Evidence map›Paper›PMID 36685239›Full record

ArticleFrontiers in neuroscience2022

Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders.

Zhengcao Cao, Xiang Xiao, Yang Zhao, Yihan Jiang, Cong Xie, Marie-Laure Paillère-Martinot, Eric Artiges, Zheng Li, Zafiris J Daskalakis, Yihong Yang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 21% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. 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

11 authors at 7 institutions in 3 countries.

Zhengcao CaoState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Xiang XiaoState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yang ZhaoState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yihan JiangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Cong XieState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Marie-Laure Paillère-MartinotDepartment of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, APHP.Sorbonne Université, Paris, France.
Eric ArtigesINSERM U A10 Developmental Trajectories and Psychiatry, Ecole Normale Supérieure Paris-Saclay, CNRS, Center Borelli, University of Paris-Saclay, Gif-sur-Yvette, France.
Zheng LiState Key Laboratory of Cognitive Neuroscience and Learning, Center for Cognition and Neuroergonomics, Beijing Normal University at Zhuhai, Zhuhai, China.
Zafiris J DaskalakisDepartment of Psychiatry, University of California, San Diego, La Jolla, CA, United States.
Yihong YangNeuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States.
Chaozhe ZhuState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Beijing Normal University · CNCentre National de la Recherche Scientifique · FRChinese Institute for Brain Research · CNNational Institute on Drug Abuse · USNational Institutes of Health · USUniversité Paris-Saclay · FRUniversity of California, San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one schizophrenia cohort with auditory verbal hallucinations. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects.

Indexed as

brain networkelectric field calculationindividualized treatmentpsychiatric disordertranscranial magnetic stimulation

Identifiers

PMID36685239
PMCPMC9846047
OpenAlexW4313585107

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.