Simulating Transcranial Magnetic Stimulation during PET with a Large-Scale Neural Network Model of the Prefrontal Cortex and the Visual System

NeuroImage 15, 58 –73 (2002) doi:10.1006/nimg.2001.0966, available online at http://www.idealibrary.com on

Simulating Transcranial Magnetic Stimulation during PET with a Large-Scale Neural Network Model of the Prefrontal Cortex and the Visual System F. T. Husain,* ,1 G. Nandipati,* A. R. Braun,* L. G. Cohen,† M-A. Tagamets,‡ and B. Horwitz* *Language Section, National Institute on Deafness and Other Communication Disorders, and †Human Cortical Physiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892; and ‡Functional Neuroimaging Laboratory, Maryland Psychiatric Research Center, University of Maryland Medical School, Baltimore, Maryland 21228 Received January 18, 2001

indirectly connected to the stimulating site were affected by TMS. © 2002 Elsevier Science

Transcranial magnetic stimulation (TMS) exerts both excitatory and inhibitory effects on the stimulated neural tissue, although little is known about the neurobiological mechanisms by which it influences neuronal function. TMS has been used in conjunction with PET to examine interregional connectivity of human cerebral cortex. To help understand how TMS affects neuronal function, and how these effects are manifested during functional brain imaging, we simulated the effects of TMS on a large-scale neurobiologically realistic computational model consisting of multiple, interconnected regions that performs a visual delayed-match-to-sample task. The simulated electrical activities in each region of the model are similar to those found in single-cell monkey data, and the simulated integrated summed synaptic activities match regional cerebral blood flow (rCBF) data obtained in human PET studies. In the present simulations, the excitatory and inhibitory effects of TMS on both locally stimulated and distal sites were studied using simulated behavioral measures and simulated PET rCBF results. The application of TMS to either excitatory or inhibitory units of the model, or both, resulted in an increased number of errors in the task performed by the model. In experimental studies, both increases and decreases in rCBF following TMS have been observed. In the model, increasing TMS intensity caused an increase in rCBF when TMS exerted a predominantly excitatory effect, whereas decreased rCBF following TMS occurred if TMS exerted a predominantly inhibitory effect. We also found that regions both directly and

INTRODUCTION Transcranial magnetic stimulation (TMS) is a technique in which a strong focal magnetic field is applied to a region on the human scalp, thereby influencing regional neuronal function. The externally generated changing magnetic field, applied by a coil placed over a subject’s head, induces electric currents in the brain. The faster the rate of change of the magnetic field, the larger is the induced current. TMS reversibly interferes with neuronal activity under the coil, resulting in a “virtual lesion” (Amassian et al., 1989; Day et al., 1989b; Seyal et al., 1992; Cohen et al., 1998). TMS can be applied as a single pulse or a double pulse, or it can be used to repetitively stimulate the cortex (called rTMS). These types of TMS and their uses have been extensively reviewed (Pascual-Leone et al., 1999; Walsh and Rushworth, 1999; Jahanshahi and Rothwell, 2000). Depending on the task being performed, the site of stimulation, and other factors such as stimulus configuration, TMS can result in either disruption or enhancement of behavioral performance. There have been a number of studies documenting the disruptive effect of TMS. For instance, Pascual-Leone and Hallett (1994) used rTMS to study the function of dorsolateral prefrontal cortex in short-term memory tasks, such as delayed match to sample (DMS). They found that TMS of sufficient intensity and frequency disrupts performance in such a task, resulting in an increased number of errors. By taking advantage of its disruptive nature, TMS has been used in attempts to determine the brain regions necessary for a particular task (Cohen et al., 1997; Gerloff et al., 1998; Mottaghy

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Language Section, NIDCD, NIH, Building 10, Room 6C420, MSC 1591, 9000 Rockville Pike, Bethesda, MD 20892. Fax: (301) 4805625. E-mail: [email protected].

1053-8119/02 $35.00 © 2002 Elsevier Science All rights reserved.

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et al., 2000) and to track propagation of information from one cortical region to another (Cracco et al., 1999b). Among the few studies showing the facilitatory effect of TMS is the report of Topper et al. (1998) who found that TMS applied to Wernicke’s area 500 to 1000 ms before visual stimulus presentation significantly decreased naming latencies. Moreover, facilitation was site-dependent; similar suprathreshold stimulation of left motor cortex and the nondominant temporal lobe had no facilitatory effect. When combined with positron emission tomography (PET), TMS becomes an even more powerful tool for examining the neural basis of cognition. PET allows us to image regional cerebral blood flow (rCBF), which provides an estimate of regional neural activity. Because the TMS/PET approach allows researchers to localize the TMS-related changes in rCBF and to detect such changes in cortical and subcortical structures, it can be used to study functional connectivity of the cortical regions in humans. In the present context, two regions are functionally connected if the TMS-induced changes in neural activity in one region result in changes in activity in the second region. In one study (Paus et al., 1997), increasing the amount of TMS applied to the left frontal eye field resulted in rCBF increases at the stimulated site and in distally connected visual cortex. However, in a later study (Paus et al., 1998), stimulation of the left primary sensorimotor cortex led to rCBF values that covaried negatively with the number of TMS pulse trains. Mottaghy et al. (2000) found that rTMS to the right or the left dorsolateral prefrontal cortex significantly worsened performance in a working memory task and induced some reduction in rCBF at the stimulation site and in distant brain regions. Changes in rCBF due to TMS have also been reported in other studies (Fox et al., 1997; Oliviero et al., 1999). Although there have been numerous studies of TMS on normal adults and patients, the neural mechanisms by which TMS exerts its effects are not well understood. For instance, the excitatory and inhibitory aspects of TMS and their neurochemical and neurophysiological effects are still debated (Cracco et al., 1999a; Paus, 1999). Researchers have not been able to determine the types of neuronal populations (excitatory, inhibitory, or both) most susceptible to stimulation in a particular brain area, and whether the effects of stimulation are confined to the site of stimulation or are transmitted to distal regions via neural pathways. Further, animal studies, which could help inform us about the effects of TMS, are limited by technical problems related to finding an appropriate coil size for small animal heads (Weissman et al., 1992; Fitzpatrick and Rothman, 2000). Neural modeling, however, does not suffer from these methodological limitations and thus may be help-

