Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level

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Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level Gesa Hartwigsen*,1, Til Ole Bergmann*, Damian Marc Herz†, Steffen Angstmann†, Anke Karabanov†, Estelle Raffin†,{, Axel Thielscher†,}, Hartwig Roman Siebner†,},1 *Department of Psychology, Christian-Albrechts-University, Kiel, Germany Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark { Grenoble Institute of Neuroscience, Research Centre U836 Inserm—UJF, Team 11 Brain Function & Neuromodulation, Grenoble, France } Biomedical Engineering Section, Technical University of Denmark, Kongens Lyngby, Denmark } Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark 1 Corresponding authors: Tel.: +49-431-880-4872, +45-3862-6541; Fax: +49 431 880 1829, +45 3635 1680. e-mail addresses: [email protected]; [email protected]

†

Abstract Noninvasive transcranial brain stimulation (NTBS) is widely used to elucidate the contribution of different brain regions to various cognitive functions. Here we present three modeling approaches that are informed by functional or structural brain mapping or behavior profiling and discuss how these approaches advance the scientific potential of NTBS as an interventional tool in cognitive neuroscience. (i) Leveraging the anatomical information provided by structural imaging, the electric field distribution in the brain can be modeled and simulated. Biophysical modeling approaches generate testable predictions regarding the impact of interindividual variations in cortical anatomy on the injected electric fields or the influence of the orientation of current flow on the physiological stimulation effects. (ii) Functional brain mapping of the spatiotemporal neural dynamics during cognitive tasks can be used to construct causal network models. These models can identify spatiotemporal changes in effective connectivity during distinct cognitive states and allow for examining how effective connectivity is shaped by NTBS. (iii) Modeling the NTBS effects based on neuroimaging can be complemented by behavior-based cognitive models that exploit variations in task performance. For instance, NTBS-induced changes in response speed and accuracy can be explicitly modeled in a cognitive framework accounting for the speed–accuracy trade-off. This enables to dissociate between behavioral NTBS effects that emerge in the context of rapid automatic responses or in Progress in Brain Research, ISSN 0079-6123, http://dx.doi.org/10.1016/bs.pbr.2015.06.014 © 2015 Elsevier B.V. All rights reserved.

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the context of slow deliberate responses. We argue that these complementary modeling approaches facilitate the use of NTBS as a means of dissecting the causal architecture of cognitive systems of the human brain.

Keywords Dynamic causal modeling, Drift diffusion modeling, Electric field calculations, Electroencephalography, Functional magnetic resonance imaging, Magnetoencephalography, Plasticity, Transcranial direct current stimulation, Transcranial magnetic stimulation, Virtual lesion

1 INTRODUCTION Noninvasive transcranial brain stimulation (NTBS) is widely used to elucidate the contribution of different brain regions to various cognitive functions (for review, see Hartwigsen, 2014; Miniussi et al., 2013; Sandrini et al., 2011). A range of interventional tools are available including transcranial magnetic stimulation (TMS), transcranial direct current stimulation (TDCS), and transcranial alternating current stimulation (TACS) (e.g., Antal and Paulus, 2013; Nitsche and Paulus, 2011; Walsh and Cowey, 2000). Traditionally, most studies examined the effects of NTBS at the descriptive level without employing a modeling framework. However, computational approaches that model the effects of NTBS are critical if one wishes to put the empirically observed NTBS effects on brain function into a systematic neurobiological context. Computational modeling also facilitates the possibility to make causal inferences and predictions and hereby pave the way for a deeper mechanistic understanding of the NTBS effects. Focusing on TMS, this chapter covers three complementary modeling approaches which deal with different aspects of NTBS: (i) Biophysical modeling and simulation of the electric field distribution induced by NTBS in the human brain, (ii) Dynamic causal modeling (DCM) of the spatiotemporal neural dynamics during cognitive tasks to unravel how effective connectivity of cognitive networks is shaped by NTBS, and (iii) Cognitive models that exploit normal variation in behavior (e.g., related to the speed–accuracy trade-off or different response strategies). We argue that these modeling approaches have the potential to greatly advance the use of NTBS as an interventional tool in cognitive neuroscience. When discussing the potential of NTBS to modulate cognitive brain functions, it is important to distinguish between “offline” and “online” NTBS. This distinction refers to the temporal relationship between NTBS and the particular readout used to probe the NTBS effects on brain function (e.g., behavioral testing, functional brain mapping, or both). NTBS can either be applied before the stimulated individual performs an experimental cognitive task (offline stimulation) or while the stimulated individual performs the experimental task (online stimulation).

ARTICLE IN PRESS 1 Introduction

1.1 ONLINE TRANSCRANIAL STIMULATION Online stimulation usually involves focal TMS to acutely and transiently perturb ongoing neural processing in the stimulated cortex during an experimental task, often referred to as a “virtual lesion” (Pascual-Leone et al., 1999). Online TMS studies generally employ a focal figure-of-eight coil to selectively target a specific cortical network node. The aim is to characterize the functional relevance of task-specific activity in the “perturbed” network node for a given task by inducing a site-specific change in task performance. TDCS and TACS can also be applied online during a task and may induce slight shifts in task performance when considering a whole session (see Antal and Paulus, 2013; Nitsche and Paulus, 2011). Compared to TMS, these NTBS modalities are not sufficiently strong to efficiently disrupt task performance on the single-trial level and lack the temporal and spatial resolution, rendering the neurobiological interpretation of behavioral online effects difficult (Miniussi et al., 2013). Online TMS comprises protocols using single or paired pulses, or short bursts of repetitive TMS (rTMS). Because a single TMS pulse interferes with ongoing neural activity for several tens of milliseconds, single-pulse online TMS provides sufficiently high temporal resolution to identify the time period during which the stimulated region makes a critical contribution to a given task in a chronometric fashion (Pascual-Leone et al., 2000). Chronometric approaches have substantially advanced the current knowledge on the time course of different (cognitive) processes such as vision (Amassian et al., 1989), language (Devlin et al., 2003; Schuhmann et al., 2012), or working memory (Mottaghy et al., 2003). The behavioral consequences of focal online TMS on task performance permit causal conclusions with respect to the contribution of a stimulated area to a specific brain function (Paus, 2008; Siebner et al., 2009b; Walsh and Cowey, 2000). Relative to studies on patients with structural lesions, online “virtual lesion” TMS studies of healthy individuals have the advantage that the brain has no time for long-term functional reorganization during the stimulation. Hence, the acute perturbation effects are not confounded by chronic plastic processes mediating functional recovery (Walsh and Cowey, 1998, 2000). Yet the online application of NTBS may be methodologically and technically challenging, especially when NTBS is combined with functional brain mapping techniques to capture the acute NTBS-induced changes in brain activity (Bestmann et al., 2003a, 2008; Miniussi and Thut, 2010; Moisa et al., 2012; Siebner et al., 2009a). Notably, the application of online NTBS is not restricted to a single brain site. Multifocal TMS allows for the simultaneous stimulation of more than one network node. Such designs have been used to probe ipsilateral premotor–motor connectivity at rest (Groppa et al., 2012a,b) or during grasp (Davare et al., 2010; Koch et al., 2010). Other studies applied multifocal TMS to investigate intrahemispheric or interhemispheric interactions during visual discrimination (Ellison and Cowey, 2009) or language (Hartwigsen et al., 2010).

