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8 Schrouff J, Monteiro JM, Portugal L, Rosa MJ, Phillips C, Mourão-Miranda J. Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models. Neuroinformatics 2018;16:117-43. [PMID: 29297140 DOI: 10.1007/s12021-017-9347-8] [Cited by in Crossref: 36] [Cited by in F6Publishing: 25] [Article Influence: 12.0] [Reference Citation Analysis]
9 Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, Pascual-Leone A, Voss HU, Casey BJ, Dubin MJ, Liston C. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017;23:28-38. [PMID: 27918562 DOI: 10.1038/nm.4246] [Cited by in Crossref: 856] [Cited by in F6Publishing: 698] [Article Influence: 142.7] [Reference Citation Analysis]
10 Xie H, Beaty RE, Jahanikia S, Geniesse C, Sonalkar NS, Saggar M. Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking. Neuroimage 2021;243:118531. [PMID: 34469816 DOI: 10.1016/j.neuroimage.2021.118531] [Reference Citation Analysis]
11 Azarmi F, Miri Ashtiani SN, Shalbaf A, Behnam H, Daliri MR. Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI. Comput Biol Med 2019;115:103495. [PMID: 31698238 DOI: 10.1016/j.compbiomed.2019.103495] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
12 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]
13 Peng L, Liu X, Ma D, Chen X, Xu X, Gao X. The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification. Front Neurosci 2022;16:913377. [DOI: 10.3389/fnins.2022.913377] [Reference Citation Analysis]
14 Li W, Xu X, Jiang W, Wang P, Gao X. Functional connectivity network estimation with an inter-similarity prior for mild cognitive impairment classification. Aging (Albany NY) 2020;12:17328-42. [PMID: 32921634 DOI: 10.18632/aging.103719] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Richter T, Fishbain B, Richter-Levin G, Okon-Singer H. Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. J Pers Med 2021;11:957. [PMID: 34683098 DOI: 10.3390/jpm11100957] [Reference Citation Analysis]
16 DAS A, Cash SS, Sejnowski TJ. Heterogeneity of Preictal Dynamics in Human Epileptic Seizures. IEEE Access 2020;8:52738-48. [PMID: 32411567 DOI: 10.1109/access.2020.2981017] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
17 Gao X, Xu X, Hua X, Wang P, Li W, Li R. Group Similarity Constraint Functional Brain Network Estimation for Mild Cognitive Impairment Classification. Front Neurosci 2020;14:165. [PMID: 32210747 DOI: 10.3389/fnins.2020.00165] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
18 Zhang J, Zhou L, Wang L. Subject-adaptive Integration of Multiple SICE Brain Networks with Different Sparsity. Pattern Recognition 2017;63:642-52. [DOI: 10.1016/j.patcog.2016.09.024] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
19 Guo H, Liu L, Chen J, Xu Y, Jie X. Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset. Front Neurosci 2017;11:639. [PMID: 29249926 DOI: 10.3389/fnins.2017.00639] [Cited by in Crossref: 22] [Cited by in F6Publishing: 13] [Article Influence: 4.4] [Reference Citation Analysis]
20 Bi XA, Cai R, Wang Y, Liu Y. Effective Diagnosis of Alzheimer's Disease via Multimodal Fusion Analysis Framework. Front Genet 2019;10:976. [PMID: 31649738 DOI: 10.3389/fgene.2019.00976] [Cited by in Crossref: 6] [Article Influence: 2.0] [Reference Citation Analysis]
21 van Esch RJC, Shi S, Bernas A, Zinger S, Aldenkamp AP, Van den Hof PMJ. A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect. Comput Biol Med 2020;127:104055. [PMID: 33157484 DOI: 10.1016/j.compbiomed.2020.104055] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
22 Smyser CD, Dosenbach NU, Smyser TA, Snyder AZ, Rogers CE, Inder TE, Schlaggar BL, Neil JJ. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 2016;136:1-9. [PMID: 27179605 DOI: 10.1016/j.neuroimage.2016.05.029] [Cited by in Crossref: 70] [Cited by in F6Publishing: 51] [Article Influence: 11.7] [Reference Citation Analysis]
