Cited by 133Mental Health Research TopicsMental Health and PsychiatryAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.

Computational Nosology and Precision Psychiatry

Open access

Friston, Karl J., Redish, A. David, Gordon, Joshua A.·2017·Computational Psychiatry

DOI 10.1162/cpsy_a_00001PMC PMC5774181

Open this paper in CoChat — free account

Abstract

This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present—and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations.

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

Crossref · 10.1162/cpsy_a_00001

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The Positive and Negative Syndrome Scale (PANSS) for schizophrenia.
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987) · Schizophrenia Bulletin · doi:10.1093/schbul/13.2.261 · Cited by 21,378
Dynamic causal modelling
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
Self-citation
Network analysis: An integrative approach to the structure of psychopathology
Borsboom, D., & Cramer, A. O. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185608 · Cited by 4,359
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423
Self-citation
Critical slowing down as early warning for the onset and termination of depression
van de Leemput, I. A., Wichers, M., Cramer, A. O. J., Borsboom, D., Tuerlinckx, F., Kuppens, P., van Nes, E. H., Viechtbauer, W., Giltay, E. J., Aggen, S. H., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312114110 · Cited by 816
Computational psychiatry
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.018 · Cited by 809
Self-citation
Bayesian model reduction and empirical Bayes for group (DCM) studies
Friston, K. J., Vladimir Litvak, A., Oswal, A., Razi, A., Stephan, K. E., van Wijk, B. C. M., Ziegler, G., & Zeidman, P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.015 · Cited by 572
Self-citation
Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models)
Kass, R. E., & Steffey, D. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478825 · Cited by 474 · cited 1×
“To illustrate this estimation, the upper right panel of Figure 4 reports the estimates (and 90% conﬁdence intervals) based on an empirical Bayesian analysis (Kass & Steffey, 1989) of eight simulated patients (using spm_dcm_peb.m).”
Method
Effective connectivity: Influence, causality and biophysical modeling
Valdes-Sosa, P. A., Roebroeck, A. F., Jean Daunizeau, A., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.058 · Cited by 399
Self-citation
Technological innovations in mental healthcare: harnessing the digital revolution
Hollis, C., Morriss, R., Martin, J., Amani, S., Cotton, R., Denis, M., & Lewis, S. (2015) · British Journal of Psychiatry · doi:10.1192/bjp.bp.113.142612 · Cited by 375

Flagged first, then by citations

Show all 52 references

1 of the 52 references couldn’t be matched to a known paper.