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ful in refining our understanding regarding the neural bases of TMS. Any region of the cortex affected by TMS contains multiple cortical columns, each of which contains multiple excitatory and inhibitory neurons. Moreover, these units interact with one another and with other brain regions. Unlike the case in a real brain, in a neural model we can examine how TMS affects each element of an interacting neural system, and how this effect manifests itself throughout the system. Specific hypotheses can be tested quantitatively and related to existing experimental data. Moreover, modeling may also lead to predictions that can be tested. Even the results from a relatively simple model can be useful in providing the bias needed to orient experimental research in one of several directions [see Horwitz et al. (1999, 2000) for a fuller discussion of the roles neural modeling can play in cognitive neuroscience]. In the present modeling study, we investigated the mechanisms underlying TMS, including those that result in excitatory and/or inhibitory effects, by simulating rTMS and PET using a previously developed largescale neural network model of the visual system (Tagamets and Horwitz, 1998; Horwitz and Tagamets, 1999). The model relates in a systematic manner experimental evidence from primate anatomical and electrophysiological studies of the visual system to human brain imaging studies, and has been successfully applied to an experimental study of a DMS task. The model was constrained in the choice of parameters, responses, and connectivity of the network to be both quantitatively and qualitatively similar to experimental data, especially nonhuman electrophysiological data (e.g., Fuster et al., 1982; Funahashi et al., 1990). The integrated summed synaptic activities in the model were used as an estimate of PET rCBF values and were similar to experimental data from Haxby et al. (1995). Therefore, this model has been shown to provide a reasonably complex framework for investigating the neural basis of PET/fMRI human neuroimaging studies. In the present study, TMS was simulated within the model in the form of diffuse excitatory currents applied to excitatory and inhibitory neural units. Its effects on a DMS task were investigated by simulated behavioral and PET rCBF values. The effect of simulated TMS on the stimulated site and in distal regions of the model was evaluated by examining simulated rCBF data. This allowed us to explore the contributions of TMS in studying the relation between functional and anatomic connectivity. For the purposes of this article we concentrate on rTMS, which has been shown to be the most effective short-term TMS capable of causing a temporary “virtual lesion” of the cortical area being stimulated, resulting in behavioral and rCBF changes.

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FIG. 3. Example showing electrical activities in some of the excitatory regions of the model. The different columns depict 9 ⫻ 9 matrices of LGN units, vertical units at stages V1 and V4, IT units, cue-selective (Fs) and delay (D1 and D2) units of PFC, and response (R) units. The y axis represents time with snapshots at 1, 3, and 5 s from the beginning of the trial. In the top half of the figure (a), when a TMS of 0.01 is given during the delay, few response units are active, in keeping with the Nonmatch (different stimuli) trial. In the bottom half of the figure (b), TMS of intensity 0.11 is given during the delay resulting in disruption of the activity of the delay units, and causing many response units to become active. This, in turn, leads to an erroneous judgment of a match trial. The color bar represents the activity index of the neuronal units.

MATERIAL AND METHODS Large-Scale Model of the Visual System The large-scale model of Tagamets and Horwitz (1998) simulates the ventral visual processing stream during a DMS task and consists of four major brain regions—primary visual cortex (V1/V2), occipitotemporal cortex (V4), inferior temporal cortex (IT), and prefrontal cortex (PFC)—as shown in Fig. 1. Each of these regions, in turn, is composed of 9 ⫻ 9 neuronal assemblies of basic units, each of which is an interacting excitatory–inhibitory neuronal pair representing a simplified cortical column. Separate regional subpopu-

lations mediate different aspects of the task. In the first two stages of the model (V1/V2 and V4), there are orientation-selective neurons, i.e., neurons that are selective for horizontal and vertical lines. In addition, V4 has corner-selective neurons, making a total of three subpopulations at this stage. The IT units integrate information received from the V4 units and generate an abstract representation of the percept that is transmitted onto the PFC. The PFC consists of four different types of neuronal units (as observed by Funahashi et al., 1990) (see Fig. 1): cue-selective units that respond when a visual stimulus is present, two types of delay units that are active during the delay period, and re-

FIG. 1. Large-scale neural network model of the ventral visual system and the prefrontal cortex. Excitatory-to-excitatory connections are depicted in blue and excitatory to inhibitory connections are depicted in red. Feedforward connections flow from left to right, and feedback connections flow from right to left. (1a) A basic unit is equivalent to a single cortical column and consists of an excitatory and an inhibitory unit. The strengths of the connections of the basic unit are depicted as numbers over the arrows of the connections. See the text and Tagamets and Horwitz (1998) for details. FIG. 2. The experimental paradigm was stimulus presentation, delay interval, and a second stimulus presentation (top). Each trial was preceded and followed by 0.5 s of rest. The second stimulus could be either the same as the first stimulus or a different stimulus. TMS (or rTMS) was applied to the PFC units for 2 s during the delay period. The different shapes used as stimuli are in the middle of the figure. The stimulus pairs used in the experiment are shown in the table (bottom). See the text for more details.

