Evidence map›Paper›PMID 41086333›Full record

ReviewPain2025

Forward and reverse engineering the pain system: from computational neuroscience to neuro-engineering.

Pranav Mahajan, Ben Seymour

Abstract readReview
In one paragraph

Review in Pain, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Pranav MahajanWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.ORCID 0009-0001-2507-5450
Ben SeymourWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0003-1724-5832

Funding

Department of Health NIHR203316IITP (Korea) MSIT 2019-0-01371Japan Society for Promotion of Science (JP) 22H04998Wellcome Trust 203139Wellcome Trust 214251Wellcome Trust 214251/Z/18/Z
6 · The paper itself

Abstract

abstractPain is a complex, multi-level phenomenon integrating sensory, motivational, and cognitive processes. Computational approaches bridge theoretical frameworks with neural and behavioural data, providing descriptive, mechanistic, and normative explanations. We review key computational approaches, including reinforcement learning, control theory, Bayesian inference, and active inference, illustrating their role in understanding pain prediction, avoidance, and modulation. Forward and reverse engineering techniques synergistically refine our models and generate testable hypotheses. This framework not only advances fundamental neuroscience but also informs clinical applications, offering potential for computational phenotyping, personalised therapies, and adaptive neuro-engineering interventions for pain management.

Indexed as

Models, NeurologicalNeurosciencesPainAnimalsHumansPain ManagementActive inferenceBayesian inferenceComputational neuroscienceControl theoryForward engineeringPain neuroscienceReinforcement learningReverse engineering

Identifiers

PMID41086333
PMCPMC7618683

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.