98%
921
2 minutes
20
Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487193 | PMC |
http://dx.doi.org/10.1038/s41398-024-03156-8 | DOI Listing |
Biosensors (Basel)
August 2025
Inter-University Semiconductor Research Center, Pusan National University, Busan 46241, Republic of Korea.
Implantable electronic devices are driving innovation in modern medical technology and have significantly improved patients' quality of life. This review comprehensively analyzes the latest technological trends in implantable electronic devices used in major organs, including the heart, brain, and skin. Additionally, it explores the potential for application in the gastrointestinal system, particularly in the field of biliary stents, in which development has been limited.
View Article and Find Full Text PDFSci Rep
August 2025
Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, 1200 N State Street, Suite 3300, Los Angeles, 90033, CA, USA.
Motor BCIs, with the help of Artificial Intelligence (AI) and machine learning, have shown promise in decoding neural signals for restoring motor function. Structures beyond motor cortex have provided additional sources for movement signals. New evidence points to the role of the insula in motor control, specifically directional hand-movements.
View Article and Find Full Text PDFNeuron
August 2025
Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine,
Intracortical arterioles are key locations for blood flow regulation and oxygen supply in the brain and are critical to brain health and disease. However, imaging such small (<100-μm-sized) vessels in humans is challenging. Here, using non-human primates as a model, we developed a capability for imaging microvasculature in vivo with a clinical 7 T MRI scanner.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
July 2025
Objective: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still predominantly adopt machine learning decoders while falling short of high BCI performance. Yet, the generalization and advantages of deep learning-based EEG decoding in realistic BCI systems remain far unclear.
View Article and Find Full Text PDFNeural Netw
November 2025
College of Computer Science and Technology, Zhejiang University, Hangzhou, 310000, China; State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, 310000, China.
Spiking Neural Networks (SNNs), inspired by the behavior of biological neurons, offer a promising direction for next-generation neural computing. Two primary methodologies have emerged for training deep SNNs: Direct Training, which optimizes SNNs using surrogate gradients, and ANN-to-SNN Conversion, which derives SNNs from Artificial Neural Networks (ANNs). In this work, we focus on the latter and investigate the conversion error that arises during the transformation.
View Article and Find Full Text PDF