The DOI matches its Crossref record; 571 of the 614 references it cites were found in a registry or an index, and 1 are not checked yet.
Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review
Open accessAbstract
Importance: Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. Objective: To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. Evidence Review: PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. Findings: A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). Conclusions and Relevance: This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures


Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“identifying only 86 studies (15.5%) included external validation”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [18]
In the citing paper
“Overall, 83% of studies were rated at high ROB18.”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [18]
In the citing paper
“examined 555 published neuroimaging-based AI models for psychiatric diagnosis”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [18]
In the citing paper
“while 97.5% included only subjects from high-income regions.”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [18]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Zhou, D., Liao, Z., & Chen, R. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/5222136 · Cited by 17
RetractedDermyshi, E., Wang, Y., Yan, C., Hong, W., Qiu, G., Gong, X., & Zhang, T. (2017) · Neonatology · doi:10.1159/000454668 · Cited by 24,586
Self-citationMeta-analysis of Observational Studies in Epidemiology<SUBTITLE>A Proposal for Reporting</SUBTITLE>
Stroup, D. F. (2000) · JAMA · doi:10.1001/jama.283.15.2008 · Cited by 20,623
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n160 · Cited by 13,262
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · BMJ · doi:10.1136/bmj.g7594 · Cited by 4,388
(2023) · IEEE Access · doi:10.1109/access.6287639 · Cited by 3,358
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,319
Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD Initiative.
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-1-200301070-00010 · Cited by 3,219
Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., & de Vet, H. C. W. (2003) · BMJ · doi:10.1136/bmj.326.7379.41 · Cited by 3,215
Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984
Understanding deep learning (still) requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021) · Communications of the ACM · doi:10.1145/3446776 · Cited by 2,400
Choosing Multiple Parameters for Support Vector Machines
Chapelle, O., Vapnik, V. N., Bousquet, O., & Mukherjee, S. (2002) · Machine Learning · doi:10.1023/a:1012450327387 · Cited by 2,232
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
IEEE Transactions on Biomedical Engineering
(2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.10 · Cited by 1,414
Reducing waste from incomplete or unusable reports of biomedical research
Glasziou, P., Altman, D. G., Bossuyt, P., Boutron, I., Clarke, M., Julious, S., Michie, S., Moher, D., & Wager, E. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62228-x · Cited by 1,319
Neuropsychiatric Disease and Treatment
(2020) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt · Cited by 1,177
Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020) · BMJ · doi:10.1136/bmj.m689 · Cited by 1,152
Early brain development in infants at high risk for autism spectrum disorder
The IBIS Network, Hazlett, H. C., Gu, H., Munsell, B. C., Kim, S. H., Styner, M., Wolff, J. J., Elison, J. T., Swanson, M. R., Zhu, H., et al. (2017) · Nature · doi:10.1038/nature21369 · Cited by 1,128
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01772-9 · Cited by 1,039
Self-citationIdentification of autism spectrum disorder using deep learning and the ABIDE dataset
Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.08.017 · Cited by 1,016
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912
Roberts, M. T., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Avilés-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.64000 · Cited by 902
Cross-validation failure: Small sample sizes lead to large error bars
Varoquaux, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.061 · Cited by 897
Machine Learning for Precision Psychiatry: Opportunities and Challenges
Bzdok, D., & Meyer‐Lindenberg, A. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.11.007 · Cited by 888
Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-048008 · Cited by 852
Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
Varoquaux, G. P., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.038 · Cited by 848
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Li, X., Zhou, Y., Dvornek, N. C., Zhang, M., Gao, S., Zhuang, J., Scheinost, D., Staib, L. H., Ventola, P. E., & Duncan, J. S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102233 · Cited by 845
