European Journal of Neuroscience, Vol. 40, pp. 3273–3283, 2014

doi:10.1111/ejn.12686

DISORDERS OF THE NERVOUS SYSTEM

Interhemispheric functional interactions between the subthalamic nuclei of patients with Parkinson’s disease €necker,2 A. A. Ku €cke,2 T. Scho €hn,2 F. U. Hohlefeld,1 C. Huchzermeyer,2 J. Huebl,2 G.-H. Schneider,3 C. Bru G. Curio1,4 and V. V. Nikulin1,4

 – Universita €tsmedizin Berlin, Campus Benjamin Franklin, Neurophysics Group, Department of Neurology, Charite Hindenburgdamm 30, Berlin, Germany 2  – Universita €tsmedizin Berlin, Berlin, Germany Department of Neurology, Charite 3  – Universita €tsmedizin Berlin, Berlin, Germany Department of Neurosurgery, Charite 4 Bernstein Center for Computational Neuroscience, Berlin, Germany 1

Keywords: basal ganglia, coherence, deep brain stimulation, levodopa, neural oscillations

Abstract Parkinson’s disease (PD) is characterized by widespread neural interactions in cortico-basal-ganglia networks primarily in beta oscillations (approx. 10–30 Hz), as suggested by previous findings of levodopa-modulated interhemispheric coherence between the bilateral subthalamic nuclei (STN) in local field potential recordings (LFPs). However, due to confounding effects of volume conduction the existence of ‘genuine’ interhemispheric subcortical coherence remains an open question. To address this issue we utilized the imaginary part of coherency (iCOH) which, in contrast to the standard coherence, is not susceptible to volume conduction. LFPs were recorded from eight patients with PD during wakeful rest before and after levodopa administration. We demonstrated genuine coherence between the bilateral STN in both 10–20 and 21–30 Hz oscillations, as revealed by a non-zero iCOH. Crucially, increased iCOH in 10–20 Hz oscillations positively correlated with the worsening of motor symptoms in the OFF medication condition across patients, which was not the case for standard coherence. Furthermore, across patients iCOH was increased after levodopa administration in 21–30 Hz oscillations. These results suggest a functional distinction between low and high beta oscillations in STN-LFP in line with previous studies. Furthermore, the observed functional coupling between the bilateral STN might contribute to the understanding of bilateral effects of unilateral deep brain stimulation. In conclusion, the present results imply a significant contribution of time-delayed neural interactions to interhemispheric coherence, and the clinical relevance of long-distance neural interactions between bilateral STN for motor symptoms in PD.

Introduction Deep brain stimulation (DBS) of the subthalamic nuclei (STN) in patients with Parkinson’s disease (PD) is an effective treatment to relieve motor symptoms (Benabid et al., 2009; Welter et al., 2014). Data recordings from deep electrodes in patients with PD have frequently demonstrated widespread neural interactions in corticobasal-ganglia networks, being particularly expressed in beta frequency ranges (approx. 10–30 Hz) in local field potentials (LFPs; Brown & Williams, 2005; K€uhn et al., 2006a, 2009; Ray et al., 2008; Eusebio et al., 2012; Little & Brown, 2014). STN-DBS is usually applied bilaterally; however, there are reports of effective unilateral DBS (Kumar et al., 1999; Chung et al., 2006; Hershey et al., 2008; Brun et al., 2012), probably mediated by macroscopic long-distance interhemispheric neural interactions (Brun et al., 2012; Little et al., 2013a). There is electrophysiological evidence for interhemispheric interactions from a few studies that demonstrated coherence in LFP recordings between both STN in beta oscillations OFF

Correspondence: Dr Friederike U. Hohlefeld, as above. E-mail: [email protected] Received 26 May 2014, revised 4 July 2014, accepted 9 July 2014

medication (De Solages et al., 2010; Silchenko et al., 2010; Little et al., 2013a), and a levodopa-induced decrease of interhemispheric coherence in 13–20 Hz oscillations (Little et al., 2013a). However, so far two crucial aspects of interhemispheric STN-LFP coherence remain to be clarified: (i) the effects of volume conduction are a confounding factor in any electrophysiological recording – especially when recording sites are close to each other (Srinivasan et al., 2007), as is the case for both STN that are separated only by a few centimeters; and (ii) while previous studies showed a relationship between motor symptoms and beta power (K€ uhn et al., 2006a, 2009; Ray et al., 2008) and intra-nuclear coherence (Hohlefeld et al., 2013), the clinical relevance of interhemispheric coherence regarding motor symptoms remains to be investigated. Accordingly, to complement previous findings on interhemispheric STN-LFP standard coherence, the present study addressed these notions in LFP recordings from patients with severe idiopathic PD during wakeful rest. (i) We utilized the imaginary part of coherency (iCOH; Nolte et al., 2004), which has the advantage of not being sensitive to effects of volume conduction, and is suitable for studying ‘genuine’, levodopa-modulated intra-STN coherence in a single nucleus (Hohlefeld et al., 2013). In contrast, the standard coherence

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd

3274 F. U. Hohlefeld et al. unavoidably leads to the detection of spurious neural interactions, as it includes zero-phase lags (Nolte et al., 2004). (ii) We investigated the relationship between motor pathology and specifically the low beta band (10–20 Hz), as previous studies suggested a functional distinction between high and low beta oscillations; only in the latter did levodopa administration decrease both interhemispheric standard coherence (Little et al., 2013a) and spectral power (Priori et al., 2004; Lopez-Azcarate et al., 2010; Hohlefeld et al., 2013). Finally, we also investigated whether interhemispheric STN-LFP coherence is modulated by levodopa administration, and whether this modulation is expressed in low rather than in high beta oscillations (cf. Little et al., 2013a).

Materials and methods Patients and surgery Eight patients (four males; mean age 59.5 years, range 39–69 years) diagnosed with idiopathic PD (mean disease duration 10 years, range 5–20 years) participated in the present study. For all patients informed consent was obtained. The experimental procedures were approved by the local ethics committee in accordance with The Code of Ethics of the World Medical Association (Rickham, 1964). The DBS electrodes were bilaterally implanted in the STN (Model 3389, Medtronic Neurological Division, Minneapolis, MN, USA). Contact 0 was the lowermost and contact 3 the uppermost (contact length 1.5 mm, contact-to-contact separation 0.5 mm; total contact separation 7.5 mm). For more details on the surgery and electrode localization refer to Hohlefeld et al. (2012). The eight patients of the present study were reported as a part of a larger data set in previous studies (Hohlefeld et al., 2012, 2013). For the present study, patients 2–9 were selected, who all received levodopa medication and showed no pronounced spectral tremor artifacts (harmonics) in the 10–30 Hz frequency range. The pre-surgery motor Unified Parkinson’s Disease Rating Scale (UPDRS) scores were assessed by an experienced clinician for OFF levodopa (score available in seven patients) and ON levodopa (score available in eight patients). Hohlefeld et al. (2012, table 1) provide scores and further clinical information about the patients. Data recordings The patients were studied post-operatively (range 2–6 days) while the DBS electrode leads were still externalized. The recordings were performed after overnight withdrawal of levodopa, referred to as ‘OFF’, and approximately 30–45 min after the 1–29 usual morning dose (100–200 mg) of oral levodopa (Madopar LT, Roche Pharma AG, Grenzach-Wyhlen, Germany), referred to as ‘ON’, when the patient and/or the treating physician observed a clinically relevant amelioration of the predominant symptoms. The recordings were obtained during wakeful rest (sitting, eyes open and fixated) in two blocks of 7 min, which were separated by a break. The OFF and ON conditions were recorded on separate days (1 day between recordings in six out of eight patients; 2 days in one patient; one patient was recorded on the same day). The order of the OFF and ON recordings was counterbalanced across patients – OFF was earlier recorded in four patients, ON was earlier recorded in three patients, and in the patient who was recorded on the same day, OFF was earlier recorded (6 h between recording sessions). LFPs were bipolarly recorded from the adjacent contact pairs 01, 12 and 23 (referred to as ‘channels’) of the macroelectrode in the left and right STN, where channel 01 was the lowermost and channel 23 was the uppermost. The data were

