Neurophysiology of Deep Brain Stimulation

CHAPTER THREE

Neurophysiology of Deep Brain Stimulation Manuela Rosa*, Gaia Giannicola*, Sara Marceglia*,†, Manuela Fumagalli*, Sergio Barbieri‡, Alberto Priori*,},1

*Centro Clinico per la Neurostimolazione, le Neurotecnologie ed i Disordini del Movimento, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy † Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy ‡ Unita` Operativa di Neurofisiopatologia Clinica, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy } Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Universita` degli Studi di Milano, Milan, Italy 1 Corresponding author: e-mail address: [email protected]

Contents 1. Electric Field and Charge Distribution 2. A Tool for Understanding the Functions of Human Deep Brain Structures 3. Neurophysiology 3.1 Single-unit 3.2 Local field potentials 3.3 Evoked potentials 3.4 Autonomic tests 3.5 Other neurophysiological variables 3.6 Synaptic plasticity 4. Behavioral Neurophysiology 5. Neurochemistry 6. Future Perspectives: Development of New Adaptive Deep Brain Stimulation (aDBS) Systems References

25 27 29 29 33 36 37 38 40 41 42 43 46

Abstract We review the data concerning the neurophysiology of deep brain stimulation (DBS) in humans, especially in reference to Parkinson's disease. The electric field generated by DBS interacts with the brain in complex ways, and several variables could influence the DBS-induced biophysical and clinical effects. The neurophysiology of DBS comprises the DBS-induced effects per se as well as neurophysiological studies designed to record electrical activity directly from the basal ganglia (single-unit or local field potential) through the electrodes implanted for DBS. In the subthalamic nucleus, DBS locally excites and concurrently inhibits at single-unit level, synchronizes low-frequency activity, and desynchronizes beta activity and also induces neurochemical changes in cyclic guanosine monophosphate (cGMP) and GABA concentrations. DBS-induced effects at International Review of Neurobiology, Volume 107 ISSN 0074-7742 http://dx.doi.org/10.1016/B978-0-12-404706-8.00004-8

#

2012 Elsevier Inc. All rights reserved.

23

24

Manuela Rosa et al.

system level can be studied through evoked potentials, autonomic tests, spinal cord segmental system, motor cortical and brainstem excitability, gait, and decision-making tasks. All these variables are influenced by DBS, suggesting also distant effects on nonmotor structures of the brain. Last, advances in understanding the neurophysiological mechanisms underlying DBS led researchers to develop a new adaptive DBS technology designed to adapt stimulation settings to the individual patient's clinical condition through a closed-loop system controlled by signals from the basal ganglia.

Even though deep brain stimulation (DBS) is an effective treatment in several disorders (Benabid et al., 1991; Gubellini, Salin, Kerkerian-Le Goff, & Baunez, 2009; Hamani, Andrade, Hodaie, Wennberg, & Lozano, 2009; Kahane & Depaulis, 2010; Krauss, Yianni, Loher, & Aziz, 2004; Kringelbach, Jenkinson, Owen, & Aziz, 2007; Moro et al., 2010; Perlmutter & Mink, 2006; Pizzolato & Mandat, 2012; Volkmann, 2004), the neurophysiological mechanisms underlying its therapeutic action remain debatable. Starting from the concept that functional inactivation can produce a lesion-like effect (Benazzouz, Gross, Feger, Boraud, & Bioulac, 1993; Benazzouz & Hallett, 2000; Beurrier, Bioulac, Audin, & Hammond, 2001; Magarinos-Ascone, Pazo, Macadar, & Buno, 2002), clear evidence now shows that DBS generates various effects at local and system levels (Carlson, Cleary, Cetas, Heinricher, & Burchiel, 2010; Dostrovsky & Lozano, 2002; Gubellini et al., 2009; Hammond, Ammari, Bioulac, & Garcia, 2008; McIntyre, Grill, Sherman, & Thakor, 2004; McIntyre & Hahn, 2010; Montgomery & Gale, 2008; Xu, Russo, Hashimoto, Zhang, & Vitek, 2008). In this chapter, we first examine DBS as a tool for understanding the neurophysiology of the basal ganglia and other DBS target areas. Second, to understand how DBS acts, we examine DBS-induced neurophysiological modulations at different levels. DBS-induced neurophysiological changes can be assessed by recording neural activity directly from a target structure or by investigating quantitative measures such as evoked potentials (EPs), autonomic and clinical neurophysiological tests, biochemical measures, and behavioral data focusing our attention on human experiments. This background information provides the rationale for possible future developments of DBS. In the past years, encouraging data obtained in patients with severe depression, obsessive compulsive disorders, and Gilles de la Tourette syndrome have extended DBS therapeutic applications to neuropsychiatric disorders (Aouizerate et al., 2009; Goodman & Alterman, 2012; Greenberg, Rauch, & Haber, 2010; Mallet et al., 2008; Porta et al., 2009; Ward, Hwynn, & Okun, 2010) and not only to movement disorders such as Parkinson’s disease (PD) (Pizzolato & Mandat, 2012; Volkmann, 2004).

Neurophysiology of Deep Brain Stimulation

25

In general, these neuropsychiatric disorders are less well understood than PD, and the DBS procedures for treating them are poorly standardized. Accordingly, progress in understanding the neurophysiological mechanism(s) underlying the therapeutic action of DBS relies mainly on information from PD, a movement disorder whose pathophysiology is relatively better known and for which we have experimental data from DBS recordings and quantitative measures in humans. Hence, our review focus mainly on DBS for PD.

1. ELECTRIC FIELD AND CHARGE DISTRIBUTION In DBS, a low-impedance electrode is implanted into specific deep brain structures to stimulate the neurons electrically. The electrode location depends on the type of neurological or neuropsychiatric disorder treated and the patient’s predominant symptoms. The electrode is connected to a subcutaneous stimulation device (pulse generator) placed in the subclavicular region. When the DBS device is turned on, an electric pulse lasting between 60 and 180 ms, with a frequency ranging from low- (15–30 Hz) to high-frequency (100–180 Hz) is delivered to the neuronal tissue in the selected target. The delivered electric pulse is usually biphasic with a waveform composed by a negative phase and a positive phase. If the two phases are balanced, the resulting net charge delivered to the tissue is null; conversely, if they are unbalanced, the net charge delivered may produce a polarization in the tissue. DBS can be either voltage- or current-controlled. DBS devices delivering voltage-controlled stimulation keep constant the applied electrical potential difference and measure the stimulation strength in volts (V). Conversely, current-controlled DBS devices maintain costant the current intensity delivered to the tissue (the electrical charge exchanged in time) and measure stimulation strength in milliamps (mA). In the past 20 years, DBS is widely administered with voltage-controlled devices, in which current is variable (Cheung & Tagliati, 2010). Current-controlled stimulation might provide a more accurate control of the spread of the electrical field than do voltage-controlled devices, because adjustments can be made to account for the potential heterogeneity in tissue impedance. A recent study showed that current-controlled DBS is safe and efficacious for the treatment of PD (Okun et al., 2012). DBS induces an electric charge exchange between the stimulating electrode and the surrounding tissue. This charge exchange affects activation properties in neurons surrounding the electrode, thus inducing therapeutic effects (Yousif, Bayford, Wang, & Liu, 2008). Even though DBS is clinically

26

Manuela Rosa et al.

effective, we still know too little about DBS-induced changes at neuronal level. Information is lacking also on the interaction between the electric field generated by DBS and the underlying neuronal tissue, mainly because DBS–neuronal tissue interactions are difficult to model (Butson & McIntyre, 2005; Yousif et al., 2008). The current density distribution on the electrode surface is estimated through computational modeling techniques (Butson & McIntyre, 2005; McIntyre & Grill, 1999, 2001, 2002; Wei & Grill, 2005). Considering an idealized electrolytic medium, the current density over the electrode surface increases toward the edges of the electrode, and multiple edges make the current density profile less uniform (Wei & Grill, 2005). Besides current density distribution on the electrode surface, DBS-induced changes in neuronal tissue depend also on the interface between the electrode and the surrounding neuronal tissue. Computational techniques used to model this interface focus on three compartments: the implanted DBS electrode, the neuronal tissue, and the peri-electrode space. The peri-electrode space varies in composition over time: immediately after DBS electrode implant (acute stage), the peri-electrode space is filled with extracellular fluid (Thoma et al., 1987). Conversely, in the chronic stage, the extracellular fluid is replaced by giant cell growth (Moss, Ryder, Aziz, Graeber, & Bain, 2004) or microglia (Griffith & Humphrey, 2006). This change takes place approximately 6–8 weeks after electrode implant (Yousif et al., 2008). The evolving electrode–brain interface affects the neuronal tissue volume activated and the stimulation intensity needed to obtain a clinically beneficial therapeutic effect. In a simulation study, Yousif et al. (2008) compared the potential distribution around the activated electrode contact during DBS when the peri-electrode space was filled with extracellular fluid with the potential distribution obtained after the electrode was encapsulated with giant cells. They showed that because the low-conductivity giant cells in chronic stage restricted current spread they produced a magnitude of the potential at a given distance consistently less that in acute stage when the peri-electrode space was filled with extracellular fluid. Hence, to maintain stimulation levels constant in the acute and chronic stages, DBS intensity should be increased, thus partially explaining why DBS settings are adjusted for the first time after DBS begins (Yousif et al., 2008). Other modeling studies showed that tissue volume activated depends not only on the electrode interface voltage drop but also on electrode and tissue capacitance (the ability of the tissue to store an electrical charge), on the tissue electrode encapsulation, and on tissue heterogeneity (anisotropy)

Neurophysiology of Deep Brain Stimulation

27

(Chaturvedi, Butson, Lempka, Cooper, & McIntyre, 2010). During voltage-controlled stimulation, the electric field transmitted to the tissue depends on electrode capacitance, whereas during current-controlled stimulation, it depends on tissue capacitance (Butson & McIntyre, 2005). During current-controlled stimulation, electrode capacitance can be ignored because, whatever the stimulus waveform, the entire stimulus current passes through the tissue, and electrode capacitance is discharged because the applied waveform is biphasic and charge-balanced. Conversely, during voltage-controlled stimulation, voltage drops across capacitances. DBS electrode capacitance is about two orders of magnitude greater than tissue capacitance and can therefore be ignored (Butson & McIntyre, 2005). This explains why, whereas the electric field generated by voltage-controlled stimulation changes according to variations in the tissue impedance, that generated by current-controlled stimulation is stable in size. In conclusion, because DBS-induced changes in the electrical field in the surrounding nervous systems are highly complex, computational models should take into account the various properties pertaining to the stimulating electrode and the electrode–tissue interface.

2. A TOOL FOR UNDERSTANDING THE FUNCTIONS OF HUMAN DEEP BRAIN STRUCTURES Despite enormous progress achieved by experimental studies and functional models (Wichmann & DeLong, 1996), little is known about the signals controlling information processing and integration in the human basal ganglia, “the dark basement of the brain” (Wilson, 1925). From a scientific viewpoint, DBS provides a unique window into the human basal ganglia thus helping to understand the neurophysiological mechanisms underlying not only motor functions but also cognitive and behavioral information processing. During DBS surgery, high-impedance exploratory microelectrodes are used to record the activity in single neurons in the target deep brain structure to optimize the electrode localization electrophysiologically before the final electrodes are definitively implanted. Besides serving clinical purposes, single-unit (SU) recordings also provide a powerful research tool to correlate anatomical, functional, and electrophysiological information (Levy, Hutchison, Lozano, & Dostrovsky, 2000; Levy et al., 2001; Romanelli et al., 2004; Stefani et al., 2002).

28

Manuela Rosa et al.

