Introduction#

Machine learning techniques are poised to become clinically useful methods that may be used for diagnosis, prognosis & treatment decisions. Despite this, they are currently underutilized in medical studies, and more so in psychiatric research because most current tools require strong programming and computational engineering skills (e.g., scikit-learn, caret, Weka, nilearn). While there are some great tools that do not require programming experience (e.g., PRoNTo), these tools are often limited to making predictions from domain specific modalities such as neuroimaging data. This highlights a pressing need for a user-friendly machine learning software that makes advanced methods available to clinical researchers from different fields, aiming at collaboratively developing diagnostic, predictive, and prognostic tools for precision medicine approaches.

NeuroMiner has been continuously developed by Nikolaos Koutsouleris since 2009 to provide clinical researchers with cutting-edge machine learning methods for the analysis of heterogeneous data domains, such as clinical and neurocognitive read-outs, structural and functional neuroimaging data, and genetic information. The program is an interface to a large variety of unsupervised and supervised pattern recognition algorithms that have been developed in the machine learning field over the last decades. Furthermore, the application implements different strategies for preprocessing, filtering and fusion of heterogeneous data, training ensembles of predictors, and for the visualization and testing of the significance of the computed predictive patterns. The current release candidate of NeuroMiner has been tested in the Section for Precision Psychiatry on a variety of datasets containing healthy controls and patients with psychiatric disorders, and it was designed specifically to create robust models with a high probability of generalization to new datasets. For reference, we include a list of papers below or see publications listed in PubMed, which were all based on previous versions of the program.

NeuroMiner Features#

More specifically, using a light-weight and interactive text-based menu system, NeuroMiner allows the user to:

a) load data easily (e.g., using spreadsheets, NIfTI images, cortical surface data, or SPM structures); b) build a variety of cross-validation frameworks for classification and regression problems that have become a gold standard in the field (e.g., repeated nested cross-validation, leave-site-out cross-validation); c) apply a number of preprocessing strategies (e.g., scaling, filtering, many forms of dimensionality reduction, etc.); d) choose and combine cutting-edge supervised algorithms (e.g., support vector machine, elastic net, random forest, etc.); e) apply feature selection procedures (e.g., wrappers), data fusion techniques, and stacked generalization; f) apply learned models to new data (external validation). To assist in selecting and analyzing data, the user can visualize the data during input, monitor accuracy during learning, and understand the results of complex analyses using multiple display options. These allow the user to accurately report the data and also to understand the underlying machine learning analyses. Furthermore, the ability to apply the learned models to completely unseen data is important because it is quickly becoming a standard requirement for all machine learning studies. In total, NeuroMiner gives the user the ability to design, implement, understand, and report machine learning analyses.

Papers using NeuroMiner#

  • Antoniades, M., Modabbernia, A., Doucet, G., Haas, S., & Frangou, S. (2020). Cognitive Ability and MRI-Predicted Age Gap in Healthy Individuals From a Large Epidemiological Sample. Biological Psychiatry, 87(9), S152–S153. https://doi.org/10.1016/j.biopsych.2020.02.405

  • Antonucci, L. A., Penzel, N., Pergola, G., Kambeitz-Ilankovic, L., Dwyer, D., Kambeitz, J., Haas, S. S., Passiatore, R., Fazio, L., Caforio, G., Falkai, P., Blasi, G., Bertolino, A., & Koutsouleris, N. (2020). Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity. Neuropsychopharmacology, 45(4), 613–621. https://doi.org/10.1038/s41386-019-0532-3

  • Antonucci, L. A., Pergola, G., Pigoni, A., Dwyer, D., Kambeitz-Ilankovic, L., Penzel, N., Romano, R., Gelao, B., Torretta, S., Rampino, A., Trojano, M., Caforio, G., Falkai, P., Blasi, G., Koutsouleris, N., & Bertolino, A. (2020). A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects. Biological Psychiatry, 87(8), 697–707. https://doi.org/10.1016/j.biopsych.2019.11.007

