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For: Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp. 2020;41:3468-3535. [PMID: 32374075 DOI: 10.1002/hbm.25013] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 7.5] [Reference Citation Analysis]
Number Citing Articles
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5 Tejavibulya L, Rolison M, Gao S, Liang Q, Peterson H, Dadashkarimi J, Farruggia MC, Hahn CA, Noble S, Lichenstein SD, Pollatou A, Dufford AJ, Scheinost D. Predicting the future of neuroimaging predictive models in mental health. Mol Psychiatry 2022. [PMID: 35697759 DOI: 10.1038/s41380-022-01635-2] [Reference Citation Analysis]
6 Cui S, Li L, Zhang Y, Lu J, Wang X, Song X, Liu J, Li K. Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study. Adv Sci (Weinh) 2021;8:2003893. [PMID: 34026445 DOI: 10.1002/advs.202003893] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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9 Knott R, Johnson BP, Tiego J, Mellahn O, Finlay A, Kallady K, Kouspos M, Mohanakumar Sindhu VP, Hawi Z, Arnatkeviciute A, Chau T, Maron D, Mercieca EC, Furley K, Harris K, Williams K, Ure A, Fornito A, Gray K, Coghill D, Nicholson A, Phung D, Loth E, Mason L, Murphy D, Buitelaar J, Bellgrove MA. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology. Mol Autism 2021;12:55. [PMID: 34353377 DOI: 10.1186/s13229-021-00457-3] [Reference Citation Analysis]
10 Cui LB, Fu YF, Liu L, Wu XS, Xi YB, Wang HN, Qin W, Yin H. Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy. Eur J Neurosci 2021;53:1961-75. [PMID: 33206423 DOI: 10.1111/ejn.15046] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
11 Colombo F, Calesella F, Mazza MG, Melloni EMT, Morelli MJ, Scotti GM, Benedetti F, Bollettini I, Vai B. Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022;:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Reference Citation Analysis]
12 Yin W, Li T, Mucha PJ, Cohen JR, Zhu H, Zhu Z, Lin W. Altered neural flexibility in children with attention-deficit/hyperactivity disorder. Mol Psychiatry. [DOI: 10.1038/s41380-022-01706-4] [Reference Citation Analysis]
13 Shi D, Yao X, Li Y, Zhang H, Wang G, Wang S, Ren K. Classification of Parkinson’s disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach. Brain Imaging and Behavior. [DOI: 10.1007/s11682-022-00685-y] [Reference Citation Analysis]
14 Calhoun VD, Pearlson GD, Sui J. Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples. Curr Opin Neurol 2021;34:469-79. [PMID: 34054110 DOI: 10.1097/WCO.0000000000000967] [Reference Citation Analysis]
15 Jiang X, Zhou C, Ao N, Gu W, Li J, Chen Y. Scarcity Mindset Neuro Network Decoding With Reward: A Tree-Based Model and Functional Near-Infrared Spectroscopy Study. Front Hum Neurosci 2021;15:736415. [PMID: 34899213 DOI: 10.3389/fnhum.2021.736415] [Reference Citation Analysis]
16 Huang S, Lin I, Yu P, Wang B, Chung C, Huang Y, Chien W, Sun C, Wu G. Exposure of Child Maltreatment Leads to a Risk of Mental Illness and Poor Prognosis in Taiwan: A Nationwide Cohort Study from 2000 to 2015. IJERPH 2022;19:4803. [DOI: 10.3390/ijerph19084803] [Reference Citation Analysis]
17 Martin CG, He BJ, Chang C. State-related neural influences on fMRI connectivity estimation. Neuroimage 2021;244:118590. [PMID: 34560268 DOI: 10.1016/j.neuroimage.2021.118590] [Reference Citation Analysis]
18 Feng C, Huang W, Xu K, Stewart JL, Camilleri JA, Yang X, Wei P, Gu R, Luo W, Eickhoff SB. Neural substrates of motivational dysfunction across neuropsychiatric conditions: Evidence from meta-analysis and lesion network mapping. Clinical Psychology Review 2022;96:102189. [DOI: 10.1016/j.cpr.2022.102189] [Reference Citation Analysis]
