The spike-timing-dependent plasticity of VIP interneurons in motor cortex.

The plasticity of inhibitory interneurons (INs) plays an important role in the organization and maintenance of cortical microcircuits. Given the many different IN types, there is an even greater diversity in synapse-type-specific plasticity learning rules at excitatory to excitatory (E→I), I→E, and I→I synapses. I→I synapses play a key disinhibitory role in cortical circuits. Because they typically target other INs, vasoactive intestinal peptide (VIP) INs are often featured in I→I→E disinhibition, which upregulates activity in nearby excitatory neurons. VIP IN dysregulation may thus lead to neuropathologies such as epilepsy. In spite of the important activity regulatory role of VIP INs, their long-term plasticity has not been described. Therefore, we characterized the phenomenology of spike-timing-dependent plasticity (STDP) at inputs and outputs of genetically defined VIP INs. Using a combination of whole-cell recording, 2-photon microscopy, and optogenetics, we explored I→I STDP at layer 2/3 (L2/3) VIP IN outputs onto L5 Martinotti cells (MCs) and basket cells (BCs). We found that VIP IN→MC synapses underwent causal long-term depression (LTD) that was presynaptically expressed. VIP IN→BC connections, however, did not undergo any detectable plasticity. Conversely, using extracellular stimulation, we explored E→I STDP at inputs to VIP INs which revealed long-term potentiation (LTP) for both causal and acausal timings. Taken together, our results demonstrate that VIP INs possess synapse-type-specific learning rules at their inputs and outputs. This suggests the possibility of harnessing VIP IN long-term plasticity to control activity-related neuropathologies such as epilepsy.

Vision Transformer-based Decision Support for Neurosurgical Intervention in Acute Traumatic Brain Injury: Automated Surgical Intervention Support Tool.

Purpose To develop an automated triage tool to predict neurosurgical intervention for patients with traumatic brain injury (TBI). Materials and Methods A provincial trauma registry was reviewed to retrospectively identify patients with TBI from 2005 to 2022 treated at a specialized Canadian trauma center. Model training, validation, and testing were performed using head CT scans with binary reference standard patient-level labels corresponding to whether the patient received neurosurgical intervention. Performance and accuracy of the model, the Automated Surgical Intervention Support Tool for TBI (ASIST-TBI), were also assessed using a held-out consecutive test set of all patients with TBI presenting to the center between March 2021 and September 2022. Results Head CT scans from 2806 patients with TBI (mean age, 57 years ± 22 [SD]; 1955 [70%] men) were acquired between 2005 and 2021 and used for training, validation, and testing. Consecutive scans from an additional 612 patients (mean age, 61 years ± 22; 443 [72%] men) were used to assess the performance of ASIST-TBI. There was accurate prediction of neurosurgical intervention with an area under the receiver operating characteristic curve (AUC) of 0.92 (95% CI: 0.88, 0.94), accuracy of 87% (491 of 562), sensitivity of 87% (196 of 225), and specificity of 88% (295 of 337) on the test dataset. Performance on the held-out test dataset remained robust with an AUC of 0.89 (95% CI: 0.85, 0.91), accuracy of 84% (517 of 612), sensitivity of 85% (199 of 235), and specificity of 84% (318 of 377). Conclusion A novel deep learning model was developed that could accurately predict the requirement for neurosurgical intervention using acute TBI CT scans. Keywords: CT, Brain/Brain Stem, Surgery, Trauma, Prognosis, Classification, Application Domain, Traumatic Brain Injury, Triage, Machine Learning, Decision Support Supplemental material is available for this article. © RSNA, 2024 See also commentary by Haller in this issue.

Self-harm presentations to hospital trauma centre emergency departments during the first year of the COVID-19 pandemic.

The COVID-19 pandemic raised concerns regarding increased suicide-related behaviours. We compared characteristics and counts of Emergency Department (ED) presentations for self-harm, an important suicide-related outcome, during versus prior to the pandemic's first year. We included patients presenting with self-harm to the ED of two trauma centres in Toronto, Canada. Time series models compared intra-pandemic (March 2020-February 2021) presentation counts to predictions from pre-pandemic data. The self-harm proportion of ED presentations was compared between the intra-pandemic period and preceding three years. A retrospective chart review of eligible patients seen from March 2019-February 2021 compared pre- vs. intra-pandemic patient and injury characteristics. While monthly intra-pandemic self-harm counts were largely within expected ranges, the self-harm proportion of total presentations increased. Being widowed (OR=9.46; 95 %CI=1.10-81.08), employment/financial stressors (OR=1.65, 95 %CI=1.06-2.58), job loss (OR=3.83; 95 %CI=1.36-10.76), and chest-stabbing self-harm (OR=2.50; 95 %CI=1.16-5.39) were associated with intra-pandemic presentations. Intra-pandemic self-harm was also associated with Intensive Care Unit (ICU) admission (OR=2.18, 95 %CI=1.41-3.38). In summary, while the number of self-harm presentations to these trauma centres did not increase during the early pandemic, their proportion was increased. The association of intra-pandemic self-harm with variables indicating medically severe injury, economic stressors, and being widowed may inform future suicide and self-harm prevention strategies.