Med;2023 (Mar 22);158:106816.

BACKGROUND: As a developmental disorder, the brain networks of autism children show abnormal patterns compared with that of typically developing. The differences between them are not stable due to the developing progress of children. It has become a choice to study the differences of developing trajectories between autistic and typically developing children by investigating the change of each group respectively. Related researches studied the developing of brain network by analyzing the relationship between network indices of the entire or sub brain networks and the cognitive developing scores. METHODS: As a matrix decomposition algorithm, non-negative matrix factorization (NMF) was applied to decompose the association matrices of brain networks. By NMF, we can obtain subnetworks in an unsupervised way. The association matrices of autism and control children were estimated by their magnetoencephalography data. NMF was applied to decompose the matrices to obtain common subnetworks of both groups. Then we calculated the expression of each subnetwork in each child’s brain network by two indices, energy and entropy. The relationship between the expression and the cognitive and development indices were investigated. RESULTS: We found a subnetwork with left lateralization pattern in α band showed different expression tendency in two groups. The expression indices of two groups were correlated with cognitive indices in autism and control group in an opposite way. In γ band, a subnetwork with strong connections on right hemisphere of brain showed a negative correlation between the expression indices and development indices in autism group. CONCLUSION: NMF algorithm can effectively decompose brain network to meaningful subnetworks. The finding of α band subnetworks confirms the results of abnormal lateralization of autistic children mentioned in relevant studies. We assume the results of decrease of expression of the subnetwork may relate to the dysfunction of mirror neuron. The decrease expression of γ subnetwork of autism may be related to the weaken process of high-frequency neurons in the neurotrophic competition.

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