One size does not fit all: Methodological considerations for brain-based predictive modeling in psychiatry

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · see citing works

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182 · see citing works

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429 · see citing works

Correspondence of the brain's functional architecture during activation and rest

Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150 · see citing works

Co-planar stereotaxic atlas of the human brain : 3-dimensional proportional system : an approach to cerebral imaging

Talairach, J., Tournoux, P., & Rayport, M. (1988) · Thieme eBooks · Cited by 5,058 · see citing works

Functional Network Organization of the Human Brain

Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124 · see citing works

A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data

Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.034 · Cited by 4,033 · see citing works

Methods to detect, characterize, and remove motion artifact in resting state fMRI

Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748 · see citing works

Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI

Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657 · see citing works

Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature

Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956 · see citing works

Prediction of individual brain maturity using fMRI

Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., et al. (2010) · Science · doi:10.1126/science.1194144 · Cited by 2,183 · see citing works

Resting-state connectivity biomarkers define neurophysiological subtypes of depression

Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170 · 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

A whole brain fMRI atlas generated via spatially constrained spectral clustering

Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2012) · Human Brain Mapping · doi:10.1002/hbm.21333 · Cited by 1,636 · 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

A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume

Buckner, R. L., Head, D. M., Parker, J. B., Fotenos, A. F., Marcus, D. S., Morris, J. C., & Snyder, A. Z. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.018 · Cited by 1,290 · see citing works

Variability in the analysis of a single neuroimaging dataset by many teams

(2020) · doi:10.1038/s41586-020-2314-9 · Cited by 1,097 · see citing works

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

Shen, X.-L., Finn, E., Scheinost, D., Rosenberg, M., Chun, M. M., Papademetris, X., & Constable, R. T. (2017) · Nature Protocols · doi:10.1038/nprot.2016.178 · Cited by 1,088 · see citing works

Building better biomarkers: brain models in translational neuroimaging

Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017) · Nature Neuroscience · doi:10.1038/nn.4478 · Cited by 1,056 · see citing works

The Diagnosis of Mental Disorders: The Problem of Reification

Hyman, S. E. (2010) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091532 · Cited by 1,052 · see citing works

Towards a consensus regarding global signal regression for resting state functional connectivity MRI

Murphy, K., & Fox, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.052 · Cited by 1,043 · see citing works

A neuromarker of sustained attention from whole-brain functional connectivity

Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2015) · Nature Neuroscience · doi:10.1038/nn.4179 · Cited by 1,004 · see citing works

Recent progress and outstanding issues in motion correction in resting state fMRI

Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.044 · Cited by 957 · see citing works

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912 · see citing works

Cross-validation failure: Small sample sizes lead to large error bars

Varoquaux, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.061 · Cited by 897 · cited 2× · see citing works

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation

Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., Nelson, S. M., Coalson, R. S., Snyder, A. Z., Schlaggar, B. L., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.035 · Cited by 846 · see citing works

Regionally Localized Thinning of the Cerebral Cortex in Schizophrenia

Kuperberg, G. R., Broome, M. R., McGuire, P. K., David, A. S., Eddy, M., Ozawa, F., Goff, D., West, W. C., Williams, S. C. R., van der Kouwe, A. J. W., et al. (2003) · Archives of General Psychiatry · doi:10.1001/archpsyc.60.9.878 · Cited by 843 · see citing works

BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment

Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J. G., & Hamarneh, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.046 · Cited by 813 · 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

636,120 Ways to Have Posttraumatic Stress Disorder

Galatzer-Levy, I. R., & Bryant, R. A. (2013) · Perspectives on Psychological Science · doi:10.1177/1745691613504115 · Cited by 778 · see citing works

Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers

Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., Keshavan, M. S., & Tamminga, C. A. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14091200 · Cited by 768 · see citing works

Decreased volume of left and total anterior insular lobule in schizophrenia

Makris, N., Goldstein, J. M., Kennedy, D. N., Hodge, S. M., Caviness, V. S., Faraone, S. V., Tsuang, M. T., & Seidman, L. J. (2006) · Schizophrenia Research · doi:10.1016/j.schres.2005.11.020 · Cited by 743 · see citing works

Task-free MRI predicts individual differences in brain activity during task performance

Tavor, I., Jones, Ō. P., Mars, R. B., Smith, S. M., Behrens, T. E. J., & Jbabdi, S. (2016) · Science · doi:10.1126/science.aad8127 · Cited by 726 · see citing works

Deterministic and probabilistic tractography based on complex fibre orientation distributions

Descoteaux, M., Deriche, R., Knosche, T. R., & Anwander, A. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004424 · Cited by 698 · see citing works

Three Approaches to Understanding and Classifying Mental Disorder: <i>ICD-11, DSM-5</i> , and the National Institute of Mental Health’s Research Domain Criteria (RDoC)

Clark, L. A., Cuthbert, B., Lewis-Fernández, R., Narrow, W. E., & Reed, G. M. (2017) · Psychological Science in the Public Interest · doi:10.1177/1529100617727266 · Cited by 688 · see citing works

Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies

Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.023 · Cited by 683 · see citing works

Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture

Amunts, K., Mohlberg, H., Bludau, S., & Zilles, K. (2020) · Science · doi:10.1126/science.abb4588 · Cited by 588 · see citing works

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Kong, R., Li, J., Orbán, C., Sabuncu, M. R., Liu, H., Schaefer, A., Sun, N., Zuo, X., Holmes, A. J., Eickhoff, S. B., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy123 · Cited by 581 · see citing works

Task-induced brain state manipulation improves prediction of individual traits

Greene, A., Gao, S., Scheinost, D., & Constable, R. T. (2018) · Nature Communications · doi:10.1038/s41467-018-04920-3 · Cited by 525 · see citing works

Drug development in the era of precision medicine

Dugger, S. A., Platt, A., & Goldstein, D. B. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.226 · Cited by 493 · see citing works

Imaging-based parcellations of the human brain

Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0071-7 · Cited by 484 · 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

Reliability and Validity of Composite Scores from the NIH Toolbox Cognition Battery in Adults

Heaton, R. K., Akshoomoff, N., Tulsky, D., Mungas, D., Weintraub, S., Dikmen, S., Beaumont, J., Casaletto, K. B., Conway, K., Slotkin, J., et al. (2014) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617714000241 · Cited by 437 · see citing works

Linked dimensions of psychopathology and connectivity in functional brain networks

Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., Calkins, M. E., Cook, P. A., García de la Garza, A., Vandekar, S. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05317-y · Cited by 437 · see citing works

NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders

Du, Y., Fu, Z., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Gao, S., Xing, Y., Lin, D., Salman, M. S., Rahaman, M. A., Abrol, A., Chen, J., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102375 · Cited by 433 · see citing works

The global signal in fMRI: Nuisance or Information?

