Global VAX: A U.S. contribution to global COVID-19 vaccination efforts, 2021-2023.

In December 2021 the U.S. Government announced a new, whole-of-government $1.8 billion effort, the Initiative for Global Vaccine Access (Global VAX) in response to the global COVID-19 pandemic. Using the foundation of decades of U.S. government investments in global health and working in close partnership with local governments and key global and multilateral organizations, Global VAX enabled the rapid acceleration of the global COVID-19 vaccine rollout in selected countries, contributing to increased COVID-19 vaccine coverage in some of the world's most vulnerable communities. Through Global VAX, the U.S. Government has supported 125 countries to scale up COVID-19 vaccine delivery and administration while strengthening primary health care systems to respond to future health crises. The progress made by Global VAX has paved the way for a stronger global recovery and improved global health security.

Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo.

To produce adaptive behavior, neural networks must balance between plasticity and stability. Computational work has demonstrated that network stability requires plasticity mechanisms to be counterbalanced by rapid compensatory processes. However, such processes have yet to be experimentally observed. Here we demonstrate that repeated optogenetic activation of excitatory neurons in monkey visual cortex (area V1) induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state, but not during rest. This new form of rapid plasticity was observed only in the correlation structure, with firing rates remaining stable across trials. A computational network model operating in the balanced regime confirmed experimental findings and revealed that inhibitory plasticity is responsible for the decrease in correlated activity in response to repeated light stimulation. These results provide the first experimental evidence for rapid homeostatic plasticity that primarily operates during wakefulness, which stabilizes neuronal interactions during strong network co-activation.

Meta-learning biologically plausible plasticity rules with random feedback pathways.

Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedback alignment offers an alternative model in which errors are propagated backward through fixed, random backward connections. This approach successfully trains shallow models, but learns slowly and does not perform well with deeper models or online learning. In this study, we develop a meta-learning approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections. The resulting plasticity rules show improved online training of deep models in the low data regime. Our results highlight the potential of meta-learning to discover effective, interpretable learning rules satisfying biological constraints.

Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks.

The brain is believed to operate in part by making predictions about sensory stimuli and encoding deviations from these predictions in the activity of "prediction error neurons." This principle defines the widely influential theory of predictive coding. The precise circuitry and plasticity mechanisms through which animals learn to compute and update their predictions are unknown. Homeostatic inhibitory synaptic plasticity is a promising mechanism for training neuronal networks to perform predictive coding. Homeostatic plasticity causes neurons to maintain a steady, baseline firing rate in response to inputs that closely match the inputs on which a network was trained, but firing rates can deviate away from this baseline in response to stimuli that are mismatched from training. We combine computer simulations and mathematical analysis systematically to test the extent to which randomly connected, unstructured networks compute prediction errors after training with homeostatic inhibitory synaptic plasticity. We find that homeostatic plasticity alone is sufficient for computing prediction errors for trivial time-constant stimuli, but not for more realistic time-varying stimuli. We use a mean-field theory of plastic networks to explain our findings and characterize the assumptions under which they apply.

On the relationship between predictive coding and backpropagation.

Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been proposed as a potentially more biologically realistic alternative to backpropagation for training neural networks. This manuscript reviews and extends recent work on the mathematical relationship between predictive coding and backpropagation for training feedforward artificial neural networks on supervised learning tasks. Implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning are discussed along with a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models.

Balanced networks under spike-time dependent plasticity.

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory-inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike-timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity-induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.

Refractory Syncope and Presyncope Associated with Atlantoaxial Instability: Preliminary Evidence of Improvement Following Surgical Stabilization.

BACKGROUND: The proclivity to atlantoaxial instability (AAI) has been widely reported for conditions such as rheumatoid arthritis and Down syndrome. Similarly, we have found a higher than expected incidence of AAI in hereditary connective tissue disorders. We demonstrate a strong association of AAI with manifestations of dysautonomia, in particular syncope and lightheadedness, and make preliminary observations as to the salutary effect of surgical stabilization of the atlantoaxial motion segment. METHODS: In an institutional review board-approved retrospective study, 20 subjects (16 women, 4 men) with hereditary connective tissue disorders had AAI diagnosed by computed tomography. Subjects underwent realignment (reduction), stabilization, and fusion of the C1-C2 motion segment. All subjects completed preoperative and postoperative questionnaires in which they were asked about performance, function, and autonomic symptoms, including lightheadedness, presyncope, and syncope. RESULTS: All patients with AAI reported lightheadedness, and 15 had refractory syncope or presyncope despite maximal medical management and physical therapy. Postoperatively, subjects reported a statistically significant improvement in lightheadedness (P = 0.003), presyncope (P = 0.006), and syncope (P = 0.03), and in the frequency (P < 0.05) of other symptoms related to autonomic function, such as nausea, exercise intolerance, palpitations, tremors, heat intolerance, gastroesophageal reflux, and sleep apnea. CONCLUSIONS: This study draws attention to the potential for AAI to present with syncope or presyncope that is refractory to medical management, and for surgical stabilization of AAI to lead to improvement of these and other autonomic symptoms.

