The DOI matches its Crossref record; 123 of the 126 references it cites were found in a registry or an index.
The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Open accessAbstract
The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The cited source says what the claim attributes to it.
In the citing paper
“One aspect of heterogeneity in ADHD symptoms and cognitive deficits is its subtypes [8, 9] which includes a predominantly hyperactive (ADHD-H), inattentive (ADHD-I), and combined (ADHD-C) subtype.”
From ADHD subtype-specific cognitive correlates and association with self-esteem: a quantitative difference (2020) · cites this paper as [9]
In the citing paper
“but that multiple routes likely contribute to the expression of EF and hyperactive/inattentive symptoms across disorders (Chan et al., 2008; Feczko et al., 2019; Molitor et al., 2018; Volkow et al., 2011).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Feczko et al., 2019
In the citing paper
“A growing body of evidence suggests multiple pathways lead to shared symptoms among a given disorder (Castellanos et al., 2006; Feczko et al., 2019).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Feczko et al., 2019
In the citing paper
“It's important for any machine learning model, in particular those using cross-validation, to further validate identified subgroups (Feczko et al., 2019).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Feczko et al., 2019
In the citing paper
“This supports the transdiagnostic approach and is consistent with previous work indicating that the underlying mechanisms leading to observed symptoms are likely shared across ASD and ADHD, even though there may be more than one (Chan et al., 2008; Feczko et al., 2019; Leitner, 2014; Molitor et al., 2018).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Feczko et al., 2019
In the citing paper
“Similar to our findings, several previous studies found linear-age-related FCS decreases in the medial prefrontal cortex, precuneus, insula, and calcarine and linear FCS increases in sensorimotor areas based on the general linear model [36-38].”
From Mapping neurophysiological subtypes of major depressive disorder using normative models of the functional connectome (2023) · cites this paper as [38]
In the citing paper
“From the homo- geneity assumption, the comorbidity (17) and heterogeneity (18,19) problems emerge and limit psychiatric research (20–26).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
In the citing paper
“The appropriate subtypes are likely going to differ based on whether one is interested in diagnosis, prognosis, treatment response, mechanism, etc. (18).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
In the citing paper
“Critically, most subtyping methods cannot discover unknown subtypes tied to a question (18).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
In the citing paper
“The heterogeneity problem comprises two tenets (18). First, different underlying mechanisms may drive the same disorder. Second, factors important for mental health outcomes may be unrelated to such mechanisms, depending on the outcome of interest.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
In the citing paper
“Newer hybrid models, such as normative modeling (19,59–61) or the functional random forest (FRF) (18,62), may overcome these limitations and enable new inferences from psychopathology studies.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
In the citing paper
“The FRF can identify trajectory subtypes from longitudinal data, such as p subtypes united or tied to ADHD outcomes.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
In the citing paper
“The FRF comprises several ap- proaches, which have been explicitly detailed elsewhere (18).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
In the citing paper
