Data-Driven Characterization of Neuronal Markers During Deep Brain Stimulation for Patients With Parkinson's Disease

Parkinson Disease Clinical Trial

Official title:

Data-Driven Characterization of Neuronal Markers During Deep Brain Stimulation for Patients With Parkinson's Disease

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03079960
• Other study ID #: P001449
• Status: Recruiting
• Phase: N/A
• Start date: April 4, 2017
• Est. completion date: December 30, 2021

Study information

• Verified date: July 2021
• Source: University Hospital Freiburg
• Contact: Volker Coenen, Prof. Dr.
• Phone: +49 761 27050510
• Email: volker.coenen[@]uniklinik-freiburg.de
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has developed into a standard therapy in the refractory stage of Parkinson's disease (PD). Implanted micro- and macroelectrodes can be used to derive neural signals from the basal ganglia (BG). Cortical signals can be obtained by measurements of the electroencephalogram (EEG) or the electrocorticogram (ECoG). Both signal types can be used to characterize the motor system of the patient and make it possible to estimate the effectiveness of a currently performed DBS. However, the relationship between such neuronal features on the one hand and the DBS stimulation parameters or the observable clinical effects on the other hand is very individual and varies from patient to patient.

The aim of the present study is to: (1) determine neuronal characteristics that are informative about the clinically relevant motor status of PD patients. (2) The investigation and description of the complex non-stationary dynamics of neuronal characteristics as a consequence of changing DBS stimulation parameters. (3) The study of the effect of changing DBS stimulation parameters on motor performance.

The three objectives form an important building block for future adaptive closed-loop DBS strategies (aDBS). Here, the stimulation parameters are to be adapted in the single-trial and depending on the currently detected motor state of the patient. Since this is accessible only to a very limited extent, it is to be investigated whether information about the motor state can be obtained from the neural features.

Description:

Deep brain stimulation of the subthalamic nucleus (STN DBS) has developed into a standard therapy for treating refractory stages of Parkinson's disease (PD). The large number of DBS systems nowadays routinely implanted represent open loop technology. These so-called continuous DBS (cDBS) systems are relatively simple from a technical perspective, as they deliver uninterrupted high-frequency stimulation pulse trains typically 24 hours a day. The stimulation is applied to the target area, like the STN, without taking into account the current level of PD symptoms or the motor state of the patient. Changes to the stimulation parameters -like pulse width, amplitude or frequency- can be applied only by a trained expert during a so-called adjustment session, which usually takes place in the clinic. This limits the number of adjustment sessions to at most a few per year. This may be sufficient to adapt the system to long-term changes of a patient's state as induced by PD progress, which take place over months and years, but certainly is not sufficient to react upon varying daily conditions or changes on even smaller temporal scales. Despite being a widely accepted approach, cDBS is known to cause several side effects such as speech impairment or tolerance to treatment due to chronic continuous stimulation, and has disadvantages with regard to energy efficiency and battery life of the implanted stimulation device.

In contrast to the available cDBS systems, it would be desirable to have adaptive DBS (aDBS) systems, that provide stimulation on demand only and, for example, reduce or stop stimulation delivery during periods of inactivity or when the motor performance of the patient is sufficiently high. Even though a few aDBS prototypes have been reported in literature, they are investigated in research contexts only and have not yet been included into clinical routines.

To realize the closed loop control of a patient's motor symptoms by an aDBS approach, at least one information source describing the motor state of the patient is required. On the one hand, this information may be accessible via external sensors or wearables, which record e.g. muscle tone, tremor, kinematic information etc. in every-day situations or during the execution of specific motor tasks. Alternatively, the information may also be expressed by specific brain signals, so-called neural markers, which correlate with the motor state and can act as its surrogate.

Informative neural markers can be extracted from several brain areas and with different recording technologies. Activity in the subthalamic nucleus (STN) and other basal ganglia can be measured both during and after the implantation of the DBS electrodes in the form of local field potentials (LFP) or microelectrode recordings (MER). Signals recorded either during stimulation, from small time windows between stimulation sequences, or with stimulation absent can provide information about the clinically relevant motor state of PD patients. Additionally, it has been shown that neural signal recordings via magneto- or electroencephalogram (MEG/EEG) and electrocorticogram (ECoG) may provide valuable complementary information compared to the signals obtained from basal ganglia.