Atlanto-axial rotary instability (Fielding type 1): characteristic clinical and radiological findings, and treatment outcomes following alignment, fusion, and stabilization.

Atlanto-axial instability (AAI) is common in the connective tissue disorders, such as rheumatoid arthritis, and increasingly recognized in the heritable disorders of Stickler, Loeys-Dietz, Marfan, Morquio, and Ehlers-Danlos (EDS) syndromes, where it typically presents as a rotary subluxation due to incompetence of the alar ligament. This retrospective, IRB-approved study examines 20 subjects with Fielding type 1 rotary subluxation, characterized by anterior subluxation of the facet on one side, with a normal atlanto-dental interval. Subjects diagnosed with a heritable connective tissue disorder, and AAI had failed non-operative treatment and presented with severe headache, neck pain, and characteristic neurological findings. Subjects underwent a modified Goel-Harms posterior C1-C2 screw fixation and fusion without complication. At 15 months, two subjects underwent reoperation following a fall (one) and occipito-atlantal instability (one). Patients reported improvement in the frequency or severity of neck pain (P < 0.001), numbness in the hands and lower extremities (P = 0.001), headaches, pre-syncope, and lightheadedness (all P < 0.01), vertigo and arm weakness (both P = 0.01), and syncope, nausea, joint pain, and exercise tolerance (all P < 0.05). The diagnosis of Fielding type 1 AAI requires directed investigation with dynamic imaging. Alignment and stabilization is associated with improvement of pain, syncopal and near-syncopal episodes, sensorimotor function, and exercise tolerance.

Neural activity during a simple reaching task in macaques is counter to gating and rebound in basal ganglia-thalamic communication.

Task-related activity in the ventral thalamus, a major target of basal ganglia output, is often assumed to be permitted or triggered by changes in basal ganglia activity through gating- or rebound-like mechanisms. To test those hypotheses, we sampled single-unit activity from connected basal ganglia output and thalamic nuclei (globus pallidus-internus [GPi] and ventrolateral anterior nucleus [VLa]) in monkeys performing a reaching task. Rate increases were the most common peri-movement change in both nuclei. Moreover, peri-movement changes generally began earlier in VLa than in GPi. Simultaneously recorded GPi-VLa pairs rarely showed short-time-scale spike-to-spike correlations or slow across-trials covariations, and both were equally positive and negative. Finally, spontaneous GPi bursts and pauses were both followed by small, slow reductions in VLa rate. These results appear incompatible with standard gating and rebound models. Still, gating or rebound may be possible in other physiological situations: simulations show how GPi-VLa communication can scale with GPi synchrony and GPi-to-VLa convergence, illuminating how synchrony of basal ganglia output during motor learning or in pathological conditions may render this pathway effective. Thus, in the healthy state, basal ganglia-thalamic communication during learned movement is more subtle than expected, with changes in firing rates possibly being dominated by a common external source.

Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance.

Balanced excitation and inhibition is widely observed in cortex. How does this balance shape neural computations and stimulus representations? This question is often studied using computational models of neuronal networks in a dynamically balanced state. But balanced network models predict a linear relationship between stimuli and population responses. So how do cortical circuits implement nonlinear representations and computations? We show that every balanced network architecture admits stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state produces nonlinear stimulus representations and nonlinear computations, is unavoidable in networks driven by multiple stimuli, is consistent with cortical recordings, and has a direct mathematical relationship to artificial neural networks.

The Neurosurgical Intraoperative Checklist for Surgery of the Craniocervical Junction and Spine.

Many sectors within healthcare have adapted checklists to improve quality control. Notwithstanding the reported successful implementation of surgical checklists in the operating theater, a dearth of literature addresses the specific challenges posed by complex surgery in the craniocervical junction and spine. The authors devised an intraoperative checklist to address the common errors and verify the completion of objectives unique to these surgeries. The data over six years is presented retrospectively; no historical control for comparison is available, as those omissions and surgical errors addressed by the checklist are not generally registered in any morbidity and mortality reports. Through six years and approximately 1200 surgeries, the checklist was implemented with 98% compliance. The checklist eliminated the occurrences of mundane surgical errors, minimized iatrogenic complications, and ensured completion of specific objectives. We discuss that preoperative checklists, now in general use in all hospitals, have not addressed the most common, intraoperative omissions. These technical omissions result in part from the complexity of spine surgery and directly impact the surgical outcome. The Neurosurgical Intraoperative Checklist is a practical, rapid, and comprehensive means to prevent common, avoidable errors and iatrogenic complications inherent to spine surgery.

