A simple transfer function for nonlinear dendritic integration

Relatively recent advances in patch clamp recordings and iontophoresis have enabled unprecedented study of neuronal post-synaptic integration (dendritic integration). Findings support a separate layer of integration in the dendritic branches before potentials reach the cell’s soma. While integration between branches obeys previous linear assumptions, proximal inputs within a branch produce threshold nonlinearity, which some authors have likened to the sigmoid function. Here we show the implausibility of a sigmoidal relation and present a more realistic transfer function in both an elegant artificial form and a biophysically derived form that further considers input locations along the dendritic arbor. As the distance between input locations determines their ability to produce nonlinear interactions, models incorporating dendritic topology are essential to understanding the computational power afforded by these early stages of integration. We use the biophysical transfer function to emulate empirical data using biophysical parameters and describe the conditions under which the artificial and biophysically derived forms are equivalent

Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects

Ultralong Dephasing Times in Solid-State Spin Ensembles via Quantum Control

Quantum spin dephasing is caused by inhomogeneous coupling to the environment, with resulting limits to the measurement time and precision of spin-based sensors. The effects of spin dephasing can be especially pernicious for dense ensembles of electronic spins in the solid-state, such as for nitrogen-vacancy (NV) color centers in diamond. We report the use of two complementary techniques, spin bath control and double quantum coherence, to enhance the inhomogeneous spin dephasing time ($T_2^*$) for NV ensembles by more than an order of magnitude. In combination, these quantum control techniques (i) eliminate the effects of the dominant NV spin ensemble dephasing mechanisms, including crystal strain gradients and dipolar interactions with paramagnetic bath spins, and (ii) increase the effective NV gyromagnetic ratio by a factor of two. Applied independently, spin bath control and double quantum coherence elucidate the sources of spin dephasing over a wide range of NV and spin bath concentrations. These results demonstrate the longest reported $T_2^*$ in a solid-state electronic spin ensemble at room temperature, and outline a path towards NV-diamond magnetometers with broadband femtotesla sensitivity.Comment: PRX versio

Exploring HIV infection and susceptibility to measles among older children and adults in Malawi: a facility-based study

SummaryBackgroundHIV infection increases measles susceptibility in infants, but little is known about this relationship among older children and adults. We conducted a facility-based study to explore whether HIV status and/or CD4 count were associated with either measles seroprotection and/or measles antibody concentration.MethodsA convenience sample was recruited comprising HIV-infected patients presenting for follow-up care, and HIV-uninfected individuals presenting for HIV testing at Chiradzulu District Hospital, Malawi, from January to September 2012. We recorded age, sex, and reported measles vaccination and infection history. Blood samples were taken to determine the CD4 count and measles antibody concentration.ResultsOne thousand nine hundred and thirty-five participants were recruited (1434 HIV-infected and 501 HIV-uninfected). The majority of adults and approximately half the children were seroprotected against measles, with lower odds among HIV-infected children (adjusted odds ratio 0.27, 95% confidence interval 0.10–0.69; p=0.006), but not adults. Among HIV-infected participants, neither CD4 count (p=0.16) nor time on antiretroviral therapy (p=0.25) were associated with measles antibody concentration, while older age (p<0.001) and female sex (p<0.001) were independently associated with this measure.ConclusionsWe found no evidence that HIV infection contributes to the risk of measles infection among adults, but HIV-infected children (including at ages older than previously reported), were less likely to be seroprotected in this sample

Preferred roles in treatment decision making among patients with cancer: A pooled analysis of studies using the control preferences scale

OBJECTIVES: To collect normative data, assess differences between demographic groups, and indirectly compare US and Canadian medical systems relative to patient expectations of involvement in cancer treatment decision making. STUDY DESIGN: Meta-analysis. METHODS: Individual patient data were compiled across 6 clinical studies among 3491 patients with cancer who completed the 2-item Control Preferences Scale indicating the roles they preferred versus actually experienced in treatment decision making. RESULTS: The roles in treatment decision making that patients preferred were 26% active, 49% collaborative, and 25% passive. The roles that patients reported actually experiencing were 30% active, 34% collaborative, and 36% passive. Roughly 61% of patients reported having their preferred role; only 6% experienced extreme discordance between their preferred versus actual roles. More men than women (66% vs 60%, P = .001) and more US patients than Canadian patients (84% vs 54%, P <.001) reported concordance between their preferred versus actual roles. More Canadian patients than US patients preferred and actually experienced (42% vs 18%, P <.001) passive roles. More women than men reported taking a passive role (40% vs 24%, P <.001). Older patients preferred and were more likely than younger patients to assume a passive role. CONCLUSIONS: Roughly half of the studied patients with cancer indicated that they preferred to have a collaborative relationship with physicians. Although most patients had the decision-making role they preferred, about 40% experienced discordance. This highlights the need for incorporation of individualized patient communication styles into treatment plans

A generalized framework to predict continuous scores from medical ordinal labels

Many variables of interest in clinical medicine, like disease severity, are recorded using discrete ordinal categories such as normal/mild/moderate/severe. These labels are used to train and evaluate disease severity prediction models. However, ordinal categories represent a simplification of an underlying continuous severity spectrum. Using continuous scores instead of ordinal categories is more sensitive to detecting small changes in disease severity over time. Here, we present a generalized framework that accurately predicts continuously valued variables using only discrete ordinal labels during model development. We found that for three clinical prediction tasks, models that take the ordinal relationship of the training labels into account outperformed conventional multi-class classification models. Particularly the continuous scores generated by ordinal classification and regression models showed a significantly higher correlation with expert rankings of disease severity and lower mean squared errors compared to the multi-class classification models. Furthermore, the use of MC dropout significantly improved the ability of all evaluated deep learning approaches to predict continuously valued scores that truthfully reflect the underlying continuous target variable. We showed that accurate continuously valued predictions can be generated even if the model development only involves discrete ordinal labels. The novel framework has been validated on three different clinical prediction tasks and has proven to bridge the gap between discrete ordinal labels and the underlying continuously valued variables