Prognosis of Parkinson Disease through Machine Learning

G.Raghavendra Prasad

Research Scholar, Department of Biomedical Engineering

National Institute of Technology, Raipur, Chhattisgarh, India

&

Assistant Professor, Amity University Chhattisgarh, Raipur,

India.

Dr. Arindam Bit

Assistant Professor, Department of Biomedical Engineering

National Institute of Technology, Raipur, Chhattisgarh,

India

Dr Shashikanta Tarai Assistant Professor, Department of Humanities and Social Sciences

National Institute of Technology, Raipur, Chhattisgarh, India

Abstract- Parkinson’s disease (PD) is one of the most widespread neurodegenerative diseases, disturbing 1% of the

inhabitants over 55 years of age (WHO Reports). Deep Brain Stimulation (DBS) being one of the successful and minimally

questioned invasive methodologies adopted for PD. Being Invasive, DBS has got its own limitations and complications. The

irresistible success of the DBS is driving most of the researchers towards noninvasive brain stimulation. The motivation

for its use is inspired from the DBS itself where the motor and non-motor problems relevant to PD are addressed

successfully. There are various attempts being suggested for Non - Invasive DBS, few of them are using transcranial

magnetic stimulation, which will address the problems related to the motor circuit related areas; transcranial direct

current stimulation , and theta-burst stimulation are few to name Electroconvulsive Therapy , formerly referred to as

Electroshock Therapy is also used to a certain extend in some cases. This Manuscript is a sincere attempt to reveal the

impactful role and scope of Machine Learning techniques to detect the PD. After illustrating the feasibilities and

limitations of existing techniques, potential ways of using fNIRS along with the Machine Learning Algorithm as a

prognostic tool in combination with other techniques for treatment of PD. We address the existing state of comprehension

of the capabilities of the non- invasive DBS with machine learning for management of PD and along with its future perspective.

Keywords – Noninvasive DBS, Parkinson’s disease, Prognosis, Neurotransmitter, Machine Learning Algorithm, fNIRS,

Stimulation.

I. INTRODUCTION

In the earlier Nineteenth century James Parkinson, a British physician termed and called PD as "the shaking palsy."

Parkinson's disease (PD) is a neuro degenerative disorder of the central nervous system. The pervasiveness of PD in

India is minimal in India when compared to other countries [1]. The total burden of PD is much higher as a result of

large population. An improvement of temporal input-output coupling of neuronal firing rates was suggested to

promote synaptic plasticity [2]. The four foremost motor sign symptoms are tremor or trembling in hands, arms, legs,

jaw, or head; rigidity, or tautness of the limbs and trunk; bradykinesia, or slothfulness of movement; and postural

instability, or impaired balance [3]. A concept called temporal interference (TI) was attained by modeling and

experimentation done on mice model. It was also proved that electric field envelope can be trialed by neuronal

activity in alive mouse. PD is a complex neurodegenerative disorder in which many poles apart pathophysiological

processes have been recognized in the nigrostriatal pathways and beyond, such as protein aggregation, oxidative

damage, mitochondrial dysfunction, lysosomal dysfunction and inflammation. It is therefore, not surprising that many diverse candidate biomarkers for disease progression in PD have been studied.

II. INFLUENCE OF DOPAMINE IN PARKINSON DISEASE

Dopamine is generally called as a signaling molecule. It transmits signals between nerve cells in order to

communicate or transfer a message. Hence it is also called a neurotransmitter. The brain cells release the dopamine to

the target cells to initiate a biological and chemical procedure. Dopamine is very effective over motor coordination.

Since it has got a huge impact factor over motor cortex, it is considered as a strong cause linked. It is proved that PD

patients on an average lose around 60% of dopamine neurons in substantia nigra which is responsible for generating

dopamine and pass to various parts of brain. In a healthy subject, the dopamine content neurons release dopamine in

the basic ganglia which will have a direct and indirect influence in initiating a movement over motor cortex region.

Whereas the PD patients will have trouble in movements due to the loss of dopamine content neurons.

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III. PRINCIPLES OF DEEP BRAIN STIMULATION

DBS has proved itself as an effective tool in the handling of Parkinson’s illness and some other critical movement

related disorders. Over last two decades, approximately 100,000 Parkinsonian primates were treated with DBS. It is

an invasive surgical procedural method by which a device like pacemaker for brain is implanted and modulated.

