Evidence map›Paper›PMID 27445865›Full record

ArticleFrontiers in psychiatry2016

Translating Neurocognitive Models of Auditory-Verbal Hallucinations into Therapy: Using Real-time fMRI-Neurofeedback to Treat Voices.

Thomas Fovet, Natasza Orlov, Miriam Dyck, Paul Allen, Klaus Mathiak, Renaud Jardri

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.8field-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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

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  3. From lab to life: challenges and perspectives of fNIRS for haemodynamic-based neurofeedback in real-world environments.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
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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

6 authors at 4 institutions in 3 countries.

Thomas FovetUniv Lille, CNRS, UMR-9193, psyCHIC team & CHU Lille, Psychiatry Dpt (CURE), Fontan Hospital , Lille , France.
Natasza OrlovDepartment of Psychosis Studies, Institute of Psychiatry Psychology and Neuroscience, King's College London , London , UK.
Miriam DyckDepartment of Psychiatry, Psychotherapy and Psychosomatics, JARA-Brain, RWTH Aachen University , Aachen , Germany.
Paul AllenDepartment of Psychosis Studies, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK; Department of Psychology, University of Roehampton, London, UK.
Klaus MathiakDepartment of Psychiatry, Psychotherapy and Psychosomatics, JARA-Brain, RWTH Aachen University , Aachen , Germany.
Renaud JardriUniv Lille, CNRS, UMR-9193, psyCHIC team & CHU Lille, Psychiatry Dpt (CURE), Fontan Hospital , Lille , France.
King's College London · GBRWTH Aachen University · DECentre Hospitalier Universitaire de Lille · FRSciences Cognitives et Sciences Affectives · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Auditory-verbal hallucinations (AVHs) are frequent and disabling symptoms, which can be refractory to conventional psychopharmacological treatment in more than 25% of the cases. Recent advances in brain imaging allow for a better understanding of the neural underpinnings of AVHs. These findings strengthened transdiagnostic neurocognitive models that characterize these frequent and disabling experiences. At the same time, technical improvements in real-time functional magnetic resonance imaging (fMRI) enabled the development of innovative and non-invasive methods with the potential to relieve psychiatric symptoms, such as fMRI-based neurofeedback (fMRI-NF). During fMRI-NF, brain activity is measured and fed back in real time to the participant in order to help subjects to progressively achieve voluntary control over their own neural activity. Precisely defining the target brain area/network(s) appears critical in fMRI-NF protocols. After reviewing the available neurocognitive models for AVHs, we elaborate on how recent findings in the field may help to develop strong a priori strategies for fMRI-NF target localization. The first approach relies on imaging-based "trait markers" (i.e., persistent traits or vulnerability markers that can also be detected in the presymptomatic and remitted phases of AVHs). The goal of such strategies is to target areas that show aberrant activations during AVHs or are known to be involved in compensatory activation (or resilience processes). Brain regions, from which the NF signal is derived, can be based on structural MRI and neurocognitive knowledge, or functional MRI information collected during specific cognitive tasks. Because hallucinations are acute and intrusive symptoms, a second strategy focuses more on "state markers." In this case, the signal of interest relies on fMRI capture of the neural networks exhibiting increased activity during AVHs occurrences, by means of multivariate pattern recognition methods. The fine-grained activity patterns concomitant to hallucinations can then be fed back to the patients for therapeutic purpose. Considering the potential cost necessary to implement fMRI-NF, proof-of-concept studies are urgently required to define the optimal strategy for application in patients with AVHs. This technique has the potential to establish a new brain imaging-guided psychotherapy for patients that do not respond to conventional treatments and take functional neuroimaging to therapeutic applications.

Indexed as

anterior cingulate cortexfMRIhallucinationsneurofeedbacktherapeutic strategies

Identifiers

PMID27445865
PMCPMC4921472
OpenAlexW2461391998

What OpenQuestion holds

Textmetadata
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.