Jan Hirschmann

Distinct oscillatory STN-cortical loops revealed by simultaneous MEG and local field potential recordings in patients with Parkinson's disease

Neuronal oscillations are assumed to play a pivotal role in the pathophysiology of Parkinsons disease (PD). Neurons in the subthalamic nucleus (STN) generate oscillations which are coupled to rhythmic population activity both in other basal ganglia nuclei and cortical areas. In order to localize these cortical areas, we recorded local field potentials (LFPs) and magnetoencephalography (MEG) simultaneously in PD patients undergoing surgery for deep brain stimulation (DBS). Patients were withdrawn from antiparkinsonian medication and recorded at rest. We scanned the entire brain for oscillations coherent with LFPs recorded from the STN with a frequency domain beamformer. Coherent activity in the low (12-20 Hz) and high (20-35 Hz) beta range was found in the ipsilateral sensorimotor and the premotor cortex. Coherence in the alpha range (7-12 Hz) was observed at various locations in the ipsilateral temporal lobe. In a subset of subjects, the superior temporal gyrus consistently showed coherent alpha oscillations. Our findings provide new insights into patterns of frequency-specific functional connectivity between basal ganglia and cortex and suggest that simultaneous inter-regional interactions may be segregated in the frequency domain. Furthermore, they demonstrate that simultaneous MEG-LFP recordings are a powerful tool to study interactions between brain areas in PD patients undergoing surgery for DBS.

A direct relationship between oscillatory subthalamic nucleus–cortex coupling and rest tremor in Parkinson’s disease

Electrophysiological studies suggest that rest tremor in Parkinsons disease is associated with an alteration of oscillatory activity. Although it is well known that tremor depends on cortico-muscular coupling, it is unclear whether synchronization within and between brain areas is specifically related to the presence and severity of tremor. To tackle this longstanding issue, we took advantage of naturally occurring spontaneous tremor fluctuations and investigated cerebral synchronization in the presence and absence of rest tremor. We simultaneously recorded local field potentials from the subthalamic nucleus, the magnetoencephalogram and the electromyogram of forearm muscles in 11 patients with Parkinsons disease (all male, age: 52-74 years). Recordings took place the day after surgery for deep brain stimulation, after withdrawal of anti-parkinsonian medication. We selected epochs containing spontaneous rest tremor and tremor-free epochs, respectively, and compared power and coherence between subthalamic nucleus, cortex and muscle across conditions. Tremor-associated changes in cerebro-muscular coherence were localized by Dynamic Imaging of Coherent Sources. Subsequently, cortico-cortical coupling was analysed by computation of the imaginary part of coherency, a coupling measure insensitive to volume conduction. After tremor onset, local field potential power increased at individual tremor frequency and cortical power decreased in the beta band (13-30 Hz). Sensor level subthalamic nucleus-cortex, cortico-muscular and subthalamic nucleus-muscle coherence increased during tremor specifically at tremor frequency. The increase in subthalamic nucleus-cortex coherence correlated with the increase in electromyogram power. On the source level, we observed tremor-associated increases in cortico-muscular coherence in primary motor cortex, premotor cortex and posterior parietal cortex contralateral to the tremulous limb. Analysis of the imaginary part of coherency revealed tremor-dependent coupling between these cortical areas at tremor frequency and double tremor frequency. Our findings demonstrate a direct relationship between the synchronization of cerebral oscillations and tremor manifestation. Furthermore, they suggest the feasibility of tremor detection based on local field potentials and might thus become relevant for the design of closed-loop stimulation systems.

Motor and cognitive placebo-/nocebo-responses in Parkinson's disease patients with deep brain stimulation.

Expectation contributes to placebo and nocebo responses in Parkinsons disease (PD). Subthalamic nucleus (STN) deep brain stimulation (DBS) improves proximal more than distal movements whereas it impairs executive cognitive function such as verbal fluency (VF). We investigated how expectation modulates the pattern of motor improvement in STN-DBS and its interaction with VF. In a within-subject-design, expectation of 24 hypokinetic-rigid PD patients regarding the impact of STN-DBS on motor symptoms was manipulated by verbal suggestions (positive [placebo], negative [nocebo], neutral [control]). Patients participated with (MedON) and without (MedOFF) antiparkinsonian medication. Motor function was assessed by Unified Parkinsons Disease Rating Scale and quantitative kinematic analysis of proximal alternating hand and distal finger tapping. VF was quantified by lexical and semantic tests. In MedOFF, expectation significantly affected proximal but not distal movements resulting in better performance in the placebo than in the nocebo condition. Placebo responders with improvement of ≥25% were characterized by a trend for impaired lexical VF. These results indicate that positive motor expectations exert both motor placebo and cognitive nocebo responses by further enhancing the STN-DBS-effect on proximal movements and by impairing VF. The placebo response on motor performance resembles the clinically known STN-DBS-effect with stronger improvement in proximal than distal movements. The nocebo response on VF is likely due to implicit learning mechanisms associated with an expectation-induced placebo response on motor performance.

