Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

Automatic classification of dyslexic children by applying machine learning to fMRI images

Garcia-Chimeno, Y., García-Zapirain, B., Prieto, I. S., & Fernández-Ruanova, B. (2014) · Bio-Medical Materials and Engineering · doi:10.3233/bme-141120 · Cited by 36

Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images

Qiu, L., Huang, X., Zhang, J., Wang, Y., Kuang, W., Li, J., Wang, X., Wang, L., Yang, X., Lui, S., et al. (2014) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.130034 · Cited by 36

Diagnostic value of structural and diffusion imaging measures in schizophrenia

Lee, J. S., Chon, M., Kim, H., Yogesh Rathi, A., Sylvain Bouix, A., Shenton, M. E., & Kubicki, M. R. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.02.007 · Cited by 36

A multimodal neuroimaging classifier for alcohol dependence

Guggenmos, M., Schmack, K., Veer, I. M., Lett, T. A. P., Sekutowicz, M., Sebold, M., Garbusow, M., Sommer, C., Wïttchen, H., Zimmermann, U. S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-019-56923-9 · Cited by 36

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Guo, Y., Qiu, J., & Lu, W. (2020) · Brain Sciences · doi:10.3390/brainsci10080562 · Cited by 36

Exploring characteristic features of attention-deficit/hyperactivity disorder: findings from multi-modal MRI and candidate genetic data

Yoo, J. H., Kim, J. I., Kim, B.-N., & Jeong, B. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00164-x · Cited by 35

Classification of obsessive-compulsive disorder from resting-state fMRI

Sen, B., Bernstein, G. A., Xu, T., Mueller, B. A., Schreiner, M. W., Cullen, K. R., & Parhi, K. K. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591508 · Cited by 35

On the use of pairwise distance learning for brain signal classification with limited observations

Calhas, D., Romero, E., & Henriques, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101852 · Cited by 35

Classification of ADHD with fMRI data and multi-objective optimization

Shao, L., You, Y., Du, H., & Fu, D. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105676 · Cited by 35

Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample

Sundermann, B., Feder, S. C., Wersching, H., Teuber, A., Schwindt, W., Kugel, H., Heindel, W. L., Arolt, V., Berger, K., & Pfleiderer, B. (2016) · Journal of Neural Transmission · doi:10.1007/s00702-016-1673-8 · Cited by 34

Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning

Zu, C., Gao, Y., Munsell, B. C., Kim, M., Peng, Z., Cohen, J. R., Zhang, D., & Guorong Wu, A. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9899-8 · Cited by 34

Multivariate classification of earthquake survivors with post‐traumatic stress disorder based on large‐scale brain networks

Zhu, H., Yuan, M., Qiu, C., Ren, Z., Li, Y., Wang, J., Huang, X., Lui, S., Gong, Q., Zhang, W., et al. (2020) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13150 · Cited by 34

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

Hu, J., Li-jie, C., Li, T., Liao, B., Dong, S., & Li, P. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/1394830 · Cited by 34

Using machine‐learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment

Ding, X., Li, Y., Li, D., Li, L., & Liu, X. (2020) · Brain and Behavior · doi:10.1002/brb3.1814 · Cited by 34

Diagnostic Prediction for Social Anxiety Disorder via Multivariate Pattern Analysis of the Regional Homogeneity

Zhang, W., Yang, X., Lui, S., Meng, Y., Li, Y., Xiao, Y., Deng, W., Zhang, W., & Gong, Q. (2015) · BioMed Research International · doi:10.1155/2015/763965 · Cited by 33

Extracting Multiscale Pattern Information of fMRI Based Functional Brain Connectivity with Application on Classification of Autism Spectrum Disorders

Wang, H., Chen, C., & Fushing, H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045502 · Cited by 33

Electroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study

Helgadóttir, H., Guðmundsson, Ó. Ó., Baldursson, G., Magnússon, P., Blin, N., Brynjólfsdóttir, B., Emilsdóttir, Á. L., Gudmundsdóttir, G. B., Lorange, M., Newman, P. K., et al. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-005500 · Cited by 33

Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation

Bhaumik, R., Pradhan, A., Das, S., & Bhaumik, D. K. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9366-0 · Cited by 33

Distinguishing Adolescents With Conduct Disorder From Typically Developing Youngsters Based on Pattern Classification of Brain Structural MRI

Zhang, J., Liu, W., Zhang, J., Wu, Q., Gao, Y., Jiang, Y., Gao, J., Yao, S., & Huang, B. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00152 · Cited by 33

Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity

Zhao, Z., Li, J., Niu, Y., Wang, C., Zhao, J., Yuan, Q., Ren, Q., Xu, Y., & Yu, Y. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.651439 · Cited by 33

Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines

Mwangi, B., Wu, M., Bauer, I. E., Modi, H. H., Zeni, C., Zunta‐Soares, G. B., Hasan, K. M., & Soares, J. C. (2015) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2015.10.002 · Cited by 32

Enhancing multi-center generalization of machine learning-based depression diagnosis from resting-state fMRI

Nakano, T., Takamura, M., Ichikawa, N., Okada, G., Okamoto, Y., Yamada, M., Suhara, T., Yamawaki, S., & Yoshimoto, J. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00400 · Cited by 32

Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients

Ebdrup, B. H., Axelsen, M., Bak, N. K., Fagerlund, B., Oranje, B., Raghava, J. M., Nielsen, M. Ø., Rostrup, E., Hansen, L. K., & Glenthoj, B. Y. (2018) · Psychological Medicine · doi:10.1017/s0033291718003781 · Cited by 32

Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity

Antonucci, L. A., Penzel, N., Pergola, G., Kambeitz‐Ilankovic, L., Dwyer, D., Kambeitz, J. P., Haas, S. S., Passiatore, R., Fazio, L., Caforio, G., et al. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0532-3 · Cited by 32

Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal

Xu, L., Hua, Q., Yu, J., & Li, J. (2020) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.12.400 · Cited by 32

Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification

Prabhakar, S. K., & Rajaguru, H. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05689 · Cited by 32

Accurate classification of schizophrenia patients based on novel resting-state fMRI features

Arbabshirani, M. R., Castro, E., & Calhoun, V. D. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945163 · Cited by 31

Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data

Algumaei, A. H., Algunaid, R. F., Rushdi, M. A., & Yassine, I. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265300 · Cited by 31

Anomalous gray matter patterns in specific reading comprehension deficit are independent of dyslexia

Bailey, S., Hoeft, F., Aboud, K. S., & Cutting, L. E. (2016) · Annals of Dyslexia · doi:10.1007/s11881-015-0114-y · Cited by 31

Auditory repetition suppression alterations in relation to cognitive functioning in fragile X syndrome: a combined EEG and machine learning approach

Knoth, I. S., Lajnef, T., Rigoulot, S., Lacourse, K., Vannasing, P., Michaud, J. L., Jacquemont, S., Major, P., Jerbi, K., & Lippé, S. (2018) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-018-9223-3 · Cited by 31

Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity

Wang, X., Jiao, Y., & Li, L. (2018) · Neuroscience Letters · doi:10.1016/j.neulet.2018.07.029 · Cited by 31

Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model

Chikara, R. K., & Ko, L. (2019) · Sensors · doi:10.3390/s19173791 · Cited by 31

Improving the predictive potential of diffusion MRI in schizophrenia using normative models—Towards subject‐level classification

Elad, D., Karayumak, S. C., Zhang, F., Cho, K. I. K., Lyall, A. E., Seitz‐Holland, J., Ben‐Ari, R., Pearlson, G. D., Tamminga, C. A., Sweeney, J. A., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25574 · Cited by 31

Autistic Spectrum Disorder Detection and Structural Biomarker Identification Using Self-Attention Model and Individual-Level Morphological Covariance Brain Networks

Wang, Z., Peng, D., Shang, Y., & Gao, J. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.756868 · Cited by 31

Identifying ADHD Individuals From Resting-State Functional Connectivity Using Subspace Clustering and Binary Hypothesis Testing