Sorted by citations

The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research
Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D. (2020) · Psychological Medicine · doi:10.1017/s0033291719003404 · Cited by 861
Automated assessment of psychiatric disorders using speech: A systematic review
Low, D. M., Bentley, K. H., & Ghosh, S. (2020) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.354 · Cited by 545
The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.03.009 · Cited by 443
Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review
Lee, Y. L., Ragguett, R., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A. P., Brietzke, E. M., Lin, K., Pan, Z., Subramaniapillai, M., et al. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.08.073 · Cited by 418
The future of rodent models in depression research
Gururajan, A., Reif, A., Cryan, J. F., & Slattery, D. A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0221-6 · Cited by 269
Deeply Felt Affect: The Emergence of Valence in Deep Active Inference
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021) · Neural Computation · doi:10.1162/neco_a_01341 · Cited by 246
Self-citation
Computational Models of Interoception and Body Regulation
Petzschner, F. H., Garfinkel, S. N., Paulus, M. P., Koch, C., & Khalsa, S. S. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.012 · Cited by 227
Modeling psychopathology: From data models to formal theories.
Haslbeck, J. M. B., Ryan, O., Robinaugh, D. J., Waldorp, L., & Borsboom, D. (2021) · Psychological Methods · doi:10.1037/met0000303 · Cited by 253
Invisible Hands and Fine Calipers: A Call to Use Formal Theory as a Toolkit for Theory Construction
Robinaugh, D. J., Haslbeck, J. M. B., Ryan, O., Fried, E. I., & Waldorp, L. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620974697 · Cited by 237
The Anatomy of Inference: Generative Models and Brain Structure
Parr, T., & Friston, K. J. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00090 · Cited by 215
Self-citation
TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.680811 · Cited by 169
Computational psychiatry: from synapses to sentience
Friston, K. J. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01743-z · Cited by 168
Self-citation
Computational Neuropsychology and Bayesian Inference
Parr, T., Rees, G. E., & Friston, K. J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00061 · Cited by 158
Self-citation
Neuropsychological tests of the future: How do we get there from here?
Bilder, R. M., & Reise, S. P. (2018) · The Clinical Neuropsychologist · doi:10.1080/13854046.2018.1521993 · Cited by 160
Predicting women with depressive symptoms postpartum with machine learning methods
Andersson, S., Bathula, D. R., Iliadis, S. I., Walter, M., & Skalkidou, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-86368-y · Cited by 147
Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference
Sandved-Smith, L., Hesp, C., Mattout, J., Friston, K. J., Antoine Lutz, A., & Ramstead, M. J. D. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab018 · Cited by 136
Self-citation
A Latent Variable Approach to Differentiating Neural Mechanisms of Irritability and Anxiety in Youth
Kircanski, K., White, L. K., Tseng, W., Wiggins, J. L., Frank, H. R., Sequeira, S. L., Zhang, S., Abend, R., Towbin, K. E., Stringaris, A. K., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.0468 · Cited by 111
A Brief Review of Artificial Intelligence Applications and Algorithms for Psychiatric Disorders
Liu, G.-D., Li, Y., Zhang, W., & Zhang, L. (2019) · Engineering · doi:10.1016/j.eng.2019.06.008 · Cited by 104
Methods and challenges for assessing heterogeneity
Feczko, E., & Fair, D. A. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.02.015 · Cited by 96
From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology
Ramstead, M. J. D., Seth, A. K., Hesp, C., Sandved-Smith, L., Mago, J., Lifshitz, M., Pagnoni, G., Smith, R. S., Dumas, G., Antoine Lutz, A., et al. (2022) · Review of Philosophy and Psychology · doi:10.1007/s13164-021-00604-y · Cited by 100
Self-citation
Mental health is biological health: Why tackling “diseases of the mind” is an imperative for biological anthropology in the 21st century
Syme, K., & Hagen, E. H. (2019) · American Journal of Physical Anthropology · doi:10.1002/ajpa.23965 · Cited by 80
Dynamic computational phenotyping of human cognition
Schurr, R., Reznik, D., Hillman, H., Bhui, R., & Gershman, S. J. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01814-x · Cited by 73
Computational Phenotyping: Using Models to Understand Individual Differences in Personality, Development, and Mental Illness
