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For: Steardo L Jr, Carbone EA, de Filippis R, Pisanu C, Segura-Garcia C, Squassina A, De Fazio P, Steardo L. Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review. Front Psychiatry 2020;11:588. [PMID: 32670113 DOI: 10.3389/fpsyt.2020.00588] [Cited by in Crossref: 9] [Cited by in F6Publishing: 26] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Zhou Y, Tang J, Sun Y, Yang WFZ, Ma Y, Wu Q, Chen S, Wang Q, Hao Y, Wang Y, Li M, Liu T, Liao Y. A brainnetome atlas-based methamphetamine dependence identification using neighborhood component analysis and machine learning on functional MRI data. Front Cell Neurosci 2022;16:958437. [DOI: 10.3389/fncel.2022.958437] [Reference Citation Analysis]
2 Levman J, Jennings M, Rouse E, Berger D, Kabaria P, Nangaku M, Gondra I, Takahashi E. A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning. Front Neurosci 2022;16:926426. [DOI: 10.3389/fnins.2022.926426] [Reference Citation Analysis]
3 Chu Y, Wu J, Wang D, Huang J, Li W, Zhang S, Ren H. Altered voxel-mirrored homotopic connectivity in right temporal lobe epilepsy as measured using resting-state fMRI and support vector machine analyses. Front Psychiatry 2022;13:958294. [DOI: 10.3389/fpsyt.2022.958294] [Reference Citation Analysis]
4 Liu L, Fan J, Zhan H, Huang J, Cao R, Xiang X, Tian S, Ren H, Tong M, Li Q. Abnormal regional signal in the left cerebellum as a potential neuroimaging biomarker of sudden sensorineural hearing loss. Front Psychiatry 2022;13:967391. [DOI: 10.3389/fpsyt.2022.967391] [Reference Citation Analysis]
5 Erdoğan SB, Yükselen G. Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers. Sensors 2022;22:5407. [DOI: 10.3390/s22145407] [Reference Citation Analysis]
6 Vergara VM, Espinoza FA, Calhoun VD. Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers. Front Psychol 2022;13:867067. [PMID: 35756267 DOI: 10.3389/fpsyg.2022.867067] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Song P, Wang Y, Yuan X, Wang S, Song X. Exploring Brain Structural and Functional Biomarkers in Schizophrenia via Brain-Network-Constrained Multi-View SCCA. Front Neurosci 2022;16:879703. [DOI: 10.3389/fnins.2022.879703] [Reference Citation Analysis]
8 Solanes A, Radua J. Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There? Front Psychiatry 2022;13:fpsyt-13-826111. [PMID: 35492715 DOI: 10.3389/fpsyt.2022.826111] [Reference Citation Analysis]
9 Li Y, Zhou Z, Li Q, Li T, Julian IN, Guo H, Chen J. Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network. Front Neurosci 2022;16:889105. [DOI: 10.3389/fnins.2022.889105] [Reference Citation Analysis]
10 He M, Cheng Y, Chu Z, Wang X, Xu J, Lu Y, Shen Z, Xu X. White Matter Network Disruption Is Associated With Melancholic Features in Major Depressive Disorder. Front Psychiatry 2022;13:816191. [DOI: 10.3389/fpsyt.2022.816191] [Reference Citation Analysis]
11 Li Y, Li Q, Li T, Zhou Z, Xu Y, Yang Y, Chen J, Guo H. Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data. Front Neurosci 2022;16:848363. [DOI: 10.3389/fnins.2022.848363] [Reference Citation Analysis]
12 Yan M, Chen J, Liu F, Li H, Zhao J, Guo W. Abnormal Default Mode Network Homogeneity in Major Depressive Disorder With Gastrointestinal Symptoms at Rest. Front Aging Neurosci 2022;14:804621. [DOI: 10.3389/fnagi.2022.804621] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Gao Y, Xiong Z, Wang X, Ren H, Liu R, Bai B, Zhang L, Li D. Abnormal degree centrality as a potential imaging biomarker for right temporal lobe epilepsy: A resting-state fMRI study and support vector machine analysis. Neuroscience 2022. [DOI: 10.1016/j.neuroscience.2022.02.004] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 12.0] [Reference Citation Analysis]