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sponse units whose activities increase when the second stimulus matches the first. Funahashi et al. (1990) further classified the delay units as being of two types: one type of delay unit (D2) is active during stimulus presentation and the subsequent delay before presentation of the second stimulus; the other type of delay unit (D1) is active only during the delay between presentations of the two stimuli. In the model, the D2 units also are the source of feedback to earlier areas. Although the delay units have been classified separately according to their function and are modeled separately in the network, they, along with the cue-selective and response units, likely represent interspersed neuronal subpopulations in the same part of the prefrontal cortex. The response module displays a brief activation if the second stimulus matches the first and the first stimulus has been remembered. Although the response units have been shown to exist in various studies (Funahashi et al., 1990; Goldman-Rakic, 1995; Miller et al., 1996) and have been shown to respond without a match, such a neuronal subpopulation must exist that mediates whether the second stimulus matches the first. The reasoning is detailed in Tagamets and Horwitz (1998). Feedforward and feedback connections between regions are based on primate neuroanatomical data. For the purposes of the present study and ease of terminology, we restrict the usage of the term PFC module to include only the cue-selective and delay units and consider the response module to be a separate brain region. Although in reality the response module cannot be spatially segregated from the other PFC modules, we consider it separately in the model, so that we can use the neuronal activity of its units as an indicator of behavior without these units being directly affected by rTMS applied to the other PFC modules. The basic unit underlying all the regions of the model, shown in Fig. 1a, consists of excitatory and inhibitory elements, with the excitatory element contributing more to the overall activation of the basic unit due to its larger synaptic weights. As has been found in nonhuman primate studies, the number and influence of excitatory units outweigh those of the inhibitory units in the cortex (Douglas et al., 1995). The excitatory element participates in feedforward and feedback connections. Parameters are chosen so that excitatory elements have simulated neuronal activities resembling those found in electrophysiological recordings from monkeys performing similar tasks (Fuster, 1973; Desimone et al., 1984; Desimone and Schein, 1987; Funahashi et al., 1990). There is low-level, random, spontaneous activity in each neural element at all times. For details on the parameters used in the model and a thorough discussion of all the assumptions employed, see Tagamets and Horwitz (1998).

Task We chose DMS for shape, as this is a well-studied condition and information associated with this task is available in the functional neuroimaging, neuroanatomical, electrophysiological, and cognitive literature. The DMS task involves presentation of a shape, a delay, and presentation of the same or different shape, as shown in Fig. 2. The model decides if the second stimulus is the same as the first. In our experiments, the shapes were composed of simple line segments such as squares, H’s, T’s, and U’s (Fig. 2, middle). Eight stimulus pairs were created as shown in the table at the bottom of Fig. 2, using various combinations of the four shapes. When a stimulus arrives at an area of the model that represents the lateral geniculate nucleus (LGN) (Fig. 1), it activates the horizontal and vertical units of V1/V2, which respond best to small horizontal and vertical lines. The next stage, V4, responds to longer horizontal and vertical lines and to corners (junctions of horizontal and vertical lines). The third stage, IT, responds to an integration of the activities seen in V4 or, in other words, to a percept of the entire stimulus. The PFC units become active during stimulus presentation and maintain a representation of the stimulus during the delay period when the stimulus is no longer present at the LGN. The units of the response module become more active when the second stimulus matches the first. The effect of attention in the model is implemented by a low-level, diffuse incoming activity to one of the delay modules (D2). The attention modulates how the delay units respond to a given stimulus; the higher the attention, the better is the representation maintained by the delay units. The effect of attention on the other modules is via feedback connections initiating from PFC. Simulating PET In our experiments, multiple trials, each involving presentation of pairs of stimuli at the LGN, were used to simulate a PET study. The rCBF data were simulated by integrating the absolute value of the synaptic activities over the time course of the study within the different regions for each task, thus assuming that increases in either excitatory or inhibitory synaptic activity, or both, are manifest as increased rCBF [see Jueptner and Weilles, 1995; it has been shown by Logothetis et al. (2001) that integrative synaptic activity also best represents the blood oxygenation signal obtained from fMRI]. This is similar to the method proposed by Arbib et al. (1995) and is further delineated in Tagamets and Horwitz (1998, 2001). Note that because only 15% of the synapses in our basic unit are inhibitory, excitatory activity will generally make a greater contribution to simulated rCBF than will inhibitory

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activity, which is consistent with the recent fMRI finding of Waldvogel et al. (2000).

duration of net inhibition, on the order of 100 to 250 ms.

Simulating TMS

Simulations

In a majority of our simulations, repetitive TMS was simulated as a 5-Hz (50 ms ON and 150 ms OFF) pulse of excitatory current, resulting in 10 pulses over a 2-s period. The current was modeled as lowlevel diffuse activity directly connected to either the excitatory or inhibitory units (or both) of each of the PFC regions (except the response region) via weighted connections. See the Appendix for details about the weights added to the model to simulate the input of TMS current. TMS was applied to these regions during the delay period as shown in Fig. 2. In all experiments, unless otherwise noted, the intensity of TMS-induced current was varied in a parametric fashion from 0.01 (low) to 0.11 (high) in steps of 0.02; note that the units of injected current represent normalized values in the range from 0 to 1. For each level of TMS, a separate simulated PET scan was performed. In our model, we are not simulating TMS pulses but rather the effect of the stimulation by injecting currents in prescribed sites of the model. Regardless of the parameter that modulates the effect of TMS in an experimental situation, we are modeling this effect by modulating the intensity of the cortical current being injected into the stimulating site. Experimentally, the time course of the induced electric current due to a single magnetic pulse is on the order of 1 ms or so (Walsh and Rushworth, 1999). Our computational model’s minimum time step is 5 ms, and we use a sigmoidal function to represent each unit’s electrical activity [see Tagamets and Horwitz (1998) for details]. As a result, for most of the simulations, we chose 50 ms as an appropriate time for representing the effects of rTMS-induced currents within a cortical column, which is what one unit represents in the model. Additionally, in one of our simulations the durations of the TMS-induced currents to the excitatory and inhibitory PFC units were varied. The ON period of the currents to the excitatory units was reduced from 50 to 5 ms and the ON period of currents to the inhibitory units was extended to 100 ms, resulting in an inhibitory ON period that was 20 times longer than the excitatory ON period. This was done in accordance with experimental studies that measured the effect of single-pulse TMS and are reviewed by Cracco et al. (1999b), Fitzpatrick and Rothman (2000), Jahanshahi and Rothwell (2000), and Pascual-Leone et al. (2000). The studies suggest that a single pulse of magnetic stimulation results in a brief period of activation of excitatory and inhibitory units followed by a longer

