Fig 1.
Distribution Tests and Reliability Analysis.
A) Empirical distribution functions of the alpha band powers from rsEEG from C3 channel from the initial test sessions (dark gray) and retest sessions (light gray) are shown. Alpha band power from C3 channel is representative of the entire sample. The p value represents the Kolmogorov-Smirnoff test statistic. B) Each pie chart in the left and middle columns displays the class distribution whereas each pie chart in the right column displays the percentage of individuals having consistent outcome measures of iTBS-induced neuromodulation across 2 sessions. The top row represents class distribution determined by the ratio of LMFP of the left motor region for the initial test session (left) and retest session (middle). The 15-45 ms window is picked at random. Similarly, the middle and bottom rows represent the MEP T5 and T25 experiments, respectively. The p values in the left column represent the Fisher’s exact test statistics whereas the p-values in the right column are computed using the z-tests for Cohen’s kappa values. C) Scatter plots with intraclass correlation coefficient (ICC) type (1,1) for the alpha band power from C3 channel (top), LMFP ratio using 15-45 ms window (second from top), p-values of t-tests for MEP T5 block (third from top) and p-values of t-tests for MEP T25 block (bottom). In all 4 cases, the p-values are computed using the F-test. Data interpretation in the main text.
Table 1.
Performance of model selection and model validation of Cross-Cohort Experiments. Square brackets represent 95% confidence interval. LDA OA stands for linear discriminant analysis with Oracle Approximating Shrinkage.
Fig 2.
Model Selection and Model Validation Performance of MEP and LMFP Ratios Cross-Cohort Experiments.
The top row represents the final models selected during the model selection process (as assessed by cross-validation using ROC-AUC metric), bottom row represents the performance of the final models on validation set during model validation. Left column represents the MEP Experiment whereas the right column represents the LMFP Ratio Experiment. The final model for the MEP Experiment is linear discriminant analysis with Oracle approximate shrinkage (LDA OA) trained on the complexity indices of composite multiscale permutation entropy from all EEG channels whereas the final model for the LMFP Ratios Experiment is decision tree trained on complexity indices of coarse-graining multiscale distribution entropy from EEG channels in the left motor region. The vertical error bars represent 95% confidence intervals and the dark horizontal bars within each vertical bars represent theoretical chance levels. 7 different metrics are assessed: accuracy (blue), sensitivity (orange), specificity (green), F1-score (red), ROC-AUC (purple), PR-AUC (brown) and precision (pink).
Fig 3.
Plot of the final decision tree from LMFP Ratios Cross-Cohort Experiment.
If the condition in the top row of the white box is true, the decision path takes the left node, or else, takes the right node. “Training Sample” in the white boxes represents the sample size of the training set before the split. “Class split” represents the class ratio of the training set as follows: [decrease in cortical excitability, increase in cortical excitability]. Yellow box represents a decrease in cortical excitability as assessed by the ratio of the AUC of the 100-131 ms window of the left motor region of LMFP. Similarly, blue box represents an increase in cortical excitability. RQS stands for regression quality score.
Fig 4.
Diagram of iTBS session, applicable to both initial test and retest sessions.
Top) Chronological order of the session with the color of the blocks corresponding to the color of the circles. Navy circle) Three blocks of single-pulse TMS were delivered to the left M1 region, one before the iTBS protocol, one 5 minutes after and another 25 minutes after the end of the iTBS protocol. Both EEG and EMG were recorded. Light blue circle: 3 minutes of resting-state EEG with eyes open. Only EEG was recorded. Yellow circle: iTBS protocol consisting a total of 600 bursts spread out in an alternating sequence of trains and silence. Only EEG was recorded.
Fig 5.
Schematic Diagram of Feature Grouping and Model Generation.
In the top panel, feature groups were iteratively generated by generating all combinations of different features (S2 Table), ROIs (S3 Table and data transformations (normalization in top or distance to the median in bottom). An example of a feature group would be normalization of the alpha band powers from the central region of interested and PreTBS Features. Each table represents one feature group. In the middle panel, feature groups with labeling of modulation in corticospinal or cortical excitability are further iteratively generated by generating all combinations of feature groups and categorization methods, represented as a green column appended to the feature group table (different windows of LMFP (S1 Table) or t-tests of peak-to-peak MEP amplitudes between post and pre-TBS protocol). In the bottom panel, each feature group with labeling is trained by different classifiers, represented as gears, to form models. An example of a model would be a logistic regression with L2 regularization trained on the same example feature group above to the iTBS responses as facilitation or suppression based on the ratio of the LMFP between post- and preTBS sessions for the 55-85 ms window. See the main text for the total number of models tested in MEP and LMFP Ratios Experiments.