  • Borgwardt, S., Koutsouleris, N., Aston, J., Studerus, E., Smieskova, R., Riecher-Rossler, A., & Meisenzahl, E. M. (2013). Distinguishing Prodromal From First-Episode Psychosis Using Neuroanatomical Single-Subject Pattern Recognition. Schizophrenia Bulletin, 39(5), 1105–1114. https://doi.org/10.1093/schbul/sbs095 Cabral, C., Kambeitz-Ilankovic, L., Kambeitz, J., Calhoun, V. D., Dwyer, D. B., von Saldern, S., Urquijo, M. F., Falkai, P., & Koutsouleris, N. (2016). Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance. Schizophrenia Bulletin, 42 Suppl 1(Suppl 1), S110-7. https://doi.org/10.1093/schbul/sbw053

  • Campabadal, A., Abos, A., Segura, B., Monte‐Rubio, G., Perez‐Soriano, A., Giraldo, D. M., Muñoz, E., Compta, Y., Junque, C., & Marti, M. J. (2022). Differentiation of multiple system atrophy subtypes by gray matter atrophy. Journal of Neuroimaging, 32(1), 80–89. https://doi.org/10.1111/jon.12927

  • Haas, S. S., Antonucci, L. A., Wenzel, J., Ruef, A., Biagianti, B., Paolini, M., Rauchmann, B.-S., Weiske, J., Kambeitz, J., Borgwardt, S., Brambilla, P., Meisenzahl, E., Salokangas, R. K. R., Upthegrove, R., Wood, S. J., Koutsouleris, N., & Kambeitz-Ilankovic, L. (2021). A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis. Neuropsychopharmacology, 46(4), 828–835. https://doi.org/10.1038/s41386-020-00877-4

  • Haidl, T. K., Hedderich, D. M., Rosen, M., Kaiser, N., Seves, M., Lichtenstein, T., Penzel, N., Wenzel, J., Kambeitz-Ilankovic, L., Ruef, A., Popovic, D., Schultze-Lutter, F., Chisholm, K., Upthegrove, R., Salokangas, R. K. R., Pantelis, C., Meisenzahl, E., Wood, S. J., Brambilla, P., … Koutsouleris, N. (2021). The non-specific nature of mental health and structural brain outcomes following childhood trauma. Psychological Medicine, 1–10. https://doi.org/10.1017/S0033291721002439

  • Hauke, D. J., Schmidt, A., Studerus, E., Andreou, C., Riecher-Rössler, A., Radua, J., Kambeitz, J., Ruef, A., Dwyer, D. B., Kambeitz-Ilankovic, L., Lichtenstein, T., Sanfelici, R., Penzel, N., Haas, S. S., Antonucci, L. A., Lalousis, P. A., Chisholm, K., Schultze-Lutter, F., Ruhrmann, S., … Borgwardt, S. (2021). Multimodal prognosis of negative symptom severity in individuals at increased risk of developing psychosis. Translational Psychiatry, 11(1), 312. https://doi.org/10.1038/s41398-021-01409-4

  • Kambeitz-Ilankovic, L., Haas, S. S., Meisenzahl, E., Dwyer, D. B., Weiske, J., Peters, H., Möller, H.-J., Falkai, P., & Koutsouleris, N. (2019). Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study. NeuroImage: Clinical, 21, 101624. https://doi.org/10.1016/j.nicl.2018.101624

  • Kambeitz-Ilankovic, L., Meisenzahl, E. M., Cabral, C., von Saldern, S., Kambeitz, J., Falkai, P., Möller, H.-J., Reiser, M., & Koutsouleris, N. (2016). Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification. Schizophrenia Research, 173(3), 159–165. https://doi.org/10.1016/j.schres.2015.03.005

