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For: Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017;145:137-65. [PMID: 27012503 DOI: 10.1016/j.neuroimage.2016.02.079] [Cited by in Crossref: 398] [Cited by in F6Publishing: 307] [Article Influence: 66.3] [Reference Citation Analysis]
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11 Chauhan S, Vig L, De Filippo De Grazia M, Corbetta M, Ahmad S, Zorzi M. A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images. Front Neuroinform 2019;13:53. [PMID: 31417388 DOI: 10.3389/fninf.2019.00053] [Cited by in Crossref: 18] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
12 Fratello M, Caiazzo G, Trojsi F, Russo A, Tedeschi G, Tagliaferri R, Esposito F. Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination. Neuroinformatics 2017;15:199-213. [PMID: 28210983 DOI: 10.1007/s12021-017-9324-2] [Cited by in Crossref: 18] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
13 Calhoun V. Data-driven approaches for identifying links between brain structure and function in health and disease. Dialogues Clin Neurosci 2018;20:87-99. [PMID: 30250386 [PMID: 30250386 DOI: 10.31887/dcns.2018.20.2/vcalhoun] [Cited by in Crossref: 10] [Article Influence: 2.5] [Reference Citation Analysis]
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15 Garcia-Dias R, Scarpazza C, Baecker L, Vieira S, Pinaya WHL, Corvin A, Redolfi A, Nelson B, Crespo-Facorro B, McDonald C, Tordesillas-Gutiérrez D, Cannon D, Mothersill D, Hernaus D, Morris D, Setien-Suero E, Donohoe G, Frisoni G, Tronchin G, Sato J, Marcelis M, Kempton M, van Haren NEM, Gruber O, McGorry P, Amminger P, McGuire P, Gong Q, Kahn RS, Ayesa-Arriola R, van Amelsvoort T, Ortiz-García de la Foz V, Calhoun V, Cahn W, Mechelli A. Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners. Neuroimage 2020;220:117127. [PMID: 32634595 DOI: 10.1016/j.neuroimage.2020.117127] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 6.0] [Reference Citation Analysis]
16 Rangaprakash D, Tadayonnejad R, Deshpande G, O'Neill J, Feusner JD. FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response. Brain Imaging Behav 2021;15:1622-40. [PMID: 32761566 DOI: 10.1007/s11682-020-00358-8] [Reference Citation Analysis]
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18 Ma X, Wang XH, Li L. Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony. Neurosci Lett 2021;742:135519. [PMID: 33246027 DOI: 10.1016/j.neulet.2020.135519] [Reference Citation Analysis]
19 Benkarim O, Paquola C, Park BY, Kebets V, Hong SJ, Vos de Wael R, Zhang S, Yeo BTT, Eickenberg M, Ge T, Poline JB, Bernhardt BC, Bzdok D. Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging. PLoS Biol 2022;20:e3001627. [PMID: 35486643 DOI: 10.1371/journal.pbio.3001627] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
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23 Vargason T, Grivas G, Hollowood-Jones KL, Hahn J. Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements. Semin Pediatr Neurol 2020;34:100803. [PMID: 32446437 DOI: 10.1016/j.spen.2020.100803] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
24 Öztekin I, Finlayson MA, Graziano PA, Dick AS. Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation. Dev Cogn Neurosci 2021;49:100966. [PMID: 34044207 DOI: 10.1016/j.dcn.2021.100966] [Reference Citation Analysis]
25 Appaji A, Harish V, Korann V, Devi P, Jacob A, Padmanabha A, Kumar V, Varambally S, Venkatasubramanian G, Rao SV, Suma HN, Webers CAB, Berendschot TTJM, Rao NP. Deep learning model using retinal vascular images for classifying schizophrenia. Schizophr Res 2022;241:238-43. [PMID: 35176722 DOI: 10.1016/j.schres.2022.01.058] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
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27 Salvador R, Canales-Rodríguez E, Guerrero-Pedraza A, Sarró S, Tordesillas-Gutiérrez D, Maristany T, Crespo-Facorro B, McKenna P, Pomarol-Clotet E. Multimodal Integration of Brain Images for MRI-Based Diagnosis in Schizophrenia. Front Neurosci 2019;13:1203. [PMID: 31787874 DOI: 10.3389/fnins.2019.01203] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
28 Zhang W, Groen W, Mennes M, Greven C, Buitelaar J, Rommelse N. Revisiting subcortical brain volume correlates of autism in the ABIDE dataset: effects of age and sex. Psychol Med 2018;48:654-68. [DOI: 10.1017/s003329171700201x] [Cited by in Crossref: 27] [Cited by in F6Publishing: 13] [Article Influence: 5.4] [Reference Citation Analysis]
29 Meehan AJ, Lewis SJ, Fazel S, Fusar-Poli P, Steyerberg EW, Stahl D, Danese A. Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges. Mol Psychiatry 2022. [PMID: 35365801 DOI: 10.1038/s41380-022-01528-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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31 Chen C, Cao X, Tian L. Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations. Front Neurosci 2019;13:1282. [PMID: 31827420 DOI: 10.3389/fnins.2019.01282] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
