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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Assessment and Communication for People with Disorders of Consciousness
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Brain-computer interfaces for neuropsychiatric disorders.

Lucine L Oganesian1, Maryam M Shanechi1,2,3,4

  • 1Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California 90089 USA.

Nature Reviews Bioengineering
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Brain-computer interfaces (BCIs) can personalize deep brain stimulation for treatment-resistant neuropsychiatric disorders. This review explores BCIs using neural biomarkers and machine learning to improve therapy efficacy.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Psychiatry

Background:

  • Neuropsychiatric disorders, like major depression, are a significant global health burden.
  • Standard treatments (psychotherapy, medication) are ineffective for many patients, highlighting the need for novel therapies.
  • Deep brain stimulation (DBS) shows promise for treatment-resistant cases but faces challenges due to individual variability.

Purpose of the Study:

  • To review the progress in developing brain-computer interfaces (BCIs) for neuropsychiatric care.
  • To focus on neural biomarkers, stimulation site selection, and closed-loop stimulation strategies for personalized therapy.
  • To outline a roadmap for realizing advanced BCIs for treatment-resistant conditions.

Main Methods:

  • Review of current research on BCIs for neuropsychiatric disorders.
  • Focus on identifying neural biomarkers for decoding patient symptom-states.
  • Exploration of data-driven machine learning and system design approaches for closed-loop stimulation.

Main Results:

  • BCIs offer a pathway to decode patient symptom-states from brain activity.
  • Personalized, closed-loop stimulation strategies can potentially enhance therapeutic efficacy.
  • Machine learning and system design are crucial for developing effective BCIs.

Conclusions:

  • Advanced BCIs are essential for overcoming limitations in current stimulation therapies.
  • Future developments require scientific and technological advances to enable next-generation BCIs.
  • BCIs hold the potential to provide alternative, personalized treatments for refractory neuropsychiatric disorders.