Parisot, S., Ktena, S. I., Ferrante, E., Lee, M., Guerrero, R., Glocker, B., & Rueckert, D. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.06.001 · Cited by 836
Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., & Hu, D. (2012) · Brain · doi:10.1093/brain/aws059 · Cited by 748
Establishment of Best Practices for Evidence for Prediction: A Review
Poldrack, R. A., Huckins, G., & Varoquaux, G. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3671 · Cited by 738
Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism
Uddin, L. Q., Supekar, K., Lynch, C. J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., & Menon, V. (2013) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.104 · Cited by 621
IEEE Transactions on Neural Systems and Rehabilitation Engineering
(2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.7333 · Cited by 570
Characterizing Thalamo-Cortical Disturbances in Schizophrenia and Bipolar Illness
Anticevic, A., Cole, M. W., Repovš, G., Murray, J. D., Brumbaugh, M. S., Winkler, A. M., Savić, A., Krystal, J. H., Pearlson, G. D., & Glahn, D. C. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht165 · Cited by 509
Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ · doi:10.1136/bmj.n2281 · Cited by 415
Disease state prediction from resting state functional connectivity
Craddock, R. C., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22159 · Cited by 411
EEG power, frequency, asymmetry and coherence in male depression
Knott, V., Mahoney, C., Kennedy, S. H., & Evans, K. (2001) · Psychiatry Research Neuroimaging · doi:10.1016/s0925-4927(00)00080-9 · Cited by 409
Functional connectivity magnetic resonance imaging classification of autism
Anderson, J. S., Nielsen, J. A., Froehlich, A. L., DuBray, M. B., Druzgal, T. J., Cariello, A. N., Cooperrider, J. R., Zielinski, B. A., Ravichandran, C., Fletcher, P. T., et al. (2011) · Brain · doi:10.1093/brain/awr263 · Cited by 405
ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data
Eslami, T., Mirjalili, V., Fong, A. C. M., Laird, A. R., & Saeed, F. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00070 · Cited by 403
Ecker, C., Rocha-Rego, V., Johnston, P., Mourão-Miranda, J., Marquand, A. F., Daly, E., Brammer, M. J., Murphy, C. M., & Murphy, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.024 · Cited by 393
Metric learning with spectral graph convolutions on brain connectivity networks
Ktena, S. I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., Glocker, B., & Rueckert, D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.052 · Cited by 382
Multisite functional connectivity MRI classification of autism: ABIDE results
Nielsen, J. A., Zielinski, B. A., Fletcher, P. T., Alexander, A. L., Lange, N., Bigler, E. D., Lainhart, J. E., & Anderson, J. S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00599 · Cited by 377
Kim, J., Calhoun, V. D., Shim, E., & Lee, J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.018 · Cited by 352
Shen, H., Wang, L., Liu, Y., & Hu, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.011 · Cited by 352
Calhoun, V. D., Maciejewski, P. K., Pearlson, G. D., & Kiehl, K. A. (2007) · Human Brain Mapping · doi:10.1002/hbm.20463 · Cited by 351
Multivariate classification of social anxiety disorder using whole brain functional connectivity
Liu, F., Guo, W., Fouché, J., Wang, Y., Wang, W., Ding, J., Zeng, L., Qiu, C., Gong, Q., Zhang, W., et al. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0641-4 · Cited by 349
Self-citationRashid, B., Arbabshirani, M. R., Damaraju, E., Cetin, M. S., Miller, R. L., Pearlson, G. D., & Calhoun, V. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.051 · Cited by 335
Sui, J., Jiang, R., Bustillo, J., & Calhoun, V. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.02.016 · Cited by 309
A small number of abnormal brain connections predicts adult autism spectrum disorder
Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., et al. (2016) · Nature Communications · doi:10.1038/ncomms11254 · Cited by 305
Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network
Sherkatghanad, Z., Akhondzadeh, M., Salari, S., Moghadam, M. Z., Abdar, M., Acharya, U. R., Khosrowabadi, R., & Salari, V. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01325 · Cited by 289
Fractality analysis of frontal brain in major depressive disorder
Ahmadlou, M., Adeli, H., & Adeli, A. (2012) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2012.05.001 · Cited by 287
Zeng, L., Wang, H., Hu, P., Yang, B., Pu, W., Shen, H., Chen, X., Liu, Z., Yin, H. J., Tan, Q., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.03.017 · Cited by 280
Presenting machine learning model information to clinical end users with model facts labels
Sendak, M. P., Gao, M., Brajer, N., & Balu, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0253-3 · Cited by 279