On a clinical level, the motor state of the patients can be assessed using part III of the Unified Parkinson's Disease Rating Scale (UPDRS-III) test battery. Its assessment, however, is rather time consuming and requires the involvement of a clinician (neurologist) and consequently the full UPDRS-III score cannot be used for a aDBS implementation. Unfortunately, with the current state of research, the information about the motor behavior cannot simply be replaced by information collected via brain signals. The reasons is, that the relation between relevant neural markers of the LFP and MER recordings, and the individual motor symptoms (e.g. as described by the UPDRS-III) is far from complete and requires further investigation.

To characterize candidates of neural markers, which can be utilized as surrogates for the motor state, it is important to investigate two questions: (1) (How) does the marker change upon applying DBS? (2) Is this change related to the clinical effects of DBS observed e.g. a change in the UPDRS-III score? In this context, selected oscillatory components have been described. The power of LFP oscillatory components in the beta range (12-30 Hz) has been reported to drop upon DBS and, despite unclear causal relation and action mechanisms, it has also been correlated to motor parkinsonian symptoms as bradykinesia and rigor. Furthermore, the interaction of band power of other frequency components with specific PD motor symptoms has been described. An example is the relation between the delta and gamma band power recorded from the STN with dyskinetic symptoms and the correlation of high gamma band power with UPDRS-III scores, and the modulation of high gamma through DBS or L-Dopa. Additionally, DBS stimulation has also been observed to influence cross-frequency coupling between cortical-cortical, cortical-subcortical and subcortical-subcortical structures.

Most studies on the effect of DBS on the motor system and on informative neural markers report on global effects observed in group studies. However, grand average findings may not provide sufficient information to control aDBS systems for an individual patient. This is underlined by many recent studies from the field of brain-computer interfaces (BCI), where informative neural signatures have been found to be subject-specific, and where subject-specific methods for extracting informative neural markers have been applied successfully. Hence we propose to refine the level of data analysis beyond the level of group statistics.

Apart from neural markers being subject-specific, the implicit dynamics of both, the neural markers and the DBS effects, should be considered:

• Dynamics of the neural markers Even within an individual user and a single day, the adaptation of DBS parameters may be required in order to compensate non-stationary characteristics displayed by neural markers on several temporal scales : (a) On the scale of hours to minutes, due to, e.g., changes in wakefulness/tiredness or circadian cycle. (b) On the scale of minutes to seconds, variations e.g. in the attention level, workload. (c) On even smaller time scales due to the current status of the motor system (task preparation vs. task onset vs. sustained ongoing tasks, high force vs. precision tasks, isometric vs. movement tasks etc.). It must be expected, that the individually informative neural markers, which can be exploited to realize the closed-loop aDBS system, are subject to change their informative content in the above-mentioned time scales and scenarios.

• Dynamics of the DBS effects Depending on the DBS parameters (e.g. intensity, frequency, duration, pulse shape) of the stimulation pattern applied in the immediate past, the effects onto (1) the motor system and onto (2) the informative neural markers are known to persist from several seconds to minutes even after stimulation has been turned off [Bronte-Stewart et al. 2009]. Due to this washout effect of DBS, the stimulation strategy of an aDBS system will probably benefit from taking the (short term) stimulation history into account. The duration and temporal dynamics of this so-called washout period depends on the kind of motor symptom studied. It has been reported to be longer for akinesia (minutes - hours) as opposed to rigidity (minutes). Thus it can be hypothesized, that the dynamics of the washout effects for the motor symptoms and for the neural markers are not the same.

The applicants of this proposal want to make a substantial step forward into the direction of a fully closed-loop aDBS system. To reach this goal, it is necessary to develop data analysis methods for brain signals, which are capable of identifying the aforementioned informative neural markers, and to utilize them as input to decode the current motor state. For both tasks, machine learning methods have been successfully investigated and utilized in the context of closed loop BCI systems. Methods developed in this field allow for single-trial decoding of non-invasive EEG signals and invasive signals like ECoG and LPF. The machine learning methods enable the detection of movement intentions in single-trial and the decoding imagined or executed movements. Furthermore, latest research of the applicants has shown, that BCI approaches allow to even predict the task performance of an upcoming motor task, which may be valuable information for brain state dependent closed-loop applications.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 120
• Est. completion date: December 30, 2021
• Est. primary completion date: December 30, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 35 Years to 75 Years

Eligibility

Inclusion Criteria:

1. Male or female patients aged = 35 and = 75 years

2. Patients with diagnosed PD according to UK PDS Brain Bank Criteria.

3. Written informed consent.

4. For PG-O and PG-pre, patients who are eligible for STN DBS Surgery according to the guidelines of the DGN (www.dgn.org)

5. For PG-chronic, patients who have received permanent DBS implantation in the past and who use the DBS treatment.

Exclusion Criteria:

1. MR Imaging shows a contraindication for microelectrode recordings. If imaging shows a high amount of blood vessels in the target region and no safe trajectory for inserting the microelectrode can be found, then the patient may receive implantation of the macroelectrode without preceding microelectrode measurements, but is excluded from the study.