During the few days after DBS surgery and before the electrode leads are connected to the subcutaneous electrical pulse generator, electrodes are accessible for electrophysiological recordings from the target structure. Because the final DBS electrodes have low-impedance, they are unsuitable for recording single neuron activity during surgery but are ideal for recording local field potentials (LFPs). LFPs reflect the synchronous presynaptic and postsynaptic activity in large neuronal populations and can detect network rhythms that are not necessarily observable in single neurons or neuron pairs (Brown & Williams, 2005; Kuhn et al., 2004, 2008; Levy et al., 2002; Marceglia, Fumagalli, & Priori, 2011; Priori et al., 2004; Rosa et al., 2011, 2012; Zaidel, Arkadir, Israel, & Bergman, 2009). LFP recordings show that oscillatory activity in the deep brain structures ranges from the classic electroencephalographic (EEG) frequencies (<50 Hz) to frequencies as high as 300 Hz, and recordings in patients with PD have consistently shown prominent oscillations between 8 and 30 Hz (beta band) (Kuhn et al., 2004, 2008; Rosa et al., 2011; Zaidel et al., 2009) Excessive betaband synchronization is considered a feature common to the whole basalganglia-cortical loop in PD with increased beta-band oscillations in the subthalamic nucleus (STN), globus pallidus, and cerebral cortex (Brown & Williams, 2005). Many LFP studies in humans revealed in the past 10 years unknown functions of basal ganglia in PD patients during the execution of motor, cognitive, and behavioral task showing the existence of a “code” in LFP oscillations corresponding to the clinical condition of patient (Marceglia et al., 2007). LFP oscillatory patterns in patients with PD change in response to antiparkinsonian drugs (Brown & Williams, 2005; Foffani et al., 2003; Giannicola et al., 2010; Giannicola, Rosa, & Marceglia, 2012; Marceglia et al., 2006; Priori et al., 2004) and are dependent on patients’ clinical and motor conditions (Brown & Williams, 2005; Kuhn et al., 2004, 2008; Priori et al., 2004). Changes in LFP activity also provide evidence that the human basal ganglia and thalamus intervene in all movement phases (Foffani, Bianchi, Baselli, & Priori, 2005; Foffani & Priori, 2004; Levy et al., 2002). More recent studies demonstrated that specific oscillations in the STN are involved also in action representation (Marceglia et al., 2009), cognitive information related to decision-making (Fumagalli et al., 2011), and emotional information (Brucke et al., 2007; Kuhn et al., 2005). The multiple rhythms recorded and differentially modulated by movement and drugs consistently confirm that pathological oscillations in the low-frequency power band (Giannicola, Rosa, &

Neurophysiology of Deep Brain Stimulation

29

Marceglia, 2012; Priori et al., 2006), beta band (Eusebio et al., 2011; Giannicola et al., 2010; Kuhn et al., 2004, 2008; Rosa et al., 2011; Zaidel et al., 2009), gamma band (Brown & Williams, 2005), and highfrequency band (Brown & Williams, 2005; Foffani et al., 2003, 2006; Ozkurt et al., 2011) are involved in the pathophysiology of movement disorders and are crucial for understanding the neurophysiological mechanisms underlying DBS clinical effects (Giannicola & Priori, 2012). Direct methods of assessing neuronal activity as SU and LFP recordings provided unique information at high temporal and spatial resolution about fine structure of rates and patterns not investigated with other indirect imaging methods such as fMRI or other functional neuroimaging techniques.

3. NEUROPHYSIOLOGY DBS-induced changes in human brain function are difficult to study. Local DBS-induced effects assessed by recording SU activity and LFPs directly from the target structure needed special technological approaches for removing stimulus artifact. Chronically after surgery experiments designed to investigate DBS can describe its effects on functions of distant systems through quantitative measures such as EPs, autonomic variables, and clinical neurophysiological tests. Neuroimaging experiments are dealt with elsewhere in the book.

3.1. Single-unit Studies using intraoperative multiple electrodes simultaneously stimulating and recording from the same structure have examined how stimulation affects adjacent neuronal activity (Carlson et al., 2010; Filali, Hutchison, Palter, Lozano, & Dostrovsky, 2004; Maltete et al., 2007; Welter et al., 2004). A major problem in such experiments is the large stimulus artifact due to the stimulating electrodes in close proximity and the high electrode impedance. To avoid this problem, early studies analyzed the DBS-induced effects on the neural activity at very low intensity or immediately after stimulation ceases—two important limiting factors. Several studies showed that high-frequency DBS inhibits neuronal cell bodies (somas) in the stimulated nucleus (Dostrovsky & Lozano, 2002; Dostrovsky et al., 2000; Filali et al., 2004; Welter et al., 2004). By activating the predominant presynaptic inhibitory neurons, DBS reduces somatic spikes in local neurons close to high-frequency stimulus sites in the target nucleus. In a study investigating the effects of STN stimulation

30

Manuela Rosa et al.

on the neuronal activity from STN neurons in patients with PD, Welter et al. (2004) showed that during stimulation at clinical settings and intensities, the mean firing rate in STN cells decreased by 77% and more than 75% of the neurons maintained persistent activity. Another study showed that intraoperative high-frequency STN DBS inhibited ipsilateral firing in 42% of neurons tested (Filali et al., 2004). These results agreed with similar experimental studies conducted on the human thalamus (Dostrovsky & Lozano, 2002) and globus pallidus internus (GPi) (Dostrovsky et al., 2000). Conversely, Carlson et al. (2010) reported that high-frequency STN DBS left the mean firing rate in the recorded STN neurons unchanged, suggesting that STN DBS might provide a null signal to the basal ganglia corticothalamic circuit. The contradictory result reported by Carlson and colleagues could depend on different current density and distribution resulting from DBS delivered through a definitive implanted macroelectrode (Butson & McIntyre, 2006) instead of the intraoperative microelectrode used in previous studies. Using a time-domain algorithm for artifact removal (Hashimoto, Elder, & Vitek, 2002), Toleikis et al. (2012) acquired SU recordings also during stimulation at effective voltage and showed that both ipsilateral and contralateral high-frequency STN DBS at therapeutic intensities and settings resulted in reversible STN firing rate suppression (Fig. 3.1A). Numerous mechanisms have been proposed to explain how DBS reduces neuronal activity within the stimulated target. These included a depolarization block hypothesis, suggesting that repetitive cell stimulation sufficiently depolarized the cell membrane to inactivate sodium channels and to prevent cell firing (Beurrier et al., 2001). Another possible mechanism is synaptic inhibition hypothesizing that DBS preferentially activates STN afferents rather than neurons (Dostrovsky et al., 2000). Because most of these afferents are inhibitory, DBS would ultimately reduce neuronal activity within the stimulated target. Conversely, several studies conducted during stimulation, including SU recording in downstream structures, suggest an excitatory hypothesis (Galati et al., 2006; Montgomery, 2006; Pralong et al., 2003; Reese et al., 2008). They showed changes in neuronal activity suggesting that DBS activates efferent axons in the stimulated nucleus. In a patient with dystonia, Montgomery (2006) demonstrated predominant decreases in ventral lateral (VL) thalamus activity after GPi stimulation implying that GPi DBS activates the inhibitory projections from GPi to VL. Studying GPi DBS-induced changes by recording neuronal activity in the ventralis

31

Neurophysiology of Deep Brain Stimulation

A f=128 Hz

l pt 1 l pt 2 l pt 3 c pt 3 c pt 4 c pt 5 c pt 6

80 70

28.1 sp/s

0.1 sp/s

24.1 sp/s

50 40 30

60 50 40 30 20

20 10

10 0

Post-stim control

Raw Processed

-0.5 60

Per-stim control

0

Firing rate (spikes/s)

Firing rate (spikes/s)

Signal (mV)

0.5

0 20

0

40

60

80

100

10 30

Time (s)

60

130

185

Stimulus Frequency (Hz)

B

C

GPi Thalamus

45 s

D

E

GPe

GPi

Splkes per second

60

GPi

40

Thalamus

20

STN

0

Pre

Post Gpe

Pre

Post Gpi

Figure 3.1 Effects of deep brain stimulation on single-unit activity. (A) Left-top: sample spike waveforms before (gray, raw) and after (black, processed) stimulus artifact removal during prestimulation and 128 Hz ipsilateral subthalamic nucleus (STN) stimulation epochs. The processed signal overlaps the raw signal during nonstimulation epochs. The large stimulus artifacts present in the raw signal are so closely spaced, they appear as a solid gray area. Randomly selected spike waveforms are plotted above the signal trace to illustrate continuity in single neuron spiking throughout the recording. Leftbottom: histogram of activity during the nonstimulation (black) and stimulation (dark gray) epochs, obtained using 1-s bins. Suppression of the average firing rate by deep brain stimulation (DBS) is over 99.6%. sp, spikes; s, second. Right: graph showing the effect of both ipsilateral (continuous lines) and contralateral (dashed lines) STN stimulation frequency on the average single-unit firing rate during stimulation compared with the rates during the prestimulation (leftmost points) and poststimulation control (rightmost points) epochs. Note that ipsilateral and contralateral STN DBS suppressed STN firing rates. (B) Effects of globus pallidus internus (GPI) DBS on the spontaneous activity from ventralis oralis anterior (VOA) neuron in a patient with dystonia.

32

Manuela Rosa et al.

oralis anterior (VOA) nucleus of thalamus, Pralong et al. (2003) showed that pallidal DBS inhibits a motor thalamic cell subpopulation (Fig. 3.1B). In these studies, the reduced firing rate recorded in the thalamic nucleus, an inhibitory GPi axonal target, suggested that DBS activates GPi efferent fibers (Liu, Postupna, Falkenberg, & Anderson, 2008) (Fig. 3.1C). Similar data came from Reese et al. (2008) who proposed that DBS excited the output axons, because during STN DBS GPi neuron firing rates increased (Fig. 3.1D). The increased GPi firing rate, confirmed by an animal study (Hashimoto, Elder, Okun, Patrick, & Vitek, 2003), implies that DBS activates excitatory projections from STN to GPi (Liu et al., 2008) (Fig. 3.1E). Data recorded from substantia nigra pars reticulata (SNr) during STN DBS in Parkinsonian patients provided convincing evidence that STN stimulation produces its therapeutic effects by exciting the STN output and driving the downstream basal ganglia output neurons (Galati et al., 2006). The results obtained by recording activity from SUs appear contradictory. How can DBS excite yet at the same time inhibit a neuron? To understand Extracellular recording of a neuronal activity located 3 mm anterodorsal from the target in the VOA. Before GPI stimulation, the mean firing rate was 17 Hz. DBS (130 Hz) applied to the GPI for 10 s reversibly inhibited this neuronal activity and 20 s after GPI stimulation ended VOA neuronal activity recovered. Note that GPI stimulation inhibited activity in a motor thalamic cell subpopulation. (C) Action potential produced by GPi stimulation: stimulation decreases activity (downward arrows) in GPi neuronal somata, but increases (upward arrow) activity in their inhibitory output axons.
, inhibitory neurons. (D) Effect of high-frequency STN DBS on pallidal neuronal activity in a patient with Parkinson's disease. On the left, neuronal recording sites in a sagittal projection onto the Schaltenbrand Wahren atlas at laterality of 22 mm. After high-frequency STN DBS, mean GPi and globus pallidus externus (GPe) firing rates increase. On the right box, plots show median (line within the box) as well as 10th, 25th, 75th, and 90th percentiles. The superimposed black dots represent the firing rate for each recorded neuron and the change before and after high-frequency STN DBS (line). (E) Action potential produced by STN stimulation: stimulation decreases discharges (downward arrows) in STN neuronal somata but increases (upward arrow) activity in their excitatory output axons, increases (upward arrows) GPi neuron excitation, and increases inhibition (decreases discharge rate, downward arrows) at the thalamus as in C.
, inhibitory neurons; þ, excitatory neurons. (A) Reprinted from Toleikis et al. (2012), Copyright (2012), with permission from JNS Publishing Group; (B) Reprinted from Pralong et al. (2003), Copyright (2003), with permission from Elsevier Masson SAS; (C) Reprinted from Liu, Postupna, Falkenberg, and Anderson (2008), Copyright (2008), with permission from Elsevier; (D) From Reese et al. (2008), Copyright (2008, Movement Disorders). This material is reproduced with permission of John & Sons, Inc.; (E) Reprinted from Liu et al. (2008), Copyright (2008), with permission from Elsevier.