“Because it is a supervised approach, the bifactor framework requires researchers to specify specific factors, such as internalizing and externalizing. Therefore, the researcher cannot discover new ones (18).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
In the citing paper
“More recently, we proposed the FRF to identify subtypes tied to an outcome (18,62).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Karalunas, S. L., Fair, D., Musser, E. D., Aykes, K., Iyer, S. P., & Nigg, J. T. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.763 · Cited by 340
Self-citationRetractedBreiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Depression and quality of life in older persons: A review
Sivertsen, H., Bjørkløf, G. H., Engedal, K., Selbæk, G., & Helvik, A.-S. (2015) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000437299 · Cited by 14,152
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
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091379 · Cited by 7,067
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231
A review of feature selection techniques in bioinformatics
Saeys, Y., Inza, I., & Larrañaga, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm344 · Cited by 5,389
Robins, L. N. (1981) · Archives of General Psychiatry · doi:10.1001/archpsyc.1981.01780290015001 · Cited by 5,219
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Flagged first, then by citations
3 of the 126 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Fair, 2012, Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD, Proc. Natl. Acad. Sci. U. S. A., 109, 6769, 10.1073/pnas.1115365109 · DOI · Paper page
- Insel, 2014, The nimh research domain criteria (rdoc) project: precision medicine for psychiatry, Am. J. Psychiatry, 171, 395, 10.1176/appi.ajp.2014.14020138 · DOI · Paper page
- Fair, 2012, Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD, Proc. Natl. Acad. Sci. U. S. A., 109, 6769, 10.1073/pnas.1115365109 · DOI · Paper page
- Karalunas, 2014, Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria, JAMA Psychiatry, 71, 1015, 10.1001/jamapsychiatry.2014.763 · DOI · Paper page
- Rosvall, 2008, Maps of random walks on complex networks reveal community structure, Proc. Natl. Acad. Sci. U. S. A., 105, 1118, 10.1073/pnas.0706851105 · DOI · Paper page
- Tang, F. and Ishwaran, H. Random forest missing data algorithms. arXiv 1701.05305. Published online January 20, 2017 · arXiv
Cited by
Sorted by citations
Items 101–200 of 443
Potential diagnostic biomarkers for schizophrenia
Yue, W., Huang, H., & Duan, J. (2022) · Medical Review · doi:10.1515/mr-2022-0009 · Cited by 24
Ben‐Zion, Z., Spiller, T. R., Keynan, J. N., Admon, R., Levy, I., Liberzon, I., Shalev, A. Y., Hendler, T., & Harpaz‐Rotem, I. (2023) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20220271 · Cited by 24
Non-canonical pathways in the pathophysiology and therapeutics of bipolar disorder
Machado‐Vieira, R., Courtes, A. C., Zarate, C. A., Henter, I. D., & Manji, H. K. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1228455 · Cited by 25
The Future of Disentangling the Heterogeneity of Autism With Neuroimaging Studies
Duan, X., Shan, X., Uddin, L. Q., & Chen, H. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.08.008 · Cited by 25
Ouden, L. D., Suo, C., Albertella, L., Greenwood, L., Lee, R. S. C., Fontenelle, L. F., Parkes, L., Tiego, J., Chamberlain, S. R., Richardson, K. E., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-021-01773-1 · Cited by 28
Tong, X., Xie, H., Carlisle, N. B., Fonzo, G. A., Oathes, D. J., Jiang, J., & Zhang, Y. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02134-2 · Cited by 22
FASD-Informed Care and the Future of Intervention
Olson, H. C., Pruner, M., Byington, N., & Jirikowic, T. (2023) · Springer eBooks · doi:10.1007/978-3-031-32386-7_13 · Cited by 22
Using in silico perturbational approach to identify critical areas in schizophrenia
