Predictive Diagnosis: A Novel Machine

Learning Method for the Early Detection

of Parkinson's Disease

Literature Review

Chaitanya Krishna Tej Suraparaju

Table of contents:

Introduction………….………….…………………………….…….………………………….………...1

Parkinson’s Disease: An Overview……………………………….……………………………….…….1

Dopaminergic Neurons…………………………….…………………………………………….1

Disease Pathophysiology ………….………………….………………………………………….2

Current Diagnostic methods ………….………………….…………………………………………….3

Clinical Examination………….…………………………………………………………………3

DaTscans ……………………………….………………..…………………………………….....5

Newly Emerging Diagnostic Methods ……………………………….……………….………..……….6

MRI Scans and Voxel Based Morphometry………………………………………...………….6

Predictive Models – An Overview ……………………………….……………….…………....……….7

Statistical models vs. Machine Learning Methods.……………………………………...…….7

Accuracy Evaluation ……………………………….……………………………………..…….8

Conclusion……………………………………………….….…………………………………………..10

Suraparaju 1 Introduction

Parkinson’s Disease (PD) is categorized as a chronic and progressive disease that affects millions of people worldwide. Some of the most prominent symptoms of PD include bradykinesia, tremors, muscle stiffness, and cognitive decline, all of which can worsen over time (Williams-Gray & Worth,

2020). As of 2020, there are approximately 60,000 Americans diagnosed with PD, and this number is projected to rise to 1.2 million Americans by the year 2030 (Marras, Beck, Bower, Robert, Ritz, Ross et al., 2018). Parkinson’s is best treated if detected early; however, even as the prevenance of PD increases in our population, there is still no diagnostic method that will accurately predict the presence of the disease. Current methods revolve around a neurologist that conducts a physical examination of the patient and evaluates the presented symptoms (Kassubek, 2014). The issue with this method is that the patient will get a diagnosis, and subsequently a treatment, only after they start showing symptoms, at which point it is too late to reverse the damage done by the disease. In order to address this concern, the project described here aims to develop a novel machine learning algorithm that can accurately predict the presence of PD in a patient before they even start showing symptoms. This is done by training the algorithm to recognize the tell-tale signs of Parkinson’s as seen on an MRI scan. The algorithm will also be trained to determine the probability of developing PD in the future if the current patient scans do not indicate any signs of the disease. This will allow for the early detection of Parkinson’s, and the patient will receive the right medication at the right time.

Parkinson’s Disease: An Overview

Dopaminergic Neurons

Parkinson’s disease (PD) is a disorder of the nervous system that is categorized by death of dopaminergic neurons (DNs) in the diencephalon, mesencephalon, and the basal ganglia of the brain.

DNs are a structurally similar group of neurons that release dopamine, which is a neurotransmitter involved in several cognitive as well as motor pathways. They are mostly found in parts of the midbrain Suraparaju 2 that are responsible for receiving and processing information from sensory as well as motor neurons

(Campbell & Reece, 2012). Special structures found in the Mesencephalon known as the Substantia

Nigra (SN) and Basal Ganglia (BG) contain large amounts of dopaminergic neurons. The neuronal pathways found in the Substantia Nigra and the Basal Ganglia play an essential role in the proper control of voluntary motor movement like walking, riding a bicycle, or even talking. Furthermore, the neurons found in the mesolimbic and mesocortical pathways are involved in facilitating cognitive and emotion- based behavior such as motivation, reward, and perception of relationships. In structures such as the hippocampus, amygdala, and the septum pellucidum, dopamine is involved in the formation/storage of generic and emotional memories (Chinta & Andersen, 2005). Refer to Figure 1 in order to get a better understanding of the anatomy of these neuronal pathways.

Figure 1: Dopaminergic pathways in the brain. This figure depicts the meso-cortic, meso-limbic, as well as the

nigrostriatal pathway.

Chinta, S. J., & Andersen, J. K. (2005). Dopaminergic neurons. The International Journal of Biochemistry & Cell

Biology, 37(5), 942–946. https://doi.org/10.1016/j.biocel.2004.09.009

Disease Pathophysiology

Although PD can be caused by several factors, symptoms are mainly experienced because of the degeneration of dopaminergic neurons. Although the exact reason as to why these neurons degenerate is unclear, prior research has revealed that DNs are more prone to oxidative stress because of their high rate of oxygen metabolism, low levels of antioxidants, and high iron content (Chinta, et al., 2005). This Suraparaju 3 prolonged stress might eventually result in cell death, and since neurons do not undergo mitosis, the damage done to the brain tissue is never repaired. Hence, as significant amounts of brain tissue start to die, the patient will begin to experience severe symptoms. The voluntary motor control center is especially affected, which is why bradykinesia, defined as a generalized slowness of movement, is the most common symptom of Parkinson’s. Additionally, the mesolimbic and mesocortical pathways are also negatively affected, resulting in a lack of emotional awareness in the patient. Lastly, destruction of brain matter in the hippocampus and amygdala causes dementia, resulting in the patients being unable to access old memories and form new ones effectively.

Current Diagnostic methods

Clinical Examination

As of right now, the standard test for PD is for a neurologist to conduct a physical examination and evaluate the presented symptoms (Kassubek, 2014). Essentially, a physician will observe the presented neurological symptoms, as well as the patient’s family history, and produce a diagnosis.