Patzelt, E. H., Hartley, C. A., & Gershman, S. J. (2018) · Personality Neuroscience · doi:10.1017/pen.2018.14 · Cited by 81
The computational neurology of movement under active inference
Parr, T., Limanowski, J., Rawji, V., & Friston, K. J. (2021) · Brain · doi:10.1093/brain/awab085 · Cited by 63
Self-citation
Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population
Benwell, C., Mohr, G., Wallberg, J., Kouadio, A., & Ince, R. A. A. (2022) · npj Mental Health Research · doi:10.1038/s44184-022-00009-4 · Cited by 65
Restoring the missing person to personalized medicine and precision psychiatry
Gómez-Carrillo, A., Paquin, V., Dumas, G., & Kirmayer, L. J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1041433 · Cited by 64
Predicting treatment outcome in depression: an introduction into current concepts and challenges
Rost, N., Binder, E. B., & Brückl, T. M. (2022) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-022-01418-4 · Cited by 66
Active Inference in OpenAI Gym: A Paradigm for Computational Investigations Into Psychiatric Illness
Cullen, M., Davey, B., Friston, K. J., & Moran, R. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.06.010 · Cited by 61
Self-citation
On the varieties of conscious experiences: Altered Beliefs Under Psychedelics (ALBUS)
Safron, A., Juliani, A., Reggente, N., Klimaj, V., & Johnson, M. W. (2025) · Neuroscience of Consciousness · doi:10.1093/nc/niae038 · Cited by 57
On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances
Safron, A., Klimaj, V., & Hipólito, I. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.688424 · Cited by 45
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Schoeller, F., Horowitz, A. H., Jain, A., Maes, P., Reggente, N., Christov-Moore, L., Pezzulo, G., Barca, L., Allen, M., Salomon, R., et al. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105478 · Cited by 42
Self-citation
Interfacing neural constructs with the Hierarchical Taxonomy of Psychopathology : ‘Why' and ‘how'
Perkins, E. R., Latzman, R. D., & Patrick, C. J. (2019) · Personality and Mental Health · doi:10.1002/pmh.1460 · Cited by 43
Reliability of Decision-Making and Reinforcement Learning Computational Parameters
Mkrtchian, A., Valton, V., & Roiser, J. P. (2023) · Computational Psychiatry · doi:10.5334/cpsy.86 · Cited by 49
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
Factors Responsible for the Success of a Start-up: A Meta-Analytic Approach
Pasayat, A. K., Bhowmick, B., & Roy, R. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2020.3016613 · Cited by 38
Integrating Cybernetic Big Five Theory with the free energy principle: A new strategy for modeling personalities as complex systems
Safron, A., & DeYoung, C. G. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819200-9.00010-7 · Cited by 34
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 30
Sophisticated Affective Inference: Simulating Anticipatory Affective Dynamics of Imagining Future Events
Hesp, C., Tschantz, A., Millidge, B., Ramstead, M. J. D., Friston, K. J., & Smith, R. S. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-64919-7_18 · Cited by 29
Self-citation
Active Inference, Epistemic Value, and Uncertainty in Conceptual Disorganization in First-Episode Schizophrenia
Limongi, R., Silva, A., Mackinley, M., Ford, S. D., & Palaniyappan, L. K. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbac125 · Cited by 27
Understanding Psychopathology: Cybernetics and Psychology on the Boundary between Order and Chaos
DeYoung, C. G., & Krueger, R. F. (2018) · Psychological Inquiry · doi:10.1080/1047840x.2018.1513690 · Cited by 27
AI ethics in computational psychiatry: From the neuroscience of consciousness to the ethics of consciousness
Wiese, W., & Friston, K. J. (2021) · Behavioural Brain Research · doi:10.1016/j.bbr.2021.113704 · Cited by 27
Self-citation
Symptoms are not the solution but the problem: Why psychiatric research should focus on processes rather than symptoms
Elbau, I., Binder, E. B., & Spoormaker, V. I. (2019) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18001000 · Cited by 24
‘Dark matter’, second waves and epidemiological modelling
Friston, K. J., Costello, A. M. D. L., & Pillay, D. (2020) · BMJ Global Health · doi:10.1136/bmjgh-2020-003978 · Cited by 24
Self-citation
Beyond Description and Deficits: How Computational Psychiatry Can Enhance an Understanding of Decision-Making in Anorexia Nervosa
Haynos, A. F., Widge, A. S., Anderson, L. M., & Redish, A. D. (2022) · Current Psychiatry Reports · doi:10.1007/s11920-022-01320-9 · Cited by 25
Self-citation
Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder
Idei, H., Murata, S., Yamashita, Y., & Ogata, T. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00762 · Cited by 24
