Cited by 209Functional Brain Connectivity StudiesStatistical Methods in EpidemiologyArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

When Optimism Hurts: Inflated Predictions in Psychiatric Neuroimaging

Whelan, Robert, Garavan, Hugh·2014·Biological Psychiatry

DOI 10.1016/j.biopsych.2013.05.014

Open this paper in CoChat — free account

Abstract

The ability to predict outcomes from neuroimaging data has the potential to answer important clinical questions such as which depressed patients will respond to treatment, which abstinent drug users will relapse, or which patients will convert to dementia. However, many prediction analyses require methods and techniques, not typically required in neuroimaging, to accurately assess a model's predictive ability. Regression models will tend to fit to the idiosyncratic characteristics of a particular sample and consequently will perform worse on unseen data. Failure to account for this inherent optimism is especially pernicious when the number of possible predictors is high relative to the number of participants, a common scenario in psychiatric neuroimaging. We show via simulated data that models can appear predictive even when data and outcomes are random, and we note examples of optimistic prediction in the literature. We provide some recommendations for assessment of model performance.

Integrity signals

Registry

Crossref · 10.1016/j.biopsych.2013.05.014

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648
Ridge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 7,001
Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression
Vittinghoff, E., & McCulloch, C. E. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwk052 · Cited by 4,359
Calculation of signal detection theory measures
Stanislaw, H., & Todorov, N. (1999) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03207704 · Cited by 2,673

