Deep brain stimulation (DBS) for secondary (acquired, combined) dystonia does not reach the high degree of efficacy achieved in primary (genetic, isolated) dystonia. We hypothesize that this may be due to variability in the underlying injury, so that different children may require placement of electrodes in different regions of basal ganglia and thalamus. We describe a new targeting procedure in which temporary depth electrodes are placed at multiple possible targets in basal ganglia and thalamus, and probing for efficacy is performed using test stimulation and recording while children remain for one week in an inpatient Neuromodulation Monitoring Unit (NMU). Nine Children with severe secondary dystonia underwent the NMU targeting procedure. In all cases, 4 electrodes were implanted. We compared the results to 6 children who had previously had 4 electrodes implanted using standard intraoperative microelectrode targeting techniques. Results showed a significant benefit, with 80% of children with NMU targeting achieving greater than 5-point improvement on the Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS), compared with 50% of children using intraoperative targeting. NMU targeting improved BFMDRS by an average of 17.1 whereas intraoperative targeting improved by an average of 10.3. These preliminary results support the use of test stimulation and recording in a Neuromodulation Monitoring Unit (NMU) as a new technique with the potential to improve outcomes following DBS in children with secondary (acquired) dystonia. A larger sample size will be needed to confirm these results.

Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. The complexity of linkages that produces pathophysiology in neurological, neurosurgical and psychiatric disease will require multiscale modeling to provide understanding that exceeds what is possible with statistical analysis or highly simplified models: how to bring together pharmacotherapeutics with neurostimulation, how to personalize therapies, how to combine novel therapies with neurorehabilitation, how to interlace periodic diagnostic updates with frequent reevaluation of therapy, how to understand a physical disease that manifests as a disease of the mind. Multiscale modeling will also help to extend the usefulness of animal models of human diseases in neuroscience, where the disconnects between clinical and animal phenomenology are particularly pronounced. Here we cover areas of particular interest for clinical application of these new modeling neurotechnologies, including epilepsy, traumatic brain injury, ischemic disease, neurorehabilitation, drug addiction, schizophrenia and neurostimulation.

Increased motor cortex excitability is a common finding in dystonia, and transcranial direct current stimulation can reduce motor cortex excitability. In an earlier study, we found that cathodal direct-current stimulation decreased motor overflow for some children with dystonia. To investigate this observation further, we performed a sham-controlled, double-blind, crossover study of 14 children with dystonia. We found a significant reduction in overflow following real stimulation, when participants performed the experimental task with the hand contralateral to the cathode. While these results suggest that cathodal stimulation may help some children to reduce involuntary overflow, the size of the effect is small. Further research will need to investigate ways to increase the magnitude of the effect of cathodal transcranial direct current stimulation.

Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways—training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)—as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care–related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favorably.