A Theoretical Framework for Evaluating Psychiatric Research Strategies
Katahira, K., & Yamashita, Y. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00008 · Cited by 22
Across-subjects multiple baseline trial of exposure-based cognitive-behavioral therapy for severe irritability: a study protocol
Naim, R., Kircanski, K., Gold, A. L., German, R. E., Davis, M., Perlstein, S., Clayton, M., Revzina, O., & Brotman, M. A. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-039169 · Cited by 22
Cross-trial prediction of depression remission using problem-solving therapy: A machine learning approach
Kannampallil, T., Dai, R., Lv, N., Xiao, L., Lu, C., Ajilore, O., Snowden, M. B., Venditti, E. M., Williams, L. M., Kringle, E. A., et al. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.04.015 · Cited by 20
In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use
Wang, J. M., Zhu, L., Brown, V. M., Garza, R. D. L., Newton, T. F., King‐Casas, B., & Chiu, P. H. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.07.009 · Cited by 20
Decision-making in brains and robots — the case for an interdisciplinary approach
Lee, S. W., & Seymour, B. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.12.012 · Cited by 19
Astrocyte-neuron interaction in the dorsal striatum-pallidal circuits and alcohol-seeking behaviors
Hong, S., Kang, S., Baker, M. R., & Choi, D. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108759 · Cited by 20
Decision Models and Technology Can Help Psychiatry Develop Biomarkers
Barron, D. S., Baker, J. T., Budde, K. S., Bzdok, D., Eickhoff, S. B., Friston, K. J., Fox, P. T., Geha, P., Heisig, S., Holmes, A. J., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.706655 · Cited by 19
Self-citation
Hidden Aspects of the Research ADOS Are Bound to Affect Autism Science
Torres, E. B., Rai, R., Mistry, S., & Gupta, B. (2020) · Neural Computation · doi:10.1162/neco_a_01263 · Cited by 19
Structured tracking of alcohol reinforcement (STAR) for basic and translational alcohol research
Brown, A. R., Branthwaite, H. E., Farahbakhsh, Z. Z., Mukerjee, S., Melugin, P. R., Song, K., Noamany, H., & Siciliano, C. A. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01994-4 · Cited by 18
Linking fast and slow: The case for generative models
Medrano, J., Friston, K. J., & Zeidman, P. (2023) · Network Neuroscience · doi:10.1162/netn_a_00343 · Cited by 17
Self-citation
Theory-driven computational models of drug addiction in humans: Fruitful or futile?
Lim, T. V., & Ersche, K. D. (2023) · Addiction Neuroscience · doi:10.1016/j.addicn.2023.100066 · Cited by 16
Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia
Zarghami, T. S., Hossein‐Zadeh, G., & Bahrami, F. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00214 · Cited by 14
Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach
Hofman, A., Lier, I., Ikram, M. A., van Wingerden, M., & Luik, A. I. (2023) · European Psychiatry · doi:10.1192/j.eurpsy.2023.13 · Cited by 13
“Apples and pears are similar, but still different things.” Bipolar disorder and schizophrenia- discrete disorders or just dimensions ?
Grunze, H., & Cetkovich, M. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.04.064 · Cited by 12
Socio-Emotional Concern Dynamics in a Model of Real-Time Dyadic Interaction: Parent-Child Play in Autism
Hesp, C., Steenbeek, H., & van Geert, P. L. C. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01635 · Cited by 11
“Social physiology” for psychiatric semiology: How TTOM can initiate an interactive turn for computational psychiatry?
Dumas, G., Gozé, T., & Micoulaud‐Franchi, J. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x19002735 · Cited by 10
Augmenting Human Selves Through Artificial Agents – Lessons From the Brain
Northoff, G., Fraser, M., Griffiths, J. D., Pinotsis, D. A., Panangaden, P., Moran, R., & Friston, K. J. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.892354 · Cited by 9
Self-citation
Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models
Idei, H., & Yamashita, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.005 · Cited by 11
Can psychopathology and neuroscience coexist in psychiatric classifications?
Crocq, M. (2018) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2018.20.3/macrocq · Cited by 9
Etiopathogenic Models of Psychosis Spectrum Illnesses Must Resolve Four Key Features
Vinogradov, S., Hamid, A., & Redish, A. D. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.06.024 · Cited by 9
Self-citation
Grievance-fueled violence can be better understood using an enactive approach
Sizoo, B. B., Strijbos, D., & Glas, G. G. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.997121 · Cited by 12