Neurophysiology of Deep Brain Stimulation

33

the mechanisms through which DBS acts, rather than investigating increased or decreased inhibition or excitation, we need to study the two effects combined. These apparently mutually exclusive findings might be reconciled by data from a modeling study conducted by McIntryre, Grill, Sherman, and Thakor (2004) indicating that DBS both inhibits and excites the target nucleus. During extracellular stimulation, the action potential begins in the axon. While DBS inhibits neuronal cell bodies in the stimulated nucleus, it simultaneously excites neuronal cell axons (McIntyre et al., 2004). Given the low probability that microelectrodes record axonal activity, SU recordings within the stimulated nucleus disclose somatic inhibition suggesting that DBS inhibits target neurons (Dostrovsky et al., 2000), whereas SU recordings in downstream nuclei reveal axonal excitation in the stimulated nucleus (Hashimoto et al., 2003). Stimulation applied to the GPI would decrease GPi activity and increase inhibitory output from basal ganglia, whereas stimulation applied to the STN would decrease STN activity, increase excitatory axons from STN to GPi, in turn, increase GPi activity, and, consequently, increase inhibitory output from basal ganglia (Fig. 3.1C and E). Although DBS-induced changes in activity assessed through SU recordings have provided important neurophysiological evidence, LFP recordings are a more suitable tool for understanding the complex mechanisms of action underlying DBS, which go beyond simple neuronal activation/inhibition.

3.2. Local field potentials Another way to study the neurophysiological effects induced by DBS is to record LFP activity directly from the target structure. The interest in LFPs arose also, thanks to several studies showing that basal ganglia functional processes are mediated not only by firing rates but also, and mainly, by firing patterns (Bevan, Magill, Terman, Bolam, & Wilson, 2002; Terman, Rubin, Yew, & Wilson, 2002), oscillatory phenomena (Gatev, Darbin, & Wichmann, 2006; Uhlhaas & Singer, 2006; Wilke, Logothetis, & Leopold, 2006), and wave propagation (Rubino, Robbins, & Hatsopoulos, 2006). Unlike SUs, basal ganglia LFPs can be recorded within days after DBS surgery before the implanted macroelectrodes are connected to the subcutaneous pulse generator. Another advantage is that because patients are typically free to move during these experimental recordings they can engage in more complex and ecological tasks than during DBS surgery. Studies conducted before our group introduced FilterDBS, a new technology allowing LFP recording during DBS from the same electrode used for stimulating (Rossi et al., 2007), reported contradictory results about

34

Manuela Rosa et al.

how DBS affects LFP activity. Whereas some suggested that in patients with PD beta oscillations (8–30 Hz) decrease after STN DBS (Bronte-Stewart et al., 2009; Brown et al., 2004; Kuhn et al., 2008), others failed to confirm this observation (Foffani et al., 2006) and reported that lowfrequency oscillations (2–7 Hz) increased (Priori et al., 2006). The use of FilterDBS-like amplifiers prompted new research and helped to study DBS-induced changes in STN LFP oscillations. Studies using therapy with levodopa and DBS combined show that the levodopa-induced effect predominates (Giannicola et al., 2010; Rossi et al., 2008) (Fig. 3.2A). One study compared the effect of DBS and antiparkinsonian drugs, showing that whereas levodopa invariably disrupted beta oscillations, DBS induced changes in beta oscillations only in patients whose LFP recordings already showed wide beta activity before DBS (Giannicola et al., 2010). Another study using a similar methodology showed that DBS suppressed global beta oscillations in all nuclei selected (Eusebio et al., 2011) (Fig. 3.2B). Eusebio and colleagues selected only nuclei that showed significant high beta peak. Data from this study confirmed the findings reported by Giannicola et al. (2010). Other studies also established that the low-frequency band increase starts during ongoing DBS and is consistent across patients (Giannicola, Rosa, Marceglia, et al., 2012; Rossi et al., 2008) (Fig. 3.2C). This observation agrees with the STN DBS-induced low-frequency band increase that lasts several minutes after DBS is turned off and also corresponds with the persistent STN DBS-induced improvement in the patients’ clinical state (Giannicola, Rosa, Marceglia, et al., 2012; Priori et al., 2006; Rossi et al., 2008). Hence, even though the exact mechanisms remain controversial, ample evidence shows that DBS specifically modulates LFP oscillations in patients with PD. Evidence on the correlation between LFP oscillations and clinical state and DBS-induced changes in LFPs suffers nevertheless from a major drawback, namely, that all experimental sessions took place within days after brain surgery when a lesion effect and local brain edema remain. To date, only two studies have investigated how DBS influences LFPs recorded more than 1 week after DBS surgery (Giannicola, Rosa, Servello, et al., 2012; Rosa et al., 2011). In a study conducted in our laboratory comparing STN LFPs recorded in PD patients immediately after and 30 days after DBS surgery (Rosa et al., 2011), we reported that weeks after DBS electrode implant STN LFPs remain stable and that in a subgroup of patients whose recordings showed a strong beta activity at baseline, LFP beta

35

Neurophysiology of Deep Brain Stimulation

B

A On DBS

On levodopa

Off DBS 930

18

3.5

16

3

14

2.5

12

2

10

1.5

Log -3 power -5 0

30

35

40

45

50

Frequency (Hz)

Frequency (Hz)

20 18 16 14 12 10

-5

20 30

z)

Time (min) 20 18 16 14 12 10

-4.5

(H

Log power (a.u.)

25

cy

20

en

15

qu

10

e Fr

5

-5.5

40

-6 -6.5 -7 0

10

20

30

40

Frequency (Hz)

C

Grand average (n = 6)

Off

40 20

t-30d

t-0h

0

2

4

6

8

10

12

10-1.2

10-1.2 PSD [AU]

7 PSD [AU]

Frequency (Hz)

4 3 2 1 0

D

Grand average (16 nuclei) On

(s)

10 -4

8 0

Time

Voltage (V)

Frequency (Hz)

20

Power (au)

Power (au)

Time (minutes) 0

10

10

-2

-1.7

-1.7

0

10

10

10

-2 -2

-2 0 2 Frequency (Hz)

0 2 Frequency (Hz)

10

0

10

20

30

Frequency (Hz)

40

0

10

20

30

40

Baseline

DBS on

DBS off

Frequency (Hz)

Figure 3.2 Effects of deep brain stimulation on local field potentials. (A) Time course of subthalamic nucleus local field potentials (STN LFP) in a representative patient with Parkinson's disease (PD). STN LFP time varying power spectrum in the beta band (8–20 Hz). Top: time–frequency plot over the entire experimental session (50 min total time, 42 min deep brain stimulation-DBS); the black dotted lines, from the left, correspond to turning DBS on, the clinical effect of levodopa, and turning DBS off. Bottom: transient states when DBS was turned on (left panel) and off (right panel). Note that levodopa and DBS both reduced beta oscillations but that the levodopa-induced effect predominates. (B) Effect of DBS on STN LFP in a representative patient with PD. On the left, power autospectrum of LFP recorded without stimulation. On the right, time–frequency log power LFP spectrum. Bars along the time axis denote ongoing DBS at 2.0–3.0 V. Note that DBS at 2.0 V suppressed the spectral peak, stimulation at 1.5 V transiently increased power peak, and stimulation at 3.0 V delayed its return after stimulation ended. (C) STN LFP power spectrum changes during DBS in patients with PD (grand average, 16 nuclei). Top: time course of LFP power spectrum changes over 12 min (DBS for 8 min). DBS begins at the vertical black dashed line on the left and ends at the vertical black dashed line on the right. Bottom: instantaneous power spectra before DBS (baseline) on the left and 4 min after DBS began on the right. Black lines: the average spectrum; gray lines spectra of individual nuclei. Note that during STN DBS the low-frequency power increases. (D) STN LFPs recorded immediately (t-0h) and 30 days (t-30d) after DBS surgery in patients with PD: grand average for nuclei showing significant beta-band (n ¼ 6 nuclei) power spectral density (PSD) in the three experimental conditions: prestimulation (baseline: black, solid line),

36

Manuela Rosa et al.

activity recorded during DBS decreased immediately after and 30 days after DBS surgery (Fig. 3.2D). Another study (Giannicola, Rosa, Servello et al., 2012) showed that the STN LFP pattern recorded at baseline without DBS and without medication a few days after DBS electrode implant remained unchanged after 7 years of chronic DBS. We also found that DBSinduced changes persist years after DBS electrode implantation and chronic stimulation: whereas in a subgroup of patients, whose LFP recordings showed strong beta activity at baseline, beta activity decreased, low-frequency activity invariably increased. Evidence that high-frequency DBS acts by modulating pathological parkinsonian oscillatory activity toward a pattern, that the basal– ganglia–cortical system finds more physiological receives strong support from several studies (Giannicola, Rosa, Servello, et al., 2012; Rosa et al., 2011; Silberstein et al., 2005). STN DBS acts not only by modulating pathological STN activity but also by projecting modulated activity to GPi and SNr (Hammond, Bergman, & Brown, 2007), the output structures in the basal ganglia. The new STN DBS-driven output might normalize basal-ganglia-cortical loops supporting the hypothesis that DBS shifts overall activity patterns toward a more physiological pattern (Kopell, Rezai, Chang, & Vitek, 2006).

3.3. Evoked potentials Some effects induced by DBS ultimately involve DBS-induced changes in cortical activity observed through scalp EEG recordings. DBS applied to the STN with single pulses or trains produces potentials that can be detected with stimulus-triggered average EEG signals in the frontocentral cortex. Several studies have described cortical EPs during low-frequency STN stimulation in patients with PD (Ashby et al., 2001; Baker, Montgomery, Rezai,

during stimulation (DBS on: gray, dashed line), and after stimulation (DBS off: light gray, dashed line). Note that beta-band power decreases in the on DBS condition both in t-0h and t-30d for nuclei with significant beta-band power. (A) Reprinted from Giannicola et al. (2010), Copyright (2010), with permission from Elsevier; (B) Reproduced from Eusebio et al. (2011), copyright notice 2011 with permission from BMJ Publishing Group Ltd.; (C) Reprinted from Rossi et al. (2008), Copyright (2008), with permission from Elsevier; (D) Reprinted from Rosa et al. (2011), Copyright (2011), with permission from Karger.

Neurophysiology of Deep Brain Stimulation

37

Burgess, & Luders, 2002; Kuriakose et al., 2009; MacKinnon et al., 2005). These cortical EPs comprise multiple components whose latencies differ. Short-latency (3–8 ms) and long-latency responses (18–25 ms) correspond to conduction from the STN stimulation site to the cortical recording location via antidromic and orthodromic pathways. Few studies have investigated cortical EPs evoked by STN DBS at clinically used frequencies (80–130 Hz) because the interstimulus interval is shorter than the latency for long-latency EPs. Rather than being DBS-related epiphenomena, cortical EPs may reflect the temporal progression of changes in cortical excitability related to clinical DBS therapeutic effects in patients with PD (Devergnas & Wichmann, 2011). More detailed information is needed on the correlation between the clinical effects of DBS and EPs. Other researchers investigated DBS-induced changes in cortical activity by studying auditory- and somatosensory-evoked potentials (AEPs and SEPs). In an early study, Pierantozzi et al. (1999) showed that GPi and STN DBS both selectively increase frontal SEP amplitude probably improving functional activity in the supplementary motor area and leaving the parietal component unchanged. In a further study, parietal SEP amplitudes, evaluated in a larger sample size, increased when DBS was turned off (Priori et al., 2001). Others subsequently reported that somatosensory P60 responses tended to increase when DBS was on, whereas auditory N100 responses in right hemisphere enhanced during ipsilateral STN DBS (Airaksinen et al., 2011). Changes in AEPs and SEPs suggest that DBS modulates thalamocortical pathways or cortical processing or both. Although the changes induced by DBS on late EPs suggest that DBS exerts its therapeutic effects partly at cortical level, the precise mechanisms through which it does so remain conjectural.

