Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support

Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.08.010 · Cited by 51,384 · see citing works

The central role of the propensity score in observational studies for causal effects

ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241 · see citing works

The REDCap consortium: building an international community of software platform partners

Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O'Neal, L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J., et al. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103208 · Cited by 24,013 · see citing works

The FAIR Guiding Principles for scientific data management and stewardship

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · cited 1× · see citing works

Adjusting batch effects in microarray expression data using empirical Bayes methods

Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · see citing works

Estimating the reproducibility of psychological science

Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515 · see citing works

False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567 · see citing works

The WU-Minn Human Connectome Project: An overview

Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651 · see citing works

Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population

Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx246 · Cited by 3,979 · see citing works

The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites

Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., Garavan, H., et al. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.03.001 · Cited by 2,167 · see citing works

Reproducible brain-wide association studies require thousands of individuals

Marek, S. A., Tervo‐Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., Donohue, M. R., Foran, W., Miller, R. L., Hendrickson, T. J., et al. (2022) · Nature · doi:10.1038/s41586-022-04492-9 · Cited by 1,949 · see citing works

The Existence of Publication Bias and Risk Factors for Its Occurrence

Dickersin, K. (1990) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1990.03440100097014 · Cited by 1,417 · see citing works

Precision Functional Mapping of Individual Human Brains

Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., Ortega, M., Hoyt-Drazen, C., Gratton, C., Sun, H., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.011 · Cited by 1,335 · see citing works

Cognition assessment using the NIH Toolbox

Weintraub, S., Dikmen, S. S., Heaton, R. K., Tulsky, D. S., Zelazo, P. D., Bauer, P. J., Carlozzi, N. E., Slotkin, J., Blitz, D., Wallner-Allen, K., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e3182872ded · Cited by 1,287 · see citing works

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

Skol, A. D., Scott, L. J., Abecasis, G. R., & Boehnke, M. (2006) · Nature Genetics · doi:10.1038/ng1706 · Cited by 1,286 · see citing works

Harmonization of multi-site diffusion tensor imaging data

Fortin, J.-P., Parker, D., Tunç, B., Watanabe, T., Elliott, M. A., Ruparel, K., Roalf, D. R., Satterthwaite, T. D., Gur, R. C., Gur, R. E., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.047 · Cited by 1,261 · see citing works

Vision assessment using the NIH Toolbox

Varma, R., McKean‐Cowdin, R., Vitale, S., Slotkin, J., & Hays, R. D. (2013) · Neurology · doi:10.1212/wnl.0b013e3182876e0a · Cited by 1,197 · see citing works

DSM-5 field trials in the United States and Canada, part II: test-retest reliability of selected categorical diagnoses

Regier, D. A., Narrow, W. E., Clarke, D. E., Kraemer, H. C., Kuramoto, S. J., Kuhl, E. A., & Kupfer, D. J. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12070999 · Cited by 1,163 · cited 1× · see citing works

The genetic architecture of the human cerebral cortex

Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., et al. (2020) · Science · doi:10.1126/science.aay6690 · Cited by 876 · see citing works

Challenges and opportunities in global mental health: a research-to-practice perspective

Wainberg, M. L., Scorza, P., Shultz, J. M., Helpman, L., Mootz, J. J., Johnson, K. A., Neria, Y., Bradford, J.-M. E., Oquendo, M. A., & Arbuckle, M. R. (2017) · Current Psychiatry Reports · doi:10.1007/s11920-017-0780-z · Cited by 875 · cited 1× · see citing works

The effect of scan length on the reliability of resting-state fMRI connectivity estimates

Birn, R. M., Molloy, E. K., Patriat, R., Parker, T., Meier, T. B., Kirk, G. R., Nair, V. A., Meyerand, M. E., & Prabhakaran, V. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.099 · Cited by 875 · see citing works

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

Littlejohns, T. J., Holliday, J., Gibson, L. M., Garratt, S., Oesingmann, N., Alfaro-Almagro, F., Bell, J. D., Boultwood, C., Collins, R., Conroy, M. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15948-9 · Cited by 812 · see citing works

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 801 · see citing works

Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking

Wicherts, J. M., Veldkamp, C. L. S., Augusteijn, H. E. M., Bakker, M., van Aert, R. C. M., & van Assen, M. A. L. M. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01832 · Cited by 754 · see citing works

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 · see citing works

Uberon, an integrative multi-species anatomy ontology

Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012) · Genome biology · doi:10.1186/gb-2012-13-1-r5 · Cited by 732 · cited 1× · see citing works

Is the Replicability Crisis Overblown? Three Arguments Examined

Pashler, H., & Harris, C. R. (2012) · Perspectives on Psychological Science · doi:10.1177/1745691612463401 · Cited by 697 · see citing works

Integration strategies of multi-omics data for machine learning analysis

Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.030 · Cited by 696 · see citing works

Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges

Molnar, C., Casalicchio, G., & Bischl, B. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-65965-3_28 · Cited by 677 · see citing works