Liu, T. T., Nalci, A., & Falahpour, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.036 · Cited by 415 · see citing works

Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier]

Santos, M. S., Soares, J. P., Abreu, P. H., Araújo, H. J., & Santos, J. A. M. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2866730 · Cited by 403 · see citing works

Ten simple rules for predictive modeling of individual differences in neuroimaging

Scheinost, D., Noble, S., Horien, C., Greene, A. S., Lake, E. M., Salehi, M., Gao, S., Shen, X., O'Connor, D., Barron, D. S., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.057 · Cited by 381 · see citing works

Global signal regression strengthens association between resting-state functional connectivity and behavior

Li, J., Kong, R., Liégeois, R., Orban, C., Tan, Y., Sun, N., Holmes, A. J., Sabuncu, M. R., Ge, T., & Yeo, B. T. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.016 · Cited by 380 · see citing works

Altered global brain signal in schizophrenia

Yang, G., Murray, J. D., Repovš, G., Cole, M. W., Savić, A., Glasser, M. F., Pittenger, C., Krystal, J. H., Wang, X., Pearlson, G. D., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405289111 · Cited by 372 · see citing works

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features

Cui, Z., & Gong, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.001 · Cited by 372 · see citing works

Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder

Calhoun, V. D., Maciejewski, P. K., Pearlson, G. D., & Kiehl, K. A. (2007) · Human Brain Mapping · doi:10.1002/hbm.20463 · Cited by 344 · see citing works

Benchmarking functional connectome-based predictive models for resting-state fMRI

Dadi, K., Rahim, M., Abraham, A., Chyzhyk, D., Milham, M. P., Thirion, B., & Varoquaux, G. P. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.062 · Cited by 343 · see citing works

The cognition battery of the NIH toolbox for assessment of neurological and behavioral function: validation in an adult sample

Weintraub, S., Dikmen, S. S., Heaton, R. K., Tulsky, D. S., Zelazo, P. D., Slotkin, J., Carlozzi, N. E., Bauer, P. J., Wallner-Allen, K., Fox, N., et al. (2014) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617714000320 · Cited by 342 · see citing works

Multi-level bootstrap analysis of stable clusters in resting-state fMRI

Bellec, P., Rosa‐Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.082 · Cited by 335 · see citing works

Which fMRI clustering gives good brain parcellations?

Thirion, B., Varoquaux, G. P., Dohmatob, E., & Poline, J. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00167 · Cited by 329 · see citing works

Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises

Sui, J., Jiang, R., Bustillo, J., & Calhoun, V. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.02.016 · Cited by 309 · see citing works

The Initial Field Trials of DSM-5: New Blooms and Old Thorns

Freedman, R., Lewis, D. A., Michels, R., Pine, D. S., Schultz, S. K., Tamminga, C. A., Gabbard, G. O., Gau, S. S.-F., Javitt, D. C., Oquendo, M. A., et al. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12091189 · Cited by 295 · see citing works

Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics

He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116276 · Cited by 271 · see citing works

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Kebets, V., Holmes, A. J., Orbán, C., Tang, S., Li, J., Sun, N., Kong, R., Poldrack, R. A., & Yeo, B. T. T. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.06.013 · Cited by 269 · see citing works

Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging.

Doehrmann, O., Ghosh, S. S., Polli, F. E., Reynolds, G. O., Horn, F., Keshavan, A., Triantafyllou, C., Saygin, Z. M., Whitfield-Gabrieli, S., Hofmann, S. G., et al. (2013) · JAMA Psychiatry · doi:10.1001/2013.jamapsychiatry.5 · Cited by 262 · see citing works

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Kong, R., Yang, Q., Gordon, E. M., Xue, A., Yan, X., Orbán, C., Zuo, X., Spreng, R. N., Ge, T., Holmes, A. J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab101 · Cited by 243 · see citing works

Machine learning in resting-state fMRI analysis

Khosla, M., Jamison, K. W., Ngo, G. H., Kuceyeski, A. F., & Sabuncu, M. R. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.031 · Cited by 239 · see citing works

Optimising network modelling methods for fMRI

Pervaiz, U., Vidaurre, D., Woolrich, M. W., & Smith, S. M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116604 · Cited by 236 · see citing works

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

Chen, J., Tam, A., Kebets, V., Orbán, C., Ooi, L. Q. R., Asplund, C. L., Marek, S. A., Dosenbach, N. U. F., Eickhoff, S. B., Bzdok, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29766-8 · Cited by 230 · see citing works

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning

Abrol, A., Fu, Z., Salman, M. S., Silva, R. F., Du, Y., Plis, S., & Calhoun, V. D. (2021) · Nature Communications · doi:10.1038/s41467-020-20655-6 · Cited by 221 · see citing works

Baseline Striatal Functional Connectivity as a Predictor of Response to Antipsychotic Drug Treatment

Sarpal, D. K., Argyelan, M., Robinson, D. G., Szeszko, P. R., Karlsgodt, K. H., John, M., Weissman, N., Gallego, J. A., Kane, J. M., Lencz, T., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14121571 · Cited by 212 · see citing works

Brain connectomics predict response to treatment in social anxiety disorder

Whitfield-Gabrieli, S., Ghosh, S. S., Nieto-Castanon, A., Saygin, Z., Doehrmann, O., Chai, X. J., Reynolds, G. O., Hofmann, S. G., Pollack, M. H., & Gabrieli, J. D. E. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.109 · Cited by 210 · see citing works

Connectome-based prediction of cocaine abstinence

Yip, S. W., Scheinost, D., Potenza, M. N., & Carroll, K. M. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17101147 · Cited by 196 · see citing works

Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?

Sarwar, T., Ramamohanarao, K., & Zalesky, A. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27471 · Cited by 176 · see citing works

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning

Janssen, R. J., Mourão-Miranda, J., & Schnack, H. G. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.04.004 · Cited by 168 · see citing works

Prediction complements explanation in understanding the developing brain

Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018) · Nature Communications · doi:10.1038/s41467-018-02887-9 · Cited by 167 · see citing works

Brain–phenotype models fail for individuals who defy sample stereotypes

Greene, A. S., Shen, X., Noble, S., Horien, C., Hahn, C. A., Arora, J., Tokoglu, F., Spann, M. N., Carrión, C. I., Barron, D. S., et al. (2022) · Nature · doi:10.1038/s41586-022-05118-w · Cited by 167 · see citing works

Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets

Yoo, K., Rosenberg, M. D., Hsu, T. W., Zhang, S., Li, C.-S. R., Scheinost, D., Constable, R. T., & Chun, M. M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.010 · Cited by 166 · see citing works

Quantifying performance of machine learning methods for neuroimaging data

Jollans, L., Boyle, R. T., Eric Artiges, A., Banaschewski, T., Desrivières, S., Grigis, A., Martinot, J., Paus, T., Smolka, M. N., Walter, H., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.082 · Cited by 165 · see citing works