14 Saba T, Rehman A, Shahzad MN, Latif R, Bahaj SA, Alyami J. Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging. Microsc Res Tech 2022. [PMID: 35088496 DOI: 10.1002/jemt.24065] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Jayakumar S, Sounderajah V, Normahani P, Harling L, Markar SR, Ashrafian H, Darzi A. Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. NPJ Digit Med 2022;5:11. [PMID: 35087178 DOI: 10.1038/s41746-021-00544-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Wang J, Ke P, Zang J, Wu F, Wu K. Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study. Front Neurosci 2022;15:785595. [DOI: 10.3389/fnins.2021.785595] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Shoeibi A, Sadeghi D, Moridian P, Ghassemi N, Heras J, Alizadehsani R, Khadem A, Kong Y, Nahavandi S, Zhang YD, Gorriz JM. Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models. Front Neuroinform 2021;15:777977. [PMID: 34899226 DOI: 10.3389/fninf.2021.777977] [Cited by in Crossref: 2] [Cited by in F6Publishing: 20] [Article Influence: 2.0] [Reference Citation Analysis]
18 de Souza Filho EM, Fernandes FA, Portela MGR, Newlands PH, de Carvalho LND, Dos Santos TF, Dos Santos AASMD, Mesquita ET, Seixas FL, Mesquita CT, Gismondi RA. Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging. Front Cardiovasc Med 2021;8:741679. [PMID: 34778403 DOI: 10.3389/fcvm.2021.741679] [Reference Citation Analysis]
19 Kuai H, Zhong N, Chen J, Yang Y, Zhang X, Liang P, Imamura K, Ma L, Wang H. Multi-source brain computing with systematic fusion for smart health. Information Fusion 2021;75:150-67. [DOI: 10.1016/j.inffus.2021.03.009] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
20 Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021;13:162. [PMID: 34583745 DOI: 10.1186/s13195-021-00900-w] [Cited by in F6Publishing: 9] [Reference Citation Analysis]
21 Kim S, Baek JH, Kwon YJ, Lee HY, Yoo JH, Shim SH, Kim JS. Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity. Transl Psychiatry 2021;11:484. [PMID: 34537812 DOI: 10.1038/s41398-021-01604-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
22 Park SM, Jeong B, Oh DY, Choi CH, Jung HY, Lee JY, Lee D, Choi JS. Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach. Front Psychiatry 2021;12:707581. [PMID: 34483999 DOI: 10.3389/fpsyt.2021.707581] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
23 Zhang Z, Li G, Xu Y, Tang X. Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics (Basel) 2021;11:1402. [PMID: 34441336 DOI: 10.3390/diagnostics11081402] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
24 Foo LL, Ng WY, Lim GYS, Tan TE, Ang M, Ting DSW. Artificial intelligence in myopia: current and future trends. Curr Opin Ophthalmol 2021;32:413-24. [PMID: 34310401 DOI: 10.1097/ICU.0000000000000791] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
25 Potvin S, Giguère CÉ, Mendrek A. Functional Connectivity During Visuospatial Processing in Schizophrenia: A Classification Study Using Lasso Regression. Neuropsychiatr Dis Treat 2021;17:1077-87. [PMID: 33888984 DOI: 10.2147/NDT.S304434] [Reference Citation Analysis]
26 Jia C, Ou Y, Chen Y, Li P, Lv D, Yang R, Zhong Z, Sun L, Wang Y, Zhang G, Guo H, Sun Z, Wang W, Wang Y, Wang X, Guo W. Decreased Resting-State Interhemispheric Functional Connectivity in Medication-Free Obsessive-Compulsive Disorder. Front Psychiatry 2020;11:559729. [PMID: 33101081 DOI: 10.3389/fpsyt.2020.559729] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
27 Guo Y, Qiu J, Lu W. Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions. Brain Sci 2020;10:E562. [PMID: 32824267 DOI: 10.3390/brainsci10080562] [Cited by in Crossref: 1] [Cited by in F6Publishing: 8] [Article Influence: 0.5] [Reference Citation Analysis]