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

Thompson, P. M., Jahanshad, N., Ching, C. R. K., Salminen, L. E., Thomopoulos, S. I., Bright, J. K., Baune, B. T., Bertolín, S., Bralten, J., Bruin, W. B., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0705-1 · Cited by 638 · see citing works

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

Pomponio, R., Erus, G., Habes, M., Doshi, J., Srinivasan, D., Mamourian, E., Bashyam, V. M., Nasrallah, I., Satterthwaite, T., Fan, Y., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116450 · Cited by 576 · see citing works

A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis

Noble, S., Scheinost, D., & Constable, R. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116157 · Cited by 560 · see citing works

The past, present and future of Registered Reports

Chambers, C. D., & Tzavella, L. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01193-7 · Cited by 499 · see citing works

Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals

Szucs, D., & Ioannidis, J. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117164 · Cited by 453 · see citing works

Inflammation-related biomarkers in major psychiatric disorders: a cross-disorder assessment of reproducibility and specificity in 43 meta-analyses

Yuan, N., Chen, Y., Xia, Y., Dai, J., & Liu, C. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0570-y · Cited by 428 · see citing works

The OpenNeuro resource for sharing of neuroscience data

Markiewicz, C. J., Gorgolewski, K. J., Feingold, F. W., Blair, R. W., Halchenko, Y. O., Miller, E., Hardcastle, N., Wexler, J., Estéban, O., Goncalves, M., et al. (2021) · eLife · doi:10.7554/elife.71774 · Cited by 410 · see citing works

Small sample sizes reduce the replicability of task-based fMRI studies

Turner, B. O., Paul, E. J., Miller, M. B., & Barbey, A. K. (2018) · Communications Biology · doi:10.1038/s42003-018-0073-z · Cited by 390 · see citing works

Dimension reduction techniques for the integrative analysis of multi-omics data

Meng, C., Zeleznik, O. A., Thallinger, G., Küster, B., Gholami, A. M., & Culhane, A. C. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv108 · Cited by 383 · see citing works

A Guide to ComBat Harmonization of Imaging Biomarkers in Multicenter Studies

Orlhac, F., Eertink, J. J., Cottereau, A., Zijlstra, J. M., Thiéblemont, C., Meignan, M. A., Boellaard, R., & Buvat, I. (2021) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.262464 · Cited by 353 · see citing works

Statistical Challenges in “Big Data” Human Neuroimaging

Smith, S. M., & Nichols, T. E. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.018 · Cited by 331 · see citing works

Harnessing reliability for neuroscience research

Zuo, X.-N., Xu, T., & Milham, M. P. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0655-x · Cited by 310 · see citing works

Emotion assessment using the NIH Toolbox

Salsman, J. M., Butt, Z. A., Pilkonis, P. A., Cyranowski, J. M., Zill, N., Hendrie, H. C., Kupst, M. J., Kelly, M. A. R., Bode, R. K., Choi, S. W., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e3182872e11 · Cited by 274 · see citing works

Motor assessment using the NIH Toolbox

Reuben, D. B., Magasi, S. R., McCreath, H. E., Bohannon, R. W., Wang, Y.-C., Bubela, D. J., Rymer, W. Z., Beaumont, J. L., Rine, R. M., Lai, J., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e3182872e01 · Cited by 264 · see citing works

Data sharing in neuroimaging research

Poline, J., Breeze, J. L., Ghosh, S., Gorgolewski, K. J., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., et al. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00009 · Cited by 262 · see citing works

Reliability and validity of severity dimensions of psychopathology assessed using the Structured Clinical Interview for DSM‐5 ( SCID )

Shankman, S. A., Funkhouser, C. J., Klein, D. N., Davila, J., Lerner, D., & Hee, D. (2017) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1590 · Cited by 259 · see citing works

Cognitive Test Scores in UK Biobank: Data Reduction in 480,416 Participants and Longitudinal Stability in 20,346 Participants

Lyall, D. M., Cullen, B., Allerhand, M. H., Smıth, D. J., Mackay, D., Evans, J. J., Anderson, J. J., Fawns‐Ritchie, C., McIntosh, A. M., Deary, I. J., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154222 · Cited by 257 · see citing works

Foundations of Data Quality Management

Fan, W., & Geerts, F. (2012) · Synthesis lectures on data management · doi:10.1007/978-3-031-01892-3 · Cited by 253 · see citing works

Illusory generalizability of clinical prediction models

Chekroud, A. M., Hawrilenko, M., Loho, H., Bondar, J., Gueorguieva, R., Hasan, A., Kambeitz, J., Corlett, P. R., Koutsouleris, N., Krumholz, H. M., et al. (2024) · Science · doi:10.1126/science.adg8538 · Cited by 251 · see citing works

