Decision letter: Differential dopaminergic modulation of spontaneous cortico–subthalamic activity in Parkinson’s disease

Abstract

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Pathological oscillations including elevated beta activity in the subthalamic nucleus (STN) and between STN and cortical areas are a hallmark of neural activity in Parkinson’s disease (PD). Oscillations also play an important role in normal physiological processes and serve distinct functional roles at different points in time. We characterised the effect of dopaminergic medication on oscillatory whole-brain networks in PD in a time-resolved manner by employing a hidden Markov model on combined STN local field potentials and magnetoencephalography (MEG) recordings from 17 PD patients. Dopaminergic medication led to coherence within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with known side effects of dopamine treatment such as deteriorated executive functions in PD. In addition, dopamine caused the beta band activity to switch from an STN-mediated motor network to a frontoparietal-mediated one. In contrast, dopamine did not modify local STN–STN coherence in PD. STN–STN synchrony emerged both on and off medication. By providing electrophysiological evidence for the differential effects of dopaminergic medication on the discovered networks, our findings open further avenues for electrical and pharmacological interventions in PD. Introduction Oscillatory activity serves crucial cognitive roles in the brain (Akam and Kullmann, 2010; Akam and Kullmann, 2014), and alterations of oscillatory activity have been linked to neurological and psychiatric diseases (Schnitzler and Gross, 2005). Different large-scale brain networks operate with their own oscillatory fingerprint and carry out specific functions (Keitel and Gross, 2016; Mellem et al., 2017; Vidaurre et al., 2018b). Given the dynamics of cognition, different brain networks need to be recruited and deployed flexibly. Hence, the duration for which a network is active, its overall temporal presence, and even the interval between the different activations of a specific network might provide a unique window to understanding brain functions. Crucially, alterations of these temporal properties or networks might be related to neurological disorders. In Parkinson’s disease (PD), beta oscillations within the subthalamic nucleus (STN) and motor cortex (13–30 Hz) correlate with the motor symptoms of PD (Marreiros et al., 2013; van Wijk et al., 2016; West et al., 2018). Beta oscillations also play a critical role in communication in a healthy brain (Engel and Fries, 2010). (For the purposes of our paper, we refer to oscillatory activity or oscillations as recurrent but transient frequency-specific patterns of network activity, even though the underlying patterns can be composed of either sustained rhythmic activity, neural bursting, or both [Quinn et al., 2019]. Disambiguating the exact nature of these patterns is, however, beyond the scope of this work.) At the cellular level, loss of nigral dopamine neurons in PD leads to widespread changes in brain networks, to varying degrees across different patients. Dopamine loss is managed in patients via dopaminergic medication. Dopamine is a widespread neuromodulator in the brain (Gershman and Uchida, 2019), raising the question of whether each medication-induced change restores physiological oscillatory networks. In particular, dopaminergic medication is known to produce cognitive side effects in PD patients (Voon et al., 2009). According to the dopamine overdose hypothesis, a reason for these effects is the presence of excess dopamine in brain regions not affected in PD (MacDonald et al., 2011; MacDonald and Monchi, 2011). Previous task-based and neuroimaging studies in PD demonstrated frontal cognitive impairment due to dopaminergic medication (Cools et al., 2002; Ray and Strafella, 2010; MacDonald et al., 2011). Using resting-state whole-brain MEG analysis, network changes related to both motor and non-motor symptoms of PD have been described (Olde Dubbelink et al., 2013a; Olde Dubbelink et al., 2013b). However, these studies could not account for simultaneous STN–STN or