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Cited by 35Functional Brain Connectivity StudiesMeta-analysis and systematic reviewsArtificial Intelligence in Healthcare

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

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Chen, Zhiyi, Hu, Bowen, Liu, Xuerong, Becker, Benjamin, Eickhoff, Simon B., Miao, Kuan, Gu, Xingmei, Tang, Yancheng +7 more·2023·BMC Medicine
DOI 10.1186/s12916-023-02941-4PMC PMC10318841
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Abstract

Background: The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. Methods: Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. Results: A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81,p < .01), varying across different countries (regions) (e.g., China,G = 0.47; the USA,G = 0.58; Germany,G = 0.78; the UK,G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = − 2.75,p < .001,R2adj = 0.40;r = − .84, 95% CI: − .41 to − .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0–87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2–56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9–90.8%)/availability (80.88% of models, 95% CI: 77.3–84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF 10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. Conclusions: Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

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  • Algorithmic bias can arise at nearly any stage of the clinical ML workflow, resulting in inequitable diagnosis and treatment outcomes across sub-populations.

    “Algorithmic bias can arise at nearly any stage of the clinical ML workflow, resulting in inequitable diagnosis and treatment outcomes across sub-populations (Chen et al., 2023).”

    From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Chen et al., 2023

    The Discussion (page unavailable) identifies sampling bias, methodological flaws, and limited technical availability as problems for psychiatric diagnostic classifiers, and warns that underrepresentation may exacerbate healthcare inequalities. But its limitations paragraph explicitly says the study focused on diagnosis, not treatment-outcome prediction. It also does not establish that bias arises at nearly every stage of the clinical ML workflow. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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  1. [1]Jordan MI Mitchell TM Machine learning: trends, perspectives, and prospects Science 2015 349 6245 255 260 10.1126/science.aaa8415 26185243 · DOI · Paper page
  2. [2]Eyre HA Singh AB Reynolds C 3rd Tech giants enter mental health World Psychiatry 2016 15 1 21 22 10.1002/wps.20297 26833598 PMC4780298 · DOI
  3. [3]Walter M Alizadeh S Jamalabadi H Lueken U Dannlowski U Walter H Olbrich S Colic L Kambeitz J Koutsouleris N Translational machine learning for psychiatric neuroimaging Prog Neuropsychopharmacol Biol Psychiatry 2019 91 113 121 10.1016/j.pnpbp.2018.09.014 30290208 · DOI
  4. [4]Rutherford S The promise of machine learning for psychiatry Biol Psychiatry 2020 88 11 e53 e55 10.1016/j.biopsych.2020.08.024 33153529 · DOI
  5. [5]Sui J Jiang R Bustillo J Calhoun V Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises Biol Psychiatry 2020 88 11 818 828 10.1016/j.biopsych.2020.02.016 32336400 PMC7483317 · DOI · Paper page
  6. [6]Bzdok D Varoquaux G Steyerberg EW Prediction, not association, paves the road to precision medicine JAMA Psychiat 2021 78 2 127 128 10.1001/jamapsychiatry.2020.2549 32804995 · DOI
  7. [7]Vayena E Blasimme A A systemic approach to the oversight of machine learning clinical translation Am J Bioeth 2022 22 5 23 25 10.1080/15265161.2022.2055216 35475963 · DOI
  8. [8]Poldrack RA Huckins G Varoquaux G Establishment of best practices for evidence for prediction: a review JAMA Psychiat 2020 77 5 534 540 10.1001/jamapsychiatry.2019.3671 PMC7250718 31774490 · DOI · Paper page
  9. [9]Nielsen AN Barch DM Petersen SE Schlaggar BL Greene DJ Machine learning with neuroimaging: evaluating its applications in psychiatry Biol Psychiatry Cogn Neurosci Neuroimaging 2020 5 8 791 798 31982357 PMC8746222
  10. [10]Varoquaux G Raamana PR Engemann DA Hoyos-Idrobo A Schwartz Y Thirion B Assessing and tuning brain decoders: cross-validation, caveats, and guidelines Neuroimage 2017 145 Pt B 166 179 10.1016/j.neuroimage.2016.10.038 27989847 · DOI
  11. [11]Dwyer DB Falkai P Koutsouleris N Machine learning approaches for clinical psychology and psychiatry Annu Rev Clin Psychol 2018 14 91 118 10.1146/annurev-clinpsy-032816-045037 29401044 · DOI · Paper page
  12. [12]Mihalik A Ferreira FS Moutoussis M Ziegler G Adams RA Rosa MJ Prabhu G de Oliveira L Pereira M Bullmore ET Multiple holdouts with stability: improving the generalizability of machine learning analyses of brain-behavior relationships Biol Psychiatry 2020 87 4 368 376 10.1016/j.biopsych.2019.12.001 32040421 PMC6970221 · DOI
  13. [13]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 27 6 2700 2708 10.1038/s41380-022-01528-4 35365801 PMC9156409 · DOI · Paper page
  14. [14]Yuan J Ran X Liu K Yao C Yao Y Wu H Liu Q Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review J Neurosci Methods 2022 368 109441 10.1016/j.jneumeth.2021.109441 34942271 · DOI
  15. [15]Davatzikos C Machine learning in neuroimaging: Progress and challenges Neuroimage 2019 197 652 656 10.1016/j.neuroimage.2018.10.003 30296563 PMC6499712 · DOI
  16. [16]Shrout PE Rodgers JL Psychology, science, and knowledge construction: broadening perspectives from the replication crisis Annu Rev Psychol 2018 69 487 510 10.1146/annurev-psych-122216-011845 29300688 · DOI
  17. [17]Maxwell SE Lau MY Howard GS Is psychology suffering from a replication crisis? What does "failure to replicate" really mean? Am Psychol 2015 70 6 487 498 10.1037/a0039400 26348332 · DOI