Confound modelling in UK Biobank brain imaging

Alfaro-Almagro, F., McCarthy, P., Afyouni, S., Andersson, J. L. R., Bastiani, M., Miller, K. L., Nichols, T. E., & Smith, S. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117002 · Cited by 249 · see citing works

Multi-level block permutation

Winkler, A. M., Webster, M., Vidaurre, D., Nichols, T. E., & Smith, S. M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.092 · Cited by 247 · see citing works

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Schmaal, L., Pozzi, E., Ho, T. C., van Velzen, L. S., Veer, I. M., Opel, N., Someren, E. J. W. V., Han, L. K. M., Aftanas, L., Aleman, A., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0842-6 · Cited by 246 · see citing works

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Raduà, J., Vieta, E., Shinohara, R. T., Kochunov, P. V., Quidé, Y., Green, M. J., Weickert, C. S., Weickert, T. W., Bruggemann, J. M., Kircher, T. T. J., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116956 · Cited by 246 · see citing works

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Brouwer, R. M., Klein, M., Grasby, K. L., Schnack, H. G., Jahanshad, N., Teeuw, J., Thomopoulos, S. I., Sprooten, E., Franz, C. E., Gogtay, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01042-4 · Cited by 227 · see citing works

Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: survey of studies published in<i>The BMJ</i>and<i>PLOS Medicine</i>

Naudet, F., Sakarovitch, C., Janiaud, P., Cristea, I., Fanelli, D., Moher, D., & Ioannidis, J. P. A. (2018) · BMJ · doi:10.1136/bmj.k400 · Cited by 227 · see citing works

Data mining for health: staking out the ethical territory of digital phenotyping

Martinez-Martin, N., Insel, T. R., Dagum, P., Greely, H. T., & Cho, M. K. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0075-8 · Cited by 218 · see citing works

The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

Gardner, D., Akil, H., Ascoli, G. A., Bowden, D. M., Bug, W. J., Donohue, D. E., Goldberg, D. H., Grafstein, B., Grethe, J. S., Gupta, A., et al. (2008) · Neuroinformatics · doi:10.1007/s12021-008-9024-z · Cited by 214 · see citing works

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

Lynch, C. J., Power, J. D., Scult, M. A., Dubin, M. J., Gunning, F. M., & Liston, C. M. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108540 · Cited by 192 · see citing works

Test-Retest Reliability of Standard and Emotional Stroop Tasks

Strauss, G. P., Allen, D. N., Jorgensen, M. L., & Cramer, S. L. (2005) · Assessment · doi:10.1177/1073191105276375 · Cited by 189 · see citing works

Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder

Anderson, K. M., Collins, M. A., Kong, R., Fang, K., Li, J., He, T., Chekroud, A. M., Yeo, B. T. T., & Holmes, A. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2008004117 · Cited by 173 · see citing works

A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

Saragosa-Harris, N., Chaku, N., MacSweeney, N., Williamson, V. G., Scheuplein, M., Feola, B., Cardenas-Iniguez, C., Demir-Lira, E., McNeilly, E., Huffman, L., et al. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101115 · Cited by 166 · see citing works

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

Trifan, A., Oliveira, M., & Oliveira, J. L. (2019) · JMIR mhealth and uhealth · doi:10.2196/12649 · Cited by 163 · see citing works

Reliability of diagnoses coding with ICD-10

Stausberg, J., Lehmann, N., Kaczmarek, D., & Stein, M. (2006) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.11.005 · Cited by 158 · see citing works

Fusing Randomized Trials With Big Data

Angus, D. C. (2015) · JAMA · doi:10.1001/jama.2015.7762 · Cited by 155 · see citing works

DataLad: distributed system for joint management of code, data, and their relationship

Halchenko, Y. O., Meyer, K., Poldrack, B., Solanky, D. S., Wagner, A. S., Gors, J. D., MacFarlane, D., Pustina, D., Sochat, V., Ghosh, S., et al. (2021) · The Journal of Open Source Software · doi:10.21105/joss.03262 · Cited by 154 · see citing works

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization

Hu, F., Chen, A. A., Horng, H., Bashyam, V., Davatzikos, C. A., Alexander‐Bloch, A. F., Li, M., Shou, H., Satterthwaite, T. D., Yu, M., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120125 · Cited by 151 · see citing works

Mapping the universe of registered reports

Hardwicke, T. E., & Ioannidis, J. P. A. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0444-y · Cited by 147 · see citing works

Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure

Cserbik, D., Chen, J., McConnell, R., Berhane, K. T., Sowell, E. R., Schwartz, J. D., Hackman, D. A., Kan, E., Fan, C. C., & Herting, M. M. (2020) · Environment International · doi:10.1016/j.envint.2020.105933 · Cited by 143 · see citing works

Accuracy and bias in retrospective symptom reporting

den Bergh, O. V., & Walentynowicz, M. (2016) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000267 · Cited by 140 · see citing works

Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression

Wen, J., Fu, C. H. Y., Tosun, D., Veturi, Y., Yang, Z., Abdulkadir, A., Mamourian, E., Srinivasan, D., Skampardoni, I., Singh, A., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0020 · Cited by 134 · see citing works

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example

Kassraian-Fard, P., Matthis, C., Balsters, J. H., Maathuis, M. H., & Wenderoth, N. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00177 · Cited by 132 · see citing works

Preregistration of secondary data analysis: A template and tutorial

van den Akker, O. R., Weston, S. J., Campbell, L., Chopik, B., Damian, R. I., Davis-Kean, P. E., Hall, A., Kosie, J. E., Kruse, E. T., Olsen, J., et al. (2021) · Meta-Psychology · doi:10.15626/mp.2020.2625 · Cited by 131 · see citing works

International Multicenter Analysis of Brain Structure Across Clinical Stages of Parkinson's Disease

Laansma, M. A., Bright, J. K., Al–Bachari, S., Anderson, T., Ard, T. D., Assogna, F., Baquero, K., Berendse, H. W., Blair, J. C., Cendes, F., et al. (2021) · Movement Disorders · doi:10.1002/mds.28706 · Cited by 127 · see citing works

Recommendations and future directions for supervised machine learning in psychiatry

Cearns, M., Hahn, T., & Baune, B. T. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0607-2 · Cited by 127 · see citing works

Whole-genome analyses of whole-brain data: working within an expanded search space

Medland, S. E., Jahanshad, N., Neale, B. M., & Thompson, P. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3718 · Cited by 119 · see citing works

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

Ge, R., Yu, Y., Qi, Y. X., Fan, Y. V., Chen, S., Gao, C., Haas, S. S., New, F., Boomsma, D. I., Brodaty, H., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00250-9 · Cited by 115 · see citing works

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Kennedy, D. N., Abraham, S. A., Bates, J. F., Crowley, A., Ghosh, S., Gillespie, T., Goncalves, M., Grethe, J. S., Halchenko, Y. O., Hanke, M., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00001 · Cited by 113 · see citing works

A review of journal policies for sharing research dataPreprint

Piwowar, H., & Chapman, W. W. (2008) · Nature Precedings · doi:10.1038/npre.2008.1700.1 · Cited by 113 · see citing works

Challenges in converting an interviewer-administered food probe database to self-administration in the National Cancer Institute automated self-administered 24-hour recall (ASA24)

Zimmerman, T. P., Hull, S. G., McNutt, S., Mittl, B., Islam, N. G., Guenther, P. M., Thompson, F. E., Potischman, N. A., & Subar, A. F. (2009) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2009.02.003 · Cited by 110 · see citing works

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

O’Connor, L. M., O’Connor, B. A., Lim, S. B., Zeng, J., & Lo, C. H. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.06.011 · Cited by 107 · see citing works

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Boedhoe, P. S. W., Heymans, M. W., Schmaal, L., Abe, Y., Alonso, P., Ameis, S. H., Anticevic, A., Arnold, P. D., Batistuzzo, M. C., Benedetti, F., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00102 · Cited by 104 · see citing works

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

Bayer, J., Thompson, P. M., Ching, C. R. K., Liu, M., Chen, A. A., Panzenhagen, A. C., Jahanshad, N., Marquand, A. F., Schmaal, L., & Sämann, P. G. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.923988 · Cited by 100 · see citing works

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

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 99 · see citing works

Archetypes of Gamification: Analysis of mHealth Apps

Schmidt-Kraepelin, M., Toussaint, P. A., Thiebes, S., Hamari, J., & Sunyaev, A. (2020) · JMIR mhealth and uhealth · doi:10.2196/19280 · Cited by 98 · see citing works

Illuminating the Black Box: Interpreting Deep Neural Network Models for Psychiatric Research

Sheu, Y.-H. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.551299 · Cited by 94 · see citing works

Multi-omics data integration methods and their applications in psychiatric disorders

Sathyanarayanan, A., Mueller, T. T., Moni, M. A., Schueler, K., Baune, B. T., Lió, P., Mehta, D., Baune, B. T., Dierssen, M., Ebert, B., et al. (2023) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2023.01.001 · Cited by 90 · see citing works

Three recommendations based on a comparison of the reliability and validity of the predominant models used in research on the empirical structure of psychopathology.