cortico–STN activity affecting these networks, which would require combined MEG/electroencephalogram (EEG)–LFP recordings (Litvak et al., 2021). Such recordings are possible during the implantation of deep brain stimulation (DBS) electrodes, an accepted treatment in the later stages of PD (Volkmann et al., 2004; Deuschl et al., 2006; Kleiner-Fisman et al., 2006). Combined MEG–LFP studies in PD involving dopaminergic intervention report changes in beta and alpha band connectivity between specific cortical regions and the STN (Litvak et al., 2011; Hirschmann et al., 2013; Oswal et al., 2016). Decreased cortico–STN coherence under dopaminergic medication (ON) correlates with improved motor functions in PD (George et al., 2013). STN–STN intra-hemispheric oscillations positively correlate to motor symptom severity in PD without dopaminergic medication (OFF), whereas dopamine-dependent nonlinear phase relationships exist between inter-hemispheric STN–STN activity (West et al., 2016). Crucially, previous studies could not rule out the influence of cortico–STN connectivity on these inter-hemispheric STN–STN interactions. To further characterise the differential effects of dopaminergic medication and delineate pathological versus physiological-relevant spectral connectivity in PD, we study PD brain activity via a hidden Markov model (HMM), a data-driven learning algorithm (Vidaurre et al., 2016; Vidaurre et al., 2018b). Due to the importance of cortico–subcortical interactions in PD, we investigated these interactions with combined spontaneous whole-brain magnetoencephalography (MEG) and STN local field potentials (LFPs) recordings from PD patients. We study whole-brain connectivity including the STN using spectral coherence as a proxy for communication based on the communication through coherence hypothesis (Fries, 2005; Fries, 2015). This will allow us to delineate differences in communication OFF and ON medication. Furthermore, we extended previous work that was limited to investigating communication between specific pairs of brain areas (Litvak et al., 2011; George et al., 2013; Hirschmann et al., 2013). Moreover, we identified the temporal properties of the networks both ON and OFF medication. The temporal properties provide an encompassing view of network alterations in PD and the effect of dopamine on these networks. We found that cortico–cortical, cortico–STN, and STN–STN networks were differentially modulated by dopaminergic medication. For the cortico–cortical network, medication led to additional connections that can be linked to the side effects of dopamine. At the same time, dopamine changed the cortico–STN network towards a pattern more closely resembling physiological connectivity as reported in the PD literature. Within the third network, dopamine only had an influence on local STN–STN coherence. These results provide novel information on the oscillatory network connectivity occurring in PD and the differential changes caused by dopaminergic intervention. These whole-brain networks, along with their electrophysiological signatures, open up new potential targets for both electric and pharmacological interventions in PD. Results Under resting-state conditions in PD patients, we simultaneously recorded whole-brain MEG activity with LFPs from the STN using directional electrodes implanted for DBS. Using an HMM, we identified recurrent patterns of transient network connectivity between the cortex and the STN, which we henceforth refer to as an ‘HMM state’. In comparison to classic sliding window analysis, an HMM solution can be thought of as a data-driven estimation of time windows of variable length (within which a particular HMM state was active): once we know the time windows when a particular state is active, we compute coherence between different pairs of regions for each of these recurrent states. Each HMM state itself is a multidimensional, time-delay embedded (TDE) covariance matrix across the whole brain, containing information about cross-regional coherence and power in the frequency domain. Additionally, the temporal evolution of the HMM states was determined. The PD data were acquired under medication (L-DOPA) OFF and ON conditions, which