Each simulated PET scan consisted of presentations of eight different stimulus pairs as shown in Fig. 2 (bottom). These stimulus pairs are combinations of the four possible stimuli. If the two stimuli of a pair are the same, the trial is a “Match” trial; otherwise it is a “Nonmatch” trial. In each simulated scan there were 3 Match and 5 Nonmatch trials. The computational modeling and simulations were generated and compiled with a C/C⫹⫹ compiler in a Unix environment and the visualization of results was accomplished using Matlab 5.3 (Mathworks, Natick, MA). SIMULATIONS The primary goals of the simulations were to explore the neural bases of TMS and to evaluate its usefulness in determining functional connectivity of brain regions involved in a particular task. The effect of simulated TMS on the model was measured by simulated PET rCBF values and by behavioral measures. For a particular TMS level, the absolute value of all synaptic activity data was determined for the different model regions for each trial (corresponding to a particular stimulus pair) and integrated over eight trials. There were seven intensity levels of the TMS currents, resulting in seven rCBF values for each study. The data were then normalized with respect to the rCBF value obtained without TMS. Behavioral data were indexed by the number of units of the response region that were active above a certain threshold (0.3; where activities of the units vary from 0 to 1). Stimulus pairs such as those shown in Fig. 2 were of two types: same (Match) and different (Nonmatch). The number of active response units was determined for each trial, and averaged across eight trials, one for each stimulus pair. 1. Simulations to Examine the Neural Basis of TMS a. Results. A series of simulations were conducted to investigate the neural basis of TMS, specifically the effects of TMS on excitatory and inhibitory neuronal populations. In the first simulation, TMS was applied to both the excitatory and inhibitory units of the PFC, excluding the response module, during the delay interval of the DMS task from 2 to 4 s after the start of a trial (see Fig. 2). The frequency of the TMS was maintained at 5 Hz while intensity was varied from a low of 0.01 to 0.11 in steps of 0.02, in separate simulated PET scans. The effect of TMS was studied by focusing on the electrical activities of the PFC and sites directly connected to it, an example of which is illustrated in Fig.

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3. The stimuli used in this example were different and the expected result was that of a Nonmatch. Activities in different regions of the model are depicted at three time steps, with time along the Y axis: at 1 s, when the first stimulus has been registered, at 3 s during the delay, and at 6 s when the second stimulus has been registered and a response generated. In Fig. 3a, during the delay (at 3 s) a low-level TMS (0.01) intensity was applied. Consequently when a second stimulus that differed from the first was registered, only a few units of the response region became active, indicating a Nonmatch. Figure 3b represents the activities of the same modules before and after stimulation with currents of high intensity (0.11). The TMS current interfered with the activity of the delay units and disrupted the integrity of the representation of the first stimulus. When the second stimulus arrived, more response units became active and responded as though it were a Match. With increasing intensity, simulated rCBF values in the PFC and response modules increased and are depicted in Fig. 4 (shown by filled diamonds). Evidence for such increases in rCBF caused by varying the parameters of magnetization have been reported in experimental studies (Fox et al., 1997; Paus et al., 1997). The averaged behavioral results are depicted in Fig. 5 (shown by filled diamonds). The number of significantly active units in the response region increased with increasing TMS intensity. To isolate the inhibitory effects of TMS, a second simulation was conducted where TMS was applied only to the inhibitory units of the PFC. Figure 4 (dot– dash curve with filled squares) depicts the effect of TMS on rCBF in the PFC and the response regions, respectively. The rCBF in the PFC and response region showed a negative correlation with TMS intensity. This is in contrast to the finding that when TMS was applied to both excitatory and inhibitory units of PFC, TMS intensity was positively correlated with increases in rCBF in the PFC and in the response region. Experimentally, such decreases have been seen in the studies of Paus et al. (1998) and Mottaghy et al. (2000). The percentage decrease in rCBF seen in this study concurs roughly with the percentage decrease in rCBF reported in Fig. 2 (p. 1105) of the Paus et al. (1998) study. In the experimental study, 10-Hz magnetic pulse trains, from 5 to 30, in steps of 5, were used to stimulate the sensorimotor cortex while the subjects underwent PET scans. Both under the coil and in regions distant from the stimulation site (e.g., SMA, medial parietal lobe), measured rCBF was negatively correlated with the number of pulse trains. Figure 5 illustrates the effect of TMS on the behavior of the model in this simulation (shown by the curve with filled squares). The number of responding units decreased with increasing intensity of TMS to the inhibitory units of PFC, resulting in

greater number of incorrect identifications of Matches as Nonmatches. We also studied the effect of stimulating the model with TMS during the entire trial as opposed to only during the delay period. In this particular simulation, TMS was applied to the excitatory and inhibitory units of the PFC throughout the duration of the trial. The rCBF values in PFC and response modules are shown in Fig. 4 (filled triangles). There was a small difference between the PET rCBF values obtained when TMS was active only during delay (Simulation 1) compared with the present simulation where TMS was applied throughout the trial. The behavioral data are shown in Fig. 5 by the curve with filled triangles. At low- to midintensity TMS, the number of responding units increased with TMS intensity; at high TMS intensities, the number of responding units decreased. Experimental studies have suggested that the effect of each pulse of an rTMS train may not be the same, but in fact may be cumulative (Pascual-Leone et al., 1994; Hallett et al., 1999). In the experimental study of Pascual-Leone et al. (1994), rTMS of different intensity and frequency applied to the motor cortex modulated motor evoked potentials (MEPs) produced in the target muscle. At 110% of the threshold intensity, there was no change, but at 150 and 200% intensity, as the number of pulses increased, the MEP amplitude increased. The researchers defined threshold intensity to be the lowest intensity of rTMS that produced MEPs of sufficient amplitude. An examination of Fig. 3a of PascualLeone and co-workers’ (1994) article suggests that there is approximately a 25% increase in the effects of 5-Hz rTMS over the entire duration of 10 pulses. To incorporate this effect, we included an incremental increase in the TMS intensity with time, so as to mimic the linear increase in the amount of excitation and inhibition during the course of the rTMS train. We presumed an increase of 2.5% with each pulse (which for 10 pulses per train would lead to the 25% increment mentioned above). This simulation led to results very similar to those obtained with no incremental increase for each pulse. A simulation also was conducted to study how changing the relative time courses of the TMS-induced currents to the excitatory and inhibitory PFC units influenced the effects of the simulated TMS. As has been reported in a number of studies (see Cracco et al., 1999b; Jahanshahi and Rothwell, 2000), a single pulse of TMS appears to result in a brief period (a few milliseconds) of synchronized excitatory activity followed by a longer period of inhibition (20 –200 ms). A recent computational model of transcranial magnetic stimulation of neocortical neurons (Kamitani et al., 2001) showed that one magnetic pulse applied to model a single pyramidal neuron can induce a brief excitatory period of firing followed by longer period of silence or

