Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage.

Decoding of cognitive states aims to identify individuals' brain states and brain fingerprints to predict behavior. Deep learning provides an important platform for analyzing brain signals at different developmental stages to understand brain dynamics. Due to their internal architecture and feature extraction techniques, existing machine-learning and deep-learning approaches are suffering from low classification performance and explainability issues that must be improved. In the current study, we hypothesized that even at the early childhood stage (as early as 3-years), connectivity between brain regions could decode brain states and predict behavioral performance in false-belief tasks. To this end, we proposed an explainable deep learning framework to decode brain states (Theory of Mind and Pain states) and predict individual performance on ToM-related false-belief tasks in a developmental dataset. We proposed an explainable spatiotemporal connectivity-based Graph Convolutional Neural Network (Ex-stGCNN) model for decoding brain states. Here, we consider a developmental dataset, N = 155 (122 children; 3-12 yrs and 33 adults; 18-39 yrs), in which participants watched a short, soundless animated movie, shown to activate Theory-of-Mind (ToM) and pain networs. After scanning, the participants underwent a ToM-related false-belief task, leading to categorization into the pass, fail, and inconsistent groups based on performance. We trained our proposed model using Functional Connectivity (FC) and Inter-Subject Functional Correlations (ISFC) matrices separately. We observed that the stimulus-driven feature set (ISFC) could capture ToM and Pain brain states more accurately with an average accuracy of 94%, whereas it achieved 85% accuracy using FC matrices. We also validated our results using five-fold cross-validation and achieved an average accuracy of 92%. Besides this study, we applied the SHapley Additive exPlanations (SHAP) approach to identify brain fingerprints that contributed the most to predictions. We hypothesized that ToM network brain connectivity could predict individual performance on false-belief tasks. We proposed an Explainable Convolutional Variational Auto-Encoder (Ex-Convolutional VAE) model to predict individual performance on false-belief tasks and trained the model using FC and ISFC matrices separately. ISFC matrices again outperformed the FC matrices in prediction of individual performance. We achieved 93.5% accuracy with an F1-score of 0.94 using ISFC matrices and achieved 90% accuracy with an F1-score of 0.91 using FC matrices.

Prediction of transport behavior of nanoparticles using machine learning algorithm: Physical significance of important features.

There is a rising concern related to the possible risk of human exposure to nanoparticles (NPs). Several studies have reported on the transport behavior of NPs in the porous media under varying conditions. Thus, there is a scope to use this information in a predictive model so that the transport behavior of any un-explored NPs could be predicted. The main focus of his study, therefore, is to apply different machine learning (ML) based models to predict the transport efficiency of a wide range of NPs and to identify the important features. To achieve the objective, first, the dataset is prepared by extracting data from published papers for selected NPs [i.e., silver (nAg), titanium dioxide (nTiO2), zinc oxide (nZnO), graphene oxide (nGO), and etc.]. Then, random forest, XGBoost, and CatBoost algorithms combined with synthetic minority oversampling technique (SMOTE) were applied where retention fraction (RF) is considered as the target feature and particle characteristics (i.e., surface charge, size, concentration), solution chemistry [pH, ionic strength (IS]), porous media properties (grain size, porosity) and flow rate are considered as the training features. The outcome of the study indicates that CatBoost combined with SMOTE performed the best in predicting RF for the entire range of NPs (R2 > 0.89 and MSE < 0.007) as well as for individual NPs. Feature importance analysis indicates four features, namely zeta potential, IS, pH, and particle diameter (the entire range of NPs, nGO, nZnO) or grain size (nAg, nTiO2) have significant weightage (>75%). The result suggests that the features overrule the prediction of transport behavior rather than the types of individual NPs. The relative importance of the features depends on the range of the parameter used. The identified important features are in accordance with the underlying physical process, which makes the prediction model more reliable.