  18. [19]Muthukrishna M Bell AV Henrich J Curtin CM Gedranovich A McInerney J Thue B Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) psychology: measuring and mapping scales of cultural and psychological distance Psychol Sci 2020 31 6 678 701 10.1177/0956797620916782 32437234 PMC7357184 · DOI · Paper page
  19. [20]Rad MS Martingano AJ Ginges J Toward a psychology of Homo sapiens: making psychological science more representative of the human population Proc Natl Acad Sci U S A 2018 115 45 11401 11405 10.1073/pnas.1721165115 30397114 PMC6233089 · DOI · Paper page
  20. [21]Arnett JJ The neglected 95%: why American psychology needs to become less American Am Psychol 2008 63 7 602 614 10.1037/0003-066X.63.7.602 18855491 · DOI
  21. [23]Cearns M Hahn T Baune BT Recommendations and future directions for supervised machine learning in psychiatry Transl Psychiatry 2019 9 1 271 10.1038/s41398-019-0607-2 31641106 PMC6805872 · DOI · Paper page
  22. [24]Tiwari P Verma R The pursuit of generalizability to enable clinical translation of radiomics Radiol Artif Intell 2021 3 1 e200227 10.1148/ryai.2020200227 33847697 PMC7845780 · DOI
  23. [26]Schultebraucks K Choi KW Galatzer-Levy IR Bonanno GA Discriminating heterogeneous trajectories of resilience and depression after major life stressors using polygenic scores JAMA Psychiat 2021 78 7 744 752 10.1001/jamapsychiatry.2021.0228 PMC8014197 33787853 · DOI
  24. [27]Lee HB Lyketsos CG Depression in Alzheimer's disease: heterogeneity and related issues Biol Psychiatry 2003 54 3 353 362 10.1016/S0006-3223(03)00543-2 12893110 · DOI
  25. [28]Arguello PA Gogos JA Genetic and cognitive windows into circuit mechanisms of psychiatric disease Trends Neurosci 2012 35 1 3 13 10.1016/j.tins.2011.11.007 22177981 · DOI
  26. [30]Peng Y Nagata MH An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data Chaos, Solitons Fractals 2020 139 110055 10.1016/j.chaos.2020.110055 32834608 PMC7324351 · DOI
  27. [31]Andaur Navarro CL Damen JAA Takada T Nijman SWJ Dhiman P Ma J Collins GS Bajpai R Riley RD Moons KGM Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review BMJ 2021 375 n2281 10.1136/bmj.n2281 34670780 PMC8527348 · DOI · Paper page
  28. [32]Hosseini M Powell M Collins J Callahan-Flintoft C Jones W Bowman H Wyble B I tried a bunch of things: the dangers of unexpected overfitting in classification of brain data Neurosci Biobehav Rev 2020 119 456 467 10.1016/j.neubiorev.2020.09.036 33035522 · DOI · Paper page
  29. [33]Varoquaux G Cheplygina V Machine learning for medical imaging: methodological failures and recommendations for the future NPJ Digit Med 2022 5 1 48 10.1038/s41746-022-00592-y 35413988 PMC9005663 · DOI
  30. [35]Chen J Patil KR Yeo BTT Eickhoff SB Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research Biol Psychiatry 2023 93 1 18 28 10.1016/j.biopsych.2022.07.025 36307328 · DOI
  31. [38]Collins GS Reitsma JB Altman DG Moons KG Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement BMJ 2015 350 g7594 10.1136/bmj.g7594 25569120 · DOI · Paper page
  32. [39]Nam CW. World Economic Outlook for 2020 and 2021. CESifo Forum. 2020;21(2):58-9. https://www.proquest.com/openview/2b714d1282ff098661c0d252c4db128b/1?cbl=43805&pq-origsite=gscholar&parentSessionId=7a4xwuy%2B60cPGopgOGEQ6SUez3gxXxwiOjjkxULCRuI%3D.
  33. [40]How does the world bank classify countries? https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741183.
  34. [41]Dagum C A new approach to the decomposition of the Gini income inequality ratio Empir Econ 1997 22 515 531 10.1007/BF01205777 · DOI
  35. [42]Hamed KH Ramachandra Rao A A modified Mann-Kendall trend test for autocorrelated data J Hydrol 1998 204 1 182 196 10.1016/S0022-1694(97)00125-X · DOI
  36. [43]Battle DE Diagnostic and Statistical Manual of Mental Disorders (DSM) CoDAS 2013 25 2 191 192 24413388
  37. [45]Ormel J VonKorff M Reducing common mental disorder prevalence in populations JAMA Psychiat 2021 78 4 359 360 10.1001/jamapsychiatry.2020.3443 33112374 · DOI
  38. [47]Woo CW Chang LJ Lindquist MA Wager TD Building better biomarkers: brain models in translational neuroimaging Nat Neurosci 2017 20 3 365 377 10.1038/nn.4478 28230847 PMC5988350 · DOI · Paper page
  39. [49]Poldrack RA Baker CI Durnez J Gorgolewski KJ Matthews PM Munafò MR Nichols TE Poline JB Vul E Yarkoni T Scanning the horizon: towards transparent and reproducible neuroimaging research Nat Rev Neurosci 2017 18 2 115 126 10.1038/nrn.2016.167 28053326 PMC6910649 · DOI · Paper page
  40. [50]Knoth IS Lajnef T Rigoulot S Lacourse K Vannasing P Michaud JL Jacquemont S Major P Jerbi K Lippé S Auditory repetition suppression alterations in relation to cognitive functioning in fragile X syndrome: a combined EEG and machine learning approach J Neurodev Disord 2018 10 1 4 10.1186/s11689-018-9223-3 29378522 PMC5789548 · DOI
  41. [51]Pedersen M Curwood EK Archer JS Abbott DF Jackson GD Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: multivariate analysis of task-free fMRI Epilepsia 2015 56 11 1767 1773 10.1111/epi.13135 26333833 · DOI
  42. [52]Wang Y Yuan L Shi J Greve A Ye J Toga AW Reiss AL Thompson PM Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis Neuroimage 2013 74 209 230 10.1016/j.neuroimage.2013.02.011 23435208 PMC3641904 · DOI
  43. [53]Hoeft F Walter E Lightbody AA Hazlett HC Chang C Piven J Reiss AL Neuroanatomical differences in toddler boys with fragile x syndrome and idiopathic autism Arch Gen Psychiatry 2011 68 3 295 305 10.1001/archgenpsychiatry.2010.153 21041609 PMC4369209 · DOI
  44. [54]Parisot S Ktena SI Ferrante E Lee M Guerrero R Glocker B Rueckert D Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer's disease Med Image Anal 2018 48 117 130 10.1016/j.media.2018.06.001 29890408 · DOI
  45. [55]Matlis S Boric K Chu CJ Kramer MA Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism BMC Neurol 2015 15 97 10.1186/s12883-015-0355-8 26111798 PMC4482270 · DOI
  46. [56]Ingalhalikar M Parker D Bloy L Roberts TP Verma R Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD Neuroimage 2011 57 3 918 927 10.1016/j.neuroimage.2011.05.023 21609768 PMC3152443 · DOI