According to Neuro Modulation Society, DBS is one of the best methodologies to rebalance the Neural Circuits. In

DBS, sequences of electric impulses are directed to the affected portion of brain. By doing so, the affected neural

circuits are balanced and restored to their original functionalities. The entire procedure starts with a surgery which comprises three components. While performing the surgery in the brain, thin electrical leads/electrodes are inserted

deep inside the brain by guided expertise by drilling the skull.

The electrical leads/electrodes are placed in the following brain areas Subthalamic nucleus (STN), Thalamus (VIM)

and Globus pallidus (GPi). The electrical leads/electrodes will be acting as a bridge to deliver the required low

voltage electrical impulse which again will be decided by the experts according to the profile of the neural instability

to modify and restore the neural activity. A small instrument comprising a battery quite similar to a pacemaker for

heart is embedded underneath the collar bone of the subject. The Electrical leads/Electrodes are connected to the

battery by wires which are kept hidden under the skin. The electrical impulse as a trigger is given externally by a

handheld remote control. The trigger parameters are suggested by the experts and can be adjusted accordingly. The

controlling parameters differ from patient to patient. Food and Drug Administration (FDA) approved DBS in the year 1997 and permitted to use it to treat motor symptoms of movement disorders. DBS was identified to reduce the

tremor or the involuntary muscle movements. Before the introduction of DBS, procedures like thalamotomy and

pallidotomy were used to administer the motor symptoms by surgically demolishing the discrete parts of the brain.

After the evolution of the DBS, it became the most preferred treatment methodology which is not destroying the

brain tissues.

In brain STN and GPi are responsible for the neural imbalances caused. By targeting those areas through DBS will

address the solution for PD. The surgery is performed by two methods i) Awake Microelectrode-guided DBS and ii)

Asleep Interventional-MRI-guided (iMRI) DBS. The known symptoms and abilities are calculated by means of

Unified Parkinson Disease Rating Scale (UPDRS).

3.1 PD Management by DBS-

The world community looked into DBS as its only hope to fight against Parkinson’s Disease. Many Brain researchers

did research and submitted reports across the globe stating that DBS is having a better chance to resolve problems in

brain areas such as motor nerves, STN, basal ganglia etc. The scientific community also went ahead and tested the

implication of DBS on rat model. PD is emerging as a preferred technique to counter PD. Vincent J J Odekerken et

al [4] went on with a trial by assessing the deep brain stimulation on STN and GPi bilateral for advanced stage of PD.

They conclude that there was a significant improvement of motor symptoms by both the methods of DBS. Christos Sidiropoulos et al [5] mentioned about the influence of variable frequencies given during DBS, both Higher order

frequency (≥130 Hz) stimulation (HFS) and Lower order frequency (≤80 Hz) stimulation (LFS). They eventually

concluded that introduction of LFS for an axial symptom instead of HFS has no difference at all. José Fidel Baizabal

Carvallo et al [6] studied around 512 patients who got around 856 electrode implantations and introspected the

hardware complications associated with movement disorder patients. He concluded that about 8.6% had a hardware

impediment or system amendment, 2.5 % had an electrode fracture followed by infections of about 1.9%, electrode

misplacement of 1.9%, electrode migration of about 1.75%, and remaining with minor complications. Schlaepfer TE

et al [7] found that DBS is one of the better ways to significantly reduce symptoms in treatment- resistant foremost

depressive chaos and justified by means of Montgomery-Asberg Depression Rating Scale and some other general

standardized scales. It was also concluded that supero-lateral medial forebrain bundle (slMFB) DBS is one of the safe

and best methods for treatment-resistant depression.