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1.2 OFFLINE TRANSCRANIAL STIMULATION Offline NTBS approaches are used to induce plasticity in the targeted brain network that outlasts the time of stimulation (Siebner and Rothwell, 2003). A wide range of offline NTBS protocols are available, involving rTMS, TDCS, and TACS. With respect to offline rTMS, the most commonly used protocols consist of regular trains at a constant frequency (e.g., regular 1 Hz rTMS) or more complex burst stimulation patterns (e.g., theta-burst stimulation). Like online TMS, offline rTMS is usually applied focally over a cortical target region using a figure-of-eight coil to induce lasting functional effects in the stimulated area and connected nodes beyond the time of the rTMS intervention (Siebner and Rothwell, 2003; Ziemann et al., 2008). Such “remote” effects in interconnected regions may occur in neighboring cortical regions close to the targeted cortex as well as in distant cortical and subcortical areas via intra- or interhemispheric connections (Bestmann et al., 2003b; Lee et al., 2006; Rossi and Rossini, 2004). Offline rTMS may also trigger rapid adaptive responses in other brain networks that have not been directly stimulated with rTMS. Hence, offline rTMS can be used to investigate rapid functional reorganization on the network level. The aftereffects of offline NTBS can be easily investigated with different neuroimaging or electrophysiological techniques. Mapping the aftereffects of offline NTBS on human brain networks can be used to detect consistent changes at the network level but also to identify neural correlates that account for the large interindividual variability in responsiveness to plasticity-inducing NTBS protocols (Hamada et al., 2013). To date, many studies of cognition combined offline rTMS, TDCS, or TACS with various functional brain mapping techniques. Functional magnetic resonance imaging (fMRI) (Andoh and Paus, 2011; Andoh and Zatorre, 2013; Liuzzi et al., 2010), electroencephalography (EEG) (Bergmann et al., 2008; De Gennaro et al., 2008; Huber et al., 2007), or, more recently, magnetoencephalography (MEG) (Marshall et al., 2015; Zwanzger et al., 2014) has been successfully used after a conditioning NTBS session to delineate stimulation-induced changes in neural activity on the network level (see Section 3). Offline and online readouts may be combined in a single experiment. For instance, the acute “online” effects of an offline NTBS protocol may be monitored with online EEG during stimulation (e.g., Veniero et al., 2011). Additionally, offline and online TMS may also be combined in a “condition-and-perturb” approach (Hartwigsen et al., 2012). Condition-and-perturb designs can be used to change the functional weight in a certain network and allow for the investigation of intraor interhemispheric compensation during task processing (Hartwigsen et al., 2012; O’Shea et al., 2007; Sack et al., 2005). For instance, Sack et al. (2005) demonstrated that online TMS over a parietal area can unmask the virtual lesion effect of offline rTMS over the contralateral homologue area during mental imagery.

1.3 PARADOXICAL TMS EFFECTS ON COGNITIVE FUNCTIONS In studies of cognition, perturbation of intrinsic brain activity with short bursts of online rTMS is often referred to as “virtual lesion” (Pascual-Leone et al., 1999),

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implying that TMS induces an impairment of brain functions. Yet the effects of TMS on neural activity might also give rise to a “paradoxical improvement” in task performance. For instance, several studies reported faster response speed with different online or offline TMS protocols over a language area (Andoh et al., 2006; Nixon et al., 2004; Sparing et al., 2001). Note that the audio-tactile input associated with TMS can be sufficient to produce unspecific facilitation effects (Duecker and Sack, 2013). Moreover, inhibition of a brain region that is either detrimental to task performance or task-irrelevant but competing for (e.g., attentional) resources can often explain “paradoxical improvements” in task performance (Walsh et al., 1998). The effects of a particular TMS protocol might also critically depend on the taskinduced neural activity or brain state (i.e., “state dependency,” cf. Pasley et al., 2009; Silvanto et al., 2008). It was argued that the TMS-induced activity or “neural noise” is not totally random (Ruzzoli et al., 2010). Depending on the neuron population that will be activated, the induced activity can be considered both as noise and as part of the (task) signal (Miniussi et al., 2010). The induced activity might be synchronized with the ongoing relevant signal, thereby rendering the signal stronger and providing an “optimum” level of noise for a specific task (Miniussi et al., 2013). Consequently, the impact of the TMS-induced “lesion” effect might also change with varying task conditions and complexity (Hartwigsen et al., 2015). In this context, the combination of TMS and different modeling approaches is particularly helpful to gain a better understanding of task-specific TMS effects by (i) identifying TMSinduced changes in neural activation and effective connectivity during different task states and (ii) providing deeper insights into the behavioral consequences of a TMSinduced perturbation by exploiting task-specific variations in performance.