Tracking and changing beliefs during social interaction: Where computational psychiatry meets cognitive behavioral therapy
Pott, J., & Schilbach, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1010012 · Cited by 9
Attention bias to negative versus non-negative faces is related to negative affectivity in a transdiagnostic youth sample
Harrewijn, A., Abend, R., Naim, R., Haller, S. P., Stavish, C. M., Bajaj, M. A., Matsumoto, C., Dombek, K., Cardinale, E. M., Kircanski, K., et al. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.04.036 · Cited by 8
Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics
Kim, E. Y., Kim, J., Jeong, J. H., Jang, J., Kang, N., Seo, J., Park, Y. E., Park, J., Jeong, H., Ahn, Y.-M., et al. (2024) · Schizophrenia Research · doi:10.1016/j.schres.2024.12.018 · Cited by 7
Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders
Bermperidis, T., Schafer, S. T., Gage, F. H., B, T. J. S. A., & Torres, E. B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.884707 · Cited by 7
Person-centred care in psychiatry: a clinical and philosophically informed approach
Glas, G. G. (2021) · BJPsych Advances · doi:10.1192/bja.2021.75 · Cited by 7
Neural correlates of extinguished threat recall underlying the commonality between pediatric anxiety and irritability
Tseng, W., Abend, R., Gold, A. L., & Brotman, M. A. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.08.117 · Cited by 7
Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer
Shane, M. S., & Denomme, W. J. (2021) · Personality Neuroscience · doi:10.1017/pen.2021.2 · Cited by 7
Disintegrating and reintegrating the self—(in)flexible self-models in depersonalization and psychedelic experiences
Ciaunica, A., & Safron, A. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780192898371.003.0004 · Cited by 8
Subjective Experience, Heterophenomenology, or Neuroimaging? A Perspective on the Meaning and Application of Mental Disorder Terms, in Particular Major Depressive Disorder
Schleim, S. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00702 · Cited by 7
Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit—A Case Study
Metzner, C., Mäki‐Marttunen, T., Zurowski, B., & Steuber, V. (2018) · Computational Psychiatry · doi:10.1162/cpsy_a_00015 · Cited by 6
Modeling the Determinants of Subjective Well-Being in Schizophrenia
Jeong, J. H., Kim, J., Kang, N., Ahn, Y.-M., Kim, Y. S., Lee, D. H., & Kim, S. H. (2024) · Schizophrenia Bulletin · doi:10.1093/schbul/sbae156 · Cited by 5
Inferring trajectories of psychotic disorders using dynamic causal modeling
Jin, J., Zeidman, P., Friston, K. J., & Kotov, R. (2023) · Computational Psychiatry · doi:10.5334/cpsy.94 · Cited by 5
Self-citation
La inteligencia artificial en la prevención de conductas suicidas: aspectos técnicos y consideraciones ético-legales
Fernández, J. M. B., del Pozo, R. B. M., & Fernández, C. J. B. (2023) · Revista de Bioética y Derecho · doi:10.1344/rbd2023.59.42759 · Cited by 5
A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals
Neville, V., Mendl, M., Paul, E. S., Seriès, P., & Dayan, P. (2023) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-023-01137-w · Cited by 5
The architecture of co-morbidity networks of physical and mental health conditions in military veterans
Alexander‐Bloch, A. F., Raznahan, A., Shinohara, R. T., Mathias, S. R., Bathulapalli, H., Bhalla, I. P., Goulet, J. L., Satterthwaite, T. D., Bassett, D. S., Glahn, D. C., et al. (2020) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2019.0790 · Cited by 5
Mental State of Inpatients With COVID-19: A Computational Psychiatry Approach
Sorokin, M. Y., Palchikova, E. I., Кибитов, А. А., Kasyanov, E. D., Khobeysh, M. A., & Zubova, E. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.801135 · Cited by 5
Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging
Limongi, R., Skelton, A. J., Tzianas, L. H., & Silva, A. (2024) · Brain Sciences · doi:10.3390/brainsci14121278 · Cited by 4
Homeostasis after injury: How intertwined inference and control underpin post-injury pain and behaviour
Mahajan, P., Dayan, P., & Seymour, B. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013538 · Cited by 4
Von der KI-Ethik zur Bewusstseinsethik: Ethische Aspekte der Computational Psychiatry
Wiese, W. (2021) · Psychiatrische Praxis · doi:10.1055/a-1369-2824 · Cited by 4
Social epistemic actions
Pezzulo, G., Barca, L., Maisto, D., & Donnarumma, F. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x19002802 · Cited by 4
Editorial: Mapping Psychopathology with fMRI and Effective Connectivity Analysis
Baojuan Li, A., Razi, A., & Friston, K. J. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00151 · Cited by 4