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FIG. 4. Effect of simulated TMS on rCBF values in the PFC (a) and the response regions (b) during different simulations investigating the neural basis of TMS. The simulated rCBF values are normalized by rCBF values for that region during PET scans with no magnetic stimulation. When both TMS currents were applied to both excitatory and inhibitory PFC units, rCBF increased with increasing TMS intensity in both the PFC and response regions (shown by filled diamonds). When TMS currents were applied to only the inhibitory PFC units, rCBF decreased with increasing TMS intensity in the PFC and response regions (shown by filled squares). TMS applied throughout the trial to both excitatory and inhibitory PFC units caused the rCBF to increase in the PFC and response regions (filled triangles). TMS current, modeled as a brief burst of excitatory current to excitatory and inhibitory units followed by a long period of inhibition (TMS intensity incremental with each pulse), led to decreased rCBF with increasing TMS intensity and is shown by open circles. See text for details.

inhibition, concurring with experimental reports of TMS. In the present simulation, while the frequency of the rTMS train was maintained at 5 Hz as in the

earlier simulations, the duration of the current to the excitatory PFC units was reduced to 5 ms and the duration of the current to the inhibitory PFC units was

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FIG. 5. Behavioral data indexed by the number of significantly active units of the response region for TMS of different intensities for the different types of simulations. The number of responding units increased with increasing TMS intensity of excitatory and inhibitory current to PFC (filled squares). However, the number of responding units decreased with increasing TMS intensity of inhibitory current to PFC (filled squares). Similarly, when the TMS currents were modeled as a brief period of excitation followed by longer-lasting inhibition (TMS intensity incremental with each pulse), the number of responding units decreased with increasing TMS intensity, ultimately going to zero (shown by open circles). At low-intensity TMS given throughout the trial (filled triangles), the number of responding units increased with TMS intensity; at high TMS intensities, the number of responding units decreased. When only D2 was stimulated (filled circles) the increase in the number of responding units with TMS intensity was less than in the simulation when TMS was applied to all modules of PFC (filled diamonds). See also legends to Figs. 4 and 6.

increased to 100 ms. This simulation also incorporated the incremental increase in TMS pulse intensity with the order of the pulses in the rTMS train as discussed in the preceding paragraph. The results of the study are shown in Fig. 4 by the open circles. The simulated rCBF exhibited a decline with increasing TMS intensity and is in close approximation with the experimental results of Paus et al. (1998). It should be noted that the more realistic modeling of rTMS (incorporating different time courses and cumulative effects of the pulses) produced results similar to those of the simpler modeling of rTMS in the earlier simulations where TMS-induced currents affected only the inhibitory PFC units. Baseline activity in the stimulated brain regions has an impact on the magnitude of the effects of TMS. In the simulations, it was found that the effect of TMS was greater on the delay units D1 and D2, which are active during the delay period when the rTMS train is applied, than on the inactive cue-selective units. However, the effect varied from a predominantly excitatory effect (increased activity) when the duration of the excitatory pulse of the TMS was long (50 ms), as in the first simulation, to a predominantly inhibitory effect (decreased activity) when the duration of the excitatory pulse was small (5 ms), as in the last simulation. b. Discussion. The correlation between brain activity and TMS has been studied using neuroimaging

techniques like PET, which measures blood flow changes resulting from neuronal activity. In our study, we found that when TMS was applied to the excitatory and inhibitory units of PFC, simulated rCBF in the PFC and its anatomically connected regions (such as the response module, IT, and V4) increased with increasing TMS intensity. However, when TMS was applied exclusively to the inhibitory units in PFC, there were reductions in rCBF in the PFC and in anatomically connected brain regions (e.g., the response module) that correlated with the intensity of the applied TMS. Similar results were obtained when TMS-induced currents were applied to both excitatory and inhibitory PFC units, but the duration of the on period of the inhibitory units was 20 times longer than the duration of the ON period of the excitatory units. The above aspects of TMS can be better understood by examining what has been observed experimentally about TMS and what is known about neuroanatomy and neurophysiology. Researchers have found somewhat contradictory results regarding the effect of TMS on rCBF values. Fox et al. (1997) performed a combined PET/TMS study of the hand area of the left primary motor cortex. They found that blood flow at the stimulating site increased by 12 to 20% in a focal manner. These researchers also found that blood flow increases in parts of ipsilateral primary and secondary somato-