Mana, L., Vila‐Vidal, M., Köckeritz, C., Aquino, K. M., Fornito, A., Kringelbach, M. L., & Deco, G. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad067 · Cited by 22
Chan, I. I., & Wu, A. M. S. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100294 · Cited by 30
Moriarity, D. P., & Alloy, L. B. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.07.002 · Cited by 22
khodadadifar, T., Soltaninejad, Z., Ebneabbasi, A., Eickhoff, C. R., Sorg, C., van Eimeren, T., Vogeley, K. T., Zarei, M., Eickhoff, S. B., & Tahmasian, M. (2021) · Human Brain Mapping · doi:10.1002/hbm.25722 · Cited by 21
Clusters of psychosis: compensation as a contributor to the heterogeneity of schizophrenia
Palaniyappan, L. K. (2023) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.230120 · Cited by 22
Deserno, M. K., Bathelt, J., Groenman, A. P., & Geurts, H. M. (2023) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-022-01986-9 · Cited by 22
Understanding heterogeneity within autism spectrum disorder: a scoping review
Puerto, M. S. C., & Vázquez, M. S. (2024) · Advances in Autism · doi:10.1108/aia-12-2023-0072 · Cited by 25
Intelligent sensing devices and systems for personalized mental health
Xing, Y., Yang, Y., Yang, K., Lu, A., Xing, L., Mackie, K., & Guo, F. (2025) · Med-X · doi:10.1007/s44258-025-00057-3 · Cited by 25
Shafiei, G., Esper, N. B., Hoffmann, M. S., Ai, L., Chen, A. A., Cluce, J., Covitz, S. C., Giavasis, S., Lane, C., Mehta, K. P., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.026 · Cited by 32
Heterogeneity in caregiving-related early adversity: Creating stable dimensions and subtypes
Nikolaidis, A., Heleniak, C. M., Fields, A., Bloom, P. A., VanTieghem, M., Vannucci, A., Camacho, N. L., Choy, T., Gibson, L., Harmon, C., et al. (2022) · Development and Psychopathology · doi:10.1017/s0954579421001668 · Cited by 26
Multiscale neural signatures of major depressive, anxiety, and stress-related disorders
Zhukovsky, P., Wainberg, M., Milic, M. S., Tripathy, S. J., Mulsant, B. H., Felsky, D., & Voineskos, A. N. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204433119 · Cited by 23
Polyneuro risk scores capture widely distributed connectivity patterns of cognition
Byington, N., Grimsrud, G. J., Mooney, M. A., Cordova, M., Doyle, O. J., Hermosillo, R. J. M., Earl, E., Houghton, A., Conan, G. M., Hendrickson, T. J., et al. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101231 · Cited by 22
Self-citationChen, Y., Li, G., Ide, J. S., Luo, X., & Li, C.-S. R. (2021) · Neuroimage: Reports · doi:10.1016/j.ynirp.2021.100025 · Cited by 16
Wan, H., Huang, W., Zhang, W., & Hu, C. (2025) · International Journal of Mental Health Promotion · doi:10.32604/ijmhp.2025.070457 · Cited by 23
Hardi, F. A., Goetschius, L. G., McLoyd, V. C., Lopez‐Duran, N. L., Mitchell, C. M., Hyde, L. W., Beltz, A. M., & Monk, C. S. (2022) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13749 · Cited by 20
Rosenblau, G., Frolichs, K., & Korn, C. W. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105181 · Cited by 20
Meijer, J., Vieira, B. H., Elleaume, C., Baranczuk‐Turska, Z., Langer, N., & Floris, D. L. (2024) · Journal of Psychopathology and Clinical Science · doi:10.1037/abn0000914 · Cited by 19
Neuroimaging-based variability in subtyping biomarkers for psychiatric heterogeneity
Wen, Z., Hammoud, M. Z., Siegel, C., LASKA, E. M., Amara, D. A., Etkin, A., Milad, M. R., & Marmar, C. R. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02807-y · Cited by 23
Stein, F., & Kircher, T. T. J. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119464 · Cited by 23
DeRosa, J., Rosch, K. S., Mostofsky, S. H., & Nikolaidis, A. (2023) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13870 · Cited by 19
Cyclodextrin-Containing Drug Delivery Systems and Their Applications in Neurodegenerative Disorders