recorded with a Digitimer D360 amplifier (Digitimer, Welwyn Garden City, UK) through a 1401power mk-II A-D converter (Cambridge Electronic Design, Cambridge, UK) onto a personal computer utilizing Spike2 software (Cambridge Electronic Design). During data acquisition the signals were amplified (950 000), bandpass-filtered between 0.5 Hz and 1 kHz (for one patient between 0.05 Hz and 1 kHz), and sampled at 5 kHz. The two recording sessions were appended for subsequent analysis, thus obtaining a total of 14 min rest recordings for a given patient. The bipolar channels in the left and right STN were tested for all possible ‘connections’ (n = 9: L01–R01, L01–R12, L01–R23, L12–R01, L12–R12, L12–R23, L23–R01, L23–R12, L23–R23). Prior to further analysis both channels of a connection were visually inspected for artifacts. Data segments containing artifacts were removed, and if one (or both) channel (which constituted a connection) contained excessive amounts of noise, this connection was excluded from further analysis. Furthermore, we used an anatomical reconstruction of the electrode trajectories (Sch€ onecker et al., 2009) to identify bipolar channels in which both contacts were outside the STN (see Hohlefeld et al., 2012 for more details). In such a case this channel and all connections with that channel were excluded. For justification of including contact pairs in the analysis and discussion of limitations of the accuracy of electrode positioning please refer to Hohlefeld et al. (2012, 2013). We use the term ‘interhemispheric STN-LFP coherence’ to refer to the functional connectivity (see below) between LFP signals obtained from the left and right STN. The usage of bipolar derivations and post-operative electrode localization was applied to minimize spatial inaccuracy and to enhance the detection of neural activity in populations predominantly within the STN (see Hohlefeld et al., 2012, 2013, for discussion). Offline data analyses were carried out with MATLAB (version 7; The MathWorks Inc., Natick, MA, USA) and SPSS (version 19; IBM SPSS Inc., New York, NY, USA). iCOH Coherency, also referred to as functional connectivity (Friston, 1994), is defined as the normalized cross-spectrum, reflecting the linear relationship between two signals at a specific frequency (Nunez et al., 1997; Nolte et al., 2004). Coherency varies between 0 (no linear relationship between the signals) and 1 (completely dependent signals). Coherence is defined as the absolute value of coherency (Nolte et al., 2004). Importantly, coherence is sensitive to both non-zero and zerolagged synchronization between two signals. Therefore, coherence is susceptible to effects of volume conduction, which represents a severe challenge in electrophysiological recordings, especially for recordings from sites that are separated only by a few centimeters (Srinivasan et al., 2007), as is the case for the left and right STN. In contrast, a non-zero iCOH indicates two signals being synchronized without zero (or pi) lag. Consequently, effects of volume conduction cannot account for a non-zero imaginary part, given the fundamental assumption of the validity of the quasi-static approximation (Nolte et al., 2004; Nicolas et al., 2011). In general, a zero iCOH can be due to (i) the absence of interacting sources (i.e. volume conduction, no delay); (ii) genuine source interaction without delay; and (iii) genuine source interaction with pi delay. While iCOH cannot inform about the phase delay or interaction strength per se, a non-zero imaginary part indicates the presence of genuine time-lagged neural interactions. Consequently, despite some of its limitations, a non-zero iCOH provides a pivotal advantage over the standard coherence by discarding spurious volume-conduction-based interactions. Further details on iCOH can be found elsewhere (Nolte et al., 2004; Nolte & M€ uller, 2010).

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

Interhemispheric functional interactions 3275 where std(iCOHjn) is estimated by the jackknife method, as described in detail by Nolte et al. (2008). Similar to the estimation of the standard z-score, values of iCOHd > 2 are considered as significant (indicating P < 0.05), as was experimentally validated for connectivity measures in simulations (Nolte et al., 2008; Nolte & M€ uller, 2010). In the present study we adopted a significance level of iCOHd ≥ 2.58 (corresponding to P ≤ 0.01). By ‘detectability’ we refer to the standardized iCOH values (iCOHd). The magnitude of iCOHd basically depends on the strength of neural synchronization, its consistency over time and the presence of bidirectional neural interactions. While macroscopic LFP recordings, however, cannot distinguish these different aspects, a larger value of iCOHd does reflect an increased likelihood to detect neural interactions (Hohlefeld et al., 2013), and hence we use the term ‘detectability’. Additionally, iCOHd was also calculated for shuffled data, by shuffling (once) the epochs of a channel out of a given connection, thus destroying the temporal relationship between the two signals. Spurious effects of analog-to-digital conversion were excluded (for details see Hohlefeld et al., 2013). The same procedure was applied to the standard COH measure (referred to as COHd).

In the present study iCOH was calculated samplewise for each connection (length of the artifact-free recording – max. 14 min, divided in non-overlapping segments of 2-s duration, corresponding to a frequency resolution of 0.5 Hz, max. n = 420 epochs). Subsequently, by iCOH we always refer to the absolute value |iCOH|. To facilitate the comparison of the present data set with previous studies (De Solages et al., 2010; Little et al., 2013a) we also calculated the standard coherence (Nolte et al., 2004; Srinivasan et al., 2007). By COH we refer to the absolute value of coherency (Nolte et al., 2004). By the term ‘interhemispheric STN-LFP coherence’ we refer to the iCOH measure throughout the text, unless noted otherwise. Furthermore, the term (i)COH refers to both measures, while the terms iCOH and COH refer to the respective single measure. Detectability and significance of iCOH ICOH was standardized by an estimate of its standard deviation (Nolte et al., 2008): iCOHd ¼ jiCOH=stdðiCOHjn Þj A

B

E

F

C

D

G

H

I

Fig. 1. Single patient example of interhemispheric STN-LFP coherence. (A,B) Power spectra of two bipolar channels in the left and right STN (channel 12) for OFF and ON levodopa for patient 2. (C,D) Relative power (percentage) of the spectral power shown in A and B. (E, F) Coherence (COH) and of the imaginary part of coherency (iCOH) of the connection L12–R12 between the left and right STN, respectively, are shown for patient 2. (G,H) Detectability of iCOH and COH estimated by the jackknife method (indicated by (i)COHd) for connection L12–R12 of the same patient. Values above the threshold of 1.96 (dashed line) indicate P < 0.05. (I) Enlarged version of H. Highly significant iCOH peaks are present up to high beta frequency ranges. The dashed line at 1.96 indicates P = 0.05. OFF, overnight withdrawal of levodopa; ON, after single dose levodopa administration. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

3276 F. U. Hohlefeld et al. Figure 1 shows the power spectral density, relative power, (i)coherence, and the detectability of (i)coherence of the connection L12–R12 for a single patient (case 2, see table 1 in Hohlefeld et al., 2012 for clinical details). Crucially, as can be seen in this example of a single patient, the standard measure COH(d) can be more than twice iCOH(d) (similar to intra-STN coherence, see Hohlefeld et al., 2013), especially in the lower frequency range (approx. < 15 Hz). However, iCOHd revealed a non-zero and significant (P < 0.01) imaginary part of coherency, which indicates genuine time-lagged synchronization between both STN-LFP. iCOH in low and high beta frequency ranges Previous studies have suggested a functional distinction between low and high beta oscillations, approx. in the range 10–20 and 21–30 Hz, respectively (K€uhn et al., 2006b; Marceglia et al., 2011), with differential reactivity of spectral power to levodopa (Priori et al., 2004; Lopez-Azcarate et al., 2010; Hohlefeld et al., 2013), which was observed also in the case of interhemispheric STN-LFP coherence (Little et al., 2013a). Effects of levodopa on intra-nuclear spectral power in the present data set have been reported elsewhere (Hohlefeld et al., 2012, 2013), which were in line with previous studies (Brown & Williams, 2005). In the low (10–20 Hz) and in the high beta band (21–30 Hz) the mean (i) COHd value (across samples) was calculated for each connection, referred to as (i)COHdm. The iCOHdm values were not preselected on the basis of COHdm. Subsequently, two analysis approaches were used: Approach 1 – spatial average across connections. For each patient the (i)COHdm values from all available connections were averaged (no pre-selection of connections based on their significance), to compensate for differences in electrode localization and missing connections due to artifacts across patients (Hohlefeld et al., 2012, 2013), resulting in a single value for a patient in a given condition. Subsequently, (i)COHdm refers to this spatial average, except in a single patient’s coherence curves or otherwise noted. Approach 2 – selecting a single connection. To facilitate the comparison of our data with previous studies of interhemispheric STN-LFP coherence, which pre-selected a single connection based on the channels showing the strongest spectral power (De Solages et al., 2010; Little et al., 2013a), we analysed our data in a similar way: For each patient and channel the spectral power was normalized as the percentage of the total power between 5 and 90 Hz (excluding a 48–52 Hz band; line noise), which is referred to as ‘relative power’. In each separate condition the channel with the maximal relative power in the range 10–32 Hz was selected separately in each STN, and these two channels constituted a connection. We set a slightly lower bound (10 instead of 13 Hz) than previous studies of interhemispheric STNLFP coherence (De Solages et al., 2010; Little et al., 2013a), as in our data set several cases showed pronounced beta peaks slightly below 13 Hz. The 10–32 Hz band selection is, however, in the range of previous studies (Brown & Williams, 2005; K€ uhn et al., 2006a, 2009; Ray et al., 2008; De Solages et al., 2010; Giannicola et al., 2010; Little et al., 2013a). The beta peak was selected by visual inspection of the oscillatory spectrum based on the fast Fourier transform (Welch method, 1-s Hanning window). Visual inspection was utilized because the LFP spectrum in the present data set had a prominent low-frequency component; therefore, an automatic detection of smaller beta peaks could be erroneous (Hohlefeld et al., 2012, 2013).