High‐frequency oscillations in Parkinson's disease: Spatial distribution and clinical relevance

The pathophysiology of Parkinsons disease (PD) has been related to excessive beta band oscillations in the basal ganglia. Recent recordings from the subthalamic nucleus of PD patients showed that beta oscillations show strong cross‐frequency coupling with high‐frequency oscillations (>200 Hz). However, little is known about the characteristics and functional properties of these oscillations. We studied the spatial distribution of high‐frequency oscillations and their relation to PD motor symptoms. We included 10 PD patients in medication OFF who underwent implantation of deep brain stimulation (DBS) electrodes. Intraoperative five‐channel microelectrode recordings were performed at 9 to 10 recording sites within the subthalamic nucleus and its immediate surroundings. We found a focal spatial distribution of high‐frequency oscillations with highest power 2 mm below the dorsolateral border of the subthalamic nucleus. Within the subthalamic nucleus, power peaked slightly anterior to the DBS target site. In addition, contralateral akinesia/rigidity scores were negatively correlated with high‐frequency oscillation power. Our results demonstrate a focal origin of high‐frequency oscillations within the subthalamic nucleus and provide further evidence for their functional association with motor state.

Parkinsonian Rest Tremor Is Associated With Modulations of Subthalamic High‐Frequency Oscillations

High frequency oscillations (>200 Hz) have been observed in the basal ganglia of PD patients and were shown to be modulated by the administration of levodopa and voluntary movement.

Bicycling and Walking are Associated with Different Cortical Oscillatory Dynamics

Although bicycling and walking involve similar complex coordinated movements, surprisingly Parkinson’s patients with freezing of gait typically remain able to bicycle despite severe difficulties in walking. This observation suggests functional differences in the motor networks subserving bicycling and walking. However, a direct comparison of brain activity related to bicycling and walking has never been performed, neither in healthy participants nor in patients. Such a comparison could potentially help elucidating the cortical involvement in motor control and the mechanisms through which bicycling ability may be preserved in patients with freezing of gait. The aim of this study was to contrast the cortical oscillatory dynamics involved in bicycling and walking in healthy participants. To this end, EEG and EMG data of 14 healthy participants were analyzed, who cycled on a stationary bicycle at a slow cadence of 40 revolutions per minute (rpm) and walked at 40 strides per minute (spm), respectively. Relative to walking, bicycling was associated with a stronger power decrease in the high beta band (23–35 Hz) during movement initiation and execution, followed by a stronger beta power increase after movement termination. Walking, on the other hand, was characterized by a stronger and persisting alpha power (8–12 Hz) decrease. Both bicycling and walking exhibited movement cycle-dependent power modulation in the 24–40 Hz range that was correlated with EMG activity. This modulation was significantly stronger in walking. The present findings reveal differential cortical oscillatory dynamics in motor control for two types of complex coordinated motor behavior, i.e., bicycling and walking. Bicycling was associated with a stronger sustained cortical activation as indicated by the stronger high beta power decrease during movement execution and less cortical motor control within the movement cycle. We speculate this to be due to the more continuous nature of bicycling demanding less phase-dependent sensory processing and motor planning, as opposed to walking.

Parkinsonian rest tremor can be detected accurately based on neuronal oscillations recorded from the subthalamic nucleus

OBJECTIVE To investigate the possibility of tremor detection based on deep brain activity. METHODS We re-analyzed recordings of local field potentials (LFPs) from the subthalamic nucleus in 10 PD patients (12 body sides) with spontaneously fluctuating rest tremor. Power in several frequency bands was estimated and used as input to Hidden Markov Models (HMMs) which classified short data segments as either tremor-free rest or rest tremor. HMMs were compared to direct threshold application to individual power features. RESULTS Applying a threshold directly to band-limited power was insufficient for tremor detection (mean area under the curve [AUC] of receiver operating characteristic: 0.64, STD: 0.19). Multi-feature HMMs, in contrast, allowed for accurate detection (mean AUC: 0.82, STD: 0.15), using four power features obtained from a single contact pair. Within-patient training yielded better accuracy than across-patient training (0.84vs. 0.78, p=0.03), yet tremor could often be detected accurately with either approach. High frequency oscillations (>200Hz) were the best performing individual feature. CONCLUSIONS LFP-based markers of tremor are robust enough to allow for accurate tremor detection in short data segments, provided that appropriate statistical models are used. SIGNIFICANCE LFP-based markers of tremor could be useful control signals for closed-loop deep brain stimulation.

