Transcranial Electrical Stimulation in Rem Sleep in Parkinson’S Disease

TRANSCRANIAL ELECTRICAL STIMULATION IN REM SLEEP IN PARKINSON’S DISEASE

THESIS FOR MASTER’S DEGREE IN BIOMEDICAL ENGINEERING

AUTHOR: SARA TERESA BRUNAL MORELO

ADVISOR: MARIO VALDERRAMA MANRRIQUE, PhD CO-ADVISOR: GUILLERMO MONSALVE, MD

UNIVERSIDAD DE LOS ANDES FACULTAD DE INGENIERÍA DEPARTAMENTO DE INGENIERÍA BIOMÉDICA BOGOTÁ D.C, COLOMBIA 2019

Transcranial electrical stimulation in REM sleep in Parkinson’s disease

Sara Teresa Brunal Morelo [email protected].

Abstract Parkinson’s disease (PD) is a neurodegenerative disease and is often accompanied by sleep disorders, mainly the REM sleep behavioral disorder. This disorder not only affects sleep quality but cognitive performance of the person. At present, the existing treatments for Parkinson’s disease are highly invasive and are focused on improving only motor performance. Due to that, in this paper, a pilot study was proposed in which a non-invasive alternative such as transcranial direct current stimulation (tDCS) during REM sleep was applied to one healthy and one PD subject in order to evaluate its implications not only in motor performance but in sleep architecture and cognitive performance. The protocol was applied for three days and motor and cognitive tests were performed by the subjects before and after the stimulation sessions. The results indicate that tDCS significantly improved motor response by decreasing the power of sensorimotor and beta rhythms. The stimulation also significantly improved the PD subject’s sleep architecture but showed no significant effect on cognitive performance. These findings suggest that tDCS in REM sleep might be an useful alternative for the treatment in PD patients who suffer from sleep disorders. Key words: Parkinson’s disease, REM sleep, transcranial direct current stimulation, motor performance, sleep architecture, cognitive performance.

1 Introduction incidence reaches 60%. RBD is a parasomnia in which the normal paralysis of skeletal muscles (atony) is lost in REM sleep, so people "act out"; their dreams and have Parkinson’s disease (PD) is a neurodegenerative disease abnormal movements and vocalizations [5]. RBD occurs which tends to manifest in adulthood. This disease is frequently in people with PD and other dementias of characterized by a progressive loss of dopaminergic neu- Lewy bodies because the subcortical, neocortical and rons located mostly in the substance nigra pars compacta brainstem areas that are damaged by these diseases are (SNpc) (in the midbrain) as well as the appearance of the same areas involved in the regulation of atony during abnormal insoluble alpha-synuclein protein aggregates, REM sleep [6]. In addition, the effects of RBD in people also known as Lewy bodies [1]. Due to the inadequate with PD have been investigated and it has been con- functioning of the dopaminergic neurons, a functional cluded that this parasomnia is an important risk factor in imbalance is generated in the projections of these towards mild cognitive impairment and cognitive dysfunction [7] [8]. the striated nucleus, which in turn generates an alteration in the circuits of the basal ganglia and an abnormal On the other hand, currently the most commonly oscillatory activity in the cortico-basal circuit. Thus, used treatment to reduce PD symptoms is pharmaco- the abnormal activity generated in the cortico-striatal- logical (specifically with Levodopa). However, such thalamic pathways leads to the manifestation of motor medication has been proven to not being effective for all problems such as tremor at rest, slowness of movement, patients and, in the long term (4 to 6 years), may lead to rigidity and postural instability. However, there are motor fluctuations and dyskinesias (involuntary move- also non-motor symptoms of PD, such as apathy, depres- ments) in these, which is undesirable [9]. Because of these sion, cognitive deficits, sleep disorders, among others [1] [2]. complications, both invasive and noninvasive treatment alternatives have been explored. The most commonly Regarding sleep disorders, it has been found that used invasive treatment is deep brain stimulation, or DBS, these are very common in PD, with prevalence as high in which high frequency electrical impulses are generated as 90% in the early stages [3]. Some sleep problems in in specific regions of the brain by implanted electrodes. PD include: difficulty initiating sleep, sleep fragmen- The subthalamic nucleus (STN) is one of the most widely tation, early awakening, excessive daytime sleepiness, used target regions in DBS, whose stimulation has been restless leg syndrome, periodic limb movement, and shown to generate improvements in akinesia symptoms, REM sleep behavior disorder [4]. Among the sleep stiffness, tremor and postural instability [10]. Although disorders mentioned above, the most frequent in people DBS generates an improvement in motor symptoms, it has with PD is the REM sleep disorder or RBD, where the

