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      Concurrent Imaging of Markers of Current Flow and Neurophysiological Changes During tDCS

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          Abstract

          Despite being a popular neuromodulation technique, clinical translation of transcranial direct current stimulation (tDCS) is hampered by variable responses observed within treatment cohorts. Addressing this challenge has been difficult due to the lack of an effective means of mapping the neuromodulatory electromagnetic fields together with the brain’s response. In this study, we present a novel imaging technique that provides the capability of concurrently mapping markers of tDCS currents, as well as the brain’s response to tDCS. A dual-echo echo-planar imaging (DE-EPI) sequence is used, wherein the phase of the acquired MRI-signal encodes the tDCS current induced magnetic field, while the magnitude encodes the blood oxygenation level dependent (BOLD) contrast. The proposed technique was first validated in a custom designed phantom. Subsequent test–retest experiments in human participants showed that tDCS-induced magnetic fields can be detected reliably in vivo. The concurrently acquired BOLD data revealed large-scale networks characteristic of a brain in resting-state as well as a ‘cathodal’ and an ‘anodal’ resting-state component under each electrode. Moreover, ‘cathodal’s BOLD-signal was observed to significantly decrease with the applied current at the group level in all datasets. With its ability to image markers of electromagnetic cause as well as neurophysiological changes, the proposed technique may provide an effective means to visualize neural engagement in tDCS at the group level. Our technique also contributes to addressing confounding factors in applying BOLD fMRI concurrently with tDCS.

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          Most cited references 56

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          Intraclass correlations: Uses in assessing rater reliability.

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            Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation

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              Decoding subject-driven cognitive states with whole-brain connectivity patterns.

              Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                21 April 2020
                2020
                : 14
                Affiliations
                1Laboratory of FMRI Technology, Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA, United States
                2Department of Neurology, University of California, Los Angeles , Los Angeles, CA, United States
                3Department of Biomedical Engineering, the City College of The City University of New York , New York, NY, United States
                Author notes

                Edited by: Amir Shmuel, McGill University, Canada

                Reviewed by: Fahmeed Hyder, Yale University, United States; Anirban Dutta, University at Buffalo, United States

                *Correspondence: Danny J. J. Wang, JJ.Wang@ 123456loni.usc.edu

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2020.00374
                7186453
                Copyright © 2020 Jog, Jann, Yan, Huang, Parra, Narr, Bikson and Wang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 8, Tables: 1, Equations: 2, References: 57, Pages: 13, Words: 0
                Funding
                Funded by: NIH Clinical Center 10.13039/100000098
                Categories
                Neuroscience
                Methods

                Neurosciences

                tdcs, dual-echo echo planar imaging (de-epi), current mapping, bold fmri, resting-state

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