Predictive models of autism spectrum disorder based on brain regional cortical thickness
Jiao, Y., Chen, R., Ke, X., Chu, K., Lu, Z., & Herskovits, E. H. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.047 · Cited by 254
Automated detection of schizophrenia using nonlinear signal processing methods
Jahmunah, V., Oh, S. L., Rajinikanth, V., Ciaccio, E. J., Cheong, K. H., Arunkumar, N., & Acharya, U. R. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.07.006 · Cited by 245
A neuroimaging biomarker for striatal dysfunction in schizophrenia
Li, A., Zalesky, A., Yue, W., Howes, O. D., Yan, H., Liu, Y., Fan, L., Whitaker, K. J., Xu, K., Rao, G., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0793-8 · Cited by 238
Zhu, C.-Z., Zang, Y.-F., Cao, Q.-J., Yan, C.-G., He, Y., Jiang, T.-Z., Sui, M.-Q., & Wang, Y.-F. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.029 · Cited by 236
Self-citationSchnack, H. G., Nieuwenhuis, M., van Haren, N. E. M., Abramovic, L., Scheewe, T. W., Brouwer, R. M., Pol, H. E. H., & Kahn, R. S. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.053 · Cited by 229
Ahmadlou, M., Adeli, H., & Adeli, A. (2010) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3181f40dc8 · Cited by 221
Meehan, A. J., Lewis, S. J., Fazel, S., Fusar-Poli, P., Steyerberg, E. W., Stahl, D., & Danese, A. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01528-4 · Cited by 219
Wavelet-Synchronization Methodology: A New Approach for EEG-Based Diagnosis of ADHD
Ahmadlou, M., & Adeli, H. (2010) · Clinical EEG and Neuroscience · doi:10.1177/155005941004100103 · Cited by 216
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
Shoeibi, A., Sadeghi, D., Moridian, P., Ghassemi, N., Heras, J., Alizadehsani, R., Khadem, A. A., Kong, Y., Nahavandi, S., Zhang, Y., et al. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.777977 · Cited by 216
A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals
Siuly, S., Khare, S. K., Bajaj, V., Wang, H., & Zhang, Y. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3022715 · Cited by 212
Mumtaz, W., Ali, S. S. A., Yasin, M. A. M., & Malik, A. S. (2017) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-017-1685-z · Cited by 208
Mwangi, B., Ebmeier, K. P., Matthews, K., & Steele, J. D. (2012) · Brain · doi:10.1093/brain/aws084 · Cited by 207
Supekar, K., Cai, W., Krishnadas, R., Palaniyappan, L. K., & Menon, V. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.07.020 · Cited by 205
Shickel (2018) · doi:10.1109/jbhi.6221020 · Cited by 203
Chen, H., Xujun, D., Liu, F., Lu, F., Ma, X., Zhang, Y., Uddin, L. Q., & Chen, H. (2015) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2015.06.014 · Cited by 202
A general prediction model for the detection of ADHD and Autism using structural and functional MRI
Sen, B., Borle, N. C., Greiner, R., & Brown, M. R. G. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194856 · Cited by 198
Saeedi, A., Saeedi, M., Maghsoudi, A., & Shalbaf, A. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09619-0 · Cited by 197
Costafreda, S. G., Fu, C. H. Y., Picchioni, M., Toulopoulou, T., McDonald, C., Kravariti, E., Walshe, M., Prata, D. P., Murray, R. M., & McGuire, P. K. (2011) · BMC Psychiatry · doi:10.1186/1471-244x-11-18 · Cited by 196
Extreme Learning Machine-Based Classification of ADHD Using Brain Structural MRI Data
Peng, X., Lin, P., Zhang, T., & Wang, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079476 · Cited by 194
Yang, H., Liu, J., Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pearlson, G. D., & Calhoun, V. D. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00192 · Cited by 193
Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network
Huang, Z.-A., Zhu, Z., Yau, C. H., & Tan, K. C. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3007943 · Cited by 189
Unsupervised classification of major depression using functional connectivity MRI
Zeng, L., Shen, H., Liu, L., & Hu, D. (2013) · Human Brain Mapping · doi:10.1002/hbm.22278 · Cited by 186
Chen, C. P., Keown, C. L., Jahedi, A., Nair, A., Pflieger, M. E., Bailey, B. A., & Müller, R.-A. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.04.002 · Cited by 183
Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach
Cui, Z., Xia, Z., Su, M., Shu, H., & Gong, G. (2016) · Human Brain Mapping · doi:10.1002/hbm.23112 · Cited by 180
Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning
Janssen, R. J., Mourão-Miranda, J., & Schnack, H. G. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.04.004 · Cited by 180
Guo, X., Dominick, K. C., Minai, A. A., Li, H., Erickson, C. A., & Lu, L. J. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00460 · Cited by 178
A hybrid deep neural network for classification of schizophrenia using EEG Data
Sun, J., Cao, R., Zhou, M., Hussain, W., Wang, B., Xue, J., & Xiang, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-83350-6 · Cited by 178
Anatomical Brain Images Alone Can Accurately Diagnose Chronic Neuropsychiatric Illnesses
Bansal, R., Staib, L. H., Laine, A. F., Hao, X., Xu, D., Liu, J., Weissman, M. M., & Peterson, B. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050698 · Cited by 177
Yan, W., Calhoun, V. D., Song, M., Cui, Y. J., Yan, H., Liu, S., Fan, L., Zuo, N., Yang, Z., Xu, K., et al. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.023 · Cited by 177
Quantifying performance of machine learning methods for neuroimaging data
Jollans, L., Boyle, R. T., Artiges, É., Banaschewski, T., Desrivières, S., Grigis, A., Martinot, J., Paus, T., Smolka, M. N., Walter, H., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.082 · Cited by 177
Resting state functional magnetic resonance imaging and neural network classified autism and control
Iidaka, T. (2014) · Cortex · doi:10.1016/j.cortex.2014.08.011 · Cited by 176
Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling
Wang, S., Lv, Y., Sui, Y., Liu, S., Wang, S., & Zhang, Y. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0845-x · Cited by 176
Sparse network-based models for patient classification using fMRI
Rosa, M. J., Portugal, L. C. L., Hahn, T., Fallgatter, A. J., Garrido, M. I., Shawe-Taylor, J. S., & Mourão-Miranda, J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.021 · Cited by 176
Patel, M. J., Andreescu, C., Price, J. C., Edelman, K. L., Reynolds, C. F., & Aizenstein, H. J. (2015) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.4262 · Cited by 174
Machine learning approach for classification of ADHD adults
Tenev, A., Markovska-Simoska, S., Kocarev, L., Pop-Jordanov, J., Müller, A. F., & Candrian, G. (2013) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2013.01.008 · Cited by 173
Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System
Gavrilescu, M., & Vizireanu, N. D. (2019) · Sensors · doi:10.3390/s19173693 · Cited by 172
Li, H., Parikh, N. A., & He, L. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00491 · Cited by 171
Shalbaf, A., Bagherzadeh, S., & Maghsoudi, A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00925-9 · Cited by 166
Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology
Daneshjou, R., Barata, C., Betz‐Stablein, B., Celebi, M. E., Codella, N., Combalia, M., Guitera, P., Gutman, D. A., Halpern, A. C., Helba, B., et al. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.4915 · Cited by 166
Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns
Liao, S., Wu, C., Huang, H.-C., Cheng, W.-T., & Liu, Y. (2017) · Sensors · doi:10.3390/s17061385 · Cited by 165
Classification of ADHD children through multimodal magnetic resonance imaging
Dai, D., Wang, J., Hua, J., & He, H. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00063 · Cited by 164
Pinaya, W. H. L., Gadelha, A., Doyle, O., Noto, C. D. S., Zugman, A., Cordeiro, Q., Jackowski, A. P., Bressan, R. A., & Sato, J. R. (2016) · Scientific Reports · doi:10.1038/srep38897 · Cited by 164
Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2379621 · Cited by 161
Automated ASD detection using hybrid deep lightweight features extracted from EEG signals
Bayğın, M., Doğan, Ş., Tuncer, T., Barua, P. D., Faust, O., Arunkumar, N., Abdulhay, E., Palmer, E. E., & Acharya, U. R. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104548 · Cited by 160
DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI
Riaz, A., Asad, M., Alonso, E., & Slabaugh, G. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108506 · Cited by 160
Liechti, M. D., Valko, L., Müller, U. C., Döhnert, M., Drechsler, R., Steinhausen, H., & Brandeis, D. (2012) · Brain Topography · doi:10.1007/s10548-012-0258-6 · Cited by 159
Sun, H., Chen, Y., Huang, Q., Lui, S., Huang, X., Shi, Y., Xu, X., Sweeney, J. A., & Gong, Q. (2017) · Radiology · doi:10.1148/radiol.2017170226 · Cited by 159
Lv, J., Biase, M. A. D., Cash, R. F. H., Cocchi, L., Cropley, V., Klauser, P., Tian, Y., Bayer, J., Schmaal, L., Karayumak, S. C., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-00882-5 · Cited by 157
Johannesen, J., Bi, J., Jiang, R., Kenney, J. G., & Chen, C. A. (2016) · Neuropsychiatric Electrophysiology · doi:10.1186/s40810-016-0017-0 · Cited by 152
Alturki, F., Alsharabi, K., Abdurraqeeb, A. M., & Aljalal, M. (2020) · Italian National Conference on Sensors · doi:10.3390/s20092505 · Cited by 152
Zhao, K., Duka, B., Xie, H., Oathes, D. J., Calhoun, V. D., & Zhang, Y. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118774 · Cited by 150
Moghaddari, M., Lighvan, . Z., & Danishvar, S. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105738 · Cited by 149
Diagnosis of Autism Spectrum Disorder Based on Functional Brain Networks with Deep Learning
Yin, W., Mostafa, S., & Wu, F. (2020) · Journal of Computational Biology · doi:10.1089/cmb.2020.0252 · Cited by 149
Whole brain resting state functional connectivity abnormalities in schizophrenia
Venkataraman, A., Whitford, T. J., Westin, C., Golland, P., & Kubicki, M. R. (2012) · Schizophrenia Research · doi:10.1016/j.schres.2012.04.021 · Cited by 147
Heus, P., Damen, J. A., Pajouheshnia, R., Scholten, R. J. P. M., Reitsma, J. B., Collins, P. G. S., Altman, D. G., Moons, K. G. M., & Hooft, L. (2018) · BMC Medicine · doi:10.1186/s12916-018-1099-2 · Cited by 147
Aghdam, M. A., Sharifi, A., & Pedram, M. M. (2018) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0093-8 · Cited by 147
Mueller, A., Candrian, G., Kropotov, J. D., Ponomarev, V. A., & Baschera, G.-M. (2010) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-4-s1-s1 · Cited by 145
Insights into multimodal imaging classification of ADHD
Colby, J. B., Rudie, J. D., Brown, J. A., Douglas, P. K., Cohen, M. S., & Shehzad, Z. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00059 · Cited by 144
Zhang, B., Yan, G., Yang, Z., Su, Y., Wang, J., & Lei, T. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3043426 · Cited by 144
Uyulan, Ç., Erguzel, T. T., Ünübol, H., Çebi, M., Sayar, G. H., Asad, M. N., & Tarhan, N. (2020) · Clinical EEG and Neuroscience · doi:10.1177/1550059420916634 · Cited by 143
Liu, F., Guo, W., Yu, D., Gao, Q. C., Gao, K., Xue, Z., Du, H., Zhang, J., Tan, C., Liu, Z., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040968 · Cited by 142
Alcoholism Identification Based on an AlexNet Transfer Learning Model
Wang, S., Xie, S., Chen, X., Guttery, D. S., Tang, C., Sun, J., & Zhang, Y. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00205 · Cited by 142
Libero, L. E., DeRamus, T. P., Lahti, A. C., Deshpande, G., & Kana, R. K. (2015) · Cortex · doi:10.1016/j.cortex.2015.02.008 · Cited by 141
Rakić, M., Cabezas, M., Kushibar, K., Oliver, A., & Lladó, X. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102181 · Cited by 140
Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm
Oh, J., Oh, B., Lee, K., Chae, J., & Yun, K. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00016 · Cited by 140
Sacchet, M. D., Prasad, G., Foland‐Ross, L. C., Thompson, P. M., & Gotlib, I. H. (2015) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2015.00021 · Cited by 138
Rozycki, M., Satterthwaite, T. D., Koutsouleris, N., Erus, G., Doshi, J. J., Wolf, D. H., Yong Fan, A., Gur, R. E., Gur, R. C., Meisenzahl, E. M., et al. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx137 · Cited by 138
Hart, H. C., Chantiluke, K. C., Cubillo, A., Smith, A. B., Simmons, A., Brammer, M. J., Marquand, A. F., & Rubia, K. (2013) · Human Brain Mapping · doi:10.1002/hbm.22386 · Cited by 137
Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry
Nielsen, A. N., Barch, D. M., Petersen, S. E., Schlaggar, B. L., & Greene, D. J. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.11.007 · Cited by 137
Pinaya, W. H. L., Mechelli, A., & Sato, J. R. (2018) · Human Brain Mapping · doi:10.1002/hbm.24423 · Cited by 136
Movahed, R. A., Jahromi, G. P., Shahyad, S., & Meftahi, G. H. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109209 · Cited by 136
EEG microstate features for schizophrenia classification
Kim, K., Duc, N. T., Choi, M., & Lee, B. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251842 · Cited by 135
Yassin, W., Nakatani, H., Zhu, Y., Kojima, M., Owada, K., Kuwabara, H., Gonoi, W., Aoki, Y. Y., Takao, H., Natsubori, T., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00965-5 · Cited by 133
Almuqhim, F., & Saeed, F. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.654315 · Cited by 133
Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study
Grossi, E., Olivieri, C., & Buscema, M. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.02.002 · Cited by 131
Kalmady, S. V., Greiner, R., Agrawal, R., Shivakumar, V., Narayanaswamy, J. C., Brown, M. R. G., Greenshaw, A. J., Dursun, S. M., & Venkatasubramanian, G. (2019) · Schizophrenia · doi:10.1038/s41537-018-0070-8 · Cited by 131
Thomas, R. M., Gallo, S., Cerliani, L., Zhutovsky, P., El-Gazzar, A., & van Wingen, G. A. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00440 · Cited by 131
Tor, H. T., Ooi, C. P., Lim-Ashworth, N., Wei, J. K. E., Jahmunah, V., Oh, S. L., Acharya, U. R., & Fung, D. S. S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.105941 · Cited by 130
EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN
Djemal, R., AlSharabi, K., Ibrahim, S., & Alsuwailem, A. (2017) · BioMed Research International · doi:10.1155/2017/9816591 · Cited by 129
Neuroanatomy of verbal working memory as a diagnostic biomarker for depression
Marquand, A. F., Mourão-Miranda, J., Brammer, M. J., Cleare, A. J., & Fu, C. H. Y. (2008) · Neuroreport · doi:10.1097/wnr.0b013e328310425e · Cited by 125
Li, F., Wang, J., Liao, Y., Yi, C., Jiang, Y., Si, Y., Peng, W., Yao, D., Zhang, Y., Dong, W., et al. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2900725 · Cited by 125
Hasanzadeh, F., Mohebbi, M., & Rostami, R. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7613 · Cited by 125
Sparse Network-Based Models for Patient Classification Using fMRI
Rosa, M. J., Portugal, L. C. L., Shawe-Taylor, J. S., & Mourão-Miranda, J. (2013) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2013.26 · Cited by 124
Deep Learning Based on Event-Related EEG Differentiates Children with ADHD from Healthy Controls
Vahid, A., Bluschke, A., Roessner, V., Stober, S., & Beste, C. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8071055 · Cited by 124
Diagnosing different binge‐eating disorders based on reward‐related brain activation patterns
Weygandt, M., Schaefer, A., Schienle, A., & Haynes, J. (2011) · Human Brain Mapping · doi:10.1002/hbm.21345 · Cited by 121
Diffusion based abnormality markers of pathology: Toward learned diagnostic prediction of ASD
Ingalhalikar, M., Parker, D., Bloy, L., Roberts, T. P. L., & Verma, R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.023 · Cited by 121
Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD)
Poil, S., Bollmann, S., Ghisleni, C., Tuura, R., Klaver, P., Ball, J., Eich‐Höchli, D., Brandeis, D., & Michels, L. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.12.118 · Cited by 121
Systematic misestimation of machine learning performance in neuroimaging studies of depression
Flint, C., Cearns, M., Opel, N., Redlich, R., Mehler, D. M. A., Emden, D., Winter, N. R., Leenings, R., Eickhoff, S. B., Kircher, T., et al. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01020-7 · Cited by 120
Fusion of fMRI and non-imaging data for ADHD classification
Riaz, A., Asad, M., Alonso, E., & Slabaugh, G. (2017) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2017.10.002 · Cited by 119
Ghiassian, S., Greiner, R., Jin, P., & Brown, M. R. G. (2016) · PLOS ONE · doi:10.1371/journal.pone.0166934 · Cited by 116
Neural Biomarkers for Dyslexia, ADHD, and ADD in the Auditory Cortex of Children
Serrallach, B. L., Groß, C., Bernhofs, V., Engelmann, D., Benner, J., Gündert, N., Blatow, M., Wengenroth, M., Seitz, A., Brunner, M., et al. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00324 · Cited by 115
Shen, M. D., Nordahl, C. W., Li, D. D., Lee, A., Angkustsiri, K., Emerson, R. W., Rogers, S. J., Ozonoff, S. J., & Amaral, D. G. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(18)30294-3 · Cited by 115
Brain MRI analysis using a deep learning based evolutionary approach
Shahamat, H., & Abadeh, M. S. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.03.017 · Cited by 115
Murdaugh, D. L., Shinkareva, S. V., Deshpande, H. R., Wang, J., Pennick, M. R., & Kana, R. K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050064 · Cited by 114
Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction
Khosla, M., Jamison, K. W., Kuceyeski, A. F., & Sabuncu, M. R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.012 · Cited by 114
Dhiman, P., Ma, J., Navarro, C. L. A., Speich, B., Bullock, G. S., Damen, J. A., Kirtley, S., Hooft, L., Riley, R. D., CALSTER, B. V., et al. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.06.024 · Cited by 113
Wu, M., Mwangi, B., Bauer, I. E., Passos, I. C., Sanches, M., Zunta‐Soares, G. B., Meyer, T. D., Hasan, K. M., & Soares, J. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.016 · Cited by 112
Individualized Prediction and Clinical Staging of Bipolar Disorders Using Neuroanatomical Biomarkers
Mwangi, B., Wu, M., Cao, B., Passos, I. C., Lavagnino, L., Keser, Z., Zunta‐Soares, G. B., Hasan, K. M., Kapczinski, F. P., & Soares, J. C. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.01.001 · Cited by 111
Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder
Chaddad, A., Desrosiers, C., Hassan, L., & Tanougast, C. (2017) · BMC Neuroscience · doi:10.1186/s12868-017-0373-0 · Cited by 109
Heunis, T.-M., Aldrich, C., Peters, J. M., Jeste, S., Şahin, M., Scheffer, C., & de Vries, P. J. (2018) · BMC Medicine · doi:10.1186/s12916-018-1086-7 · Cited by 109
Park, S. M., Jeong, B., Oh, D. Y., Choi, C.-H., Jung, H. Y., Lee, J., Lee, D., & Choi, J. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.707581 · Cited by 109
Calderoni, S., Retico, A., Biagi, L., Tancredi, R., Muratori, F., & Tosetti, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.070 · Cited by 108
Kazeminejad, A., & Sotero, R. C. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.01018 · Cited by 108
EEG characteristics of children with attention-deficit/hyperactivity disorder
Chen, H., Chen, W., Song, Y., Sun, L., & Li, X. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2019.03.048 · Cited by 108
Retico, A., Giuliano, A., Tancredi, R., Cosenza, A., Apicella, F., Narzisi, A., Biagi, L., Tosetti, M., Muratori, F., & Calderoni, S. (2016) · Molecular Autism · doi:10.1186/s13229-015-0067-3 · Cited by 107
Whole brain white matter connectivity analysis using machine learning: An application to autism
Zhang, F., Savadjiev, P., Cai, W., Song, Y., Yogesh Rathi, A., Tunç, B., Parker, D., Kapur, T., Schultz, R. T., Makris, N., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.029 · Cited by 107
Siqueira, A. D. S., Biazoli, C. E., Comfort, W. E., Rohde, L. A., & Sato, J. R. (2014) · BioMed Research International · doi:10.1155/2014/380531 · Cited by 105
Qureshi, M. N. I., Min, B., Jo, H. J., & Lee, B. (2016) · PLOS ONE · doi:10.1371/journal.pone.0160697 · Cited by 105
Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals
Duan, L., Duan, H., Qiao, Y., Sha, S., Qi, S., Zhang, X., Huang, J., Huang, X., & Wang, C. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00284 · Cited by 105
Detection of Depression and Scaling of Severity Using Six Channel EEG Data
Mahato, S., Goyal, N., Ram, D., & Paul, S. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01573-y · Cited by 104
Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder
Wang, X., Jiao, Y., Tang, T., Wang, H., & Lu, Z. (2013) · European Journal of Radiology · doi:10.1016/j.ejrad.2013.04.009 · Cited by 103
Gehricke, J.-G., Kruggel, F., Thampipop, T., Alejo, S. D., Tatos, E., Fallon, J., & Muftuler, L. T. (2017) · PLOS ONE · doi:10.1371/journal.pone.0175433 · Cited by 103
Bruin, W. B., Taylor, L., Thomas, R. M., Shock, J. P., Zhutovsky, P., Abe, Y., Alonso, P., Ameis, S. H., Anticevic, A., Arnold, P. D., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01013-y · Cited by 103
Johnston, B. A., Mwangi, B., Matthews, K., Coghill, D., Konrad, K., & Steele, J. D. (2014) · Human Brain Mapping · doi:10.1002/hbm.22542 · Cited by 101
Lim, L., Marquand, A., Cubillo, A. A., Smith, A. B., Chantiluke, K., Simmons, A., Mehta, M., & Rubia, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063660 · Cited by 101
Lu, X., Yang, Y., Wu, F., Gao, M., Xu, Y., Zhang, Y., Yao, Y., Du, X., Li, C., Wu, L., et al. (2016) · Medicine · doi:10.1097/md.0000000000003973 · Cited by 101
Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals
Pham, T., Vicnesh, J., Wei, J. K. E., Oh, S. L., Arunkumar, N., Abdulhay, E., Ciaccio, E. J., & Acharya, U. R. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17030971 · Cited by 101
Baradits, M., Bitter, I., & Czobor, P. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.112938 · Cited by 101
Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder
Liao, M., Duan, H., & Wang, G. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/9340027 · Cited by 101
Neuroanatomical Differences in Toddler Boys With Fragile X Syndrome and Idiopathic Autism
Hoeft, F., Walter, E., Lightbody, A. A., Hazlett, H. C., Chang, C., Piven, J., & Reiss, A. L. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.153 · Cited by 100
Arribas, J. I., Calhoun, V. D., & Adalı, T. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2080679 · Cited by 100
Lei, D., Pinaya, W. H. L., Young, J., van Amelsvoort, T., Marcelis, M. C., Donohoe, G. J., Mothersill, D., Corvin, A. P., Vieira, S., Huang, X., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24863 · Cited by 100
Nicholson, A. A., Harricharan, S., Densmore, M., Neufeld, R. W. J., Ros, T., McKinnon, M. C., Frewen, P., Théberge, J., Jetly, R., Pedlar, D., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102262 · Cited by 100
Fang, P., Zeng, L., Shen, H., Wang, L., Baojuan Li, A., Liu, L., & Hu, D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045972 · Cited by 98
Cheng, W., Ji, X., Zhang, J., & Feng, J. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00058 · Cited by 97
Dey, S., Rao, A. R., & Shah, M. A. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00064 · Cited by 97
Sacchet, M. D., Livermore, E. E., Iglesias, J. E., Glover, G. H., & Gotlib, I. H. (2015) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2015.06.002 · Cited by 97
Doan, N. T., Kaufmann, T., Bettella, F., Jørgensen, K. N., Brandt, C. L., Moberget, T., Alnæs, D., Douaud, G., Duff, E. P., Djurovic, S., et al. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.014 · Cited by 97
(21875336), W. J., (7803908), S. D., (21875339), I.-A. O., (21875267), K. M., (19191715), F. A., & (21875342), F. S. (2025) · Figshare · Cited by 97
Multiple-Network Classification of Childhood Autism Using Functional Connectivity Dynamics
Price, T., Chong-yaw Wee, A., Gao, W., & Shen, D. G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_23 · Cited by 96
Harricharan, S., Nicholson, A. A., Thome, J., Densmore, M., McKinnon, M. C., Théberge, J., Frewen, P. A., Neufeld, R. W. J., & Lanius, R. A. (2020) · Psychophysiology · doi:10.1111/psyp.13472 · Cited by 94