2 been observed that it is not very effective with respect to – To adapt a real-time sleep stage classification al- the improvement of non-motor symptoms and sometimes gorithm. worsens such conditions (apathy, cognitive and affective – To validate the REM sleep detection algorithm disorders, temporary psychosis). In particular, it has been in real time. observed that DBS does not lead to an improvement in RBD or restless legs syndrome [11]. – To design an electrical stimulation system (tDCS) in REM sleep in real time. Due to the fact that DBS does not offer comprehensive treatment of PD symptoms and is highly costly and • PART 2: System implementation in Subjects risky, noninvasive stimulation treatment alternatives have been addressed, most commonly transcranial magnetic – To implement and validate the system devel- stimulation (TMS) and transcranial direct current electrical oped in healthy subjects. stimulation (tDCS). Such stimulations have been shown to generate improvements in either motor or non-motor – To implement and validate the system devel- symptoms in people with Parkinson’s disease. However, oped in Parkinson’s disease patients. these studies have been performed on a reduced number – To quantify the effect on motor and cognitive of people, so they do not correspond to standardized performance and sleep architecture of tDCS in treatments yet. In particular, the application of anodal healthy and PD patients. tDCS on the motor cortex (M1) has been observed to result in improved motor symptoms of PD ( [12], [13], [14]), while tDCS on the dorsolateral prefrontal cortex generates an improvement in the cognitive performance and executive 3 Materials and methods functions of the patient ( [15], [16], [17]). 3.1 REM sleep detection algorithm In addition, this type of electrical stimulation can be applied during sleep. In a study by Speth and co- The algorithm used to classify the different sleep stages workers [18], it was found that tDCS during REM sleep was implemented by Diego Martínez Mejía, a master’s in healthy people can considerably improve the motor degree in biomedical engineering student at Los Andes imagination of them. Thus, the authors propose this type University. This algorithm was initially created by Hassan of stimulation as a possible treatment for people with and collaborators in 2015 [19]. It receives as input a motor deficits where the motor imagination is widely single EEG channel and this signal is divided in 30 affected, as is the case of Parkinson’s disease. Taking this seconds epochs. Then, 10 different spectral features hypothesis into account, the present study corresponds are extracted from each epoch: the mean, variance, to a pilot study in which the application of tDCS in REM skewness, kurtosis, spectral flatness, spectral centroid, sleep in PD patients is addressed, in order to observe spectral spread, spectral decrease, spectral roll-off and if there is a significant improvement in the motor and spectral slope. This features has been proved to be cognitive performance of the patient, as well as in the statistically different among the sleep stages: awake (0), quality of the patient’s sleep. light sleep (1 and 2), deep sleep (3 and 4) and REM sleep (5).

The training of the algorithm was done with the Phys- ionet Sleep EDF-Database [20], This signals correspond to caucasic men and women with ages between 21 and 2 Objectives 35 years without any sleep disorders and no medication. The signals were sampled at 100 Hz and each one has 2.1 General Objective three polysomnography signals: Fpz-Cz (channel 1), Pz-Oz (channel 2) and electrooculogram (EOG; channel 3). For To construct and analyze the impact of a transcranial direct the training and test of the model, 29 randomly selected current stimulation (tDCS) in REM sleep system in people signals were used. For the validation of the algorithm, 9 who suffer from Parkinson’s disease (PD) in terms of motor signals also randomly selected were used. Various mod- and congnitive performance as well as sleep architecture. els were trained using the three channels mentioned above in order to observe which one showed the highest detec- 2.2 Specific objectives tion accuracy (compared to the expert annotations in the database). Once the classification model was chosen, dif- The study is divided into two main parts: ferent tests were performed where a simulated or EEG real time signal of a person was sent to the algorithm in order to • PART 1: Construction of the REM sleep detection observe if it was capable of performing an efficient analysis and stimulation system. in a short period of time.