Forbes, M. K., Greene, A. L., Levin-Aspenson, H. F., Watts, A. L., Hallquist, M. N., Lahey, B. B., Markon, K. E., Patrick, C. J., Tackett, J. L., Waldman, I. D., et al. (2021) · Journal of Abnormal Psychology · doi:10.1037/abn0000533 · Cited by 88 · see citing works

Precision Neuroscience: Dense Sampling of Individual Brains

Poldrack, R. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.002 · Cited by 86 · see citing works

Evaluation of integrative clustering methods for the analysis of multi-omics data

Chauvel, C., Novoloaca, A., Veyre, P., Reynier, F., & Becker, J. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz015 · Cited by 84 · see citing works

Mega‐analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group

Zugman, A., Harrewijn, A., Cardinale, E. M., Zwiebel, H., Freitag, G. F., Werwath, K., Bas-Hoogendam, J., Groenewold, N., Aghajani, M., Hilbert, K., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25096 · Cited by 83 · see citing works

Integrating imaging and omics data: A review

Antonelli, L., Guarracino, M. R., Maddalena, L., & Sangiovanni, M. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.04.032 · Cited by 81 · see citing works

Persistent and distressing psychotic-like experiences using adolescent brain cognitive development℠ study data

Karcher, N. R., Loewy, R. L., Savill, M., Avenevoli, S., Huber, R. S., Makowski, C., Sher, K. J., & Barch, D. M. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01373-x · Cited by 77 · see citing works

Pragmatic Precision Psychiatry—A New Direction for Optimizing Treatment Selection

Kessler, R. C., & Luedtke, A. R. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.2500 · Cited by 74 · see citing works

Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models

Bayer, J., Dinga, R., Kia, S. M., Kottaram, A., Wolfers, T., Lv, J., Zalesky, A., Schmaal, L., & Marquand, A. F. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119699 · Cited by 72 · see citing works

Mental health in the UK Biobank: A roadmap to self‐report measures and neuroimaging correlates

Dutt, R., Hannon, K., Easley, T. O., Griffis, J. C., Zhang, W., & Bijsterbosch, J. D. (2021) · Human Brain Mapping · doi:10.1002/hbm.25690 · Cited by 71 · see citing works

How to remove or control confounds in predictive models, with applications to brain biomarkers

Chyzhyk, D., Varoquaux, G., Milham, M., & Thirion, B. (2022) · GigaScience · doi:10.1093/gigascience/giac014 · Cited by 65 · see citing works

A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

Torbati, M. E., Minhas, D. S., Ahmad, G., O’Connor, E. E., Muschelli, J., Laymon, C. M., Yang, Z., Cohen, A. D., Aizenstein, H. J., Klunk, W. E., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118703 · Cited by 64 · see citing works

Suboptimal phenotypic reliability impedes reproducible human neurosciencePreprint

Nikolaidis, A., Chen, A. A., He, X., Shinohara, R. T., Vogelstein, J. T., Milham, M. P., & Shou, H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.22.501193 · Cited by 63 · see citing works

Implementing a "publish, then review" model of publishing

Eisen, M. B., Akhmanova, A. S., Behrens, T. E. J., Harper, D. M., Weigel, D., & Zaidi, M. (2020) · eLife · doi:10.7554/elife.64910 · Cited by 60 · see citing works

An integrative review on methodological considerations in mental health research – design, sampling, data collection procedure and quality assurance

Badu, E., O’Brien, A. P., & Mitchell, R. J. (2019) · Archives of Public Health · doi:10.1186/s13690-019-0363-z · Cited by 59 · see citing works

Transdiagnostic and Illness-Specific Functional Dysconnectivity Across Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Huang, C., Luo, Q., Palaniyappan, L. K., Yang, A. C., Hung, C., Chou, K., Lo, C. Z., Liu, M., Tsai, S., Barch, D. M., et al. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.01.010 · Cited by 57 · see citing works

Removing the Reliability Bottleneck in Functional Magnetic Resonance Imaging Research to Achieve Clinical Utility

Milham, M. P., Vogelstein, J. T., & Xu, T. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.4272 · Cited by 55 · see citing works

Raising Awareness for the Replication Crisis in Clinical Psychology by Focusing on Inconsistencies in Psychotherapy Research: How Much Can We Rely on Published Findings from Efficacy Trials?

Hengartner, M. P. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00256 · Cited by 52 · see citing works

Sharing Research Data to Improve Public Health

Carr, D. L., & Littler, K. (2015) · Journal of Empirical Research on Human Research Ethics · doi:10.1177/1556264615593485 · Cited by 51 · see citing works

Preregistration of Analyses of Preexisting Data

Mertens, G., & Krypotos, A. (2019) · Psychologica Belgica · doi:10.5334/pb.493 · Cited by 49 · see citing works

A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Sun, D., Rakesh, G., Haswell, C. C., Logue, M. W., Baird, C. L., O’Leary, E. N., Cotton, A. S., Xie, H., Tamburrino, M., Chen, T., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119509 · Cited by 49 · see citing works

Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection

Liu, M., Zhu, A. H., Maiti, P., Thomopoulos, S. I., Gadewar, S. P., Chai, Y., Kim, H., Jahanshad, N., & Initiative, A. D. N. (2023) · Human Brain Mapping · doi:10.1002/hbm.26422 · Cited by 49 · see citing works

Multisite test–retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3

Haddad, E., Pizzagalli, F., Zhu, A. H., Bhatt, R. R., Islam, T., Gari, I. B., Dixon, D., Thomopoulos, S. I., Thompson, P. M., & Jahanshad, N. (2022) · Human Brain Mapping · doi:10.1002/hbm.26147 · Cited by 47 · see citing works