2 MODELING THE DISTRIBUTION OF THE NTBS-INDUCED ELECTRICAL FIELDS Only a few coil or electrode positions can be tested in a single NTBS study. The success or failure of the study therefore depends on the careful selection of the cortical target sites and the ability to selectively stimulate the targeted cortical areas. Hypotheses on the brain areas that are involved in a task can be derived from the literature and from group- or subject-specific neuroimaging and should be covered by models describing neural computations on the network level (e.g., Marshall et al., 2015; Reichenbach et al., 2011; Thut et al., 2011). Once a target site has been chosen, computational methods for electric field calculations can be helpful for accurate planning by maximizing the stimulation effects in the target region while sparing other brain areas to the best possible degree. Electric field calculations have been used since the early days of TMS to characterize the field shape of different coil types ( Jalinous, 1991; Ravazzani et al., 1996; Thielscher and Kammer, 2004). Note that these modeling studies were mostly based on no or very simple head models. In recent years, the usage of increasingly more accurate head models and numerical methods such as finite-element methods (FEMs) has substantially advanced our understanding of the impact of the individual

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anatomy on the field distribution (Datta et al., 2010; Thielscher et al., 2011). FEM calculations are based on volume meshes that represent the head geometry by means of basic geometrical elements such as tetrahedron, a polyhedron composed of four triangular faces, three of which meet at each corner or vertex. This allows for a flexible geometrical modeling of the diverse tissue compartments. Further, FEM models can account for inhomogeneous and anisotropic conductivity distributions such as that of brain white matter. This flexibility enables for a high-resolution and accurate estimation of the field distribution caused by NTBS. The results obviously depend on the careful creation of the head models based on imaging data (mostly MRI). Currently, most models distinguish between skin, skull, cerebrospinal fluid (CSF), and brain gray and white matter. Sometimes, additional compartments such as the eyes and subdural fat are taken into account. Often, the skull is modeled as a single compartment without distinguishing between compact and spongy parts. While more detailed head meshes with many compartments are generally desirable, a trade-off between the complexity of the process to create the head mesh and the study goals has to be performed in practice. For instance, a detailed modeling of the skull (ideally also based on data received from computed tomography) might be preferable for electrode-based methods such as TDCS or TACS. However, this is not needed for TMS. While the currents that hit the brain have to pass through the skull (i.e., are influenced by its fine structure) in the former case, they are contained within the skull in the latter. Accurate field calculations help to constrain neural modeling efforts by providing an estimation of the spatial stimulation pattern that is likely to be achieved in practice. Hence, field calculations complement the information gained from neuroimaging or electrophysiological measures and assist the neurobiological interpretation of NTBS effects on the neural network and behavioral level. Increasing the spatial accuracy of NTBS approaches is particularly desirable, especially for low-intensity transcranial electrical stimulation methods such as TDCS or TACS. Previous TDCS studies have mainly used bipolar electrode arrangements and have not been conclusive with respect to the question which brain areas are stimulated by TDCS (e.g., Antal et al., 2011; Lang et al., 2005). The distribution of the TDCS-induced electrical field in the brain is not intuitive and straightforward, and electrical field modeling suggests that bipolar TDCS induces cluttered stimulation patterns (Datta et al., 2010; Neuling et al., 2012; Opitz et al., 2015). Additionally, the strongest stimulation might not necessarily occur directly underneath the electrode pads, but somewhere in between them. Note that on a finer spatial scale, the problem of spatial selectivity also arises for TMS. Descriptive and neural network models of I- and D-wave generation for motor cortex stimulation and for the interacting neural activity in case of paired-pulse experiments have been introduced (Di Lazzaro et al., 2004, 2008; Esser et al., 2005). These models are based on a large experimental effort from several researchers and are particularly relevant when trying to decipher the physiological effects of TMS of the primary motor cortex (Di Lazzaro et al., 2008). However, such models will necessarily remain ambiguous as long as a better understanding of the location of the

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primary neural effect of TMS is lacking. Moreover, validation studies are needed to test whether the predictions of a particular model can reliably predict the stimulationinduced modulation of behavioral or neuroimaging readouts at an individual level. This link is required to prove that such models are practically useful. Electric field calculations can help in this respect, e.g., by deriving novel and biophysically motivated hypotheses on the origin of the orientation dependence of TMS responses (Thielscher et al., 2011) (Fig. 1A). Further improvements in this field will require the combination of field calculations with estimations of the excitability of various

FIGURE 1 (A) Example showing how the field strength inducted by TMS depends on the direction of the currents relative to the underlying gyri. A figure-of-eight coil was simulated, with its center indicated as open circle and the direction of current flow in the brain area underneath the center as arrow. Those parts of the gyri that are locally perpendicular to the current flow receive the strongest electric field. (B) Example showing the effect of the cerebrospinal fluid (CSF) layer above the brain on the field strength injected in the cortex by tDCS. The two electrode pads were positioned above the left motor region and the right supraorbital region. The left panel shows the electric field distribution in the cortex. The middle panel shows the same distribution, but on the inflated cortex. The right panel shows the thickness of the CSF layer above the brain. Regions with thin CSF layers (an example is indicated by the red (dark gray in the print version) circle) are prone to high electric fields. Adapted and reprinted from Opitz et al. (2015), Figure 3. Copyright (2015), with permission from Elsevier.

