Marc Benayoun

Avalanches in a stochastic model of spiking neurons.

Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be “critical” for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality.

Emergent Oscillations in Networks of Stochastic Spiking Neurons

Networks of neurons produce diverse patterns of oscillations, arising from the networks global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the networks connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework.

Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations

In the cat or primate primary visual cortex (V1), normal vision corresponds to a state where neural excitation patterns are driven by external visual stimuli. A spectacular failure mode of V1 occurs when such patterns are overwhelmed by spontaneously generated spatially self-organized patterns of neural excitation. These are experienced as geometric visual hallucinations. The problem of identifying the mechanisms by which V1 avoids this failure is made acute by recent advances in the statistical mechanics of pattern formation, which suggest that the hallucinatory state should be very robust. Here, we report how incorporating physiologically realistic long-range connections between inhibitory neurons changes the behavior of a model of V1. We find that the sparsity of long-range inhibition in V1 plays a previously unrecognized but key functional role in preserving the normal vision state. Surprisingly, it also contributes to the observed regularity of geometric visual hallucinations. Our results provide an explanation for the observed sparsity of long-range inhibition in V1—this generic architectural feature is an evolutionary adaptation that tunes V1 to the normal vision state. In addition, it has been shown that exactly the same long-range connections play a key role in the development of orientation preference maps. Thus V1’s most striking long-range features—patchy excitatory connections and sparse inhibitory connections—are strongly constrained by two requirements: the need for the visual state to be robust and the developmental requirements of the orientational preference map.

EEG, temporal correlations, and avalanches.

Epileptiform activity in the EEG is frequently characterized by rhythmic, correlated patterns or synchronized bursts. Long-range temporal correlations (LRTC) are described by power law scaling of the autocorrelation function and have been observed in scalp and intracranial EEG recordings. Synchronous large-amplitude bursts (also called neuronal avalanches) have been observed in local field potentials both in vitro and in vivo. This article explores the presence of neuronal avalanches in scalp and intracranial EEG in the context of LRTC. Results indicate that both scalp and intracranial EEG show LRTC, with larger scaling exponents in scalp recordings than intracranial. A subset of analyzed recordings also show avalanche behavior, indicating that avalanches may be associated with LRTC. Artificial test signals reveal a linear relationship between the scaling exponent measured by detrended fluctuation analysis and the exponent of the avalanche size distribution. Analysis and evaluation of simulated data reveal that preprocessing of EEG (squaring the signal or applying a filter) affect the ability of detrended fluctuation analysis to reliably measure LRTC.

Sacrum and Coccyx Radiographs Have Limited Clinical Impact in the Emergency Department

OBJECTIVE The purpose of this study was to determine the yield and clinical impact of sacrum and coccyx radiographs in the emergency department (ED). MATERIALS AND METHODS Consecutive sacrum and coccyx radiographs obtained in the EDs of four hospitals over a 6-year period were categorized as positive for acute fracture or dislocation, negative, or other. Five follow-up metrics were analyzed: follow-up advanced imaging in the same ED visit, follow-up advanced imaging within 30 days, new analgesic prescriptions, clinic follow-up, and surgical intervention within 60 days. RESULTS Sacrum and coccyx radiographs from 687 patients (mean age, 48.1 years; 61.6% women and 38.4% men) obtained at level-1 (n = 335) and level-2 (n = 352) trauma centers showed a positivity rate of 8.4% ± 2.1% (n = 58/687). None of the 58 positive cases had surgical intervention. At the level-1 trauma centers, there was no significant association between sacrum and coccyx radiograph positivity and analgesic prescription or clinical follow-up (p = 0.12; odds ratio [OR], 2.3; 95% CI, 0.81-6.20). At the level-2 trauma centers, 97.1% (n = 34/35) of patients with positive sacrum and coccyx radiographs received analgesic prescriptions or clinical referrals, whereas negative cases were at 82.9% (OR, 7.0; 95% CI, 0.94-52.50). Of all cases, 5.7% (n = 39) and 4.3% (n = 29) had advanced imaging in the same ED visit and within 30 days, respectively. Sacrum and coccyx radiography results had no significant correlation with advanced imaging in the same ED visit (level-1, p = 0.351; level-2, p = 0.179). There was no significant difference in 30-day advanced imaging at the level-1 trauma centers (p = 0.8), but there was at the level-2 trauma centers (p = 0.0493). CONCLUSION ED sacrum and coccyx radiographs showed a low positivity rate and had no quantifiable clinical impact. We recommend that sacrum and coccyx radiographs be eliminated from ED practice and patients treated conservatively on the basis of clinical parameters.

Utility of computed tomographic imaging of the cervical spine in trauma evaluation of ground-level fall

BACKGROUND Computed tomography (CT) of the cervical spine (C-spine) is routinely ordered for low-risk mechanisms of injury, including ground-level fall. Two commonly used clinical decision rules (CDRs) to guide C-spine imaging in trauma are the National Emergency X-Radiography Utilization Study (NEXUS) and the Canadian Cervical Spine Rule for Radiography (CCR). METHODS Retrospective cross-sectional study of 3,753 consecutive adult patients presenting to an urban Level I emergency department who received C-spine CT scans were obtained over a 6-month period. The primary outcome of interest was prevalence of C-spine fracture. Secondary outcomes included fracture stability, appropriateness of imaging by NEXUS and CCR criteria, and estimated radiation dose exposure and costs associated with C-spine imaging studies. RESULTS Of the 760 patients meeting inclusion criteria, 7 C-spine fractures were identified (0.92% ± 0.68%). All fractures were identified by NEXUS and CCR criteria with 100% sensitivity. Of all these imaging studies performed, only 69% met NEXUS indications for imaging (50% met CCR indications). C-spine CT scans in patients not meeting CDR indications were associated with costs of

Chapter 31 – Decision Theory

15,500 to

Systematic comparison of the behaviors produced by computational models of epileptic neocortex.

22,000 by NEXUS (

Functional Magnetic Imaging

14,600–

Frequency Analysis Part II

25,600 by CCR) in this single center during the 6-month study period. CONCLUSION For ground-level fall, C-spine CT is overused. The consistent application of CDR criteria would reduce annual nationwide imaging costs in the United States by