Evaluating the Machine Learning Literature: A Primer and User’s Guide for Psychiatrists

Grzenda, A. L., Kraguljac, N. V., McDonald, W. M., Nemeroff, C. B., Torous, J. B., Alpert, J. E., Rodríguez, C. I., & Widge, A. S. (2021) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20030250 · Cited by 45 · see citing works

The Burden of Reliability: How Measurement Noise Limits Brain-Behaviour PredictionsPreprint

Gell, M., Eickhoff, S. B., Omidvarnia, A., Küppers, V., Patil, K. R., Satterthwaite, T. D., Müller, V. I., & Langner, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.09.527898 · Cited by 44 · see citing works

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis

Jiang, M.-Z., Aguet, F., Ardlie, K., Chen, J. C., Cornell, E. S., Cruz, D., Durda, P., Gabriel, S., Gerszten, R. E., Guo, X., et al. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010517 · Cited by 43 · see citing works

Data Sharing and Reanalyses Among Randomized Clinical Trials Published in Surgical Journals Before and After Adoption of a Data Availability and Reproducibility Policy

Bergeat, D., Lombard, N., Gasmi, A., Le Floch, B., & Naudet, F. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.15209 · Cited by 41 · see citing works

A step-by-step guide on preregistration and effective data sharing for psychopathology research.

Krypotos, A., Klugkist, I. G., Mertens, G., & Engelhard, I. M. (2019) · Journal of Abnormal Psychology · doi:10.1037/abn0000424 · Cited by 40 · see citing works

Test–retest reliability of Attention Network Test measures in schizophrenia

Hahn, E. W., Ta, T. M. T., Hahn, C., Kuehl, L. K., Ruehl, C., Neuhaus, A. H., & Dettling, M. (2011) · Schizophrenia Research · doi:10.1016/j.schres.2011.09.026 · Cited by 37 · see citing works

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

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 · see citing works

Concurrent validity and reliability of suicide risk assessment instruments: A meta-analysis of 20 instruments across 27 international cohorts.

Campos, A. I., van Velzen, L. S., Veltman, D. J., Pozzi, E., Ambrogi, S., Ballard, E. D., Banaj, N., Başgöze, Z., Bellow, S., Benedetti, F., et al. (2023) · Neuropsychology · doi:10.1037/neu0000850 · Cited by 34 · see citing works

Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI

Zhu, A. H., Nir, T. M., Javid, S., Villalón‐Reina, J. E., Rodrigue, A., Strike, L. T., de Zubicaray, G. I., McMahon, K. L., Wright, M. J., Medland, S. E., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-05028-2 · Cited by 33 · see citing works

Federated Analysis of Neuroimaging Data: A Review of the Field

Rootes-Murdy, K., Gazula, H., Verner, E., Kelly, R., DeRamus, T. P., Plis, S., Sarwate, A. D., Turner, J. A., & Calhoun, V. D. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09550-7 · Cited by 32 · see citing works

Neither carrots nor sticks? Challenges surrounding data sharing from the perspective of research funding agencies—A qualitative expert interview study

Anger, M., Wendelborn, C., Winkler, E. C., & Schickhardt, C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0273259 · Cited by 31 · see citing works

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication mattersPreprint

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. (2019) · medRxiv · doi:10.1101/19012567 · Cited by 30 · see citing works

Challenges and Opportunities in dMRI Data Harmonization

Zhu, A. H., Moyer, D., Nir, T. M., Thompson, P. M., & Jahanshad, N. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_13 · Cited by 27 · see citing works

Genetic Contribution to the Heterogeneity of Major Depressive Disorder: Evidence From a Sibling-Based Design Using Swedish National Registers

Nguyen, T.-D., Kowalec, K., Pasman, J. A., Larsson, H., Lichtenstein, P., Dalman, C., Sullivan, P. F., Kuja‐Halkola, R., & Lu, Y. (2023) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20220906 · Cited by 27 · see citing works

Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian modelsPreprint

Bayer, J., Dinga, R., Kia, S. M., Kottaram, A., Wolfers, T., Lv, J., Zalesky, A., Schmaal, L., & Marquand, A. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.09.430363 · Cited by 25 · see citing works

Resample aggregating improves the generalizability of connectome predictive modeling

O’Connor, D. A., Lake, E. M. R., Scheinost, D., & Constable, R. T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118044 · Cited by 25 · see citing works

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps

Zhao, C., Jarecka, D., Covitz, S. C., Chen, Y., Eickhoff, S. B., Fair, D. A., Franco, A. R., Halchenko, Y. O., Hendrickson, T. J., Hoffstaedter, F., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00074 · Cited by 25 · see citing works

Assessment of Psychopathology

Pavlová, B., & Uher, R. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.0108 · Cited by 25 · see citing works