Xing, Y., Meng, B., & Chen, Q. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251910834 · Cited by 19
Similarities and dissimilarities between psychiatric cluster disorders
Smail, M. A., Wu, X., Henkel, N., Eby, H., Herman, J. P., McCullumsmith, R. E., & Shukla, R. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01030-3 · Cited by 17
Idei, H., Murata, S., Yamashita, Y., & Ogata, T. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.01.033 · Cited by 16
Artificial Intelligence in Science and Society: The Vision of USERN
Dorigo, T., Brown, G. D., Casonato, C., Cerdà, A., Ciarrochi, J., Lio, M. D., D’Souza, N., Gauger, N. R., Hayes, S. C., Hofmann, S. G., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3529357 · Cited by 21
Latent Profiles of Childhood Adversity, Adolescent Mental Health, and Neural Network Connectivity
Hardi, F. A., Beltz, A. M., McLoyd, V. C., Jeanne Brooks-gunn, B., Huntley, E. D., Mitchell, C. M., Hyde, L. W., & Monk, C. S. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.30711 · Cited by 17
Neuroimmunological effects of early life experiences
Brydges, N. M., & Reddaway, J. (2020) · Brain and Neuroscience Advances · doi:10.1177/2398212820953706 · Cited by 20
Lawn, T., Giacomel, A., Martins, D., Veronese, M., Howard, M. A., Turkheimer, F., & Dipasquale, O. (2024) · Communications Biology · doi:10.1038/s42003-024-06391-3 · Cited by 22
Group-to-Individual Generalizability and Individual-Level Inferences in Cognitive Neuroscience
Mattoni, M., Fisher, A. J., Gates, K. M., Chein, J. M., & Olino, T. M. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106024 · Cited by 22
Misiak, B., Pytel, A., & Stańczykiewicz, B. (2025) · Psychological Medicine · doi:10.1017/s0033291725000261 · Cited by 20
Kang, E., Heo, D.-W., Lee, J., & Suk, H. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3337074 · Cited by 17
Towards personalized precision functional mapping in infancy
Moore, L. A., Hermosillo, R. J. M., Feczko, E., Moser, J., Koirala, S., Allen, M. C., Buß, C., Conan, G., Juliano, A., Marr, M. C., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00165 · Cited by 18
Self-citationBiomarkers for Psychosis: Are We There Yet? Umbrella Review of 1478 Biomarkers
Fuentes‐Claramonte, P., Estradé, A., Solanes, A., Ramella‐Cravaro, V., García‐León, M. Á., de Diego-Adeliño, J., Molins, C., Fung, E., Valentí, M., Anmella, G., et al. (2024) · Schizophrenia Bulletin Open · doi:10.1093/schizbullopen/sgae018 · Cited by 25
Colombo, F., Calesella, F., Bravi, B., Fortaner-Uyà, L., Monopoli, C., Tassi, E., Carminati, M., Zanardi, R., Bollettini, I., Poletti, S., et al. (2024) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2024.05.015 · Cited by 18
Brucar, L. R., Rawls, E., Haynos, A. F., Peterson, C. B., & Zilverstand, A. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03408-1 · Cited by 16
Talmon, A., Dixon, M. L., Goldin, P. R., Heimberg, R. G., & Gross, J. J. (2021) · Clinical Psychological Science · doi:10.1177/21677026211004452 · Cited by 16
Machine Learning for Neurodevelopmental Disorders
Moreau, C. A., Deruelle, C., & Auzias, G. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_31 · Cited by 15
Enrico, P., Delvecchio, G., Turtulici, N., Aronica, R., Pigoni, A., Squarcina, L., Villa, F. M., Perlini, C., Rossetti, M. G., Bellani, M., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-022-01911-1 · Cited by 17
Biotypes of major depressive disorder identified by a multiview clustering framework
Chen, X., Dai, Z., & Lin, Y. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.02.118 · Cited by 15
Wang, Z., Li, D., Chen, Y., Tao, Z., Jiang, L., He, X., & Zhang, W. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.115816 · Cited by 16
Okpete, U. E., & Byeon, H. (2024) · World Journal of Psychiatry · doi:10.5498/wjp.v14.i8.1148 · Cited by 18