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36 Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; Alzheimer's Disease Neuroimaging Initiative., Australian Imaging Biomarkers and Lifestyle flagship study of ageing. Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data. Neuroimage 2018;183:504-21. [PMID: 30130647 DOI: 10.1016/j.neuroimage.2018.08.042] [Cited by in Crossref: 46] [Cited by in F6Publishing: 29] [Article Influence: 11.5] [Reference Citation Analysis]
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38 Nielsen AN, Gratton C, Church JA, Dosenbach NUF, Black KJ, Petersen SE, Schlaggar BL, Greene DJ. Atypical Functional Connectivity in Tourette Syndrome Differs Between Children and Adults. Biol Psychiatry 2020;87:164-73. [PMID: 31472979 DOI: 10.1016/j.biopsych.2019.06.021] [Cited by in Crossref: 30] [Cited by in F6Publishing: 22] [Article Influence: 10.0] [Reference Citation Analysis]
39 Liu K, Li Q, Yao L, Guo X. The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification. Front Neurosci 2022;16:902528. [DOI: 10.3389/fnins.2022.902528] [Reference Citation Analysis]
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42 Ma D, Yee E, Stocks JK, Jenkins LM, Popuri K, Chausse G, Wang L, Probst S, Beg MF. Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods. J Alzheimers Dis 2021;80:715-26. [PMID: 33579858 DOI: 10.3233/JAD-201591] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
43 Xu Z, Yang X, Gao M, Liu L, Sun J, Liu P, Qin W. Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach. Front Neurosci 2019;13:448. [PMID: 31139043 DOI: 10.3389/fnins.2019.00448] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 5.3] [Reference Citation Analysis]
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46 De D, Nayak T, Chowdhury S, Dhal PK. Insights of Host Physiological Parameters and Gut Microbiome of Indian Type 2 Diabetic Patients Visualized via Metagenomics and Machine Learning Approaches. Front Microbiol 2022;13:914124. [DOI: 10.3389/fmicb.2022.914124] [Reference Citation Analysis]
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48 Komatsu H, Watanabe E, Fukuchi M. Psychiatric Neural Networks and Precision Therapeutics by Machine Learning. Biomedicines 2021;9:403. [PMID: 33917863 DOI: 10.3390/biomedicines9040403] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
49 Zhang J, Li X, Li Y, Wang M, Huang B, Yao S, Shen L. Three dimensional convolutional neural network-based classification of conduct disorder with structural MRI. Brain Imaging and Behavior 2020;14:2333-40. [DOI: 10.1007/s11682-019-00186-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
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52 Oddo-sommerfeld S, Hänggi J, Coletta L, Skoruppa S, Thiel A, Stirn AV. Brain activity elicited by viewing pictures of the own virtually amputated body predicts xenomelia. Neuropsychologia 2018;108:135-46. [DOI: 10.1016/j.neuropsychologia.2017.11.025] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 2.8] [Reference Citation Analysis]
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54 Xi C, Lai J, Du Y, Ng CH, Jiang J, Wu L, Zhang P, Xu Y, Hu S. Abnormal functional connectivity within the reward network: a potential neuroimaging endophenotype of bipolar disorder. J Affect Disord 2021;280:49-56. [PMID: 33221607 DOI: 10.1016/j.jad.2020.11.072] [Reference Citation Analysis]
55 Lombardi A, Guaragnella C, Amoroso N, Monaco A, Fazio L, Taurisano P, Pergola G, Blasi G, Bertolino A, Bellotti R, Tangaro S. Modelling cognitive loads in schizophrenia by means of new functional dynamic indexes. NeuroImage 2019;195:150-64. [DOI: 10.1016/j.neuroimage.2019.03.055] [Cited by in Crossref: 18] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
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57 Morgan SE, Young J, Patel AX, Whitaker KJ, Scarpazza C, van Amelsvoort T, Marcelis M, van Os J, Donohoe G, Mothersill D, Corvin A, Arango C, Mechelli A, van den Heuvel M, Kahn RS, McGuire P, Brammer M, Bullmore ET. Functional Magnetic Resonance Imaging Connectivity Accurately Distinguishes Cases With Psychotic Disorders From Healthy Controls, Based on Cortical Features Associated With Brain Network Development. Biol Psychiatry Cogn Neurosci Neuroimaging 2020:S2451-9022(20)30138-5. [PMID: 32800754 DOI: 10.1016/j.bpsc.2020.05.013] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
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60 Lamichhane B, Jayasekera D, Jakes R, Ray WZ, Leuthardt EC, Hawasli AH. Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability. Front Neurol 2021;12:669076. [PMID: 34335444 DOI: 10.3389/fneur.2021.669076] [Reference Citation Analysis]
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