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types of neural elements, as started in a first study by Miranda and coworkers (Salvador et al., 2011). This step is needed to derive stronger hypotheses on the physiological impact of NTBS from the calculated field distribution than currently possible. In principle, analogous lines of research can be performed for TMS of nonmotor cortical areas using the TMS-evoked cortical potentials (TEPs) as measured with EEG as readout and relate orientation-dependent variations in TEPs with orientation-dependent models of electrical field distributions. Some of the findings from field modeling are robust and can be expected to generalize across different individuals and thus act as “rules of thumb.” This comprises the general differences between the field distributions of different TMS coil types and the (above mentioned) dependence of field strength induced by TMS on the orientation of currents relative to the local gyrus orientation. Likewise, for tDCS, factors such as the thickness of the CSF layer (Fig. 1B) and the composition of the skull can also be expected to have a consistent impact (Datta et al., 2010; Opitz et al., 2015). Recent simulation results also highlight that interindividual variation in cortical anatomy may have substantial impact on the spatial distribution of the injected fields. This motivates the increased usage of field calculations for individual stimulation planning and analysis in the future. They can inform the experimenter, but also the computational neuroscientist, on the likely brain areas that have been directly targeted by NTBS and might help to shed light on the causes of interindividual differences in the responsiveness to NTBS. It is worth noting that the usage of electric field calculations can already proof beneficial even when a profound understanding of the effects of the field on the various types of cortical neurons is still missing. For example, the excitability threshold to TMS differs from person to person and it is known that the interindividual variability can to some extent be explained by simple measurements of coil–cortex distance (Kozel et al., 2000; McConnell et al., 2001; Stokes et al., 2007). Given the clear impact of the gyrification pattern on the field pattern (Thielscher et al., 2011), the usage of more realistic field estimates might help to increase the amount of variance that is explained by anatomical factors and to better match the “cortical” stimulation strength across subjects. Clearly, this still requires a careful demonstration of the correlation between field variations derived from realistic head models and outcome parameters such as the motor threshold in a larger group of subjects. While biophysical modeling of the induced electrical fields represents a promising area of NTBS-related research, several steps are still needed to unveil the full potential of field calculations for improving NTBS applications. Apart from technical aspects (e.g., improving software usability for automated head generation; Windhoff et al., 2013), this mainly refers to missing knowledge on how the neural elements are influenced by the injected fields. A better understanding of the dependence of the local stimulation effects on the field direction relative to the gray matter sheet or fiber directions would allow deriving more reliable estimates on the affected brain regions. The integration of existing histological and electrophysiological data might help to further refine biophysical models on the stimulation of neural elements (Salvador et al., 2011), but reliable conclusions will need verification through

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invasive experiments in animal models (see Rahman et al., 2013 for one of the few studies targeting this question). This also holds for the uncertainties on the values assumed for some of the tissue impedances that currently still limit the reliability of the field estimates (Miranda, 2013). Hence, the usefulness of estimating the electrical field distributions in the brain to predict electrophysiological or behavioral NTBS effects still has to be fully demonstrated in order to pave the way for a more standard usage in the field of brain stimulation.

3 MODELING OF NTBS-INDUCED CHANGES IN EFFECTIVE CONNECTIVITY A range of brain mapping techniques can be used as functional readouts to capture NTBS-induced changes in brain network activity and connectivity after NTBS (see Siebner et al., 2009a for review). This includes positron emission tomography (PET), blood oxygenation level-dependent (BOLD) fMRI, magnetic resonance spectroscopy, near-infrared spectroscopy, or electrophysiological methods such as EEG or MEG. These mapping techniques can provide spatial and temporal information about task-related or spontaneous neuronal processes of interest and detect lasting changes in brain activity and effective connectivity after NTBS (Sandrini et al., 2011; Siebner et al., 2009a). A better understanding of the direct and remote effects of NTBS may provide important new insights into the adaptive plasticity and shortterm reorganization of neural networks. While the combined use of NTBS and brain mapping is now feasible, important conceptual questions remain to be resolved. One important challenge is to identify functional readouts that are most closely related to the neurobiological mechanisms by which NTBS shapes the function of cognitive brain networks. In this context, it is worth to bear in mind that cognitive processes are not mediated by isolated neural areas, but the dynamic interactions among relevant areas (Rowe, 2010). For instance, patients suffering from vascular lesions of connections between motor and sensory speech areas show marked impairment in speech repetition (termed “conduction aphasia”), even though the neural “nodes” of this network remain intact. Therefore, we argue that functional readouts which capture NTBS-induced changes in the causal network organization, including the stimulated area, are more closely related to the neurobiological mechanisms by which NTBS changes a cognitive function compared to only analyzing regional changes in neural activity. Across the last two decades, several approaches have been developed to study connectivity in neural networks (Rowe, 2010). In general, connectivity measures can be divided into functional and effective connectivity (Friston, 2002; Horwitz, 2003). Functional connectivity refers to temporal covariance between neural areas, which does not allow inferences on the causality of interactions. Conversely, effective connectivity reflects causal interactions between areas, i.e., how one region exerts influence over another. In the following sections, we will focus on measures of effective connectivity derived by two modeling methods, namely

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psychophysiological interactions (PPIs) and DCM. Other measures of neural connectivity, as well as analysis of resting-state connectivity, are beyond the scope of this chapter. A number of previous studies combined offline rTMS with subsequent fMRI or PET to map TMS-induced changes in the task-related effective connectivity between different network nodes for cognitive or motor functions (Boudrias et al., 2012; Hartwigsen et al., 2013; Lee et al., 2003; Megumi et al., 2015; Moisa et al., 2012; Ward et al., 2010). These studies will be discussed in detail below. Together, they provided converging evidence that NTBS can induce lasting changes in effective brain connectivity and suggest that these changes might mediate the NTBS-induced changes in task performance.