Advantages of the spatial average approach. While the selection of a single ‘best’ connection, e.g. based on the two channels with maximal spectral beta power (K€ uhn et al., 2009; De Solages et al., 2010; Little et al., 2013a), might enhance the signal-tonoise ratio and thus might facilitate the detection of levodopainduced modulations of neural synchronization, this approach has the crucial implication of being associated with a selection bias. Such bias will be necessarily present if the number of connections differs within or across patients, i.e. if channels/connections had to be removed due to artifacts or due to suboptimal electrode placement. Consequently, after removal the probability of choosing a single connection differs across data sets. Moreover, the selection probability is also confounded by differences in electrode placement (Chen et al., 2006; Pogosyan et al., 2010) and different orientations of neural sources, within both STN and across patients. These disadvantages can be circumvented by using a spatial average across channels/connections, which represents a robust regional measure of neural activity and is less sensitive to data heterogeneity across patients, as employed for STN-LFP recordings by others (Litvak et al., 2011) and us (Hohlefeld et al., 2012, 2013), similar to spatial averaging across scalp sensors in electroencephalography/magnetoencephalography research (Lehmann & Skrandies, 1980; Murray et al., 2008). Statistical analysis The (i)COHdm values were subjected to a repeated-measures analysis of variance (ANOVA; post hoc testing: Bonferroni), separately for the low and high beta band, with the factors ‘condition’ (OFF vs. ON) and ‘measure’ (COHdm vs. iCOHdm). Differences between the number of total connections (all available connections pooled across patients) for the OFF and ON condition were analysed with binomial testing. To determine a possible relationship with clinical scores, the (i)COHdm values were correlated (Pearson) with the presurgery motor UPDRS scores separately in the OFF and ON condition. In the case of violation of prerequisites (e.g. normality) the analogous non-parametric analyses were utilized (Friedman analysis, Wilcoxon’s signed rank test, Spearman’s rank correlation). Averaged values are given as mean  standard of the mean (SEM).

Results The analyses were performed for eight patients. After artifact rejection and anatomical evaluation of the lead contacts (see above) pooled across patients in each condition, n = 50 out of 72 (69%) connections were available in the OFF condition and n = 53 out of 72 (74%) connections were available in the ON condition. Thus, in total (across both medication conditions) 28.47% of connections (n = 41 out of 144) had to be removed, out of which 15.97% (n = 23 out of 144) were removed due to technical/physiological noise, and 12.5% (n = 18 out of 144) due to suboptimal electrode placement (in two patients) according to the anatomical reconstruction of the electrode trajectory (cf. Materials and Methods). For the spatial average the mean number of available connections per patient in the OFF condition was 6.25  0.675 (range n = 4–9 connections) and in the ON condition 6.625  0.754 (range n = 4–9 connections). Significant interhemispheric STN-LFP coherence The first aim of the present study was to investigate the existence of significant interhemispheric coherence in low (10–20 Hz) and high (21–30 Hz) beta oscillations, which was confirmed by the present

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

Interhemispheric functional interactions 3277 results (based on iCOH), thus being in line with previous studies utilizing the standard coherence (De Solages et al., 2010; Silchenko et al., 2010; Little et al., 2013a). The iCOH revealed a considerable number of significant connections (fewer compared to the standard coherence). The statistical results are presented below. Spatial average approach Figure 2 shows the percentage of significant connections (P < 0.01), pooled across patients, for each condition separately in the 10–20 and 21–30 Hz bands. COHdm revealed on average (across all conditions and bands) 79% of significant connections (range 56–93%) with P < 0.01, whereas iCOHdm revealed on average (across all conditions and bands) 30% of significant connections (range 8– 40%) with P < 0.01. Moreover, when each condition and band was considered separately, iCOHdm showed 45–53% fewer significant connections compared with COHdm, as confirmed by statistical testing (see Fig. 2 and below). In the 10–20 Hz band binomial testing revealed a significant difference between the number of significant connections for COHdm and iCOHdm in both the OFF condition (COHdm vs. iCOHdm: 45 vs. 19, P < 0.01) and in the ON condition (COHdm vs. iCOHdm: 49 vs. 21, P < 0.01). Almost all patients contributed at least one significant connection to each data set (OFF COHdm vs. iCOHdm: eight vs. seven patients; ON COHdm vs. iCOHdm: seven vs. seven patients). Similarly, in the 21–30 Hz band binomial testing revealed a significant difference between the number of significant connections for COHdm and iCOHdm in both the OFF condition (COHdm vs. iCOHdm: 28 vs. four, P < 0.01) and the ON condition (COHdm vs. iCOHdm: 42 vs. 18, P < 0.01). Almost all patients contributed at least one significant connection to the majority of data sets (OFF COHdm vs. iCOHdm: seven vs. one patient; ON COHdm vs. iCOHdm: eight vs. six patients). There were no significant connections in the case of (i)COHdm calculated on shuffled signals in any of the measures, frequency bands and conditions, showing that the significance threshold for (i)COH at P < 0.01 was sufficiently conservative, and thus did not require a correction for multiple comparisons. A

Interhemispheric iCOH in the 10–20 Hz band OFF medication relates to motor UPDRS The second aim was to investigate the relationship between motor symptoms and interhemispheric neural synchronization. This relationship was selectively expressed in low beta oscillations but not in high beta oscillations in line with our hypothesis. The statistical results are presented below. Spatial average approach In the 10–20 Hz band OFF medication there was a significant correlation across patients between the UPDRS scores and iCOHdm OFF medication (Pearson r = 0.854, P = 0.0145; uncorrected; Bonferroni threshold 0.05/2 = 0.025 for both frequency bands), see Fig. 3. However, for COHdm this correlation was not significant (Pearson r = 0.001, P = 0.999). Correlations for the ON condition were not significant for any of the measures (0.13 < P < 0.41). In the 21– 30 Hz band there were no significant correlations for any of the measures and conditions (0.4 < P < 0.86). Additionally, the OFF– ON differences in UPDRS scores were correlated with the corresponding OFF–ON differences in (i)COHdm, without significant correlations in any measure and frequency band (0.11 < P < 0.78). Single connection approach In both the 10–20 and the 21–30 Hz bands there were no significant correlations for any of the measures and conditions (P-values across all correlations: 0.15 < P < 0.92). Additionally, the OFF–ON differences in UPDRS scores were not correlated with the corresponding OFF–ON differences in (i)COHdm in any measure or frequency band (0.26 < P < 0.97). Moreover, the non-significant results remained unchanged after including 12.5% of connections that were previ-

B

Fig. 2. Significant interhemispheric STN-LFP connections across patients. (A) Total significant (P < 0.01) connections (displayed as percentage for convenience) pooled across patients for the low beta band (10–20 Hz). Standard coherence revealed a more than two-fold larger number of significant connections, compared with the imaginary part of coherency, as confirmed by binomial testing (indicated by the P-values). The white digits at the bottom of each bar indicate the number of patients contributing at least one significant connection to the total data set. (B) Total significant (P < 0.01) connections (percentage) pooled across patients for the high beta band (21– 30 Hz). (i)COHdm, mean detectability of (i)COH in the denoted frequency range; OFF, overnight withdrawal of levodopa; ON, after single dose levodopa administration.