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24 Patel MJ, Khalaf A, Aizenstein HJ. Studying depression using imaging and machine learning methods. Neuroimage Clin 2016;10:115-23. [PMID: 26759786 DOI: 10.1016/j.nicl.2015.11.003] [Cited by in Crossref: 73] [Cited by in F6Publishing: 37] [Article Influence: 10.4] [Reference Citation Analysis]
25 Chouinard B, Volden J, Cribben I, Cummine J. Neurological evaluation of the selection stage of metaphor comprehension in individuals with and without autism spectrum disorder. Neuroscience 2017;361:19-33. [DOI: 10.1016/j.neuroscience.2017.08.001] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
26 Won D, Manzour H, Chaovalitwongse W. Convex Optimization for Group Feature Selection in Networked Data. INFORMS Journal on Computing 2020;32:182-98. [DOI: 10.1287/ijoc.2018.0868] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
27 Scheid BH, Ashourvan A, Stiso J, Davis KA, Mikhail F, Pasqualetti F, Litt B, Bassett DS. Time-evolving controllability of effective connectivity networks during seizure progression. Proc Natl Acad Sci U S A 2021;118:e2006436118. [PMID: 33495341 DOI: 10.1073/pnas.2006436118] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
28 Yu R, Zhang H, An L, Chen X, Wei Z, Shen D. Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification. Hum Brain Mapp 2017;38:2370-83. [PMID: 28150897 DOI: 10.1002/hbm.23524] [Cited by in Crossref: 46] [Cited by in F6Publishing: 33] [Article Influence: 9.2] [Reference Citation Analysis]
29 Stolicyn A, Steele JD, Seriès P. Prediction of depression symptoms in individual subjects with face and eye movement tracking. Psychol Med 2020;:1-9. [PMID: 33161920 DOI: 10.1017/S0033291720003608] [Reference Citation Analysis]
30 Shao J, Dai Z, Zhu R, Wang X, Tao S, Bi K, Tian S, Wang H, Sun Y, Yao Z, Lu Q. Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI. Bipolar Disord 2019;21:774-84. [PMID: 31407477 DOI: 10.1111/bdi.12819] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 4.3] [Reference Citation Analysis]
31 Das A, Sexton D, Lainscsek C, Cash SS, Sejnowski TJ. Characterizing Brain Connectivity From Human Electrocorticography Recordings With Unobserved Inputs During Epileptic Seizures. Neural Comput 2019;31:1271-326. [PMID: 31113298 DOI: 10.1162/neco_a_01205] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
32 Chung A, Schirmer M, Krishnan M, Ball G, Aljabar P, Edwards A, Montana G. Characterising brain network topologies: A dynamic analysis approach using heat kernels. NeuroImage 2016;141:490-501. [DOI: 10.1016/j.neuroimage.2016.07.006] [Cited by in Crossref: 19] [Cited by in F6Publishing: 8] [Article Influence: 3.2] [Reference Citation Analysis]
33 Hay E, Ritter P, Lobaugh NJ, McIntosh AR. Multiregional integration in the brain during resting-state fMRI activity. PLoS Comput Biol 2017;13:e1005410. [PMID: 28248957 DOI: 10.1371/journal.pcbi.1005410] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 1.2] [Reference Citation Analysis]
34 Dadi K, Rahim M, Abraham A, Chyzhyk D, Milham M, Thirion B, Varoquaux G; Alzheimer's Disease Neuroimaging Initiative. Benchmarking functional connectome-based predictive models for resting-state fMRI. Neuroimage 2019;192:115-34. [PMID: 30836146 DOI: 10.1016/j.neuroimage.2019.02.062] [Cited by in Crossref: 87] [Cited by in F6Publishing: 60] [Article Influence: 29.0] [Reference Citation Analysis]
35 Yang P, Zhao C, Yang Q, Wei Z, Xiao X, Shen L, Wang T, Lei B, Peng Z. Diagnosis of obsessive-compulsive disorder via spatial similarity-aware learning and fused deep polynomial network. Med Image Anal 2021;75:102244. [PMID: 34700244 DOI: 10.1016/j.media.2021.102244] [Reference Citation Analysis]
36 Adeli E, Kwon D, Zhao Q, Pfefferbaum A, Zahr NM, Sullivan EV, Pohl KM. Chained regularization for identifying brain patterns specific to HIV infection. Neuroimage 2018;183:425-37. [PMID: 30138676 DOI: 10.1016/j.neuroimage.2018.08.022] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 2.8] [Reference Citation Analysis]