Confound-leakage: confound removal in machine learning leads to leakage

Hamdan, S., Love, B. C., von Polier, G. G., Weis, S., Schwender, H. R., Eickhoff, S. B., & Patil, K. R. (2022) · GigaScience · doi:10.1093/gigascience/giad071 · Cited by 23 · see citing works

Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan

Gonzales, S., Carson, M. B., & Holmes, K. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010397 · Cited by 20 · see citing works

Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only

Watson, H., Gallifant, J., Lai, Y., Radunsky, A. P., Villanueva, C., Martinez‐Martin, N., Gichoya, J. W., Huynh, U. K., & Celi, L. A. (2023) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100771 · Cited by 19 · see citing works

inMTSCCA: An Integrated Multi-Task Sparse Canonical Correlation Analysis for Multi-Omic Brain Imaging Genetics

Du, L., Zhang, J., Zhao, Y., Shang, M., Guo, L., Han, J., & Initiative, T. A. D. N. (2023) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.03.005 · Cited by 15 · see citing works

Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRIPreprint

Zhu, A. H., Nir, T. M., Javid, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.22.581646 · Cited by 14 · see citing works

Convergence, preliminary findings and future directions across the four human connectome projects investigating mood and anxiety disorders

Tozzi, L., Anene, E. T., Gotlib, I. H., Wintermark, M., Kerr, A. B., Wu, H., Seok, D., Narr, K. L., Sheline, Y. I., Whitfield, S., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118694 · Cited by 13 · see citing works

Extending and using anatomical vocabularies in the stimulating peripheral activity to relieve conditions project

Surles-Zeigler, M. C., Sincomb, T., Gillespie, T., de Bono, B., Bresnahan, J. C., Mawe, G. M., Grethe, J. S., Tappan, S. J., Heal, M., & Martone, M. E. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.819198 · Cited by 13 · see citing works

The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease

Carmody, L., Gargano, M. A., Toro, S., Vasilevsky, N., Adam, M. P., Blau, H., Chan, L., Gómez‐Andrés, D., Horváth, R., Kraus, M. L., et al. (2023) · Med · doi:10.1016/j.medj.2023.10.003 · Cited by 12 · see citing works

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks

Cali, R. J., Bhatt, R. R., Thomopoulos, S. I., Gadewar, S. P., Gari, I. B., Chattopadhyay, T., Jahanshad, N., Thompson, P. M., & Initiative, T. A. D. N. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340740 · Cited by 12 · see citing works

Clinical trial data-sharing policies among journals, funding agencies, foundations, and other professional organizations: a scoping review

Johnson, A. L., Anderson, J. M., Bouvette, M., Pinero, I., Rauh, S., Johnson, B., Kee, M., Heigle, B., Tricco, A. C., Page, M. J., et al. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.11.009 · Cited by 11 · see citing works

ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability

Turner, J. A., Calhoun, V. D., Thompson, P. M., Jahanshad, N., Ching, C. R. K., Thomopoulos, S. I., Verner, E., Strauss, G. P., Ahmed, A. O., Turner, M. D., et al. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09559-y · Cited by 10 · see citing works

The Three NITRCs: A Guide to Neuroimaging Neuroinformatics Resources

Kennedy, D. N., Haselgrove, C., Riehl, J., Preuss, N., & Buccigrossi, R. (2015) · Neuroinformatics · doi:10.1007/s12021-015-9263-8 · Cited by 9 · see citing works

Policies to regulate data sharing of cohorts via data infrastructures: An interview study with funding agencies

Devriendt, T., Shabani, M., & Borry, P. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104900 · Cited by 8 · see citing works

Higher amplitudes of visual networks are associated with trait- but not state-depression

Zhang, W., Dutt, R., Lew, D., Barch, D. M., & Bijsterbosch, J. D. (2024) · Psychological Medicine · doi:10.1017/s0033291724003167 · Cited by 8 · see citing works

Identification from MRI with Face-Recognition Software

Juluru, K., Siegel, E., & Mazura, J. (2020) · New England Journal of Medicine · doi:10.1056/nejmc1915674 · Cited by 8 · see citing works

Feasibility of Race by Sex Intersectionality Research on Suicidality in the Adolescent Brain Cognitive Development (ABCD) Study

Assari, S., Boyce, S., & Bazargan, M. (2021) · Children · doi:10.3390/children8060437 · Cited by 7 · see citing works

Confound-leakage: Confound Removal in Machine Learning Leads to Leakage

Hamdan, S., Love, B. C., von Polier, G. G., Weis, S., Schwender, H., Eickhoff, S. B., & Patil, K. R. (2022) · arXiv · doi:10.48550/arxiv.2210.09232 · Cited by 4 · see citing works

A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents

Shiohama, T., Maikusa, N., Kawaguchi, M., Natsume, J., Hirano, Y., Saito, K., Takanashi, J., Levman, J. E. D., Takahashi, E., Matsumoto, K., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13172774 · Cited by 4 · see citing works