Bicycling suppresses abnormal beta synchrony in the Parkinsonian basal ganglia

Freezing of gait is a poorly understood symptom of Parkinson disease, and can severely disrupt the locomotion of affected patients. However, bicycling ability remains surprisingly unaffected in most patients suffering from freezing, suggesting functional differences in the motor network. The purpose of this study was to characterize and contrast the oscillatory dynamics underlying bicycling and walking in the basal ganglia.

Rejecting deep brain stimulation artefacts from MEG data using ICA and mutual information.

BACKGROUND Recording brain activity during deep brain stimulation (DBS) using magnetoencephalography (MEG) can potentially help clarifying the neurophysiological mechanism of DBS. The DBS artefact, however, distorts MEG data significantly. We present an artefact rejection approach to remove the DBS artefact from MEG data. NEW METHODS We developed an approach consisting of four consecutive steps: (i) independent component analysis was used to decompose MEG data to independent components (ICs); (ii) mutual information (MI) between stimulation signal and all ICs was calculated; (iii) artefactual ICs were identified by means of an MI threshold; and (iv) the MEG signal was reconstructed using only non-artefactual ICs. This approach was applied to MEG data from five Parkinsons disease patients with implanted DBS stimulators. MEG was recorded with DBS ON (unilateral stimulation of the subthalamic nucleus) and DBS OFF during two experimental conditions: a visual attention task and alternating right and left median nerve stimulation. RESULTS With the presented approach most of the artefact could be removed. The signal of interest could be retrieved in both conditions. COMPARISON WITH EXISTING METHODS In contrast to existing artefact rejection methods for MEG-DBS data (tSSS and S(3)P), the proposed method uses the actual artefact source, i.e. the stimulation signal, as reference signal. CONCLUSIONS Using the presented method, the DBS artefact can be significantly rejected and the physiological data can be restored. This will facilitate research addressing the impact of DBS on brain activity during rest and various tasks.

Oscillatory coupling of the subthalamic nucleus in obsessive compulsive disorder

Sir, We read, with utmost interest, the recently published paper in Brain (Accolla et al., 2016) that used the combination of neurophysiological recordings of local field potentials (LFPs) with MRI to investigate functional organization of the subthalamic nucleus (STN) in patients with Parkinson’s disease. Deep brain stimulation (DBS) electrode contacts that picked up beta oscillations, a well known signature of STNs in Parkinson’s disease, were used for whole brain probabilistic tractography seeding. Based on their data, the authors stated “that (i) beta oscillations are not restricted to a ‘motor’ STN area; and (ii) that the ‘motor’ STN is not connected exclusively with motor cortical areas”. The authors concluded ‘that beta oscillations have a main but not exclusive motor significance, and that STN might be organized following a topographical specialization’. We would like to provide additional data that underscore some of these assumptions by recordings from the ‘nonmotor’ STN and its oscillatory network in a different disease entity: obsessive compulsive disorder (OCD). DBS of the STN is a powerful treatment for Parkinson’s disease and recently also for severe OCD (Mallet et al., 2008). STN-cognitive-limbic oscillatory loops (e.g. with the cingulate gyrus) may play a key role in the modulatory effect of STN-DBS in OCD and emotional-cognitive side effects in Parkinson’s disease. Local STN oscillations (Bastin et al., 2014) but not patterns of STN connections in OCD have been investigated so far. For details of the scientific background, please see the online Supplementary material. To address this issue, especially concerning its differences to Parkinson’s disease, we now report on a case of OCD with combined LFP-MEG (magnetoencephalography) recordings. A 42-year-old female patient with severe OCD (washing compulsions) underwent bilateral DBS of the anteromedial (‘non-motor’) STN. Intraoperatively, resting multi-unit activity and LFPs were recorded simultaneously. DBS electrodes where externalized and postoperatively, resting simultaneous recordings of MEG and STN-LFP were performed (details in Supplementary material). Over a follow-up of 3 years the patient showed a stable and even ameliorating effect of DBS on the YaleBrown Obsessive-Compulsive Scale (Y-BOCS) compared to baseline (3 versus 39 points) (Wojtecki et al., 2016). Intraoperative LFP showed that local theta-, alphaand beta-oscillations were observed with dominance on the posterior trajectory in both hemispheres. In postoperative LFPMEG recordings, high beta coherence was apparent between the STN and the sensorimotor cortex, whereas theta-coupling was evident between the STN and the anterior cingulate cortex (ACC) (Fig. 1). Our example gives first insight into coherent STN-cortical oscillations in OCD. doi:10.1093/brain/awx164 BRAIN 2017: Page 1 of 3 | e1