PlosOne_DR_Data

This .mat file includes experimental data (data{i} for subject i, in which data{i}.mg for task 1 and data{i}.ss for task 2), and model output of sensorimotor speed and motivation

Goal stopping position and motivation estimated from Task 2.

<p>a. Average goal stopping position across blocks as a function of BDI. b. Goal stopping position over time in three blocks in four depressive groups. c. Estimated motivation for each subject, taking into consideration of individual differences in both sensorimotor speed and goal state. d. Estimated motivation for each subject, not considering individual differences in sensorimotor speed and goal state.</p

Fixed effects for model predicting goal stop distance.

<p>Fixed effects for model predicting goal stop distance.</p

Sensorimotor speed.

<p>a. Sensory speed. b. Motor speed.</p

Supplemental material for PTSD and the War of Words

<p>Supplemental material for PTSD and the War of Words by Adam M. Chekroud, Hieronimus Loho, Martin Paulus and John H. Krystal in Chronic Stress</p

Data_Sheet_1_Predicting Age From Brain EEG Signals—A Machine Learning Approach.DOCX

<p>Objective: The brain age gap estimate (BrainAGE) is the difference between the estimated age and the individual chronological age. BrainAGE was studied primarily using MRI techniques. EEG signals in combination with machine learning (ML) approaches were not commonly used for the human age prediction, and BrainAGE. We investigated whether age-related changes are affecting brain EEG signals, and whether we can predict the chronological age and obtain BrainAGE estimates using a rigorous ML framework with a novel and extensive EEG features extraction.</p><p>Methods: EEG data were obtained from 468 healthy, mood/anxiety, eating and substance use disorder participants (297 females) from the Tulsa-1000, a naturalistic longitudinal study based on Research Domain Criteria framework. Five sets of preprocessed EEG features across channels and frequency bands were used with different ML methods to predict age. Using a nested-cross-validation (NCV) approach and stack-ensemble learning from EEG features, the predicted age was estimated. The important features and their spatial distributions were deduced.</p><p>Results: The stack-ensemble age prediction model achieved R² = 0.37 (0.06), Mean Absolute Error (MAE) = 6.87(0.69) and RMSE = 8.46(0.59) in years. The age and predicted age correlation was r = 0.6. The feature importance revealed that age predictors are spread out across different feature types. The NCV approach produced a reliable age estimation, with features consistent behavior across different folds.</p><p>Conclusion: Our rigorous ML framework and extensive EEG signal features allow a reliable estimation of chronological age, and BrainAGE. This general framework can be extended to test EEG association with and to predict/study other physiological relevant responses.</p