ArticleHuman brain mapping2020
Multi-dimensional predictions of psychotic symptoms via machine learning.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Development and evaluation of a risk prediction model for social disability in schizophrenia patients.Frontiers in psychiatry · 2025Article
- Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.Schizophrenia (Heidelberg, Germany) · 2024Article
- RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive Systems Domain.Computational psychiatry (Cambridge, Mass.) · 2024Review
- Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume.Frontiers in neuroscience · 2023Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Computational Modeling of Electroencephalography and Functional Magnetic Resonance Imaging Paradigms Indicates a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia.Biological psychiatry · 2022Article
- Multi-dimensional predictions of psychotic symptoms via machine learning.Human brain mapping · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.