Functional brain network alterations in the co-occurrence of autism spectrum disorder and attention deficit hyperactivity disorder

Lin, Qiwen; Shi, Yafei; Huang, Huiyuan; Jiao, Bingqing; Kuang, Changyi; Chen, Jiawen; Rao, Yuyang; Zhu, Yunpeng; Liu, Wenting; Huang, Ruiwang; Lin, Jiabao; Ma, Li

doi:10.1007/s00787-023-02165-0

Cited by 9 publications

(10 citation statements)

References 91 publications

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“…(Su et al, 2023) have shown that the PCG and IOG had abnormal brain connections. Q. Lin et al (2023) found abnormalities in the orbitofrontal and FFG.…”

Section: Discussionmentioning

confidence: 94%

“…The connection between the STG.L and ORBmid.L, and between the INS.L and ORBmid.L were significantly enhanced in the interaction between core block #9 and periphery block #10. Some studies (Q. Lin et al, 2023; Y.…”

Section: Discussionmentioning

confidence: 99%

“…Q. Lin et al (2023) found abnormalities in the orbitofrontal and FFG. The connection between the STG.L and ORBmid.L, and between the INS.L and ORBmid.L were significantly enhanced in the interaction between core block #9 and periphery block #10.…”

Section: Increased Cs Between Core and Periphery Blocks In Adhdmentioning

confidence: 97%

“…Q. Lin et al (2023) found the functional connection enhancement of the ANG in ADHD. The ORBmid belongs to the frontal lobe and is mainly related to thinking functions (Ji & Zhang, 2022).…”

Section: Increased Intra-block Cs Of Periphery Block In Adhdmentioning

confidence: 97%

“…Some studies (Q. Lin et al, 2023;Y. Tang et al, 2022) have revealed that these brain regions are abnormal.…”

Section: Increased Cs Between Core and Periphery Blocks In Adhdmentioning

confidence: 99%

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Aberrant Functional Connectivity in Core–Periphery Structure Based on WSBM in ADHD

Li,

Hou,

Zhang

et al. 2023

J Atten Disord

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Objective: Brain network studies have revealed that the community structure of ADHD is altered. However, these studies have only focused on modular community structure, ignoring the core–periphery community structure. Method: This paper employed the weighted stochastic block model to divide the functional connectivity (FC) into 10 communities. And we adopted core score to define the core–periphery structure of FC. Finally, connectivity strength (CS) and disruption index (DI) were used to evaluate the changes of core–periphery structure in ADHD. Results: The core community of visual network showed reduced CS and a positive value of DI, while the CS of periphery community was enhanced. In addition, the interaction between core communities (involving the sensorimotor and visual network) and periphery community of attention network showed increased CS and a negative valve of DI. Conclusion: Anomalies in core–periphery community structure provide a new perspective for understanding the community structure of ADHD.

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“…(Su et al, 2023) have shown that the PCG and IOG had abnormal brain connections. Q. Lin et al (2023) found abnormalities in the orbitofrontal and FFG.…”

Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Increased Cs Between Core and Periphery Blocks In Adhdmentioning

confidence: 97%

“…Q. Lin et al (2023) found the functional connection enhancement of the ANG in ADHD. The ORBmid belongs to the frontal lobe and is mainly related to thinking functions (Ji & Zhang, 2022).…”

Section: Increased Intra-block Cs Of Periphery Block In Adhdmentioning

confidence: 97%

“…Some studies (Q. Lin et al, 2023;Y. Tang et al, 2022) have revealed that these brain regions are abnormal.…”

Section: Increased Cs Between Core and Periphery Blocks In Adhdmentioning

confidence: 99%

See 3 more Smart Citations

Aberrant Functional Connectivity in Core–Periphery Structure Based on WSBM in ADHD

Li,

Hou,

Zhang

et al. 2023

J Atten Disord

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Diagnostics in Neurotribe Identification: Case Studies

Hutson,

Harper-Nichols

2024

Sensemaking and Neuroaesthetics

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ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis

Zhang,

Guo,

et al. 2024

Sci Rep

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The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.

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Functional brain network alterations in the co-occurrence of autism spectrum disorder and attention deficit hyperactivity disorder

Cited by 9 publications

References 91 publications

Aberrant Functional Connectivity in Core–Periphery Structure Based on WSBM in ADHD

Aberrant Functional Connectivity in Core–Periphery Structure Based on WSBM in ADHD

Diagnostics in Neurotribe Identification: Case Studies

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis

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