Clinical prediction from structural brain MRI scans: A large-scale empirical study

Initiative, F. T. A. D. N., Sabuncu, M. R., & Konukoglu, E. (2015) · Neuroinformatics · doi:10.1007/s12021-014-9238-1 · Cited by 161 · see citing works

Altered Frontal Cortical Volume and Decision Making in Adolescent Cannabis Users

Churchwell, J. C., Lopez-Larson, M., & Yurgelun-Todd, D. A. (2010) · Frontiers in Psychology · doi:10.3389/fpsyg.2010.00225 · Cited by 159 · see citing works

Exploring the impact of analysis software on task fMRI results

Bowring, A., Maumet, C., & Nichols, T. E. (2019) · Human Brain Mapping · doi:10.1002/hbm.24603 · Cited by 158 · see citing works

Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data

Ricard, J. A., Parker, T., Dhamala, E., Kwasa, J. A. C., Allsop, A. S., & Holmes, A. J. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01218-y · Cited by 153 · see citing works

Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety

Goldstein‐Piekarski, A. N., Ball, T. M., Samara, Z., Staveland, B. R., Keller, A. S., Fleming, S. L., Grisanzio, K. A., Holt-Gosselin, B., Stetz, P. C., Ma, J., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.06.024 · Cited by 150 · see citing works

Reconciling Dimensional and Categorical Models of Autism Heterogeneity: A Brain Connectomics and Behavioral Study

Tang, S., Sun, N., Floris, D. L., Zhang, X., Martino, A. D., & Yeo, B. T. T. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.11.009 · Cited by 145 · see citing works

Understanding Mental Illness Stigma Toward Persons With Multiple Stigmatized Conditions: Implications of Intersectionality Theory

Oexle, N., & Corrigan, P. W. (2018) · Psychiatric Services · doi:10.1176/appi.ps.201700312 · Cited by 141 · see citing works

Predicting personality from network-based resting-state functional connectivity

Nostro, A. D., Müller, V. I., Varikuti, D. P., Pläschke, R. N., Hoffstaedter, F., Langner, R., Patil, K. R., & Eickhoff, S. B. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1651-z · Cited by 138 · see citing works

Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity

Li, J., Bzdok, D., Chen, J., Tam, A., Ooi, L. Q. R., Holmes, A. J., Ge, T., Patil, K. R., Jabbi, M., Eickhoff, S. B., et al. (2022) · Science Advances · doi:10.1126/sciadv.abj1812 · Cited by 132 · see citing works

Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder

Hong, S., Valk, S. L., Martino, A. D., Milham, M. P., & Bernhardt, B. C. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx229 · Cited by 131 · see citing works

Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness

Wang, D., Li, M., Wang, M., Schoeppe, F., Ren, J., Chen, H., Öngür, D., Brady, R. O., Baker, J. T., & Liu, H. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0276-1 · Cited by 131 · see citing works

Predicting Treatment Response to Cognitive Behavioral Therapy in Panic Disorder With Agoraphobia by Integrating Local Neural Information

Hahn, T., Kircher, T. T. J., Straube, B., Wïttchen, H., Konrad, C., Ströhle, A., Wittmann, A., Pfleiderer, B., Reif, A., Arolt, V., et al. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1741 · Cited by 130 · see citing works

Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry

Nielsen, A. N., Barch, D. M., Petersen, S. E., Schlaggar, B. L., & Greene, D. J. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.11.007 · Cited by 129 · see citing works

Considerations for MRI study design and implementation in pediatric and clinical populations

Greene, D. J., BLACK, K. J., & Schlaggar, B. L. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2015.12.005 · Cited by 126 · see citing works

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

Kalmady, S. V., Greiner, R., Agrawal, R., Shivakumar, V., Narayanaswamy, J. C., Brown, M. R. G., Greenshaw, A. J., Dursun, S. M., & Venkatasubramanian, G. (2019) · Schizophrenia · doi:10.1038/s41537-018-0070-8 · Cited by 126 · see citing works

Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores

Jiang, R., Calhoun, V. D., Fan, L., Zuo, N., Jung, R., Qi, S., Lin, D., Li, J., Zhuo, C., Song, M., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhz134 · Cited by 126 · see citing works

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI

Ooi, L. Q. R., Chen, J., Zhang, S., Kong, R., Tam, A., Li, J., Dhamala, E., Zhou, J. H., Holmes, A. J., & Yeo, B. T. T. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119636 · Cited by 125 · see citing works

Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction

Khosla, M., Jamison, K. W., Kuceyeski, A. F., & Sabuncu, M. R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.012 · Cited by 114 · see citing works

Network communication models improve the behavioral and functional predictive utility of the human structural connectome

Seguin, C., Tian, Y., & Zalesky, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00161 · Cited by 114 · see citing works

Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study

Ji, C., Patil, K. R., Weis, S., Sim, K., Nickl‐Jockschat, T., Zhou, J. H., Aleman, A., Sommer, I. E., Liemburg, E. J., Hoffstaedter, F., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.08.031 · Cited by 111 · see citing works

High-resolution connectomic fingerprints: Mapping neural identity and behavior

Sina Mansour, L., Tian, Y., Yeo, B. T. T., Cropley, V., & Zalesky, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117695 · Cited by 110 · see citing works

What do clinicians treat: Diagnoses or symptoms? The incremental validity of a symptom-based, dimensional characterization of emotional disorders in predicting medication prescription patterns

Waszczuk, M. A., Zimmerman, M., Ruggero, C., Li, K., MacNamara, A., Weinberg, A., Hajcak, G., Watson, D., & Kotov, R. (2017) · Comprehensive Psychiatry · doi:10.1016/j.comppsych.2017.04.004 · Cited by 106 · see citing works

Connectome-based Models Predict Separable Components of Attention in Novel Individuals

Rosenberg, M. D., Hsu, T. W., Scheinost, D., Constable, R. T., & Chun, M. M. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01197 · Cited by 103 · see citing works

Transdiagnostic dimensions of psychopathology explain individuals’ unique deviations from normative neurodevelopment in brain structure

Parkes, L., Moore, T. M., Calkins, M. E., Cook, P. A., Cieslak, M., Roalf, D. R., Wolf, D. H., Gur, R. C., Gur, R. E., Satterthwaite, T. D., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01342-6 · Cited by 103 · see citing works

Connectome-Based Predictive Modeling of Individual Anxiety

Wang, Z., Goerlich, K. S., Ai, H., Aleman, A., Luo, Y., & Xu, P. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa407 · Cited by 102 · see citing works

Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults

Dhamala, E., Jamison, K. W., Jaywant, A., Dennis, S., & Kuceyeski, A. F. (2021) · Human Brain Mapping · doi:10.1002/hbm.25420 · Cited by 98 · see citing works