  47. [57]Shahamat H Saniee Abadeh M Brain MRI analysis using a deep learning based evolutionary approach Neural Netw 2020 126 218 234 10.1016/j.neunet.2020.03.017 32259762 · DOI
  48. [58]Bajestani GS Behrooz M Khani AG Nouri-Baygi M Mollaei A Diagnosis of autism spectrum disorder based on complex network features Comput Methods Programs Biomed 2019 177 277 283 10.1016/j.cmpb.2019.06.006 31319956 · DOI
  49. [59]Lanka P Rangaprakash D Dretsch MN Katz JS Denney TS Jr Deshpande G Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets Brain Imaging Behav 2020 14 6 2378 2416 10.1007/s11682-019-00191-8 31691160 PMC7198352 · DOI
  50. [60]Zhang L Wang XH Li L Diagnosing autism spectrum disorder using brain entropy: a fast entropy method Comput Methods Programs Biomed 2020 190 105240 10.1016/j.cmpb.2019.105240 31806393 · DOI
  51. [61]Rakić M Cabezas M Kushibar K Oliver A Lladó X Improving the detection of autism spectrum disorder by combining structural and functional MRI information NeuroImage Clin 2020 25 102181 10.1016/j.nicl.2020.102181 31982680 PMC6994708 · DOI
  52. [62]Ahmadlou M Adeli H Adeli A Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder J Clin Neurophysiol 2010 27 5 328 333 10.1097/WNP.0b013e3181f40dc8 20844443 · DOI
  53. [63]Libero LE DeRamus TP Lahti AC Deshpande G Kana RK Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates Cortex 2015 66 46 59 10.1016/j.cortex.2015.02.008 25797658 PMC4782775 · DOI
  54. [64]Pham TH Vicnesh J Wei JKE Oh SL Arunkumar N Abdulhay EW Ciaccio EJ Acharya UR Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals Int J Environ Res Public Health 2020 17 3 971 10.3390/ijerph17030971 32033231 PMC7038220 · DOI
  55. [65]Graa O Rekik I Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes J Neurosci Methods 2019 327 108344 10.1016/j.jneumeth.2019.108344 31421161 · DOI
  56. [66]Ingalhalikar M Smith AR Bloy L Gur R Roberts TP Verma R Identifying sub-populations via unsupervised cluster analysis on multi-edge similarity graphs Med Image Comput Comput Assist Interv 2012 15 Pt 2 254 261 23286056 PMC4023482
  57. [67]Khosla M Jamison K Kuceyeski A Sabuncu MR Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction Neuroimage 2019 199 651 662 10.1016/j.neuroimage.2019.06.012 31220576 PMC6777738 · DOI
  58. [68]Li H Parikh NA He L A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes Front Neurosci 2018 12 491 10.3389/fnins.2018.00491 30087587 PMC6066582 · DOI
  59. [70]Xu L Hua Q Yu J Li J Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal Clin Neurophysiol 2020 131 6 1365 1374 10.1016/j.clinph.2019.12.400 32311592 · DOI
  60. [71]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 10.1016/j.neulet.2020.135519 33246027 · DOI
  61. [72]Rakhimberdina Z Liu X Murata AT Population graph-based multi-model ensemble method for diagnosing autism spectrum disorder Sensors (Basel) 2020 20 21 6001 10.3390/s20216001 33105909 PMC7660214 · DOI
  62. [73]Tsiaras V Simos PG Rezaie R Sheth BR Garyfallidis E Castillo EM Papanicolaou AC Extracting biomarkers of autism from MEG resting-state functional connectivity networks Comput Biol Med 2011 41 12 1166 1177 10.1016/j.compbiomed.2011.04.004 21592470 · DOI
  63. [75]Hu J Cao L Li T Liao B Dong S Li P Interpretable learning approaches in resting-state functional connectivity analysis: the case of autism spectrum disorder Comput Math Methods Med 2020 2020 1394830 10.1155/2020/1394830 32508974 PMC7251440 · DOI
  64. [76]Jung M Tu Y Park J Jorgenson K Lang C Song W Kong J Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder Br J Psychiatry 2019 214 6 339 344 10.1192/bjp.2018.248 31088591 PMC6521835 · DOI
  65. [77]Heinsfeld AS Franco AR Craddock RC Buchweitz A Meneguzzi F Identification of autism spectrum disorder using deep learning and the ABIDE dataset NeuroImage Clinical 2018 17 16 23 10.1016/j.nicl.2017.08.017 29034163 PMC5635344 · DOI · Paper page
  66. [78]Bhaumik R Pradhan A Das S Bhaumik DK Predicting autism spectrum disorder using domain-adaptive cross-site evaluation Neuroinformatics 2018 16 2 197 205 10.1007/s12021-018-9366-0 29455363 · DOI
  67. [79]Wang L Wee CY Tang X Yap PT Shen D Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder Brain Imaging Behav 2016 10 1 33 40 10.1007/s11682-015-9360-1 25761828 PMC4714957 · DOI
  68. [80]Ecker C Rocha-Rego V Johnston P Mourao-Miranda J Marquand A Daly EM Brammer MJ Murphy C Murphy DG Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach Neuroimage 2010 49 1 44 56 10.1016/j.neuroimage.2009.08.024 19683584 · DOI
  69. [81]Payabvash S Palacios EM Owen JP Wang MB Tavassoli T Gerdes M Brandes-Aitken A Cuneo D Marco EJ Mukherjee P White matter connectome edge density in children with autism spectrum disorders: potential imaging biomarkers using machine-learning models Brain Connect 2019 9 2 209 220 10.1089/brain.2018.0658 30661372 PMC6444925 · DOI
  70. [82]Price T Wee CY Gao W Shen D Multiple-network classification of childhood autism using functional connectivity dynamics Med Image Comput Comput Assist Interv 2014 17 Pt 3 177 184 25320797
  71. [83]Haweel R Shalaby A Mahmoud A Seada N Ghoniemy S Ghazal M Casanova MF Barnes GN El-Baz A A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI Med Phys 2021 48 5 2315 2326 10.1002/mp.14692 33378589 · DOI
  72. [86]Uddin LQ Supekar K Lynch CJ Khouzam A Phillips J Feinstein C Ryali S Menon V Salience network-based classification and prediction of symptom severity in children with autism JAMA Psychiat 2013 70 8 869 879 10.1001/jamapsychiatry.2013.104 PMC3951904 23803651 · DOI · Paper page
  73. [87]Nielsen JA Zielinski BA Fletcher PT Alexander AL Lange N Bigler ED Lainhart JE Anderson JS Multisite functional connectivity MRI classification of autism: ABIDE results Front Hum Neurosci 2013 7 599 10.3389/fnhum.2013.00599 24093016 PMC3782703 · DOI · Paper page
  74. [88]Jahedi A Nasamran CA Faires B Fan J Müller RA Distributed intrinsic functional connectivity patterns predict diagnostic status in Large Autism Cohort Brain Connect 2017 7 8 515 525 10.1089/brain.2017.0496 28825309 PMC5653144 · DOI
