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Cited by in F6Publishing
For: Wang Y, Qin Y, Li H, Yao D, Sun B, Gong J, Dai Y, Wen C, Zhang L, Zhang C, Luo C, Zhu T. Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study. Front Neurosci 2021;15:665578. [PMID: 34220426 DOI: 10.3389/fnins.2021.665578] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Liu JL, Sun JT, Hu HL, Wang HY, Kang YX, Chen TQ, Chen ZH, Shang YX, Li YT, Hu B, Liu R. Structural and Functional Neural Alterations in Internet Addiction: A Study Protocol for Systematic Review and Meta-Analysis. Psychiatry Investig 2023;20:69-74. [PMID: 36721888 DOI: 10.30773/pi.2021.0383] [Reference Citation Analysis]
2 Sun S, Yang J, Chen Y, Miao J, Sawan M. EEG Signals Based Internet Addiction Diagnosis Using Convolutional Neural Networks. Applied Sciences 2022;12:6297. [DOI: 10.3390/app12136297] [Reference Citation Analysis]
3 Ramos H, Alacreu M, Guerrero MD, Sánchez R, Moreno L. Lifestyle Variables Such as Daily Internet Use, as Promising Protective Factors against Cognitive Impairment in Patients with Subjective Memory Complaints. Preliminary Results. J Pers Med 2021;11:1366. [PMID: 34945838 DOI: 10.3390/jpm11121366] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]