Leveraging pleiotropy for the improved treatment of psychiatric disorders
Woodward, D. J., Thorp, J. G., Middeldorp, C. M., Akóṣílè, W., Derks, E. M., & Gerring, Z. F. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02771-7 · Cited by 15
Emerging biophysical techniques for probing synaptic transmission in neurodegenerative disorders
Kale, M. B., Wankhede, N. L., Bishoyi, A. K., Ballal, S., Kalia, R., Arya, R., Kumar, S., Khalid, M., Gulati, M., Umare, M. D., et al. (2024) · Neuroscience · doi:10.1016/j.neuroscience.2024.11.055 · Cited by 17
Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models
Yao, Y., & Stephan, K. E. (2021) · Human Brain Mapping · doi:10.1002/hbm.25431 · Cited by 15
Assessment of Population Well-being With the Mental Health Quotient: Validation Study
Newson, J. J., Pastukh, V., & Thiagarajan, T. C. (2022) · JMIR Mental Health · doi:10.2196/34105 · Cited by 19
Chen, P., Zhang, S., Zhao, K., Kang, X., Rittman, T., & Liu, Y. (2023) · Brain Research · doi:10.1016/j.brainres.2023.148675 · Cited by 19
A data-driven latent variable approach to validating the research domain criteria framework
Quah, S. K. L., Jo, B., Geniesse, C., Uddin, L. Q., Mumford, J. A., Barch, D. M., Fair, D. A., Gotlib, I. H., Poldrack, R. A., & Saggar, M. (2025) · Nature Communications · doi:10.1038/s41467-025-55831-z · Cited by 15
Self-citationThe challenges raised by comorbidity in psychiatric research: The case of autism
Petrolini, V., & Vicente, A. (2022) · Philosophical Psychology · doi:10.1080/09515089.2022.2052829 · Cited by 20
Ebadi, A., Allouch, S., Mheich, A., Tabbal, J., Kabbara, A., Robert, G., Lefebvre, A., Iftimovici, A., Rodríguez‐Herreros, B., Chabane, N., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03441-0 · Cited by 24
Wearables in ADHD: Monitoring and Intervention—Where Are We Now?
Olinic, M.-S., Stretea, R., & Cherecheș, C. (2025) · Diagnostics · doi:10.3390/diagnostics15182359 · Cited by 16
Treatment selection using prototyping in latent-space with application to depression treatment
Kleinerman, A., Rosenfeld, A., Benrimoh, D. A., Fratila, R., Armstrong, C., Mehltretter, J., Shneider, E., Yaniv‐Rosenfeld, A., Karp, J. F., Reynolds, C. F., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258400 · Cited by 14
Developmental Profiles in Autism Spectrum Disorder
Silleresi, S. (2023) · Language acquisition & language disorders · doi:10.1075/lald.68 · Cited by 15
Richter, M. F., Storck, M., Blitz, R., Goltermann, J., Seipp, J., Dannlowski, U., Baune, B. T., Dugas, M., & Opel, N. (2020) · JMIR Mental Health · doi:10.2196/24066 · Cited by 14
High spatial frequency filtered primes hastens happy faces categorization in autistic adults
Lacroix, A., Nalborczyk, L., Dutheil, F., Kovarski, K., Chokron, S., Garrido, M., Gomot, M., & Mermillod, M. (2021) · Brain and Cognition · doi:10.1016/j.bandc.2021.105811 · Cited by 12
Circuit-Based Approaches to Understanding the Anterior Cingulate Cortex (ACC)
Myers, D. C., Simon, J. R., Oh, J., Kabotyanski, K. E., Fujimoto, S., Oathes, D., Rudebeck, P., Amemori, K., Sheth, S., & Fudge, J. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1311-25.2025 · Cited by 12
Extracting interpretable signatures of whole-brain dynamics through systematic comparison
Bryant, A. G., Aquino, K. M., Parkes, L., Fornito, A., & Fulcher, B. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012692 · Cited by 13
Lee, W., Min, I. K., Yang, K. I., Kim, D., Yun, C., & Chu, M. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-01107-7 · Cited by 14
Li, T., Hoogman, M., Mota, N. R., Buitelaar, J. K., The ENIGMA‐ASD working group, Vásquez, A. A., Franke, B., & van Rooij, D. (2021) · Autism Research · doi:10.1002/aur.2627 · Cited by 12
van Rensburg, D. J., Lindeque, J. Z., Harvey, B. H., & Steyn, S. F. (2022) · Mitochondrion · doi:10.1016/j.mito.2022.03.002 · Cited by 13
Linking Personality Traits to Individual Differences in Affective Spaces