3 3.2 Transcranial electrical stimulation device in which he suffered from tremors. In addition, the patient suffered from mild cognitive dysfunction that started a year The stimulation device used was the one designed by ago, with subtle changes in spelling and memory. He had David Henao (PhD student at Los Andes University). It normal sleep and eating patterns. The healthy subject was consists of various components in order to modify the stim- a 22 year old male, without any type of sleep disorders. ulation input signal and bring it to the desired current in- The protocol was approved by the Ethics Committee of Los tensity. Thus, the device is connected to an Arduino, which Andes University with the correspondent informed consent sends a certain voltage (maximum of 5 volts) to an INA 128 where the procedure was explained in detail along with its instrumentation amplifier, which eliminates the offset cre- possible secondary events. ated by the Arduino, this signal is sent to a second INA 128, which amplifies the signal according to the resistance (Rg) that is placed. The amplified signal is sent to an operational amplifier LM324n, which does not amplify, but 3.4 Intervention montage and protocol eliminates the incoming impedance. Finally, this signal is sent to terminals where the electrical stimulation electrodes In this study, a tDCS protocol was applied with a total are connected. In the figure 1 the different parts of the de- duration of three consecutive days on a daytime three- vice can be seen: hours nap. An anodal stimulation was performed using the device previously described through a pair of saline- 1. Arduino signal input ports, two triggers, power and soaked sponge electrodes (5 x 7 cm) over the motor cortex ground voltages. with a current intensity of 2 mA. The anode was placed over the C3 position according to the international 10- 2. First INA 128 instrumentation amplifier. 20 EEG system, whereas the cathode was positioned in 3. Second INA 128 instrumentation amplifier. the supra-orbital contralateral area (above the right eye). Motor and cognitive tests were applied to the PD subject 4. Terminals for manual or digital potentiometer. one week before the first session and one week after the last session. The motor assessment was administered by 5. LM324 operational amplifier. Guillermo Monsalve, neurosurgeon, and the cognitive as- 6. Output current terminals of the two stimulation elec- sessment by Pilar Mayorga, neuropsychologist, both from trodes with their respective ground. Santa Fe Foundation. For the healthy subject, a cognitive task was applied one day before the first stimulation ses- 7. Ouput terminals for the triggers with their respective sion and one day after the last stimulation session. ground.

3.5 Sleep architecture analysis and motor and cognitive performance tests

For motor assessment of the PD subject, the UPDRS test, part III: motor examination was administered, which in- cludes scores for tremor, slowness, stiffness and balance. On the other side, for cognitive assessment, a battery of tests was applied that evaluated the following functions: language (semantic and phonological verbal fluency), attention (TMT parts A and B), memory (CERAD, ADAS-COG), visual-constructional performance (complex figure of Rey- Osterrieth and bells test) and cognitive functions (Stroop test and spatial working memory). For the healthy subject, only the attention test (TMT-A) was applied, as well as a hand-eye coordination task in which speed and ac- Figure 1: Transcranial electrical stimulation device with its curacy was obtained. Finally, to quantify the change in parts. sleep architecture in both subjects, for each stage of sleep the power was extracted from its most representative fre- 3.3 Subjects quency bands (see figure 2). After this, the power was compared in the same EEG signal before and after the stimula- The stimulation procedure was applied on one healthy and tion. Additionally, the change of power in the sensorimo- one PD subject for comparative purposes. The PD patient tor and beta rhythms, which play important roles in the was a 62 year old male, with a disease duration of 3 years. process of activation of the motor cortex and motor imag- The patient had main affectation in the right upper limb, ination, was analyzed.

4 patient anyway in stimulation sessions in order to visually conﬁrm REM sleep presence.

4.1.2 Real-time simulations and system refinement The above mentioned tests were performed offline, i. e. the full signal analysis was performed at the same time. After this, different simulations were performed with Figure 2: Representative frequency bands extracted for Matlab R2015b software, where the signal was sent in real each sleep stage. time (by TCP/IP communication) and where the algorithm was executed in 30-second windows of the signal, with no overlap. The algorithm showed a delay of 2 seconds, 3.6 Statistical analysis which, for the purposes of the study, is insignificant. For each test, an ANOVA one-way analysis was performed, A simulation was also performed with the OpenBCI with a 5% significance. Thus, pre and post-stimulation Cyton, in order to see the quality of the signal acquired. scores were compared for motor tests, as well as cogni- Thus, the simulated signal (from the validation database) tive tests. In the latter, the results for each cognitive func- was sent to a voltage divider (see figure 4) through an NI tion were grouped and normalized with respect to the pre- ELVIS II, in order to reduce the amplitude of the voltage stimulation score. Regarding the sleep architecture, the to a microvolt scale. The signal was received through powers of the different frequency bands were also nor- Python (version 2. 1), which, through the lab streaming malized with respect to the pre-stimulation powers of the layer software, was sent to Matlab, where the signal was signal. analyzed by 30-second windows as mentioned before.

4 Results

4.1 REM sleep detection and stimulation system 4.1.1 REM sleep detection algorithm

Figure 4: Voltage divider scheme.