3.4. Autonomic tests Evidence that DBS influences the human autonomic nervous system comes from a study showing that DBS improves the sympathetic skin response (Priori et al., 2001). In the same study, DBS-induced changes in cardiovascular variables were assessed with plasma renin activity assay and the upright tilt test. When DBS was turned off, plasma renin increased but arterial blood pressure remained unchanged. These findings suggested that STN DBS improved sympathetic and cardiovascular reactivity probably by interfering with nonmotor functions in the basal ganglia and stimulus spread to nearby structures (Priori et al., 2001).

38

Manuela Rosa et al.

In a study designed to assess STN DBS-induced changes in cardiovascular function during DBS surgery, Sauleau et al. (2005) directly monitored autonomic side effects in patients undergoing STN DBS for PD: in 88% of the patients, tachycardia arose within seconds after stimulation began, whereas hypertension arose within about a minute. In two prospective studies assessed to evaluate STN DBS-induced changes in urinary function in patients with PD, Finazzi-Agro et al. (2003) reported significantly increased bladder capacity and reflex volume and Seif et al. (2004) found a reduced voiding desire.

3.5. Other neurophysiological variables DBS modifies certain clinical neurophysiological abnormalities that may not have a direct clinical correlate with movement disorder symptoms (Hallett, 1998; Rossini, Filippi, & Vernieri, 1998; Valls-Sole & Valldeoriola, 2002). Ample evidence describes STN or GPi DBS-induced changes in motor cortex excitability (Chen, Garg, Lozano, & Lang, 2001; Cunic et al., 2002; Kuhn et al., 2003). For example, Chen et al. (2001) reported the effects of GPi DBS on motor threshold, motor evoked potentials (MEP) recruitment curve, silent period (SP) duration, short-interval intracortical inhibition (SICI), long-interval intracortical inhibition, and intracortical facilitation in patients with PD. No significant differences were found, except a reduced cortical SP duration. When they switched GPi stimulation off, however, motor threshold increased, and the size of contralateral responses in the stimulus-response curves in relaxed muscles diminished. Similarly, in patients with dystonia, Kuhn et al. (2003) reported that GPi DBS decreased motor cortex excitability, as reflected by an increase in motor thresholds, and no GPi DBS-related changes in spinal excitability were found. Conversely, when they examined whether STN DBS affected motor cortical excitability in parkinsonian patients, Cunic et al. (2002) reported results that opposed those of GPi DBS on motor cortex excitability: STN DBS induced no changes in SP duration, motor threshold, or MEP recruitment curve. In experiments investigating the effects of STN and GPi DBS on resting SICI, Pierantozzi et al. (2002) found that SICI increased during either bilateral STN or GPi DBS at an interstimulus interval of 3 ms and during bilateral STN DBS at interstimulus intervals of 2 ms, suggesting that SICI may improve because DBS restores thalamocortical motor pathway function. In another study testing the blink reflex in patients with primary torsion dystonia, Tisch, Limousin, Rothwell, Asselman,

Neurophysiology of Deep Brain Stimulation

39

Quinn et al. (2006) measured changes in blink reflex excitability after GPi DBS and showed that GPi DBS results in a functional reorganization in the nervous system and long-term increase in brainstem inhibition. Others documented reduced blink reflex inhibition after a prepulse stimulus (prepulse inhibition) (Schicatano, Peshori, Gopalaswamy, Sahay, & Evinger, 2000; Valls-Sole, Munoz, & Valldeoriola, 2004). In a study designed to investigate whether a single electrical STN DBS pulse induced prepulse effects on the blink reflex and how they compared with the effects induced by single auditory and somatosensory stimuli, Costa, Valls-Sole, Valldeoriola, Pech, and Rumia (2006) found that a single STN DBS pulse induced significant prepulse blink reflex inhibition in all PD patients, who have abnormally reduced auditory and somatosensory prepulse effects. They proposed that the abnormal reduction in prepulse inhibition lies at a point before the circuit reaches the structures activated by STN DBS or that DBS causes the prepulse through a circuit other than that for auditory and somatosensory stimuli. Others reported DBS-induced changes in spinal cord circuitry (Potter, Illert, Wenzelburger, Deuschl, & Volkmann, 2004; Tisch, Limousin, Rothwell, Asselman, Zrinzo, et al., 2006). In a study testing the H-reflex reciprocal inhibition and clinical outcome in eight patients with primary torsion dystonia, Tisch, Limousin, Rothwell, Asselman, Zrinzo, et al. (2006) reported a progressive improvement in the reciprocal inhibitory effect induced by a radialnerve stimulus on the median nerve H-reflex, at 1, 3, and 6 months during GPi DBS, and suggested that DBS causes a functional nervous system reorganization that includes the spinal machinery. When they investigated the effect of highfrequency STN DBS on autogenic inhibition in PD, Potter et al. (2004) reported an increase in soleus H-reflex autogenic inhibition, a spinal inhibitory phenomenon that is abnormal in PD (Delwaide, Pepin, & Maertens de Noordhout, 1991). They measured the soleus H-reflex alone or conditioned by previous gastrocnemius nerve stimulation at a 2–10-ms interstimulus interval in patients with PD. STN DBS increased the conditioning stimulus inhibition and the increase correlated significantly with patients’ clinical improvement in gait and posture. Several studies investigated DBS-induced changes in gait and postural stability in patients with PD and described motor function improvements (Ferrarin et al., 2005; Johnsen, Mogensen, Sunde, & Ostergaard, 2009; Joundi et al., 2012; St George et al., 2012). To investigate how STN and GPI DBS affected autonomic postural responses, St George et al. (2012) tested patients with PD before and 6 months after surgery and reported that DBS improved automatic postural response stability for both STN

40

Manuela Rosa et al.

and GPi sites. Similar results came from Johnsen et al. (2009), who found that STN DBS facilitated symmetric gait and thereby improved balance during gait. Another study analyzed full body gait during overground walking in patients with PD and reported a significantly increased gait speed after DBS surgery, stride length, and lower limb joint range of motion during DBS than in the DBS off condition (Ferrarin et al., 2005). During STN DBS, Joundi et al. (2012) tested parkinsonian patients by capturing simple ballistic movements across four joints using kinematic motion analysis. They observed that velocity significantly improved during DBS thus showing that STN DBS can enhance performance of ballistic movements.

3.6. Synaptic plasticity Several researchers tried to explain why the time course of clinical benefit differs in patients with PD or dystonia treated with GPi DBS, by investigating how DBS influences synaptic plasticity. Instance, Ruge, Tisch, et al. (2011) measured TMS-paired associative plasticity (a test of long-term potentiation-like synaptic plasticity abnormal in patients with dystonia) (Quartarone et al., 2008; Schwingenschuh et al., 2010; Weise et al., 2006), after DBS surgery. Changes in synaptic plasticity were absent 1 month after surgery and then over the following months increased toward levels observed in healthy individuals. When DBS is turned on, it disrupts abnormal basal ganglia signals, reducing abnormal synaptic plasticity but the clinical benefit is delayed because “memory” for abnormal movement persists. In another study, Ruge, Cif, et al. (2011) showed no change in physiological or clinical status when DBS was turned off for 2 days, suggesting that synaptic plasticity may also drive the long-term therapeutic effects induced by DBS in dystonia although considerable variation between patients exists. Patients who had higher synaptic plasticity levels during DBS retained clinical benefit when DBS was stopped and vice versa. These findings suggested that shaping DBS in the individual patient might maximize the beneficial neurophysiological patterns that influence clinical status. Although no study has investigated DBS-induced changes in synaptic plasticity in human PD, one study investigated it in rat STN (Shen, Zhu, Munhall, & Johnson, 2003). High-frequency DBS in the STN from a rat slice preparation induces three forms of synaptic plasticity at cortico-STN synapses: short-term potentiation (STP), long-term potentiation (LTP), and long-term depression (LTD) in excitatory synaptic potentials. DBS-induced STP and

Neurophysiology of Deep Brain Stimulation

41

LTD have paired-pulse characteristics consistent with changes in presynaptic action, whereas LTP has features more consistent with a postsynaptic action. If synaptic plasticity, especially long-term plasticity, is among the mechanisms by which high-frequency DBS changes symptoms in PD, the effect should persist for an appropriate time after the stimulator is turned off. This interesting possibility merits confirmation in a study designed to analyze in detail the poststimulation symptom time course in humans.

4. BEHAVIORAL NEUROPHYSIOLOGY Despite the increasing interest in nonmotor functions of deep brain structures, in particular, in cognitive functions and behavior, only three studies investigated the neurophysiological correlate from the STN during decisionmaking processes in PD (Cavanagh et al., 2011; Fumagalli et al., 2011; Fumagalli & Priori, 2012; Zaghloul et al., 2012). Fumagalli et al. (2011) showed that STN LFP changes during decision-making related specifically to moral conflict. In a similar study, Cavanagh et al. (2011) tested EEG and LFP changes during a choice conflict task on and off STN DBS and suggested that in high-conflict decisions the prefrontal-STN network increases the decision threshold thus delaying the response. Another neurophysiological study investigated the role of the STN in decisionmaking by analyzing microelectrode recordings. When participants engaged in a high-conflict decision-making task, STN single-unit activity increased (Zaghloul et al., 2012). These neurophysiological studies provided direct evidence for the basal ganglia’s role in decision-making, suggesting that basal ganglia DBS might modulate cognitive functions and behavior. In the past years, scientific literature investigated the behavioral effect of STN DBS on decision-making in patients with PD (Balaz, Bockova, Rektorova, & Rektor, 2011; Marceglia et al., 2011) thus providing useful information that helps to explain the mechanisms underlying DBS. The first study evaluated patients with PD undergoing bilateral STN DBS during a stimulus reward learning task and a gambling task. The results showed that STN stimulation had no influence on reward learning and economic decisions (Czernecki et al., 2005). Using a computational model of the cortico-basal-thalamocortical network (Frank, 2006), Frank, Samanta, Moustafa, and Sherman (2007) tested a group of mildly medicated PD patients with a reward-based probabilistic learning task, during DBS, and compared their performance with a control group. Patients were slower than controls in learning probabilistic reinforcements and STN DBS-induced impulsive responses specifically in high-conflict

42

Manuela Rosa et al.

decisions (Frank et al., 2007). To study STN DBS effects on reward-based decision-learning, a group of patients with PD bilaterally implanted with STN DBS did a probabilistic learning task while on and off stimulation. STN stimulation improved the action-oriented learning functions, enabling patients to use feedback more effectively during STN DBS and consequently to respond optimally to a situation requiring a decision (van Wouwe et al., 2011). In a study aiming to clarify the specific effects of STN DBS on gambling behavior, parkinsonian patients with chronically implanted STN electrodes for DBS executed a loss-chasing game during DBS. The task required them to choose between gambling to recover a loss or quitting. The results indicated that STN DBS left the tendency to chase losses unchanged but increased the value of losses that patients are willing to pay (Rogers, 2011). To assess acute effects of STN DBS on decision-making, PD patients did the Iowa Gambling Task before bilateral STN DBS surgery, and 2–4 weeks post surgery in on and off stimulation conditions. Although no significant differences emerged in their task performance before surgery, on stimulation and off stimulation, patients performed worse in the on condition than in the off condition only in the last block of the task. The investigators concluded that STN DBS may affect decision-making in the acute postoperative stage (Oyama et al., 2011). The conflicting results reported in the aforementioned studies probably depend on methodological differences (task used, time after surgery, dopaminergic medication). Although the mechanisms through which STN DBS influences decision-making are still unclear, two studies provided interesting insights in this field through a neuro-computational model in which the STN provides a self-adaptive dynamic “hold-your-horses” signal that functions as a brake that temporarily prevents subjects from responding to highconflict decisions and allows them more time to integrate all the necessary information and settle on the optimal choice (Cavanagh et al., 2011; Frank et al., 2007; Oyama et al., 2011). According to this model, the STN relays a global NoGo signal via excitatory projections to the pallidum, thereby inhibiting thalamocortical activity. STN DBS reduces the NoGo signal thus impulsively speeding high-conflictual decisions (Frank et al., 2007).