  75. [89]Retico A Giuliano A Tancredi R Cosenza A Apicella F Narzisi A Biagi L Tosetti M Muratori F Calderoni S The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study Mol Autism 2016 7 5 10.1186/s13229-015-0067-3 26788282 PMC4717545 · DOI
  76. [90]Calderoni S Retico A Biagi L Tancredi R Muratori F Tosetti M Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses Neuroimage 2012 59 2 1013 1022 10.1016/j.neuroimage.2011.08.070 21896334 · DOI
  77. [91]Yamagata B Itahashi T Fujino J Ohta H Nakamura M Kato N Mimura M Hashimoto RI Aoki Y Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder Brain Imaging Behav 2019 13 6 1689 1698 10.1007/s11682-018-9973-2 30280304 · DOI
  78. [92]Gori I Giuliano A Muratori F Saviozzi I Oliva P Tancredi R Cosenza A Tosetti M Calderoni S Retico A Gray matter alterations in young children with autism spectrum disorders: comparing morphometry at the voxel and regional level J Neuroimaging 2015 25 6 866 874 10.1111/jon.12280 26214066 · DOI
  79. [93]Leming M Górriz JM Suckling J Ensemble deep learning on large, mixed-site fMRI datasets in autism and other tasks Int J Neural Syst 2020 30 7 2050012 10.1142/S0129065720500124 32308082 · DOI
  80. [94]Shen MD Nordahl CW Li DD Lee A Angkustsiri K Emerson RW Rogers SJ Ozonoff S Amaral DG Extra-axial cerebrospinal fluid in high-risk and normal-risk children with autism aged 2–4 years: a case-control study The lancet Psychiatry 2018 5 11 895 904 10.1016/S2215-0366(18)30294-3 30270033 PMC6223655 · DOI
  81. [95]Grossi E Olivieri C Buscema M Diagnosis of autism through EEG processed by advanced computational algorithms: a pilot study Comput Methods Programs Biomed 2017 142 73 79 10.1016/j.cmpb.2017.02.002 28325448 · DOI
  82. [96]Gupta S Rajapakse JC Welsch RE Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder NeuroImage Clinical 2020 25 102186 10.1016/j.nicl.2020.102186 32000101 PMC7042673 · DOI
  83. [97]Ghiassian S Greiner R Jin P Brown MR Using functional or structural magnetic resonance images and personal characteristic data to identify ADHD and Autism PLoS One 2016 11 12 e0166934 10.1371/journal.pone.0166934 28030565 PMC5193362 · DOI · Paper page
  84. [98]Zu C Gao Y Munsell B Kim M Peng Z Cohen JR Zhang D Wu G Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning Brain Imaging Behav 2019 13 4 879 892 10.1007/s11682-018-9899-8 29948906 PMC6513717 · DOI
  85. [100]Li Q Becker B Jiang X Zhao Z Zhang Q Yao S Kendrick KM Decreased interhemispheric functional connectivity rather than corpus callosum volume as a potential biomarker for autism spectrum disorder Cortex 2019 119 258 266 10.1016/j.cortex.2019.05.003 31167156 · DOI
  86. [101]Dekhil O Hajjdiab H Shalaby A Ali MT Ayinde B Switala A Elshamekh A Ghazal M Keynton R Barnes G Using resting state functional MRI to build a personalized autism diagnosis system PLoS One 2018 13 10 e0206351 10.1371/journal.pone.0206351 30379950 PMC6209234 · DOI
  87. [102]Yamagata B Itahashi T Fujino J Ohta H Takashio O Nakamura M Kato N Mimura M Hashimoto RI Aoki YY Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder Psychiatry Clin Neurosci 2019 73 7 409 415 10.1111/pcn.12854 31026100 · DOI
  88. [103]Iidaka T Resting state functional magnetic resonance imaging and neural network classified autism and control Cortex 2015 63 55 67 10.1016/j.cortex.2014.08.011 25243989 · DOI
  89. [104]Jamal W Das S Oprescu IA Maharatna K Apicella F Sicca F Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates J Neural Eng 2014 11 4 046019 10.1088/1741-2560/11/4/046019 24981017 · DOI · Paper page
  90. [105]Zhang F Savadjiev P Cai W Song Y Rathi Y Tunç B Parker D Kapur T Schultz RT Makris N Whole brain white matter connectivity analysis using machine learning: an application to autism Neuroimage 2018 172 826 837 10.1016/j.neuroimage.2017.10.029 29079524 PMC5910272 · DOI
  91. [106]Sujit SJ Coronado I Kamali A Narayana PA Gabr RE Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks J Magn Reson Imaging 2019 50 4 1260 1267 10.1002/jmri.26693 30811739 PMC6711839 · DOI
  92. [107]Huang H Liu X Jin Y Lee SW Wee CY Shen D Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis Hum Brain Mapp 2019 40 3 833 854 10.1002/hbm.24415 30357998 PMC6865533 · DOI
  93. [108]Eill A Jahedi A Gao Y Kohli JS Fong CH Solders S Carper RA Valafar F Bailey BA Müller RA Functional connectivities are more informative than anatomical variables in diagnostic classification of autism Brain Connect 2019 9 8 604 612 10.1089/brain.2019.0689 31328535 PMC6798803 · DOI
  94. [109]Xiao X Fang H Wu J Xiao C Xiao T Qian L Liang F Xiao Z Chu KK Ke X Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder Autism Res 2017 10 4 620 630 10.1002/aur.1711 27874271 · DOI
  95. [110]Kam TE Suk HI Lee SW Multiple functional networks modeling for autism spectrum disorder diagnosis Hum Brain Mapp 2017 38 11 5804 5821 10.1002/hbm.23769 28845892 PMC6866928 · DOI
  96. [111]Ktena SI Parisot S Ferrante E Rajchl M Lee M Glocker B Rueckert D Metric learning with spectral graph convolutions on brain connectivity networks Neuroimage 2018 169 431 442 10.1016/j.neuroimage.2017.12.052 29278772 · DOI
  97. [112]Aghdam MA Sharifi A Pedram MM Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks J Digit Imaging 2019 32 6 899 918 10.1007/s10278-019-00196-1 30963340 PMC6841914 · DOI
  98. [113]Sadeghi M Khosrowabadi R Bakouie F Mahdavi H Eslahchi C Pouretemad H Screening of autism based on task-free fMRI using graph theoretical approach Psychiatry Res Neuroimaging 2017 263 48 56 10.1016/j.pscychresns.2017.02.004 28324694 · DOI
  99. [114]Chaddad A Desrosiers C Hassan L Tanougast C Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder BMC Neurosci 2017 18 1 52 10.1186/s12868-017-0373-0 28821235 PMC6389224 · DOI
  100. [115]Djemal R AlSharabi K Ibrahim S Alsuwailem A EEG-based computer aided diagnosis of autism spectrum disorder using wavelet, entropy, and ANN Biomed Res Int 2017 2017 9816591 10.1155/2017/9816591 28484720 PMC5412163 · DOI
