Multi-modality connectome-based predictive modeling of individualized compulsions in obsessive-compulsive disorder

Zhu, Chunyan; Fu, Zhao; Yu, Fengqiong; Zhang, Junfeng; Zhang, Yuxuan; Ai, Hui; Chen, Lu; Sui, Pengjiao; Wu, Qiyan; Luo, Yudan; Xu, Pengfei; Wang, Kai

doi:10.1016/j.jad.2022.05.120

Cited by 5 publications

(1 citation statement)

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“…We then found that the functional connectivity of triple networks (SN, DMN, and FPN), specifically SN-FPN, is primarily helpful in predicting individual forcing severity, with SN being a key predictor in structural prediction networks. These results suggest that structural SN and triple network jointly contribute to forcing (Zhu et al, 2022).…”

Section: Functional Magnetic Resonance Imagingmentioning

confidence: 70%

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Li,

Kang,

2023

Front. Hum. Neurosci.

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Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.

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Section: Functional Magnetic Resonance Imagingmentioning

confidence: 70%