  • Koutsouleris, N., Davatzikos, C., Borgwardt, S., Gaser, C., Bottlender, R., Frodl, T., Falkai, P., Riecher-Rossler, A., Moller, H.-J., Reiser, M., Pantelis, C., & Meisenzahl, E. (2014). Accelerated Brain Aging in Schizophrenia and Beyond: A Neuroanatomical Marker of Psychiatric Disorders. Schizophrenia Bulletin,40(5), 1140–1153. https://doi.org/10.1093/schbul/sbt142

  • Koutsouleris, Nikolaos, Borgwardt, S., Meisenzahl, E. M., Bottlender, R., Möller, H.-J., & Riecher-Rössler, A. (2012). Disease Prediction in the At-Risk Mental State for Psychosis Using Neuroanatomical Biomarkers: Results From the FePsy Study. Schizophrenia Bulletin, 38(6), 1234–1246. https://doi.org/10.1093/schbul/sbr145

  • Koutsouleris, Nikolaos, Davatzikos, C., Bottlender, R., Patschurek-Kliche, K., Scheuerecker, J., Decker, P., Gaser, C., Möller, H.-J., & Meisenzahl, E. M. (2012). Early Recognition and Disease Prediction in the At-Risk Mental States for Psychosis Using Neurocognitive Pattern Classification. Schizophrenia Bulletin, 38(6), 1200–1215. https://doi.org/10.1093/schbul/sbr037

  • Koutsouleris, Nikolaos, Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., Popovic, D., Oeztuerk, O., Haas, S. S., Weiske, J., Ruef, A., Kambeitz-Ilankovic, L., Antonucci, L. A., Neufang, S., Schmidt-Kraepelin, C., Ruhrmann, S., Penzel, N., Kambeitz, J., Haidl, T. K., … Meisenzahl, E. (2021). Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry, 78(2), 195. https://doi.org/10.1001/jamapsychiatry.2020.3604

  • Koutsouleris, Nikolaos, Gaser, C., Bottlender, R., Davatzikos, C., Decker, P., Jäger, M., Schmitt, G., Reiser, M., Möller, H.-J., & Meisenzahl, E. M. (2010). Use of neuroanatomical pattern regression to predict the structural brain dynamics of vulnerability and transition to psychosis. Schizophrenia Research, 123(2–3), 175–187. https://doi.org/10.1016/j.schres.2010.08.032

  • Koutsouleris, Nikolaos, Kahn, R. S., Chekroud, A. M., Leucht, S., Falkai, P., Wobrock, T., Derks, E. M., Fleischhacker, W. W., & Hasan, A. (2016). Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. The Lancet Psychiatry, 3(10), 935–946. https://doi.org/10.1016/S2215-0366(16)30171-7

  • Koutsouleris, Nikolaos, Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., Paolini, M., Chisholm, K., Kambeitz, J., & Haidl, T. (2018). Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis. JAMA Psychiatry, 75(11), 1156–1172.

  • Koutsouleris, Nikolaos, Meisenzahl, E. M., Borgwardt, S., Riecher-Rössler, A., Frodl, T., Kambeitz, J., Köhler, Y., Falkai, P., Möller, H. J., Reiser, M., & Davatzikos, C. (2015). Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain, 138(7), 2059–2073. https://doi.org/10.1093/brain/awv111

  • Koutsouleris, Nikolaos, Meisenzahl, E. M., Davatzikos, C., Bottlender, R., Frodl, T., Scheuerecker, J., Schmitt, G., Zetzsche, T., Decker, P., Reiser, M., Möller, H.-J., & Gaser, C. (2009). Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition. Archives of General Psychiatry, 66(7), 700. https://doi.org/10.1001/archgenpsychiatry.2009.62

  • Koutsouleris, Nikolaos, Riecher-Rössler, A., Meisenzahl, E. M., Smieskova, R., Studerus, E., Kambeitz-Ilankovic, L., von Saldern, S., Cabral, C., Reiser, M., Falkai, P., & Borgwardt, S. (2015). Detecting the Psychosis Prodrome Across High-Risk Populations Using Neuroanatomical Biomarkers. Schizophrenia Bulletin, 41(2), 471–482. https://doi.org/10.1093/schbul/sbu078

  • Koutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., … & PRONIA Consortium. (2021). Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA psychiatry, 78(2), 195-209.