Show all 40 references

Research works printed but not linked: 1

Efron, 1993, Vol. 57

Sorted by citations

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
Leakage and the reproducibility crisis in machine-learning-based science
Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387
Computational psychiatry as a bridge from neuroscience to clinical applications
Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4238 · Cited by 1,002
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
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966
Building a Science of Individual Differences from fMRI
Dubois, J., & Adolphs, R. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.014 · Cited by 644
Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience
Gabrieli, J. D. E., Ghosh, S., & Whitfield, S. (2015) · Neuron · doi:10.1016/j.neuron.2014.10.047 · Cited by 547
Task-induced brain state manipulation improves prediction of individual traits
Greene, A. S., Gao, S., Scheinost, D., & Constable, R. T. (2018) · Nature Communications · doi:10.1038/s41467-018-04920-3 · Cited by 498
Neuropsychosocial profiles of current and future adolescent alcohol misusers
Whelan, R., Watts, R., Orr, C., Althoff, R. R., Artiges, É., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Büchel, C., Carvalho, F. M., et al. (2014) · Nature · doi:10.1038/nature13402 · Cited by 454
Self-citation
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
Can brain state be manipulated to emphasize individual differences in functional connectivity?
Finn, E. S., Scheinost, D., Finn, D. M., Shen, X., Papademetris, X., & Constable, R. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.064 · Cited by 344
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
A small number of abnormal brain connections predicts adult autism spectrum disorder
Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., et al. (2016) · Nature Communications · doi:10.1038/ncomms11254 · Cited by 305
General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks
Elliott, M. L., Knodt, A. R., Cooke, M. E., Kim, M. J., Melzer, T. R., Keenan, R. J., Ireland, D. R., Ramrakha, S., Poulton, R., Caspi, A., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.068 · Cited by 308
Deep neural networks in psychiatry
Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 297
Advances in fMRI Real-Time Neurofeedback
Watanabe, T., Sasaki, Y., Shibata, K., & Kawato, M. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.09.010 · Cited by 242
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
Data leakage inflates prediction performance in connectome-based machine learning models
Rosenblatt, M., Tejavibulya, L., Jiang, R., Noble, S., & Scheinost, D. (2024) · Nature Communications · doi:10.1038/s41467-024-46150-w · Cited by 236
Pavlovian-to-instrumental transfer effects in the nucleus accumbens relate to relapse in alcohol dependence
Garbusow, M., Schad, D. J., Sebold, M., Friedel, E., Bernhardt, N. R., Koch, S., Steinacher, B., Kathmann, N., Geurts, D. E. M., Sommer, C., et al. (2015) · Addiction Biology · doi:10.1111/adb.12243 · Cited by 178
Resting‐state functional connectivity and nicotine addiction: prospects for biomarker development
Fedota, J. R., & Stein, E. A. (2015) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12882 · Cited by 167
A neuromarker for drug and food craving distinguishes drug users from non-users
Koban, L., Wager, T. D., & Hedy Kober, A. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01228-w · Cited by 158
Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine
Gupta, N. S., & Kumar, P. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107051 · Cited by 170
Biomarkers in Substance Use Disorders
Volkow, N. D., Koob, G., & Baler, R. (2015) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.5b00067 · Cited by 141
Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study
Pinaya, W. H. L., Mechelli, A., & Sato, J. R. (2018) · Human Brain Mapping · doi:10.1002/hbm.24423 · Cited by 136
Reward Region Responsivity Predicts Future Weight Gain and Moderating Effects of the TaqIA Allele
Stice, E. M., Burger, K. S., & Yokum, S. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3607-14.2015 · Cited by 136
Combining Multiple Connectomes Improves Predictive Modeling of Phenotypic Measures
Gao, S., Greene, A., Constable, R. T., & Scheinost, D. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116038 · Cited by 149
Treating refractory mental illness with closed-loop brain stimulation: Progress towards a patient-specific transdiagnostic approach
Widge, A. S., Ellard, K. K., Paulk, A. C., Basu, I., Yousefi, A., Zorowitz, S., Gilmour, A., Afzal, A., Deckersbach, T., Cash, S. S., et al. (2017) · Experimental Neurology · doi:10.1016/j.expneurol.2016.07.021 · Cited by 118
Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence
Ahn, W., & Vassileva, J. (2016) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2016.02.008 · Cited by 119
Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers
Yamada, T., Hashimoto, R., Yahata, N., Ichikawa, N., Yoshihara, Y., Okamoto, Y., Kato, N., Takahashi, H., & Kawato, M. (2017) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyx059 · Cited by 137
Working Memory-Related Neural Activity Predicts Future Smoking Relapse
Loughead, J., Wileyto, E. P., Ruparel, K., Falcone, M., Hopson, R. D., Gur, R. C., & Lerman, C. E. (2014) · Neuropsychopharmacology · doi:10.1038/npp.2014.318 · Cited by 149
Neural preservation underlies speech improvement from auditory deprivation in young cochlear implant recipients