5. NEUROCHEMISTRY The various mechanisms through which DBS exerts its therapeutic actions, including its ability to modulate pathological activity (Hammond et al., 2007) and induce a depolarization blockade (Beurrier et al., 2001), may in part depend on altered neurotransmitter levels in the basal ganglia.

Neurophysiology of Deep Brain Stimulation

43

In an intracerebral microdialysis study in rats, Windels et al. (2000) found that high-frequency STN DBS significantly increases extracellular glutamate levels in GPi and SNr. These results were confirmed in human parkinsonian disease in intraoperative microdialysis studies in GPi (Stefani et al., 2005) and SNr (Galati et al., 2006). Stefani et al. (2005) found elevated cyclic guanosine monophosphate (cGMP) extracellular concentrations in GPi during STN DBS indicating increased glutamatergic transmission or increased glutamate effectiveness on GPi. Again confirming the importance of neurochemical changes during DBS in parkinsonian patients, Galati et al. (2006) showed that STN DBS increased cGMP levels in SNr suggesting increased excitation resulting from overactivity in glutamatergic afferent fibers to the SNr from the STN (Fig. 3.3A). In parkinsonian patients, Stefani et al. (2011) studied the biochemical effect of STN DBS on GPi and basal ganglia output to the motor thalamus, the crucial structure conveying motor information to cortex. Effective STN DBS increased GPi cGMP levels and reduced extracellular GABA concentrations in ventral anterior nucleus (VA) supporting thalamic disinhibition, in turn, reestablishing more physiological thalamocortical transmission, thereby improving motor symptoms (Fig. 3.3B). Equally important, they indirectly suggest refreshing the standard basal ganglia model. The dogma indicating a hyperactive indirect pathway as a crucial hallmark of hypokinetic signs in PD probably needs rethinking because DBS relieves akinesia and rigidity even in the absence of reduced GPi excitability. Clinical improvement nevertheless requires fast changes in thalamic GABA, confirming the standard basal ganglia model, in which the core player in determining thalamocortical transmission is VA (Stefani et al., 2011). Because evidence in animals shows the involvement of neurotransmitters such as GABA during cognitive processes and behavior (Martins, Shahrokh, & Powell, 2011; Mora, Segovia, & Del Arco, 2008; Skirzewski et al., 2011), altered basal ganglia GABA levels during DBS might explain DBS-induced effects on motor function and also on cognitive functions and behavior.

6. FUTURE PERSPECTIVES: DEVELOPMENT OF NEW ADAPTIVE DEEP BRAIN STIMULATION (aDBS) SYSTEMS Despite its widespread acceptance and therapeutic effectiveness, DBS suffers a major limitation, namely, it is delivered with constant stimulation settings, adjusted only during control visits. Neurological and neuropsychiatric disease treated with DBS, especially PD, the main indication for DBS, typically

44

Manuela Rosa et al.

A 10

12

8

10 8

6

6

4

4

2 2

UPDRS lll (selected items)

cGMP concentration (fmol/50 ml)

STN-DBS 14

0

0 10

20

30

40

50

70

60

80

90

Time (min)

B

STN-DBS

4

8

3

6

2

4

1

2 0

GABA (pmol/50 ml)

0 1

2

3

4

5

1

2

3

4

5

6

1

2

3

4

5

6

Fractions

Clinical Worsening 10

GPi 10 8

8

6

6

4

4

2

2

0

UPDRS

cGMP (fmol/50 ml)

UPDRS

5

Clinical Worsening 10

VA

0 1

2

3

4

5

1

2

3

4

5

6

1

2

3

4

5

6

Fractions

Figure 3.3 Effects of deep brain stimulation on neurotransmitters. (A) Microdialysis data from substantia nigra pars reticulata (SNr) 30 min before, during and after subthalamic nucleus deep brain stimulation (STN DBS) in patients with Parkinson's disease (PD). Cyclic guanosine monophosphate (cGMP) levels are reported every 10 min. The first three fractions (white bars) were used for baseline evaluation. Monopolar STN DBS was then switched on (black bars). After 30 min, STN DBS was discontinued and 10 min fractions were collected for an additional 30 min. Bars represent mean  standard deviation. Note a significant cGMP increase during STN DBS at P <0.01 versus T10–T20–T30–T40–T80–T90. The clinical scores validate the effectiveness of STN DBS (dotted line, selected items from the Unified Parkinson's disease rating scale-UPDRS:

Neurophysiology of Deep Brain Stimulation

45

fluctuates over time. The constant stimulation strategy therefore leaves symptoms partly uncontrolled (Deuschl et al., 2006; Krack et al., 2003; Romito & Albanese, 2011). DBS could achieve an even better clinical result by adapting moment-by-moment to the patient’s clinical condition. Another concern arises from increasing evidence attributing several cognitive and behavioral adverse effects, observed years after DBS therapy started, to the amount of stimulation delivered (Bronstein et al., 2011; Romito & Albanese, 2011). DBS therapy could be optimized to control fluctuations and adverse effects better by “fine-tuning” DBS settings (including frequency, amplitude, pulse width, and waveform) in a short time-window. A possible solution would be an “intelligent” DBS system, able to change stimulation settings and variables when it detects fluctuations and adverse effects as well as more subtle pathophysiological changes, without patients waiting until the next clinic visit for reprogramming (Burgess et al., 2010; Marceglia et al., 2007; Priori, Foffani, & Rossi, 2005a, 2005b; Rosin et al., 2011; Santaniello, Fiengo, Glielmo, & Grill, 2011; Winestone, Zaidel, Bergman, & Israel, 2012). This strategy is known as “closed-loop” or “adaptive” DBS (aDBS). aDBS consists of a simple closed-loop model designed to measure and analyze a control variable reflecting the patient’s clinical condition to elaborate new stimulation device settings and send them to an intelligent stimulator implanted in the chest. The closed-loop concept works on the principle that a physiological variable measurable in patients undergoing DBS, possibly without burdening patients with implants other than those they already carry (electrodes, extensions, and pulse generator), is able to reflect changes in the patient’s condition. Research conducted over the past 10 years suggests as a possible control variable neuronal oscillations, measured through LFPs from DBS target

rigidity 0–4; finger tapping 0–4; hand movement 0–4; 0 corresponds to “normal”; 12 is the maximum score). The scale is given on the right y axis. In all patients, rigidity and akinesia in the upper contralateral arm improved by >30% without side effects within the first 10 min after stimulation began. (B) Microdialysis data from ventral anterior nucleus (VA) and globus pallidus internus (GPi), and clinical evaluation before, during, and after STN DBS in patients with PD. Clinically effective STN DBS significantly reduced GABA in VA and clearly increased cGMP in the GPi. Dotted lines represent the clinical changes as in A. The data are expressed as the mean  standard deviation. (A) Reproduced from Galati et al. (2006) with permission from John Wiley & Sons Ltd.; (B) Reprinted from Stefani et al. (2011) with permission (http://creativecommons.org/licenses/by-nc-nd/3.0/).

46

Manuela Rosa et al.

Frequency (Hz)

LFP-based control signal 40 3.5 3 2.5 2 1.5 1 0.5

30 20 10 0 0

20

40

60 80 Time (s)

100

FilterDBS

New parameters Parameters adjustment

120

0

LFPs

1

Figure 3.4 The concept of closed-loop adaptive Deep Brain Stimulation (aDBS). DBS is delivered through implanted electrodes represented in the radiography. A control variable (local field potentials, LFPs) is measured automatically from the implanted electrodes and analyzed. FilterDBS provides artifact-free LFP. A LFP-based controlling system evaluates the results of the analysis and provides new parameters to be delivered by the implanted pulse generator. Reprinted from Marceglia et al. (2007) with permission from Expert Reviews Ltd.

structure (Priori, Foffani, Rossi, & Marceglia, 2012) (Fig. 3.4). This choice receives support from the following observations: LFPs are recordable without additional implants, correlate with the patient’s clinical condition (Brown & Williams, 2005; Kuhn et al., 2004, 2008; Priori et al., 2004), and are easy to capture and process also during DBS on, without artifacts (Rossi et al., 2007). Another advantage is that LFPs recorded over time after electrode implant (Rosa et al., 2010) show DBS-induced modulations similar to those recorded a few days after electrode implant (Rosa et al., 2011). Hence, even though the new aDBS approaches based on LFP recordings now seem feasible, before aDBS systems can be applied in patients, further research is needed to define the control variables that best reflect the patient’s clinical state, refine feedback algorithms, develop a prototype device for use in patients, and conduct a clinical study to compare the new aDBS system with the current “reference-standard,” open-loop STN DBS.

REFERENCES Airaksinen, K., Makela, J. P., Taulu, S., Ahonen, A., Nurminen, J., Schnitzler, A., et al. (2011). Effects of DBS on auditory and somatosensory processing in Parkinson’s disease. Human Brain Mapping, 32, 1091–1099. Aouizerate, B., Cuny, E., Bardinet, E., Yelnik, J., Martin-Guehl, C., Rotge, J. Y., et al. (2009). Distinct striatal targets in treating obsessive-compulsive disorder and major depression. Journal of Neurosurgery, 111, 775–779.