  • Koutsouleris, N., Pantelis, C., Velakoulis, D., McGuire, P., Dwyer, D. B., Urquijo-Castro, M. F., … & Hodges, J. (2022). Exploring links between psychosis and frontotemporal dementia using multimodal machine learning: Dementia praecox revisited. JAMA psychiatry, 79(9), 907-919.

  • Koutsouleris, Nikolaos, Worthington, M., Dwyer, D. B., Kambeitz-Ilankovic, L., Sanfelici, R., Fusar-Poli, P., Rosen, M., Ruhrmann, S., Anticevic, A., Addington, J., Perkins, D. O., Bearden, C. E., Cornblatt, B. A., Cadenhead, K. S., Mathalon, D. H., McGlashan, T., Seidman, L., Tsuang, M., Walker, E. F., … Cannon, T. D. (2021). Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort. Biological Psychiatry, 90(9), 632–642. https://doi.org/10.1016/j.biopsych.2021.06.023

  • Lalousis, P. A., Wood, S. J., Schmaal, L., Chisholm, K., Griffiths, S. L., Reniers, R. L. E. P., Bertolino, A., Borgwardt, S., Brambilla, P., Kambeitz, J., Lencer, R., Pantelis, C., Ruhrmann, S., Salokangas, R. K. R., Schultze-Lutter, F., Bonivento, C., Dwyer, D., Ferro, A., Haidl, T., … Upthegrove, R. (2021). Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach. Schizophrenia Bulletin, 47(4), 1130–1140. https://doi.org/10.1093/schbul/sbaa185

  • Lieslehto, J., Jääskeläinen, E., Kiviniemi, V., Haapea, M., Jones, P. B., Murray, G. K., Veijola, J., Dannlowski, U., Grotegerd, D., Meinert, S., Hahn, T., Ruef, A., Isohanni, M., Falkai, P., Miettunen, J., Dwyer, D. B., & Koutsouleris, N. (2021). The progression of disorder-specific brain pattern expression in schizophrenia over 9 years. Npj Schizophrenia, 7(1), 32. https://doi.org/10.1038/s41537-021-00157-0

  • Opel, N., Redlich, R., Kaehler, C., Grotegerd, D., Dohm, K., Heindel, W., Kugel, H., Thalamuthu, A., Koutsouleris, N., Arolt, V., Teuber, A., Wersching, H., Baune, B. T., Berger, K., & Dannlowski, U. (2017). Prefrontal gray matter volume mediates genetic risks for obesity. Molecular Psychiatry, 22(5), 703–710. https://doi.org/10.1038/mp.2017.51

  • Pigoni, A., Dwyer, D., Squarcina, L., Borgwardt, S., Crespo-Facorro, B., Dazzan, P., Smesny, S., Spaniel, F., Spalletta, G., Sanfelici, R., Antonucci, L. A., Reuf, A., Oeztuerk, O. F., Schmidt, A., Ciufolini, S., Schönborn-Harrisberger, F., Langbein, K., Gussew, A., Reichenbach, J. R., … Brambilla, P. (2021). Classification of first-episode psychosis using cortical thickness: A large multicenter MRI study. European Neuropsychopharmacology, 47, 34–47. https://doi.org/10.1016/j.euroneuro.2021.04.002