Fig. 3. Relationship between interhemispheric STN-LFP coherence and motor symptoms. Significant Pearson correlation between the pre-surgery motor UPDRS and iCOHdm (10–20 Hz, spatial average) in the OFF condition. The line indicates the least squares fit. Dots represent single patients. The positive correlation indicates that across patients worse motor symptoms relate to higher detectability of interhemispheric iCOH in the low beta band. (i)COHdm, mean detectability of (i)COH in the denoted frequency range; OFF, overnight withdrawal of levodopa; ON, after single dose levodopa administration.

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

3278 F. U. Hohlefeld et al. A

B

Fig. 4. Grand-average of interhemispheric STN-LFP coherence. (A) Raw values of coherence and the absolute imaginary part of coherency are shown, averaged across all available connections for each condition. (B) Detectability of coherence and the imaginary part of coherency are shown (sample-wise corresponding to A), averaged across all available connections for each condition. Absolute values above the threshold of 2.58 (dashed line) indicate P < 0.01. Grey shadings indicate the low beta (10–20 Hz) and high beta (21–30 Hz) band. OFF, overnight withdrawal of levodopa; ON, after single dose levodopa administration.

ously removed due to suboptimal electrode positioning (cf. Materials and Methods). More details are given in the Appendix.

A

B

Levodopa administration modulates interhemispheric STN-LFP coherence in the 21–30 Hz band The third aim was to investigate whether interhemispheric STN-LFP coherence was modulated by levodopa administration, being expressed in low rather than in high beta oscillations. The present results showed, however, a selective increase of interhemispheric coherence in the high beta band but no significant changes in the low beta band. The statistical results are presented below. Figure 4 shows the grand average of the (i)coherence curves across patients. The grey shadings indicate the low beta (10–20 Hz) and high beta (21–30 Hz) bands, in which connectivity values were averaged for statistical comparison. The results of statistical testing across patients are shown in Fig. 5. Spatial average approach In the 10–20 Hz (see Fig. 5A) band the repeated-measures ANOVA revealed a significant main effect of ‘measure’ (F1,7 = 22.52, P = 0.002) with COHdm > iCOHdm (6.587  0.526 vs. 2.756  0.246). Neither the factor ‘condition’ nor the interaction ‘condition 9 measure’ was significant (P > 0.38). In the 21–30 Hz band (see Fig. 5B) the Friedman analysis revealed significant differences (v2(3) = 19.5, P < 0.001), where post hoc tests (Wilcoxon’s signed rank test; uncorrected P-values are presented here; Bonferroni threshold 0.05/4 = 0.0125) showed that COHdm > iCOHdm in both conditions (OFF: 3.375  0.561 vs. 1.75  0.357, P = 0.012; ON: 4.692  0.614 vs. 2.48  0.453; P = 0.012). Furthermore, post hoc testing showed that ON > OFF in both measures (COHdm: 4.692  0.614 vs. 3.375  0.561, P = 0.025; iCOHdm: 2.48  0.453 vs. 1.75  0.357, P = 0.017).

Fig. 5. Grand-average of interhemispheric STN-LFP coherence (spatial average). (A) Grand-average of the detectability of standard coherence and imaginary part of coherency in the low beta band (10–20 Hz). The detectability of the standard coherence was more than twice that of the imaginary part of coherency, as confirmed by repeated-measures ANOVA with a significant main effect ‘measure’ (indicated by the P-value). (B) Grand-average of the detectability of standard coherence and imaginary part of coherency in the high beta band (21–30 Hz). The Friedman analysis revealed that the detectability of standard coherence was almost twice that of the imaginary part of coherency (post hoc Wilcoxon’s signed rank test), and there was a significant increase of iCOHdm and COHdm in the ON compared with the OFF condition (post hoc Wilcoxon’s signed rank test). (i)COHdm, mean detectability of (i)COH in the denoted frequency range; OFF, overnight withdrawal of levodopa; ON, after single dose levodopa administration.

COHdm > iCOHdm) was not affected by including 12.5% of connections that were previously removed due to suboptimal electrode positioning (cf. Materials and Methods). More details are given in the Appendix.

Discussion Single connection approach The results were similar to those obtained with the spatial average approach (see above). Additional analysis revealed that the presence of significant results in the 21–30 Hz band (i.e. ON > OFF and

We demonstrated the presence of genuine neural synchronization in the range 10–30 Hz between the left and right STN in post-operative LFP recordings. This interhemispheric functional connectivity was revealed by a non-zero iCOH, which is only sensitive to time-

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

Interhemispheric functional interactions 3279 lagged neural interactions and thereby excludes effects of volume conduction, which was not addressed in previous studies of interhemispheric STN-LFP coherence (De Solages et al., 2010; Silchenko et al., 2010; Little et al., 2013a). In the present study interhemispheric STN-LFP 10–20 Hz coherence in the OFF medication state correlated with worse motor symptoms across patients. Moreover, interhemispheric coherence was increased with ON medication in 21–30 Hz oscillations compared with OFF medication. These results highlight the relevance of macroscopic subcortical–cortical neural interactions across hemispheres in beta frequency ranges for PD pathology, and might contribute to our understanding of unilateral DBS efficacy, as discussed below. Contributions to interhemispheric STN-LFP coherence The present data extend our previous finding of genuine intra-STN functional connectivity based on iCOH (Hohlefeld et al., 2013) and confirmed findings of interhemispheric connectivity between both STNs, as demonstrated by standard coherence in 10–30 Hz LFP oscillations in patients with PD (De Solages et al., 2010; Silchenko et al., 2010; Little et al., 2013a). The interhemispheric STN-LFP coherence is an indirect indicator probably of polysynaptic cross-hemispheric neural interactions (Little et al., 2013a), as direct subcortical anatomical projections between both STNs are not known (Carpenter et al., 1981; De Solages et al., 2010; Little et al., 2013a). The demonstration of interhemispheric STN-LFP coherence complements previous data supporting the notion of long-distance neural synchronizations in the beta frequency ranges via (sub)cortical loops, as suggested by the demonstration of cortico-cortical coherence (Silberstein et al., 2005; Litvak et al., 2011) and cortico-STN coherence in patients with PD (Lalo et al., 2008; Litvak et al., 2011). The STN receives main inputs via the globus pallidus externus (GPe) and input from cortical motor regions via the hyperdirect pathway (Brunenberg et al., 2012). Efferent projections from the STN reach the globus pallidus internal (GPi) and substantia nigra pars reticulata (SNr), i.e. the major output structures of the basal ganglia via the thalamus (Parent & Hazrati, 1995; Hamani et al., 2004). Given these considerations, in general diverse anatomical projections are likely to mediate the observed interhemispheric STN-LFP coherence, as discussed below. (i)Cortico-subcortical projections. We might assume the existence of cortical sources being synchronized across both hemispheres via the corpus callosum (Gooijers & Swinnen, 2014), and these synchronous activity patterns are ipsilaterally projected down to both STNs via the hyperdirect pathway (Brunenberg et al., 2012) and/or via the striatum/pallidum (Parent & Hazrati, 1995; Plenz & Kital, 1999). This notion would be supported by the existence of robust cortico-STN coherence in beta frequency ranges, as evident in simultaneous STN-LFP and electroencephalographic or magnetoencephalographic studies (Silberstein et al., 2005; Lalo et al., 2008; Litvak et al., 2011), with the cortex leading neural activity in STN (Lalo et al., 2008; Litvak et al., 2011). (ii)Subcortico-cortico-subcortical projections. We might also consider a more complex scenario, in which the driving force underlying interhemispheric STN synchronization might rather be of subcortical origin, i.e. the activity from both STN via the STN-GPi/ SNr route culminates in thalamo-cortical projections, being bilaterally synchronized via the corpus callosum. Another indirectly synchronizing structure could be the thalamus per se via thalamic projections from bilateral GPi and SNr, which both receive excitatory input from the STN (Novak et al., 2009). In general, the spatio-temporally widespread neural coupling between both basal ganglia might be also realized by a combination of cortically and

subcortically driving sources (as discussed above). While we cannot differentiate these possibilities based on coherence measures, the complexity of neural mechanisms underlying interhemispheric synchrony between the bilateral basal ganglia nevertheless highlights the relevance of distributed oscillatory beta networks for PD pathology (Litvak et al., 2011; Little et al., 2013a).

