The Deep Brain Stimulation (DBS) Think Tank XII was held on August 21st to 23rd. This year we showcased groundbreaking advancements in neuromodulation technology, focusing heavily on the novel uses of existing technology as well as next-generation technology. Our keynote speaker shared the vision of using neuro artificial intelligence to predict depression using brain electrophysiology. Innovative applications are currently being explored in stroke, disorders of consciousness, and sleep, while established treatments for movement disorders like Parkinson's disease are being refined with adaptive stimulation. Neuromodulation is solidifying its role in treating psychiatric disorders such as depression and obsessive-compulsive disorder, particularly for patients with treatment-resistant symptoms. We estimate that 300,000 leads have been implanted to date for neurologic and neuropsychiatric indications. Magnetoencephalography has provided insights into the post-DBS physiological changes. The field is also critically examining the ethical implications of implants, considering the long-term impacts on clinicians, patients, and manufacturers.
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http://dx.doi.org/10.3389/fnhum.2025.1544994 | DOI Listing |
Brain
March 2025
Krembil Brain Institute, University Health Network, Toronto, ON M5T 1M8, Canada.
Parkinson's disease is characterized, in part, by hypoactivity of direct pathway inhibitory projections from striatum to the globus pallidus internus (GPi) and indirect pathway inhibitory projections from globus pallidus externus (GPe) to the subthalamic nucleus (STN). In people with Parkinson's disease (n=32), we explored the potential use of intracranial stimulation for eliciting long-term potentiation (LTP) of these underactive pathways to produce improvement of symptoms that persists beyond stimulation cessation. During GPi deep brain stimulation (DBS) surgery, we found strong evidence (p<.
View Article and Find Full Text PDFSci Adv
March 2025
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Brain age gap (BAG), the deviation between estimated brain age and chronological age, is a promising marker of brain health. However, the genetic architecture and reliable targets for brain aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)-based brain age using deep learning models trained on the UK Biobank and validated with three external datasets.
View Article and Find Full Text PDFGigascience
January 2025
Concordia University, Department of Computer Science and Software Engineering, 1455 Blvd. De Maisonneuve Ouest, Montreal, Quebec H3G 1M8, Canada.
Magnetic resonance imaging (MRI) preprocessing is a critical step for neuroimaging analysis. However, the computational cost of MRI preprocessing pipelines is a major bottleneck for large cohort studies and some clinical applications. While high-performance computing and, more recently, deep learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers.
View Article and Find Full Text PDFObjective: To enable fast and stable neonatal brain MR imaging by integrating learned neonate-specific subspace model and model-driven deep learning.
Methods: Fast data acquisition is critical for neonatal brain MRI, and deep learning has emerged as an effective tool to accelerate existing fast MRI methods by leveraging prior image information. However, deep learning often requires large amounts of training data to ensure stable image reconstruction, which is not currently available for neonatal MRI applications.
J Neurol
March 2025
Centre for Neurology, Department of Neurodegenerative Diseases, and Hertie Institute for Clinical Brain Research, University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
Next-generation neurostimulators capable of running closed-loop adaptive deep brain stimulation (aDBS) are about to enter the clinical landscape for the treatment of Parkinson's disease. Already promising results using aDBS have been achieved for symptoms such as bradykinesia, rigidity and motor fluctuations. However, the heterogeneity of freezing of gait (FoG) with its wide range of clinical presentations and its exacerbation with cognitive and emotional load make it more difficult to predict and treat.
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