Afther that, the algorithm detection tests with the Open- BCI (sampling frequency of 250 Hz) were performed on 6 healthy subjects (age 21-25 years) during a daytime nap. Figure 3: ACA for each trained model either with channel The bipolar channel Fpz-Cz was connected to the Open- 1, 2 or 3, for the 9 patients of the validation database. BCI, as well as a reference. The subjects slept for an hour and a half to two hours. Figure 5 shows a schematic of the Figure 3 shows the Average Classification Accuracy (ACA) EEG signal processing performed. In these sessions, al- obtained for the model trained with each channel for each though the stimulation signal was sent to Arduino One, no signal in the validation database.As can be seen in this electrical stimulation of the individuals was performed, i. figure, the highest ACA was obtained by the model trained e. no electrodes were connected for such stimulation. This with channel 1 (Fpz-Cz), with an ACA of approximately is because the goal was only to evaluate the algorithm’s 85%. Because of this, this was the model chosen for REM behavior with real EEG signals. However, it was observed sleep detection. Although the model trained with the EOG that, even though the signal was filtered between 0.3 and channel had a low ACA, this channel was connected to the 30 Hz and saturated to 100 and -100 in amplitude, some

5 noise was still present, so the detection algorithm wrongly cess of the right mastoid bone. In figure 7 it can be seen detected some sleep stages. This was verified by doing that the acquisition with this device considerably reduced the wavelet transform of the signal and comparing it to the the noise in the signal and, in consequence, the sleep stage hypnogram obtained (see figure 6). classification was more accurate.

Figure 7: Sleep stage classiﬁcation and wavelet transform in Micromed LTM. Figure 5: Signal processing scheme.

4.2 System implementation in healthy subject

4.2.1 Motor performance

In order to see if there was a change in the motor cortex excitability during sleep, the power of sensorimotor and beta rhythms were analyzed before and after the stimulation. Figure 6: Sleep stage classiﬁcation and wavelet transform These rhythms were chosen given their close relationship in OpenBCI. to movement in vigilance and motor imagery in sleep.

Given this inconvenience, other sessions of real-time acqui- Figure 8 shows the mean normalized power of the sen- sition was performed on the Micromed LTM device (sam- sorimotor and beta rhythms before and after the stimula- pling frequency of 256 Hz) in order to reduce the amount tion. The results sugest that there was a decrease in the of noise in the signal. The signal processing was the same power of both sensorimotor (22% reduction) and beta (14% as the one shown in 5. These sessions were done in 5 reduction) rhythms after the stimulation. In this case, the healthy subjects with ages between 21 and 25 years in day- one-way ANOVA analysis indicated that the diﬀerence be- time naps of an hour and half of duration. The channel tween pre and post stimulation was only signiﬁcant in the recorded was the same (Fpz-Cz) with ground in the pro- sensorimotor rhythm.

6 Figure 8: Normalized power in the sensorimotor and beta band before and after stimulation. *p<0.05 Figure 10: Normalized power of sleep stages frequency bands pre and post stimulation in healthy subject.

4.3 System implementation in PD subject 4.2.2 Cognitive performance 4.3.1 Motor performance Figure 9 shows the normalized results of the three cognitive Figure 11 shows the power of the sensorimotor and beta tests applied. It can be seen that the speed of realization rhythms before and after the stimulation. A consider- of the task slightly decreased, the accuracy stayed stable able decrease in the power of both sensorimotor and beta and the score on the attention test improved. However, rhythms after the stimulation can be seen. The difference the ANOVA analysis showed that none of these changes between pre and post stimulation was significant in both were statistically significant. rhythms, with a 53% and a 76% reduction, respectively.

Figure 9: Cognitive performance of healthy subject, normalized scores. Figure 11: Normalized power in the sensorimotor and beta band before and after stimulation. *p<0.05

4.2.3 Sleep architecture 4.3.2 Cognitive performance Figure 12 shows the results of the cognitive tests applied Figure 11 shows a comparison of the normalized power of to the PD subject. It can be seen that the scores on the ex- the representative frequency bands of the different sleep ecutive functions, visual constructional, memory and lan- stages, between pre and post stimulation signals. It can guage tasks slightly decreased in comparison to the pre- be seen that the stimulation slightly reduced the power of stimulation tests. It can also be seen that there was an in- sleep stages 0 (awake) and 1 (light sleep). On the contrary, crease in the score of the attention test. However, although the stimulation increased the power of sleep stages 2 (light none of these differences were statistically significant, the sleep), 3 (deep sleep), 4 (deep sleep) and 5 (REM). However, neuropsychologist reported to have observed in the patient none of these changes were statistically significant. an improvement in the speed of processing and response,

