Developing the surgeon-machine interface: Using a novel instance-segmentation framework for intraoperative landmark labelling

Introduction: The utilisation of artificial intelligence (AI) augments intraoperative safety, surgical training, and patient outcomes. We introduce the term Surgeon-Machine Interface (SMI) to describe this innovative intersection between surgeons and machine inference. A custom deep computer vision (CV) architecture within a sparse labelling paradigm was developed, specifically tailored to conceptualise the SMI. This platform demonstrates the ability to perform instance segmentation on anatomical landmarks and tools from a single open spinal dural arteriovenous fistula (dAVF) surgery video dataset. Methods: Our custom deep convolutional neural network was based on SOLOv2 architecture for precise, instance-level segmentation of surgical video data. Test video consisted of 8520 frames, with sparse labelling of only 133 frames annotated for training. Accuracy and inference time, assessed using F1-score and mean Average Precision (mAP), were compared against current state-of-the-art architectures on a separate test set of 85 additionally annotated frames. Results: Our SMI demonstrated superior accuracy and computing speed compared to these frameworks. The F1-score and mAP achieved by our platform were 17% and 15.2% respectively, surpassing MaskRCNN (15.2%, 13.9%), YOLOv3 (5.4%, 11.9%), and SOLOv2 (3.1%, 10.4%). Considering detections that exceeded the Intersection over Union threshold of 50%, our platform achieved an impressive F1-score of 44.2% and mAP of 46.3%, outperforming MaskRCNN (41.3%, 43.5%), YOLOv3 (15%, 34.1%), and SOLOv2 (9%, 32.3%). Our platform demonstrated the fastest inference time (88ms), compared to MaskRCNN (90ms), SOLOV2 (100ms), and YOLOv3 (106ms). Finally, the minimal amount of training set demonstrated a good generalisation performance -our architecture successfully identified objects in a frame that were not included in the training or validation frames, indicating its ability to handle out-of-domain scenarios. Discussion: We present our development of an innovative intraoperative SMI to demonstrate the future promise of advanced CV in the surgical domain. Through successful implementation in a microscopic dAVF surgery, our framework demonstrates superior performance over current state-of-the-art segmentation architectures in intraoperative landmark guidance with high sample efficiency, representing the most advanced AI-enabled surgical inference platform to date. Our future goals include transfer learning paradigms for scaling to additional surgery types, addressing clinical and technical limitations for performing real-time decoding, and ultimate enablement of a real-time neurosurgical guidance platform.</p

Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics

Brain computer interfaces (BCIs) have been applied to sensorimotor systems for many years. However, BCI technology has broad potential beyond sensorimotor systems. The emerging field of cognitive prosthetics, for example, promises to improve learning and memory for patients with cognitive impairment. Unfortunately, our understanding of the neural mechanisms underlying these cognitive processes remains limited in part due to the extensive individual variability in neural coding and circuit function. As a consequence, the development of methods to ascertain optimal control signals for cognitive decoding and restoration remains an active area of inquiry. To advance the field, robust tools are required to quantify time-varying and task-dependent brain states predictive of cognitive performance. Here, we suggest that network science is a natural language in which to formulate and apply such tools. In support of our argument, we offer a simple demonstration of the feasibility of a network approach to BCI control signals, which we refer to as network BCI (nBCI). Finally, in a single subject example, we show that nBCI can reliably predict online cognitive performance and is superior to certain common spectral approaches currently used in BCIs. Our review of the literature and preliminary findings support the notion that nBCI could provide a powerful approach for future applications in cognitive prosthetics

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Translating Molecular Approaches to Oligodendrocyte-Mediated Neurological Circuit Modulation

The central nervous system (CNS) exhibits remarkable adaptability throughout life, enabled by intricate interactions between neurons and glial cells, in particular, oligodendrocytes (OLs) and oligodendrocyte precursor cells (OPCs). This adaptability is pivotal for learning and memory, with OLs and OPCs playing a crucial role in neural circuit development, synaptic modulation, and myelination dynamics. Myelination by OLs not only supports axonal conduction but also undergoes adaptive modifications in response to neuronal activity, which is vital for cognitive processing and memory functions. This review discusses how these cellular interactions and myelin dynamics are implicated in various neurocircuit diseases and disorders such as epilepsy, gliomas, and psychiatric conditions, focusing on how maladaptive changes contribute to disease pathology and influence clinical outcomes. It also covers the potential for new diagnostics and therapeutic approaches, including pharmacological strategies and emerging biomarkers in oligodendrocyte functions and myelination processes. The evidence supports a fundamental role for myelin plasticity and oligodendrocyte functionality in synchronizing neural activity and high-level cognitive functions, offering promising avenues for targeted interventions in CNS disorders

The fasciola cinereum of the hippocampal tail as an interventional target in epilepsy.