  101. [116]Yahata N Morimoto J Hashimoto R Lisi G Shibata K Kawakubo Y Kuwabara H Kuroda M Yamada T Megumi F A small number of abnormal brain connections predicts adult autism spectrum disorder Nat Commun 2016 7 11254 10.1038/ncomms11254 27075704 PMC4834637 · DOI · Paper page
  102. [117]Heunis T Aldrich C Peters JM Jeste SS Sahin M Scheffer C de Vries PJ Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder - a systematic methodological exploration of technical and demographic confounders in the search for biomarkers BMC Med 2018 16 1 101 10.1186/s12916-018-1086-7 29961422 PMC6027554 · DOI
  103. [118]Chen H Duan X Liu F Lu F Ma X Zhang Y Uddin LQ Chen H Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity–A multi-center study Prog Neuropsychopharmacol Biol Psychiatry 2016 64 1 9 10.1016/j.pnpbp.2015.06.014 26148789 · DOI
  104. [119]Just MA Cherkassky VL Buchweitz A Keller TA Mitchell TM Identifying autism from neural representations of social interactions: neurocognitive markers of autism PLoS One 2014 9 12 e113879 10.1371/journal.pone.0113879 25461818 PMC4251975 · DOI
  105. [120]Akhavan Aghdam M Sharifi A Pedram MM Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network J Digit Imaging 2018 31 6 895 903 10.1007/s10278-018-0093-8 29736781 PMC6261184 · DOI
  106. [121]Alturki FA AlSharabi K Abdurraqeeb AM Aljalal M EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques Sensors (Basel) 2020 20 9 2505 10.3390/s20092505 32354161 PMC7361958 · DOI
  107. [122]Spiegel A Mentch J Haskins AJ Robertson CE Slower binocular rivalry in the autistic brain Curr Biol 2019 29 17 2948 2953.e2943 10.1016/j.cub.2019.07.026 31422885 · DOI
  108. [123]Conti E Retico A Palumbo L Spera G Bosco P Biagi L Fiori S Tosetti M Cipriani P Cioni G Autism spectrum disorder and childhood apraxia of speech: early language-related hallmarks across structural MRI study J Pers Med 2020 10 4 275 10.3390/jpm10040275 33322765 PMC7768516 · DOI
  109. [124]Bi XA Liu Y Jiang Q Shu Q Sun Q Dai J The diagnosis of autism spectrum disorder based on the random neural network cluster Front Hum Neurosci 2018 12 257 10.3389/fnhum.2018.00257 29997489 PMC6028564 · DOI
  110. [125]Pollonini L Patidar U Situ N Rezaie R Papanicolaou AC Zouridakis G Functional connectivity networks in the autistic and healthy brain assessed using Granger causality Annu Int Conf IEEE Eng Med Biol Soc 2010 2010 1730 1733 21096408
  111. [126]Eldridge J Lane AE Belkin M Dennis S Robust features for the automatic identification of autism spectrum disorder in children J Neurodev Disord 2014 6 1 12 10.1186/1866-1955-6-12 24936212 PMC4039057 · DOI
  112. [127]Khan NA Waheeb SA Riaz A Shang X A three-stage teacher, student neural networks and sequential feed forward selection-based feature selection approach for the classification of autism spectrum disorder Brain Sci 2020 10 10 754 10.3390/brainsci10100754 33086634 PMC7603385 · DOI
  113. [128]Sherkatghanad Z Akhondzadeh M Salari S Zomorodi-Moghadam M Abdar M Acharya UR Khosrowabadi R Salari V Automated detection of autism spectrum disorder using a convolutional neural network Front Neurosci 2019 13 1325 10.3389/fnins.2019.01325 32009868 PMC6971220 · DOI
  114. [129]Gao J Chen M Li Y Gao Y Li Y Cai S Wang J Multisite autism spectrum disorder classification using convolutional neural network classifier and individual morphological brain networks Front Neurosci 2020 14 629630 10.3389/fnins.2020.629630 33584183 PMC7877487 · DOI
  115. [130]Liu Y Xu L Li J Yu J Yu X Attentional connectivity-based prediction of autism using heterogeneous rs-fMRI data from CC200 Atlas Exp Neurobiol 2020 29 1 27 37 10.5607/en.2020.29.1.27 32122106 PMC7075658 · DOI
  116. [131]Yang M Cao M Chen Y Chen Y Fan G Li C Wang J Liu T Large-scale brain functional network integration for discrimination of autism using a 3-D Deep Learning Model Front Hum Neurosci 2021 15 687288 10.3389/fnhum.2021.687288 34149385 PMC8206477 · DOI
  117. [132]Huang ZA Zhu Z Yau CH Tan KC Identifying autism spectrum disorder from resting-state fMRI using deep belief network IEEE Trans Neural Netw Learn Syst 2021 32 7 2847 2861 10.1109/TNNLS.2020.3007943 32692687 · DOI
  118. [133]Zhao J Song J Li X Kang J A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method Brain Behav 2020 10 12 e01721 10.1002/brb3.1721 33125837 PMC7749618 · DOI
  119. [134]Almuqhim F Saeed F ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data Front Comput Neurosci 2021 15 654315 10.3389/fncom.2021.654315 33897398 PMC8060560 · DOI
  120. [135]Xu L Sun Z Xie J Yu J Li J Wang J Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network Clin Neurophysiol 2021 132 2 457 468 10.1016/j.clinph.2020.11.037 33450566 · DOI
  121. [136]Lu J Kishida K De Asis CJ Lohrenz T Deering DT Beauchamp M Montague PR Single stimulus fMRI produces a neural individual difference measure for Autism Spectrum Disorder Clin Psychol Sci 2015 3 3 422 432 10.1177/2167702614562042 26722624 PMC4692192 · DOI
  122. [137]Ahmed MR Zhang Y Liu Y Liao H Single volume image generator and deep learning-based ASD classification IEEE J Biomed Health Inform 2020 24 11 3044 3054 10.1109/JBHI.2020.2998603 32750917 · DOI
  123. [138]Xu L Geng X He X Li J Yu J Prediction in autism by deep learning short-time spontaneous hemodynamic fluctuations Front Neurosci 2019 13 1120 10.3389/fnins.2019.01120 31780879 PMC6856557 · DOI
  124. [139]Wee CY Wang L Shi F Yap PT Shen D Diagnosis of autism spectrum disorders using regional and interregional morphological features Hum Brain Mapp 2014 35 7 3414 3430 10.1002/hbm.22411 25050428 PMC4109659 · DOI · Paper page
  125. [140]Sewani H Kashef R An autoencoder-based deep learning classifier for efficient diagnosis of autism Children (Basel, Switzerland) 2020 7 10 182 33066454 PMC7602176
  126. [141]Shi C Xin X Zhang J Domain adaptation using a three-way decision improves the identification of autism patients from multisite fMRI data Brain Sci 2021 11 5 603 10.3390/brainsci11050603 34066816 PMC8150603 · DOI
