EP 58. Corticomuscular coherence during isotonic contractions with DBS and medication in PD patients

To study the effects of deep brain stimulation (DBS) of bilateral subthalamic nucleus (STN) on the motor and non-motor symptoms in moderate or advanced Parkinson's disease (PD) patients. From August 2006 to January 2010, 21 consecutive PD patients with refractory motor fluctuations or dyskinesia underwent operations at our hospital. All patients were evaluated by unified Parkinson's disease rating scale (UPDRS), Hoehn & Yahr (H&Y) stage, Parkinson's disease questionnaire (PDQ-39), mini mental state examination (MMSE), Parkinson's disease sleep scale-Chinese vision (PDSS-CV), Pittsburgh sleep quality index (PSQI), Hamilton depression rating scale (HAMD) and Hamilton anxiety rating scale (HAMA). And the daily dosage of dopaminergic agents was recorded at 1 week pre-operation and 3, 6 and 12 months post-operation. Ten patients finished a 12-month follow-up. Their motor functions showed significant improvement. And the scores of UPDRS-motor, tremor, rigidity, bradykinesia and axial symptoms reduced significantly in the on-stimulation-off-medication condition and the on-stimulation-on-medication condition vs the on-medication condition pre-operation. And the improvement of tremor was the most pronounced (52.1% and 77.7% respectively). The H&Y stage decreased significantly from 3.2 ± 0.7 to 2.5 ± 0.4 post-operation. The activities of daily living improved while PDQ-39 declined significantly from 56 ± 9 pre-operation to 32 ± 13 at 12 months follow-up. The score changes of MMSE, PDSS-CV, PSQI, HAMA and HAMD were statistically insignificant. The levo-dopa equivalent dose of 1-year post-operation decreased significantly by 49.2% versus that of pre-operation (P < 0.05). Bilateral STN-DBS can significant ameliorate the motor symptoms of moderate or advanced PD patients, reduce the dosage of anti-PD medications and improve the quality of life. This procedure has the advantages of a greater safety, minor side effects and an easy controllability.

Over the past two decades, the development of functional imaging methods has greatly promoted our understanding on the changes of neurons following neurodegenerative disorders, such as Parkinson's disease (PD). The application of a spatial covariance analysis on 18F-FDG PET imaging has led to the identification of a distinctive disease-related metabolic pattern. This pattern has proven to be useful in clinical diagnosis, disease progression monitoring as well as assessment of the neuronal changes before and after clinical treatment. It may potentially serve as an objective biomarker on disease progression monitoring, assessment, histological and functional evaluation of related diseases. PD is one of the most common neurodegenerative disorders in the elderly. It is characterized by progressive loss of dopamine neurons in the substantia nigra pars compacta. Throughout the course of disease, the most obvious symptoms are movement-related, such as resting tremor, muscle rigidity, hypokinesia and postural instability (Worth, 2013). Currently, a definite diagnosis of PD is made by clinical evaluation with at least 2 years of follow-up (Hughes et al., 2002; Bhidayasiri and Reichmann, 2013), due to the overlap of motor symptoms between early PD and atypical parkinsonism including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). However, this classic diagnostic criterion does not benefit the early diagnosis of disease. The prognostic outcome and treatment option are substantially different between PD and atypical parkinsonism. Thus it is critical to develop biomarkers for earlier and more accurate diagnosis of PD. Generally, appropriate diagnostic biomarker for PD ought to cover several key characteristics: (i) minimal invasiveness to detect the biomarker in easily accessible body tissue or fluids, (ii) excellent sensitivity to explore the patients with PD, (iii) high specificity to prevent false-positive results in PD-free individuals, and (iv) robustness against potential affecting factors. A PD-related spatial covariance pattern (PDRP) with quantifiable expression on 18F-FDG PET imaging has been gradually detected using a spatial covariance method during the last two decades and it has been demonstrated to be the right diagnostic biomarker for PD (Eidelberg et al., 1994). PDRP has proven not only to be effective in early discrimination of PD from atypical parkinsonian disorders, but also to be able to assess the disease progression and treatment response. Thus it is considered as a multifunctional biomarker. In this review, we aim to provide an overview of the development in pattern-based biomarker for PD.

