Bringing Anatomical Information into Neuronal Network Models

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of `predictive connectomics' estimate connectivity where the data are lacking based on statistical relationships with known quantities. It is instructive, and in certain cases necessary, to use organizational principles that link the plethora of data within a unifying framework where regularities of brain structure can be exploited to inform computational models. In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Characterization and modeling of primate cortical anatomy and activity

Neuroscience is the study of the brain and all the complex mechanisms that make thought and cognition possible. The cerebral cortex is where some of the most complex cognitive processes are believed to occur. This work primarily focuses on the macaque, since it is a close relative to humans and a widely studied model animal. While experimental studies are limited to a few neurons and locations, computational models can compensate these limitations since they allow to study the entire system at will. However, there are many hurdles on the way to reliable and realistic brain models, some of which we addressed in this dissertation. We identified some specific gaps in the knowledge that impede the creation of comprehensive brain models. These include: the lack of resting state extracellular neural recordings and its subsequent analysis, the lack of comprehensive neuron density estimates and their statistical distribution, and the lack of connectivity data within cortical areas. The aim of this dissertation is to address these gaps in the knowledge in order to construct comprehensive models of the macaque cortex at a neuronal level.In this dissertation, we present high-resolution resting state data from macaque V1 and V4 areas, along with exhaustive quality controls and all the relevant metadata about the experiment. We then study the resting state data and show distinct structures in the population dynamics, which our analysis and simulations suggest could be modulated by feedback from V4 to V1. Moreover, we show that the distribution of neuron densities across and within the cortex of mammals is compatible with a lognormal distribution, which could easily emerge from a noisy cell division process. In addition, we present new measurements of neuron density in the macaque cortex, in an area and layer resolved manner. These measurements required a 3D reconstruction from histological slices and constitute, to the best of our knowledge, the first comprehensive data set of neuron densities in a single macaque. Finally, we present a method to estimate local microcircuit connectivity from resting state spiking activity, using single unit spiking statistics and the Wasserstein distance. We show that the activity is significantly different across the cortex and demonstrate the validity of our parameter estimation method using synthetic data. In conclusion, this work provides activity and anatomical data for the neuroscience community, as well as several methods that will be applicable beyond the scope of this thesis. All in all, this work brings the field a small step closer to a comprehensive understanding of the cerebral cortex

Feedback modulation of neural manifolds in macaque primary visual cortex

High-dimensional brain activity is in many cases organized into lower-dimensional neural manifolds [1,2]. Feedback from V4 to V1 is known to mediate visual attention [3] and computational work has shown that it can also rotate neural manifolds in a context-dependent manner [4]. However, whether feedback signals can modulate neural manifolds in vivo remains to be ascertained. Here, we studied the neural manifolds in macaque (Macaca mulatta) visual cortex during resting state [5] and found two distinct high-dimensional clusters in the activity. The clusters were primarily correlated with behavioral state (eye closure) and had distinct dimensionality. Granger causality analysis revealed that feedback from V4 to V1 was significantly stronger during the eyes-open periods. Finally, spiking neuron model simulations confirmed that signals mimicking V4-to-V1 feedback can modulate neural manifolds. Taken together, the data analysis and simulations suggest that feedback signals actively modulate neural manifolds in the visual cortex of the macaque.References:[1] Stringer et al. (2020). Nature 571, 361-365. https://doi.org/10.1038/s41586-019-1346-5[2] Singh et al. (2008). Journal of Vision 8(8), 11. https://doi.org/10.1167/8.8.11[3] Poort et al. (2012). Neuron 75 (1), 143-156. https://doi.org/10.1016/j.neuron.2012.04.032[4] Naumann et al. (2022). eLife 11, 76096. https://doi.org/10.7554/eLife.76096[5] Chen*, Morales-Gregorio* et al. (2022). Scientific Data 9 (1), 77. https://doi.org/10.1038/s41597-022-01180-

Characterization and modeling of primate cortical anatomy and activity

Neuroscience is the study of the brain and all the complex mechanisms that make thought and cognition possible. The cerebral cortex is where some of the most complex cognitive processes are believed to occur. This work primarily focuses on the macaque, since it is a close relative to humans and a widely studied model animal. While experimental studies are limited to a few neurons and locations, computational models can compensate these limitations since they allow to study the entire system at will. However, there are many hurdles on the way to reliable and realistic brain models, some of which we addressed in this dissertation. We identified some specific gaps in the knowledge that impede the creation of comprehensive brain models. These include: the lack of resting state extracellular neural recordings and its subsequent analysis, the lack of comprehensive neuron density estimates and their statistical distribution, and the lack of connectivity data within cortical areas. The aim of this dissertation is to address these gaps in the knowledge in order to construct comprehensive models of the macaque cortex at a neuronal level. In this dissertation, we present high-resolution resting state data from macaque V1 and V4 areas, along with exhaustive quality controls and all the relevant metadata about the experiment. We then study the resting state data and show distinct structures in the population dynamics, which our analysis and simulations suggest could be modulated by feedback from V4 to V1. Moreover, we show that the distribution of neuron densities across and within the cortex of mammals is compatible with a lognormal distribution, which could easily emerge from a noisy cell division process. In addition, we present new measurements of neuron density in the macaque cortex, in an area and layer resolved manner. These measurements required a 3D reconstruction from histological slices and constitute, to the best of our knowledge, the first comprehensive data set of neuron densities in a single macaque. Finally, we present a method to estimate local microcircuit connectivity from resting state spiking activity, using single unit spiking statistics and the Wasserstein distance. We show that the activity is significantly different across the cortex and demonstrate the validity of our parameter estimation method using synthetic data. In conclusion, this work provides activity and anatomical data for the neuroscience community, as well as several methods that will be applicable beyond the scope of this thesis. All in all, this work brings the field a small step closer to a comprehensive understanding of the cerebral cortex