3.1 THE PSYCHOPHYSIOLOGICAL INTERACTION METHOD PPI is a data-driven, model-free approach assessing interactions between physiological activity of prespecified brain areas of interest and experimental contexts (Friston et al., 1997). A PPI thereby aims at explaining regionally specific responses in terms of the interaction between a psychological variable (i.e., a specific task condition) and the activity in a specified seed area. Specifically, the analysis tests for differences in the regression slope of activity in all areas of the brain, on the activity in the seed area (Lee et al., 2003). These regression slopes are taken as a metric of the “functional coupling” between the areas. Note that the presence of a significant change in coupling between the seed region and other brain areas under a specific task condition can be interpreted in two distinct ways: either as a change in the influence of the seed area on other brain regions or as a change in the responsiveness of the seed area to inputs from other brain regions (Lee et al., 2003). An advantage of PPI is that apart from defining a seed region, no other regions-of-interest need to be preselected, as the changes in connectivity of the seed area are tested for all other voxels in the brain. Employing a PPI approach on PET data, Lee et al. (2003) studied adaptive plasticity in the motor system after 1 Hz rTMS over the left primary motor cortex (M1) in the healthy motor system. Relative to sham rTMS, effective rTMS over M1 increased neural activity in the stimulated area and connected (premotor and motor) regions during finger movements, with the strongest effect being observed in the contralateral premotor cortex. A PPI analysis revealed that the stimulated part of M1 became less responsive to input from premotor and mesial motor areas. Moreover, the authors found increased movement-related coupling between an inferomedial portion of left M1 and anterior motor areas (including left sensorimotor cortex, dorsal premotor cortex (PMd), and supplementary motor area) after rTMS. These findings were interpreted as evidence for rapid TMS-induced remodeling of motor representations, potentially providing a neural substrate for acute compensatory plasticity of the motor system in response to focal perturbations. In particular, the upregulation of contralateral premotor regions might have helped to maintain motor task performance after focal disruption of M1.

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In another study, Ward et al. (2010) applied a PPI approach to explore how offline “inhibitory” 1 Hz rTMS to left PMd changes effective connectivity of the stimulated PMd in the context of action reprogramming. Healthy volunteers performed a Posner-like S1–S2 paradigm while task-related neural activation was mapped with fMRI. Effective 1 Hz rTMS over the PMd led to increased functional coupling during action reprogramming between left PMd and the ipsilateral parietal cortex (i.e., left supramarginal gyrus (SMG) and adjacent anterior intraparietal sulcus) relative to sham rTMS. In that study, rTMS improved the accuracy of trials that required the updating of motor plans. Notably, the individual decrease in error rates was predicted by the individual increase in coupling between parieto-premotor regions. This suggests that rTMS over the premotor cortex improved the ability to dynamically update a motor plan by strengthening the connectivity between the parietal and premotor regions. Together, these studies have shown that rTMS induces changes in the network connectivity of the stimulated area rather than modulating neural activity solely in the targeted area. However, employing a PPI approach does not allow for (post hoc) conclusions on the direction of the regional interactions.

3.2 DYNAMIC CAUSAL MODELING DCM uses neurobiological plausible forward models to infer on changes in neural connectivity underlying observed changes in neural activity (e.g., the BOLD response in fMRI or time–frequency spectra in EEG) (Friston et al., 2012). DCM examines the instantaneous rates of changes in neural activity in response to inputs (i.e., experimental perturbations). This approach thus allows for an investigation of the interaction of a predefined set of brain regions during different experimental contexts (Friston et al., 2003). The strength and the direction of regional interactions are computed by comparing the observed regional BOLD responses with the BOLD responses that are predicted by a neurobiologically plausible model (Stephan et al., 2010). The model describes how activity in and interactions among regional neuronal populations are modulated by external inputs (i.e., the experimental task conditions) and how the ensuing neuronal dynamics translate into the measured BOLD signal (Leff et al., 2008). DCM is used for hypothesis testing, employing Bayesian methods to compare alternate hypotheses that are formalized as alternative generative networks (or models) (Stephan et al., 2010). In DCM, these a priori defined models are compared based on their estimated likelihood given the data while taking into account model complexity, referred to as free energy bound on the log model evidence. Note that model comparison in the DCM framework is conducted to compare models, not to validate them (see Friston et al., 2013). As a result, three types of parameters are calculated for the winning model: the direct influences of the external input or stimuli on regional activity (i.e., the driving input); the strength of the intrinsic connections between two regions in the absence of modulating experimental effects; and the changes in the intrinsic connectivity between regions induced by the experimental design (Mechelli et al., 2005). DCM can thus be used to (1) identify the most likely source of the driving input in a network for a specific

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task, (2) identify the connections modulated by a task condition, and (3) identify whether this modulation had a facilitatory or inhibitory effect on regional neural processing. It thus provides important insights into neurobiological mechanisms underlying observed differences in neural activity, which cannot be inferred using other connectivity analyses such as PPI. A schematic illustration of DCM of NTBS-induced changes in effective connectivity in the study of cognition is given in Fig. 2. Applying DCM, previous TMS-fMRI studies have provided converging evidence that focal rTMS can consistently change effective connectivity in specific neural networks, including brain regions that are not directly targeted by rTMS. This might involve changes in effective interhemispheric corticocortical (Hartwigsen et al., 2013) or intrahemispheric cortico-subcortical connectivity (Herz et al, 2014a). For instance, Hartwigsen et al. (2013) used DCM to show that a transient perturbation caused by offline rTMS can induce changes in the effective connectivity between homologous speech regions in the healthy language system (Fig. 3). In that study, offline continuous theta-burst stimulation (cTBS) of the left posterior inferior frontal gyrus (pIFG) reduced neural activity during speech production in the

FIGURE 2 Dynamic causal modeling of NTBS-induced changes in effective connectivity. Schematic illustration of a dynamic causal model capturing a network of three brain regions that contribute to a given task. Relative to sham TMS, effective offline TMS over one network node significantly decreases the task-related activity in the stimulated area (orange (light gray in the print version) circle). This may result in an upregulation of another area within this network (big red (gray in the print version) circle). TMS might also influence the task-related (facilitatory or inhibitory) influence between the two nodes (red (gray in the print version) arrows between regions).