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sensory areas, premotor areas, and supplementary motor areas were positively correlated with the increases in blood flow at the stimulating site. However, a negative correlation was observed between the blood flow changes at the stimulating site and the contralateral primary motor areas. Mottaghy et al. (2000) studied the neuronal substrate of short-term working memory using a combined PET/TMS study. They found that rTMS to the right or left dorsolateral prefrontal cortex during the entirety of a 2-back task induced reductions in the rCBF at the stimulating site and at the connected sites. Paus and colleagues performed two studies that examined the effect of TMS on rCBF values. In the first study (Paus et al., 1997), they reported a significant positive correlation between rCBF and the number of TMS pulse trains at the left frontal eye fields. When they studied the effect of TMS on the left primary sensorimotor cortex, with an identical stimulation paradigm, Paus et al. (1998) found the cerebral blood flow at the stimulated site covaried significantly in a negative fashion with the number of stimulation trains. In our simulations, we demonstrated that excitatory and inhibitory TMS-induced currents cause the integrated rCBF to increase in the stimulating PFC site. However, when the TMS-induced current was applied only to inhibitory PFC units, rCBF decreased with increasing intensity of the current. In one simulation we changed the temporal relationship of the TMSinduced currents to the excitatory and inhibitory PFC units, resulting in a brief excitatory burst followed by longer duration of inhibition. The results demonstrated that the inhibitory effects dominated the excitatory effects, leading to a decline in rCBF with increasing TMS intensity. These two simulations, where inhibitory effects predominated, resulted in a decrease in rCBF with increasing TMS intensity. Our results suggest that in studies such as Fox et al. (1997) and Paus et al. (1997), the activation of either the excitatory population alone or both the excitatory and inhibitory neural elements of the stimulating site [for instance, frontal eye fields in the Paus et al. (1997) study or motor cortex in the Fox et al. (1997) study] led to increased rCBF and that the activation primarily of the inhibitory neuronal population led to decreased rCBF values in other studies [sensorimotor cortex in Paus et al. (1998) and prefrontal cortex in the study by Mottaghy et al. (2000)]. Further, in one instance where numeric data are available on the changes in rCBF with changes in TMS (Paus et al., 1998) we can make quantitative comparisons (see Fig. 4 of this article and Fig. 2 of Paus et al., 1998). The decline in rCBF was approximately 8 –10% of the initial rCBF value in both the experimental and modeling results. Our modeling results thus support the suggestion of Paus et al. (1998) that preferential activation of excitatory corti-

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cocortical neurons resulted in the rCBF increase seen in the first study (Paus et al., 1997) and activation of GABAergic inhibitory interneurons resulted in the rCBF decrease seen in the second study (Paus et al., 1998). The question then arises as to why these populations were differentially activated. One hypothesis that could account for the preceding results would be the preponderance of a particular type of neuron at a given site. Generally, researchers have suggested that excitatory effects of TMS are produced by activation of the excitatory corticocortical neurons, and the inhibitory effects, by activation of the GABAergic inhibitory interneurons (Di Lazzaro et al., 1998; Paus et al., 1998; Cracco et al., 1999b). The excitatory effects of TMS are seen in peripheral muscles as a result of sufficient or suprathreshold magnetic stimulation, usually measured via electromyographic (EMG) responses. Inhibitory effects of TMS have been demonstrated (1) by evaluating the silent periods of EMG responses evoked by a single suprathreshold magnetic stimulus (Day et al., 1989a) and (2) by measuring the “masking” or suppressing effect of a small conditioning subthreshold stimulus occurring 1–5 ms before a single suprathreshold magnetic pulse (Kujirai et al., 1993; Ziemann et al., 1996; Tokimura et al., 2000). The activation of the excitatory and inhibitory cortical neurons in turn modulates the cortical blood flow (Roland and Friberg, 1988; Northington et al., 1992). Roland and Friberg (1988) have reported decreases in rCBF due to pharmacological stimulation of GABAergic receptors in awake humans, suggesting that inhibitory postsynaptic transmission can be related to decreases in rCBF. However, a note of caution is sounded by a recent exploration of synaptic inhibition on cortical activation during PET and fMRI studies using the large-scale model (Tagamets and Horwitz, 1999, 2001). The studies suggested that the effects of excitatory and inhibitory synaptic activations or currents on rCBF values can depend on a number of factors apart from the currents themselves, including context (the activity of excitatory afferent connections), local interconnections, and the type (direct or indirect) of inhibitory connections. The modeling suggested that when low local excitatory recurrence is available, or if the region is not being driven by afferent excitation, then neuronal inhibition can raise rCBF values. However, in the opposite situation, with high recurrence or active afferent excitation, inhibition can lower observed rCBF values. Hence, while in our simulations the effects of the excitatory and inhibitory TMS currents are clearly defined, in actuality, the effects of these TMS-induced currents on rCBF may be more complex, depending on the stimulus configuration, context, local circuitry, and interconnections with the remainder of the network involved in the task. A further caveat is that, unlike in the model, the effects of TMS on excitatory and inhib-

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itory neurons cannot be separated experimentally, and that excitatory and inhibitory neurons are located in the same spatial locations (at the level of spatial resolution we are dealing with). Nonetheless, because there are differences in the cellular anatomy and physiology between excitatory and inhibitory neurons, it may be that TMS has different effects on each type of neuronal population. One of the advantages of modeling, as shown above, is that we can investigate what are the consequences of assuming that TMS affects each population in the same or in different ways. Another explanation for the results of negative correlation between TMS and rCBF may be the activation of different neuronal populations due to the orientation of the magnetic coil relative to the underlying neural tissue. TMS preferentially activates neural elements parallel to the coil (Amassian et al., 1987; Day et al., 1987). Exactly what neural elements are activated for a given coil orientation remains unclear, depending on their position relative to the cortical surface and the presence or absence of cortical indentations (Maccabee et al., 1990). The effect of the orientation of the TMS coil has been reported for the motor cortex (Wilson et al., 1996; Ziemann et al., 1999), occipital cortex (Meyer et al., 1991), and PFC (Hill et al., 2000). In the last study, the researchers examined the effects of eight coil orientations on the PFC and found that they differed in the magnitude of the error in memory-guided saccades, with the most effective orientation (resulting in the most errors) being anterolateral, with respect to the vertex (top of the head; see Fig. 2 of Hill et al., 2000). A factor contributing to the effect of orientation is the occurrence of excitatory pyramidal neurons perpendicular to the brain surface and of inhibitory interneurons parallel to the brain surface. Generally speaking, the magnetic coil preferentially excites neural elements parallel to its orientation. Other variables that can influence the effect of TMS are intensity and frequency of the current in the magnetic coil. However, in the two Paus et al. (1997, 1998) studies, the parameters for intensity and frequency remained the same, and are not discussed further. In the modeling study we found that the effect of TMS on the PFC was exacerbated when stimulation occurred during the entire task as opposed to only during the delay period. In the experimental studies investigating the effect of TMS on the neuronal circuit (PFC) subserving working memory, magnetic stimulation was performed either during the delay period of the working memory task (as in Pascual-Leone and Hallett, 1994) or during the entire task (as in Mottaghy et al., 2000). Magnetic stimulation in both studies led to increased errors in the memory tasks when compared with subjects’ performance without TMS. The latter study was combined with PET imaging of the cerebral blood flow changes in the brain due to TMS.