allowed us to delineate the physiological versus pathological spatio-spectral and temporal changes observed in PD. To allow the system to dynamically evolve, we use time delay embedding. Theoretically, delay embedding can reveal the state space of the underlying dynamical system (Packard et al., 1980). Thus, by delay-embedding PD time series OFF and ON medication, we uncover the differential effects of a neurotransmitter such as dopamine on underlying whole-brain connectivity. OFF medication, patients had on average a Unified Parkinson’s Disease Rating Scale (UPDRS) part III of 29.24 ± 10.74. This was reduced by L-DOPA (176.5 ± 56.2 mg) to 19.47 ± 8.52, indicating an improvement in motor symptoms. Spontaneous brain activity in PD can be resolved into distinct states Using an HMM, we delineated cortico–subthalamic spectral changes from both global source-level cortical interactions as well as local STN–STN interactions. Three of the six HMM states could be attributed to physiologically interpretable connectivity patterns. We could not interpret the other three states within the current physiological frameworks both OFF and ON medication and they are therefore not considered in the following (see Figure 2—figure supplement 1). The connectivity between different brain regions for each state was visualised for the frequency modes shown in Figure 1. Figures 2–4 show the connectivity patterns for the three physiologically meaningful states in both the OFF (top row) and ON medication condition (bottom row). We refer to the state obtained in Figure 2 as the cortico–cortical state (Ctx–Ctx). This state was characterised mostly by local coherence within segregated networks OFF medication in the alpha and beta band. In contrast, there was a widespread increase in coherence across the brain from OFF to ON medication. Therefore, ON medication, the connectivity strength in the alpha and beta band was not significantly different from the mean noise level. Figure 3 displays the second state. A large proportion of spectral connections in this state enable cortico–STN communication via spectral coherence (Lalo et al., 2008; Litvak et al., 2011; Hirschmann et al., 2013; Oswal et al., 2013; van Wijk et al., 2016) and thus we labelled this as the cortico–STN state (Ctx–STN). This state was characterised by connectivity between multiple cortical regions and the STN OFF medication, but increased specificity of cortical–STN connectivity ON medication. Finally, Figure 4 shows the third state. Within this state, highly synchronous STN–STN spectral connectivity emerged, both OFF and ON medication and therefore we named it the STN–STN state (STN–STN). The spectral characteristics of this state largely remain unaffected under the influence of dopaminergic medication. In the following sections, we describe these three states in detail. Figure 1 Download asset Open asset Data-driven frequency modes. Each plotted curve shows a different spectral band. The x-axis represents frequency in Hz and the y-axis represents the weights obtained from the non-negative matrix factorisation (NNMF) in arbitrary units. The NNMF weights are like regression coefficients. The frequency resolution of the modes is 0.5 Hz. Panels A and B show the OFF and ON medication frequency modes, respectively. Source data are provided as Figure 1—source data 1–2. Figure 1—source data 1 Source data of Figure 1a. https://cdn.elifesciences.org/articles/66057/elife-66057-fig1-data1-v1.mat Download elife-66057-fig1-data1-v1.mat Figure 1—source data 2 Source data of Figure 1b. https://cdn.elifesciences.org/articles/66057/elife-66057-fig1-data2-v1.mat Download elife-66057-fig1-data2-v1.mat Figure 2 with 1 supplement see all Download asset Open asset Cortico–cortical state. The cortico–cortical state was characterised by a significant increase in coherence ON compared to OFF medication (see panel B). Due to this, no connections within the alpha and beta band ON medication were significantly higher than the mean (panel C). However, in the delta band, ON medication medial prefrontal–orbitofrontal connectivity emerged. (A and C) Each node in the circular graph represents a brain region based on the Mindboggle atlas. The regions