Levine, S. M., Alahäivälä, A.-L. I., Wechsler, T. F., Wackerle, A. M., Rupprecht, R., & Schwarzbach, J. V. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00448 · Cited by 14
Yoon, L., Rohrsetzer, F., Battel, L., Anés, M., Manfro, P. H., Rohde, L. A., Viduani, A. C., Zajkowska, Z., Mondelli, V., Kieling, C., et al. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13496 · Cited by 13
Together is better: Let's overcome the heterogeneity problem
Oliva, V., & Prisco, M. D. (2022) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2022.10.007 · Cited by 12
Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence
Allen, B. (2023) · Biomedicines · doi:10.3390/biomedicines11030771 · Cited by 13
Thomson, M., Cavelti, M., Lerch, S., Koenig, J., Reichl, C., Mürner-Lavanchy, I. M., Wyssen, A., & Kaess, M. (2024) · Borderline Personality Disorder and Emotion Dysregulation · doi:10.1186/s40479-024-00252-5 · Cited by 15
Schenk, P. M., Hastings, J., Santilli, M., Potts, J., Kennett, J., Friedrich, C., & Michie, S. (2024) · Wellcome Open Research · doi:10.12688/wellcomeopenres.20701.2 · Cited by 13
Narrative simulation of social experiences in naturalistic context – A neurocinematic approach
Tikka, P., Kaipainen, M., & Salmi, J. (2023) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2023.108654 · Cited by 17
Putting behaviour back into brain-behaviour correlation analysesPreprint
Tiego, J., & Fornito, A. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/g84j2 · Cited by 12
Understanding mental health through computers: An introduction to computational psychiatry
Martínez, J. C. C., & Santamaría‐García, H. A. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1092471 · Cited by 11
Sodium-Glucose Cotransporter-2 Inhibitors in Depression
Liebers, D. T., Ebina, W., & Iosifescu, D. V. (2023) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000374 · Cited by 13
Countering essentialism in psychiatric narratives
Broeker, M. D., & Arnaud, S. (2024) · Philosophical Psychology · doi:10.1080/09515089.2024.2407502 · Cited by 11
Mulc, D., Vukojević, J., Kalafatic, E., Cifrek, M., Vidović, D., & Jović, A. (2025) · Sensors · doi:10.3390/s25020409 · Cited by 14
Multiscale Heterogeneity of White Matter Morphometry in Psychiatric Disorders
Segal, A., Smith, R. E., Chopra, S., Oldham, S., Parkes, L., Aquino, K. M., Kia, S. M., Wolfers, T., Franke, B., Hoogman, M., et al. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.03.014 · Cited by 11
Wearable fNIRS platform for dense sampling and precision functional neuroimaging
Jounghani, A. R., Kumar, A., Carbonell, L. M., Aguilar, E. P. L., Picardi, T. B., Crawford, S., Bowden, A. K., & Hosseini, S. M. H. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01690-3 · Cited by 11
Mattoni, M., Smith, D. V., & Olino, T. M. (2023) · Network Neuroscience · doi:10.1162/netn_a_00306 · Cited by 11
Clustering and disease subtyping in Neuroscience, toward better methodological adaptations
Poulakis, K., & Westman, E. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1243092 · Cited by 13
Ashar, Y. K., Clark, J., Gunning, F. M., Goldin, P. R., Gross, J. J., & Wager, T. D. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01366-y · Cited by 13
Sefik, E., Duan, K., Li, Y., Sholar, B., Evans, L., Pincus, J. E., Ammar, Z., Murphy, M. M., Klaiman, C., Saulnier, C. A., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02584-8 · Cited by 11
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
Generative artificial intelligence model for simulating structural brain changes in schizophrenia
Yamaguchi, H., Sugihara, G., Shimizu, M., & Yamashita, Y. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1437075 · Cited by 10
Seghete, K. L. M., Graham, A. M., Shank, T. M., Alsup, S., Fisher, P. A., Wilson, A. C., & Ewing, S. W. F. (2020) · Current Addiction Reports · doi:10.1007/s40429-020-00296-x · Cited by 10