Imaging Neuroscience opening editorial

Smith, S. M., Bergmann, T. O., Forstmann, B. U., Dagher, A., Keilholz, S. D., Kennedy, K. M., Kotz, S. A., Lustig, C., Pike, G. B., Tittgemeyer, M., et al. (2023) · Imaging Neuroscience · doi:10.1162/imag_e_00007 · Cited by 4 · see citing works

Ethical Considerations in Global Mental Health Research

Loue, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-66296-7_4 · Cited by 3 · see citing works

Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions

Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works

Towards closed-loop precision psychiatry: Integrating MRI biomarkers for individualized care of major depressive disorder

Gao, Q., Chen, X., Castellanos, F. X., Lu, B., & Yan, C. (2025) · Psychoradiology · doi:10.1093/psyrad/kkaf024 · Cited by 21 · see citing works

Psychiatric neuroimaging designs for individualised, cohort, and population studies

Gell, M., Noble, S., Laumann, T. O., Nelson, S. M., & Tervo‐Clemmens, B. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01918-y · Cited by 22 · see citing works

Precision neurodiversity: personalized brain network architecture as a window into cognitive variability

Mohammad, S. I., Azzam, E. R., Vasudevan, A., Ismail, S. M., Ayaz, H., & Prasad, K. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1669431 · Cited by 8 · see citing works

External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging

Rosenblatt, M., Foster, M. L., Adkinson, B. D., Tejavibulya, L., Khaitova, M., Ye, J., Sun, H., Rodriguez, R. X., Camp, C., Chinta, A., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03115-9 · Cited by 6 · see citing works

Multimodal brain network analysis: Research advances and challenges

Hou, T., Dai, W., Huang, J., Kong, Y., & Ding, W. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.104096 · Cited by 1 · see citing works

Structural Brain Correlates of Childhood Inhibited Temperament: An ENIGMA-Anxiety Mega-Analysis

Bas‐Hoogendam, J. M., Bernstein, R. A., Benson, B. E., Frank, S. E. C., Buss, K. A., Gunther, K. E., Pérez‐Edgar, K. E., Salum, G. A., Jackowski, A. P., Bressan, R. A., et al. (2025) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2025.06.026

White Matter Microstructure Alterations in Social Anxiety Disorder: A Mega-Analysis Across Twelve Cohorts in the ENIGMA-Anxiety Working Group

Roelofs, E. F., Groenewold, N. A., Farkas, K., Zhu, A. H., Gao, S., Borgers, T., Dannlowski, U., Flinkenflügel, K., Grotegerd, D., Hahn, T., et al. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.11.007

Metadata Driven Optimization of Distributed ETL Pipelines in Cloud Native Data Warehouses

Gopaluni, J. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.4742

Evaluating biomarkers and prediction models with E2P SimulatorPreprint

Karvelis, P., Felsky, D., Abi-Dargham, A., Horga, G., & Diaconescu, A. O. (2026) · eLife · doi:10.7554/elife.110646.1

Evaluating biomarkers and prediction models with E2P SimulatorPreprint

Karvelis, P., Felsky, D., Abi-Dargham, A., Horga, G., & Diaconescu, A. O. (2026) · eLife · doi:10.7554/elife.110646

HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disordersPreprint

Jarukasemkit, S., Harms, M. P., Lenzini, P. A., Chen, A., Glasser, M. F., Hamilton, K., Li, L., Luo, X., Myers, M., Pines, A. R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.26.732748

Big Data Initiatives in Psychiatry: Global Neuroimaging Studies

Thompson, P. M., Ching, C. R. K., Dennis, E. L., Salminen, L. E., Turner, J. A., van Erp, T. G. M., & Jahanshad, N. (2020) · Springer eBooks · doi:10.1007/978-3-030-35206-6_21 · Cited by 5 · see citing works

Big connectome imaging data in psychiatric disorders

Xia, M., Sun, X., Bu, X., Li, Q., & Yong He, A. (2024) · Medicine Plus · doi:10.1016/j.medp.2024.100038 · Cited by 10 · see citing works

14. IS BIGGER BETTER? PROMISES AND PITFALLS OF BIG DATA IN NEUROIMAGING OF PSYCHOSIS

Pasternak, O. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz022.053

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Lu, B., Chen, X., Castellanos, F. X., Thompson, P. M., Zuo, X., Zang, Y., & Yan, C. (2024) · 中国科学通报：英文版 · doi:10.1016/j.scib.2024.03.006 · Cited by 26 · see citing works

Trends in big data analyses by multicenter collaborative translational research in psychiatry

Onitsuka, T., Hirano, Y., Nemoto, K., Hashimoto, N., Kushima, I., Koshiyama, D., Koeda, M., Takahashi, T., Noda, Y., Matsumoto, J., et al. (2021) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13311 · Cited by 49 · see citing works