G325R GBA mutation in Parkinson's disease: Disease course and long-term DBS outcome

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. Download Figure shows the temporal properties for the three states for both the OFF and ON medication on the temporal properties of the HMM states revealed an effect of HMM states on the interval of and lifetime was no effect of medication (L-DOPA) on and lifetime had a significant effect on the interval of Finally, we found an between the HMM states and medication on the interval of and lifetime But there was no between HMM states and medication on Figure 6 Download asset Open asset properties of states. Panel A shows the for the three states for the cortico–cortical cortico–STN and the STN–STN (STN–STN). Each represents the mean for a state and the represents ON medication data and OFF medication Panel B shows the mean interval of of the three states ON and OFF medication. Panel C shows the lifetime for the three states. Figure are used for in are not The y-axis of each the same as the main Source data are provided as Figure data Figure data 1 Source data of Figure OFF medication. Download Figure data 2 Source data of Figure ON medication. Download Figure data 3 Source data of Figure OFF medication. Download Figure data 4 Source data of Figure ON medication. Download Figure data 5 Source data of Figure OFF medication. Download Figure data 6 Source data of Figure ON medication. Download We on the results. OFF medication, the STN–STN state was the one with the lifetime Ctx, STN–STN The Ctx–STN state OFF medication had the lifetime all three states Ctx–STN and the interval between of Ctx–STN Ctx–STN The interval between was for the Ctx–Ctx state OFF medication Ctx–Ctx The for the STN–STN and Ctx–STN states was but significantly higher than for the Ctx–Ctx state STN–STN Ctx–STN ON medication, the comparison between temporal properties of all three states the same levels as OFF medication, for the lifetime of the Ctx–STN state, which was no longer significantly different from that of the Ctx–Ctx state Within each medication condition, the states their temporal characteristics to each 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 but the lifetime for the Ctx–Ctx state was not significantly by medication. The Ctx–Ctx state was even often ON medication ON >OFF The interval between remained unchanged for the STN–STN and Ctx–STN states. The for all three states was not significantly changed from OFF to ON medication. In summary, the cortico–cortical state was 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 we simultaneously recorded into time-resolved states to reveal distinct spectral communication patterns. We identified three states distinct coherence patterns ON and OFF a cortico–cortical, a cortico–STN, and a STN–STN state. Our results a of neural activity to in connectivity patterns in which coherence under the effect of dopaminergic medication and which selective cortico–STN connectivity and STN–STN 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 and task-based PD studies et al., 2009; West et al., 2016; et al., The differential effect of dopamine allowed us to delineate pathological and

Differentiated effects of deep brain stimulation and medication on somatosensory processing in Parkinson's disease.

Parkinson’s disease (PD) is a neurodegenerative disorder cardinally marked by motor symptoms, but also sensory symptoms and several other non-motor symptoms. PD patients are typically treated with dopaminergic medication for several years. Many patients eventually experience bouts of periods where medication might not be able to effectively control symptoms as well as experience side-effects of long-term dopaminergic treatments. Deep brain stimulation (DBS) is an option as the next therapeutic recourse for such patients. DBS treatment essentially involves placement of stimulating electrodes in the subthalamic nucleus (STN) or the globus pallidus internum (GPi) along with an implanted pulse generator (IPG) in the sub-clavicular space. STN-DBS alleviates motor symptoms and leads to substantial improvements in quality of life for PD patients. Although DBS is known to improve several classes of symptoms, the effect mechanism of DBS is still not clear. While there is a lack of electrophysiological investigation of sensory processing and the effects of treatments in PD altogether, the electrophysiological studies of the cortical dynamics during motor tasks and at rest lack consensus.We recorded magnetoencephalography (MEG) and electromyography (EMG) from PD patients in three studies: (i) at rest, (ii) during median nerve stimulation, and (iii) while performing phasic contractions (hand gripping). The three studies focused on cortical oscillatory dynamics at rest, during somatosensory processing and during movement, respectively. The measurements were conducted in DBS-treated, untreated (DBS washout) and dopaminergic-medicated states. While both treatments (DBS and dopaminergic medication) ameliorated motor symptoms similarly in all studies, they showed differentiated effects on: (i) increased sensorimotor cortical low-gamma spectral power (31-45 Hz) (but no changes in beta power (13-30 Hz)) at rest only during DBS, (ii) somatosensory processing with higher gamma augmentation (31-45 Hz, 20-60 ms) in the dopaminergic-medicated state compared to DBS-treated and untreated states, and (iii) hand gripping with increased motor-related beta corticomuscular coherence (CMC, 13-30 Hz) during dopaminergic medication in contrast to increased gamma power (31-45 Hz) during DBS.Firstly, we infer from the three studies that DBS and dopaminergic medication employ partially different anatomo-functional pathways and functional strategies when improving PD symptoms. Secondly, we suggest that treatments act on pathological oscillatory dynamics differently at cortical and sub-cortical levels and may do so through more sophisticated mechanisms than mere suppression of the pathological spectral power in a particular band. And thirdly, we urge exploring effect mechanisms of PD treatments beyond the motor system. The effects of dopaminergic medication on early somatosensory processing has opened the door for exploring the effects of treatments and studying their mechanisms using electrophysiology, especially in higher order sensory deficits. Integration of such research findings into a holistic view on mechanisms of treatments could pave way for better disease management paradigms.