  • Rosen, M., Betz, L. T., Kaiser, N., Penzel, N., Dwyer, D., Lichtenstein, T. K., Schultze-Lutter, F., Kambeitz-Ilankovic, L., Bertolino, A., Borgwardt, S., Brambilla, P., Lencer, R., Meisenzahl, E., Pantelis, C., Salokangas, R. K. R., Upthegrove, R., Wood, S., Ruhrmann, S., Koutsouleris, N., & Kambeitz, J. (2022). Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations. The British Journal of Psychiatry, 220(6), 318–321. https://doi.org/10.1192/bjp.2021.141

  • Sakamoto, S., Mallah, D., Medeiros, D. J., Dohi, E., Imai, T., Rose, I. V. L., Matoba, K., Zhu, X., Kamiya, A., & Kano, S. (2021). Alterations in circulating extracellular vesicles underlie social stress‐induced behaviors in mice. FEBS Open Bio, 11(10), 2678–2692. https://doi.org/10.1002/2211-5463.13204

  • Schröder, R., Faiola, E., Fernanda Urquijo, M., Bey, K., Meyhöfer, I., Steffens, M., Kasparbauer, A.-M., Ruef, A., Högenauer, H., Hurlemann, R., Kambeitz, J., Philipsen, A., Wagner, M., Koutsouleris, N., & Ettinger, U. (2022). Neural Correlates of Smooth Pursuit Eye Movements in Schizotypy and Recent Onset Psychosis: A Multivariate Pattern Classification Approach. Schizophrenia Bulletin Open, 3(1). https://doi.org/10.1093/schizbullopen/sgac034

  • Schwarzer, J. M., Meyhoefer, I., Antonucci, L. A., Kambeitz-Ilankovic, L., Surmann, M., Bienek, O., Romer, G., Dannlowski, U., Hahn, T., Korda, A., Dwyer, D. B., Ruef, A., Haas, S. S., Rosen, M., Lichtenstein, T., Ruhrmann, S., Kambeitz, J., Salokangas, R. K. R., Pantelis, C., … Piccin, S. (2022). The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis. Neuropsychopharmacology. https://doi.org/10.1038/s41386-022-01385-3

  • Shang, J., Fisher, P., Bäuml, J. G., Daamen, M., Baumann, N., Zimmer, C., Bartmann, P., Boecker, H., Wolke, D., Sorg, C., Koutsouleris, N., & Dwyer, D. B. (2019). A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm. Human Brain Mapping, 40(14), 4239–4252. https://doi.org/10.1002/hbm.24698

  • Upthegrove, R., Lalousis, P., Mallikarjun, P., Chisholm, K., Griffiths, S. L., Iqbal, M., Pelton, M., Reniers, R., Stainton, A., Rosen, M., Ruef, A., Dwyer, D. B., Surman, M., Haidl, T., Penzel, N., Kambeitz-llankovic, L., Bertolino, A., Brambilla, P., Borgwardt, S., … Koutsouleris, N. (2021). The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis. Schizophrenia Bulletin, 47(1), 249–258. https://doi.org/10.1093/schbul/sbaa094

  • Wenzel, J., Haas, S. S., Dwyer, D. B., Ruef, A., Oeztuerk, O. F., Antonucci, L. A., von Saldern, S., Bonivento, C., Garzitto, M., & Ferro, A. (2021). Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints? Neuropsychopharmacology, 1–9.

Disclaimer

Please note that NeuroMiner is supplied as is and no formal maintenance is provided or implied. In no event shall the authors of the software (from now on called the Authors) be liable to any party for direct, indirect, special, incidental, or consequential damages, including lost profits, arising out of the use of this software and its documentation, even if the Authors have been advised of the possibility of such damage. The Authors specifically disclaim any warranties, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. The software and accompanying documentation provided hereunder is provided ’as is’. The Authors have no obligation to provide maintenance, support, updates, enhancements, or modifications (but we plan to). This is the beta release version of the software and the software is undergoing regular updates Please send any comments, questions, or bug reports to email.neurominer@gmail.com or open an issue using the GitHub button in the top right cornerof this page. ::