Spatially extended balanced networks without translationally invariant connectivity.

Networks of neurons in the cerebral cortex exhibit a balance between excitation (positive input current) and inhibition (negative input current). Balanced network theory provides a parsimonious mathematical model of this excitatory-inhibitory balance using randomly connected networks of model neurons in which balance is realized as a stable fixed point of network dynamics in the limit of large network size. Balanced network theory reproduces many salient features of cortical network dynamics such as asynchronous-irregular spiking activity. Early studies of balanced networks did not account for the spatial topology of cortical networks. Later works introduced spatial connectivity structure, but were restricted to networks with translationally invariant connectivity structure in which connection probability depends on distance alone and boundaries are assumed to be periodic. Spatial connectivity structure in cortical network does not always satisfy these assumptions. We use the mathematical theory of integral equations to extend the mean-field theory of balanced networks to account for more general dependence of connection probability on the spatial location of pre- and postsynaptic neurons. We compare our mathematical derivations to simulations of large networks of recurrently connected spiking neuron models.

Optimizing Alignment Parameters During Craniocervical Stabilization and Fusion: A Technical Note.

Proper craniocervical alignment during craniocervical reduction, stabilization, and fusion optimizes cerebrospinal fluid (CSF) flow through the foramen magnum, establishes the appropriate "gaze angle", avoids dysphagia and dyspnea, and, most importantly, normalizes the clival-axial angle (CXA) to reduce ventral brainstem compression. To illustrate the metrics of reduction that include CXA, posterior occipital cervical angle, orbital-axial or "gaze angle", and mandible-axial angle, we present a video illustration of a patient presenting with signs and symptoms of the cervical medullary syndrome along with concordant radiographic findings of craniocervical instability as identified on dynamic imaging and through assessment of the CXA, Harris, and Grabb-Oakes measurements.

Inference of synaptic connectivity and external variability in neural microcircuits.

A major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activity in vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. For in vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many studies use in silico simulations as benchmarks for investigation, but such approaches necessarily rely upon a variety of simplifying assumptions about the simulated network and can depend on numerous simulation parameters. We combine neuronal network simulations, mathematical analysis, and calcium imaging data to address the question of when and how functional connectivity, synaptic connectivity, and latent external input variability can be untangled. We show numerically and analytically that, even though the precision matrix of recorded spiking activity does not uniquely determine synaptic connectivity, it is in practice often closely related to synaptic connectivity. This relation becomes more pronounced when the spatial structure of neuronal variability is jointly considered.

Correlated states in balanced neuronal networks.

Understanding the magnitude and structure of interneuronal correlations and their relationship to synaptic connectivity structure is an important and difficult problem in computational neuroscience. Early studies show that neuronal network models with excitatory-inhibitory balance naturally create very weak spike train correlations, defining the "asynchronous state." Later work showed that, under some connectivity structures, balanced networks can produce larger correlations between some neuron pairs, even when the average correlation is very small. All of these previous studies assume that the local network receives feedforward synaptic input from a population of uncorrelated spike trains. We show that when spike trains providing feedforward input are correlated, the downstream recurrent network produces much larger correlations. We provide an in-depth analysis of the resulting "correlated state" in balanced networks and show that, unlike the asynchronous state, it produces a tight excitatory-inhibitory balance consistent with in vivo cortical recordings.

A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity.

Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopamine-modulated plasticity in the basal ganglia that trains parallel cortical pathways through unsupervised plasticity as a motor task becomes well learned. We developed a novel learning algorithm for reservoir computing that models the interaction between reinforcement and unsupervised learning observed in experiments. This novel learning algorithm converges on simulated motor tasks on which previous reservoir computing algorithms fail and reproduces experimental findings that relate Parkinson's disease and its treatments to motor learning. Hence, incorporating biological theories of motor learning improves the effectiveness and biological relevance of reservoir computing models.

Special Issue from the 2017 International Conference on Mathematical Neuroscience.

The ongoing acquisition of large and multifaceted data sets in neuroscience requires new mathematical tools for quantitatively grounding these experimental findings. Since 2015, the International Conference on Mathematical Neuroscience (ICMNS) has provided a forum for researchers to discuss current mathematical innovations emerging in neuroscience. This special issue assembles current research and tutorials that were presented at the 2017 ICMNS held in Boulder, Colorado from May 30 to June 2. Topics discussed at the meeting include correlation analysis of network activity, information theory for plastic synapses, combinatorics for attractor neural networks, and novel data assimilation methods for neuroscience-all of which are represented in this special issue.

Circuit Models of Low-Dimensional Shared Variability in Cortical Networks.

Trial-to-trial variability is a reflection of the circuitry and cellular physiology that make up a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional. Previous model cortical networks cannot explain this global variability, and rather assume it is from external sources. We show that if the spatial and temporal scales of inhibitory coupling match known physiology, networks of model spiking neurons internally generate low-dimensional shared variability that captures population activity recorded in vivo. Shifting spatial attention into the receptive field of visual neurons has been shown to differentially modulate shared variability within and between brain areas. A top-down modulation of inhibitory neurons in our network provides a parsimonious mechanism for this attentional modulation. Our work provides a critical link between observed cortical circuit structure and realistic shared neuronal variability and its modulation.

The shipboard Beirut terrorist bombing experience: a historical account and recommendations for preparedness in events of mass neurological injuries.

On a Sunday morning at 06:22 on October 23, 1983, in Beirut, Lebanon, a semitrailer filled with TNT sped through the guarded barrier into the ground floor of the Civilian Aviation Authority and exploded, killing and wounding US Marines from the 1st Battalion 8th Regiment (2nd Division), as well as the battalion surgeon and deployed corpsmen. The truck bomb explosion, estimated to be the equivalent of 21,000 lbs of TNT, and regarded as the largest nonnuclear explosion since World War II, caused what was then the most lethal single-day death toll for the US Marine Corps since the Battle of Iwo Jima in World War II. Considerable neurological injury resulted from the bombing. Of the 112 survivors, 37 had head injuries, 2 had spinal cord injuries, and 9 had peripheral nerve injuries. Concussion, scalp laceration, and skull fracture were the most common cranial injuries.Within minutes of the explosion, the Commander Task Force 61/62 Mass Casualty Plan was implemented by personnel aboard the USS Iwo Jima. The wounded were triaged according to standard protocol at the time. Senator Humphreys, chairman of the Preparedness Committee and a corpsman in the Korean War, commented that he had never seen such a well-executed evolution. This was the result of meticulous preparation that included training not only of the medical personnel but also of volunteers from the ship's company, frequent drilling with other shipboard units, coordination of resources throughout the ship, the presence of a meticulous senior enlisted man who carefully registered each of the wounded, the presence of trained security forces, and a drilled and functioning communication system.Viewed through the lens of a neurosurgeon, the 1983 bombings and mass casualty event impart important lessons in preparedness. Medical personnel should be trained specifically to handle the kinds of injuries anticipated and should rehearse the mass casualty event on a regular basis using mock-up patients. Neurosurgery staff should participate in training and planning for events alongside other clinicians. Training of nurses, corpsmen, and also nonmedical personnel is essential. In a large-scale evolution, nonmedical personnel may monitor vital signs, work as scribes or stretcher bearers, and run messages. It is incumbent upon medical providers and neurosurgeons in particular to be aware of the potential for mass casualty events and to make necessary preparations.

What does the research say about androgen use and cerebrovascular events?

Many studies have investigated the benefits of androgen therapy and neurosteroids in aging men, while concerns remain about the potential associations of exogenous steroids and incidents of cerebrovascular events and ischemic stroke (IS). Testosterone is neuroprotective, neurotrophic and a potent stimulator of neuroplasticity. These benefits are mediated primarily through conversion of a small amount of testosterone to estradiol by the catalytic activity of estrogen synthetase (aromatase cytochrome P450 enzyme). New studies suggest that abnormal serum levels of the nonaromatized potent metabolite of testosterone, either high or low dihydrotestosterone (DHT), is a risk factor for stroke. Associations between pharmacologic androgen use and the incidence of IS are questionable, because a significant portion of testosterone is converted to DHT. There is also insufficient evidence to reject a causal relationship between the pro-testosterone adrenal androgens and incidence of IS. Moreover, vascular intima-media thickness, which is a predictor of stroke and myocardial symptoms, has correlations with sex hormones. Current diagnostic and treatment criteria for androgen therapy for cerebrovascular complications are unclear. Confounding variables, including genetic and metabolic alterations of the key enzymes of steroidogenesis, ought to be considered. Information extracted from pharmacogenetic testing may aid in expounding the protective-destructive properties of neurosteroids, as well as the prognosis of androgen therapy, in particular their cerebrovascular outcomes. This investigative review article addresses relevant findings of the clinical and experimental investigations of androgen therapy, emphasizes the significance of genetic testing of androgen responsiveness towards individualized therapy in post-IS injuries as well as identifying pertinent questions.