Self-citation
Time-driven interventions for affective disorders: on resonance and the Oberth effect
Argôlo, F. C., Gadelha, A., Pan, P. M., & Bressan, R. A. (2020) · Trends in Psychiatry and Psychotherapy · doi:10.1590/2237-6089-2019-0067 · Cited by 4
Changes in oscillations in anterior cingulate and medial prefrontal cortex are associated with altered signatures of Bayesian predictive coding in trait anxietyPreprint
Hein, T. P., Gong, Z., Ivanova, M., Fedele, T., Nikulin, V. V., & Ruiz, M. H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.24.493280 · Cited by 4
Embracing Complexity in Clinical Practice: Operationalizing a Systems-Based Perspective in Mental-Health Diagnostics
Driessen, J., Goekoop, R., Voerman, S. A., Mulder, A., Verhaar, M., Bos, J. H., Glas, G. G., & van den Berg, D. P. G. (2026) · Clinical Psychological Science · doi:10.1177/21677026251414040 · Cited by 3
Towards Understanding Persons and Their Personalities with Cybernetic Big 5 Theory and the Free Energy Principle and Active Inference (FEP-AI) Framework
Safron, A., & Sheikhbahaee, Z. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-47958-8_6 · Cited by 4
The concept of the schizophrenic lifeworld revisited
Krämer, M., Mavrogiorgou, P., & Juckel, G. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1625364 · Cited by 2
A Prognostic Theory of Treatment Response for Major Depressive Disorder: A Dynamic Systems Framework for Forecasting Clinical Trajectories
Ngabo-Woods, H., Dunai, L., & Verdú, I. S. (2025) · Applied Sciences · doi:10.3390/app152312524 · Cited by 2
Resistance to depth inversion illusions: A biosignature of psychosis with potential utility for monitoring positive symptom emergence and remission in schizophrenia
Fradkin, S. I., & Silverstein, S. M. (2022) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2022.100050 · Cited by 3
Electrophysiological Markers of Aberrant Cue-Specific Exploration in Hazardous Drinkers
Campbell, E. M., Singh, G., Claus, E. D., Witkiewitz, K. A., Costa, V. D., Hogeveen, J., & Cavanagh, J. F. (2023) · Computational Psychiatry · doi:10.5334/cpsy.96 · Cited by 2
A Broken Duet: Multistable Dynamics in Dyadic Interactions
Medrano, J., & Sajid, N. (2024) · Entropy · doi:10.3390/e26090731 · Cited by 2
Computational Modelling Reveals Slower Safety Learning and Threat Extinction are Associated With Higher Anxiety Severity in Remote Fear Conditioning
Kerr, T., Purves, K. L., McGregor, T., Craske, M. G., Barry, T., Lester, K. J., Constantinou, E., Sun, M. M. C., Robinson, O. J., & Eley, T. C. (2026) · Computational Psychiatry · doi:10.5334/cpsy.138 · Cited by 1
Responsible Artificial Intelligence in Precision Psychiatry
Vesterinen, T. (2025) · Studies in applied philosophy, epistemology and rational ethics · doi:10.1007/978-3-031-99654-2_8 · Cited by 1
Writing Through Sympathetic Arousal: A Perceptual Active Inference Model on the Effects of Sympathetic Arousal Interoception on Written and Spoken Grammatical Complexity
Skelton, A. J., Vries, L. A. D., Silva, A. M., & Limongi, R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-16955-6_14 · Cited by 1
Uncertainty Meets Disordered Eating and Body Image: A Transdiagnostic Network Study Across Depressive, Anxiety and Anorexia Nervosa Symptoms Including a Control Group
Granero, R., Krug, I., & Kiropoulos, L. A. (2026) · Nutrients · doi:10.3390/nu18091370 · Cited by 1

Authors: Friston, K. J., Redish, A. D., & Gordon, J. A.
Affiliations: Wellcome Centre for Human Neuroimaging (GB)
University of Minnesota (US)
National Institute of Mental Health (US)
Corresponding author: Friston, Karl J.
ORCID: Friston, Karl J.
Redish, A. David
Gordon, Joshua A.
Venue: Computational Psychiatry
Publisher: Ubiquity Press, Ltd.
Published: 2017-10-01
Volume: 1
Issue: 0
Pages: 2
ISSN: 2379-6227 (ISSN-L)
DOI: 10.1162/cpsy_a_00001
PMC: PMC5774181
OpenAlex: W2625121567
Cited by: 126
References: 52
Citations by year: 2026: 14 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 18 · 2021: 20 · 2020: 14 · 2019: 15 · 2018: 14 · 2017: 3
FWCI: 16.38
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Computational Psychiatry (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
UCL Discovery (University College London) (submittedVersion)
Funders: Wellcome Trust (088130/Z/09/Z)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords: precision psychiatry, psychiatric diagnosis, psychopathology, pathophysiology, Research Domain Criteria, Bayesian model selection, posterior predictive distribution, treatment response prediction

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

Computational Nosology and Precision Psychiatry

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks