Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation

Li, Lucia M.; Violante, Inês R.; Leech, Robert; Ross, Ewan; Hampshire, Adam; Opitz, Alexander; Rothwell, John C.; Carmichael, David W.; Sharp, David J.

doi:10.1101/179556

Cited by 15 publications

(42 citation statements)

References 60 publications

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“…The study protocol is selected based on several assumptions and hypotheses such as time course of tDCS effect (Nitsche & Paulus, 2000), cellular mechanism of stimulation (Bikson et al, 2004;Rahman et al, 2013), state dependency of tDCS as a subthreshold stimulation method (Hsu, Juan, & Tseng, 2016;Li et al, 2019), and dose-response relationship. Ultimately, these considerations get to the heart of how tDCS research benefits from functional MR imaging and how to address inherent challenges in experimental design.…”

Section: Tdcs-mr Imaging: Trial Design Parameter Spacementioning

confidence: 99%

Methodology for tDCS integration with fMRI

Esmaeilpour

Shereen

Ghobadi‐Azbari

et al. 2019

Human Brain Mapping

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Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state‐level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS‐fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS‐fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS‐fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.

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Section: Tdcs-mr Imaging: Trial Design Parameter Spacementioning

confidence: 99%

Methodology for tDCS integration with fMRI

Esmaeilpour

Shereen

Ghobadi‐Azbari

et al. 2019

Human Brain Mapping

View full text Add to dashboard Cite

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“…Peak electric field was confirmed via computational, finite element method (FEM) head modelling as in Li et al [9] (Fig 1d). Outside the scanner, participants had two 15-second blocks of anodal and cathodal tDCS, and were asked to rate sensations of itching, pain, metallic taste, burning, anxiety, and tingling on a 1-5 scale, with 1=nil and 5=unbearable.…”

Section: Delivery Of Tdcsmentioning

confidence: 60%

“…Brain state is a key factor determining the physiological response to tDCS. Previous work from our group and others have found that brain state influences the brain network effects of tDCS in healthy controls [7] [8] [9]. Specifically, our previous tDCS-fMRI work shows that tDCS reinforces the underlying pattern of brain network activity, and that this interacts with stimulation polarity [9].…”

Section: Introductionmentioning

confidence: 70%

“…Additionally, the CRT is a simple cognitive task with which both TBI patients and healthy controls can engage with high (>90.0%) levels of accuracy [15], producing the core pattern of SN activation and DMN deactivation in both healthy control and TBI cohorts. In our previous work, stimulation did not improve behavioral performance in healthy participants [9], enabling changes in brain network activity to be interpreted independently of changes in performance.…”

Section: Introductionmentioning

confidence: 89%

“…The average ratings for all categories found no difference between anodal or cathodal tDCS. Further details of the experimental paradigm and stimulation can be found in [9].…”

Section: Delivery Of Tdcsmentioning

confidence: 99%

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Investigating the interaction between white matter and brain state on tDCS-induced changes in brain network activity

Kurtin

Violante

Zimmerman

et al. 2020

Preprint

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BackgroundTranscranial direct current stimulation (tDCS) is a form of noninvasive brain stimulation whose potential as a cognitive therapy is hindered by our limited understanding of how participant and experimental factors influence its effects. Using functional MRI to study brain networks, we have previously shown in healthy controls that the physiological effects of tDCS are strongly influenced by brain state. We have additionally shown, in both healthy and traumatic brain injury (TBI) populations, that the behavioral effects of tDCS are positively correlated with white matter (WM) structure.ObjectivesIn this study we investigate how these two factors, WM structure and brain state, interact to shape the effect of tDCS on brain network activity.MethodsWe applied anodal, cathodal and sham tDCS to the right inferior frontal gyrus (rIFG) of healthy (n=22) and TBI participants (n=34). We used the Choice Reaction Task (CRT) performance to manipulate brain state during tDCS. We acquired simultaneous fMRI to assess activity of cognitive brain networks and used Fractional Anisotropy (FA) as a measure of WM structure.ResultsWe find that the effects of tDCS on brain network activity in TBI participants are highly dependent on brain state, replicating findings from our previous healthy control study in a separate, patient cohort. We then show that WM structure further modulates the brain-state dependent effects of tDCS on brain network activity. These effects are not unidirectional – in the absence of task with anodal and cathodal tDCS, FA is positively correlated with brain activity in several regions of the default mode network. Conversely, with cathodal tDCS during CRT performance, FA is negatively correlated with brain activity in a salience network region.ConclusionsOur results show that experimental and participant factors interact to have unexpected effects on brain network activity, and that these effects are not fully predictable by studying the factors in isolation.

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Modulation of network centrality and gray matter microstructure using multi‐session brain stimulation and memory training

Thams

Külzow

Flöel

et al. 2022

Human Brain Mapping

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Neural mechanisms of behavioral improvement induced by repeated transcranial direct current stimulation (tDCS) combined with cognitive training are yet unclear. Previously, we reported behavioral effects of a 3‐day visuospatial memory training with concurrent anodal tDCS over the right temporoparietal cortex in older adults. To investigate intervention‐induced neural alterations we here used functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) datasets available from 35 participants of this previous study, acquired before and after the intervention. To delineate changes in whole‐brain functional network architecture, we employed eigenvector centrality mapping. Gray matter alterations were analyzed using DTI‐derived mean diffusivity (MD). Network centrality in the bilateral posterior temporooccipital cortex was reduced after anodal compared to sham stimulation. This focal effect is indicative of decreased functional connectivity of the brain region underneath the anodal electrode and its left‐hemispheric homolog with other “relevant” (i.e., highly connected) brain regions, thereby providing evidence for reorganizational processes within the brain's network architecture. Examining local MD changes in these clusters, an interaction between stimulation condition and training success indicated a decrease of MD in the right (stimulated) temporooccipital cluster in individuals who showed superior behavioral training benefits. Using a data‐driven whole‐brain network approach, we provide evidence for targeted neuromodulatory effects of a combined tDCS‐and‐training intervention. We show for the first time that gray matter alterations of microstructure (assessed by DTI‐derived MD) may be involved in tDCS‐enhanced cognitive training. Increased knowledge on how combined interventions modulate neural networks in older adults, will help the development of specific therapeutic interventions against age‐associated cognitive decline.

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Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation

Cited by 15 publications

References 60 publications

Methodology for tDCS integration with fMRI

Methodology for tDCS integration with fMRI

Investigating the interaction between white matter and brain state on tDCS-induced changes in brain network activity

Modulation of network centrality and gray matter microstructure using multi‐session brain stimulation and memory training

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