Curated Papers > Recent high-impact research on "brain-computer interfaces" (IF ≥ 30, last 5 years)

Brain-computer interfaces for neuropsychiatric disorders.

Literature Information

DOI	10.1038/s44222-024-00177-2
PMID	40988938
Journal	Nature reviews bioengineering
Impact Factor	37.6
JCR Quartile	Q1
Publication Year	2024
Times Cited	0
Keywords	Brain-computer interfaces, Neuropsychiatric disorders, Deep brain stimulation, Personalized therapy, Machine learning
Literature Type	Journal Article
ISSN	2731-6092
Pages	653-670
Issue	2(8)
Authors	Lucine L Oganesian, Maryam M Shanechi

TL;DR

This paper reviews the development of brain-computer interfaces (BCIs) aimed at personalizing deep brain stimulation therapy for treatment-resistant neuropsychiatric disorders, such as major depression, which often do not respond to standard treatments. By focusing on decoding symptom-states using neural biomarkers and employing data-driven machine learning techniques, the research highlights the potential of BCIs to enhance therapeutic efficacy and outlines a roadmap for future advancements in this field.

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Brain-computer interfaces · Neuropsychiatric disorders · Deep brain stimulation · Personalized therapy · Machine learning

Abstract

Neuropsychiatric disorders such as major depression are a leading cause of disability worldwide with standard treatments, including psychotherapy or medication, failing many patients. Deep brain stimulation holds great potential as an alternative therapy for treatment-resistant cases; however, improving the efficacy of stimulation therapy for neuropsychiatric disorders is hindered by the complexity as well as inter- and/or intra-individual variability in symptom manifestations, neural representations and response to therapy. These challenges motivate the development of brain-computer interfaces (BCIs) that can decode a patient's symptom-state from brain activity as feedback to personalize the stimulation therapy in closed loop. Here, we review progress on developing BCIs for neuropsychiatric care, focusing on neural biomarkers for decoding symptom-states, stimulation site selection and closed-loop stimulation strategies. Moreover, we highlight promising data-driven machine learning and system design approaches and provide a roadmap for realizing these BCIs. Finally, we review current limitations, discuss extensions to other treatment modalities, and outline the required scientific and technological advances. These advances can enable next-generation BCIs that provide an alternative therapy for treatment-resistant neuropsychiatric disorders.

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Primary Questions Addressed

1. What are the specific neural biomarkers currently being researched for decoding symptom-states in neuropsychiatric disorders?
2. How do the challenges of inter-individual variability in symptom manifestations affect the development of personalized brain-computer interfaces?
3. In what ways can machine learning techniques enhance the efficacy of closed-loop stimulation strategies in treating neuropsychiatric disorders?
4. What potential extensions of brain-computer interfaces could be explored for other treatment modalities beyond deep brain stimulation?
5. What are the key technological advances needed to overcome the current limitations in brain-computer interface development for neuropsychiatric care?

Key Findings

Research Background and Objectives

Neuropsychiatric disorders, particularly major depression, significantly contribute to global disability. Standard treatments, including psychotherapy and medication, often do not yield satisfactory results for many patients. This has led to interest in deep brain stimulation (DBS) as a potential alternative for treatment-resistant cases. However, the effectiveness of DBS is complicated by the variability in symptoms and individual responses. This study aims to explore the development of brain-computer interfaces (BCIs) to personalize stimulation therapy based on real-time brain activity.

Main Methods/Materials/Experimental Design

The research employs a systematic review approach to evaluate the current state of BCIs in neuropsychiatric care. The methodology focuses on the following key components:

1. Neural Biomarkers: Identifying specific brain activity patterns that correlate with different symptom states.
2. Stimulation Site Selection: Determining optimal locations in the brain for stimulation based on individual neural representations.
3. Closed-Loop Stimulation Strategies: Implementing a feedback system where brain activity informs real-time adjustments to stimulation parameters.

The technical route can be illustrated as follows:

Key Results and Findings

• Neural Biomarkers: Advances in identifying neural signatures associated with specific symptoms have been noted, allowing for better decoding of patient states.
• Stimulation Efficacy: Evidence suggests that personalized stimulation based on real-time brain data can enhance therapeutic outcomes.
• Machine Learning Approaches: Data-driven techniques have shown promise in improving the accuracy of symptom-state identification and tailoring stimulation protocols.

Main Conclusions/Significance/Innovativeness

The review highlights the transformative potential of BCIs in neuropsychiatric treatment, emphasizing that personalized stimulation could lead to better outcomes for treatment-resistant patients. The integration of machine learning and system design into BCI development represents a significant innovation in this field, potentially reshaping therapeutic strategies for neuropsychiatric disorders.

Research Limitations and Future Directions

Despite the promising developments, several limitations were identified:

• Variability in Neural Responses: Individual differences in brain activity can complicate the personalization of therapies.
• Technical Challenges: Implementing effective BCIs requires advancements in both hardware and software.

Future research directions include:

• Expanding the range of neuropsychiatric disorders addressed by BCIs.
• Enhancing machine learning algorithms to improve the accuracy and reliability of symptom-state decoding.
• Investigating the integration of BCIs with other treatment modalities to provide comprehensive care.

Aspect	Current Status	Future Directions
Neural Biomarkers	Identified for major symptoms	Broaden to more disorders
Stimulation Site Selection	Personalized based on neural representation	Optimize further with advanced imaging
Closed-Loop Strategies	Initial implementations showing promise	Develop more robust feedback systems
Machine Learning Integration	Early-stage applications	Refine algorithms for better performance
Overall Impact	Potential for improved patient outcomes	Aim for widespread clinical application

References

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9. Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression. - Patricio Riva-Posse;Ki Sueng Choi;Paul E Holtzheimer;Cameron C McIntyre;Robert E Gross;Ashutosh Chaturvedi;Andrea L Crowell;Steven J Garlow;Justin K Rajendra;Helen S Mayberg - Biological psychiatry (2014)
10. Patient-specific connectomic models correlate with, but do not reliably predict, outcomes in deep brain stimulation for obsessive-compulsive disorder. - Alik S Widge;Fan Zhang;Aishwarya Gosai;George Papadimitrou;Peter Wilson-Braun;Magdalini Tsintou;Senthil Palanivelu;Angela M Noecker;Cameron C McIntyre;Lauren O'Donnell;Nicole C R McLaughlin;Benjamin D Greenberg;Nikolaos Makris;Darin D Dougherty;Yogesh Rathi - Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology (2022)

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