The Role of Functional MRI in Understanding the Origin of Speech Delay in Autism Spectrum Disorders

Клюев, Е. А.; Sheyko, G.E.; Dunayev, M.G.; Abramov, S.A.; Dvoryaninova, V.V.; Balandina, Oksana V.; Karyakin, N. N.; Белова, А. Н.

doi:10.17691/stm2019.11.3.09

Cited by 4 publications

(8 citation statements)

References 21 publications

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“…Meanwhile, ASD groups showed a loss of this left hemisphere lateralization. Such results in those infants and toddlers who developed ASD is consistent with the hypothesis that failure of proper language development and neural circuitry is an important indicator of autism 13,15,16,40–44 …”

Section: Discussionsupporting

confidence: 85%

“…Such results in those infants and toddlers who developed ASD is consistent with the hypothesis that failure of proper language development and neural circuitry is an important indicator of autism. 13,15,16,[40][41][42][43][44] Our aforementioned inference that only selected brain ROI have variating activation responses to certain task leads to the potential of inclusion of only such brain regions in our proposed classification model. The achieved classification results support the hypothesis that efficient extraction and reduction of input feature dimensionality yields higher diagnostic accuracy.…”

Section: Discussionmentioning

confidence: 99%

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A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI

et al. 2021

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Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment. Methods: To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard-Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Second, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K-means clustering technique is performed on such significant brain areas. Informative blood oxygen level-dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step. Results: Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using tenfold cross validation (with sensitivity = 84%, specificity = 76%). Conclusion:The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI

et al. 2021

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“…Whereas those within the mild group and non-ASD group showed typical left-sided dominance in response to a language task, this larger group of ASD subjects (those with moderate and severe ADOS CSS) showed a loss of this left hemisphere lateralization. These results in those toddler and infants who developed ASD is again consistent with the hypothesis that failure of proper language development and neural circuitry is an important indicator of autism and its severity [13], [15]- [18], [20]- [22]. We used multiple classification systems to delineate the mild/non-ASD groups from moderate and severe ASD groups with an average accuracy, sensitivity and specificity of 70% to 80%.…”

Section: Discussionsupporting

confidence: 80%

“…Previous studies suggest a multi-modal approach may lead to more accurate classification [15]. These modalities may include the transcriptome, ERP/EEG, MEG, fNIRs, and DTI/Rs/Structural MRI [13]- [18], [20]- [22]. Such works need to combine evidence of MRI modalities, genetics, and early clinical behavioral assessment in larger sample sizes where the prevalence of ASD subgroups could be accurately quantitated.…”

Section: Discussionmentioning

confidence: 99%

“…These researchers investigated NBR and found reduced activation and increased functional connectivity between the STG and NBR exhibited regions in ASD compared to TD subjects. A similar study is presented in [22]. They have included two matched groups of autistic and TD, each having 6 children (5 boys and 1 girl) aged from 3.8 years to 6.6 years.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study

et al. 2021

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Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

Alam,

Raja,

Gulzar

et al. 2023

IJACSA

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Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction, verbal and non-verbal communication, and is often associated with cognitive and neurobehavioral challenges. Timely screening and diagnosis of ASD are crucial for early educational planning, treatment, family support, and timely medical intervention. Manual diagnostic methods are time-consuming and labor-intensive, underscoring the need for automated approaches to assist caretakers and parents. While various researchers have employed machine learning and deep learning techniques for ASD diagnosis, existing models often fall short in capturing the complexity of multisite meltdowns and fully leveraging the interdependence among these meltdowns for severity assessment in acquired facial images of children, hindering the development of a comprehensive grading system. This paper introduces a novel approach using a Long Short Term Memory (LSTM) integrated Convolution Neural Network (CNN) designed to identify multisite meltdowns and exploit their interdependence for severity assessment in ASD. The process begins with image pre-processing, involving discrete convolution filters for noise removal and contrast enhancement to improve image quality. The enhanced image then undergoes instance segmentation using the Segment Anything model to identify significant regions in the child's image. The segmented region is subjected to principal component analysis for feature extraction, and these features are utilized by the LSTM-integrated CNN for meltdown detection and severity classification. The model is trained using children's images extracted from videos, and testing is performed on videos captured during children's observations. Performance analysis reveals superior results, with a training accuracy of 88% and validation accuracy of 84%, outperforming conventional methods. This innovative approach not only enhances the efficiency of ASD diagnosis but also provides a more nuanced understanding of multisite meltdowns and their impact on severity, contributing to the development of a robust grading system.

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The Role of Functional MRI in Understanding the Origin of Speech Delay in Autism Spectrum Disorders

Cited by 4 publications

References 21 publications

A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI

A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI

A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study

Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

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