ArticleNature reviews bioengineering2024
Brain-computer interfaces for neuropsychiatric disorders.
Article in Nature reviews bioengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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.
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.
Who cites it
19 citing papers in PubMed.
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Pain Medicine Specialist: Championing High-Quality Development for Neuromodulation Technology.Current medical science · 2026Review
- State-Dependent Modulation of a Theta-Responsive Subnetwork by High-Frequency Stimulation of the Ventral Striatum.Neuroscience bulletin · 2026Article
- Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review
- The application of AI-driven digital phenotyping and interventions in neuropsychiatric disorders.Frontiers in psychiatry · 2026Review
- Ethics of brain-computer interface in mental healthcare.Frontiers in human neuroscience · 2026Article
- Adaptive neuromodulation dialogues: navigating current challenges and emerging innovations in neuromodulation system development.Journal of neural engineering · 2025Review
- Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.ArXiv · 2025Article
- Prioritized learning of cross-population neural dynamics.Journal of neural engineering · 2025Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.Proceedings of machine learning research · 2025Article
- Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling.Advances in neural information processing systems · 2025Article
- BaRISTA: Brain Scale Informed Spatiotemporal Representation of Human Intracranial Neural Activity.Advances in neural information processing systems · 2025Article
- Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference.Advances in neural information processing systems · 2025Article
- New technology and emerging theories driving progress in neuropsychiatric disorders.Fundamental research · 2024Article
- Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks.Nature neuroscience · 2024Article
- Caregivers in implantable brain-computer interface research: a scoping review.Frontiers in human neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Identifiers
What Socratic holds
Registered trials
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.