Translational perspectives for computational neuroimaging
Open accessAbstract
Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches-biophysical network models, generative models, and model-based fMRI analyses of neuromodulation-strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
A major limitation of BNMs is that their high degree of complexity renders the estimation of connection-specific parameters challenging.
“While their biological level of detail is attractive, a major limitation of BNMs is that their high degree of complexity renders the estimation of connection-specific parameters challenging (for review, see Stephan et al., 2015).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Stephan et al., 2015
Embracing computational models opens the door to identifying biomarkers based on circuit function rather than just phenomenology.
“Embracing computational models opens the door to identifying biomarkers based on circuit function rather than just phenomenology (71–73).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [71]
Establishing computational neuroimaging probes of neuromodulation is a theme of general importance in computational psychiatry, since this may provide a physiologically interpretable stratification of patients from spectrum disorders with direct implications for individual treatment.
“Establishing computational neuroimaging probes of neuromodulation is a theme of general importance in computational psychiatry, since this may provide a physiologically interpretable stratification of patients from spectrum disorders with direct implications for individual treatment (118).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [118]
The citation lists haven’t been fetched yet
References 147 · Cited by 169
- Authors
- Stephan, K. E., Iglesias, S., Heinzle, J., & Diaconescu, A. O.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- University of Zurich (CH)
- Corresponding author
- Stephan, Klaas E.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-08
- Volume
- 87
- Issue
- 4
- Pages
- 716-732
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W1756616509
- Cited by
- 169
- References
- 147
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 8 · 2023: 7 · 2022: 9 · 2021: 16 · 2020: 17 · 2019: 15 · 2018: 18 · 2017: 26 · 2016: 31 · 2015: 2
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (091593)
- René und Susanne Braginsky Stiftung
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Keywords
- computational neuroimaging, forward model, generative models, model-based fMRI, neuromodulation, schizophrenia, psychiatric diagnosis
- MeSH terms
- Animals, Brain, Computer Simulation, Humans, Models, Neurological, Nerve Net, Translational Research, Biomedical, Neuroimaging, Functional 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