Jamal, W., Das, S., Oprescu, I.-A., Maharatna, K., Apicella, F., & Sicca, F. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/4/046019 · Cited by 93
Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM
Chyzhyk, D., Savio, A. M., & Graña, M. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.04.002 · Cited by 93
Just, M. A., Cherkassky, V. L., Buchweitz, A., Keller, T. A., & Mitchell, T. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0113879 · Cited by 93
Wu, C., Huang, H.-C., Huang, S., Chen, I., Liao, S., Chen, C., Lin, C., Lee, S., Chen, M., Tsai, C.-F., et al. (2021) · Biosensors · doi:10.3390/bios11120499 · Cited by 93
Evaluating risk prediction models for adults with heart failure: A systematic literature review
Tanna, G. L. D., Wirtz, H. S., Burrows, K. L., & Globe, G. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0224135 · Cited by 92
Classification of ASD based on fMRI data with deep learning
Shao, L., Fu, C., You, Y., & Fu, D. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09683-0 · Cited by 92
Mueller, A., Candrian, G., Grane, V. A., Kropotov, J. D., Ponomarev, V. A., & Baschera, G.-M. (2011) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-5-5 · Cited by 91
Bürger, C., Redlich, R., Grotegerd, D., Meinert, S., Dohm, K., Schneider, I., Zaremba, D., Förster, K., Alferink, J., Bölte, J., et al. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.36 · Cited by 91
Identifying major depressive disorder using Hurst exponent of resting-state brain networks
Wei, M., Qin, J., Yan, R., Li, H., Yao, Z., & Lü, Q. (2013) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2013.09.008 · Cited by 90
Wang, N., Yao, D., Ma, L., & Liu, M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102279 · Cited by 90
Aydın, S., Arıca, N., Ergül, E., & Tan, O. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500100 · Cited by 89
Extracting biomarkers of autism from MEG resting-state functional connectivity networks
Tsiaras, V., Simos, P. G., Rezaie, R., Sheth, B. R., Garyfallidis, E., Castillo, E. M., & Papanicolaou, A. C. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.04.004 · Cited by 89
Wee, C.-Y., Wang, L., Shi, F., Yap, P.-T., & Shen, D. (2014) · Human Brain Mapping · doi:10.1002/hbm.22411 · Cited by 89
Singh, K., Singh, S., & Malhotra, J. (2020) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/0954411920966937 · Cited by 89
Cited by
Sorted by citations
Bias recognition and mitigation strategies in artificial intelligence healthcare applications
Hasanzadeh, F., Josephson, C. B., Waters, G., Adedinsewo, D., Azizi, Z., & White, J. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01503-7 · Cited by 320
Data leakage inflates prediction performance in connectome-based machine learning models
Rosenblatt, M., Tejavibulya, L., Jiang, R., Noble, S., & Scheinost, D. (2024) · Nature Communications · doi:10.1038/s41467-024-46150-w · Cited by 236
BrainAGE, brain health, and mental disorders: A systematic review
Seitz‐Holland, J., Haas, S. S., Penzel, N., Reichenberg, A., & Pasternak, O. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105581 · Cited by 38
Chen, Z., Hu, B., Liu, X., Becker, B., Eickhoff, S. B., Miao, K., Gu, X., Tang, Y., Dai, X., Li, C., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-02941-4 · Cited by 35
Self-citationAlmarie, B., González-González, L. F., Barbosa, L. A. D. S., Lutz, A. M., Grosse, U., & Fregni, F. (2025) · Biomedicines · doi:10.3390/biomedicines13123005 · Cited by 39
Metadata
- Authors
- Chen, Z., Liu, X., Yang, Q., Wang, Y.-J., Miao, K., Gong, Z., Yu, Y., Leonov, A., Liu, C., Feng, Z., & Chuan-Peng, H.
- Affiliations
- Army Medical University (CN)
- Clark University (US)
- Qufu Normal University (CN)
- Nanjing Normal University (CN)
- Corresponding author
- Chen, Zhiyi
- Feng, Zhengzhi
- ORCID
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2023-03-06
- Volume
- 6
- Issue
- 3
- Pages
- e231671
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC9989906
- OpenAlex
- W4323295479
- Cited by
- 43
- References
- 614
- Citations by year
- 2026: 20 · 2025: 10 · 2024: 10 · 2023: 3
- FWCI
- 0.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- JAMA Network Open (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Clark Digital Commons (Clark University) (submittedVersion, cc-by-nc-nd)
- Funders
- Chinese People’s Liberation Army
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- psychiatric diagnosis, reporting quality, PROBAST, clinical applicability, model validation, sample size adequacy, model calibration
- MeSH terms
- Artificial Intelligence, Calibration, Humans, Bias, Benchmarking, 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 ›
Are you an author, or do you see a problem with this page? Request a review