Functional distinction of low and high beta oscillations I – relationship to motor symptoms and levodopa administration The clinically most relevant finding of our study was the significant correlation of interhemispheric iCOH in low beta oscillations (10– 20 Hz) with the severity of UPDRS scores across patients in the OFF medication condition (see Fig. 3), but which was not significant for high beta oscillations (21–30 Hz). In general, this result implies that the relationship between motor symptoms and interhemispheric coherence is mediated by time-lagged neural synchronizations in spatially widespread beta networks. Interestingly, previous studies have reported opposing results on the relationship between motor symptoms with OFF medication and beta oscillations in STN-LFP – some studies found no significant correlations (power: Ray et al., 2008; K€ uhn et al., 2009; percentage of beta oscillatory cells: Weinberger et al., 2006) whereas others did (LFP complexity: Chen et al., 2010; power: L opez-Azcarate et al., 2010; Degen et al., 2014). However, these studies investigated broad beta frequency ranges (approx. 8– 35 Hz) without dividing into beta sub-bands, which might have masked clinically relevant correlations. Indeed, previous STN-LFP data suggested a functional distinction (rather than spatial segregation: Hirschmann et al., 2011; but see also Fogelson et al., 2006) between the low and high beta band, as reflected in a number of heterogeneous findings. (i) Only in low beta oscillations were a levodopa-induced decrease of interhemispheric standard coherence (Little et al., 2013a) and a decrease of intra-nuclear spectral power (Priori et al., 2004; L opez-Azcarate et al., 2010; Hohlefeld et al., 2013) observed. (ii) In contrast, only in high beta oscillations did levodopa-induced motor improvement correlate with increased intra-nuclear coherence after levodopa administration (Hohlefeld et al., 2013), and worse UPDRS scores correlate with decreased amplitude variability recorded from a single nucleus in the OFF medication state (Little et al., 2012). However, in the case of interhemispheric STN-LFP coherence we have observed a correlation with motor scores only in low beta oscillations (10–20 Hz, OFF medication), whereas levodopa administration rather increased interhemispheric coherence (both iCOH and COH) in high beta oscillations (21–30 Hz), while the latter is at odds with previous data (Little et al., 2013a; possible technical reasons are discussed below). Given the acknowledged complexity of beta oscillations (Brown & Eusebio, 2008; Eusebio et al., 2012), the present findings highlight that frequency-specific contributions of beta sub-bands to neural pathology in PD remains to be further clarified (Little et al., 2012). A step toward an improved understanding of the evident distinction between low and high beta oscillations might be to investigate in future studies whether the interrelationship between both rhythms (e.g. amplitude co-modulation, cross-frequency coupling) relates to the severity of motor symptoms. Functional distinction of low and high beta oscillations II – potential confounds to the frequency-specific modulation of interhemispheric coherence after levodopa administration In general, we might hypothesize that the absence of a clinical correlation between interhemispheric coherence and motor symptoms in

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

3280 F. U. Hohlefeld et al. the ON medication condition might be related to the variability of levodopa responses, for example in terms of motor symptom reduction (Jansson et al., 1998), and pharmacokinetic and chronic pharmacodynamic aspects (Obeso et al., 1989; Chan et al., 2005). In contrast to a baseline ‘floor’ effect in the OFF medication condition, levodopa might induce variable and heterogeneous modulations of neural connectivity, thus possibly diluting an observable clinical correlation at the time point of the experiment. Furthermore, we did not observe a significant correlation between the OFF–ON differences in (i)COHdm and the OFF–ON differences in motor scores; however, the presence of a correlation between motor symptoms and (i) COHdm in the OFF medication condition does not automatically imply a correlation between the corresponding measures when calculating the OFF–ON differences. This is because the latter is also sensitive to the efficacy of the drug treatment, while the former is based on a neurophysiologic relationship between neuronal dynamics and the clinical manifestation of the disease. In general, we might tentatively hypothesize that increased interhemispheric STNLFP coherence in the high beta oscillations after levodopa administration could reflect (i) a direct levodopa-associated effect or (ii) a release of rather otherwise suppressed ‘physiological’ (i.e. less disease-affected) neural synchronizations. These aspects, however, cannot be differentiated by the present study settings. Furthermore, another potential confound might be surgery-related stun effects (Ray et al., 2008; Mann et al., 2009; Pogosyan et al., 2010; Rosa et al., 2010). However, as the order of the ON and OFF recordings was counterbalanced across patients, but seven out of eight patients had increased interhemispheric STN-LFP coherence in the high beta frequency band ON levodopa (results of single patients not shown; cf. Fig. 5B for the statistical significance across patients), the resolution of stun effects over time probably did not contribute to the frequency-band specificity of the present result. In general, it might also be the case that stun effects differentially affect low and high beta oscillations, such that levodopa-induced modulations of interhemispheric coherence in low beta oscillations might be less pronounced (due to floor effects); however, these situations cannot be disentangled by the present study. Differences of the present results from previous studies of interhemispheric STN-LFP coherence To our knowledge, only a few studies have investigated interhemispheric STN-LFP coherence (utilizing, however, only the standard coherence measure). Two studies investigated interhemispheric betacoherence only with OFF medication, in a large (n = 28 patients; De Solages et al., 2010) and a small data set (n = 2 patients; Silchenko et al., 2010). The latter study showed phase differences between both STN of approx. 3–300 ms (for 3–30 Hz oscillations, respectively; Silchenko et al., 2010), which despite potential confounds due to volume conduction is in line with our finding of genuine non-zero lagged interhemispheric neural synchronization (revealed by a nonzero imaginary part of coherency). Little et al. (2013a) investigated effects of levodopa medication on interhemispheric connectivity in patients with PD, and showed a decrease of standard coherence OFF medication in 13–20 Hz oscillations without significant changes in the high beta band. However, in the present study we observed a significant levodopa-induced increase of interhemispheric coherence only in the high beta frequency band (21–30 Hz). These divergent results might be due to several differences between our study and that of Little et al. (2013a). (i)Smaller sample size. In our study the data set was much smaller (n = 8 patients vs. n = 23 patients). This might have prevented the

detection of significant effects due to different population sampling; yet our sample size is comparable to other STN-LFP studies (Fogelson et al., 2006; K€ uhn et al., 2006b; Trottenberg et al., 2007; Ray et al., 2008; Giannicola et al., 2010; Silchenko et al., 2010). However, despite the small sample size we nevertheless demonstrated a significant levodopa-induced modulation of iCOH and standard coherence in the high beta band, but which was not detected with a larger number of patients in Little et al. (2013a). (ii)Anatomical electrode rejection. Another factor contributing to the differing results could be that we used a reconstruction of the patient-individual electrode tracts to reject connections with suboptimal electrode placement (cf. Materials and Methods), which was not performed by Little et al. (2013a,b). The rejection procedure emphasizes the contribution of neural signals that originate rather focally in the STN (or in the very close vicinity; see Hohlefeld et al., 2012, 2013 for a detailed discussion on electrode placement). This might enhance the detection of levodopa-induced effects in neural synchronization, given that multiple studies showed that oscillatory beta activity indeed originates rather within the STN (K€ uhn et al., 2005; Trottenberg et al., 2007; Zaidel et al., 2010; de Solages et al., 2011). We observed that in two patients, in whom some connections had to be removed due to suboptimal anatomical electrode positioning (cf. Materials and Methods), the selection of the ‘best’ connection based on the maximal spectral beta power (according to the approach of Little et al., 2013a) changed after removal (data not shown). Although the levodopa-induced increase of interhemispheric coherence in high beta oscillations was significant with and without removing such ‘suboptimal’ connections (and thus the anatomical rejection probably not being a statistically relevant factor in the present data), the observation in these two patients suggests that effects of suboptimal electrode positioning (i.e. picking up source activity rather in neighboring structures) on physiological measures, such as spectral power, needs further evaluation in larger data sets. (iii)Selection bias. In addition to being sensitive to differences in electrode positioning, pre-selecting a single connection (Little et al., 2013a) might introduce a selection bias when connections have to be excluded due to artifacts or electrode misplacement (Chen et al., 2006; Pogosyan et al., 2010). However, a spatial average across channels/connections represents a robust regional measure of neural activity and is less sensitive to data heterogeneity across patients (Litvak et al., 2011; Hohlefeld et al., 2012, 2013). Moreover, in the present study a clinically relevant correlation of motor scores and interhemispheric coherence was only observed in the ‘spatial average’ approach and not in the ‘single connection’ approach. While in the present data set we cannot differentiate whether this is due to technical reasons (i.e. selection bias due to removal of electrodes/connections based on suboptimal anatomical placement and physiological/technical noise) and/or due to physiological reasons (e.g. clinically relevant interhemispheric coherence being more widespread in the nucleus across several channels), our results nevertheless represent further evidence of the practical usefulness of the spatial average approach.