Tang, Y., Wang, C., Chen, Y., Sun, N., Jiang, A., & Wang, Z. (2019) · Journal of Attention Disorders · doi:10.1177/1087054719837749 · Cited by 30

Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes

Graa, O., & Rekik, I. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108344 · Cited by 30

Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism

Usman, O. L., Muniyandi, R. C., Omar, K., & Mohamad, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245579 · Cited by 30

Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder

Lee, M. H., Kim, N., Yoo, J., Kim, H., Son, Y., Kim, Y.-B., Oh, S., Kim, S., Lee, H., Jeon, J., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-88845-w · Cited by 30

The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results

Elnakieb, Y. A., Ali, M. T., Elnakib, A. A., Shalaby, A. M., Soliman, A., Mahmoud, A. H., Ghazal, M. A., Barnes, G. N., & El-Baz, A. S. (2021) · Sensors · doi:10.3390/s21248171 · Cited by 30

Locally Linear Embedding and fMRI Feature Selection in Psychiatric Classification

Sidhu, G. (2019) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2019.2936348 · Cited by 30

ADHD diagnosis from multiple data sources with batch effects

Olivetti, E., Greiner, S., & Avesani, P. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00070 · Cited by 29

Prediction of pediatric bipolar disorder using neuroanatomical signatures of the amygdala

Mwangi, B., Spiker, D. E., Zunta‐Soares, G. B., & Soares, J. C. (2014) · Bipolar Disorders · doi:10.1111/bdi.12222 · Cited by 29

Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification

Qin, J., Wei, M., Liu, H., Chen, J., Yan, R., Hua, L., Zhao, K., Yao, Z., & Lü, Q. (2014) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2014.08.037 · Cited by 29

Diagnosing Developmental Dyscalculia on the Basis of Reliable Single Case FMRI Methods: Promises and Limitations

Dinkel, P. J., Willmes, K., Krinzinger, H., Konrad, K., & Koten, J. W., Jr. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083722 · Cited by 29

White Matter Microstructure in Pediatric Bipolar Disorder and Disruptive Mood Dysregulation Disorder

Linke, J. O., Adleman, N. E., Sarlls, J. E., Ross, A., Perlstein, S., Frank, H. R., Towbin, K. E., Pine, D. S., Leibenluft, E., & Brotman, M. A. (2019) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2019.05.035 · Cited by 29

Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: A machine learning study

Liang, S., Deng, W., Li, X., Wang, Q., Greenshaw, A. J., Guo, W., Kong, X., Li, M., Zhao, L., Meng, Y., et al. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.03.022 · Cited by 29

A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method

Zhao, J., Song, J., Xiaoli Li, A., & Kang, J. (2020) · Brain and Behavior · doi:10.1002/brb3.1721 · Cited by 29

Advanced Signal Processing Methods for Characterization of Schizophrenia

Masychev, K., Ciprian, C., Ravan, M., Reilly, J. P., & MacCrimmon, D. J. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3011842 · Cited by 29

Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine

Squarcina, L., Nosari, G., Marin, R., Castellani, U., Bellani, M., Bonivento, C., Fabbro, F., Molteni, M., & Brambilla, P. (2021) · Brain and Behavior · doi:10.1002/brb3.2238 · Cited by 29

Single-subject classification of schizophrenia using event-related potentials obtained during auditory and visual oddball paradigms

Neuhaus, A. H., Popescu, F. C., Bates, J. A., Goldberg, T. E., & Malhotra, A. K. (2012) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-012-0326-7 · Cited by 28

Classification of borderline personality disorder based on spectral power of resting-state fMRI

Xu, T., Cullen, K. R., Houri, A. K., Lim, K. O., Schulz, S. C., & Parhi, K. K. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944756 · Cited by 28

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

de Pierrefeu, A., Löfstedt, T., Laidi, C., Hadj‐Selem, F., Bourgin, J., Hájek, T., Španiel, F., Kolenič, M., Ciuciu, P., Hamdani, N., et al. (2018) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12964 · Cited by 28

Machine learning identifies unaffected first‐degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

Jing, R., Li, P., Ding, Z., Lin, X., Zhao, R., Shi, L., Yan, H., Liao, J., Zhuo, C., Lü, L., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24678 · Cited by 28