Neurophysiology of Deep Brain Stimulation

47

Ashby, P., Paradiso, G., Saint-Cyr, J. A., Chen, R., Lang, A. E., & Lozano, A. M. (2001). Potentials recorded at the scalp by stimulation near the human subthalamic nucleus. Clinical Neurophysiology, 112, 431–437. Baker, K. B., Montgomery, E. B., Jr., Rezai, A. R., Burgess, R., & Luders, H. O. (2002). Subthalamic nucleus deep brain stimulus evoked potentials: Physiological and therapeutic implications. Movement Disorders, 17, 969–983. Balaz, M., Bockova, M., Rektorova, I., & Rektor, I. (2011). Involvement of the subthalamic nucleus in cognitive functions—A concept. Journal of the Neurological Sciences, 310, 96–99. Benabid, A. L., Pollak, P., Gervason, C., Hoffmann, D., Gao, D. M., Hommel, M., et al. (1991). Long-term suppression of tremor by chronic stimulation of the ventral intermediate thalamic nucleus. Lancet, 337, 403–406. Benazzouz, A., Gross, C., Feger, J., Boraud, T., & Bioulac, B. (1993). Reversal of rigidity and improvement in motor performance by subthalamic high-frequency stimulation in MPTP-treated monkeys. The European Journal of Neuroscience, 5, 382–389. Benazzouz, A., & Hallett, M. (2000). Mechanism of action of deep brain stimulation. Neurology, 55, S13–S16. Beurrier, C., Bioulac, B., Audin, J., & Hammond, C. (2001). High-frequency stimulation produces a transient blockade of voltage-gated currents in subthalamic neurons. Journal of Neurophysiology, 85, 1351–1356. Bevan, M. D., Magill, P. J., Terman, D., Bolam, J. P., & Wilson, C. J. (2002). Move to the rhythm: Oscillations in the subthalamic nucleus-external globus pallidus network. Trends in Neurosciences, 25, 525–531. Bronstein, J. M., Tagliati, M., Alterman, R. L., Lozano, A. M., Volkmann, J., Stefani, A., et al. (2011). Deep brain stimulation for Parkinson disease: An expert consensus and review of key issues. Archives of Neurology, 68, 165. Bronte-Stewart, H., Barberini, C., Koop, M. M., Hill, B. C., Henderson, J. M., & Wingeier, B. (2009). The STN beta-band profile in Parkinson’s disease is stationary and shows prolonged attenuation after deep brain stimulation. Experimental Neurology, 215, 20–28. Brown, P., Mazzone, P., Oliviero, A., Altibrandi, M. G., Pilato, F., Tonali, P. A., et al. (2004). Effects of stimulation of the subthalamic area on oscillatory pallidal activity in Parkinson’s disease. Experimental Neurology, 188, 480–490. Brown, P., & Williams, D. (2005). Basal ganglia local field potential activity: Character and functional significance in the human. Clinical Neurophysiology, 116, 2510–2519. Brucke, C., Kupsch, A., Schneider, G. H., Hariz, M. I., Nuttin, B., Kopp, U., et al. (2007). The subthalamic region is activated during valence-related emotional processing in patients with Parkinson’s disease. The European Journal of Neuroscience, 26, 767–774. Burgess, J. G., Warwick, K., Ruiz, V., Gasson, M. N., Aziz, T. Z., Brittain, J. S., et al. (2010). Identifying tremor-related characteristics of basal ganglia nuclei during movement in the Parkinsonian patient. Parkinsonism & Related Disorders, 16, 671–675. Butson, C. R., & McIntyre, C. C. (2005). Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation. Clinical Neurophysiology, 116, 2490–2500. Butson, C. R., & McIntyre, C. C. (2006). Role of electrode design on the volume of tissue activated during deep brain stimulation. Journal of Neural Engineering, 3, 1–8. Carlson, J. D., Cleary, D. R., Cetas, J. S., Heinricher, M. M., & Burchiel, K. J. (2010). Deep brain stimulation does not silence neurons in subthalamic nucleus in Parkinson’s patients. Journal of Neurophysiology, 103, 962–967. Cavanagh, J. F., Wiecki, T. V., Cohen, M. X., Figueroa, C. M., Samanta, J., Sherman, S. J., et al. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14, 1462–1467.

48

Manuela Rosa et al.

Chaturvedi, A., Butson, C. R., Lempka, S. F., Cooper, S. E., & McIntyre, C. C. (2010). Patient-specific models of deep brain stimulation: Influence of field model complexity on neural activation predictions. Brain Stimulation, 3, 65–67. Chen, R., Garg, R. R., Lozano, A. M., & Lang, A. E. (2001). Effects of internal globus pallidus stimulation on motor cortex excitability. Neurology, 56, 716–723. Cheung, T., & Tagliati, M. (2010). Deep brain stimulation: Can we do it better? Clinical Neurophysiology, 121, 1979–1980. Costa, J., Valls-Sole, J., Valldeoriola, F., Pech, C., & Rumia, J. (2006). Single subthalamic nucleus deep brain stimuli inhibit the blink reflex in Parkinson’s disease patients. Brain, 129, 1758–1767. Cunic, D., Roshan, L., Khan, F. I., Lozano, A. M., Lang, A. E., & Chen, R. (2002). Effects of subthalamic nucleus stimulation on motor cortex excitability in Parkinson’s disease. Neurology, 58, 1665–1672. Czernecki, V., Pillon, B., Houeto, J. L., Welter, M. L., Mesnage, V., Agid, Y., et al. (2005). Does bilateral stimulation of the subthalamic nucleus aggravate apathy in Parkinson’s disease? Journal of Neurology, Neurosurgery, and Psychiatry, 76, 775–779. Delwaide, P. J., Pepin, J. L., & Maertens de Noordhout, A. (1991). Short-latency autogenic inhibition in patients with Parkinsonian rigidity. Annals of Neurology, 30, 83–89. Deuschl, G., Schade-Brittinger, C., Krack, P., Volkmann, J., Schafer, H., Botzel, K., et al. (2006). A randomized trial of deep-brain stimulation for Parkinson’s disease. The New England Journal of Medicine, 355, 896–908. Devergnas, A., & Wichmann, T. (2011). Cortical potentials evoked by deep brain stimulation in the subthalamic area. Frontiers in Systems Neuroscience, 5, 30. Dostrovsky, J. O., Levy, R., Wu, J. P., Hutchison, W. D., Tasker, R. R., & Lozano, A. M. (2000). Microstimulation-induced inhibition of neuronal firing in human globus pallidus. Journal of Neurophysiology, 84, 570–574. Dostrovsky, J. O., & Lozano, A. M. (2002). Mechanisms of deep brain stimulation. Movement Disorders, 17(Suppl. 3), S63–S68. Eusebio, A., Thevathasan, W., Doyle Gaynor, L., Pogosyan, A., Bye, E., Foltynie, T., et al. (2011). Deep brain stimulation can suppress pathological synchronisation in parkinsonian patients. Journal of Neurology, Neurosurgery, and Psychiatry, 82, 569–573. Ferrarin, M., Rizzone, M., Bergamasco, B., Lanotte, M., Recalcati, M., Pedotti, A., et al. (2005). Effects of bilateral subthalamic stimulation on gait kinematics and kinetics in Parkinson’s disease. Experimental Brain Research, 160, 517–527. Filali, M., Hutchison, W. D., Palter, V. N., Lozano, A. M., & Dostrovsky, J. O. (2004). Stimulation-induced inhibition of neuronal firing in human subthalamic nucleus. Experimental Brain Research, 156, 274–281. Finazzi-Agro, E., Peppe, A., D’Amico, A., Petta, F., Mazzone, P., Stanzione, P., et al. (2003). Effects of subthalamic nucleus stimulation on urodynamic findings in patients with Parkinson’s disease. The Journal of Urology, 169, 1388–1391. Foffani, G., Ardolino, G., Egidi, M., Caputo, E., Bossi, B., & Priori, A. (2006). Subthalamic oscillatory activities at beta or higher frequency do not change after high-frequency DBS in Parkinson’s disease. Brain Research Bulletin, 69, 123–130. Foffani, G., Bianchi, A. M., Baselli, G., & Priori, A. (2005). Movement-related frequency modulation of beta oscillatory activity in the human subthalamic nucleus. The Journal of Physiology, 568, 699–711. Foffani, G., & Priori, A. (2004). Involvement of the human subthalamic nucleus in movement preparation. Neurology, 63, 195–196 author reply 196. Foffani, G., Priori, A., Egidi, M., Rampini, P., Tamma, F., Caputo, E., et al. (2003). 300-Hz subthalamic oscillations in Parkinson’s disease. Brain, 126, 2153–2163. Frank, M. J. (2006). Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making. Neural Networks, 19, 1120–1136.

Neurophysiology of Deep Brain Stimulation

49

Frank, M. J., Samanta, J., Moustafa, A. A., & Sherman, S. J. (2007). Hold your horses: Impulsivity, deep brain stimulation, and medication in parkinsonism. Science, 318, 1309–1312. Fumagalli, M., Giannicola, G., Rosa, M., Marceglia, S., Lucchiari, C., Mrakic-Sposta, S., et al. (2011). Conflict-dependent dynamic of subthalamic nucleus oscillations during moral decisions. Social Neuroscience, 6, 243–256. Fumagalli, M., & Priori, A. (2012). Functional and clinical neuroanatomy of morality. Brain, 135(Pt 7), 2006–2021. Galati, S., Mazzone, P., Fedele, E., Pisani, A., Peppe, A., Pierantozzi, M., et al. (2006). Biochemical and electrophysiological changes of substantia nigra pars reticulata driven by subthalamic stimulation in patients with Parkinson’s disease. The European Journal of Neuroscience, 23, 2923–2928. Gatev, P., Darbin, O., & Wichmann, T. (2006). Oscillations in the basal ganglia under normal conditions and in movement disorders. Movement Disorders, 21, 1566–1577. Giannicola, G., Marceglia, S., Rossi, L., Mrakic-Sposta, S., Rampini, P., Tamma, F., et al. (2010). The effects of levodopa and ongoing deep brain stimulation on subthalamic beta oscillations in Parkinson’s disease. Experimental Neurology, 226, 120–127. Giannicola, G., & Priori, A. (2012). Stormy weather in human basal ganglia. Clinical Neurophysiology, http://dx.doi.org/10.1016/j.clinph.2012.09.012. Giannicola, G., Rosa, M., Marceglia, S., Scelzo, E., Rossi, L., Servello, D., et al. (2012). The effects of levodopa and deep brain stimulation on subthalamic local field lowfrequency oscillations in Parkinson’s disease. Neurosignals, http://dx.doi.org/10.1159/ 000336543. Giannicola, G., Rosa, M., Servello, D., Menghetti, C., Carrabba, G., Pacchetti, C., Zangaglia, R., Cogiamanian, F., Scelzo, E., Marceglia, S., Rossi, L., & Priori, A. (2012). Subthalamic Local Field Potentials After Seven-Years Deep Brain Stimulation in Parkinson’s Disease. Experimental Neurology, 237(2), 312–317. Goodman, W. K., & Alterman, R. L. (2012). Deep brain stimulation for intractable psychiatric disorders. Annual Review of Medicine, 63, 511–524. Greenberg, B. D., Rauch, S. L., & Haber, S. N. (2010). Invasive circuitry-based neurotherapeutics: Stereotactic ablation and deep brain stimulation for OCD. Neuropsychopharmacology, 35, 317–336. Griffith, R. W., & Humphrey, D. R. (2006). Long-term gliosis around chronically implanted platinum electrodes in the Rhesus macaque motor cortex. Neuroscience Letters, 406, 81–86. Gubellini, P., Salin, P., Kerkerian-Le Goff, L., & Baunez, C. (2009). Deep brain stimulation in neurological diseases and experimental models: From molecule to complex behavior. Progress in Neurobiology, 89, 79–123. Hallett, M. (1998). Physiology of dystonia. Advances in Neurology, 78, 11–18. Hamani, C., Andrade, D., Hodaie, M., Wennberg, R., & Lozano, A. (2009). Deep brain stimulation for the treatment of epilepsy. International Journal of Neural Systems, 19, 213–226. Hammond, C., Ammari, R., Bioulac, B., & Garcia, L. (2008). Latest view on the mechanism of action of deep brain stimulation. Movement Disorders, 23, 2111–2121. Hammond, C., Bergman, H., & Brown, P. (2007). Pathological synchronization in Parkinson’s disease: Networks, models and treatments. Trends in Neurosciences, 30, 357–364. Hashimoto, T., Elder, C. M., Okun, M. S., Patrick, S. K., & Vitek, J. L. (2003). Stimulation of the subthalamic nucleus changes the firing pattern of pallidal neurons. The Journal of Neuroscience, 23, 1916–1923. Hashimoto, T., Elder, C. M., & Vitek, J. L. (2002). A template subtraction method for stimulus artifact removal in high-frequency deep brain stimulation. Journal of Neuroscience Methods, 113, 181–186.

50

Manuela Rosa et al.