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

He, T., An, L., Chen, P., Chen, J., Feng, J., Bzdok, D., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01059-9 · Cited by 96 · see citing works

Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction

Sanchis‐Segura, C., Ibáñez, M. V., Aguirre, N., Cruz-Gómez, Á. J., & Forn, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-69361-9 · Cited by 95 · see citing works

La medicina personalizada aplicada a la salud mental: la psiquiatría de precisión

Vieta, E. (2015) · Revista de Psiquiatría y Salud Mental · doi:10.1016/j.rpsm.2015.03.003 · Cited by 93 · see citing works

Genetic Markers of ADHD-Related Variations in Intracranial Volume

Klein, M., Walters, R. K., Demontis, D., Stein, J. L., Hibar, D. P., Adams, H. H. H., Bralten, J., Mota, N. R., Schachar, R. J., Sonuga-Barke, E. J. S., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.18020149 · Cited by 89 · see citing works

Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup mega-analysis

Wang, X., Xie, H., Chen, T., Cotton, A. S., Salminen, L. E., Logue, M. W., Clarke‐Rubright, E. K., Wall, J. T., Dennis, E. L., O’Leary, E. N., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-00967-1 · Cited by 89 · see citing works

Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling

Lichenstein, S. D., Scheinost, D., Potenza, M. N., Carroll, K. M., & Yip, S. W. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0586-y · Cited by 84 · see citing works

Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis

Koutsouleris, N., Wobrock, T., Guse, B., Langguth, B., Landgrebe, M., Eichhammer, P., Frank, E., Cordes, J., Wölwer, W., Musso, F., et al. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx114 · Cited by 83 · see citing works

Structural connectivity predicts clinical outcomes of deep brain stimulation for Tourette syndrome

Johnson, K. A., Duffley, G., Anderson, D. N., Ostrem, J. L., Welter, M., Baldermann, J. C., Kuhn, J., Huys, D., Visser-Vandewalle, V. E. R. M., Foltynie, T., et al. (2020) · Brain · doi:10.1093/brain/awaa188 · Cited by 80 · see citing works

Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research

Yip, S. W., Kiluk, B. D., & Scheinost, D. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.11.001 · Cited by 77 · see citing works

Data-driven clustering differentiates subtypes of major depressive disorder with distinct brain connectivity and symptom features

Wang, Y., Tang, S., Zhang, L., Bu, X., Lu, L., Li, H., Gao, Y., Hu, X., Kuang, W., Jia, Z., et al. (2021) · The British Journal of Psychiatry · doi:10.1192/bjp.2021.103 · Cited by 77 · see citing works

Structural MRI-Based Predictions in Patients with Treatment-Refractory Depression (TRD)

Johnston, B. A., Steele, J. D., Tolomeo, S., Christmas, D. M., & Matthews, K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0132958 · Cited by 72 · see citing works

Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning

Mellema, C., Nguyen, K. P., Treacher, A., & Montillo, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-06459-2 · Cited by 72 · see citing works

Reduced Anterior Temporal and Hippocampal Functional Connectivity During Face Processing Discriminates Individuals with Social Anxiety Disorder from Healthy Controls and Panic Disorder, and Increases Following Treatment

Pantazatos, S. P., Talati, A., Schneier, F. R., & Joy Hirsch, C. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2013.211 · Cited by 71 · see citing works

Organizational Principles of Human Visual Cortex Revealed by Receptor Mapping

Eickhoff, S. B., Rottschy, C., Kujovic, M., Palomero‐Gallagher, N., & Zilles, K. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn024 · Cited by 70 · see citing works

Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture

Ji, C., Müller, V. I., Dukart, J., Hoffstaedter, F., Baker, J. T., Holmes, A. J., Vatansever, D., Nickl‐Jockschat, T., Liu, X., Derntl, B., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.09.024 · Cited by 70 · see citing works

Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery

Traut, N., Heuer, K., Lemaître, G., Beggiato, A., Germanaud, D., Elmaleh, M., Bethegnies, A., Bonnasse‐Gahot, L., Cai, W., Chambon, S., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119171 · Cited by 69 · see citing works

Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses

Bhagwat, N., Barry, A., Dickie, E. W., Brown, S. T., Devenyi, G. A., Hatano, K., DuPré, E., Dagher, A., Chakravarty, M. M., Greenwood, C. M. T., et al. (2021) · GigaScience · doi:10.1093/gigascience/giaa155 · Cited by 67 · see citing works

Multimodal data revealed different neurobiological correlates of intelligence between males and females

Jiang, R., Calhoun, V. D., Cui, Y. J., Qi, S., Zhuo, C., Li, J., Jung, R. E., Yang, J., Du, Y., Jiang, T., et al. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00146-z · Cited by 61 · see citing works

Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders

Barron, D. S., Gao, S., Dadashkarimi, J., Greene, A. S., Spann, M. N., Noble, S., Lake, E. M. R., Krystal, J. H., Constable, R. T., & Scheinost, D. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa371 · Cited by 61 · see citing works

Prediction of Alzheimer's disease using individual structural connectivity networks

Shao, J., Myers, N. E., Yang, Q., Feng, J., Plant, C., Böhm, C., Förstl, H., Kurz, A., Zimmer, C., Meng, C., et al. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.01.017 · Cited by 59 · see citing works

Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development

Dhamala, E., Ooi, L. Q. R., Chen, J., Kong, R., Anderson, K. M., Chin, R., Yeo, B. T. T., & Holmes, A. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119485 · Cited by 56 · see citing works

Identifying schizophrenia subgroups using clustering and supervised learning

Talpalaru, A., Bhagwat, N., Devenyi, G. A., Lepage, M., & Chakravarty, M. M. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.05.044 · Cited by 53 · see citing works

Connectome-based models can predict early symptom improvement in major depressive disorder

Ju, Y., Horien, C. L., Chen, W., Guo, W., Lu, X., Sun, J., Dong, Q., Liu, B., Liu, J., Yan, D., et al. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.04.028 · Cited by 51 · see citing works

Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series

Yan, W., Zhao, M., Fu, Z., Pearlson, G. D., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., & Calhoun, V. D. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.02.007 · Cited by 51 · see citing works

Neurostructural Heterogeneity in Youth with Internalizing Symptoms

Kaczkurkin, A., Sotiras, A., Baller, E. B., Barzilay, R., Calkins, M., Chand, G., Cui, Z., Erus, G., Fan, Y., Gur, R., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.09.005 · Cited by 50 · see citing works

Construct validity of the NIH Toolbox Cognition Battery in individuals with stroke.