Feng, G., Ingvalson, E. M., Grieco‐Calub, T. M., Roberts, M. Y., Ryan, M. E., Birmingham, P. K., Burrowes, D. M., Young, N. M., & Wong, P. C. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717603115 · Cited by 118
Computational neuroscience approach to biomarkers and treatments for mental disorders
Yahata, N., Kasai, K., & Kawato, M. (2016) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12502 · Cited by 135
What big data can do for treatment in psychiatry
Gillan, C. M., & Whelan, R. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.003 · Cited by 114
Self-citation
Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis
Boyle, R. T., Jollans, L., Rueda‐Delgado, L. M., Rizzo, R., Yener, G., McMorrow, J. P., Knight, S. P., Carey, D., Robertson, I. H., Emek‐Savaş, D. D., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00260-3 · Cited by 115
Self-citation
Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis
Quaak, M., van de Mortel, L. A., Thomas, R. M., & van Wingen, G. A. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102584 · Cited by 114
Your evidence? Machine learning algorithms for medical diagnosis and prediction
Heinrichs, B., & Eickhoff, S. B. (2019) · Human Brain Mapping · doi:10.1002/hbm.24886 · Cited by 104
Feeling How Old I Am: Subjective Age Is Associated With Estimated Brain Age
Kwak, S., Kim, H., Chey, J., & Youm, Y. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00168 · Cited by 103
Utility of Machine-Learning Approaches to Identify Behavioral Markers for Substance Use Disorders: Impulsivity Dimensions as Predictors of Current Cocaine Dependence
Ahn, W., Ramesh, D., Moeller, F. G., & Vassileva, J. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00034 · Cited by 95
A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity
Yamashita, M., Yoshihara, Y., Hashimoto, R., Yahata, N., Ichikawa, N., Sakai, Y., Yamada, T., Matsukawa, N., Okada, G., Tanaka, S., et al. (2018) · eLife · doi:10.7554/elife.38844 · Cited by 92
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis
Khosla, A., Khandnor, P., & Chand, T. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.12.005 · Cited by 89
The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review
Kusuma, K., Larsen, M. E., Quiroz, J. C., Gillies, M. B., Burnett, A., Qian, J., & Torok, M. H. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.09.050 · Cited by 94
Generalizable brain network markers of major depressive disorder across multiple imaging sites
Yamashita, A., Sakai, Y., Yamada, T., Yahata, N., Kunimatsu, A., Okada, N., Itahashi, T., Hashimoto, R., Mizuta, H., Ichikawa, N., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000966 · Cited by 83
EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD
Kiiski, H. S. M., Bennett, M., Rueda‐Delgado, L. M., Farina, F. R., Knight, R., Boyle, R. T., Roddy, D. W., Grogan, K., Bramham, J., Kelly, C. A. M., et al. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14645 · Cited by 81
Self-citation
Neuroscience and the Criminal Justice System
Greely, H. T., & Farahany, N. A. (2018) · Annual Review of Criminology · doi:10.1146/annurev-criminol-011518-024433 · Cited by 77
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
Translational machine learning for psychiatric neuroimaging
Walter, M., Alizadeh, S., Jamalabadi, H., Lueken, U., Dannlowski, U., Walter, H., Olbrich, S., Čolić, L., Kambeitz, J. P., Koutsouleris, N., et al. (2018) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2018.09.014 · Cited by 84
Transdiagnostic Connectome-Based Prediction of Craving
Garrison, K. A., Sinha, R., Potenza, M. N., Gao, S., Liang, Q., Lacadie, C., & Scheinost, D. (2023) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21121207 · Cited by 71
A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity
Takagi, Y., Sakai, Y., Lisi, G., Yahata, N., Abe, Y., Nishida, S., Nakamae, T., Morimoto, J., Kawato, M., Narumoto, J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-07792-7 · Cited by 67
Few-Shot Domain-Adaptive Anomaly Detection for Cross-Site Brain Images
Su, J., Shen, H., Peng, L., & Hu, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3125686 · Cited by 68
Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse
Gowin, J. L., Ball, T. M., Wittmann, M., Tapert, S. F., & Paulus, M. P. (2015) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2015.04.018 · Cited by 64
SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets
Jiang, R., Abbott, C., Jiang, T., Du, Y., Espinoza, R. T., Narr, K. L., Wade, B., Yu, Q., Song, M., Lin, D., et al. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.165 · Cited by 64
Predicting the knowledge–recklessness distinction in the human brain
Vilares, I., Wesley, M. J., Ahn, W., Bonnie, R. J., Hoffman, M. B., Jones, O. D., Morse, S. J., Yaffe, G., Lohrenz, T. M., & Montague, P. R. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619385114 · Cited by 62
Prediction of alcohol drinking in adolescents: Personality-traits, behavior, brain responses, and genetic variations in the context of reward sensitivity
Heinrich, A., Müller, K. U., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Bromberg, U., Büchel, C., Conrod, P., Fauth‐Bühler, M., Orfanos, D. P., et al. (2016) · Biological Psychology · doi:10.1016/j.biopsycho.2016.05.002 · Cited by 66
Self-citation
Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine- learning study
Mikolas, P., Melicher, T., Skoch, A., Matejka, M., Slovakova, A., Bakstein, E., Hajek, T., & Spaniel, F. (2016) · Psychological Medicine · doi:10.1017/s0033291716000878 · Cited by 58
Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma
Zhang, B., Qi, S., Pan, X., Li, C., Yao, Y., Qian, W., & Guan, Y. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.598721 · Cited by 58
Neuromarkers for Mental Disorders: Harnessing Population Neuroscience
Jollans, L., & Whelan, R. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00242 · Cited by 56
Self-citation
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 58
Association of Neuroimaging Measures of Emotion Processing and Regulation Neural Circuitries With Symptoms of Bipolar Disorder in Offspring at Risk for Bipolar Disorder
Acuff, H. E., Versace, A., Bertocci, M. A., Ladouceur, C. D., Hanford, L. C., Manelis, A., Monk, K., Bonar, L. K., Mccaffrey, A., Goldstein, B. I., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2318 · Cited by 55
Baseline and follow-up activity and functional connectivity in reward neural circuitries in offspring at risk for bipolar disorder
Acuff, H. E., Versace, A., Bertocci, M. A., Ladouceur, C. D., Hanford, L. C., Manelis, A., Monk, K., Bonar, L. K., Mccaffrey, A., Goldstein, B. I., et al. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0339-2 · Cited by 49
Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample
Boeke, E. A., Holmes, A. J., & Phelps, E. A. (2020) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.05.018 · Cited by 50
Psychotherapy and Neuroimaging
Fournier, J. C. J., & Price, R. B. (2014) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.12.3.290 · Cited by 51
Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review
Čukić, M., López, V., & Pavón, J. (2020) · Journal of Medical Internet Research · doi:10.2196/19548 · Cited by 45
Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls
Kiiski, H. S. M., Jollans, L., Donnchadha, S. Ó., Nolan, H., Lonergan, R. M., Kelly, S. B., O’Brien, M. C., Kinsella, K., Bramham, J., Burke, T., et al. (2018) · Brain Topography · doi:10.1007/s10548-018-0620-4 · Cited by 45
Self-citation
Challenges and promises for translating computational tools into clinical practice
Ahn, W., & Busemeyer, J. R. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.02.001 · Cited by 43
Reduced subgenual cingulate–dorsolateral prefrontal connectivity as an electrophysiological marker for depression
Benschop, L., Vanhollebeke, G., Li, J., Leahy, R. M., Vanderhasselt, M., & Baeken, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-20274-9 · Cited by 44
Bridging the gap between the neurocognitive lab and the addiction clinic
Franken, I. H. A., & van de Wetering, B. J. M. (2014) · Addictive Behaviors · doi:10.1016/j.addbeh.2014.11.034 · Cited by 47
The initiation of cannabis use in adolescence is predicted by sex‐specific psychosocial and neurobiological features
Spechler, P. A., Allgaier, N. A., Chaarani, B., Whelan, R., Watts, R., Orr, C., Albaugh, M. D., D’Alberto, N., Higgins, S. T., Hudson, K. E., et al. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.13989 · Cited by 40
Self-citation
Identifying the signature of prospective motor control in children with autism
Cavallo, A., Romeo, L., Ansuini, C., Battaglia, F. M., Nobili, L., Pontil, M., Panzeri, S., & Becchio, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-82374-2 · Cited by 42
Predicting clinical outcome from reward circuitry function and white matter structure in behaviorally and emotionally dysregulated youth
Bertocci, M. A., Bebko, G. M., Versace, A., Fournier, J. C. J., Iyengar, S., Olino, T. M., Bonar, L. K., Almeida, J., Perlman, S. B., Schirda, C., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2016.5 · Cited by 38
Psychiatric Neural Networks and Precision Therapeutics by Machine Learning
Komatsu, H., Watanabe, E., & Fukuchi, M. (2021) · Biomedicines · doi:10.3390/biomedicines9040403 · Cited by 39
Reporting details of neuroimaging studies on individual traits prediction: A literature survey
Yeung, A. W. K., More, S., Wu, J., & Eickhoff, S. B. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119275 · Cited by 36
Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging
Jiang, R., Woo, C., Qi, S., Wu, J., & Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2022.3155951 · Cited by 36
The Clinical Added Value of Imaging: A Perspective From Outcome Prediction
Jollans, L., & Whelan, R. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.04.005 · Cited by 39
Self-citation
Predicting full-scale and verbal intelligence scores from functional Connectomic data in individuals with autism Spectrum disorder
Dryburgh, E., McKenna, S. J., & Rekik, I. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00111-w · Cited by 34
Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity
Antonucci, L. A., Penzel, N., Pergola, G., Kambeitz‐Ilankovic, L., Dwyer, D., Kambeitz, J. P., Haas, S. S., Passiatore, R., Fazio, L., Caforio, G., et al. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0532-3 · Cited by 32
Closed-Loop Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals
Fukuma, R., Yanagisawa, T., Yorifuji, S., Kato, R., Yokoi, H., Hirata, M., Saitoh, Y., Kishima, H., Kamitani, Y., & Yoshimine, T. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131547 · Cited by 32
The potential of neuroimaging for identifying predictors of adolescent alcohol use initiation and misuse
O’Halloran, L., Nymberg, C., Jollans, L., Garavan, H., & Whelan, R. (2016) · Addiction · doi:10.1111/add.13629 · Cited by 32
Self-citation
Predicting adolescent depression and anxiety from multi-wave longitudinal data using machine learning
