A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks

Ning, Qiang; Gao, Jie; Dong, Qinglin; Li, Jin; Zhang, Shu; Liang, Hongtao; Sun, Yifei; Ge, Bao; Liu, Zhengliang; Wu, Zihao; Liu, Tianming; Yue, Huiji; Zhao, Shijie

doi:10.1016/j.bbr.2023.114603

Cited by 8 publications

(7 citation statements)

References 52 publications

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“…Recent trends in research also highlight the profound impact of technological advancements on ASD studies. Advancements in techniques such as single-cell RNA sequencing ( 65 ), brain imaging technologies [such as fMRI ( 66 ) and MEG ( 67 )], and genomics, among others, have provided us with opportunities for a deeper understanding of the mechanisms and characteristics of ASD.…”

Section: Discussionmentioning

confidence: 99%

A bibliometric analysis of autism spectrum disorder signaling pathways research in the past decade

Lyu,

Li,

Chen

et al. 2024

Front. Psychiatry

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BackgroundThis study employs bibliometric methods to comprehensively understand the fundamental structure of research about Autism Spectrum Disorder (ASD) Signaling Pathways by examining key indicators such as nations, institutions, journals, authors, and keywords.MethodologyWe utilized the WoScc database to retrieve literature relevant to ASD Signaling Pathways published between 2013 and 2023. Through visual analysis and tools like CiteSpace and VosViewer, we explored nations, institutions, journals, authors, and keywords, thereby constructing relevant networks.Results26 The study encompasses 1,396 articles, revealing a consistent increase in publications. The United States, China, and Germany are leading nations in this literature. Regarding research institutions, the University of California system and Eric Klann have garnered significant attention due to their substantial contributions to the field of ASD Signaling Pathways. Most relevant research is published in the journal “Molecular Autism.” Research interests are concentrated across various themes, including “elevating neuronal β-catenin levels,” “Tunisian children,” “Fmr1 knockout (KO) mice,” “de novo mutations,” “autistic children,” “local translation,” “propionic acid-induced mouse models,” “neurosystems,” “glucose metabolism,” and “neuronal migration.” Future research may emphasize exploring aspects such as gut microbiota, genes, stress, maternal immune activation, memory, and neurodevelopmental disorders of ASD.ConclusionThis study, through bibliometric analysis of key indicators such as nations, institutions, journals, authors, and keywords, provides a comprehensive overview of the current state of research on ASD Signaling Pathways. These investigations predominantly focus on molecular mechanisms, animal model studies, population-based research, and the structure and function of neurosystems. Future research directions are also clearly proposed. First, in-depth research on the genes and neurodevelopmental disorders associated with ASD will continue to reveal the genetic basis and provide support for precise treatments. At the same time, attention to the gut microbiota will help explore its association with ASD, which may provide clues for new treatments. In addition, the relationship between stress and ASD will become the focus of research to understand better the emotional and behavioral characteristics of ASD patients in stressful situations. Maternal immune activation will also be further studied to explore how environmental factors influence the risk and development of ASD. Finally, a deeper understanding of the cognitive functions of patients with ASD, especially memory and learning, will help develop individualized treatment strategies to improve patients’ quality of life. These directions will work together and are expected to provide a more comprehensive understanding of Signaling Pathways research in ASD and provide new ideas and opportunities for future intervention and treatment.

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Section: Discussionmentioning

confidence: 99%

A bibliometric analysis of autism spectrum disorder signaling pathways research in the past decade

Lyu,

Li,

Chen

et al. 2024

Front. Psychiatry

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“…The subject provides the address (line 9) and the nurse then states the details of the possible help on the way, as well as furnishes his/ her details for a call back in case of any problem (line 10). The dialogue ends in the exchange of greetings between the subject and the nurse (lines [11][12]. The block diagram of the sample dialog is shown in Fig.…”

Section: Scenario 6: Reporting the Fall Of Elderly At Homementioning

confidence: 99%

“…Early detection of ASD is better because as the age of a person increases, the detection of ASD becomes difficult due to the onset of overlap of it's symptoms with those of other diseases. Lately, ASD has gained a lot of attention and interest as a research field, which is evident from numerous recent publications [6][7][8][9][10][11][12]. In majority of these works, machine learning (ML)/ deep learning (DL) models are trained on the (publicly) available datasets and then their performances are tested using the instances from the publicly available datasets or through a set of real-life data values.…”

Section: Introductionmentioning

confidence: 99%

“…We feel that the use of artificial intelligence (AI) for design of patient centric platforms is quite crucial for achieving this SDG. In all the published works [6][7][8][9][10][11][12], the focus has been 1 Cognitive-based neurological disorders encompass a wide spectrum of conditions affecting the cognitive function(s) in children, with diverse causes, symptoms, and impacts. These disorders are classified into different categories based on their underlying pathology.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Autism Spectrum Disorder (ASD) care agent for social interaction

Gupta,

Mazumdar,

Komaragiri

et al. 2024

Preprint

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Autism Spectrum Disorder (ASD) is a neurological disorder that impacts a subject's ability to be involved in a social interaction. A large body of work exists on the detection of ASD using machine learning (ML) and deep learning (DL) algorithms. Further the use of eXplainable artificial intelligence (XAI) algorithms is being advocated in the healthcare domain (on account of the 'black-box' nature of DL algorithms), to the best of our knowledge, no social interaction model based eXplainable Autism Spectrum Disorder (ASD) care agent exists for social interaction training of an ASD subject. Therefore, we make a beginning in this direction through this work. We present a novel architecture of an eXplainable ASD care agent for social interaction training of ASD subjects, with an underlying intelligent AI algorithm. We elucidate its working through six representative social interaction scenarios and put forward some practical considerations when the agent is put in practice with the ASD subject. We hope that the interested researchers can develop further on the work in future.

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“…A cluster of articles [83,85,88,90,93,95,99,101,104,106,111,113,114] focuses on using advanced computational techniques, including machine learning, deep learning, and graph analysis, to classify and diagnose autism. These articles represent the growing interest in leveraging data-driven approaches to understand and categorize individuals with ASD.…”

Section: Machine Learning and Graph Analysis For Asd Classificationmentioning

confidence: 99%

An Umbrella Review of the Fusion of fMRI and AI in Autism

Giansanti

2023

Diagnostics

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The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study’s objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.

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A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks

Cited by 8 publications

References 52 publications

A bibliometric analysis of autism spectrum disorder signaling pathways research in the past decade

A bibliometric analysis of autism spectrum disorder signaling pathways research in the past decade

Towards an Autism Spectrum Disorder (ASD) care agent for social interaction

An Umbrella Review of the Fusion of fMRI and AI in Autism

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