Ilse S. Pienaar et al [8] indicated that STN-DBS reverts back the degree of vascular pathology in PD case. They

confirmed that the microvasculature of the STN takes an extent of degeneration in PD and those levels of proteins

which will alter this process. The actions are adapted through DBS stimulation. Fedor Panov et al [9] had studied 180

patients undergoing DBS for movement disorders. They also recorded abnormal oscillatory activity in basal ganglia-

thalamocortical motor loops from the brain cortex using electrocorticography (ECoG); which can provide high

amplitude signals. The study concluded that ECoG is a very good option for accessing critical regions in basal

ganglia and motor cortex for activation of neurons locally. Simon Little et al [10] concluded after a principle study

and successfully demonstrated that it is possible to perform Brain Computer Interface (BCI) controlled DBS in PD

patients. The results came out suggested that the BCI-DBS for PD was 30% more effective than conventional one, in

spite of delivering <50% of the stimulation performed using conventional DBS. Martijn Figee et al [11] subjected 16

OCD patients to fMRI data for anatomical registration purpose and EEG symptom-provocation procedure and concluded that DBS is one of the dependable solutions to restore fronto- spatial network activity and connectivity of

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the stimulated region. Jill L. Ostrema et al [12] studied the effect of frequency (LFS Versus HFS) by means of the

STN target in dystonia true for all types of primary dystonia and concluded that stimulating frequencies for basal

ganglia thalamocortical circuit varies for STN and GPi also suggested for a requirement of a better mechanism to

understand DBS in dystonia. Dorval, Alan D et al [13] proposed that the neuronal firing rates in PD

electrophysiology circuits should be replaced with the measures of neuronal information. It was also proved by the 6-

OHDA rat model that the therapeutic DBS partially restored information transmission.

IV. COMPLEXITIES ASSOCIATED TO DBS

Despite the procedure's efficacy, the community at large continued to be cautious about presumed allied risks. DBS

is a comparatively multifaceted remedy that requires regular neurological follow-up and battery changes often. DBS

surgery is advisable only when the eminence of existence is not adequate on therapeutical means as administered by

a movement disorders neurologist. There is a possibility that about 1% of the surgical candidates are vulnerable to

stroke causing a permanent death, due to bleeding in the brain, and a 2 -5% chance of infection. The complications of

DBS were majorly divided into three categories; i) Operation-related, ii) Hardware-related, iii) Stimulation-related

[14]. Since being a surgical procedure, there is always a possibility of internal haemorrage. There is also a possibility

of wrong placement of electrode leading to miss the stimulation for the physiological target. Hardware-failures or related complications are unpredictable such as fracture of the electrode, electrode migration. There exists a severe

threat that there may be a possibility of contagion after the removal of the electrode. The last of the three categories

comes to stimulation-oriented issues. One of them is sensorimotor conditional problems which is most common but

most of them are reversible and adjustable. The second important stimulation related problem is Psychiatric

conditions which seems to be bit hassle after surgery at certain cases. There were certain Life-threatening conditions

reported due to severe brain damage [14, 15]. According to Melbourne DBS association, around 1% of the people

who undergo DBS are experiencing a seizure after their surgery. 5% of the patients are prone to infection and around

2 % of patients require re- adjustment of the position of electrodes implanted.

V. STATE OF NON-INVASIVE DBS

Anthony Barker and colleagues first demonstrated Transcranial Magnetic Stimulation (TMS) in 1985 which is one is one of the most universally adept brain stimulus techniques. It is based on the principle of electromagnetic induction.

Two pieces of wires were placed nearby where the first one stimulates the coil and the second one was placed at the

targeted region of the brain. It was proved to be as one of the trustable techniques which is now also used in

combination with electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Electric pulses

are given either in single or in repetitive cycles. The repetitive TMS stimulates low or high frequency repeatedly and

is referred as (rTMS). Excitatory or Inhibitory stimulation effects are determined by the choice of stimulation

parameters. TMS has a greater impact in the cortical region and also has a limited impact on connected areas of

cortex [16].

TMS is also referred as an interference technique. It is majority now combined with all other potential available

techniques like MRI and fMRI to determine the stimulation site for a TMS study. They are also being used along with EEG to enhance our temporal partition of behaviour [16]. It was also proved that rTMS of motor cortex has a

positive impact on the release of dopamine in the putamen. The electromagnetic induction created by the rTMS is

much stronger when compared to the TMS; hence rTMS has long lasting changes in the brain when compared to the

other. The limitations of these mechanisms are that they cannot penetrate thick into the brain due to the property of

electromagnetism. The coils used are also hefty; hence it is a bit difficult to target specific area of brain with this

mechanism. Transcranial Direct Current Stimulation (tDCS) is called as one of the simplest neurostimulation