2. Contraindication for stereotactical neurosurgery.

3. Dementia (Mattis Dementia Rating Score = 130)

4. Acute psychosis stated by a psychiatric physician

5. Unable to give written informed consent

6. Surgical contraindications

7. Medications that are likely to cause interactions in the opinion of the investigator

8. Fertile women not using adequate contraceptive methods: female condoms, diaphragm or coil, each used in combination with spermicides; intra-uterine device; hormonal contraception in combination with a mechanical method of contraception;

9. Current or planned pregnancy, nursing period

10. Contraindications according to device instructions or Investigator's Brochure:

1. Diathermy (shortwave, microwave, and/or therapeutic ultrasound diathermy)

2. Magnetic Resonance Imaging (MRI)

3. Patient incapability

11. Patients to be expected poor surgical candidates

For PG-chronic, only exclusion criteria 3, 4, 5, 7, 8, 9, 10 are applicable, since electrodes are already implanted, thus, no surgical procedure is necessary.

Related Conditions & MeSH terms

• Parkinson Disease

Intervention

Device:
• Electrophysiological recording and measurement devices
Externalization of DBS connectors and macroelectrodes for simultaneous STN stimulation LFP recordings by the use of electrophysiological recording and measurement devices.

Locations

Country	Name	City	State
Germany	Medical Center - University of Freiburg - Clinic for Neurosurgery - Dept. of Stereotactical and Functional Neurosurgery	Freiburg im Breisgau	Baden-Württemberg

Sponsors (2)

Lead Sponsor	Collaborator
Prof. Dr. Volker Arnd Coenen	University of Freiburg

Country where clinical trial is conducted

Germany, 

References & Publications (42)

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* Note: There are 42 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Correlation of stimulation parameters and motor performance	For each patient, a linear regression model will be trained to predict motor performance (target variable) given a stimulation parameter set (predictor). The r-value of each of the trained models across all subjects will be compared against the r-values obtained from resampled bootstrap models. Statistical significant differences between estimated and bootstrapped models will be assessed by a Wilcoxon test with a significance level of 5%. Endpoint is prediction of motor performance as assessed by the r-values of the estimated models.; Stimulation parameters will include current (mA), frequency (Hz) and impulse width (µs). Motor performance will be evaluated by various motor tests (comparable to UPDRS).	Days 1-4 after neurosurgery
Secondary	Correlation of motor performance and informative neural markers	For each patient, the Pearson correlation between (1) the beta band power and the performance in the short motor tasks and (2) the best multivariate neural marker obtained by our models with the performance in the short motor tasks will be computed. The correlations obtained across all subjects will then be compared under the two conditions. Statistical significant difference between multivariate and beta markers will be estimated by a pairwise Wilcoxon test (significance level of 5%). Endpoint is prediction of motor performance as assessed by the r-values of the estimated models.; Motor performance will be evaluated by various motor tests (comparable to UPDRS) and beta band frequency levels. Informative neural markers will be assessed by electroencephalograms (EEG), electromyelograms (EMG) and physiological parameters (e.g. respiratory frequency).	Days 1-4 after neurosurgery
Secondary	Correlation of stimulation parameters and informative neural markers	Analogue to the primary endpoint, a linear regression model is trained, which learns to predict the values of multivariate neural markers based on stimulation parameters. Again, we compare the r-values of the estimated models and of the corresponding models obtained after bootstrap resampling for each subject. Statistical significant differences between them will be assessed by a Wilcoxon test (significance level of 5%). Endpoint is prediction of neural marker values as assessed by the r-values of the estimated models.; Informative neural markers will be assessed by electroencephalograms (EEG), electromyelograms (EMG) and physiological parameters (e.g. respiratory frequency).	Days 1-4 after neurosurgery