Johnsen, E. L., Mogensen, P. H., Sunde, N. A., & Ostergaard, K. (2009). Improved asymmetry of gait in Parkinson’s disease with DBS: Gait and postural instability in Parkinson’s disease treated with bilateral deep brain stimulation in the subthalamic nucleus. Movement Disorders, 24, 590–597. Joundi, R. A., Brittain, J. S., Punt, T. D., Green, A. L., Jenkinson, N., & Aziz, T. Z. (2012). Stimulation of the subthalamic nucleus improves velocity of ballistic movements in Parkinson’s disease. NeuroReport, 23, 390–394. Kahane, P., & Depaulis, A. (2010). Deep brain stimulation in epilepsy: What is next? Current Opinion in Neurology, 23, 177–182. Kopell, B. H., Rezai, A. R., Chang, J. W., & Vitek, J. L. (2006). Anatomy and physiology of the basal ganglia: Implications for deep brain stimulation for Parkinson’s disease. Movement Disorders, 21(Suppl. 14), S238–S246. Krack, P., Batir, A., Van Blercom, N., Chabardes, S., Fraix, V., Ardouin, C., et al. (2003). Five-year follow-up of bilateral stimulation of the subthalamic nucleus in advanced Parkinson’s disease. The New England Journal of Medicine, 349, 1925–1934. Krauss, J. K., Yianni, J., Loher, T. J., & Aziz, T. Z. (2004). Deep brain stimulation for dystonia. Journal of Clinical Neurophysiology, 21, 18–30. Kringelbach, M. L., Jenkinson, N., Owen, S. L., & Aziz, T. Z. (2007). Translational principles of deep brain stimulation. Nature Reviews. Neuroscience, 8, 623–635. Kuhn, A. A., Hariz, M. I., Silberstain, P., Tisch, S., Kupsch, A., Schneider, G. H., Limousin-Dowsey, P., Yarrow, K., & Brown, P. (2005). Activation of the subthalamic region during emotional processing in Parkinson disease. Neurology, 65, 707–713. Kuhn, A. A., Kempf, F., Brucke, C., Gaynor Doyle, L., Martinez-Torres, I., Pogosyan, A., et al. (2008). High-frequency stimulation of the subthalamic nucleus suppresses oscillatory beta activity in patients with Parkinson’s disease in parallel with improvement in motor performance. The Journal of Neuroscience, 28, 6165–6173. Kuhn, A. A., Meyer, B. U., Trottenberg, T., Brandt, S. A., Schneider, G. H., & Kupsch, A. (2003). Modulation of motor cortex excitability by pallidal stimulation in patients with severe dystonia. Neurology, 60, 768–774. Kuhn, A. A., Williams, D., Kupsch, A., Limousin, P., Hariz, M., Schneider, G. H., et al. (2004). Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance. Brain, 127, 735–746. Kuriakose, R., Saha, U., Castillo, G., Udupa, K., Ni, Z., Gunraj, C., et al. (2009). The nature and time course of cortical activation following subthalamic stimulation in Parkinson’s disease. Cerebral Cortex, 20, 1926–1936. Levy, R., Ashby, P., Hutchison, W. D., Lang, A. E., Lozano, A. M., & Dostrovsky, J. O. (2002). Dependence of subthalamic nucleus oscillations on movement and dopamine in Parkinson’s disease. Brain, 125, 1196–1209. Levy, R., Dostrovsky, J. O., Lang, A. E., Sime, E., Hutchison, W. D., & Lozano, A. M. (2001). Effects of apomorphine on subthalamic nucleus and globus pallidus internus neurons in patients with Parkinson’s disease. Journal of Neurophysiology, 86, 249–260. Levy, R., Hutchison, W. D., Lozano, A. M., & Dostrovsky, J. O. (2000). High-frequency synchronization of neuronal activity in the subthalamic nucleus of parkinsonian patients with limb tremor. The Journal of Neuroscience, 20, 7766–7775. Liu, Y., Postupna, N., Falkenberg, J., & Anderson, M. E. (2008). High frequency deep brain stimulation:What are the therapeutic mechanisms? Neuroscience & Biobehavioral Reviews, 32, 343–351. MacKinnon, C. D., Webb, R. M., Silberstein, P., Tisch, S., Asselman, P., Limousin, P., et al. (2005). Stimulation through electrodes implanted near the subthalamic nucleus activates projections to motor areas of cerebral cortex in patients with Parkinson’s disease. The European Journal of Neuroscience, 21, 1394–1402.

Neurophysiology of Deep Brain Stimulation

51

Magarinos-Ascone, C., Pazo, J. H., Macadar, O., & Buno, W. (2002). High-frequency stimulation of the subthalamic nucleus silences subthalamic neurons: A possible cellular mechanism in Parkinson’s disease. Neuroscience, 115, 1109–1117. Mallet, L., Polosan, M., Jaafari, N., Baup, N., Welter, M. L., Fontaine, D., et al. (2008). Subthalamic nucleus stimulation in severe obsessive-compulsive disorder. The New England Journal of Medicine, 359, 2121–2134. Maltete, D., Jodoin, N., Karachi, C., Houeto, J. L., Navarro, S., Cornu, P., et al. (2007). Subthalamic stimulation and neuronal activity in the substantia nigra in Parkinson’s disease. Journal of Neurophysiology, 97, 4017–4022. Marceglia, S., Fiorio, M., Foffani, G., Mrakic-Sposta, S., Tiriticco, M., Locatelli, M., et al. (2009). Modulation of beta oscillations in the subthalamic area during action observation in Parkinson’s disease. Neuroscience, 161, 1027–1036. Marceglia, S., Foffani, G., Bianchi, A. M., Baselli, G., Tamma, F., Egidi, M., et al. (2006). Dopamine-dependent non-linear correlation between subthalamic rhythms in Parkinson’s disease. The Journal of Physiology, 571, 579–591. Marceglia, S., Fumagalli, M., & Priori, A. (2011). What neurophysiological recordings tell us about cognitive and behavioral functions of the human subthalamic nucleus. Expert Review of Neurotherapeutics, 11, 139–149. Marceglia, S., Rossi, L., Foffani, G., Bianchi, A., Cerutti, S., & Priori, A. (2007). Basal ganglia local field potentials: Applications in the development of new deep brain stimulation devices for movement disorders. Expert Review of Medical Devices, 4, 605–614. Martins, G. J., Shahrokh, M., & Powell, E. M. (2011). Genetic disruption of Met signaling impairs GABAergic striatal development and cognition. Neuroscience, 176, 199–209. McIntyre, C. C., & Grill, W. M. (1999). Excitation of central nervous system neurons by nonuniform electric fields. Biophysical Journal, 76, 878–888. McIntyre, C. C., & Grill, W. M. (2001). Finite element analysis of the current-density and electric field generated by metal microelectrodes. Annals of Biomedical Engineering, 29, 227–235. McIntyre, C. C., & Grill, W. M. (2002). Extracellular stimulation of central neurons: Influence of stimulus waveform and frequency on neuronal output. Journal of Neurophysiology, 88, 1592–1604. McIntyre, C. C., Grill, W. M., Sherman, D. L., & Thakor, N. V. (2004). Cellular effects of deep brain stimulation: Model-based analysis of activation and inhibition. Journal of Neurophysiology, 91, 1457–1469. McIntyre, C. C., & Hahn, P. J. (2010). Network perspectives on the mechanisms of deep brain stimulation. Neurobiology of Disease, 38, 329–337. Montgomery, E. B., Jr. (2006). Effects of GPi stimulation on human thalamic neuronal activity. Clinical Neurophysiology, 117, 2691–2702. Montgomery, E. B., Jr., & Gale, J. T. (2008). Mechanisms of action of deep brain stimulation (DBS). Neuroscience and Biobehavioral Reviews, 32, 388–407. Mora, F., Segovia, G., & Del Arco, A. (2008). Glutamate-dopamine-GABA interactions in the aging basal ganglia. Brain Research Reviews, 58, 340–353. Moro, E., Lozano, A. M., Pollak, P., Agid, Y., Rehncrona, S., Volkmann, J., et al. (2010). Long-term results of a multicenter study on subthalamic and pallidal stimulation in Parkinson’s disease. Movement Disorders, 25, 578–586. Moss, J., Ryder, T., Aziz, T. Z., Graeber, M. B., & Bain, P. G. (2004). Electron microscopy of tissue adherent to explanted electrodes in dystonia and Parkinson’s disease. Brain, 127, 2755–2763. Okun, M. S., Gallo, B. V., Mandybur, G., Jagid, J., Foote, K. D., Revilla, F. J., et al. (2012). Subthalamic deep brain stimulation with a constant-current device in Parkinson’s disease: An open-label randomised controlled trial. Lancet Neurology, 11, 140–149.

52

Manuela Rosa et al.

Oyama, G., Shimo, Y., Natori, S., Nakajima, M., Ishii, H., Arai, H., et al. (2011). Acute effects of bilateral subthalamic stimulation on decision-making in Parkinson’s disease. Parkinsonism & Related Disorders, 17, 189–193. Ozkurt, T. E., Butz, M., Homburger, M., Elben, S., Vesper, J., Wojtecki, L., et al. (2011). High frequency oscillations in the subthalamic nucleus: A neurophysiological marker of the motor state in Parkinson’s disease. Experimental Neurology, 229, 324–331. Perlmutter, J. S., & Mink, J. W. (2006). Deep brain stimulation. Annual Review of Neuroscience, 29, 229–257. Pierantozzi, M., Mazzone, P., Bassi, A., Rossini, P. M., Peppe, A., Altibrandi, M. G., et al. (1999). The effect of deep brain stimulation on the frontal N30 component of somatosensory evoked potentials in advanced Parkinson’s disease patients. Clinical Neurophysiology, 110, 1700–1707. Pierantozzi, M., Palmieri, M. G., Mazzone, P., Marciani, M. G., Rossini, P. M., Stefani, A., et al. (2002). Deep brain stimulation of both subthalamic nucleus and internal globus pallidus restores intracortical inhibition in Parkinson’s disease paralleling apomorphine effects: A paired magnetic stimulation study. Clinical Neurophysiology, 113, 108–113. Pizzolato, G., & Mandat, T. (2012). Deep brain stimulation for movement disorders. Frontiers in Integrative Neuroscience, 6, 2. Porta, M., Brambilla, A., Cavanna, A. E., Servello, D., Sassi, M., Rickards, H., et al. (2009). Thalamic deep brain stimulation for treatment-refractory Tourette syndrome: Two-year outcome. Neurology, 73, 1375–1380. Potter, M., Illert, M., Wenzelburger, R., Deuschl, G., & Volkmann, J. (2004). The effect of subthalamic nucleus stimulation on autogenic inhibition in Parkinson disease. Neurology, 63, 1234–1239. Pralong, E., Debatisse, D., Maeder, M., Vingerhoets, F., Ghika, J., & Villemure, J. G. (2003). Effect of deep brain stimulation of GPI on neuronal activity of the thalamic nucleus ventralis oralis in a dystonic patient. Neurophysiologie Clinique, 33, 169–173. Priori, A., Ardolino, G., Marceglia, S., Mrakic-Sposta, S., Locatelli, M., Tamma, F., et al. (2006). Low-frequency subthalamic oscillations increase after deep brain stimulation in Parkinson’s disease. Brain Research Bulletin, 71, 149–154. Priori, A., Cinnante, C., Genitrini, S., Pesenti, A., Tortora, G., Bencini, C., et al. (2001). Non-motor effects of deep brain stimulation of the subthalamic nucleus in Parkinson’s disease: Preliminary physiological results. Neurological Sciences, 22, 85–86. Priori, A., Foffani, G., Pesenti, A., Tamma, F., Bianchi, A. M., Pellegrini, M., et al. (2004). Rhythm-specific pharmacological modulation of subthalamic activity in Parkinson’s disease. Experimental Neurology, 189, 369–379. Priori, A., Foffani, G., Rossi, L., & Marceglia, S. (2012). Adaptive deep brain stimulation (aDBS) controller by local field potential oscillations. Experimental Neurology, http:// dx.doi.org/10.1016/j.expneurol.2012.09.013. Priori, A., Foffani, G., & Rossi, L. (2005a). Apparatus for treating neurological disorders by means of adaptive electro-stimulation retroacted by biopotentials. E.P. Office 1940508. Priori, A., Foffani, G., & Rossi, L. (2005b). Apparatus for treating neurological disorders by means of adaptive electro-stimulation retroacted by biopotentials. Office U.S. 8078281. Quartarone, A., Morgante, F., Sant’angelo, A., Rizzo, V., Bagnato, S., Terranova, C., et al. (2008). Abnormal plasticity of sensorimotor circuits extends beyond the affected body part in focal dystonia. Journal of Neurology, Neurosurgery, and Psychiatry, 79, 985–990. Reese, R., Winter, C., Nadjar, A., Harnack, D., Morgenstern, R., Kupsch, A., et al. (2008). High-frequency stimulation of the subthalamic nucleus increases pallidal neuronal firing rate in a patient with Parkinson’s disease. Movement Disorders, 23, 1945–1947. Rogers, R. D. (2011). The roles of dopamine and serotonin in decision making: Evidence from pharmacological experiments in humans. Neuropsychopharmacology, 36, 114–132.