Carlozzi, N. E., Tulsky, D. S., Wolf, T., Goodnight, S. M., Heaton, R. K., Casaletto, K. B., Wong, A., Baum, C. M., Gershon, R. C., & Heinemann, A. W. (2017) · Rehabilitation Psychology · doi:10.1037/rep0000195 · Cited by 49 · see citing works

Genetic correlations between subcortical brain volumes and psychiatric disorders

Ohi, K., Shimada, T., Kataoka, Y., Yasuyama, T., Kawasaki, Y., Shioiri, T., & Thompson, P. M. (2020) · The British Journal of Psychiatry · doi:10.1192/bjp.2019.277 · Cited by 43 · see citing works

Machine Learning Models Identify Multimodal Measurements Highly Predictive of Transdiagnostic Symptom Severity for Mood, Anhedonia, and Anxiety

Mellem, M. S., Liu, Y., González, H., Kollada, M., Martin, W. J., & Ahammad, P. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.07.007 · Cited by 41 · see citing works

Connectivity‐based whole brain dual parcellation by group ICA reveals tract structures and decreased connectivity in schizophrenia

Wu, L., Calhoun, V. D., Jung, R. E., & Caprihan, A. (2015) · Human Brain Mapping · doi:10.1002/hbm.22945 · Cited by 40 · see citing works

Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology

Saggar, M., & Uddin, L. Q. (2019) · eneuro · doi:10.1523/eneuro.0384-19.2019 · Cited by 38 · see citing works

Isolating the sources of pipeline‐variability in group‐level task‐fMRI results

Bowring, A., Nichols, T. E., & Maumet, C. (2021) · Human Brain Mapping · doi:10.1002/hbm.25713 · Cited by 32 · see citing works

Sex classification using long‐range temporal dependence of resting‐state functional MRI time series

Dhamala, E., Jamison, K. W., Sabuncu, M. R., & Kuceyeski, A. F. (2020) · Human Brain Mapping · doi:10.1002/hbm.25030 · Cited by 31 · see citing works

Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females

Dhamala, E., Jamison, K. W., Jaywant, A., & Kuceyeski, A. F. (2021) · Human Brain Mapping · doi:10.1002/hbm.25709 · Cited by 30 · see citing works

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Zhao, X., Rangaprakash, D., Yuan, B., Denney, T. S., Katz, J. S., Dretsch, M. N., & Deshpande, G. (2018) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2018.00025 · Cited by 28 · see citing works

Brain connectivity at rest predicts individual differences in normative activity during movie watching

Gruskin, D. C., & Patel, G. H. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119100 · Cited by 23 · see citing works

The classification of psychiatric disorders according to DSM-5 deserves an internationally standardized psychological test battery on symptom level

der Kloet, D. V. H., & van Heugten, T. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01108 · Cited by 21 · see citing works

Predicting individual traits from unperformed tasks

Gal, S., Tik, N., Bernstein‐Eliav, M., & Tavor, I. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118920 · Cited by 21 · see citing works

Association vs. Prediction: The Impact of Cortical Surface Smoothing and Parcellation on Brain Age

Zeighami, Y., & Evans, A. C. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.637724 · Cited by 11 · see citing works

Neuroimaging correlates of emotional response-inhibition discriminate between young depressed adults with and without sub-threshold bipolar symptoms (Emotional Response-inhibition in Young Depressed Adults)

Cha, J., Speaker, S. L., Hu, B., Altinay, M. I., Koirala, P., Karne, H. S., Spielberg, J. M., Kuceyeski, A. F., Dhamala, E., & Anand, A. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.12.037 · Cited by 9 · see citing works

Disease versus Dimension in Diagnosis

Shorter, E., & van Praag, H. M. (2010) · The Canadian Journal of Psychiatry · doi:10.1177/070674371005500201 · Cited by 7 · see citing works

Balancing Strengths and Weaknesses in Dimensional PsychiatryPreprint

Alexander, L. M., Salum, G. A., Swanson, J. M., & Milham, M. P. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/207019 · Cited by 2 · see citing works

A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder

Winter, N. R., Blanke, J., Leenings, R., Ernsting, J., Fisch, L., Sarink, K., Barkhau, C., Emden, D., Thiel, K., Flinkenflügel, K., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.5083 · Cited by 133 · see citing works

Mapping neurophysiological subtypes of major depressive disorder using normative models of the functional connectome

Sun, X., Sun, J., Lu, X., Dong, Q., Zhang, L., Wang, W., Liu, J., Ma, Q., Wang, X., Wei, D., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.05.021 · Cited by 129 · see citing works

Towards a biologically annotated brain connectome

Bazinet, V., Hansen, J. Y., & Mišić, B. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00752-3 · Cited by 108 · see citing works

Functional connectomics in depression: insights into therapies

Chai, Y., Sheline, Y. I., Oathes, D. J., Balderston, N. L., Rao, H., & Yu, M. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.05.006 · Cited by 108 · see citing works

The history and future of resting-state functional magnetic resonance imaging

Biswal, B. B., & Uddin, L. Q. (2025) · Nature · doi:10.1038/s41586-025-08953-9 · Cited by 80 · see citing works

Practical AI application in psychiatry: historical review and future directions

Sun, J., Lu, T., Shao, X., Han, Y., Xia, Y., Zheng, Y., Wang, Y., Li, X., Ravindran, A. V., Fan, L., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03072-3 · Cited by 78 · see citing works

The challenges and prospects of brain-based prediction of behaviour

Wu, J., Li, J., Eickhoff, S. B., Scheinost, D., & Genon, S. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01670-1 · Cited by 57 · see citing works

Brain-Based Predictions of Psychiatric Illness–Linked Behaviors Across the Sexes

Dhamala, E., Ooi, L. Q. R., Chen, J., Ricard, J. A., Berkeley, E., Chopra, S., Qu, Y. L., Zhang, X.-H., Lawhead, C., Yeo, B. T. T., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.03.025 · Cited by 43 · see citing works

Functional brain networks are associated with both sex and gender in children

Dhamala, E., Bassett, D. S., Yeo, B. T. T., & Holmes, A. J. (2024) · Science Advances · doi:10.1126/sciadv.adn4202 · Cited by 44 · see citing works

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Liloia, D., Zamfira, D. A., Tanaka, M., Manuello, J., Crocetta, A., Keller, R., Cozzolino, M., Duca, S., Cauda, F., & Costa, T. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105791 · Cited by 44 · 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

Replicability and generalizability in population psychiatric neuroimaging

Marek, S. A., & Laumann, T. O. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01960-w · Cited by 31 · see citing works

Considering the interconnected nature of social identities in neuroimaging research

Dhamala, E., Ricard, J. A., Uddin, L. Q., Galea, L. A. M., Jacobs, E. G., Yip, S. W., Yeo, B. T. T., Chakravarty, M. M., & Holmes, A. J. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01832-y · Cited by 31 · see citing works

Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness

Chopra, S., Dhamala, E., Lawhead, C., Ricard, J. A., Orchard, E. R., An, L., Chen, P., Wulan, N., Kumar, P., Rubenstein, A., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn1862 · Cited by 37 · see citing works

Individual variability in neural representations of mind-wandering

Kucyi, A., Anderson, N. L., Bounyarith, T., Braun, D., Shareef-Trudeau, L., Treves, I. N., Braga, R. M., Hsieh, P., & Hung, S. (2024) · Network Neuroscience · doi:10.1162/netn_a_00387 · Cited by 27 · see citing works

Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

Ananna, F. F., Nowreen, R., Jahwari, S. S. R. A., Costa, E. A., Angeline, L., & Sindiramutty, S. R. (2023) · International Journal of Emerging Multidisciplinaries Computer Science & Artificial Intelligence · doi:10.54938/ijemdcsai.2023.02.1.254 · Cited by 26 · see citing works

Functional networks in the infant brain during sleep and wake states

Yates, T. S., Ellis, C. T., & Turk‐Browne, N. B. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad327 · Cited by 24 · see citing works

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities

Fu, Z., Batta, I., Wu, L., Abrol, A., Agcaoglu, O., Salman, M. S., Du, Y., Iraji, A., Shultz, S., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120617 · Cited by 25 · see citing works

Computational whole-body-exposome models for global precision brain health

Ibáñez, A. M., Duran-Aniotz, C. A., Migeot, J., Báez, S., Fittipaldi, S., Coronel‐Oliveros, C., Eyre, H. A., Udeh‐Momoh, C. T., Zetterberg, H., Alladi, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67448-3 · Cited by 24 · 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

Transdiagnostic findings across major depressive disorder, bipolar disorder and schizophrenia: A qualitative review

Stein, F., & Kircher, T. T. J. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119464 · Cited by 21 · see citing works

Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights

Anbarasi, J. D., Kumari, R., Ganesh, M., & Agrawal, R. (2024) · BMC Neurology · doi:10.1186/s12883-024-03864-0 · Cited by 39 · see citing works

Using precision approaches to improve brain-behavior prediction

Lee, H. J., Dworetsky, A., Labora, N., & Gratton, C. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.09.007 · Cited by 20 · see citing works

Tailoring Psychiatric Neuroimaging to Translational Goals

Tervo‐Clemmens, B., Marek, S. A., & Barch, D. M. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.1416 · Cited by 16 · see citing works

Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing

Petrican, R., & Fornito, A. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101229 · Cited by 15 · see citing works

Identifying brain functional subtypes and corresponding task performance profiles in autism spectrum disorder

Liu, Q., Lai, H., Le, J., Lan, C., Zhang, X., Huang, L., Xu, D., Jiang, X., Li, F. R., Kendrick, K. M., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03086-x · Cited by 16 · see citing works

Toward neuromarkers for tailored smoking cessation treatments

Versace, F., Robinson, J. D., & Cinciripini, P. M. (2023) · Addiction Neuroscience · doi:10.1016/j.addicn.2023.100075 · Cited by 13 · see citing works

Influences of sex and gender on the associations between risk and protective factors, brain, and behavior

Brosch, K., & Dhamala, E. (2024) · Biology of Sex Differences · doi:10.1186/s13293-024-00674-4 · Cited by 14 · see citing works

Mapping the white-matter functional connectome: a personal perspective

Li, J., Chen, H., & Liao, W. (2025) · Psychoradiology · doi:10.1093/psyrad/kkaf028 · Cited by 11 · see citing works

Lifestyle Factors Counteract the Neurodevelopmental Impact of Genetic Risk for Accelerated Brain Aging in Adolescence

Petrican, R., Fornito, A., & Boyland, E. J. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.06.023 · Cited by 12 · see citing works

The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research

Zhao, Q., Nooner, K. B., Tapert, S. F., Adeli, E., Pohl, K. M., Kuceyeski, A. F., & Sabuncu, M. R. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100397 · Cited by 11 · see citing works

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Zhu, J., & Yu, Y. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.10.009 · Cited by 10 · see citing works

The Promise of Investigating Neural Variability in Psychiatric Disorders

Tsikonofilos, K., Kumar, A., Ampatzis, K., Garrett, D. D., & Månsson, K. N. T. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.02.004 · Cited by 9 · see citing works

A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across ModalitiesPreprint

Winter, N. R., Blanke, J., Leenings, R., Ernsting, J., Fisch, L., Sarink, K., Barkhau, C., Thiel, K., Flinkenflügel, K., Winter, A., et al. (2023) · medRxiv · doi:10.1101/2023.02.27.23286311 · Cited by 9 · see citing works

Personalized identification and intervention of depression in adolescents: A tertiary-level framework

Zhou, Y., Chen, X., Gu, R., Xiang, Y., Hajcak, G., & Wang, G. (2024) · 中国科学通报：英文版 · doi:10.1016/j.scib.2024.01.024 · Cited by 8 · see citing works

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry

Olia, J. B. H., Raman, A., Hsu, C., Alkhayyat, A., & Nourazarian, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109984 · Cited by 9 · see citing works

Neuroimaging insights into adolescent depression risk and development

MacSweeney, N., Toenders, Y. J., & Tamnes, C. K. (2025) · Nature Mental Health · doi:10.1038/s44220-025-00453-z · Cited by 7 · see citing works

Biologically Annotated Heterogeneity of Depression Through Neuroimaging Normative Modeling

Li, J., Chen, H., & Liao, W. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.07.002 · Cited by 7 · see citing works

Rescuing missing data in connectome-based predictive modeling

Liang, Q., Jiang, R., Adkinson, B. D., Rosenblatt, M., Mehta, S., Foster, M. L., Dong, S., You, C., Negahban, S. N., Zhou, H. H., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00071 · Cited by 7 · see citing works

Sex classification from functional brain connectivity: Generalization to multiple datasets

Wiersch, L., Friedrich, P., Hamdan, S., Komeyer, V., Hoffstaedter, F., Patil, K. R., Eickhoff, S. B., & Weis, S. (2024) · Human Brain Mapping · doi:10.1002/hbm.26683 · Cited by 7 · 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

Building diagnostic neuroimaging biomarkers for psychiatric disorders using reverse inference approaches: A viable route?