19 Ji X, Zhao J, Fan L, Li H, Lin P, Zhang P, Fang S, Law S, Yao S, Wang X. Highlighting psychological pain avoidance and decision-making bias as key predictors of suicide attempt in major depressive disorder-A novel investigative approach using machine learning. J Clin Psychol 2021. [PMID: 34542183 DOI: 10.1002/jclp.23246] [Reference Citation Analysis]
20 Aryutova K, Paunova R, Kandilarova S, Todeva-Radneva A, Stoyanov D. Implications from translational cross-validation of clinical assessment tools for diagnosis and treatment in psychiatry. World J Psychiatr 2021; 11(5): 169-180 [PMID: 34046313 DOI: 10.5498/wjp.v11.i5.169] [Cited by in CrossRef: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
21 Xu M, Calhoun V, Jiang R, Yan W, Sui J. Brain imaging-based machine learning in autism spectrum disorder: methods and applications. J Neurosci Methods 2021;361:109271. [PMID: 34174282 DOI: 10.1016/j.jneumeth.2021.109271] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
22 Faria AV, Zhao Y, Ye C, Hsu J, Yang K, Cifuentes E, Wang L, Mori S, Miller M, Caffo B, Sawa A. Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup. Hum Brain Mapp 2021;42:1034-53. [PMID: 33377594 DOI: 10.1002/hbm.25276] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
23 Nielson DM, Keren H, O'Callaghan G, Jackson SM, Douka I, Vidal-Ribas P, Pornpattananangkul N, Camp CC, Gorham LS, Wei C, Kirwan S, Zheng CY, Stringaris A. Great Expectations: A Critical Review of and Suggestions for the Study of Reward Processing as a Cause and Predictor of Depression. Biol Psychiatry 2021;89:134-43. [PMID: 32797941 DOI: 10.1016/j.biopsych.2020.06.012] [Cited by in Crossref: 18] [Cited by in F6Publishing: 10] [Article Influence: 9.0] [Reference Citation Analysis]
24 Itani S, Rossignol M. At the Crossroads Between Psychiatry and Machine Learning: Insights Into Paradigms and Challenges for Clinical Applicability. Front Psychiatry 2020;11:552262. [PMID: 33192664 DOI: 10.3389/fpsyt.2020.552262] [Reference Citation Analysis]
25 Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp. 2020;41:3468-3535. [PMID: 32374075 DOI: 10.1002/hbm.25013] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 7.5] [Reference Citation Analysis]
26 Pereira-Sanchez V, Castellanos FX. Neuroimaging in attention-deficit/hyperactivity disorder. Curr Opin Psychiatry 2021;34:105-11. [PMID: 33278156 DOI: 10.1097/YCO.0000000000000669] [Reference Citation Analysis]
27 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]
28 Boer OD, El Marroun H, H A Franken I. Brain Morphology Predictors of Alcohol, Tobacco, and Cannabis Use in Adolescence: A Systematic Review. Brain Res 2022;:148020. [PMID: 35853511 DOI: 10.1016/j.brainres.2022.148020] [Reference Citation Analysis]
29 Uribe C, Junque C, Gómez-Gil E, Díez-Cirarda M, Guillamon A. Brain connectivity dynamics in cisgender and transmen people with gender incongruence before gender affirmative hormone treatment. Sci Rep 2021;11:21036. [PMID: 34702875 DOI: 10.1038/s41598-021-00508-y] [Reference Citation Analysis]
30 Meehan AJ, Danese A. Progress and barriers to the implementation of prediction modelling in child and adolescent mental health—A commentary on Senior et al. (2021). JCPP Advances 2021;1. [DOI: 10.1002/jcv2.12052] [Reference Citation Analysis]
31 Livezey JA, Glaser JI. Deep learning approaches for neural decoding across architectures and recording modalities. Brief Bioinform 2021;22:1577-91. [PMID: 33372958 DOI: 10.1093/bib/bbaa355] [Cited by in F6Publishing: 1] [Reference Citation Analysis]