  127. [142]Kazeminejad A Sotero RC The importance of anti-correlations in graph theory based classification of autism spectrum disorder Front Neurosci 2020 14 676 10.3389/fnins.2020.00676 32848533 PMC7426475 · DOI
  128. [143]Yin W Mostafa S Wu FX Diagnosis of autism spectrum disorder based on functional brain networks with deep learning J Comput Biol 2021 28 2 146 165 10.1089/cmb.2020.0252 33074746 · DOI
  129. [144]Jiao Y Chen R Ke X Chu K Lu Z Herskovits EH Predictive models of autism spectrum disorder based on brain regional cortical thickness Neuroimage 2010 50 2 589 599 10.1016/j.neuroimage.2009.12.047 20026220 PMC2823830 · DOI
  130. [146]Song Y Epalle TM Lu H Characterizing and predicting autism spectrum disorder by performing resting-state functional network community pattern analysis Front Hum Neurosci 2019 13 203 10.3389/fnhum.2019.00203 31258470 PMC6587437 · DOI
  131. [147]Irimia A Lei X Torgerson CM Jacokes ZJ Abe S Van Horn JD Support vector machines, multidimensional scaling and magnetic resonance imaging reveal structural brain abnormalities associated with the interaction between autism spectrum disorder and sex Front Comput Neurosci 2018 12 93 10.3389/fncom.2018.00093 30534065 PMC6276724 · DOI
  132. [148]Eslami T Mirjalili V Fong A Laird AR Saeed F ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data Front Neuroinform 2019 13 70 10.3389/fninf.2019.00070 31827430 PMC6890833 · DOI
  133. [149]Sarovic D Hadjikhani N Schneiderman J Lundström S Gillberg C Autism classified by magnetic resonance imaging: a pilot study of a potential diagnostic tool Int J Methods Psychiatr Res 2020 29 4 1 18 10.1002/mpr.1846 32945591 PMC7723195 · DOI
  134. [150]Kazeminejad A Sotero RC Topological properties of resting-state fMRI functional networks improve machine learning-based autism classification Front Neurosci 2018 12 1018 10.3389/fnins.2018.01018 30686984 PMC6335365 · DOI
  135. [151]Guo X Dominick KC Minai AA Li H Erickson CA Lu LJ Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method Front Neurosci 2017 11 460 10.3389/fnins.2017.00460 28871217 PMC5566619 · DOI
  136. [152]Thomas RM Gallo S Cerliani L Zhutovsky P El-Gazzar A van Wingen G Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks Front Psych 2020 11 440 10.3389/fpsyt.2020.00440 PMC7242627 32477198 · DOI
  137. [153]Chen H Chen W Song Y Sun L Li X EEG characteristics of children with attention-deficit/hyperactivity disorder Neuroscience 2019 406 444 456 10.1016/j.neuroscience.2019.03.048 30926547 · DOI
  138. [154]Guo X Yao D Cao Q Liu L Zhao Q Li H Huang F Wang Y Qian Q Wang Y Shared and distinct resting functional connectivity in children and adults with attention-deficit/hyperactivity disorder Transl Psychiatry 2020 10 1 65 10.1038/s41398-020-0740-y 32066697 PMC7026417 · DOI
  139. [155]Müller A Vetsch S Pershin I Candrian G Baschera GM Kropotov JD Kasper J Rehim HA Eich D EEG/ERP-based biomarker/neuroalgorithms in adults with ADHD: Development, reliability, and application in clinical practice World J Biol Psychiatry 2020 21 3 172 182 10.1080/15622975.2019.1605198 30990349 · DOI
  140. [156]Gao MS Tsai FS Lee CC Learning a phenotypic-attribute attentional brain connectivity embedding for ADHD Classification using rs-fMRI Annu Int Conf IEEE Eng Med Biol Soc 2020 2020 5472 5475 33019218
  141. [157]Chen Y Tang Y Wang C Liu X Zhao L Wang Z ADHD classification by dual subspace learning using resting-state functional connectivity Artif Intell Med 2020 103 101786 10.1016/j.artmed.2019.101786 32143793 · DOI
  142. [158]Muthuraman M Moliadze V Boecher L Siemann J Freitag CM Groppa S Siniatchkin M Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders Sci Rep 2019 9 1 20028 10.1038/s41598-019-56398-8 31882672 PMC6934806 · DOI
  143. [159]McNorgan C Judson C Handzlik D Holden JG Linking ADHD and behavioral assessment through identification of shared diagnostic task-based functional connections Front Physiol 2020 11 583005 10.3389/fphys.2020.583005 33391011 PMC7773605 · DOI
  144. [160]Vahid A Bluschke A Roessner V Stober S Beste C Deep learning based on event-related EEG differentiates children with ADHD from Healthy Controls J Clin Med 2019 8 7 1055 10.3390/jcm8071055 31330961 PMC6679086 · DOI
  145. [161]Riaz A Asad M Alonso E Slabaugh G DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI J Neurosci Methods 2020 335 108506 10.1016/j.jneumeth.2019.108506 32001294 · DOI
  146. [162]Rostami M Farashi S Khosrowabadi R Pouretemad H Discrimination of ADHD subtypes using decision tree on behavioral, neuropsychological, and neural markers Basic Clin Neurosci 2020 11 3 359 367 32963728 PMC7502189
  147. [163]Kiiski H Rueda-Delgado LM Bennett M Knight R Rai L Roddy D Grogan K Bramham J Kelly C Whelan R Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms Clin Neurophysiol 2020 131 1 330 342 10.1016/j.clinph.2019.08.010 31506235 · DOI
  148. [164]Tang Y Wang C Chen Y Sun N Jiang A Wang Z Identifying ADHD individuals from resting-state functional connectivity using subspace clustering and binary hypothesis testing J Atten Disord 2021 25 5 736 748 10.1177/1087054719837749 30938224 · DOI
  149. [165]Sun Y Zhao L Lan Z Jia XZ Xue SW Differentiating boys with ADHD from those with typical development based on whole-brain functional connections using a machine learning approach Neuropsychiatr Dis Treat 2020 16 691 702 10.2147/NDT.S239013 32210565 PMC7071874 · DOI
  150. [166]Sidhu G Locally linear embedding and fMRI feature selection in psychiatric classification IEEE J Transl Eng Health Med 2019 7 2200211 10.1109/JTEHM.2019.2936348 31497410 PMC6726465 · DOI
  151. [167]Riaz A Asad M Alonso E Slabaugh G Fusion of fMRI and non-imaging data for ADHD classification Comput Med Imaging Graph 2018 65 115 128 10.1016/j.compmedimag.2017.10.002 29137838 · DOI
  152. [168]Kaur S Singh S Arun P Kaur D Bajaj M Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults Clin EEG Neurosci 2020 51 2 102 113 10.1177/1550059419876525 31533446 · DOI
