Evidence map›Paper›PMID 36937818›Full record

ReviewFrontiers in neural circuits2023

Hierarchical predictive coding in distributed pain circuits.

Zhe Sage Chen

Open access · goldFull text readReview
In one paragraph

Review in Frontiers in neural circuits, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
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

14 citing papers in PubMed, 19 citations in OpenAlex.

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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

1 author at 1 institution in 1 country.

Zhe Sage ChenDepartment of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States.
NYU Langone Health · US

Funding

Cortical information integration as a model for pain perception and behaviorRF1NS121776 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI CHEN, ZHE SAGE, WANG, JING · 2021 to 2021
$2.0M
Cortical Information Integration as a Model for Pain Perception and BehaviorR01NS121776 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI CHEN, ZHE SAGE, WANG, JING · 2024 to 2025
$1.2M
NINDS NIH HHS R01 NS121776NINDS NIH HHS RF1 NS121776
6 · The paper itself

Abstract

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

Indexed as

Gyrus CinguliPainAnimalsBrainHumansPain PerceptionSensationactive inferencecingulate-insula hubhierarchical predictive codingneural oscillationsneurotransmitterpain networkprediction errortraveling waves

Identifiers

PMID36937818
PMCPMC10020379
OpenAlexW4323045062

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read4
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.