Liloia, D., Costa, T., Cauda, F., & Manuello, J. (2024) · Advances in Clinical and Experimental Medicine · doi:10.17219/acem/186816 · Cited by 5 · see citing works

Distinct brain network features predict internalizing and externalizing traits in children, adolescents and adults

Qu, Y. L., Chen, J., Tam, A., Ooi, L. Q. R., Dhamala, E., Cocuzza, C. V., Zhang, S., Zeng, T., Lawhead, C., Yeo, B. T. T., et al. (2025) · Nature Mental Health · doi:10.1038/s44220-025-00388-5 · Cited by 10 · see citing works

Reliable and generalizable brain-based predictions of cognitive functioning across common psychiatric illnessPreprint

Chopra, S., Dhamala, E., Lawhead, C., Ricard, J. A., Orchard, E. R., An, L., Chen, P., Wulan, N., Kumar, P., Rubenstein, A., et al. (2022) · medRxiv · doi:10.1101/2022.12.08.22283232 · Cited by 5 · see citing works

Distinct brain network features predict internalizing and externalizing traits in children and adultsPreprint

Qu, Y. L., Chen, J., Tam, A., Ooi, L. Q. R., Dhamala, E., Cocuzza, C. V., Lawhead, C., Yeo, B. T. T., & Holmes, A. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.20.541490 · Cited by 5 · see citing works

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use

Weng, Y., Kruschwitz, J., Rueda‐Delgado, L. M., Ruddy, K. L., Boyle, R. T., Franzen, L., Serin, E., Nweze, T., Hanson, J. L., Smyth, A., et al. (2024) · eLife · doi:10.7554/elife.97150.3 · Cited by 4 · see citing works

Representing Brain-Behavior Associations by Retaining High-Motion Minoritized Youth

Ramduny, J., Uddin, L. Q., Vanderwal, T., Feczko, E., Fair, D. A., Kelly, C. A. M., & Baskin-sommers, A. R. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.01.014 · Cited by 4 · see citing works

Machine Learning-Based Algorithm for Tacrolimus Dose Optimization in Hospitalized Kidney Transplant Patients

Park, D. J., Kim, M.-H., Cho, H.-J., Kim, J. S., Hwang, J. K., & Lee, J. (2025) · Diagnostics · doi:10.3390/diagnostics15232948 · Cited by 3 · see citing works

Genetic risk predicts adolescent mood pathology via sexual differentiation of brain function and physiological aging

Petrican, R., Fornito, A., Murgatroyd, C., Boyland, E. J., & Hardman, C. A. (2025) · Nature Communications · doi:10.1038/s41467-025-60686-5 · Cited by 5 · see citing works

Functional brain network dynamics mediate the relationship between female reproductive aging and interpersonal adversity

Petrican, R., Chopra, S., Segal, A., Fallon, N., & Fornito, A. (2025) · Nature Mental Health · doi:10.1038/s44220-024-00352-9 · Cited by 3 · see citing works

Emerging Topics in Computational Psychiatric Research: Clarity Through Complexity?

Yip, S. W., & Konova, A. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.02.005 · Cited by 3 · see citing works

Neuroanatomy reflects individual variability in impulsivity in youth

Dhamala, E., Christensen, E., Hanson, J. L., Ricard, J. A., Arcaro, N., Bhola, S., Wiersch, L., Brosch, K., Yeo, B. T. T., Holmes, A. J., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03526-2 · Cited by 4 · see citing works

Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers

Adkinson, B. D., Rosenblatt, M., Sun, H.-L., Dadashkarimi, J., Tejavibulya, L., Horien, C., Westwater, M., Rodriguez, R., Noble, S., & Scheinost, D. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02447-y · Cited by 2 · see citing works

Large-Scale Neuroimaging of Mental Illness

Ching, C. R. K., Kang, M. J. Y., & Thompson, P. M. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_462 · Cited by 2 · see citing works

Using effective connectivity-based predictive modeling to predict MDD scale scores from multisite rs-fMRI data

Dai, P., He, Z., Luo, J., Huang, K., Hu, T., Chen, Q., Liao, S., & Yi, X. (2025) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2025.110406 · Cited by 2 · see citing works

Comparing and scaling fMRI features for brain-behavior prediction

Schöttner, M., Bolton, T. A. W., Patel, J. H., & Hagmann, P. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.141 · Cited by 2 · see citing works

A Framework for Advancing Mechanistic Neurobehavioral Biomarkers in Psychiatry

Lee, K., Ji, J. L., Helmer, M., Murray, J. D., Krystal, J. H., & Anticevic, A. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.09.013 · Cited by 5 · see citing works

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use

Weng, Y., Kruschwitz, J., Rueda‐Delgado, L. M., Ruddy, K. L., Boyle, R. T., Franzen, L., Serin, E., Nweze, T., Hanson, J. L., Smyth, A., et al. (2024) · eLife · doi:10.7554/elife.97150 · Cited by 2 · see citing works

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance usePreprint

Weng, Y., Kruschwitz, J., Rueda‐Delgado, L. M., Ruddy, K. L., Boyle, R. T., Franzen, L., Serin, E., Nweze, T., Hanson, J. L., Smyth, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.03.587900 · Cited by 2 · see citing works

Psychiatric Neuroimaging Designs for Individualised, Cohort, and Population StudiesPreprint

Gell, M., Noble, S., Laumann, T. O., Nelson, S. M., & Tervo‐Clemmens, B. (2024) · doi:10.31234/osf.io/278ef · Cited by 2 · see citing works

Shared Neural Signatures of Socioeconomic Status, Scarcity, and Neighborhood Threat in YouthPreprint

Brosch, K., Wiersch, L., Christensen, E., Rakesh, D., Hanson, J. L., Schwartz, R. M., & Dhamala, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.11.659188 · Cited by 2 · see citing works

Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach

Früh, D., Mendl-Heinisch, C., Bittner, N., Weis, S., & Caspers, S. (2025) · Human Brain Mapping · doi:10.1002/hbm.70191 · Cited by 2 · see citing works

Deriving Generalizable and Interpretable Brain-Behavior Phenotypes of Cannabis Use

Konova, A. B., & Zilverstand, A. (2023) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2023.01.003 · Cited by 1 · see citing works

Environmental Neurorehabilitation: Harnessing the Power of the Environment in Rehabilitation Following Neurological Conditions

Norwood, M. F., Pretty, D., Marsh, C., Shirota, C., Gillet, C., Zeeman, H., & Kendall, E. (2024) · Springer eBooks · doi:10.1007/978-3-031-64699-7_15 · Cited by 1 · see citing works

Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging

Paschali, M., Jiang, Y. Y., Siegel, S., González, C., Pohl, K. M., Chaudhari, A. S., & Zhao, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-74561-4_3 · Cited by 1 · see citing works

Sex differences in the functional network underpinnings of psychotic-like experiences in childrenPreprint

Dhamala, E., Chopra, S., Ooi, L. Q. R., Rubio, J. M., Yeo, B. T. T., Malhotra, A. K., & Holmes, A. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.22.590660 · Cited by 1 · see citing works

Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical NeurosciencePreprint

Wang, R., Erus, G., Chaudhari, P., & Davatzikos, C. A. (2023) · PubMed · doi:10.48550/arxiv.2308.03175 · Cited by 1 · see citing works

Functional brain connectome-transcriptional landscape linking to transdiagnostic factors of psychopathological symptomsPreprint