  153. [169]Chen M Li H Wang J Dillman JR Parikh NA He L A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection Radiol Artif Intell 2019 2 1 e190012 10.1148/ryai.2019190012 32076663 PMC6996597 · DOI
  154. [170]Sutoko S Monden Y Tokuda T Ikeda T Nagashima M Funane T Sato H Kiguchi M Maki A Yamagata T Exploring attentive task-based connectivity for screening attention deficit/hyperactivity disorder children: a functional near-infrared spectroscopy study Neurophotonics 2019 6 4 045013 10.1117/1.NPh.6.4.045013 31853459 PMC6917048 · DOI
  155. [171]Luo Y Alvarez TL Halperin JM Li X Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques NeuroImage Clin 2020 26 102238 10.1016/j.nicl.2020.102238 32182578 PMC7076568 · DOI
  156. [172]Wang XH Jiao Y Li L Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity Neurosci Lett 2018 685 30 34 10.1016/j.neulet.2018.07.029 30031733 · DOI
  157. [174]Yasumura A Omori M Fukuda A Takahashi J Yasumura Y Nakagawa E Koike T Yamashita Y Miyajima T Koeda T Applied machine learning method to predict children with ADHD using prefrontal cortex activity: a multicenter study in Japan J Atten Disord 2020 24 14 2012 2020 10.1177/1087054717740632 29154696 · DOI
  158. [175]Qureshi MNI Oh J Min B Jo HJ Lee B Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI Front Hum Neurosci 2017 11 157 28420972 PMC5378777
  159. [176]Biederman J Hammerness P Sadeh B Peremen Z Amit A Or-Ly H Stern Y Reches A Geva A Faraone SV Diagnostic utility of brain activity flow patterns analysis in attention deficit hyperactivity disorder Psychol Med 2017 47 7 1259 1270 10.1017/S0033291716003329 28065167 · DOI
  160. [178]Sato JR Hoexter MQ Fujita A Rohde LA Evaluation of pattern recognition and feature extraction methods in ADHD prediction Front Syst Neurosci 2012 6 68 10.3389/fnsys.2012.00068 23015782 PMC3449288 · DOI
  161. [179]Iannaccone R Hauser TU Ball J Brandeis D Walitza S Brem S Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging Eur Child Adolesc Psychiatry 2015 24 10 1279 1289 10.1007/s00787-015-0678-4 25613588 · DOI · Paper page
  162. [180]Gu Y Miao S Han J Liang Z Ouyang G Yang J Li X Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy J Neural Eng 2018 15 3 035005 10.1088/1741-2552/aa9ee9 29199636 · DOI
  163. [181]Du J Wang L Jie B Zhang D Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA Comput Med Imaging Graph 2016 52 82 88 10.1016/j.compmedimag.2016.04.004 27166430 · DOI
  164. [182]Wang XH Jiao Y Li L Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity Sci Rep 2018 8 1 11789 10.1038/s41598-018-30308-w 30087369 PMC6081414 · DOI
  165. [183]Hart H Chantiluke K Cubillo AI Smith AB Simmons A Brammer MJ Marquand AF Rubia K Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD Hum Brain Mapp 2014 35 7 3083 3094 10.1002/hbm.22386 24123508 PMC4190683 · DOI
  166. [184]Dai D Wang J Hua J He H Classification of ADHD children through multimodal magnetic resonance imaging Front Syst Neurosci 2012 6 63 10.3389/fnsys.2012.00063 22969710 PMC3432508 · DOI
  167. [185]Liu S Zhao L Wang X Xin Q Zhao J Guttery DS Zhang YD Deep spatio-temporal representation and ensemble classification for attention deficit/hyperactivity disorder IEEE Trans Neural Syst Rehabil Eng 2021 29 1 10 10.1109/TNSRE.2020.3019063 32833639 · DOI
  168. [186]Wang X Jiao Y Tang T Wang H Lu Z Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder Eur J Radiol 2013 82 9 1552 1557 10.1016/j.ejrad.2013.04.009 23684384 · DOI
  169. [187]Sidhu GS Asgarian N Greiner R Brown MR Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD Front Syst Neurosci 2012 6 74 10.3389/fnsys.2012.00074 23162439 PMC3494168 · DOI
  170. [188]Liechti MD Valko L Müller UC Döhnert M Drechsler R Steinhausen HC Brandeis D Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan Brain Topogr 2013 26 1 135 151 10.1007/s10548-012-0258-6 23053601 · DOI
  171. [189]Zhu CZ Zang YF Cao QJ Yan CG He Y Jiang TZ Sui MQ Wang YF Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder Neuroimage 2008 40 1 110 120 10.1016/j.neuroimage.2007.11.029 18191584 · DOI · Paper page
  172. [190]Olivetti E Greiner S Avesani P ADHD diagnosis from multiple data sources with batch effects Front Syst Neurosci 2012 6 70 10.3389/fnsys.2012.00070 23060755 PMC3465911 · DOI
  173. [191]Johnston BA Mwangi B Matthews K Coghill D Konrad K Steele JD Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification Hum Brain Mapp 2014 35 10 5179 5189 10.1002/hbm.22542 24819333 PMC6869620 · DOI · Paper page
  174. [192]Aradhya AMS Subbaraju V Sundaram S Sundararajan N Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI) Annu Int Conf IEEE Eng Med Biol Soc 2018 2018 5541 5544 30441592
  175. [193]Chang CW Ho CC Chen JH ADHD classification by a texture analysis of anatomical brain MRI data Front Syst Neurosci 2012 6 66 10.3389/fnsys.2012.00066 23024630 PMC3444803 · DOI
  176. [194]Smith JL Johnstone SJ Barry RJ Aiding diagnosis of attention-deficit/hyperactivity disorder and its subtypes: discriminant function analysis of event-related potential data J Child Psychol Psychiatry 2003 44 7 1067 1075 10.1111/1469-7610.00191 14531589 · DOI
  177. [195]dos Santos SA Biazoli Junior CE Comfort WE Rohde LA Sato JR Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data Biomed Res Int 2014 2014 380531 25309910 PMC4163359
  178. [196]Sun H Chen Y Huang Q Lui S Huang X Shi Y Xu X Sweeney JA Gong Q Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis Radiology 2018 287 2 620 630 10.1148/radiol.2017170226 29165048 · DOI
  179. [197]Mueller A Candrian G Kropotov JD Ponomarev VA Baschera GM Classification of ADHD patients on the basis of independent ERP components using a machine learning system Nonlinear Biomed Phys 2010 4 Suppl 1 Suppl 1 S1 10.1186/1753-4631-4-S1-S1 20522259 PMC2880795 · DOI
  180. [198]Cheng W Ji X Zhang J Feng J Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques Front Syst Neurosci 2012 6 58 10.3389/fnsys.2012.00058 22888314 PMC3412279 · DOI · Paper page