Possible overestimation of functional connectivity with the standard coherence measure ‘By looking at the imaginary part of coherency, we take an extreme position. We see, at best, only half of the picture. But that half is safe.’ (Nolte et al., 2004; p. 2306). Given that iCOH is only sensitive to non-zero lagged neural synchronization and therefore necessarily would miss volume conduction and true zero-lagged neural synchronization (cf. Materials and Methods), it becomes evident that

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

Interhemispheric functional interactions 3281 this measure is reasonably complementary to the standard coherence (Nolte et al., 2004). Moreover, the advantages of iCOH outweigh its limitations depending on the research question. In the present study our main interest was to investigate the presence of ‘genuine’ (i.e. not due to volume conduction) synchronization between interhemispheric STN-LFP, which was confirmed by the iCOH measure revealing up to 40% significant connections (see Fig. 2). Moreover, we were interested in possible differences between the iCOH and COH measures, when being calculated on the very same signals. Statistical testing confirmed that iCOH was about half the standard coherence (in the case of raw values, detectability and number of significant connections), thus confirming our observations in the case of intra-STN coherence (Hohlefeld et al., 2013). However, based on these observations we cannot differentiate to what extent this massive decrease of iCOH is due to removing volume conduction per se or due to removing genuine zero-lagged neural interactions (cf. Materials and Methods). Finally and most crucially with respect to the clinical relevance of utilizing coherence measures in the context of deep brain recordings, the present results showed that only iCOH correlated with the severity of motor symptoms across patients in the OFF condition (see Fig. 3; as discussed above). For the standard coherence, however, there was no significant correlation with clinical scores. Consequently, these results suggest that utilizing the iCOH measure has both theoretical (cf. Materials and Methods) and practical advantages for electrophysiological connectivity studies investigating neural mechanisms underlying PD pathology. Implications of interhemispheric STN-LFP coherence for unilateral DBS Previous studies have reported clinically effective unilateral DBS to reduce bilateral motor symptoms (Kumar et al., 1999; Chung et al., 2006; Hershey et al., 2008; Brun et al., 2012). Moreover, it has been shown that unilateral STN stimulation modulates single-cell activity and LFPs in the non-stimulated STN, which can be due to subcortico-cortical antidromic and orthodromic activation spread (Novak et al., 2009; Brun et al., 2012). Our results support this causal evidence on a functional level by showing interhemispheric STN-LFP synchronization, which is indicative of long-distance neural interactions between the basal ganglia (as discussed above). Further evidence for interhemispheric neural synchronization might be considered in future studies, for example by unilateral stimulation of the STN and measuring directly after cessation of the stimulation the volume conduction-free interhemispheric directionality of information flow (Nolte et al., 2008). Here, the congruence between the stimulation site and information flow between both nuclei would represent causal evidence for neural synchronization between both basal ganglia, which might underlie the improvement in bilateral motor symptoms, as behaviorally observed after unilateral STN-DBS (Chung et al., 2006). Finally, the present findings suggesting a functional distinction between low and high beta oscillations in terms of interhemispheric STN-LFP coherence (as discussed above) might be of relevance for developing adaptive DBS schedules (Little et al., 2013b; Hariz, 2014) taking into account different beta sub-bands.

Conclusions We have demonstrated the presence of genuine, non-zero lagged interhemispheric neural synchronization (10–30 Hz oscillations) in STN-LFP recordings obtained from patients with PD, as revealed by a non-zero iCOH. The interhemispheric STN-LFP coherence corre-

lated with the severity of motor symptoms OFF medication (10– 20 Hz) and was increased after levodopa administration (21–30 Hz). These results highlight the significance of long-distance neural interactions between both hemispheres for PD pathology, indicate a clinically relevant distinction between oscillatory beta sub-bands and are in line with previous findings of efficacious unilateral DBS.

Acknowledgements The research was supported by the German Research Foundation (DFG) grant no. KFO 247. We thank the anonymous reviewers for their constructive comments. The authors report no conflict of interest.

Abbreviations COH, coherence; COHd, detectability of COH; DBS, deep brain stimulation; iCOH, imaginary part of coherency; iCOHd, detectability of iCOH; LFP, local field potential; PD, Parkinson’s disease; SEM, standard error of the mean; STN, subthalamic nucleus; UPDRS, Unified Parkinson’s Disease Rating Scale.

References Benabid, A.L., Chabardes, S., Mitrofanis, J. & Pollak, P. (2009) Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson’s disease. Lancet Neurol., 8, 67–81. Brown, P. & Eusebio, A. (2008) Paradoxes of functional neurosurgery: clues from basal ganglia recordings. Movement Disord., 23, 12–20. Brown, P. & Williams, D. (2005) Basal ganglia local field potential activity: character and functional significance in the human. Clin. Neurophysiol., 116, 2510–2519. Brun, Y., Karachi, C., Fernandez-Vidal, S., Jodoin, N., Grabli, D., Bardinet, E., Mallet, L., Agid, Y., Yelnik, J. & Welter, M.-L. (2012) Does unilateral basal ganglia activity functionally influence the contralateral side? What we can learn from STN stimulation in patients with Parkinson’s disease. J. Neurophysiol., 108, 1575–1583. Brunenberg, E.J.L., Moeskops, P., Backes, W.H., Pollo, C., Cammoun, L., Vilanova, A., Janssen, M.L.F., Visser-Vandewalle, V.E.R.M., ter Haar Romeny, B.M., Thiran, J.-P. & Platel, B. (2012) Structural and resting state functional connectivity of the subthalamic nucleus: identification of motor STN parts and the hyperdirect pathway. PLoS One, 7, e39061. Carpenter, M.B., Carleton, S.C., Keller, J.T. & Conte, P. (1981) Connections of the subthalamic nucleus in the monkey. Brain Res., 224, 1–29. Chan, P.L.S., Nutt, J.G. & Holford, N.H.G. (2005) Importance of within subject variation in levodopa pharmacokinetics: a 4 year cohort study in Parkinson’s disease. J. Pharmacokinet. Phar., 32, 307–331. Chen, C.C., Pogosyan, A., Zrinzo, L.U., Tisch, S., Limousin, P., Ashkan, K., Yousry, T., Hariz, M.I. & Brown, P. (2006) Intra-operative recordings of local field potentials can help localize the subthalamic nucleus in Parkinson’s disease surgery. Exp. Neurol., 198, 214–221. Chen, C.C., Hsu, Y.T., Chan, H.L., Chiou, S.M., Tu, P.H., Lee, S.T., Tsai, C.H., Lu, C.S. & Brown, P. (2010) Complexity of subthalamic 13–35 Hz oscillatory activity directly correlates with clinical impairment in patients with Parkinson’s disease. Exp. Neurol., 224, 234–240. Chung, S.J., Jeon, S.R., Kim, S.R., Sung, Y.H. & Lee, M.C. (2006) Bilateral effects of unilateral subthalamic nucleus deep brain stimulation in advanced Parkinson’s disease. Eur. Neurol., 56, 127–132. De Solages, C., Hill, B.C., Koop, M.M., Henderson, J.M. & Bronte-Stewart, H. (2010) Bilateral symmetry and coherence of subthalamic nuclei beta band activity in Parkinson’s disease. Exp. Neurol., 221, 260–266. Degen, K., Neumann, W.J., H€ ubl, J., Br€ ucke, C., Schneider, G.-H. & K€ uhn, A. (2014) Die Power der oszillatorischen 8–30 Hz STN-Aktivit€at korreliert mit dem motorischen Defizit im UPDRS III bei Patienten mit idiopathischem Parkinson-Syndrom. Klin. Neurophysiol., 45, V17. Eusebio, A., Cagnan, H. & Brown, P. (2012) Does suppression of oscillatory synchronisation mediate some of the therapeutic effects of DBS in patients with Parkinson’s disease? Front. Integr. Neurosci., 6, 47. Fogelson, N., Williams, D., Tijssen, M., van Bruggen, G., Speelman, H. & Brown, P. (2006) Different functional loops between cerebral cortex and the subthalamic area in Parkinson’s disease. Cereb. Cortex, 16, 64–75. Friston, K.J. (1994) Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp., 2, 56–78.