Hawes, M. T., Schwartz, H. A., Son, Y., & Klein, D. N. (2022) · Psychological Medicine · doi:10.1017/s0033291722003452 · Cited by 36
Combined effect of genetic variants in the GluN2B coding gene(GRIN2B)on prefrontal function during working memory performance
Pergola, G., Carlo, P. D., Andriola, I., Gelao, B., Torretta, S., Attrotto, M. T., Fazio, L., Raio, A., Albergo, D., Masellis, R., et al. (2015) · Psychological Medicine · doi:10.1017/s0033291715002639 · Cited by 32
Uniting adolescent neuroimaging and treatment research: Recommendations in pursuit of improved integration
Ewing, S. W. F., Tapert, S. F., & Molina, B. S. G. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.12.011 · Cited by 31
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 38
Closed-Loop Stimulation in Emotional Circuits for Neuro-Psychiatric Disorders
Widge, A. S., & Moritz, C. T. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802452-2.00017-2 · Cited by 29
Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up
Habets, P. C., Thomas, R. M., Milaneschi, Y., Jansen, R., Pool, R., Peyrot, W. J., Penninx, B. W. J. H., Meijer, O. C., van Wingen, G. A., & Vinkers, C. H. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.05.024 · Cited by 31
Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer
Scheinost, D., Pollatou, A., Dufford, A. J., Jiang, R., Farruggia, M. C., Rosenblatt, M., Peterson, H., Rodriguez, R. X., Dadashkarimi, J., Liang, Q., et al. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.10.014 · Cited by 35
Individual Cortical Entropy Profile: Test–Retest Reliability, Predictive Power for Cognitive Ability, and Neuroanatomical Foundation
Liu, M., Liu, X., Hildebrandt, A., & Zhou, C. (2020) · Cerebral Cortex Communications · doi:10.1093/texcom/tgaa015 · Cited by 25
Recent advances of deep learning in psychiatric disorders
Chen, L., Xia, C., & Sun, H. (2020) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbaa029 · Cited by 28
The Application of Connectome-Based Predictive Modeling to the Maternal Brain: Implications for Mother-Infant Bonding.
Rutherford, H., Potenza, M., Mayes, L., & Scheinost, D. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz185 · Cited by 24
Connectome‐based predictive modelling of cognitive reserve using task‐based functional connectivity
Boyle, R. T., Connaughton, M., McGlinchey, E., Knight, S. P., Looze, C. D., Carey, D., Stern, Y., Robertson, I. H., Kenny, R. A., & Whelan, R. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15896 · Cited by 27
Self-citation
Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction
Bailey, N. W., Fulcher, B., Caldwell, B., Hill, A. T., Fitzgibbon, B. M., van Dijk, H., & Fitzgerald, P. B. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.12.007 · Cited by 25
Combined genetic influence of the nicotinic receptor gene cluster CHRNA5/A3/B4 on nicotine dependence
Lee, S., Ahn, W., Seweryn, M. T., & Sadée, W. (2018) · BMC Genomics · doi:10.1186/s12864-018-5219-3 · Cited by 27
Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain–computer interface
Widge, A. S., & Moritz, C. T. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/024001 · Cited by 29
The Ability of Functional Magnetic Resonance Imaging to Predict Heavy Drinking and Alcohol Problems 5 Years Later
Schuckit, M. A., Smith, T. L., Paulus, M. P., Tapert, S. F., Simmons, A. N., Tolentino, N. J., & Shafir, A. (2016) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.12935 · Cited by 26
Predicting Sex From EEG: Validity and Generalizability of Deep-Learning-Based Interpretable Classifier
Bučková, B. R., Brunovský, M., Bareš, M., & Hlinka, J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.589303 · Cited by 26
Examination of the association between exposure to childhood maltreatment and brain structure in young adults: a machine learning analysis
Price, M., Albaugh, M. D., Hahn, S., Juliano, A., Fani, N., Brier, Z. M. F., Legrand, A. C., van Stolk‐Cooke, K., Chaarani, B., Potter, A., et al. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-00987-7 · Cited by 23
Self-citation
Multiscale functional connectome abnormality predicts cognitive outcomes in subcortical ischemic vascular disease
Liu, M., Wang, Y., Zhang, H., Yang, Q., Shi, F., Zhou, Y. F., & Shen, D. G. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhab507 · Cited by 25
Classifying Conduct Disorder Using a Biopsychosocial Model and Machine Learning Method
Chan, L., Simmons, C., Tillem, S., Conley, M. I., Brazil, I. A., & Baskin-sommers, A. R. (2022) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2022.02.004 · Cited by 30
Quality over quantity: powering neuroimaging samples in psychiatry
Makowski, C., Nichols, T. E., & Dale, A. M. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01893-4 · Cited by 22
Personalizing neuromodulation
Medaglia, J. D., Erickson, B. A., Zimmerman, J. P., & Kelkar, A. S. (2019) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2019.01.002 · Cited by 23
Brain event-related potentials predict individual differences in inhibitory control
Rueda‐Delgado, L. M., O’Halloran, L., Enz, N., Ruddy, K. L., Kiiski, H. S. M., Bennett, M., Farina, F. R., Jollans, L., Vahey, N. A., & Whelan, R. (2019) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2019.03.013 · Cited by 24
Self-citation
Identification of a brain fingerprint for overweight and obesity
Farruggia, M. C., van Kooten, M. J., Perszyk, E. E., Burke, M. V., Scheinost, D., Constable, R. T., & Small, D. M. (2020) · Physiology & Behavior · doi:10.1016/j.physbeh.2020.112940 · Cited by 25