37 Adeli E, Li X, Kwon D, Zhang Y, Pohl KM. Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection. IEEE Trans Pattern Anal Mach Intell 2020;42:1713-28. [PMID: 30835210 DOI: 10.1109/TPAMI.2019.2901688] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
38 Yang X, Zhang N, Schrader P. A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. Machine Learning with Applications 2022;8:100290. [DOI: 10.1016/j.mlwa.2022.100290] [Reference Citation Analysis]
39 Zheng S, Ding C. Sparse classification using Group Matching Pursuit. Neurocomputing 2019;338:83-91. [DOI: 10.1016/j.neucom.2019.02.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
40 Zhu H, Yuan M, Qiu C, Ren Z, Li Y, Wang J, Huang X, Lui S, Gong Q, Zhang W, Zhang Y. Multivariate classification of earthquake survivors with post-traumatic stress disorder based on large-scale brain networks. Acta Psychiatr Scand 2020;141:285-98. [PMID: 31997301 DOI: 10.1111/acps.13150] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
41 Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, Pascual-Leone A, Voss HU, Casey BJ, Dubin MJ, Liston C. Erratum: Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017;23:264. [PMID: 28170383 DOI: 10.1038/nm0217-264d] [Cited by in Crossref: 24] [Cited by in F6Publishing: 20] [Article Influence: 4.8] [Reference Citation Analysis]
42 Li Y, Liu J, Gao X, Jie B, Kim M, Yap PT, Wee CY, Shen D. Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification. Med Image Anal 2019;52:80-96. [PMID: 30472348 DOI: 10.1016/j.media.2018.11.006] [Cited by in Crossref: 22] [Cited by in F6Publishing: 14] [Article Influence: 5.5] [Reference Citation Analysis]
43 Miyazaki T, Kanda T, Tsujino N, Ishii R, Nakatsuka D, Kizuka M, Kasagi Y, Hino H, Yanagisawa M. Dynamics of Cortical Local Connectivity during Sleep-Wake States and the Homeostatic Process. Cereb Cortex 2020;30:3977-90. [PMID: 32037455 DOI: 10.1093/cercor/bhaa012] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
44 Li M, Das T, Deng W, Wang Q, Li Y, Zhao L, Ma X, Wang Y, Yu H, Li X, Meng Y, Palaniyappan L, Li T. Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression. Acta Psychiatr Scand 2017;136:288-99. [PMID: 28504840 DOI: 10.1111/acps.12752] [Cited by in Crossref: 41] [Cited by in F6Publishing: 34] [Article Influence: 8.2] [Reference Citation Analysis]
45 Bakhtiari R, Cummine J, Reed A, Fox CM, Chouinard B, Cribben I, Boliek CA. Changes in brain activity following intensive voice treatment in children with cerebral palsy. Hum Brain Mapp 2017;38:4413-29. [PMID: 28580693 DOI: 10.1002/hbm.23669] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 1.4] [Reference Citation Analysis]
46 Yang Z, Chai Y, Chen T, Qu J. Smoothed $$\ell _1$$ ℓ 1 -regularization-based line search for sparse signal recovery. Soft Comput 2017;21:4813-28. [DOI: 10.1007/s00500-016-2423-4] [Cited by in Crossref: 2] [Article Influence: 0.3] [Reference Citation Analysis]
47 Zou H, Yang J. Multiple functional connectivity networks fusion for schizophrenia diagnosis. Med Biol Eng Comput 2020;58:1779-90. [DOI: 10.1007/s11517-020-02193-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
48 Schreiner M, Forsyth JK, Karlsgodt KH, Anderson AE, Hirsh N, Kushan L, Uddin LQ, Mattiacio L, Coman IL, Kates WR, Bearden CE. Intrinsic Connectivity Network-Based Classification and Detection of Psychotic Symptoms in Youth With 22q11.2 Deletions. Cereb Cortex 2017;27:3294-306. [PMID: 28383675 DOI: 10.1093/cercor/bhx076] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 3.5] [Reference Citation Analysis]
49 Cremers HR, Wager TD, Yarkoni T. The relation between statistical power and inference in fMRI. PLoS One 2017;12:e0184923. [PMID: 29155843 DOI: 10.1371/journal.pone.0184923] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
50 Coloigner J, Phlypo R, Coates TD, Lepore N, Wood JC. Graph Lasso-Based Test for Evaluating Functional Brain Connectivity in Sickle Cell Disease. Brain Connect 2017;7:443-53. [PMID: 28747064 DOI: 10.1089/brain.2016.0474] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