method. Two electrodes; cathode and anode are connected to a source and placed on various specific locations on the

scalp, permitting the delivery of current between the devices and the electrodes. The cathode stimulates the left

dorsolateral prefrontal cortex and the anode stimulates the frontopolar. tDCS technique can be opted for personal

means without the requirement of a physician prescription. This technique found to improve various cognitive

functions. Electroconvulsive Therapy is also a part of non-invasive brain stimulus techniques which is strictly

implemented in controlled and guided atmosphere and only opted as a last choice of treatment due to the usage of intentional, controlled high current levels of around 800 milliamps which lasts for few seconds. This technique has

got severe side effects.

5.1 Non Invasive DBS management for PD

Robert Schulz et al [17] commented that transcranial magnetic stimulation (TMS) and transcranial direct current

stimulation (tDCS) did wonder to the motor cortex and cognitive functions in PD patients .It was also addressed that

these noninvasive stimulation techniques should be addressing following concerns i) optimal time point of stimulation merits, ii) simultaneous stimulation to different cortical areas, iii) motor recovery, and iv)stimulation

parameters from various age groups. Fregni, et al [18] concluded that noninvasive methods such as TMS exhibit

various fundamental uses such as modulation activity in focal epilepsy and PD. They also suggested that it helps in

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the resto ration of disrupted neural network. David H. Benningera et al [19] reviewed that the therapeutic potential of

rTMS, and tDCS had got minimal side effects regarding the excellence of the life of the recipients. It was also

suggested that a closed loop stimulation system is required to decode the optimal stimulation pattern which may

include real time processing of signals. Nir Grossman et al [20] reported a new noninvasive strategy for stimulating

neurons in brain. They delivered a dynamic range of frequencies at higher frequency to stimulate neuron and they

validated the temporal interference (TI) by various modeling and other physical experiments.

VI. FUNCTIONAL NEAR INFRARED SPECTROSCOPY (FNIRS); ALTERNATIVE NONINVASIVE

MANAGEMENT FOR PD

The recent shift in trend focusing towards fNIRS emphasizes the significance of non-invasive brain stimulation. The

current trend signifies the flexibility in diagnosing particular neurodegenerative disease. The approach towards

fNIRS along with some other signals like EEG may be lethal enough to unlock the potential towards these

noninvasive brain stimulation methods. In 1881, Abney and Festing measured near infrared spectroscopy (NIRS)

using photographic plates. NIRS shows tremendous promise for a range of research and clinical applications and also

it uses safe non -ionizing light. It is a best potential prospect for clinical research perspective. fNIRS is a type of

functional neuroimaging technology which helps in monitoring the brain functionalities such as blood flow in frontal brain either directly or indirectly. The sensor belonging to the technique tracks and records the movement of the

subject with respect to the oxygenation levels of the brain before and after the task. There are very few studies

combining NIRS and DBS which can contribute to hybrid therapeutic techniques. The studies done over fNIRS

depicts that it decreases the supplementary motor area activity and premotor areas of subjects of PD [21]. The study

went on and concluded that fNIRS detected cortical activity.

Cheng-Hsuan Chena et al [22] proved that visually-induced NIRS can be taken advantage to devise a brain computer

interface (BCI) system. The precision received with that experimentation was around 90%. Ahsan Abdullah et al [23]

studied and mentioned that NIRS can be used to understand specific cognitive function in brain since it is having a

high spatial and temporal resolution. In order to do that they did one-way clustering which can automatically reduce

the complexity of generating large group of spatio -temporal activity results. Noman Naseer et al [24] examined the

effects of classification modalities of two fNIRS based BCI and found that Artificial Neural Networks (ANN) has the highest classification accuracies when used in two dimensional and three-dimensional feature sets. Yvonne Blokland

et al [25] suggested methods for improving performance of brain switches based on sensorimotor rhythms (SMR) for

recipients with tetraplegia. They pooled and used EEG-fNIRS classifier to attain good performances. Audrey

Girouard et al [26] devised a new real-time fNIRS system (i.e. online fNIRS analysis and classification: OFAC).

They performed an online classification with the previous input and the current one. The study achieved precision of

85% which was the first of its kind working real time passive BCI using fNIRS. Bailei Sun [27] suggested that

optical neuronal signals in the visual cortex region can be detected using continuous wave near-infrared

spectroscopy. He performed this by using continuous wave (CW) NIRS system with high uncovering understanding.

Electrophysiological data plays an important role in determining the preferential aspect and effect of stimulation. It is

also notified that high frequency stimulations may have an effect over cortical and sub cortical motor loops [28].

Stimulation method improved cardinal features and activities of daily living [29].

VII. MACHINE LEARNING AND HYBRID APPROACH

Various researchers have used a variety and combination of features to predict Parkinson disease. The Robust

contribution factor and dependability aspect of Machine Learning algorithms made them a stand out choice of

researchers to implement them in hybrid systems where they can minimize the errors.

In recent past Supervised machine learning has been used as a key tool for discovering medical imaging biomarkers.

MRI biomarkers act as a major player in the diagnostic process. The support vector machine technique provides the entire hybrid system flexibility to sensitize and classify the differences that are subtle and spatially distributed [30].

Parkinson’s disease diagnosis can be targeted by using a combination of signal processing features and machine

learning methods. By quantifying and classifying the features from the voice signal the PD is addressed with a

customized machine learning algorithm [31]. Further such as combinations of non-motor imaging markers along with

SVM classifiers is capable of addressing neurodegenerative disorders thanks to the multimodal approach [32]. As a

further scope, there may be wide compatible multimodality applications developed to take care of wide range of

neurological disease that affects distributed regions of neuronal network.

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VIII. PROPOSED HYBRID FLOW DESIGN FOR PD PROGNOSIS

Figure 1: PD Prognosis

The Proposed model aims at the potential parkinsonian primates based on their clinical symptoms. Signals

like fNIRS, EEG are applied over the target group and possible biomarkers are identified. The signals from

the test subjects are analyzed and features are extracted and classified from them by designing specific

classifiers for the need of PD. A machine learning algorithm will be taking care of train ing and testing of the

data sets which are obtained from the target groups. Finally, analysis of the trained and tested output pattern

gives the verdict. The suggested prognosis method is a simple attempt to combine fNIRS signal and tDCS

signals effectively in order t o m on i t o r the p r e-frontal cortex r egions to obser ve th e a bn or m a l i t i e s

exhibited such as dopamine emission, oxygenated level of brain regions in the specific area of requirement.

Figure 2: Likelihood estimation of PD

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The training and testing phase of the data sets are unfolded by selecting appropriate features and extracting the

feature vectors from them by customized algorithms and classification techniques. Noninvasive brain stimulation techniques using fNIRS can be a solution to address the cortex area activities. It is possible to

develop real-time data analysis schemes to assess brain activation at the same time as data collection. The real

time-based signal monitoring in combination with other techniques for complex targeted areas of brain could be

useful in finding an optimal signal in PD affected patients for deriving a diagnostic pattern. This could also

contribute to the diagnosis and treatment of PD. It can be helpful in brain mapping through which PD can be

diagnosed. fNIRS can also be used to understand the pathogenesis of PD.

IX. CONCLUSION

In this review a detailed discussion is done comparing the conventional DBS with the new emerging non-

invasive multimodal hybrid techniques. There is a requirement of state-of-the-art technology and technique to

prognosis the PD which in large bothers the world. There are researches rising to occasions with multimodality

approaches trying to address different aspects of the disease. Combining with emerging technologies, it can be lethally implemented to activate neurons. The firing of the neurons can be made possible by coupling with

techniques like tCDS. fNIRS has got a huge potential and it will be playing a very important role not only in

prognosis of PD, but also in the diagnosis mechanism. There exist some other models such as Closed-loop

cortical stimulation for the treatment of advanced PD [33]. But still, fNIRS looking at its potential it may the

area where the new revelations start in terms of prognosis of PD. The future of PD prognosis may go smart,

sensible, mobile and economical in the natural environment due to the versatile approach of Machine learning

tools which will be an inspiring factor and works constantly towards the possibility of minimizing mean errors

in prognosis with respect to neurodegenerative diseases. Artificial Neural Networks implications in terms of

neurodegenerative disease prognosis is becoming a reality which will change the insight of approaching diseases

such as PD in rear future.

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