Neurophysiology of Deep Brain Stimulation

53

Romanelli, P., Heit, G., Hill, B. C., Kraus, A., Hastie, T., & Bronte-Stewart, H. M. (2004). Microelectrode recording revealing a somatotopic body map in the subthalamic nucleus in humans with Parkinson disease. Journal of Neurosurgery, 100, 611–618. Romito, L. M., & Albanese, A. (2011). Dopaminergic therapy and subthalamic stimulation in Parkinson’s disease: A review of 5-year reports. Journal of Neurology, 257, S298–S304. Rosa, M., Franzini, A., Giannicola, G., Messina, G., Altamura, A. C., & Priori, A. (2012). Hypothalamic Oscillations in Human Pathological Aggressiveness. Biological Psychiatry, http://dx.doi.org/10.1016/j.biopsych.2012.06.007. Rosa, M., Giannicola, G., Servello, D., Marceglia, S., Pacchetti, C., Porta, M., et al. (2011). Subthalamic local field beta oscillations during ongoing deep brain stimulation in Parkinson’s disease in hyperacute and chronic phases. Neurosignals, 19, 151–162. Rosa, M., Marceglia, S., Servello, D., Foffani, G., Rossi, L., Sassi, M., et al. (2010). Time dependent subthalamic local field potential changes after DBS surgery in Parkinson’s disease. Experimental Neurology, 222, 184–190. Rosin, B., Slovik, M., Mitelman, R., Rivlin-Etzion, M., Haber, S. N., Israel, Z., et al. (2011). Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron, 72, 370–384. Rossi, L., Foffani, G., Marceglia, S., Bracchi, F., Barbieri, S., & Priori, A. (2007). An electronic device for artefact suppression in human local field potential recordings during deep brain stimulation. Journal of Neural Engineering, 4, 96–106. Rossi, L., Marceglia, S., Foffani, G., Cogiamanian, F., Tamma, F., Rampini, P., et al. (2008). Subthalamic local field potential oscillations during ongoing deep brain stimulation in Parkinson’s disease. Brain Research Bulletin, 76, 512–521. Rossini, P. M., Filippi, M. M., & Vernieri, F. (1998). Neurophysiology of sensorimotor integration in Parkinson’s disease. Clinical Neuroscience, 5, 121–130. Rubino, D., Robbins, K. A., & Hatsopoulos, N. G. (2006). Propagating waves mediate information transfer in the motor cortex. Nature Neuroscience, 9, 1549–1557. Ruge, D., Cif, L., Limousin, P., Gonzalez, V., Vasques, X., Hariz, M. I., et al. (2011). Shaping reversibility? Long-term deep brain stimulation in dystonia: The relationship between effects on electrophysiology and clinical symptoms. Brain, 134, 2106–2115. Ruge, D., Tisch, S., Hariz, M. I., Zrinzo, L., Bhatia, K. P., Quinn, N. P., et al. (2011). Deep brain stimulation effects in dystonia: Time course of electrophysiological changes in early treatment. Movement Disorders, 26, 1913–1921. Santaniello, S., Fiengo, G., Glielmo, L., & Grill, W. M. (2011). Closed-loop control of deep brain stimulation: A simulation study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19, 15–24. Sauleau, P., Raoul, S., Lallement, F., Rivier, I., Drapier, S., Lajat, Y., et al. (2005). Motor and non motor effects during intraoperative subthalamic stimulation for Parkinson’s disease. Journal of Neurology, 252, 457–464. Schicatano, E. J., Peshori, K. R., Gopalaswamy, R., Sahay, E., & Evinger, C. (2000). Reflex excitability regulates prepulse inhibition. The Journal of Neuroscience, 20, 4240–4247. Schwingenschuh, P., Ruge, D., Edwards, M. J., Terranova, C., Katschnig, P., Carrillo, F., et al. (2010). Distinguishing SWEDDs patients with asymmetric resting tremor from Parkinson’s disease: A clinical and electrophysiological study. Movement Disorders, 25, 560–569. Seif, C., Herzog, J., van der Horst, C., Schrader, B., Volkmann, J., Deuschl, G., et al. (2004). Effect of subthalamic deep brain stimulation on the function of the urinary bladder. Annals of Neurology, 55, 118–120. Shen, K. Z., Zhu, Z. T., Munhall, A., & Johnson, S. W. (2003). Synaptic plasticity in rat subthalamic nucleus induced by high-frequency stimulation. Synapse, 50, 314–319. Silberstein, P., Pogosyan, A., Kuhn, A. A., Hotton, G., Tisch, S., Kupsch, A., et al. (2005). Cortico-cortical coupling in Parkinson’s disease and its modulation by therapy. Brain, 128, 1277–1291.

54

Manuela Rosa et al.

Skirzewski, M., Lopez, W., Mosquera, E., Betancourt, L., Catlow, B., Chiurillo, M., et al. (2011). Enhanced gabaergic tone in the ventral pallidum: Memory of unpleasant experiences? Neuroscience, 196, 131–146. St George, R. J., Carlson-Kuhta, P., Burchiel, K. J., Hogarth, P., Frank, N., & Horak, F. B. (2012). The effects of subthalamic and pallidal deep brain stimulation on postural responses in patients with Parkinson disease. Journal of Neurosurgery, 116, 1347–1356. Stefani, A., Bassi, A., Mazzone, P., Pierantozzi, M., Gattoni, G., Altibrandi, M. G., et al. (2002). Subdyskinetic apomorphine responses in globus pallidus and subthalamus of parkinsonian patients: Lack of clear evidence for the ‘indirect pathway’. Clinical Neurophysiology, 113, 91–100. Stefani, A., Fedele, E., Galati, S., Pepicelli, O., Frasca, S., Pierantozzi, M., et al. (2005). Subthalamic stimulation activates internal pallidus: Evidence from cGMP microdialysis in PD patients. Annals of Neurology, 57, 448–452. Stefani, A., Fedele, E., Vitek, J., Pierantozzi, M., Galati, S., Marzetti, F., Peppe, A., Bassi, M. S., Bernardi, G., & Stanzione, P. (2011). The clinical efficacy of L-DOPA and STN-DBS share a common marker: reduced GABA content in the motor thalamus. Cell Death and Disease, 5(2), e154. Terman, D., Rubin, J. E., Yew, A. C., & Wilson, C. J. (2002). Activity patterns in a model for the subthalamopallidal network of the basal ganglia. The Journal of Neuroscience, 22, 2963–2976. Thoma, H., Gerner, H., Holle, J., Kluger, P., Mayr, W., Meister, B., et al. (1987). The phrenic pacemaker. Substitution of paralyzed functions in tetraplegia. ASAIO Transactions, 33, 472–479. Tisch, S., Limousin, P., Rothwell, J. C., Asselman, P., Quinn, N., Jahanshahi, M., et al. (2006). Changes in blink reflex excitability after globus pallidus internus stimulation for dystonia. Movement Disorders, 21, 1650–1655. Tisch, S., Limousin, P., Rothwell, J. C., Asselman, P., Zrinzo, L., Jahanshahi, M., et al. (2006). Changes in forearm reciprocal inhibition following pallidal stimulation for dystonia. Neurology, 66, 1091–1093. Toleikis, J. R., Metman, L. V., Pilitsis, J. G., Barborica, A., Toleikis, S. C., & Bakay, R. A. (2012). Effect of intraoperative subthalamic nucleus DBS on human single-unit activity in the ipsilateral and contralateral subthalamic nucleus. Journal of Neurosurgery, 116, 1134–1143. Uhlhaas, P. J., & Singer, W. (2006). Neural synchrony in brain disorders: Relevance for cognitive dysfunctions and pathophysiology. Neuron, 52, 155–168. Valls-Sole, J., Munoz, J. E., & Valldeoriola, F. (2004). Abnormalities of prepulse inhibition do not depend on blink reflex excitability: A study in Parkinson’s disease and Huntington’s disease. Clinical Neurophysiology, 115, 1527–1536. Valls-Sole, J., & Valldeoriola, F. (2002). Neurophysiological correlate of clinical signs in Parkinson’s disease. Clinical Neurophysiology, 113, 792–805. van Wouwe, N. C., Ridderinkhof, K. R., van den Wildenberg, W. P., Band, G. P., Abisogun, A., Elias, W. J., et al. (2011). Deep brain stimulation of the subthalamic nucleus improves reward-based decision-learning in Parkinson’s disease. Frontiers in Human Neuroscience, 5, 30. Volkmann, J. (2004). Deep brain stimulation for the treatment of Parkinson’s disease. Journal of Clinical Neurophysiology, 21, 6–17. Ward, H. E., Hwynn, N., & Okun, M. S. (2010). Update on deep brain stimulation for neuropsychiatric disorders. Neurobiology of Disease, 38, 346–353. Wei, X. F., & Grill, W. M. (2005). Current density distributions, field distributions and impedance analysis of segmented deep brain stimulation electrodes. Journal of Neural Engineering, 2, 139–147.

Neurophysiology of Deep Brain Stimulation

55

Weise, D., Schramm, A., Stefan, K., Wolters, A., Reiners, K., Naumann, M., et al. (2006). The two sides of associative plasticity in writer’s cramp. Brain, 129, 2709–2721. Welter, M. L., Houeto, J. L., Bonnet, A. M., Bejjani, P. B., Mesnage, V., Dormont, D., et al. (2004). Effects of high-frequency stimulation on subthalamic neuronal activity in parkinsonian patients. Archives of Neurology, 61, 89–96. Wichmann, T., & DeLong, M. R. (1996). Functional and pathophysiological models of the basal ganglia. Current Opinion in Neurobiology, 6, 751–758. Wilke, M., Logothetis, N. K., & Leopold, D. A. (2006). Local field potential reflects perceptual suppression in monkey visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 103, 17507–17512. Wilson, K. (1925). Disorders of motility and of muscle tone, with special reference to the corpus striatum. Lancet, 2, 1–10, 53–62, 169–178, 215–219, 268–276. Windels, F., Bruet, N., Poupard, A., Urbain, N., Chouvet, G., Feuerstein, C., et al. (2000). Effects of high frequency stimulation of subthalamic nucleus on extracellular glutamate and GABA in substantia nigra and globus pallidus in the normal rat. The European Journal of Neuroscience, 12, 4141–4146. Winestone, J. S., Zaidel, A., Bergman, H., & Israel, Z. (2012). The use of macroelectrodes in recording cellular spiking activity. Journal of Neuroscience Methods, 206, 34–39. Xu, W., Russo, G. S., Hashimoto, T., Zhang, J., & Vitek, J. L. (2008). Subthalamic nucleus stimulation modulates thalamic neuronal activity. The Journal of Neuroscience, 28, 11916–11924. Yousif, N., Bayford, R., Wang, S., & Liu, X. (2008). Quantifying the effects of the electrodebrain interface on the crossing electric currents in deep brain recording and stimulation. Neuroscience, 152, 683–691. Zaghloul, K. A., Weidemann, C. T., Lega, B. C., Jaggi, J. L., Baltuch, G. H., & Kahana, M. J. (2012). Neuronal activity in the human subthalamic nucleus encodes decision conflict during action selection. The Journal of Neuroscience, 32, 2453–2460. Zaidel, A., Arkadir, D., Israel, Z., & Bergman, H. (2009). Akineto-rigid vs. tremor syndromes in Parkinsonism. Current Opinion in Neurology, 22, 387–393.