Evidence map›Paper›PMID 39690407›Full record

ReviewMilitary Medical Research2024

Revolutionizing treatment for disorders of consciousness: a multidisciplinary review of advancements in deep brain stimulation.

Yi Yang, Tian-Qing Cao, Sheng-Hong He, Lu-Chen Wang, Qi-Heng He, Ling-Zhong Fan, Yong-Zhi Huang, Hao-Ran Zhang, Yong Wang, Yuan-Yuan Dang and 7 more

Abstract readReview
In one paragraph

Review in Military Medical Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
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

17 authors.

Yi Yang *Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China. yangyi_81nk@163.com.
Tian-Qing Cao *Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Sheng-Hong HeMedical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 7BN, UK.
Lu-Chen WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
Qi-Heng HeDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Ling-Zhong FanNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China.
Yong-Zhi HuangInstitute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
Hao-Ran ZhangNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China.
Yong WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100080, China.
Yuan-Yuan DangDepartment of Neurosurgery, Chinese PLA General Hospital, Beijing, 100080, China.
Nan WangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Xiao-Ke ChaiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Dong WangDepartment of Neurosurgery, Ganzhou People's Hospital, Ganzhou, 341000, Jiangxi, China.
Qiu-Hua JiangDepartment of Neurosurgery, Ganzhou People's Hospital, Ganzhou, 341000, Jiangxi, China.
Xiao-Li LiSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
Chen LiuSchool of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China. liuchen715@tju.edu.cn.
Shou-Yan WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China. shouyan@fudan.edu.cn.

Funding

2022 Open Project of Key Laboratory and Engineering Technology Research Center in the Rehabilitation Field of the Ministry of Civil Affairs 2022GKZS0003Beijing Natural Science Foundation 7232049Chinese Institute for Brain Research Youth Scholar Program 2022-NKX-XM-02Guarantors of Brain HMR04170International (Hong Kong, Macao, and Taiwan) Science and Technology Cooperation Project Z221100002722014Royal Society IES\R3\213123Science and Technology Innovation 2030-Young Scientists Project of Brain Science and brain-like Research 2022ZD0205300
6 · The paper itself

Abstract

Among the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis. The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions, providing a framework for refining DBS targets. We also discuss the multimodal approaches for assessing patients with DOC, encompassing clinical behavioral scales, electrophysiological assessment, and neuroimaging techniques methods. During the evolution of DOC therapy, the segmentation of central nuclei, the recording of single-neurons, and the analysis of local field potentials have emerged as favorable technical factors that enhance the efficacy of DBS treatment. Advances in computational models have also facilitated a deeper exploration of the neural dynamics associated with DOC, linking neuron-level dynamics with macroscopic behavioral changes. Despite showing promising outcomes, challenges remain in patient selection, precise target localization, and the determination of optimal stimulation parameters. Future research should focus on conducting large-scale controlled studies to delve into the pathophysiological mechanisms of DOC. It is imperative to further elucidate the precise modulatory effects of DBS on thalamo-cortical and cortico-cortical functional connectivity networks. Ultimately, by optimizing neuromodulation strategies, we aim to substantially enhance therapeutic outcomes and greatly expedite the process of consciousness recovery in patients.

Indexed as

Consciousness DisordersDeep Brain StimulationHumansComputational modelingDeep brain stimulationDisorders of consciousnessLocal field potentialsSegmentation of thalamic nuclei

Identifiers

PMID39690407
PMCPMC11654023

What Socratic 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 Socratic graph.