A significant obstacle in employing these models stems from the inherently complex and unresolved nature of parameter inference. To meaningfully employ observed neural dynamics and discern differences across experimental conditions, pinpointing distinctive parameter distributions is crucial. 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's strategy for overcoming the absence of a likelihood function, a bottleneck for inference methods in these types of models, involves the application of deep learning for density estimation. While the substantial methodological gains from SBI are promising, difficulties arise when incorporating them into large-scale biophysically detailed models, with no established procedures, particularly when attempting to infer parameters reflecting 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. Our approach to estimating and contrasting results from oscillatory and event-related potential simulations is articulated below. We further elaborate on how diagnostic tools can be employed to evaluate the caliber and distinctiveness of the posterior estimations. In numerous applications that employ detailed models of neural dynamics, the described methods present a principled foundation to guide future SBI applications.
Computational neural modeling faces the significant challenge of identifying model parameters that accurately reflect observed neural activity. Several approaches exist to infer parameters in specific types of abstract neural models, but correspondingly few strategies are available for sizable, biophysically realistic neural models. This study details the challenges and solutions in applying a deep learning statistical framework to determine parameters within a large-scale, biophysically detailed neural model, emphasizing the particular difficulties when using time-series data for parameter estimation. Our example utilizes a multi-scale model specifically developed to connect human MEG/EEG measurements with their generators at the cellular and circuit levels. Our methodology provides a crucial understanding of how cellular properties interact to generate quantifiable neural activity, and offers protocols for evaluating the reliability and uniqueness of predictions concerning diverse MEG/EEG biomarkers.
Estimating model parameters that accurately reflect observed activity patterns constitutes a core problem in computational neural modeling. While several techniques exist for parameter inference within specific classes of abstract neural models, there are remarkably few strategies applicable to the substantial scale and biophysical detail of large-scale neural models. XL413 The study details the application of a deep learning statistical method to parameter estimation in a detailed large-scale neural model, highlighting the specific difficulties in estimating parameters from time series data and presenting potential solutions. 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.
Local ancestry markers in an admixed population provide a critical understanding of the genetic architecture underpinning complex diseases or traits, as indicated by their heritability. Due to the structuring of ancestral populations, estimation procedures may be susceptible to biases. We present HAMSTA, a novel approach to estimate heritability using admixture mapping summary statistics, correcting for biases arising from ancestral stratification to isolate the effects of local ancestry. Our extensive simulations reveal that HAMSTA's estimates exhibit near-unbiasedness and robustness against ancestral stratification, contrasting favorably with existing methods. When ancestral stratification is present, our HAMSTA-derived sampling strategy delivers a calibrated family-wise error rate (FWER) of 0.05 for admixture mapping, distinguishing it from existing FWER estimation methods. 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. Across the 20 phenotypes, values range from 0.00025 to 0.0033 (mean), corresponding to a range of 0.0062 to 0.085 (mean). Admixture mapping studies, when applied to these diverse phenotypes, show little inflation resulting from ancestral population stratification, with the mean inflation factor calculated at 0.99 ± 0.0001. From a comprehensive perspective, HAMSTA provides a high-speed and forceful approach for estimating genome-wide heritability and evaluating biases in the test statistics employed within admixture mapping studies.
The intricate nature of human learning, exhibiting significant inter-individual variation, correlates with the microscopic structure of crucial white matter pathways across diverse learning domains, though the influence of pre-existing myelin sheaths in white matter tracts on subsequent learning performance remains uncertain. Our investigation used a machine-learning model selection framework to determine if existing microstructure might forecast individual differences in learning a sensorimotor task, and to further probe whether the connection between white matter tract microstructure and learning outcomes was selective to learning outcomes. Using diffusion tractography, we gauged the average fractional anisotropy (FA) of white matter pathways in 60 adult participants, followed by training and subsequent testing to assess learning outcomes. Participants engaged in the repetitive task of drawing a set of 40 new symbols on a digital writing tablet during training. Draw duration’s rate of change during practice served as the measure of drawing learning, and visual recognition learning was measured via performance accuracy on a 2-AFC task for images classified as new or old. The research findings showcased a selective influence of major white matter tract microstructure on learning outcomes. Left hemisphere pArc and SLF 3 tracts were found to predict drawing learning, and the left hemisphere MDLFspl tract predicted visual recognition learning. The findings were consistently observed in an independent, held-out dataset and backed up by supporting analytical methods. XL413 The results, in their entirety, indicate that variations in the internal structure of human white matter tracts may be uniquely linked to future learning outcomes, necessitating further exploration of the correlation between existing tract myelination and the aptitude for learning.
A demonstrable link between tract microstructure and future learning potential has been observed in mice, but has not, as far as we are aware, been replicated in humans. A data-driven strategy isolated two key tracts, the two most posterior sections of the left arcuate fasciculus, as indicators of skill acquisition in a sensorimotor task (symbol drawing). However, this predictive model proved ineffective when applied to different learning domains, such as visual symbol recognition. Learning differences among individuals may be tied to distinct characteristics in the tissue of major white matter tracts within the human brain, the findings indicate.
In murine models, a selective relationship between tract microstructure and future learning aptitude has been observed; however, a similar relationship in humans remains, to our knowledge, undiscovered. 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. XL413 The study's results hint at a possible selective connection between individual learning differences and the tissue properties of crucial white matter tracts within the human brain.
Within the infected host, lentiviruses' non-enzymatic accessory proteins exert control over the cell's internal operations. The clathrin adaptor system is exploited by the HIV-1 accessory protein Nef to degrade or mislocate host proteins that actively participate in antiviral defense strategies. In genome-edited Jurkat cells, using quantitative live-cell microscopy, we delve into the interaction between Nef and clathrin-mediated endocytosis (CME), a crucial pathway for internalizing membrane proteins in mammalian cells. Plasma membrane CME sites recruit Nef, a process accompanied by increased recruitment and prolonged lifespan of the CME coat protein AP-2 and the subsequent arrival of dynamin2. In our study, we ascertained that CME sites which enlist Nef exhibit a higher tendency to also enlist dynamin2. This suggests that Nef recruitment to CME sites accelerates CME site maturation to enable robust host protein degradation.
A precision medicine approach to type 2 diabetes management necessitates the identification of reproducible clinical and biological characteristics linked to divergent responses to various anti-hyperglycemic therapies in terms of clinical outcomes. Proven differences in the effectiveness of therapies for type 2 diabetes, backed by robust evidence, could underpin more personalized clinical decision-making regarding optimal treatment.
A pre-registered systematic review of meta-analyses, randomized controlled trials, and observational studies was conducted to evaluate clinical and biological characteristics related to varied treatment responses to SGLT2-inhibitors and GLP-1 receptor agonists, focusing on glycemic, cardiovascular, and renal outcomes.