Classification of Social Anxiety Disorder With Support Vector Machine Analysis Using Neural Correlates of Social Signals of Threat

Xing, M., Fitzgerald, J. M., & Klumpp, H. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00144 · Cited by 28

Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

Shi, C., Xin, X., & Zhang, J. (2021) · Brain Sciences · doi:10.3390/brainsci11050603 · Cited by 28

Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis

Hua, M., Peng, Y., Zhou, Y., Qin, W., Yu, C., & Liang, M. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2019.109837 · Cited by 27

Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders

Muthuraman, M., Moliadze, V., Boecher, L., Siemann, J., Freitag, C. M., Groppa, S., & Siniatchkin, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-56398-8 · Cited by 27

Deep Neural Network to Differentiate Brain Activity Between Patients With First-Episode Schizophrenia and Healthy Individuals: A Multi-Channel Near Infrared Spectroscopy Study

Chou, P., Yao, Y.-H., Zheng, R.-X., Liou, Y.-L., Liu, T.-T., Lane, H., Yang, A. C., & Wang, S.-C. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.655292 · Cited by 27

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Zang, J., Huang, Y., Kong, L., Lei, B., Ke, P., Li, H., Zhou, J., Xiong, D., Li, G., Chen, J., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.697168 · Cited by 27

Altered global brain network topology as a trait marker in patients with anorexia nervosa

Geisler, D., Borchardt, V., Boehm, I., King, J. A., Tam, F. I., Marxen, M., Biemann, R., Roessner, V., Walter, M., & Ehrlich, S. (2019) · Psychological Medicine · doi:10.1017/s0033291718004002 · Cited by 26

Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions

Korda, A. I., Ruef, A., Neufang, S., Davatzikos, C. A., Borgwardt, S., Meisenzahl, E. M., & Koutsouleris, N. (2021) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2021.111303 · Cited by 26

Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression

Wu, Y., Ren, P., Chen, R., Xu, H., Xu, J., Zeng, L., Wu, D., Jiang, W., Tang, N., & Liu, X. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00501-z · Cited by 26

Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants

Lee, L., Chen, C.-H., Chang, W., Lee, P., Shyu, K., Chen, M., Hsu, J.-W., Bai, Y., Su, T., & Tu, P. (2021) · European Psychiatry · doi:10.1192/j.eurpsy.2021.2248 · Cited by 26

Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder

Hammer, R., Cooke, G. E., Stein, M. A., & Booth, J. R. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.08.015 · Cited by 25

Support Vector Machines, Multidimensional Scaling and Magnetic Resonance Imaging Reveal Structural Brain Abnormalities Associated With the Interaction Between Autism Spectrum Disorder and Sex

Andrei Irimia, A., Lei, X., Torgerson, C. M., Jacokes, Z., Abe, S., & Horn, J. D. V. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00093 · Cited by 25

Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures

Erguzel, T. T., Noyan, C. O., Eryılmaz, G., Ünsalver, B. Ö., Çebi, M., Taş, C., Dılbaz, N., & Tarhan, N. (2019) · Clinical EEG and Neuroscience · doi:10.1177/1550059418824450 · Cited by 25

Support vector machine‐based multivariate pattern classification of methamphetamine dependence using arterial spin labeling

Li, Y., Cui, Z., Liao, Q., Dong, H., Zhang, J., Shen, W., & Zhou, W. (2019) · Addiction Biology · doi:10.1111/adb.12705 · Cited by 25

Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements

Ramasubbu, R., Brown, E. C., Marcil, L. D., Talai, A. S., & Forkert, N. D. (2019) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12862 · Cited by 25

Involvement of the habenula in the pathophysiology of autism spectrum disorder

Germann, J., Gouveia, F. V., Brentani, H., Bedford, S. A., Tullo, S., Chakravarty, M. M., & Devenyi, G. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-00603-0 · Cited by 25

Selection of spectro-temporal patterns in multichannel MEG with support vector machines for schizophrenia classification

Ince, N. F., Goksu, F., Pellizzer, G., Tewfik, A. H., & Stephane, M. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649973 · Cited by 24

Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach

de Moura, A. M., Pinaya, W. H. L., Gadelha, A., Zugman, A., Noto, C. D. S., Cordeiro, Q., Belangero, S. I., Jackowski, A. P., Bressan, R. A., & Sato, J. R. (2018) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2018.03.003 · Cited by 24

Functional Connectivities Are More Informative Than Anatomical Variables in Diagnostic Classification of Autism

Eill, A., Jahedi, A., Gao, Y., Kohli, J. S., Fong, C., Solders, S. K., Carper, R. A., Valafar, F., Bailey, B., & Müller, R. (2019) · Brain Connectivity · doi:10.1089/brain.2019.0689 · Cited by 24

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites

Yamamoto, M., Bagarinao, E., Kushima, I., Takahashi, T., Sasabayashi, D., Inada, T., Suzuki, M., Iidaka, T., & Ozaki, N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239615 · Cited by 24

The progression of disorder-specific brain pattern expression in schizophrenia over 9 years

Lieslehto, J., Jääskeläinen, E., Kiviniemi, V. J., Haapea, M., Jones, P. B., Murray, G. K., Veijola, J. M., Dannlowski, U., Grotegerd, D., Meinert, S., et al. (2021) · Schizophrenia · doi:10.1038/s41537-021-00157-0 · Cited by 24

Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence

Sánchez‐Morla, E. M., Fuentes, J. L., Miguel, J. M., Boquete, L., del Castillo, M. O., Orduna, E., Satué, M., & García‐Martín, E. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11080803 · Cited by 24

Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms

Guo, X., Wang, J., Wang, X., Liu, W., Yu, H., Xu, L., Li, H., Wu, J., Dong, M., Tan, W., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08239-4 · Cited by 24

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

Wang, L., Chong-yaw Wee, A., Tang, X., Yap, P., & Shen, D. G. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9360-1 · Cited by 23

Aiding diagnosis of childhood attention-deficit/hyperactivity disorder of the inattentive presentation: Discriminant function analysis of multi-domain measures including EEG

Johnstone, S. J., Parrish, L., Jiang, H., Zhang, D., Williams, V., & Sun, L. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108080 · Cited by 23

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

Guo, H., Qin, M., Chen, J., Xu, Y. J., & Xiang, J. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/4820935 · Cited by 23

Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children

Menon, S. S., & Krishnamurthy, K. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.742807 · Cited by 23

MDD-TSVM: A novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals

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Brain regions with abnormal network properties in severe epilepsy of Lennox‐Gastaut phenotype: Multivariate analysis of task‐free fMRI

Pedersen, M., Curwood, E. K., Archer, J. S., Abbott, D. F., & Jackson, G. D. (2015) · Epilepsia · doi:10.1111/epi.13135 · Cited by 22

Parallel principal component neural networks for classification of event-related potential waveforms

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Tech giants enter mental health

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Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging

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Multivoxel pattern analysis of structural MRI in children and adolescents with conduct disorder

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Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals

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ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia

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Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning

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Machine Learning-Based Electroencephalographic Phenotypes of Schizophrenia and Major Depressive Disorder

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Diagnostic utility of brain activity flow patterns analysis in attention deficit hyperactivity disorder

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Abnormal Neural Oscillations in Schizophrenia Assessed by Spectral Power Ratio of MEG During Word Processing

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Dynamic thresholding networks for schizophrenia diagnosis

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Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder

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Using deep learning to classify pediatric posttraumatic stress disorder at the individual level

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Additional Brain Functional Network in Adults with Attention-Deficit/Hyperactivity Disorder: A Phase Synchrony Analysis

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Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism

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Predicting alcohol dependence from multi‐site brain structural measures

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Linking ADHD and Behavioral Assessment Through Identification of Shared Diagnostic Task-Based Functional Connections

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Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

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Neural connectome prospectively encodes the risk of post-traumatic stress disorder (PTSD) symptom during the COVID-19 pandemic

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Elucidating brain connectivity networks in major depressive disorder using classification-based scoring

