Engaging curriculum for the middle years

Downes, John M.

doi:10.18296/cm.0096

Cited by 3 publications

(4 citation statements)

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“…Bayes’ rule specifies a closed form for the desired posterior distribution p ( θ ∣ x ) as being proportional to the likelihood p ( x ∣ θ ) multiplied with the prior p ( θ ) [ 40 ]. Unfortunately, in detailed biophysical models the likelihood function p ( x ∣ θ ), which encodes the relationship between model parameters θ and outputs x , is often analytically intractable but can be approximated from a large number of simulations.…”

Section: Resultsmentioning

confidence: 99%

Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference

Tolley,

Rodrigues,

Gramfort

et al. 2024

PLoS Comput Biol

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Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.

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Section: Resultsmentioning

confidence: 99%

Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference

Tolley,

Rodrigues,

Gramfort

et al. 2024

PLoS Comput Biol

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“…The R2 score measures the proportion of variance in the target variable explained by the model and is calculated as 1 minus the ratio of the model’s MSE to that of a mean baseline model. The R2 score is a coefficient of determination and, theoretically, it can range from -∞ to 1, where 1 indicates perfect prediction, 0 indicates no improvement over the mean model, and values less than 0 indicate poorer performance than the baseline (Bishop, 2006; James et al, 2017). MSE and MAE represent the average squared and absolute differences between predicted and actual scores, respectively, and are used to assess the model’s prediction accuracy (Bishop, 2006; James et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…The R2 score is a coefficient of determination and, theoretically, it can range from -∞ to 1, where 1 indicates perfect prediction, 0 indicates no improvement over the mean model, and values less than 0 indicate poorer performance than the baseline (Bishop, 2006; James et al, 2017). MSE and MAE represent the average squared and absolute differences between predicted and actual scores, respectively, and are used to assess the model’s prediction accuracy (Bishop, 2006; James et al, 2017). For both MSE and MAE, lower values signify better model performance.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Models Predict the Emergence of Depression in Argentinean College Students during Periods of COVID-19 Quarantine

López Steinmetz,

Sison,

Zhumagambetov

et al. 2024

Preprint

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INTRODUCTION: The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need. AIMS: 1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction. METHODS: A longitudinal dataset (N = 1492 college students) captured T1 and T2 measurements during the Argentinean COVID-19 quarantine. ML models, including linear logistic regression classifiers/ridge regression (LogReg/RR), random forest classifiers/regressors, and support vector machines/regressors (SVM/SVR), are employed. Assessed features encompass depression and anxiety scores (at T1), mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age. For classification, models' performance on test data is evaluated using Area Under the Precision-Recall Curve (AUPRC), Area Under the Receiver Operating Characteristic curve, Balanced Accuracy, F1 score, and Brier loss. For regression, R-squared (R2), Mean Absolute Error, and Mean Squared Error are assessed. Univariate analyses are conducted to assess the predictive strength of each individual feature with respect to the target variable. The performance of multi- vs univariate models is compared using the mean AUPRC score for classifiers and R2 score for regressors. RESULTS: The highest performance is achieved by SVM and LogReg (e.g., AUPRC: 0.76, 95% CI: 0.69, 0.81) and SVR and RR models (e.g., R2 for SVR and RR: 0.56, 95% CI: 0.45, 0.64 and 0.45, 0.63, respectively). Univariate models, particularly LogReg and SVM using depression (AUPRC: 0.72, 95% CI: 0.64, 0.79) or anxiety scores (AUPRC: 0.71, 95% CI: 0.64, 0.78) and RR using depression scores (R2: 0.48, 95% CI: 0.39, 0.57) exhibit performance levels close to those of the multivariate models, which include all features. DISCUSSION: These findings highlight the relevance of pre-existing depression and anxiety conditions in predicting depression during quarantine, underscoring their comorbidity. ML models, particularly SVM/SVR and LogReg/RR, demonstrate potential in timely detection of at-risk students, enabling preventive measures for improved mental health outcomes.

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“…However, it is important to acknowledge the fundamental differences between ML and traditional statistics. ML methods can automatically capture complex relationships in the data, whereas traditional statistical models often assume a linear association . This distinction is crucial for understanding the value proposition of ML in clinical research, as it enables the development of more accurate and robust models for prediction and decision-making.…”

mentioning

confidence: 99%