There was reduced rCBF at the stimulation site and in distal brain regions. However, because the earlier Pascual-Leone and Hallett (1994) study did not employ PET imaging, we cannot compare the effects of TMS applied during the delay period on rCBF with its effects when applied throughout the task. Further, the tasks used in the two studies and the experimental paradigms differed. Our simulations also resulted in deficient performance of the model. While it is known that TMS disrupts behavioral performance (Amassian et al., 1989; Day et al., 1989b; Pascual-Leone and Hallett, 1994; Gerloff et al., 1998), but in some cases enhances it (Topper et al., 1998; Boroojerdi et al., 2001), not much is known about the neural mechanisms by which this is accomplished. In our simulations we found that TMS applied to the excitatory and the inhibitory units of PFC caused the number of active response units to increase, regardless of the type of trial (Match or Nonmatch). The number of false positives increased, resulting in greater number of errors. This result is similar to that demonstrated by Pascual-Leone and Hallett (1994), who found that applying TMS during the delay period of a DMS task impaired performance by increasing errors (false responses). When TMS was applied predominantly to the inhibitory PFC units of our model, the number of active response units dropped dramatically, almost reaching 0 at a TMS intensity of 0.11. Errors in the form of incorrect identifications of Matches increased. TMS adds neural noise to the organized activity in the model and in this fashion causes a “virtual lesion.” TMS to the excitatory units results in greater excitability and TMS to inhibitory units results in lower excitability. In either case, the result is noise added to the system, resulting in impaired performance. When TMS is applied to both excitatory and inhibitory units, the higher weighting given to the excitatory half of the basic unit or cortical column (see Fig. 1) results in the excitatory effects overriding the inhibitory effects. The larger weighting given to excitatory units is based on the belief that most synapses in the cortex are excitatory and that a vast majority of the interregional connections are excitatory-to-excitatory (Douglas and Martin, 1991; Douglas et al., 1995). 2. Simulations to Examine TMS Functional Connectivity a. Results. The aim of the second set of simulations was to investigate the use of TMS as a tool to study functional connectivity. TMS has been used to determine interregional functional connectivity in particular cognitive tasks, in conjunction with PET (Paus et

SIMULATING TMS/PET WITH A NEURAL NETWORK MODEL

al., 1998; Mottaghy et al., 2000) or other functional imaging techniques such as EEG (Ilmoniemi et al., 1997). The functional connectivity between two brain regions is indexed by the correlation of their activities. Generally, two regions may have a large correlation in activity if they are anatomically linked and that link is functional in a specific task. But, they also could have a large correlation if they are indirectly connected via other regions. In this regard, it is important to distinguish functional connectivity from effective connectivity (Friston, 1994; Horwitz, 1994; Horwitz et al., 2000). Effective connectivity is the effect of one brain region on another via the explicit anatomical pathway linking the two regions. In the current simulation we attempted to dissociate effects of TMS on directly linked and indirectly linked, but functionally connected, sites. TMS was applied, during the delay period, to the excitatory and inhibitory units of region D2 of the PFC, which is indirectly connected to the response region via the delay region D1. None of the other regions of PFC were stimulated. The increase in rCBF values of the response region was positively correlated with the TMS intensity, as shown in Fig. 6 by filled circles. When contrasted with the earlier simulation involving injection of currents into all PFC modules, reproduced in Fig. 6 by filled diamonds, it is evident that stimulating D2 alone leads to a smaller increase in rCBF in the PFC and response modules with increasing TMS intensity. In terms of the active response module units, when D2 was stimulated there was an increase in the number of responding units with increasing intensity of TMS (shown in Fig. 5 by filled circles). However, this increase in the number of active units is less than that obtained when currents were injected in all PFC modules. b. Discussion. A major concern of TMS has been to determine the functional networks involved in the normal processing of a task. Paus et al. (1997, 1998), for example, used PET to analyze the spatial distribution of the effects of TMS applied to the frontal eye fields and the sensorimotor cortex. Experimentally, the changes in rCBF were seen not only at the stimulating site but also at sites that were anatomically connected to the frontal eye fields and the sensorimotor cortex. In our simulations, we found that when TMS stimulated the D2 module, effects were seen in the response region, which is not anatomically connected via a direct pathway with the D2 module. This demonstrates that TMS is a tool that assesses functional connectivity and not effective connectivity. In other words, TMS effects can be seen in indirectly linked areas, and thus conclusions about the anatomic connectivity of a region to the stimulating site cannot be made based solely on the effects of TMS.