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

3282 F. U. Hohlefeld et al. Giannicola, G., Marceglia, S., Rossi, L., Mrakic-Sposta, S., Rampini, P., Tamma, F., Cogiamanian, F., Barbieri, S. & Priori, A. (2010) The effects of levodopa and ongoing deep brain stimulation on subthalamic beta oscillations in Parkinson’s disease. Exp. Neurol., 226, 120–127. Gooijers, J. & Swinnen, S.P. (2014) Interactions between brain structure and behavior: the corpus callosum and bimanual coordination. Neurosci. Biobehav. R., 43C, 1–19. Hamani, C., Saint-Cyr, J.A., Fraser, J., Kaplitt, M. & Lozano, A.M. (2004) The subthalamic nucleus in the context of movement disorders. Brain, 127, 4–20. Hariz, M. (2014) Deep brain stimulation: new techniques. Parkinsonism Relat. D., 20, S192–S196. Hershey, T., Wu, J., Weaver, P.M., Perantie, D.C., Karimi, M., Tabbal, S.D. & Perlmutter, J.S. (2008) Unilateral vs. bilateral STN DBS effects on working memory and motor function in Parkinson disease. Exp. Neurol., 210, 402–408. € Hirschmann, J., Ozkurt, T.E., Butz, M., Homburger, M., Elben, S., Hartmann, C.J., Vesper, J., Wojtecki, L. & Schnitzler, A. (2011) Distinct oscillatory STN-cortical loops revealed by simultaneous MEG and local field potential recordings in patients with Parkinson’s disease. NeuroImage, 55, 1159–1168. Hohlefeld, F.U., Huebl, J., Huchzermeyer, C., Schneider, G.-H., Sch€ onecker, T., K€ uhn, A.A., Curio, G. & Nikulin, V.V. (2012) Long-range temporal correlations in the subthalamic nucleus of patients with Parkinson’s disease. Eur. J. Neurosci., 36, 2812–2821. Hohlefeld, F.U., Huchzermeyer, C., Huebl, J., Schneider, G.-H., Nolte, G., Br€ucke, C., Sch€onecker, T., K€uhn, A.A., Curio, G. & Nikulin, V.V. (2013) Functional and effective connectivity in subthalamic local field potential recordings of patients with Parkinson’s disease. Neuroscience, 250, 320–332. Jansson, Y., Eriksson, B. & Johnels, B. (1998) Dispersible levodopa has a fast and more reproducible onset of action than the conventional preparation in Parkinson’s disease. A study with optoelectronic movement analysis. Parkinsonism Relat. D., 4, 201–206. K€uhn, A.A., Trottenberg, T., Kivi, A., Kupsch, A., Schneider, G.-H. & Brown, P. (2005) The relationship between local field potential and neuronal discharge in the subthalamic nucleus of patients with Parkinson’s disease. Exp. Neurol., 194, 212–220. K€uhn, A.A., Kupsch, A., Schneider, G.-H. & Brown, P. (2006a) Reduction in subthalamic 8-35 Hz oscillatory activity correlates with clinical improvement in Parkinson’s disease. Eur. J. Neurosci., 23, 1956–1960. K€uhn, A.A., Doyle, L., Pogosyan, A., Yarrow, K., Kupsch, A., Schneider, G.-H., Hariz, M.I., Trottenberg, T. & Brown, P. (2006b) Modulation of beta oscillations in the subthalamic area during motor imagery in Parkinson’s disease. Brain, 129, 695–706. K€uhn, A.A., Tsui, A., Aziz, T., Ray, N., Br€ucke, C., Kupsch, A., Schneider, G.-H. & Brown, P. (2009) Pathological synchronisation in the subthalamic nucleus of patients with Parkinson’s disease relates to both bradykinesia and rigidity. Exp. Neurol., 215, 380–387. Kumar, R., Lozano, A.M., Sime, E., Halket, E. & Lang, A.E. (1999) Comparative effects of unilateral and bilateral subthalamic nucleus deep brain stimulation. Neurology, 53, 561–566. Lalo, E., Thobois, S., Sharott, A., Polo, G., Mertens, P., Pogosyan, A. & Brown, P. (2008) Patterns of bidirectional communication between cortex and basal ganglia during movement in patients with Parkinson disease. J. Neurosci., 28, 3008–3016. Lehmann, D. & Skrandies, W. (1980) Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroen. Clin. Neuro., 48, 609–621. Little, S. & Brown, P. (2014) The functional role of beta oscillations in Parkinson’s disease. Parkinsonism Relat. D., 20, S44–S48. Little, S., Pogosyan, A., Kuhn, A.A. & Brown, P. (2012) b band stability over time correlates with Parkinsonian rigidity and bradykinesia. Exp. Neurol., 236, 383–388. Little, S., Tan, H., Anzak, A., Pogosyan, A., K€uhn, A. & Brown, P. (2013a) Bilateral functional connectivity of the basal ganglia in patients with Parkinson’s disease and its modulation by dopaminergic treatment. PLoS One, 8, e82762. Little, S., Pogosyan, A., Neal, S., Zavala, B., Zrinzo, L., Hariz, M., Foltynie, T., Limousin, P., Ashkan, K., FitzGerald, J., Green, A.L., Aziz, T.Z. & Brown, P. (2013b) Adaptive deep brain stimulation in advanced Parkinson disease. Ann. Neurol., 74, 449–457. Litvak, V., Jha, A., Eusebio, A., Oostenveld, R., Foltynie, T., Limousin, P., Zrinzo, L., Hariz, M.I., Friston, K. & Brown, P. (2011) Resting oscillatory