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FIGURE 3 Dynamic causal modeling of NTBS-induced changes in effective connectivity during speech production. TMS induces adaptive plasticity during speech repetition in the healthy brain. (A) Without perturbation or after sham TMS, the left posterior inferior frontal gyrus (pIFG) shows increased neural activation during speech repetition (big red (gray in the print version) circle), while the right homologue does not significantly contribute (small blue (dark gray in the print version) circle). There is no significant task-related connectivity between both regions. (B) TMS over left pIFG decreases task-related activity in the stimulated area (small blue (dark gray in the print version) circle) and increases the activity of the right homologue (large red (light gray in the print version) circle). TMS over left pIFG is followed by an increase in the task-related facilitatory drive from the right to the left pIFG. (C) Illustration of the significant negative correlation between individual speech onsets and increased facilitatory connectivity from the right to the left pIFG indicating faster response speed with increased influence from the right pIFG to the left pIFG. Adapted and reprinted from Hartwigsen et al. (2013), Figure 4. Copyright (2013), with permission from the National Academy of Sciences, USA.

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stimulated area relative to sham offline cTBS or effective offline cTBS over a control site in the anterior IFG (Fig. 3A and B). In turn, the (nondominant) right pIFG showed increased neural activity during speech production after effective offline cTBS. The upregulation of the right pIFG was associated with a stronger facilitatory drive from the right to the left pIFG, indicating a cTBS-induced change in effective connectivity from the nonstimulated to the stimulated hemisphere. Moreover, individual response speed became faster with increased influence from the right pIFG to the left pIFG (Fig. 3C). These results demonstrate that the right hemisphere actively contributes to speech production after a focal left hemispheric lesion. Using a similar conditioning approach, Herz et al. (2014a) applied 1 Hz rTMS over the presupplementary motor area in healthy participants who subsequently performed an interference task requiring the quick cancellation of impulsive response tendencies in order to avoid erroneous responses. They found that rTMS improved the participants’ ability to control impulsive responses, which was mediated by increased activation and connectivity of a cortico-subcortical network between the IFG and subthalamic nucleus (STN). This suggests that the behavioral improvement was not mediated by the stimulated area per se, but by spatially remote areas, which are part of the same inhibitory control network. Moreover, the relationship between rTMS-induced changes in effective connectivity and individual variations in task performance that was observed in both of our previous DCM studies (i.e., Hartwigsen et al., 2013; Herz et al., 2014a) suggests that the respective connectivity changes might mediate the NTBS-induced changes on the behavioral level. DCM can also be used to elucidate compensational and pathophysiological mechanisms in neurological disorders (e.g., in motor recovery after stroke) (Grefkes et al., 2010; Volz et al., 2015). For instance, Grefkes et al. (2010) applied 1 Hz offline rTMS over the M1 of the unaffected (contralesional) hemisphere in subacute stroke patients suffering from motor impairments. Relative to rTMS over the vertex, rTMS over the contralesional M1 significantly improved the motor performance of the paretic hand. Behavioral improvements were positively correlated with a reduction of the inhibitory influences from contralesional M1 during paretic hand movements. At the same time, endogenous coupling between ipsilesional supplementary motor area and M1 was enhanced after rTMS over the contralesional M1. The authors suggested that rTMS might have remodeled the disturbed functional network architecture of the motor system. Specifically, motor improvements were associated with a reduction of pathological transcallosal influences from the contralesional M1 and a restitution of ipsilesional effective connectivity between the supplementary motor area and M1. Not all patients benefit from TMS interventions and the beneficial effects of NTBS in stroke recovery might strongly depend on lesion size and location as well as the degree of impairment and treatment sessions (Grefkes and Fink, 2012). In the study by Grefkes et al. (2010), the strongest beneficial effects of contralesional 1 Hz rTMS were observed in patients in whom pathologically enhanced inhibition

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between the primary motor cortices could be resolved by the intervention. Hence, effective connectivity analyses might be particularly helpful in identifying characteristic network configurations that are susceptible to the neuromodulatory effects of rTMS (Grefkes and Fink, 2011). In sum, the combination of TMS and DCM complements standard fMRI analyses reflecting task-related changes in neural activation by capturing the direction and magnitude of the TMS-induced effects on the network level in both the healthy system and in patients with brain lesions. Of note, the function of neural networks can not only be determined by their spatial properties (i.e., which regions are engaged in the network) but also by their functional/timing properties. For instance, the same spatial networks may be activated during different types of movements, but oscillate in different frequencies (Engel and Fries, 2010; Herz et al., 2012). The aforementioned studies (Grefkes et al., 2010; Hartwigsen et al., 2013; Herz et al., 2014a; Ward et al., 2010) used fMRI, which has a good spatial resolution and is thus suitable for assessing spatial reorganization of neural networks. However, electrophysiological recordings with a high temporal resolution, such as EEG or MEG, are better suited to capture the functional properties of neural networks. DCM has recently been extended to electrophysiological data. For instance, induced oscillatory activity in cortical areas can be used to infer on modulation of neural networks (Chen et al., 2008). This method has successfully been applied in patients with Parkinson’s disease demonstrating marked alterations in corticocortical oscillatory coupling during movements (Herz et al., 2014b,c). This approach has been suggested as a neurobiological readout for closed-loop deep brain stimulation in patients with Parkinson’s disease (Alhourani et al., 2015), but to the best of our knowledge, there are no studies combining DCM for EEG and NTBS yet. Such studies would be important to assess whether NTBS-induced modulation of abnormal network connectivity in neurological disorders can help to restore physiological network properties alleviating the patient’s clinical impairment. They may also help to elucidate those remote effects in oscillatory activity following NTBS that cannot be accounted for by mere inhibition of the targeted brain area. For instance, a recent offline TMS-MEG study in the healthy brain (Marshall et al., 2015) found that offline inhibition of the frontal eye fields caused a subsequent disruption of the normal attentional modulation of occipitoparietal alpha oscillations contralateral (but not ipsilateral) to the stimulated frontal eye fields. This unexpected contralaterality might be best explained by a transient (compensatory) reorganization of the connectivity within the dorsal frontoparietal attention network as a whole instead of the mere inhibition of the respective frontal eye fields (Marshall et al., 2015). In sum, effective connectivity analyses complement standard fMRI (and M/EEG) analyses by modulating how different network nodes influence and drive each other during different (TMS-induced) brain states. However, one limitation of the DCM approach is the relatively small number of network nodes that can be successfully modeled within the same DCM. As yet, such models are restricted to small networks comprising approximately 3–4 network nodes with full connectivity.

