Background: Alongside the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, the number of patients with persistent symptoms following acute infection with SARS-CoV-2 is of concern. It is estimated that at least 65 million people worldwide meet criteria for what the World Health Organization (WHO) defines as "post-COVID-19 condition" - a multisystem disease comprising a wide range of symptoms. Effective treatments are lacking. In the present review, we aim to summarize the current evidence for the effectiveness of non-invasive or minimally invasive brain stimulation techniques in reducing symptoms of post-COVID-19.
Methods: After pre-registration with PROSPERO, the review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA). The four electronic databases PubMed/MEDLINE, PsycINFO, Web of Science and Scopus were systematically searched for all relevant studies through April 2nd, 2024. Two independent investigators selected empirical papers that reported on the application of non- or minimally invasive brain stimulation in patients with post-COVID-19 conditions.
Results: A total of 19 studies were identified, one using transcutaneous vagus nerve stimulation (tVNS), another using transorbital alternating current stimulation (toACS), 6 studies on transcranial magnetic stimulation (TMS) and 11 studies on transcranial direct current stimulation (tDCS) for the treatment of post-COVID-19 symptoms.
Conclusions: Existing studies report first promising results, illustrating improvement in clinical outcome parameters. Yet, the mechanistic understanding of post-COVID-19 and how brain stimulation techniques may be benefitial are limited. Directions for future research in the field are discussed.
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http://dx.doi.org/10.1016/j.amjmed.2024.07.007 | DOI Listing |
NPJ Digit Med
January 2025
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Rehabilitation Medicine (Rehabilitation Center), Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan , Shandong, 250012, China.
Background: Mild cognitive impairment (MCI) is a high-risk factor for dementia and dysphagia; therefore, early intervention is vital. The effectiveness of intermittent theta burst stimulation (iTBS) targeting the right dorsal lateral prefrontal cortex (rDLPFC) remains unclear.
Methods: Thirty-six participants with MCI were randomly allocated to receive real (n = 18) or sham (n = 18) iTBS.
Brain Stimul
January 2025
Department of Physical Medicine and Rehabilitation, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas; TIRR Memorial Hermann Hospital, Houston, Texas. Electronic address:
Brain Stimul
January 2025
Department of Biomedical Engineering, 36 S Wasatch Dr, Salt Lake City, 84112, UT, United States.
Emerging neurostimulation methods aim to selectively modulate deep brain structures. Guiding these therapies has presented a substantial chal- lenge, since imaging modalities such as MRI limit the spectrum of benefi- ciaries. In this study, we assess the guidance accuracy of a neuronavigation method that does not require taking MRI scans.
View Article and Find Full Text PDFBrain Stimul
January 2025
Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA, 01609; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129; Department of Mathematics, Worcester Polytechnic Institute, Worcester, MA, USA, 01609.
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