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1. ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data

   https://doi.org/10.3389/fncom.2021.654315  

   Almuqhim, Fahad; Saeed, Fahad (April 2021, Frontiers in Computational Neuroscience)

   null (Ed.)

   Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understanding of the neurological mechanisms underlying ASD necessitates the identification of new biomarkers that can aid in study of brain development, and functioning, and can lead to accurate and early detection of ASD. In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features that can be used for classification. These features are then fed into a deep neural network (DNN) which results in superior classification of fMRI brain scans more prone to ASD. Our proposed model is trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error. We evaluated our proposed deep-learning model using publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset collected from 17 different research centers, and include more than 1,035 subjects. Our extensive experimentation demonstrate that ASD-SAENet exhibits comparable accuracy (70.8%), and superior specificity (79.1%) for the whole dataset as compared to other methods. Further, our experiments demonstrate superior results as compared to other state-of-the-art methods on 12 out of the 17 imaging centers exhibiting superior generalizability across different data acquisition sites and protocols. The implemented code is available on GitHub portal of our lab at: https://github.com/pcdslab/ASD-SAENet . 

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2. Vocational Interventions for Individuals with ASD: Umbrella Review

   https://doi.org/10.1007/s40489-023-00368-4  

   Tincani, Matt; Ji, Hyangeun; Upthegrove, Maddie; Garrison, Elizabeth; West, Michael; Hantula, Donald; Vucetic, Slobodan; Dragut, Eduard (January 2023, Review Journal of Autism and Developmental Disorders)
3. A novel approach to training attention and gaze in ASD: A feasibility and efficacy pilot study: Training Attention and Gaze in ASD

   https://doi.org/10.1002/dneu.22563  

   Chukoskie, Leanne; Westerfield, Marissa; Townsend, Jeanne (May 2018, Developmental Neurobiology)
4. Heightened sensory sensitivity and subsequent engagement among individuals with ASD

   https://doi.org/10.1016/j.jpsychires.2025.07.032  

   Barnard-Brak, Lucy; Hutchison, Amy; Renda, Caitlin (August 2025, Journal of Psychiatric Research)
5. Robot-mediated Group Instruction for Children with ASD: A Pilot Study

   https://doi.org/10.1109/RO-MAN53752.2022.9900584  

   Trombly, Madeline; Shahverdi, Pourya; Huang, Nathan; Chen, Qinghua; Korneder, Jessica; Louie, Wing-Yue Geoffrey (August 2022, 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN))

   Children diagnosed with autism spectrum disorder (ASD) typically work towards acquiring skills to participate in a regular classroom setting such as attending and appropriately responding to an instructor’s requests. Social robots have the potential to support children with ASD in learning group-interaction skills. However, the majority of studies that target children with ASD’s interactions with social robots have been limited to one-on-one interactions. Group interaction sessions present unique challenges such as the unpredictable behaviors of the other children participating in the group intervention session and shared attention from the instructor. We present the design of a robot-mediated group interaction intervention for children with ASD to enable them to practice the skills required to participate in a classroom. We also present a study investigating differences in children's learning behaviors during robot-led and human-led group interventions over multiple intervention sessions. Results of this study suggests that children with ASD's learning behaviors are similar during human and robot instruction. Furthermore, preliminary results of this study suggest that a novelty effect was not observed when children interacted with the robot over multiple sessions. 

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