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4 MODELING THE BEHAVIORAL EFFECTS OF NTBS Since the early days of NTBS, TMS has been used to modulate behavior and perception (Amassian et al., 1989). Depending on the nature of the specific task of interest, different types of dependent variables may be investigated. Changes in response speed or accuracy are among the most commonly used indicators of TMS-induced changes on the behavioral level. In most cases, these parameters are quantified with simple composite measures like the mean of response speed or the rate of accuracy. However, these composite measures provide relatively poor statistical sensitivity and only a limited information exploitation (Voss et al., 2013). As all information is projected onto one single parameter for each measure, they may remain blind toward slight changes in response strategies, for instance conflicting response tendencies in Stroop or Eriksen flanker tasks (Eriksen and Eriksen, 1974; Stroop, 1935). Such changes might emerge only in a relatively small proportion of the trials or in a certain range of behavioral variables of interest (e.g., the fastest response) and might thus not be reflected in simple composite measures. TMS-induced modulations per se are often relatively subtle so that behavioral changes will only occur if task demands are sufficiently high and a small drop in performance will have immediate consequences for the behavioral outcome. This is for instance the case in stimulus detection tasks during which stimuli are presented close to the perceptual threshold (Amassian et al., 1989; Kammer, 2007). Hence, a careful tuning of both the task of interest and the stimulation is required, which may be a demanding procedure. Yet again, changes might only occur in a certain subset of trials or a certain range of the outcome space spanned by the task. These problems might render composite measures too coarse to reveal the effects of interest. However, testing multiple more specific behavioral measures in the same study will result in a multiple comparisons problem. One way to overcome this dilemma is to employ distributional analyses. Critically, these analyses can be related to cognitive models of response conflict solving (e.g., “activation-suppression hypothesis,” Ridderinkhof, 2002, see below). For instance, so-called delta plots determine effect size as a function of response speed. Delta plots are constructed by rank ordering the response times across both correct and incorrect responses per condition (e.g., TMS site) for each subject and dividing them into equal-sized response time quartiles (see Fig. 4). Mean response speed and accuracy can then be determined for each quartile in each condition. They can be used to explore the processing dynamics in conflicting tasks by capturing different, conflicting response strategies. Within the theoretical framework of a dual-process model and the activation-suppression hypothesis (Ridderinkhof, 2002), delta plots capture both the direct stimulus-based activation as well as the suppression of irrelevant stimulus features. Delta plots thus account for the whole set of responses, making analysis less sensitive to outliers and basing information exploitation on a much wider range. This makes distributional analysis more sensitive toward changes in a specific aspect of the response profile. Delta plots have the potential to capture speed–accuracy relationships and allow for separate analysis of response tendencies. In a recent study (Hartwigsen et al., 2012), we combined TMS and delta plots in a condition-and-perturb approach to investigate parieto-premotor interactions during

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FIGURE 4 Modeling NTBS-induced changes in behavior using delta plots. (A) Schematic illustration of a dual-process model for stimulus–response mapping in trials with conflicting information and assumed role of left PMd and SMG. The increasing size of the arrows for selective inhibition represents the operation dynamics for this process (i.e., suppression is not initiated immediately after signal onset but takes time to build up). S1: invalid precue, S2: target stimulus. (B) Delta plots for the four combinations of offline and online TMS in the conditionand-perturb approach. Prior to the spatially precued reaction time task, offline rTMS was applied over left PMd or vertex (control site). During the task, subjects received effective or sham online TMS over left SMG. The delta plots illustrate the interference effect of invalidly precued trials as a function of response speed. The y-axis shows the relative increase in reaction times (RTs, in milliseconds) for invalidly compared with validly precued trials (the validity effect). The four data points of each curve represent the mean data for equally sized reaction time quartiles on the x-axis (0–25%, 26–50%, 51–75%, and 76–100%). Adapted and reprinted from Hartwigsen et al. (2012), Figure 5. Copyright (2012), with permission from the Society for Neuroscience.

action reprogramming. We found that online TMS over left SMG significantly impaired the accuracy of invalidly precued trials that required the subject to discard a prepared but invalid response and replace it with a valid alternative. Accordingly, delta plots indicated that the increase in errors induced by online perturbation of SMG was limited to trials in which subjects made fast responses, resulting in a steeper increase in the “accuracy slopes” of the delta plots at earlier quantile segments of the response speed distribution. This shows that online TMS of left SMG attenuated the ability to suppress the prepared but incorrect response, providing

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evidence for a key role of the SMG in response updating. Additionally, the response speed of invalidly precued trials was delayed with online SMG perturbation, but only after left PMd had been additionally preconditioned with offline rTMS. Hence, delta plots for response speed indicated that the difference between invalidly and validly precued trials (with the latter referring to trials that did not require the updating of motor plans) was more pronounced at slower response times after combined premotor–parietal TMS (Fig. 4B). Interpreting the profile of the behavioral effects within the theoretical framework of a dual-process model for stimulus–response mapping (see Ridderinkhof, 2002), we inferred that left SMG has a key role in the suppression of the precued but incorrect response (Fig. 4A). Consequently, online perturbation of SMG disrupted the ability to suppress the prepared but incorrect response at earlier quantile segments of the RT distribution. The delta plots relating the RT validity effect (i.e., the difference between validly and invalidly precued trials) with response speed showed that a combined online–offline perturbation of SMG and PMd was necessary to interfere with deliberate response decision processes (Ridderinkhof, 2002), i.e., processes that depend on the (valid) response stimulus. This suggests that the “lesion effect” caused by acute disruption of neural processing in SMG was not sufficient to impair response updating because intact neural processing in PMd was still sufficient to maintain a normal level of deliberate response activation unless it was additionally preconditioned with offline rTMS. Together, our results indicate that the functional weight within a network can be rapidly redistributed. Moreover, conditioning one network node can increase the disruptive effects of TMS in another area of the same network. In this context, the combination of TMS and delta plots might capture different response decision processes. An important challenge with speeded response time or choice tasks refers to the possibility of different (subject-specific) cognitive response strategies, emphasizing either the speed or accuracy of the response. This speed–accuracy trade-off phenomenon may occur both within and between participants. In case of diverging directions of speed and accuracy, the interpretation of task performance might become ambiguous (Voss et al., 2004, 2013). A way to deal with this is to rely on generative models such as sequential sampling models. These models are assumed to reflect the underlying processes that contribute to a particular response distribution. Sequential sampling models comprise parameters as speed and variability of information uptake, response threshold(s), or nondecision time(s). A prominent sequential sampling variant is the drift diffusion model (Ratcliff, 1978) (see Fig. 5). In this model, the putative processes are fitted to the observed distribution and their numerical values are obtained. By this, a network of parameters is created. This allows for a combined analysis of speed and accuracy and is sensitive toward possible adaptations of response strategies (Ratcliff and McKoon, 2008; Voss and Voss, 2007). Numerous processes have been captured with such models, including perceptual decision making (e.g., moving dot motion coherence paradigms), cognitive tasks and problems (e.g., recognition memory) (Ratcliff, 1978), numerosity recognition (Leite and Ratcliff, 2011), voluntary decision making (Zhang et al., 2012), and even aging (Ratcliff et al., 2004). In a recent study (Philiastides et al., 2011), drift diffusion modeling

