Understanding the dose-response relationship is crucial in studying the effects of brain stimulation techniques, such as transcranial direct current stimulation (tDCS). The dose-response relationship refers to the relationship between the received stimulation dose and the resulting response, which can be described as a function of the dose at various levels, including single/multiple neurons, clusters, regions, or networks. Here, we are focused on the received stimulation dose obtained from computational head models and brain responses which are quantified by functional magnetic resonance imaging (fMRI) data. In this randomized, triple-blind, sham-controlled clinical trial, we recruited sixty participants with methamphetamine use disorders (MUDs) as a sample clinical population who were randomly assigned to receive either sham or active tDCS. Structural and functional MRI data, including high-resolution T1 and T2-weighted MRI, resting-state functional MRI, and a methamphetamine cue-reactivity task fMRI, were acquired before and after tDCS. Individual head models were generated using the T1 and T2-weighted MRI data to simulate electric fields. In a linear approach, we investigated the associations between electric fields (received dose) and changes in brain function (response) at four different levels: voxel level, regional level (using atlas-based parcellation), cluster level (identifying active clusters), and network level (task-based functional connectivity). At the voxel level, regional level, and cluster level, no FDR-corrected significant correlation was observed between changes in functional activity and electric fields. However, at the network level, a significant positive correlation was found between frontoparietal connectivity and the electric field at the frontopolar stimulation site (r = 0.42, p corrected = 0.02; medium effect size). Our proposed pipeline offers a methodological framework for analyzing tDCS effects by exploring dose-response relationships at different levels, enabling a direct link between electric field variability and the neural response to tDCS. The results indicate that network-based analysis provides valuable insights into the dependency of tDCS neuromodulatory effects on the individual's regional current dose. Integration of dose-response relationships can inform dose optimization, customization, or the extraction of predictive/treatment-response biomarkers in future brain stimulation studies.

SUMMARY: Feature selection can improve the accuracy of machine-learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy-preserving noise mechanism to identify features that are stable between training and holdout sets.We develop consensus nested cross-validation (cnCV) that combines the idea of feature stability from differential privacy with nCV. Feature selection is applied in each inner fold and the consensus of top features across folds is used as a measure of feature stability or reliability instead of classification accuracy, which is used in standard nCV. We use simulated data with main effects, correlation and interactions to compare the classification accuracy and feature selection performance of the new cnCV with standard nCV, Elastic Net optimized by cross-validation, differential privacy and private evaporative cooling (pEC). We also compare these methods using real RNA-seq data from a study of major depressive disorder.The cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter run times because it does not construct classifiers in the inner folds. The cnCV method chooses a more parsimonious set of features with fewer false positives than nCV. The cnCV method has similar accuracy to pEC and cnCV selects stable features between folds without the need to specify a privacy threshold. We show that cnCV is an effective and efficient approach for combining feature selection with classification. AVAILABILITY AND IMPLEMENTATION: Code available at https://github.com/insilico/cncv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate the transdiagnostic emergence of psychopathology. Specifically, it has been suggested that, in certain psychiatric disorders, interoceptive processing mechanisms either over-weight prior beliefs or under-weight signals from the viscera (or both), leading to a failure to accurately update beliefs about the body. However, this has not been directly tested empirically. To evaluate the potential roles of prior beliefs and interoceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagnostic patient sample during an interoceptive awareness (heartbeat tapping) task. Modelling revealed that, during an interoceptive perturbation condition (inspiratory breath-holding during heartbeat tapping), healthy individuals (N = 52) assigned greater precision to ascending cardiac signals than individuals with symptoms of anxiety (N = 15), depression (N = 69), co-morbid depression/anxiety (N = 153), substance use disorders (N = 131), and eating disorders (N = 14)-who failed to increase their precision estimates from resting levels. In contrast, we did not find strong evidence for differences in prior beliefs. These results provide the first empirical computational modeling evidence of a selective dysfunction in adaptive interoceptive processing in psychiatric conditions, and lay the groundwork for future studies examining how reduced interoceptive precision influences visceral regulation and interoceptively-guided decision-making.

In this letter, we present a new method for integration of sensor-based multifrequency bands of electroencephalography and magnetoencephalography data sets into a voxel-based structural-temporal magnetic resonance imaging analysis by utilizing the general joint estimation using entropy regularization (JESTER) framework. This allows enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities, thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.

Accurately differentiating between what are truly unpredictably random and systematic changes that occur at random can have profound effect on affect and cognition. To examine the underlying computational principles that guide different learning behavior in an uncertain environment, we compared an R-W model and a Bayesian approach in a visual search task with different volatility levels. Both R-W model and the Bayesian approach reflected an individual's estimation of the environmental volatility, and there is a strong correlation between the learning rate in R-W model and the belief of stationarity in the Bayesian approach in different volatility conditions. In a low volatility condition, R-W model indicates that learning rate positively correlates with lose-shift rate, but not choice optimality (inverted U shape). The Bayesian approach indicates that the belief of environmental stationarity positively correlates with choice optimality, but not lose-shift rate (inverted U shape). In addition, we showed that comparing to Expert learners, individuals with high lose-shift rate (sub-optimal learners) had significantly higher learning rate estimated from R-W model and lower belief of stationarity from the Bayesian model.