Adapting to post-COVID19 research in Parkinson's disease: Lessons from a multinational experience

Deep brain stimulation and refractory freezing of gait in Parkinson’s disease: Improvement with high-frequency current steering co-stimulation of subthalamic nucleus and substantia Nigra

Recent evidence suggests that deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) mediates its clinical effects by modulating cortical oscillatory activity, presumably via a direct cortico-subthalamic connection. This observation might pave the way for novel closed-loop approaches comprising a cortical sensor. Enhanced beta oscillations (13-35 Hz) have been linked to the pathophysiology of PD and may serve as such a candidate marker to localize a cortical area reliably modulated by DBS. However, beta-oscillations are widely distributed over the cortical surface, necessitating an additional signal source for spotting the cortical area linked to the pathologically synchronized cortico-subcortical motor network. In this context, both cortico-subthalamic coherence and cortico-muscular coherence (CMC) have been studied in PD patients. Whereas, the former requires invasive recordings, the latter allows for non-invasive detection, but displays a rather distributed cortical synchronization pattern in motor tasks. This distributed cortical representation may conflict with the goal of detecting a cortical localization with robust biomarker properties which is detectable on a single subject basis. We propose that this limitation could be overcome when recording CMC at rest. We hypothesized that—unlike healthy subjects—PD would show CMC at rest owing to the enhanced beta oscillations observed in PD. By performing source space analysis of beta CMC recorded during resting-state magnetoencephalography, we provide preliminary evidence in one patient for a cortical hot spot that is modulated most strongly by subthalamic DBS. Such a spot would provide a prominent target region either for direct neuromodulation or for placing a potential sensor in closed-loop DBS approaches, a proposal that requires investigation in a larger cohort of PD patients.

Mental and Behavioral Dysfunction in Movement Disorders

Deep brain stimulation on the subthalamic nucleus has been used to relieve Parkinsonian motor symptoms. However, the underlying physiological mechanism has not been fully understood. Beta-band cortico-muscular coherence increases when healthy humans perform isometric contraction. We hypothesized that this might be a measure of symptomatic improvement in motor performance after subthalamic nucleus deep brain stimulation. Here, we measured the beta-band cortico-muscular coherence with magnetoencephalography from three Parkinson's disease patients. We then compared the coherence values for stimulator on-state and off-state. We found that when the stimulator is on, the beta cortico-muscular coherence elevates significantly for the tremorous hand compared with that when the stimulator is off. This suggests that deep brain stimulation resulted in better cortico-muscular coordination.

Objective assessments of Parkinson's disease (PD) patients' motor state using motion capture techniques are still rarely used in clinical practice, even though they may improve clinical management. One major obstacle relates to the large dimensionality of motor abnormalities in PD. We aimed to extract global motor performance measures covering different everyday motor tasks, as a function of a clinical intervention, i.e., deep brain stimulation (DBS) of the subthalamic nucleus. We followed a data-driven, machine-learning approach and propose performance measures that employ Random Forests with probability distributions. We applied this method to 14 PD patients with DBS switched-off or -on, and 26 healthy control subjects performing the Timed Up and Go Test (TUG), the Functional Reach Test (FRT), a hand coordination task, walking 10-m straight, and a 90° curve. For each motor task, a Random Forest identified a specific set of metrics that optimally separated PD off DBS from healthy subjects. We noted the highest accuracy (94.6%) for standing up. This corresponded to a sensitivity of 91.5% to detect a PD patient off DBS, and a specificity of 97.2% representing the rate of correctly identified healthy subjects. We then calculated performance measures based on these sets of metrics and applied those results to characterize symptom severity in different motor tasks. Task-specific symptom severity measures correlated significantly with each other and with the Unified Parkinson's Disease Rating Scale (UPDRS, part III, correlation of r2 = 0.79). Agreement rates between different measures ranged from 79.8 to 89.3%. The close correlation of PD patients' various motor abnormalities quantified by different, task-specific severity measures suggests that these abnormalities are only facets of the underlying one-dimensional severity of motor deficits. The identification and characterization of this underlying motor deficit may help to optimize therapeutic interventions, e.g., to "automatically" adapt DBS settings in PD patients.