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Quantifying patterns of brain activity: Distinguishing unaffected siblings from participants with ADHD and healthy individuals

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Deep Feature Extraction for Resting-State Functional MRI by Self-Supervised Learning and Application to Schizophrenia Diagnosis

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Entropy-Based Quantitative Electroencephalogram Analysis for Diagnosing Attention-Deficit Hyperactivity Disorder in Girls

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Sparse deep neural networks on imaging genetics for schizophrenia case–control classification

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Classification of bipolar disorder using basal-ganglia-related functional connectivity in the resting state

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A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder

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Automated and ERP-Based Diagnosis of Attention-Deficit Hyperactivity Disorder in Children

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Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models

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Learning a Phenotypic-Attribute Attentional Brain Connectivity Embedding for ADHD Classification using rs-fMRI

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Abnormal functional connectivity within the reward network: a potential neuroimaging endophenotype of bipolar disorder

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Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony

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A CNN Deep Local and Global ASD Classification Approach with Continuous Wavelet Transform Using Task-Based FMRI

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A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis

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Altered spontaneous activity in antisocial personality disorder revealed by regional homogeneity

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Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data

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Abnormal early dynamic individual patterns of functional networks in low gamma band for depression recognition

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Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier

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Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI)

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Comparison of Extreme Learning Machine and K-Nearest Neighbour Performance in Classifying EEG Signal of Normal, Poor and Capable Dyslexic Children

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Borderline personality disorder classification based on brain network measures during emotion regulation

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Disrupted pathways from frontal-parietal cortex to basal ganglia and cerebellum in patients with unmedicated obsessive compulsive disorder as observed by whole-brain resting-state effective connectivity analysis – a small sample pilot study

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Visualizing Functional Network Connectivity Difference between Healthy Control and Major Depressive Disorder Using an Explainable Machine-learning Method

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Cortical thickness distinguishes between major depression and schizophrenia in adolescents

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The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures

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Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

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On Classifying Disease-Induced Patterns in the Brain Using Diffusion Tensor Images

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Altered Fractional Amplitude of Low-Frequency Fluctuation in Major Depressive Disorder and Bipolar Disorder

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Classification of ADHD subgroup with recursive feature elimination for structural brain MRI

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Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder

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Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals

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Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity

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Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification

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Modeling essential connections in obsessive–compulsive disorder patients using functional MRI

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Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological and Neural Markers

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Functional Connectivity Combined With a Machine Learning Algorithm Can Classify High-Risk First-Degree Relatives of Patients With Schizophrenia and Identify Correlates of Cognitive Impairments

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Fusing multimodal neuroimaging data with a variational autoencoder

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Decreased Nucleus Accumbens Connectivity at Rest in Medication-Free Patients with Obsessive-Compulsive Disorder

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Establishment of Effective Biomarkers for Depression Diagnosis With Fusion of Multiple Resting-State Connectivity Measures

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Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features

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Single-Stimulus Functional MRI Produces a Neural Individual Difference Measure for Autism Spectrum Disorder

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Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research

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Diagnosis of major depressive disorder using whole-brain effective connectivity networks derived from resting-state functional MRI

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Selecting the Most Relevant Brain Regions to Classify Children with Developmental Dyslexia and Typical Readers by Using Complex Magnocellular Stimuli and Multiple Kernel Learning

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Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

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Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach

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A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images

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Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling

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The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification

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A comparative analysis of functional connectivity data in resting and task-related conditions of the brain for disease signature of OCD

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A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification

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Identifying Differences in the P600 Component of ERP-Signals between OCD Patients and Controls Employing a PNN-based Majority Vote Classification Scheme

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Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

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Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder

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A machine learning approach using P300 responses to investigate effect of clozapine therapy

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Accurate assessment of low-function autistic children based on EEG feature fusion

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A combination of support vector machine and voxel-based morphometry in adult male alcohol use disorder patients with cognitive deficits

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A Hybrid Learning Pipeline for Automated Diagnosis of First-Episode Schizophrenia Utilizing T1-weighted Images

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Bias recognition and mitigation strategies in artificial intelligence healthcare applications

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Data leakage inflates prediction performance in connectome-based machine learning models

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