Self-citationFunctional activity level reported by an informant is an early predictor of Alzheimer’s disease
Vik, A., Kociński, M., Rye, I., Lundervold, A. J., & Lundervold, A. S. (2023) · BMC Geriatrics · doi:10.1186/s12877-023-03849-7 · Cited by 13
A Biased Perspective on Brain Imaging of ADHD
Castellanos, F. X. (2021) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2021.21060609 · Cited by 9
Finding Similarities in Differences Between Autistic Adults: Two Replicated Subgroups
Radhoe, T. A., van Rentergem, J. A. A., Torenvliet, C., Groenman, A. P., van der Putten, W., & Geurts, H. M. (2023) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-023-06042-2 · Cited by 11
Towards robust and replicable sex differences in the intrinsic brain function of autismPreprint
Floris, D. L., Filho, J. O. A., Lai, M., Giavasis, S., Oldehinkel, M., Mennes, M., Charman, T., Tillmann, J., Dumas, G., Ecker, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.09.142471 · Cited by 10
Liang, Q., Zhou, Z., Chen, S., Lin, S., Lin, X., Li, Y., Zhang, Y., Peng, B., Hou, G., & Qiu, Y. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03238-1 · Cited by 11
Kashiwagi, Y., Tokuda, T., Takahara, Y., Masaki, Y., Sakai, Y., Yoshimoto, J., Yamashita, A., Yoshioka, T., Ogawa, K., Okada, G., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03224-5 · Cited by 11
Stout, D. M., Harlé, K. M., Norman, S. B., Simmons, A. N., & Spadoni, A. D. (2021) · Psychological Medicine · doi:10.1017/s0033291721001513 · Cited by 9
West, A., Hamlin, N., Frangou, S., Wilson, T. W., & Doucet, G. E. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab215 · Cited by 9
Martinez, S., Brandt, L., Comer, S. D., LEVIN, F. R., & Jones, J. D. (2022) · Addiction Neuroscience · doi:10.1016/j.addicn.2022.100034 · Cited by 11
Idei, H., & Yamashita, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.005 · Cited by 11
On the nature, predictors, and outcomes of undergraduate students' psychological distress profiles
Gilbert, W., Demanins, S. A., Bureau, J. S., Guay, F., & Morin, A. J. S. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102378 · Cited by 13
Latent subtypes of manic and/or irritable episode symptoms in two population-based cohorts
Arathimos, R., Fabbri, C., Vassos, E., Davis, K. A. S., Pain, O., Gillett, A. C., Coleman, J. R. I., Hanscombe, K. B., Hagenaars, S. P., Jermy, B. S., et al. (2022) · The British Journal of Psychiatry · doi:10.1192/bjp.2021.184 · Cited by 9
Olsen, A., Babikian, T., Bigler, E. D., Caeyenberghs, K., Conde, V., Dams-O’Connor, K., Dobryakova, E., Genova, H. M., Grafman, J. H., Haberg, A. K., et al. (2019) · doi:10.31234/osf.io/jnsb2 · Cited by 9
Metadata
- Authors
- Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A.
- Affiliations
- Oregon Health & Science University (US)
- Corresponding author
- Feczko, Eric
- Fair, Damien A.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 23
- Issue
- 7
- Pages
- 584-601
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC6821457
- OpenAlex
- W2947752575
- Cited by
- 443
- References
- 126
- Citations by year
- 2026: 51 · 2025: 69 · 2024: 83 · 2023: 88 · 2022: 59 · 2021: 43 · 2020: 40 · 2019: 9
- FWCI
- 5.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- DeStefano Family Foundation
- National Library of Medicine (T15 LM007088)
- National Institute of Mental Health (R01 MH096773, R00 MH091238, R01 MH096773-03S1, R01 MH 096773–05, R01 MH086654, R01 MH59107)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- psychiatric classification, dimensional models, curse of dimensionality, supervised learning, unsupervised learning, precision psychiatry, mental illness, causal mechanisms
- MeSH terms
- Mental Disorders, Brain, Cognition, Humans, Individuality, Models, Statistical
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