51 Kong Y, Gao S, Yue Y, Hou Z, Shu H, Xie C, Zhang Z, Yuan Y. Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity. Hum Brain Mapp 2021;42:3922-33. [PMID: 33969930 DOI: 10.1002/hbm.25529] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
52 Meszlényi RJ, Buza K, Vidnyánszky Z. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture. Front Neuroinform 2017;11:61. [PMID: 29089883 DOI: 10.3389/fninf.2017.00061] [Cited by in Crossref: 45] [Cited by in F6Publishing: 24] [Article Influence: 9.0] [Reference Citation Analysis]
53 Li Y, Liu J, Peng Z, Sheng C, Kim M, Yap PT, Wee CY, Shen D. Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification. Neuroinformatics 2020;18:1-24. [PMID: 30982183 DOI: 10.1007/s12021-019-09418-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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55 Sundermann B, Feder S, Wersching H, Teuber A, Schwindt W, Kugel H, Heindel W, Arolt V, Berger K, Pfleiderer B. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm (Vienna) 2017;124:589-605. [PMID: 28040847 DOI: 10.1007/s00702-016-1673-8] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 1.5] [Reference Citation Analysis]
56 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]
57 Ge Y, Pan Y, Wu Q, Dou W. A Distance-Based Neurorehabilitation Evaluation Method Using Linear SVM and Resting-State fMRI. Front Neurol 2019;10:1105. [PMID: 31736850 DOI: 10.3389/fneur.2019.01105] [Reference Citation Analysis]
58 Das A, Sampson AL, Lainscsek C, Muller L, Lin W, Doyle JC, Cash SS, Halgren E, Sejnowski TJ. Interpretation of the Precision Matrix and Its Application in Estimating Sparse Brain Connectivity during Sleep Spindles from Human Electrocorticography Recordings. Neural Comput 2017;29:603-42. [PMID: 28095202 DOI: 10.1162/NECO_a_00936] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 2.6] [Reference Citation Analysis]
59 Yu R, Qiao L, Chen M, Lee SW, Fei X, Shen D. Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification. Pattern Recognit 2019;90:220-31. [PMID: 31579345 DOI: 10.1016/j.patcog.2019.01.015] [Cited by in Crossref: 20] [Cited by in F6Publishing: 10] [Article Influence: 6.7] [Reference Citation Analysis]
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61 Li W, Wang Z, Zhang L, Qiao L, Shen D. Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification. Front Neuroinform 2017;11:55. [PMID: 28912708 DOI: 10.3389/fninf.2017.00055] [Cited by in Crossref: 24] [Cited by in F6Publishing: 18] [Article Influence: 4.8] [Reference Citation Analysis]
62 Cremers H, van Zutphen L, Duken S, Domes G, Sprenger A, Waldorp L, Arntz A. Borderline personality disorder classification based on brain network measures during emotion regulation. Eur Arch Psychiatry Clin Neurosci 2021;271:1169-78. [PMID: 33263789 DOI: 10.1007/s00406-020-01201-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
63 Shim M, Hwang HJ, Kuhl U, Jeon HA. Resting-State Functional Connectivity in Mathematical Expertise. Brain Sci 2021;11:430. [PMID: 33800679 DOI: 10.3390/brainsci11040430] [Reference Citation Analysis]
64 Moreno-Ortega M, Prudic J, Rowny S, Patel GH, Kangarlu A, Lee S, Grinband J, Palomo T, Perera T, Glasser MF, Javitt DC. Resting state functional connectivity predictors of treatment response to electroconvulsive therapy in depression. Sci Rep 2019;9:5071. [PMID: 30911075 DOI: 10.1038/s41598-019-41175-4] [Cited by in Crossref: 22] [Cited by in F6Publishing: 17] [Article Influence: 7.3] [Reference Citation Analysis]
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66 Guo H, Zhang F, Chen J, Xu Y, Xiang J. Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease. Front Neurosci 2017;11:615. [PMID: 29209156 DOI: 10.3389/fnins.2017.00615] [Cited by in Crossref: 21] [Cited by in F6Publishing: 11] [Article Influence: 4.2] [Reference Citation Analysis]
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