cortico-subthalamic connectivity in patients with Parkinson’s disease. Brain, 134, 359–374. L opez-Azcarate, J., Tainta, M., Rodrıguez-Oroz, M.C., Valencia, M., Gonzalez, R., Guridi, J., Iriarte, J., Obeso, J.A., Artieda, J. & Alegre, M. (2010) Coupling between beta and high-frequency activity in the human subthalamic nucleus may be a pathophysiological mechanism in Parkinson’s disease. J. Neurosci., 30, 6667–6677. Mann, J.M., Foote, K.D., Garvan, C.W., Fernandez, H.H., Jacobson, C.E. 4th, Rodriguez, R.L., Haq, I.U., Siddiqui, M.S., Malaty, I.A., Morishita, T., Hass, C.J. & Okun, M.S. (2009) Brain penetration effects of microelectrodes and DBS leads in STN or GPi. J. Neurol. Neurosur. Ps., 80, 794– 797. Marceglia, S., Fumagalli, M. & Priori, A. (2011) What neurophysiological recordings tell us about cognitive and behavioral functions of the human subthalamic nucleus. Expert Rev. Neurother., 11, 139–149. Murray, M.M., Brunet, D. & Michel, C.M. (2008) Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr., 20, 249–264. Nicolas, M.J., L opez-Azcarate, J., Valencia, M., Alegre, M., Perez-Alcazar, M., Iriarte, J. & Artieda, J. (2011) Ketamine-induced oscillations in the motor circuit of the rat basal ganglia. PLoS One, 6, e21814. Nolte, G. & M€ uller, K.-R. (2010) Localizing and estimating causal relations of interacting brain rhythms. Front. Hum. Neurosci., 4, 209. Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S. & Hallett, M. (2004) Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol., 115, 2292–2307. Nolte, G., Ziehe, A., Nikulin, V.V., Schl€ ogl, A., Kr€amer, N., Brismar, T. & M€ uller, K.-R. (2008) Robustly estimating the flow direction of information in complex physical systems. Phys. Rev. Lett., 100, 234101. Novak, P., Klemp, J.A., Ridings, L.W., Lyons, K.E., Pahwa, R. & Nazzaro, J.M. (2009) Effect of deep brain stimulation of the subthalamic nucleus upon the contralateral subthalamic nucleus in Parkinson disease. Neurosci. Lett., 463, 12–16. Nunez, P.L., Srinivasan, R., Westdorp, A.F., Wijesinghe, R.S., Tucker, D.M., Silberstein, R.B. & Cadusch, P.J. (1997) EEG coherency I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroen. Clin. Neuro., 103, 499–515. Obeso, J.A., Vaamonde, J., Grandas, F., Luquin, M.R., Rodriguez, M., Lera, G. & Martınez-Lage, J.M. (1989) Overcoming pharmacokinetic problems in the treatment of Parkinson’s disease. Movement Disord., 4, S70–S85. Parent, A. & Hazrati, L.N. (1995) Functional anatomy of the basal ganglia. II. The place of subthalamic nucleus and external pallidum in basal ganglia circuitry. Brain Res. Brain Res. Rev., 20, 128–154. Plenz, D. & Kital, S.T. (1999) A basal ganglia pacemaker formed by the subthalamic nucleus and external globus pallidus. Nature, 400, 677–682. Pogosyan, A., Yoshida, F., Chen, C.C., Martinez-Torres, I., Foltynie, T., Limousin, P., Zrinzo, L., Hariz, M.I. & Brown, P. (2010) Parkinsonian impairment correlates with spatially extensive subthalamic oscillatory synchronization. Neuroscience, 171, 245–257. Priori, A., Foffani, G., Pesenti, A., Tamma, F., Bianchi, A.M., Pellegrini, M., Locatelli, M., Moxon, K.A. & Villani, R.M. (2004) Rhythm-specific pharmacological modulation of subthalamic activity in Parkinson’s disease. Exp. Neurol., 189, 369–379. Ray, N.J., Jenkinson, N., Wang, S., Holland, P., Brittain, J.S., Joint, C., Stein, J.F. & Aziz, T. (2008) Local field potential beta activity in the subthalamic nucleus of patients with Parkinson’s disease is associated with improvements in bradykinesia after dopamine and deep brain stimulation. Exp. Neurol., 213, 108–113. Rickham, P.P. (1964) Human experimentation. Code of ethics of the World Medical Association. Declaration of Helsinki. Brit. Med. J., 13, 177. Rosa, M., Marceglia, S., Servello, D., Foffani, G., Rossi, L., Sassi, M., Mrakic-Sposta, S., Zangaglia, R., Pacchetti, C., Porta, M. & Priori, A. (2010) Time dependent subthalamic local field potential changes after DBS surgery in Parkinson’s disease. Exp. Neurol., 222, 184–190. Sch€ onecker, T., Kupsch, A., K€ uhn, A.A., Schneider, G.-H. & Hoffmann, K.T. (2009) Automated optimization of subcortical cerebral MR imagingatlas coregistration for improved postoperative electrode localization in deep brain stimulation. Am. J. Neuroradiol., 30, 1914–1921. Silberstein, P., Pogosyan, A., K€ uhn, A.A., Hotton, G., Tisch, S., Kupsch, A., Dowsey-Limousin, P., Hariz, M.I. & Brown, P. (2005) Cortico-cortical coupling in Parkinson’s disease and its modulation by therapy. Brain, 128, 1277–1291. Silchenko, A.N., Adamchic, I., Pawelczyk, N., Hauptmann, C., Maarouf, M., Sturm, V. & Tass, P.A. (2010) Data-driven approach to the estimation of connectivity and time delays in the coupling of interacting neuronal subsystems. J. Neurosci. Meth., 191, 32–44.

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283

Interhemispheric functional interactions 3283 de Solages, C., Hill, B.C., Yu, H., Henderson, J.M. & Bronte-Stewart, H. (2011) Maximal subthalamic beta hypersynchrony of the local field potential in Parkinson's disease is located in the central region of the nucleus. J. Neurol. Neurosur. Ps., 82, 1387–1389. Srinivasan, R., Winter, W.R., Ding, J. & Nunez, P.L. (2007) EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J. Neurosci. Meth., 166, 41–52. Trottenberg, T., Kupsch, A., Schneider, G.-H., Brown, P. & K€uhn, A.A. (2007) Frequency-dependent distribution of local field potential activity within the subthalamic nucleus in Parkinson’s disease. Exp. Neurol., 205, 287–291. Weinberger, M., Mahant, N., Hutchison, W.D., Lozano, A.M., Moro, E., Hodaie, M., Lang, A.E. & Dostrovsky, J.O. (2006) Beta oscillatory

activity in the subthalamic nucleus and its relation to dopaminergic response in Parkinson’s disease. J. Neurophysiol., 96, 3248–3256. Welter, M.-L., Schüpbach, M., Czernecki, V., Karachi, C., Fernandez-Vidal, S., Golmard, J.-L., Serra, G., Navarro, S., Welaratne, A., Hartmann, A., Mesnage, V., Pineau, F., Cornu, P., Pidoux, B., Worbe, Y., Zikos, P., Grabli, D., Galanaud, D., Bonnet, A.M., Belaid, H., Dormont, D., Vidailhet, M., Mallet, L., Houeto, J.L., Bardinet, E., Yelnik, J. & Agid, Y. (2014) Optimal target localization for subthalamic stimulation in patients with Parkinson disease. Neurology, 82, 1352–1361. Zaidel, A., Spivak, A., Grieb, B., Bergman, H. & Israel, Z. (2010) Subthalamic span of beta oscillations predicts deep brain stimulation efficacy for patients with Parkinson’s disease. Brain, 133, 2007–2021.

Appendix

of the connections (cf. Results). The results remained similar to those obtained after removing connections based on anatomical grounds, i.e. COHdm > iCOHdm in both the 10–20 and 21–30 Hz bands, and ON > OFF in the 21–30 Hz band, and no correlation with UPDRS scores, as reported below: in the 10–20 Hz band the Friedman analysis revealed significant differences (v2(3) = 17.7, P = 0.001), where post hoc tests (Wilcoxon’s signed rank test; uncorrected P-values are presented here; Bonferroni threshold 0.05/4 = 0.0125) showed that COHdm > iCOHdm in both conditions (OFF: 5.654  1.046 vs. 2.682  0.323, P = 0.012; ON: 5.161  1.261 vs. 2.199  0.388; P = 0.012). There were no significant differences between the OFF and ON conditions for any of the measures (P > 0.26). In the 21– 30 Hz band the repeated-measures ANOVA revealed a significant main effect ‘measure’ (F1,7 = 12.145, P = 0.01) with COHdm > iCOHdm (4.027  0.562 vs. 2.363  0.463) and a significant main effect ‘condition’ (F1,7 = 5.742, P = 0.048) with ON > OFF (3.638  0.639 vs. 2.752  0.434). The interaction ‘measure 9 condition’ was not significant (P = 0.625). Moreover, in both the 10–20 and the 21–30 Hz band there were no significant correlations with UPDRS scores for any of the measures and conditions (P-values across all correlations: 0.22 < P < 0.92). Additionally, the OFF–ON differences in UPDRS scores were not correlated with the corresponding OFF–ON differences in (i)COHdm in any measure or frequency band (0.14 < P < 0.9).

Results of multiple testing, single connection approach In the 10–20 Hz band the Friedman analysis revealed significant differences (v2(3) = 16.65, P = 0.001), where post hoc tests (Wilcoxon’s signed rank test; uncorrected P-values are presented here; Bonferroni threshold 0.05/4 = 0.0125) showed that COHdm > iCOHdm in both conditions (OFF: 5.572  1.053 vs. 2.695  0.329, P = 0.012; ON: 5.666  1.239 vs. 2.309  0.368; P = 0.012). There were no significant differences between the OFF and ON conditions for any of the measures (P > 0.26). In the 21–30 Hz band the repeated-measures ANOVA revealed a significant main effect ‘measure’ (F1,7 = 11.05, P = 0.013) with COHdm > iCOHdm (4.351  0.599 vs. 2.791  0.508) and a significant main effect ‘condition’ (F1,7 = 6.599, P = 0.037) with ON > OFF (4.349  0.663 vs. 2.793  0.421). The interaction ‘measure 9 condition’ was not significant (P = 0.63).

Investigation without rejecting electrodes based on anatomical positioning Additionally, to determine a possible contribution of bipolar channels due to suboptimal electrode placement according to the anatomical reconstruction of the electrode trajectory (cf. Materials and Methods), the analyses were performed for both bands without removing 12.5%

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3273–3283