Impacts of ABO-incompatible platelet transfusions on platelet recovery and outcomes after intracerebral hemorrhage.

Acute platelet transfusion after intracerebral hemorrhage (ICH) given in efforts to reverse antiplatelet medication effects and prevent ongoing bleeding does not appear to improve outcome and may be associated with harm. Although the underlying mechanisms are unclear, the influence of ABO-incompatible platelet transfusions on ICH outcomes has not been investigated. We hypothesized that patients with ICH who receive ABO-incompatible platelet transfusions would have worse platelet recovery (using absolute count increment [ACI]) and neurological outcomes (mortality and poor modified Rankin Scale [mRS 4-6]) than those receiving ABO-compatible transfusions. In a single-center cohort of consecutively admitted patients with ICH, we identified 125 patients receiving acute platelet transfusions, of whom 47 (38%) received an ABO-incompatible transfusion. Using quantile regression, we identified an association of ABO-incompatible platelet transfusion with lower platelet recovery (ACI, 2 × 103cells per µL vs 15 × 103cells per µL; adjusted coefficient ß, -19; 95% confidence interval [CI], -35.55 to -4.44; P = .01). ABO-incompatible platelet transfusion was also associated with increased odds of mortality (adjusted odds ratio [OR], 2.59; 95% CI, 1.00-6.73; P = .05) and poor mRS (adjusted OR, 3.61; 95% CI, 0.97-13.42; P = .06); however, these estimates were imprecise. Together, these findings suggest the importance of ABO compatibility for platelet transfusions for ICH, but further investigation into the mechanism(s) underlying these observations is required.

Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy.

Information processing in the cerebral cortex depends not only on the nature of incoming stimuli, but also on the state of neuronal networks at the time of stimulation. That is, the same stimulus will be processed differently depending on the neuronal context in which it is received. A major factor that could influence neuronal context is the background, or ongoing neuronal activity before stimulation. In visual cortex, ongoing activity is known to play a critical role in the development of local circuits, yet whether it influences the coding of visual features in adult cortex is unclear. Here, we investigate whether and how the information encoded by individual neurons and populations in primary visual cortex (V1) depends on the ongoing activity before stimulus presentation. We report that when individual neurons are in a "low" prestimulus state, they have a higher capacity to discriminate stimulus features, such as orientation, despite their reduction in evoked responses. By measuring the distribution of prestimulus activity across a population of neurons, we found that network discrimination accuracy is improved in the low prestimulus state. Thus, the distribution of ongoing activity states across the network creates an "internal context" that dynamically filters incoming stimuli to modulate the accuracy of sensory coding. The modulation of stimulus coding by ongoing activity state is consistent with recurrent network models in which ongoing activity dynamically controls the balanced background excitation and inhibition to individual neurons.

Sensory coding accuracy and perceptual performance are improved during the desynchronized cortical state.

Cortical activity changes continuously during the course of the day. At a global scale, population activity varies between the 'synchronized' state during sleep and 'desynchronized' state during waking. However, whether local fluctuations in population synchrony during wakefulness modulate the accuracy of sensory encoding and behavioral performance is poorly understood. Here, we show that populations of cells in monkey visual cortex exhibit rapid fluctuations in synchrony ranging from desynchronized responses, indicative of high alertness, to highly synchronized responses. These fluctuations are local and control the trial variability in population coding accuracy and behavioral performance in a discrimination task. When local population activity is desynchronized, the correlated variability between neurons is reduced, and network and behavioral performance are enhanced. These findings demonstrate that the structure of variability in local cortical populations is not noise but rather controls how sensory information is optimally integrated with ongoing processes to guide network coding and behavior.