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FIGURE 5 Modeling NTBS-induced changes in behavior using cognitive models. Simulated data obtained from a drift diffusion model. Depicted are correct responses and errors for a hypothetical task resulting from 100,000 simulations, as well as random walks leading to correct responses (in blue (dark gray in the print version)) and error responses (in red (light gray in the print version)). Parameters of the model include the starting point, the nondecision time, the drift rate, and the boundary separation. To investigate the influences of effective TMS, a decrease in drift rate of 33% as compared to sham TMS was assumed. Other model parameters remained unchanged. Note that TMS perturbation results in a rightward shift of the reaction time distribution and increase in error rate.

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was combined with TMS to investigate perceptual categorization processes. Conditioning 1 Hz offline rTMS was used to decrease activity in the dorsolateral prefrontal cortex (DLPFC) before subjects underwent a speeded perceptual categorization task. The authors reported significantly delayed responses and reduced accuracy after DLPFC TMS. Drift diffusion modeling revealed that particularly the drift rate (i.e., the rate of information uptake) was decreased after TMS, while nondecision times (i.e., constant processing times in the system) were unaffected. Likewise, Soto et al. (2012) found a specific modulation of the drift rate when applying online TMS during a working memory task. It remains unclear, however, how well behavioral modeling approaches such as sequential sampling models capture TMS-induced modulations of behavioral variables. For instance, when combining sequential sampling models with TMS, it might be important to take into account whether accumulation processes in the brain take place locally or centrally (Lo and Wang, 2006; Wong and Wang, 2006). Another aspect is related to the TMS protocol and the induced stimulation effects. Hence, a long-lasting offline rTMS protocol will probably result in more global changes, whereas single-pulse online TMS might have to be modeled differently. One possibility would be to include them as “shock events” which contaminate a distribution based on a diffusion-like process (Ratcliff and Tuerlinckx, 2002). A more general implication of the findings discussed above is that by accounting for the response distribution and different response strategies, behavioral modeling approaches may capture subtle TMS-induced changes on the behavioral level that might not be reflected in simple composite measures. The application of behavioral models in TMS studies might thus increase the overall proportion of variance explained on the total variance of a model. This might ultimately improve the sensitivity and specificity of different behavioral modeling approaches to capture TMSinduced modulations of behavior.

5 FUTURE PERSPECTIVES ON COMPUTATIONAL NEUROSTIMULATION IN THE STUDY OF COGNITION Within the past few years, the combination of NTBS with modeling approaches based on neuroimaging, electrophysiological, or behavioral data in studies of cognition has substantially increased our knowledge about the causal role of different brain regions in various aspects of cognition. Moreover, these studies provided insights into short-term reorganization and adaptive plasticity on the network level. The way forward is to use multimodal approaches that integrate information from different neuroimaging or electrophysiological techniques such as fMRI and EEG, which will substantially increase the validity and reliability of the NTBS-induced effects. In this context, the simultaneous application of NTBS and fMRI/EEG would advance the current knowledge on the neurophysiology of the NTBS-induced effects. Future studies should also consider the degree of intra- and interhemispheric interaction and compensation of different cognitive networks. Here, the use of focal minicoils (or

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electrodes) that allow for the simultaneous application of NTBS over multiple brain sites (e.g., Groppa et al., 2012a,b) seems promising. These studies should also account for the relatively strong interindividual variation with respect to the direction and intensity of the NTBS-induced modulation using advanced biophysical models. Moreover, the inclusion of behavioral parameters in (DCM) models of neuroimaging data could provide additional information with respect to the interaction of key nodes for specific cognitive processes and NTBS-induced changes on the neural network and behavioral level. The combination of relatively novel techniques such as TACS or transcranial random noise stimulation with neuroimaging and behavioral modeling approaches might also help to advance current models of cognition. Both approaches have been applied to synchronize or desynchronize cortical oscillations during different cognitive tasks and might prove effective for entrainment of cognitive functions. In this context, stimulation-induced modulation of behavioral variables and changes in the effective connectivity between network nodes would be important to provide deeper insights into the functional network architecture beyond simple changes in neural activity. This might deepen our understanding of the compensatory potential and adaptive plasticity of different task-specific networks. Finally, these approaches might help to establish models of cognition that can provide a better understanding of the cognitive consequences of neurological diseases such as stroke or Parkinson’s disease. It needs to be borne in mind, however, that aberrant networks (as for instance in Parkinson’s disease) might exhibit different responsiveness to stimulation. Therefore, response properties that are expressed in healthy brains may not be suited to predict the response to transcranial stimulation in the lesioned brain.

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