Funder: Stanford MCHRI Tashia and John Morgridge Endowed Fellowship, LGS Foundation Cure 365Funder: Stanford Resident Neuroscience Scholar TrackTargeted tissue ablation involving the anterior hippocampus is the standard of care for patients with drug-resistant mesial temporal lobe epilepsy. However, a substantial proportion continues to suffer from seizures even after surgery. We identified the fasciola cinereum (FC) neurons of the posterior hippocampal tail as an important seizure node in both mice and humans with epilepsy. Genetically defined FC neurons were highly active during spontaneous seizures in epileptic mice, and closed-loop optogenetic inhibition of these neurons potently reduced seizure duration. Furthermore, we specifically targeted and found the prominent involvement of FC during seizures in a cohort of six patients with epilepsy. In particular, targeted lesioning of the FC in a patient reduced the seizure burden present after ablation of anterior mesial temporal structures. Thus, the FC may be a promising interventional target in epilepsy

Focused Ultrasound Thalamotomy with Dentato-Rubro-Thalamic Tractography in Patients with Spinal Cord Stimulators and Cardiac Pacemakers

Magnetic resonance image-guided high-intensity focused ultrasound (MRgFUS)-based thermal ablation of the ventral intermediate nucleus of the thalamus (VIM) is a minimally invasive treatment modality for essential tremor (ET). Dentato-rubro-thalamic tractography (DRTT) is becoming increasingly popular for direct targeting of the presumed VIM ablation focus. It is currently unclear if patients with implanted pulse generators (IPGs) can safely undergo MRgFUS ablation and reliably acquire DRTT suitable for direct targeting. We present an 80-year-old male with a spinal cord stimulator (SCS) and an 88-year-old male with a cardiac pacemaker who both underwent MRgFUS for medically refractory ET. Clinical outcomes were measured using the Clinical Rating Scale for Tremor (CRST). DRTT was successfully created and imaging parameter adjustments did not result in any delay in procedural time in either case. In the first case, 7 therapeutic sonications were delivered. The patient improved immediately and durably with a 90% CRST-disability improvement at 6-week follow-up. In our second case, 6 therapeutic sonications were delivered with durable, 75% CRST-disability improvement at 6 weeks. These are the first cases of MRgFUS thalamotomy in patients with IPGs. DRTT targeting and MRgFUS-based thermal ablation can be safely performed in these patients using a 1.5-T MRI

Thalamic Deep Brain Stimulation for Essential Tremor: Relation of the Dentatorubrothalamic Tract with Stimulation Parameters

BACKGROUND: In deep brain stimulation (DBS) for essential tremor, the primary target ventrointermedius (VIM) nucleus cannot be clearly visualized with structural imaging. As such, there has been much interest in the dentatorubrothalamic tract (DRTT) for target localization, but evidence for the DRTT as a putative stimulation target in tremor suppression is lacking. We evaluated proximity of the DRTT in relation to DBS stimulation parameters. METHODS: This is a retrospective analysis of 26 consecutive patients who underwent DBS with microelectrode recordings (46 leads). Fiber tracking was performed with a published deterministic technique. Clinically optimized stimulation parameters were obtained in all patients at the time of most recent follow-up (6.2 months). Volume of tissue activated (VTA) around contacts was calculated from a published model. RESULTS: Tremor severity was reduced in all treated hemispheres, with 70% improvement in the treated hand score of the Clinical Rating Scale for Tremor. At the level of the active contact (2.9 ± 2.0 mm superior to the commissural plane), the center of the DRTT was lateral to the contacts (5.1 ± 2.1 mm). The nearest fibers of the DRTT were 2.4 ± 1.7 mm from the contacts, whereas the radius of the VTA was 2.9 ± 0.7 mm. The VTA overlapped with the DRTT in 77% of active contacts. The distance from active contact to the DRTT was positively correlated with stimulation voltage requirements (Kendall τ = 0.33, P = 0.006), whereas distance to the atlas-based VIM coordinates was not. CONCLUSIONS: Active contacts in proximity to the DRTT had lower voltage requirements. Data from a large cohort provide support for the DRTT as an effective stimulation target for tremor control