from the atlas are listed in Table 1 along with their corresponding numbers that are used in the circular graph. The colour code in the circular graph represents a group of regions clustered according to the atlas (starting from node number 1) STN contacts (contacts 1, 2, 3 = right STN and contacts 4, 5, 6 = left STN), frontal, medial frontal, temporal, sensorimotor, parietal, and visual cortices. In the circular graph, only the significant connections (p<0.05; corrected for multiple comparisons, IntraMed analysis) are displayed as black curves connecting the nodes. The circles from left to right represent the delta/theta, alpha, and beta bands. Panel A shows results for OFF medication data and panel C for the ON medication condition. For every circular graph, we also show a corresponding top view of the brain with the connectivity represented by yellow lines and the red dot represents the anatomical seed vertex of the brain region. Only the cortical connections are shown. Panel B shows the result for inter-medication analysis (InterMed) for the cortico–cortical state. In each symmetric matrix, every row and column corresponds to a specific atlas cluster denoted by the dot colour on the side of the matrix. Each matrix entry is the result of the InterMed analysis where OFF medication connectivity between ith row and jth column was compared to the ON medication connectivity between the same connections. A cell is white if the comparison mentioned on top of the matrix (either ON >OFF or OFF >ON) was significant at a threshold of p<0.05. The connectivity maps of states 4–6 are provided in Figure 2—figure supplement 1. Source data are provided as Figure 2—source data 1–3. Figure 2—source data 1 Source data of Figure 2a. https://cdn.elifesciences.org/articles/66057/elife-66057-fig2-data1-v1.mat Download elife-66057-fig2-data1-v1.mat Figure 2—source data 2 Source data of Figure 2b. https://cdn.elifesciences.org/articles/66057/elife-66057-fig2-data2-v1.mat Download elife-66057-fig2-data2-v1.mat Figure 2—source data 3 Source data of Figure 2c. https://cdn.elifesciences.org/articles/66057/elife-66057-fig2-data3-v1.mat Download elife-66057-fig2-data3-v1.mat Figure 3 Download asset Open asset Cortico–STN state. For the general description, see the note to Figure 2. The cortico–STN state was characterised by preservation of spectrally selective cortico–STN connectivity ON medication. Also, ON medication, a sensorimotor–frontoparietal network emerged. Source data are provided as Figure 3—source data 1–3. Figure 3—source data 1 Source data of Figure 3a. https://cdn.elifesciences.org/articles/66057/elife-66057-fig3-data1-v1.mat Download elife-66057-fig3-data1-v1.mat Figure 3—source data 2 Source data of Figure 3b. https://cdn.elifesciences.org/articles/66057/elife-66057-fig3-data2-v1.mat Download elife-66057-fig3-data2-v1.mat Figure 3—source data 3 Source data of Figure 3c. https://cdn.elifesciences.org/articles/66057/elife-66057-fig3-data3-v1.mat Download elife-66057-fig3-data3-v1.mat Figure 4 Download asset Open asset STN–STN state. For the general description, see the note to Figure 2. The STN–STN state was characterised by preservation of STN–STN coherence in the alpha and beta band OFF versus ON medication. STN–STN theta/delta coherence was no longer significant ON medication. Source data are provided as Figure 4—source data 1–3. Figure 4—source data 1 Source data of Figure 4a. https://cdn.elifesciences.org/articles/66057/elife-66057-fig4-data1-v1.mat Download elife-66057-fig4-data1-v1.mat Figure 4—source data 2 Source data of Figure 4b. https://cdn.elifesciences.org/articles/66057/elife-66057-fig4-data2-v1.mat Download elife-66057-fig4-data2-v1.mat Figure 4—source data 3 Source data of Figure 4c. https://cdn.elifesciences.org/articles/66057/elife-66057-fig4-data3-v1.mat Download elife-66057-fig4-data3-v1.mat Table 1 Regions of the Mindboggle atlas used. STN, subthalamic nucleus; Vis, visual; Par, parietal; Smtr, sensory motor; Tmp, temporal; Mpf, medial prefrontal; Frnt, frontal; Ctx, cortex. The colour code is for the ring figures presented as part of the results. STN1Contact one rightSmtr-Ctx12Postcentral2Contact two right13Precentral3Contact three