Evidence map›Paper›PMID 36464521›Full record

ReviewBiological psychiatry2023

Computationally Informed Interventions for Targeting Compulsive Behaviors.

Thorsten Kahnt

Open access · greenAbstract readReview
In one paragraph

Review in Biological psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Decoding and modifying dynamic attentional bias in gaming disorder.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Article
  5. Article
  6. Article
  7. Article
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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.

Thorsten KahntNational Institute on Drug Abuse Intramural Research Program, Baltimore, Maryland. Electronic address: thorsten.kahnt@nih.gov.
National Institute on Drug Abuse · US

Funding

Neurocomputational mechanisms of learning and decision-making and their disruption in addiction ZIADA000642 · NIDA · NATIONAL INSTITUTE ON DRUG ABUSE · PI KAHNT, THORSTEN · 2022 to 2025
$7.2M
Intramural NIH HHS ZIA DA000642
6 · The paper itself

Abstract

Compulsive behaviors are central to addiction and obsessive-compulsive disorder and can be understood as a failure of adaptive decision making. Particularly, they can be conceptualized as an imbalance in behavioral control, such that behavior is guided predominantly by learned rather than inferred outcome expectations. Inference is a computational process required for adaptive behavior, and recent work across species has identified the neural circuitry that supports inference-based decision making. This includes the orbitofrontal cortex, which has long been implicated in disorders of compulsive behavior. Inspired by evidence that modulating orbitofrontal cortex activity can alter inference-based behaviors, here we discuss noninvasive approaches to target these circuits in humans. Specifically, we discuss the potential of network-targeted transcranial magnetic stimulation and real-time neurofeedback to modulate the neural underpinnings of inference. Both interventions leverage recent advances in our understanding of the neurocomputational mechanisms of inference-based behavior and may be used to complement current treatment approaches for behavioral disorders.

Indexed as

Behavior, AddictiveObsessive-Compulsive DisorderCompulsive BehaviorHumansPrefrontal CortexTranscranial Magnetic StimulationCompulsive behaviorInference-based decision makingModel-basedModel-freeNeurofeedbackReinforcement leaningTranscranial magnetic stimulation

Identifiers

PMID36464521
PMCPMC9989040
OpenAlexW4294838860

What OpenQuestion holds

Textmetadata
LicenceTDM
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.