7 as well as a better attentional performance in front of pro- 5 Discussion cesses of sustainment, division, tracking and selection of the attention. She also reported a slight decrease in patient per- In the present study, a REM sleep detection and stimula- formance regarding executive function tasks, specifically in tion system was constructed, validated and implemented the working memory and creation of access routes to infor- in order to analyze the consequences of applying tDCS mation. Finally, she reported not having observed changes during REM sleep on motor and cognitive aspects as in the form and content of language, verbal memory and well as on sleep architecture in a healthy person and in constructional praxias. a person with Parkison’s disease. The results show that this type of stimulation resulted in a significant change in sleep architecture and in the power of sensorimotor and beta rhythms in the PD subject. Likewise, it was observed that tDCS did not generate a significant change in the cognitive performance of neither subject and did not generate a change in the sleep architecture of the healthy subject.

In the motor aspect, a significant decrease in the power of the sensorimotor and beta rhythms was observed after applying the stimulation in the subject with PD and only of the first rhythm in the healthy subject. In different studies, the close relationship between the power of these rhythms and the activation of the motor cortex has been observed. Thus, there is a desynchronization or reduction in the power of the sensorimotor and beta rhythms in tasks that involve the activation of the motor cortex, such as performing a movement (during wakefulness) or even Figure 12: Cognitive performance of PD subject, normal- in tasks of motor imagination (in sleep or wakefulness), in ized scores. which the same areas of the cortex are activated as when performing a movement [21] [22] [23]. Thus, the decrease in the power of the sensorimotor and beta rhythms 4.3.3 Sleep architecture observed in this study reflect an activation of the motor cortex, which is in line with previous studies where it has Figure 13 shows a comparison of the normalized power of been reported that this type of stimulation increases the the representative frequency bands of the different sleep excitability of the motor cortex, both in wakefulness [24] stages, between pre and post stimulation signals. It can be and in sleep through an increase in motor imagination [18]. seen that the stimulation considerably reduced the power On the other hand, it has been reported that the power of sleep stages 0 (awake) by 31% and 1 (light sleep) by 44%. of the aforementioned rhythms presents an imbalance On the other hand, the stimulation increased the power in people with PD, in whom there is no decrease but of sleep stages 2 (light sleep) by 37%, 3 (deep sleep) and 4 an increase in the power of these oscillations in motor (deep sleep) by 17% and 5 (REM) by 46%. The ANOVA one- tasks [25], so that the stimulation carried out in this study way analysis indicated that all of the changes mentioned would suppose a normalization of these when decreasing above were statistically significant. them, thus improving their motor response.

Regarding cognitive performance, although the neuropsychologist stated that she observed an improvement in the processing speed and attentional performance of the PD patient, the statistical analysis revealed that there was no significant difference in any of the evaluated areas (language, memory, executive functions, visual- constructional and attention) in the PD patient, as well as no significant difference in the tests performed by the healthy subject. This is consistent with several authors in the literature, who show improvements exclusively in motor performance of people with PD by performing tDCS on motor cortex, rather than cognitive [12] [13] [14].

Figure 13: Power of sleep stages frequency bands pre and Finally, regarding sleep architecture, it was found post stimulation in PD subject, normalized. *p<0.05 that tDCS signiﬁcantly modiﬁed, in the PD patient, the

8 power of the frequency bands representative of each 6 Conclusion sleep stage thus generating: a significant decrease in the potency of the alpha (stage 0) and theta (stage 1) rhythm Parkinson’s disease is a complex disease that encompasses and a significant increase in the power of the sleep both motor and cognitive symptoms as well as sleep dis- spindles (stage 2), delta rhythm (stages 3 and 4) and eye orders. The treatments that currently exist for this disease movement (stage 5 or REM) and this same effect but not have proven not to treat its symptoms comprehensively. statistically significant in the healthy subject. This result is In the study carried out here it was found that transcra- in accordance with studies in which transcranial electrical nial electrical stimulation applied during REM sleep can stimulation was applied in REM sleep, in which a slight become a new alternative treatment for this disease, as it but not significant decrease in the potency of stages 0 and generated a significant improvement in the activation of 1 and an increase in stages 3, 4 and 5 of sleep is reported the motor cortex and the architecture of the patient’s sleep. in healthy subjects; thus maintaining the sleep architecture However, studies covering a larger population and a longer in these as the difference [26]. On the other hand, with stimulation time are required in order to standardize such respect to the change in sleep architecture in the PD a protocol. patient, as there are yet no studies of tDCS during sleep in this disease, it is not possible to compare results with the References literature. 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