Objective Deep brain stimulation of the subthalamic nucleus (STN-DBS) in Parkinson’s disease (PD) patients does not halt disease progression, as these patients will progress and develop disabling non-levodopa responsive symptoms. These features may act as milestones that represent the overall functionality of patients after DBS. The objective of this study was to investigate the development of clinical milestones in advanced PD patients who underwent bilateral STN-DBS.Methods The study evaluated PD patients who underwent STN-DBS at baseline up to their last follow-up using the Unified Parkinson’s Disease Rating Scale and Hoehn and Yahr scale. The symptoms of hallucinations, dysarthria, dysphagia, frequent falls, difficulty walking, cognitive impairment and the loss of autonomy were chosen as the clinical milestones.Results A total of 106 patients with a mean age of 47.21 ± 10.52 years at disease onset, a mean age of 58.72 ± 8.74 years at surgery and a mean disease duration of 11.51 ± 4.4 years before surgery were included. Initial improvement of motor symptoms was seen after the surgery with the appearance of clinical milestones over time. Using the moderately disabling criteria, 81 patients (76.41%) developed at least one clinical milestone, while 48 patients (45.28%) developed a milestone when using the severely disabling criteria.Conclusion STN-DBS has a limited effect on axial and nonmotor symptoms of the PD patients, in contrast to the effect on motor symptoms. These symptoms may serve as clinical milestones that can convey the status of PD patients and its impact on the patients and their caregivers. Therefore, advanced PD patients, even those treated with bilateral STN-DBS, will still require assistance and cannot live independently in the long run.

In this paper, we proposed Approximate Entropy (ApEn) measure for nonlinear analysis of patterns of two daily common movements, that is sit-to-stand and stand-to-sit, in patients with Parkinson's disease (PD) using deep brain stimulation (DBS). ApEn value of a signal indicates its complexity and irregularity, such that larger the ApEn measure is, more stochastic and less regular the signal is. Here, this nonlinear measure is used for classifying three groups of 1) healthy subjects, 2) PD patients with DBS off, and 3) the same PD patients with DBS on, based on their sit-to-stand and stand-to-sit patterns. To this end, the area under receiver operating characteristic (ROC) plots (AUC) is used to evaluate the capability of ApEn. For stand-to-sit patterns and for discrimination between two groups of normal vs. PD with DBS off, normal vs. PD with DBS on, and PD with DBS off vs. PD with DBS on, we achieved the AUC values of up to 0.9625, 0.9, and 0.9583, respectively. Also, there was high correlation between ApEn values of PD patients obtained from different sensors and the severity of their Parkinson's disease i.e. UPDRS scores (up to 0.7671 for PD with DBS off and 0.774 for PD with DBS on). For sit-to-stand patterns and to distinguish between normal vs. PD with DBS off, normal vs. PD with DBS on, and PD with DBS off vs. PD with DBS on, we achieved the AUC values of up to 1, 0.9778, and 0.9167, respectively. Moreover, for this pattern, there was higher correlation between ApEn values of PD patients and their UPDRS scores (up to 0.9573 for PD with DBS off and 0.7145 for PD with DBS on). Furthermore, in both sit-to-stand and stand-to-sit patterns, it was observed that PD patients have larger complexity values than healthy controls and PD subjects in DBS on state had smaller irregularity values than PD subjects in DBS off state. In general, we may conclude that sit-to-stand patterns can distinguish PD patients from healthy subjects and also be used to estimate the severity of Parkinson's disease better than stand-to-sit patterns.

Coherence is the degree of time-locked correlation between two signals as a function of frequency. The purpose of this study was to test the following hypotheses: (1) corticomuscular coherence is abnormally increased in those Parkinson's disease (PD) patients with small amplitude cortical myoclonus, and (2) corticomuscular coherence peaks around the time of the myoclonus electromyographic (EMG) discharge. We studied Parkinson's disease patients with and without myoclonus and controls. The data were digitally collected and processed off-line with EMG rectification, creation of 511-msec epochs, Fast-Fourier transform, and coherence analysis. In the 12 to 30 Hz frequency band, but not at 30 to 60 Hz or above, coherence peaks were observed in the PD subjects with myoclonus that were significantly greater than in the control subjects (P < 0.001) and in PD subjects without myoclonus (P < 0.001). The abnormal coherence values are evidence for abnormal rhythmic activity in cortical motor areas in those Parkinson's disease patients with myoclonus. In combination with previous findings on back-averaging, our results show that this myoclonus occurs when neuronal populations are driven to an extreme amount of synchronous activity with higher corticomuscular coherence values. These results have mechanistic implications for cortical dysfunction in Parkinson's disease and for cortical myoclonus in general.