Chen, Z., Liu, X., Li, W., Hu, Y., Hu, B., Xu, T., Zhang, R., Tang, Y., Xia, L., Zhang, J., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3890054/v1

A Comprehensive Analysis of Psychiatric Disorders Using Deep Learning

Manickam, N., & Ponnusamy, V. (2023) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon60755.2023.10442235

Embracing Model Heterogeneity for Better Brain-Behavior Associations

Scheinost, D., & Spann, M. N. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100425

Persistent brain network signatures in psychosis: implications for diagnosis, prognosis, and treatment

Dhamala, E., & Rubio, J. M. (2025) · Neuropsychopharmacology · doi:10.1038/s41386-025-02079-2

Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkersPreprint

Adkinson, B. D., Rosenblatt, M., Sun, H.-L., Dadashkarimi, J., Tejavibulya, L., Horien, C., Westwater, M., Rodriguez, R., Noble, S., & Scheinost, D. (2025) · bioRxiv · doi:10.1101/2025.03.12.642878 · Cited by 3 · see citing works

Bird’s Eye View of Artificial Intelligence in Neuroscience

Zhou, M. H., Lin, E., Ye, A. Q., Mukherjee, P., & Yuh, E. L. (2025) · AI in neuroscience. · doi:10.1089/ains.2024.0001

Meta-Learning for Generalizable Connectome Modeling Across Heterogeneous Atlas Spaces

Liang, Q., Adkinson, B. D., & Scheinost, D. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981255

Imagerie cérébrale et premiers succès de la psychiatrie de précision

Laidi, C., Bouaziz, N., Haffen, É., Domenech, P., Duchesnay, É., Favre, P., Fakra, É., Houenou, J., Iftimovici, A., Antoine Lefrere, A., et al. (2025) · médecine/sciences · doi:10.1051/medsci/2025061

Identifying Neuroimaging Biomarkers of Resilience and Vulnerability to Chronic Stress in An Animal Model: An Exploratory AnalysisPreprint

Dai, T., Algar, S. D., SMALL, M. P., Zalesky, A., & Rodger, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.05.667822

Current limitations in functional connectivity assessment: suggestions for analysis improvements

Xu, T., Bayrak, R. G., Fischbach, A. K., Bridgeford, E., Vogelstein, J. T., Noble, S., & Anteraper, S. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-19099-5.00002-7

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes

Riley, S., Cheng, A., Wang, Y.-W., Shen, X.-L., Dhamala, E., Zhao, Y.-Z., Holmes, A. J., Constable, R., & Yip, S. W. (2026) · Imaging neuroscience · doi:10.1162/imag.a.1287

Multimodal neuroimaging data boosts the prediction of multifaceted cognitionPreprint

Wu, J., Li, J., Jung, K., Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2025) · Research Square · doi:10.21203/rs.3.rs-7721822/v1

Personalized Prediction of Regional Brain Atrophy in Parkinson’s Disease through Longitudinal AI ModelingPreprint

Wu, Y., Rundek, T., Nguyen, P., Yesha, Y., Feldman, M., Atit, P., Miller, J., Najafi, T., & Haq, I. U. (2025) · medRxiv · doi:10.64898/2025.12.29.25343190

A Multi-Modal Framework for Major Depressive Disorder Prediction Integrating Variant-Based Genomics, Wearables, and Clinical Data

Lee, T., Ha, S., Kang, K., Kim, J., Jhon, M., & Lee�, H. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356566

Artificial Intelligence in Diabetes Management: A New Frontier in Precision Medicine

Gaur, A., Paul, M., Singh, N., & Pant, G. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80592-911-620261015

Revamping neuroimaging analysis to reveal biomarkers of adolescent mental health

Busch, E. L., Turk‐Browne, N. B., & Baskin-sommers, A. R. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00610-y

Baseline clinical features outperform structural MRI in predicting rapid cognitive and motor decline in Parkinson’s disease

Wu, Y., Santiago, J. A., Rundek, T., Nguyen, P., Yesha, Y., Feldman, M., Sarno, M., Atit, P., Miller, J., Najafi, T., et al. (2026) · npj Parkinson s Disease · doi:10.1038/s41531-026-01530-5

Investigating the impact of stressful life events on neuroanatomy across adolescencePreprint

Wiersch, L., Brosch, K., Christensen, E., & Dhamala, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723376

The geometry of disorder: fractal analysis in psychiatric neuroimaging

Rehwald, R. (2026) · European Radiology · doi:10.1007/s00330-026-12750-x

How does machine learning bias in prediction of behavioral phenotypes relate to individual characteristics?Preprint

Wu, J., Li, J., Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2026) · Research Square · doi:10.21203/rs.3.rs-10604176/v1

Spinal Manipulation Therapy Remodels CA1-Centered Default Mode Network to Alleviate Comorbid Low Back Pain and Depression-Like Behaviors in Rats

Zhou, X., Hu, Q., Lei, S.-Y., Yao, Z., Lyu, L.-J., & Chen, Z.-B. (2026) · Chinese Journal of Integrative Medicine · doi:10.1007/s11655-026-4169-x

Multimodal Machine Learning Approaches to Detect Attention-Deficit/Hyperactivity Disorder (ADHD) Using Physiological and Behavioural Biomarkers: A Systematic Review

Walle, A. D., & Bargshady, G. (2026) · Healthcare · doi:10.3390/healthcare14193259

Addressing the between-group comparison problem: Detecting differences between correlation matrix populations due to single-variable perturbations for resting state fMRI

Faran, I., Peer, M., Arzy, S., & Benjamini, Y. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122239

Effect sizes for brain-based predictive models of cognition across the lifespan

Christensen, E., Wiersch, L., Wang, M., McPherson, B., Shearer, H., Noble, S., Lam, K., Poline, J., & Dhamala, E. (2026) · Communications Psychology · doi:10.1038/s44271-026-00528-2

124. Large-Scale Machine Learning and Neuroimaging in Psychiatry

Thompson, P. M. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.02.142 · Cited by 1 · see citing works

Computational approaches and machine learning for individual-level treatment predictions

Paulus, M. P., & Thompson, W. K. (2019) · Psychopharmacology · doi:10.1007/s00213-019-05282-4 · Cited by 29 · see citing works

The Promise of Machine Learning for Psychiatry

Rutherford, S. E. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.08.024 · Cited by 9 · see citing works

Predicting the future of neuroimaging predictive models in mental health

Tejavibulya, L., Rolison, M. J., Gao, S., Liang, Q., Peterson, H., Dadashkarimi, J., Farruggia, M. C., Hahn, C. A., Noble, S., Lichenstein, S. D., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01635-2 · Cited by 57 · see citing works

Towards a brain‐based predictome of mental illness

Rashid, B., & Calhoun, V. D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25013 · Cited by 160 · see citing works