rightTmp-Ctx14Middle temporal1Contact four left15Superior temporal2Contact five leftMpf-Ctx16Caudal middle frontal3Contact six left17Medial orbitofrontalVis-Ctx4CuneusFrnt-Ctx18Insula5Lateral occipital19Lateral orbitofrontal6Lingual20Pars opercularisPar-Ctx7Inferior parietal21Pars orbitalis8Para central22Pars triangularis9Precuneus23Rostral middlefrontal10Superior parietal24Superior frontal11Supramarginal Ctx–Ctx state is characterised by increased frontal coherence due to elevated dopamine levels Supporting the dopamine overdose hypothesis in PD (Kelly et al., 2009; MacDonald and Monchi, 2011), we identified a delta/theta oscillatory network involving intra-hemispheric connections between the lateral and medial orbitofrontal cortex as well as the pars orbitalis. The delta/theta network emerged between the lateral and medial orbitofrontal as well as left and right pars orbitalis cortex ON medication (p<0.05, Figure 2C delta). On the contrary, OFF medication no significant connectivity was detected in the delta/theta band. In the alpha and beta band OFF medication there was significant connectivity within the frontal regions, STN, and to a limited extent in the posterior parietal regions (p<0.05, Figure 2A). Another effect of excess dopamine was significantly increased connectivity of frontal cortex and temporal cortex both with the STN and multiple cortical regions across all frequency modes (p<0.01, Figure 2 delta, alpha, and beta). The change in sensorimotor–STN connectivity primarily took place in the alpha band with an increased ON medication. Sensorimotor–cortical connectivity was increased ON medication across multiple cortical regions in both the alpha and beta band (p<0.01, Figure 2 alpha and beta). However, STN–STN coherence remained unchanged OFF versus ON medication across all frequency modes. Viewed together, the Ctx–Ctx state captured increased coherence across the cortex ON medication within the alpha and beta band. This, however, implies that ON medication, no connectivity strength was significantly higher than the mean noise level within the alpha or beta band. ON medication, significant coherence emerged in the delta/theta band primarily between different regions of the orbitofrontal cortex. Dopaminergic medication selectively reduced connectivity in the Ctx–STN state Our analysis revealed that the Ctx–STN state ON medication was characterised by selective cortico–STN spectral connectivity and an overall shift in cortex-wide activity towards physiologically relevant network connectivity. In particular, ON medication, connectivity between STN and cortex became more selective in the alpha and beta band. OFF medication, STN–pre-motor (sensory), STN–frontal, and STN–parietal connectivity was present (p<0.05, Figure 3A alpha and beta). Importantly, coherence OFF medication was significantly larger than ON medication between STN and sensorimotor, STN and temporal, and STN and frontal cortices (p<0.05 for all connections, Figure 3B alpha and beta). Furthermore, ON medication, in the alpha band only the connectivity between temporal, parietal, and medial orbitofrontal cortical regions and the STN was preserved (p<0.05, Figure 3C alpha). Finally, ON medication, a sensorimotor–frontoparietal network emerged (p<0.05, Figure 3C beta), where sensorimotor, medial prefrontal, frontal, and parietal regions were no longer connected to the STN, but instead directly communicated with each other in the beta band. Hence, there was a transition from STN-mediated sensorimotor connectivity to the cortex OFF medication to a more direct cortico–cortical connectivity ON medication. Simultaneously to STN–cortico and cortico–cortical, STN–STN connectivity changed. In the ON condition, STN–STN connectivity was significantly different from the mean noise level across all three frequency modes (p<0.05, Figure 3C). But on the other hand, there was no significant change in the STN–STN connectivity OFF versus ON medication (p=0.21 delta/theta; p=0.25 alpha; p=0.10 beta; Figure 3B). To summarise, coherence decreased ON medication across a wide range of cortical regions both at the cortico–cortical and cortico–STN level. Still, significant connectivity was selectively preserved in a spectrally specific manner ON medication both at the cortico–cortical (sensorimotor–frontoparietal network) and the cortico–STN levels. The most surprising aspect of this state was the emergence of bilateral STN–STN coherence ON medication across all frequency modes. Dopamine selectively modifies delta/theta oscillations within the STN–STN state In this STN–STN state, dopaminergic intervention had only a limited effect on STN–STN connectivity. OFF medication, STN–STN coherence was present across all three frequency modes (p<0.05, Figure 4A), while ON medication, significant STN–STN coherence emerged only in the alpha and beta band (p<0.05, Figure 4C alpha and beta). ON medication, STN–STN delta/theta connectivity strength was not significantly different from the mean noise level (p<0.05, Figure 4C delta). OFF compared to ON medication, coherence was reduced across the entire cortex both at the inter-cortical and the STN–cortex level across all frequency modes. The most affected areas were similar to the ones in the Ctx–STN state, in other words, the sensorimotor, frontal, and temporal regions. Their coherence with the STN was also significantly reduced, ON compared to OFF medication (STN–sensorimotor, p<0.01 delta/theta, beta; p<0.05 alpha; STN–temporal, p<0.01 delta/theta, alpha, beta; and STN–frontal, p<0.01 delta/theta, alpha and beta; Figure 4B). In summary, STN–STN connectivity was not significantly altered OFF to ON medication. At the same time, coherence decreased from OFF to ON medication at both the cortico–cortical and the cortico–STN level. Therefore, only significant STN–STN connectivity existed both OFF and ON medication, while cortico–STN or cortico–cortical connectivity changes remained at the mean noise level. States with a generic coherence decrease have longer lifetimes Using the temporal properties of the identified networks, we investigated whether states showing a shift towards physiological connectivity patterns lasted longer ON medication. A state that is physiological should exhibit increased lifetime and/or should occur more often ON medication. An example of the state time courses is shown in Figure 5. Figure 5 Download asset Open asset Example of a probability time course for the six hidden Markov model (HMM) states OFF medication. Note that within the main text of the paper, we are only discussing the first three states. The connectivity maps of states 4–6 are provided in Figure 2—figure supplement 1. Source data are provided as Figure 5—source data 1–2. Figure 5—source data 1 Probability time course first half in relation to Figure 5. https://cdn.elifesciences.org/articles/66057/elife-66057-fig5-data1-v1.mat Download elife-66057-fig5-data1-v1.mat Figure 5—source data 2 Probability time course second half in relation to Figure 5. https://cdn.elifesciences.org/articles/66057/elife-66057-fig5-data2-v1.mat Download elife-66057-fig5-data2-v1.mat Figure 6A-C shows the temporal properties for the three states for both the OFF and ON medication conditions. Two-way repeated measures ANOVA on the temporal properties of the HMM states revealed an effect of HMM states on the fractional occupancy (FO) (F(2,96)=10.49, p<0.01), interval of visits (F(2,221513)=9783.13, p<0.01), and lifetime (F(2,214818)=50.36, p<0.01). There was no effect of medication (L-DOPA) on FO (F(1,96)=2.00, p=0.16) and lifetime (F(1,214818)=0.15, p=0.7026). Medication had a significant effect on the interval of visits (F(1,221513)=4202.96, p<0.01). Finally, we found an interaction between the HMM states and medication on the interval of visits (F(2,221513)=1949.98, p<0.01) and lifetime (F(2,214818)=172.25, p<0.01). But there was no interaction between HMM states and medication on FO (F(2,96)=0.54, p=0.5855). Figure 6 Download asset Open asset Temporal properties of states. Panel A shows the fractional occupancy for the three states for the cortico–cortical (Ctx–Ctx), cortico–STN (Ctx–STN), and the STN–STN (STN–STN). Each point represents the mean for a state and the error bar represents standard error. Orange denotes ON medication data and blue OFF medication data. Panel B shows the mean interval of visits (in milliseconds) of the three states ON and OFF medication. Panel C shows the lifetime (in milliseconds) for the three states. Figure insets are used for clarity in case error bars are not clearly visible. The y-axis of each figure inset has the same units as the main figure. Source data are provided as Figure 6—source data 1–6. Figure 6—source data 1 Source data of Figure 6a OFF medication. https://cdn.elifesciences.org/articles/66057/elife-66057-fig6-data1-v1.mat Download elife-66057-fig6-data1-v1.mat Figure 6—source data 2 Source data of Figure 6a ON medication. https://cdn.elifesciences.org/articles/66057/elife-66057-fig6-data2-v1.mat Download elife-66057-fig6-data2-v1.mat Figure 6—source data 3 Source data of Figure 6b OFF medication. https://cdn.elifesciences.org/articles/66057/elife-66057-fig6-data3-v1.mat Download elife-66057-fig6-data3-v1.mat Figure 6—source data 4 Source data of Figure 6b ON medication. https://cdn.elifesciences.org/articles/66057/elife-66057-fig6-data4-v1.mat Download elife-66057-fig6-data4-v1.mat Figure 6—source data 5 Source data of Figure 6c OFF medication. https://cdn.elifesciences.org/articles/66057/elife-66057-fig6-data5-v1.mat Download elife-66057-fig6-data5-v1.mat Figure 6—source data 6 Source data of Figure 6c ON medication. https://cdn.elifesciences.org/articles/66057/elife-66057-fig6-data6-v1.mat Download elife-66057-fig6-data6-v1.mat We performed post hoc testing on the ANOVA results. OFF medication, the STN–STN state was the one with the longest lifetime (STN–STN >Ctx Ctx, p<0.01; STN–STN >Ctx-STN, p<0.01). The Ctx–STN state OFF medication had the shortest lifetime among all three states (Ctx–STN <Ctx-Ctx, p<0.01; Ctx–STN <STN-STN, p<0.01) and the shortest interval between visits (interval of visit Ctx–STN <Ctx-Ctx, p<0.01; Ctx–STN <STN-STN, p<0.01). The largest interval between visits was for the Ctx–Ctx state OFF medication (Ctx–Ctx >STN-STN, p<0.01; Ctx–Ctx >Ctx-STN, p<0.01). The FO for the STN–STN and Ctx–STN states was similar, but significantly higher than for the Ctx–Ctx state (STN–STN >Ctx-Ctx, p<0.01; STN–STN ≈ Ctx–STN, p=0.82; Ctx–STN >Ctx-Ctx, p<0.01). ON medication, the comparison between temporal properties of all three states retained the same significance levels as OFF medication, except for the lifetime of the Ctx–STN state, which was no longer significantly different from that of the Ctx–Ctx state (p=0.98). Within each medication condition, the states retained their temporal characteristics relative to each other. Across medication conditions, significant changes were present in the temporal properties of the states. The lifetimes for both the STN–STN and Ctx–STN state were significantly increased by medication (ON >OFF: STN–STN, p<0.01; Ctx–STN, p≤0.01) but the lifetime for the Ctx–Ctx state was not significantly influenced by medication. The Ctx–Ctx state was visited even less often ON medication (interval: ON >OFF Ctx–Ctx, p<0.01). The interval between visits remained unchanged for the STN–STN and Ctx–STN states. The FO for all three states was not significantly changed from OFF to ON medication. In summary, the cortico–cortical state was visited least often compared to the other two states both OFF and ON medication. The cortico–STN and STN–STN states showing physiologically relevant spectral connectivity lasted significantly longer ON medication. Discussion In this study, we parsed simultaneously recorded MEG–STN LFP signals into discrete time-resolved states to reveal distinct spectral communication patterns. We identified three states exhibiting distinct coherence patterns ON and OFF medication: a cortico–cortical, a cortico–STN, and a STN–STN state. Our results indicate a tendency of neural activity to engage in connectivity patterns in which coherence decreases under the effect of dopaminergic medication and which maintain selective cortico–STN connectivity (Ctx–STN and STN–STN states). Only within the Ctx–Ctx state did coherence increase under dopaminergic medication. These results are in line with the multiple effects of dopaminergic medication reported in resting and task-based PD studies (Jubault et al., 2009; West et al., 2016; Tinkhauser et al., 2017). The differential effect of dopamine allowed us to delineate pathological and