Authors: Whelan, R., & Garavan, H.
Affiliations: University of Vermont (US)
Corresponding author: Garavan, Hugh
ORCID: Whelan, Robert
Garavan, Hugh
Venue: Biological Psychiatry
Publisher: Elsevier BV
Published: 2014-05
Volume: 75
Issue: 9
Pages: 746-748
ISSN: 0006-3223 (ISSN-L)
DOI: 10.1016/j.biopsych.2013.05.014
OpenAlex: W1979450493
Cited by: 209
References: 40
Citations by year: 2026: 13 · 2025: 12 · 2024: 11 · 2023: 17 · 2022: 24 · 2021: 22 · 2020: 17 · 2019: 22 · 2018: 17 · 2017: 15 · 2016: 20 · 2015: 12 · 2014: 6
FWCI: 7.09
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Biological Psychiatry (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: optimism bias, overfitting, model evaluation, psychiatric neuroimaging, outcome prediction, high-dimensional predictors, regression models, depression treatment response, recurrence prediction, conversion to dementia
MeSH terms: Brain, Computer Simulation, Humans, Regression Analysis, ROC Curve, Signal Processing, Computer-Assisted, Neuroimaging

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

When Optimism Hurts: Inflated Predictions in Psychiatric Neuroimaging

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks