A major problem in the implementation of these models is the inherently difficult and unsolved problem of parameter inference. Essential for interpreting observed neural dynamics meaningfully and differentiating across experimental conditions is the identification of unique parameter distributions. As a recent development, simulation-based inference (SBI) has been suggested as a methodology for Bayesian inference to calculate parameters in sophisticated neural models. SBI circumvents the limitation of lacking a likelihood function, a critical constraint on inference methods in similar models, by applying cutting-edge deep learning techniques for density estimation. Although the substantial methodological advancements of SBI show potential, translating these advancements into applications for large-scale biophysically detailed models proves difficult, with currently lacking methods, particularly in the realm of inferring parameters that can account for time-series waveforms. Utilizing the Human Neocortical Neurosolver's large-scale framework, we present guidelines and considerations for SBI's application in estimating time series waveforms within biophysically detailed neural models. This begins with a simplified example and advances to specific applications for common MEG/EEG waveforms. This document outlines the process of estimating and comparing outcomes from simulated oscillatory and event-related potentials. Moreover, we describe the application of diagnostic tools for determining the quality and distinctiveness of posterior estimates. Detailed models of neural dynamics are crucial for numerous applications that can utilize the principles presented in these SBI methods, guiding future implementations.
A fundamental problem within computational neural modeling involves pinpointing model parameters that can explain observed neural activity patterns. Despite the presence of several techniques for performing parameter inference in selected subclasses of abstract neural models, the repertoire of methods for large-scale biophysically detailed neural models remains comparatively sparse. In this research, we describe the obstacles and solutions encountered while utilizing a deep learning-based statistical approach to estimate parameters within a large-scale, biophysically detailed neural model, placing emphasis on the particular challenges posed by time-series data. Our illustrative example showcases a multi-scale model, linking human MEG/EEG recordings to the underlying cellular and circuit-level generators. Our strategy illuminates the connection between cellular properties and the generation of measured neural activity, and simultaneously delivers protocols for evaluating the precision and uniqueness of predictions related to diverse MEG/EEG markers.
A significant concern in computational neural modeling centers on the estimation of model parameters to reflect the patterns of activity observed. Several approaches exist for parameter inference within specific categories of abstract neural models, yet the number of viable methods dwindles drastically for the significant task of parameter estimation in large-scale, biophysically detailed neural models. TW-37 This study details the hurdles and remedies encountered when applying a deep learning-driven statistical framework to parameter estimation within a large-scale, biophysically detailed neural model, highlighting the specific challenges associated with estimating parameters from time series data. Our illustration involves a multi-scale model, intentionally structured to connect human MEG/EEG recordings to their cellular and circuit-level sources. Crucially, our approach allows us to understand how cell-level properties contribute to measured neural activity, and provides a framework for evaluating the quality and uniqueness of the predictions for diverse MEG/EEG biomarkers.
The genetic architecture of a complex disease or trait is significantly illuminated by the heritability of local ancestry markers within an admixed population. Ancestral population structures may introduce biases into the estimations. This work introduces a novel approach, HAMSTA (Heritability Estimation from Admixture Mapping Summary Statistics), inferring heritability explained by local ancestry from admixture mapping summary statistics, adjusting for any biases from ancestral stratification. Our findings, based on extensive simulations, indicate that the HAMSTA estimates are nearly unbiased and resistant to ancestral stratification, surpassing the accuracy of other available methods. Given ancestral stratification, we find that a HAMSTA-generated sampling methodology produces a calibrated family-wise error rate (FWER) of 5% for admixture mapping analyses, contrasting with other FWER estimation strategies. Utilizing HAMSTA, we analyzed 20 quantitative phenotypes among up to 15,988 self-reported African American individuals participating in the Population Architecture using Genomics and Epidemiology (PAGE) study. The 20 phenotypes' values span from 0.00025 to 0.0033 (mean), which is equivalent to a range of 0.0062 to 0.085 (mean). In current admixture mapping studies examining various phenotypes, there is scant indication of inflation arising from ancestral population stratification. The average inflation factor observed was 0.99 ± 0.0001. Generally, HAMSTA offers a rapid and potent method for determining genome-wide heritability and assessing biases in test statistics used in admixture mapping studies.
The multifaceted nature of human learning, demonstrating substantial differences amongst individuals, is associated with the structural characteristics of key white matter tracts in diverse learning domains, however, the influence of pre-existing myelination of these tracts on future learning remains unknown. We applied a machine-learning model selection framework to assess whether existing microstructure could forecast variations in individual learning potential for a sensorimotor task, and further, whether the correlation between major white matter tracts' microstructure and learning outcomes was specific to those learning outcomes. In 60 adult participants, we assessed the average fractional anisotropy (FA) of white matter tracts employing diffusion tractography. Subsequent training and testing sessions were used to evaluate learning proficiency. Participants engaged in repeated practice using a digital writing tablet, drawing a collection of 40 unique symbols during training. Visual recognition learning was measured using accuracy in an old/new 2-AFC recognition task; conversely, the rate of change in drawing duration across the practice session determined drawing learning. The results highlighted a selective correlation between white matter tract microstructure and learning outcomes, with the left hemisphere's pArc and SLF 3 tracts linked to drawing acquisition and the left hemisphere MDLFspl tract tied to visual recognition learning. These outcomes were duplicated in a held-out, repeated dataset, strengthened by accompanying analytical studies. TW-37 The collective outcomes hint that individual differences in the microarchitecture of human white matter tracts might be selectively linked to future learning achievements, prompting further inquiry into the effect of current tract myelination on the ability to learn.
The murine model has provided evidence of a selective correspondence between tract microstructure and future learning; this relationship has not, to our knowledge, been seen in human subjects. We utilized a data-informed methodology to identify just two tracts, namely the most posterior segments of the left arcuate fasciculus, that predicted success in a sensorimotor task—specifically, learning to draw symbols. This predictive model, however, failed to transfer to other learning objectives, such as visual symbol recognition. Variations in individual learning capacities might be correlated with the properties of key white matter tracts in the human brain, as suggested by the research.
The murine model has demonstrated a selective relationship between tract microstructure and future learning performance; however, to the best of our knowledge, this relationship remains unestablished in human subjects. Using a data-driven strategy, we discovered two key tracts—the most posterior parts of the left arcuate fasciculus—predictive of learning a sensorimotor task (drawing symbols), but this model failed to transfer to other learning goals, for instance, visual symbol recognition. TW-37 Learning differences between individuals could be selectively associated with the tissue properties of key white matter pathways in the human brain, according to the results.
The infected host's cellular machinery is exploited by non-enzymatic accessory proteins that are generated by lentiviruses. Nef, an HIV-1 accessory protein, commandeers clathrin adaptors, leading to the degradation or mislocalization of host proteins critical for antiviral responses. In genome-edited Jurkat cells, we scrutinize the interaction between Nef and clathrin-mediated endocytosis (CME), a pivotal pathway for membrane protein internalization in mammalian cells, via quantitative live-cell microscopy. Recruitment of Nef to plasma membrane CME sites demonstrates a pattern of concomitant increase in the recruitment of CME coat protein AP-2 and its extended lifetime, together with the later arrival of dynamin2. We additionally found that CME sites which recruit Nef are more likely to also recruit dynamin2, indicating that Nef recruitment is a key factor in the maturation of CME sites, thereby maximizing host protein downregulation.
A key element in a precision medicine strategy for type 2 diabetes is the determination of clinical and biological markers consistently associated with distinct treatment responses when utilizing various anti-hyperglycemic medications. Heterogeneity in treatment effects, robustly evidenced, could underpin more tailored clinical choices for optimal type 2 diabetes management.
Our pre-registered systematic review encompassed meta-analysis studies, randomized controlled trials, and observational studies, exploring clinical and biological traits influencing heterogeneous treatment outcomes for SGLT2-inhibitor and GLP-1 receptor agonist therapies, with a particular focus on their impact on glucose control, heart health, and kidney function.