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GENERAL DISCUSSION AND CONCLUSION In this study, we attempted to investigate two different aspects of TMS within a neural modeling framework: its excitatory and inhibitory effects, and its usefulness in probing functional connectivity. Simulated TMS was used to stimulate excitatory and inhibitory units of the model, and its effects were noted in regions that were directly or indirectly connected to the stimulating site. PET rCBF values, and the excitability of the response units of the model, indexed the effect of TMS as its intensity was varied in a parametric manner. When applied to both excitatory and inhibitory units, increasing TMS intensity caused an increase in rCBF; when primarily the inhibitory PFC units were stimulated, there was a decrease in rCBF with increasing TMS intensity. When TMS was applied to the PFC units throughout the trial, compared with only during the delay period, the rCBF values increased marginally. TMS affected not only the sites directly coupled to the stimulating site, but also sites that were both directly and indirectly connected to the stimulating site. The simulated TMS interfered with the performance of the model and the number of errors increased with increasing TMS intensity in all the simulations. When TMS of sufficient intensity (level 0.09 or higher) was applied to both the excitatory and inhibitory units of the PFC, the response units did not differentiate between a Match and a Nonmatch stimulus. Applying TMS to the inhibitory units alone, to the inhibitory units for a longer period than to the excitatory units, throughout the trial, or only to the delay module D2 also led to impaired performance by the model. One reason why a neural modeling study is valuable is the lack of information available on the neurochemical and neurophysiological effects of TMS, due primarily to the paucity of animal studies. As explained in the review by Fitzpatrick and Rothman (2000), TMS experiments on animals are difficult because of the limitations on the size of the TMS coils needed to stimulate different cortical regions. Smaller coils require larger currents and this creates heating and mechanical stability problems. There are, as yet, no single-cell studies in combination with TMS, and therefore, we do not have precise answers about the neural effects of TMS. However, there are certain animal studies that suggest that TMS can result in long-term potentiation and depression (Fitzpatrick and Rothman, 2000). In such circumstances modeling studies may provide the much-needed framework in which to formulate questions and provide tentative answers regarding different aspects of TMS in humans. The simulated TMS is an abstracted form of the experimental TMS, designed to capture the salient details of actual TMS. During TMS, a brief magnetic pulse is applied over the scalp of a subject, overlying a

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FIG. 6. Effect of simulated TMS on rCBF values in the PFC (a) and the response regions (b) during different simulations investigating functional connectivity using TMS. When both excitatory and inhibitory currents were induced in PFC (50 ms on, 150 ms off), which is directly connected to the response regions, the rCBF increased with increasing TMS intensity in both the PFC and response regions (shown by filled diamonds). However, when TMS currents were induced only in the indirectly connected excitatory and inhibitory D2 units, rCBF in the PFC and response regions increased (filled circles), although this effect was not as pronounced as in the simulation when currents were applied to all modules of PFC. See text for details.

specific cortical area. The effect of the magnetic field at the cortex is to induce charges on the scalp and a current, which can be both excitatory and inhibitory in nature, resulting in altered neural activity. In the model a number of neuroanatomical and neurophysiological details are abstracted out. For instance, the fundamental unit of the model, as illustrated in Fig. 1,

represents a cortical column, which actually is composed of six layers. This type of simplified column, called a Wilson–Cowan unit (Wilson and Cowan, 1972), has been used extensively by modelers as a reasonable representation of a cortical column. Given the lack of complexity in the model, the internal dynamics arising out of TMS-induced currents in a human brain cannot

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be entirely depicted. However, the model captures the behavior of the brain while performing a visual DMS task and is neurobiologically realistic. It is worth emphasizing that all details of the computational model we used in this study, except for those associated with the explicit simulation of TMS, were identical to those used by Tagamets and Horwitz (1998) in the paper first reporting this model. The point is that we did nothing to “fine-tune” the model to account for our simulation results. The fact that the simulated rCBF curve shown in Fig. 4a closely matches the experimental values obtained by Paus et al. (1998) indicates that our model provides a reasonable representation of the type of neural behavior we are attempting to study. The effect of TMS in our simulations always resulted in disrupted performance. Many experimental TMS studies likewise lead to behavioral impairment, although there have been some reports demonstrating improved behavioral performance during TMS. The improvements in performance either were seen in cases with higher cognitive tasks and were measured via reaction time studies (Topper et al., 1998) or were seen in stroke patients who had prior lesions (Cracco et al., 1999b), possibly due to suppression of inefficient pathways. Enhancement of performance by TMS could occur for a number of reasons, for example, (1) increasing the baseline activity of a region, thus making it more susceptible to respond during a task; (2) suppressing inefficient neural pathways, especially in a task that could be performed using different cognitive strategies; and (3) augmenting neuromodulation of neural activity. The model as it stands has no built-in neuromodulation or plasticity that could allow it to perform a task along multiple pathways or to learn different patterns. Thus the last two mechanisms of enhancement of performance cannot be examined in the present study. However, our simulated results do address the first option. Although baseline activity in the PFC units was increased by simulated TMS, we found no enhancement of performance. On the contrary, the number of errors increased, even at low levels of TMS. The lack of improved performance by the model suggests that the simplest type of baseline increase, by adding current to a region, is not sufficient to bring about an enhancement of performance. We therefore conclude that in the studies of improved performance due to TMS, the enhancement likely results either from suppression of inefficient pathways, or through neuromodulation of activity, or by some other yet to be specified mechanism. CONCLUSION In this simulation study, we used a neurobiologically realistic, large-scale neural model to investigate the

biological correlates of TMS and the relation between TMS and changes in rCBF, as measured by PET. We demonstrated that our manner of simulating TMS led to performance errors on the DMS task, which was the case when TMS was applied to both excitatory and inhibitory units and also only to inhibitory units. We also showed that our simulated values closely matched the experimental findings of increased and decreased rCBF following TMS in brain regions functionally connected to the stimulation site. However, the former occurred when TMS exerted a predominantly excitatory effect and the latter when it exerted a predominantly inhibitory effect. Indirectly connected distal sites were affected by TMS. APPENDIX The additional weights added to the model are as follows: Fanout

Mean value and variability

% to create

TMS PF, except R

5⫻5

9 @ 0.05 ⫹ 0.003

100

To excitatory units

TMS PF except R

5⫻5

16 @ 0.03 ⫹ 0.003 9 @ 0.05 ⫹ 0.003

100

To inhibitory units

From

To

Comments

16 @ 0.03 ⫹ 0.003

ACKNOWLEDGMENTS We thank Dr. Felix Mottaghy for useful discussions and for reading an earlier version of the manuscript. We also thank an anonymous reviewer who provided several useful suggestions that were incorporated into our simulations. This work was supported in part by the NIDCD Intramural Research Program.

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