  181. [199]Ahmadlou M Adeli H Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD Clin EEG Neurosci 2010 41 1 1 10 10.1177/155005941004100103 20307009 · DOI
  182. [200]Abibullaev B An J Decision support algorithm for diagnosis of ADHD using electroencephalograms J Med Syst 2012 36 4 2675 2688 10.1007/s10916-011-9742-x 21671069 · DOI
  183. [201]Colby JB Rudie JD Brown JA Douglas PK Cohen MS Shehzad Z Insights into multimodal imaging classification of ADHD Front Syst Neurosci 2012 6 59 10.3389/fnsys.2012.00059 22912605 PMC3419970 · DOI · Paper page
  184. [202]Mueller A Candrian G Grane VA Kropotov JD Ponomarev VA Baschera GM Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study Nonlinear Biomed Phys 2011 5 5 10.1186/1753-4631-5-5 21771289 PMC3149569 · DOI
  185. [203]Yu D Additional brain functional network in adults with attention-deficit/hyperactivity disorder: a phase synchrony analysis PLoS One 2013 8 1 e54516 10.1371/journal.pone.0054516 23382908 PMC3561351 · DOI
  186. [204]Poil SS Bollmann S Ghisleni C O'Gorman RL Klaver P Ball J Eich-Höchli D Brandeis D Michels L Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD) Clin Neurophysiol 2014 125 8 1626 1638 10.1016/j.clinph.2013.12.118 24582383 · DOI
  187. [205]Qureshi MN Min B Jo HJ Lee B Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study PLoS One 2016 11 8 e0160697 10.1371/journal.pone.0160697 27500640 PMC4976974 · DOI · Paper page
  188. [206]Hart H Marquand AF Smith A Cubillo A Simmons A Brammer M Rubia K Predictive neurofunctional markers of attention-deficit/hyperactivity disorder based on pattern classification of temporal processing J Am Acad Child Adolesc Psychiatry 2014 53 5 569 578.e561 10.1016/j.jaac.2013.12.024 24745956 · DOI
  189. [207]Qureshi MN Boreom L Classification of ADHD subgroup with recursive feature elimination for structural brain MRI Annu Int Conf IEEE Eng Med Biol Soc 2016 2016 5929 5932 28269602
  190. [208]Deshpande G Wang P Rangaprakash D Wilamowski B Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data IEEE Trans Cybern 2015 45 12 2668 2679 10.1109/TCYB.2014.2379621 25576588 · DOI · Paper page
  191. [210]Moghaddari M Lighvan MZ Danishvar S Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG Comput Methods Programs Biomed 2020 197 105738 10.1016/j.cmpb.2020.105738 32927404 · DOI
  192. [211]Pereda E García-Torres M Melián-Batista B Mañas S Méndez L González JJ The blessing of dimensionality: feature selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation PLoS One 2018 13 8 e0201660 10.1371/journal.pone.0201660 30114248 PMC6095525 · DOI
  193. [213]Öztoprak H Toycan M Alp YK Arıkan O Doğutepe E Karakaş S Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity Clin Neurophysiol 2017 128 12 2400 2410 10.1016/j.clinph.2017.09.105 29096213 · DOI
  194. [214]Tor HT Ooi CP Lim-Ashworth NS Wei JKE Jahmunah V Oh SL Acharya UR Fung DSS Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals Comput Methods Programs Biomed 2021 200 105941 10.1016/j.cmpb.2021.105941 33486340 · DOI
  195. [215]Tosun M Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning Phys Eng Sci Med 2021 44 3 693 702 10.1007/s13246-021-01018-x 34043150 · DOI
  196. [216]Johnstone SJ Parrish L Jiang H Zhang DW Williams V Li S Aiding diagnosis of childhood attention-deficit/hyperactivity disorder of the inattentive presentation: Discriminant function analysis of multi-domain measures including EEG Biol Psychol 2021 161 108080 10.1016/j.biopsycho.2021.108080 33744372 · DOI
  197. [217]Dey S Rao AR Shah M Exploiting the brain's network structure in identifying ADHD subjects Front Syst Neurosci 2012 6 75 10.3389/fnsys.2012.00075 23162440 PMC3499771 · DOI
  198. [218]Yoo JH Kim JI Kim BN Jeong B Exploring characteristic features of attention-deficit/hyperactivity disorder: findings from multi-modal MRI and candidate genetic data Brain Imaging Behav 2020 14 6 2132 2147 10.1007/s11682-019-00164-x 31321662 · DOI
  199. [219]Tenev A Markovska-Simoska S Kocarev L Pop-Jordanov J Müller A Candrian G Machine learning approach for classification of ADHD adults Int J Psychophysiol 2014 93 1 162 166 10.1016/j.ijpsycho.2013.01.008 23361114 · DOI
  200. [220]Rezaeezadeh M Shamekhi S Shamsi M Attention deficit hyperactivity disorder diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study Phys Eng Sci Med 2020 43 2 577 592 10.1007/s13246-020-00858-3 32524443 · DOI

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Authors
Chen, Z., Hu, B., Liu, X., Becker, B., Eickhoff, S. B., Miao, K., Gu, X., Tang, Y., Dai, X., Li, C., Leonov, A., Xiao, Z., Feng, Z., Chen, J., & Chuan-Peng, H.
Affiliations
  • Southwest University (CN)
  • Army Medical University (CN)
  • University of Electronic Science and Technology of China (CN)
  • Heinrich Heine University Düsseldorf (DE)
  • Shanghai International Studies University (CN)
  • Sun Yat-sen University (CN)
  • Clark University (US)
  • Beijing Normal University (CN)
  • Nanjing Normal University (CN)
Corresponding author
Chen, Zhiyi
ORCID
Venue
BMC Medicine
Publisher
Springer Science and Business Media LLC
Published
2023-07-03
Volume
21
Issue
1
Pages
241-241
ISSN
1741-7015 (ISSN-L)
DOI
10.1186/s12916-023-02941-4
PMC
PMC10318841
OpenAlex
W4382987693
Cited by
35
References
25
Citations by year
2026: 13 · 2025: 11 · 2024: 8 · 2023: 3
FWCI
5.24
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Clark Digital Commons (Clark University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
Funders
  • National Natural Science Foundation of China (STI2030, 82201658)
  • University of Electronic Science and Technology of China
  • Southwest University
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
generalizability, psychiatric diagnosis, model performance, quality assessment checklist, clinical translation of AI, mental illness
MeSH terms
Machine Learning, Humans, Psychiatry, Psychotic Disorders, Research Design, Neuroimaging

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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