Fig 1 -

a) Concurrent tDCS-fMRI setup. b) Timing diagram for MRI experiment. Red colored lines show the application of tDCS during the 24-minute long rs-fMRI scan.</p

The top 2 ROI pairs contributing towards the classification of multi-electrode stimulation.

The top 2 ROI pairs contributing towards the classification of multi-electrode stimulation.</p

VAS tolerability scores (min = 0, max = 10) of all sessions of all participants.

VAS tolerability scores (min = 0, max = 10) of all sessions of all participants.</p

Fig 6 -

Importance rate and contribution of ROI pairs towards classifying ME stimulation using Gini impurity decrease and SHAP value: (a & b) 26 ROIs and (c & d) 22 ROIs. Table G and Table H in S1 Document lists the ROI pairs associated with the codes.</p

Accuracy of models to classify different stimulations.

Accuracy of models to classify different stimulations.</p

Time taken for data processing up to prediction, varying the number of ROIs used.

Time taken for data processing up to prediction, varying the number of ROIs used.</p

Data preprocessing flow-chart.

Transcranial direct current stimulation (tDCS) can noninvasively modulate behavior, cognition, and physiologic brain functions depending on polarity and dose of stimulation as well as montage of electrodes. Concurrent tDCS-fMRI presents a novel way to explore the parameter space of non-invasive brain stimulation and to inform the experimenter as well as the participant if a targeted brain region or a network of spatially separate brain regions has been engaged and modulated. We compared a multi-electrode (ME) with a single electrode (SE) montage and both active conditions with a no-stimulation (NS) control condition to assess the engagement of a brain network and the ability of different electrode montages to modulate network activity. The multi-electrode montage targeted nodal regions of the right Arcuate Fasciculus Network (AFN) with anodal electrodes placed over the skull position of the posterior superior temporal/middle temporal gyrus (STG/MTG), supramarginal gyrus (SMG), posterior inferior frontal gyrus (IFG) and a return cathodal electrode over the left supraorbital region. In comparison, the single electrode montage used only one anodal electrode over a nodal brain region of the AFN, but varied the location between STG/MTG, SMG, and posterior IFG for different participants. Whole-brain rs-fMRI was obtained every three seconds. The tDCS-stimulator was turned on at 3 minutes after the scanning started. A 4D rs-fMRI data set was converted to dynamic functional connectivity (DFC) matrices using a set of ROI pairs belonging to the AFN as well as other unrelated brain networks. In this study, we evaluated the performance of five algorithms to classify the DFC matrices from the three conditions (ME, SE, NS) into three different categories. The highest accuracy of 0.92 was obtained for the classification of the ME condition using the K Nearest Neighbor (KNN) algorithm. In other words, applying the classification algorithm allowed us to identify the engagement of the AFN and the ME condition was the best montage to achieve such an engagement. The top 5 ROI pairs that made a major contribution to the classification of participant’s rs-fMRI data were identified using model performance parameters; ROI pairs were mainly located within the right AFN. This proof-of-concept study using a classification algorithm approach can be expanded to create a near real-time feedback system at a participant level to detect the engagement and modulation of a brain network that spans multiple brain lobes.</div

Accuracy across different ROIs for the classification shown by various models.

Accuracy across different ROIs for the classification shown by various models.</p

Fig 2 -

(a) Representative image of the right hemispheric Arcuate Fasciculus tract shown in standard brain and the canonical location of three nodal regions of the AFN targeted by brain-stimulation (dashed circles in 2a). tDCS electrodes were placed on the scalp directly above the brain regions marked with circles. (b) Anatomically identified spherical ROIs representing cortical regions within and outside of the AFN (shown here for the right hemisphere, but mirror left hemisphere regions were identified as well). Side and oblique views are shown to demonstrate the mesial location of AF tract and ROIs. (c) Shows electrode placement for ME montage for one of the participant and Electric field distributions simulated using SimNIBS software [39].</p

Fig 5 -

Confusion matrices for the prediction of knn model on the 22ROIs (a) and 26 ROIs(b) dataset. Confusion matrices for the prediction of random forest model on 22 ROIs (c) and 26 ROIs (d) dataset.</p