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.