Evidence map›Paper›PMID 38059513›Full record

ArticleWIREs mechanisms of disease

A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices.

Wander L Valentim, Daniel S Tylee, Renato Polimanti

Open access · greenAbstract read
In one paragraph

Article in WIREs mechanisms of disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 66% 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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Article
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

3 authors at 2 institutions in 2 countries.

Wander L ValentimFaculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil.ORCID 0000-0002-2677-5889
Daniel S TyleeDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.
Renato PolimantiDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.
Yale University · USUniversidade Federal de Minas Gerais · BR

Funding

Leveraging genomic data to dissect the association of internalizing disorders with the risk, onset, and vulnerability of COVID-19RF1MH132337 · NIMH · YALE UNIVERSITY · PI POLIMANTI, RENATO · 2022 to 2022
$3.7M
Investigating the Systems Genetics of the Patterns of Polysubstance Abuse and AddictionR33DA047527 · NIDA · YALE UNIVERSITY · PI POLIMANTI, RENATO · 2021 to 2023
$2.0M
Genome-wide Investigation of the Interplay Between Age-Related Hearing Loss and Smoking BehaviorsR21DC018098 · NIDCD · YALE UNIVERSITY · PI POLIMANTI, RENATO · 2019 to 2021
$440k
NIDA NIH HHS R33 DA047527NIDCD NIH HHS R21 DC018098NIH HHS R21 DC018098NIH HHS R33 DA047527NIH HHS RF1MH132337NIMH NIH HHS RF1 MH132337
6 · The paper itself

Abstract

Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.

Indexed as

Brain-Computer InterfacesDeep Brain StimulationMental DisordersBrainGenomicsHumansbrain functionbrain-machine interfacegenome-wide association studiesneuropsychiatric disorderstranslational research

Identifiers

PMID38059513
PMCPMC11163995
OpenAlexW4389452661

What OpenQuestion holds

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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.