When conducting the exam, the neurologist will specifically look for the following symptoms: bradykinesia, rigidity, tremors, and gait/balance abnormalities. Bradykinesia is defined as a generalized slowness of movement, and it can especially be seen when patients struggle to initiate a particular action of motion. This symptom is observed in almost every patient with Parkinson’s disease, which makes it an essential marker for diagnosis (Stanford Medicine 25, 2020). In order to determine the presence of bradykinesia, the neurologist will instruct the patient perform rapid alternating movements. If the range of motion and/or the speed of motion appears to reduce over time, it is likely that the patient is showing signs of bradykinesia (Stanford Medicine 25, 2020). Next, the physician will check if the patient is exhibiting signs relating to muscle rigidity. This is defined as increased resistance to passive movement within multiple joints, and usually starts on one half of the lateral plan and later spreads to the other half.

To determine if a patient is showing symptoms of muscle rigidity, the examiner will passively rotate the Suraparaju 4 patient’s wrist and evaluate the subjective amount of resistance given by the patient’s joint (Stanford

Medicine 25, 2020). If they feel that rigidity is present, then they will mark this symptom off as being present.

After checking for muscle rigidity, the physician will then observe the presence of tremors, which are categorized as an involuntary contractions and relaxations in the patient’s voluntary muscles.

To test for the presence of this symptom, the neurologist will ask the patient to remain seated and not to make any movements. If the patient’s appendages begin to involuntarily shake, then the existence of tremors is likely observed (Sandford Medicine 25, 2020). Lastly, the examiner will evaluate the patient’s gait and balance abilities. Patients with PD usually develop alterations in the postural reflexes that causes stability in gait and balance control. Such alterations are documented to develop later in the course of the illness and have high potential to cause serious injuries or even death. To perform this part of the clinical exam, the neurologist asks the patient to walk back and forth several times, ideally, in a hallway that is at least 10 feet long. While the patient is walking, the neurologist will look for any signs of loss of balance on the turns, reduced step length, loss of heel strike, and loss of arm swings. If any of these signs are present, the neurologist will finally produce a Parkinson’s diagnosis for the patient.

Although conducting a clinical test has been an industry standard ever since the discovery of PD, conducting exams as described above actually have several drawbacks. It is clear that such a method for disease diagnosis is extremely subjective, which means that diagnostic results vary based on the individual physician. For example, a doctor might dismiss some subtle Parkinson’s symptoms, such as muscle rigidity, as simply a sign of fatigue if the patient admits that they are sleep deprived when detailing their family history. This will result in a misdiagnosis and may be fatal to the patient.

Furthermore, a clinical examination can diagnose the patient only after they start showing the major symptoms of Parkinson’s. Once a patient begins to show severe motor or cognitive deficiencies, it means that PD has already started running its destructive course, and there is not much that can be done Suraparaju 5 to slow it down. As a result, clinical examinations are an extremely poor tool to use for the early detection of Parkinson’s disease.

DaTscans

Because of the drawbacks of clinical examinations, the need for a more objective diagnostic method arose. Hence, researchers in the field turned towards neuroimaging as a way to produce objective and infallible evidence for the exitance of PD. In 2011, the FDA approved a new type of brain imaging known as DaTscan in hopes that a more objective diagnosis system for Parkinson’s Disease will become the new industry standard (Gilbert, 2020). These scans involve the injection of a radioactive tracer molecule, known as Ioflupane (123I), into the bloodstream. Once this tracer crosses the blood-brain barrier and enters the circulatory system of the brain, it binds with the dopamine neurotransmitter vesicles, which are molecules found in dopaminergic neurons. Several hours after the tracer has been injected, images showing the presence of the tracer molecule are taken (Gilbert, 2020).

If the patient has a less than normal presence of Ioflupane (123I), it means that they do not have the normal amount of DNs in their brain, and a PD diagnosis is likely to be given. Refer to Figure 2 in order to see these results in action.

Figure 2: Side-by-side comparison of DaTscans of a healthy patient and a patient with PD

Gilbert, R. (2020, February 27). What is a DaTscan and should I get one? American Parkinson Disease Association.

Retrieved December 08, 2020, from https://www.apdaparkinson.org/article/what-is-a-datscan-and-should-i-

get-one/ Suraparaju 6

While DaTscans are definitely more objective than a clinical examination, it still has its drawbacks. Firstly, conducting a DaTscan is extremely expensive, with prices ranging from $2,500 to

$5,000 per scan (Gilbert, 2020). More importantly, however, DaTscans are also only effective at determining the presence of PD if the disease has already started running its destructive course.

Systematic review has shown that DaTscans only have a 38% accuracy rate when detecting early

Parkinson’s (Galbraith, 2016). In other words, most patients with early Parkinson’s showed a normal

DaTscan. Hence, there is no clear evidence that DaTscan is accurate in diagnosing early Parkinson’s.

Newly Emerging Diagnostic Methods

MRI scans and Voxel Based Morphometry

An MRI scan, which stands for Magnetic Resonance Imaging, is a versatile imaging system that produces three dimensional pictures of the internal body without exposing the patient to an ionizing radiation agent. The most common form of MRIs come in T1 or T2 weighted images. T1 refers to the time taken for regrowth of a magnetization constant, and T2 refers to the time taken by the magnetics signal to decay in the transverse plane (Weiger & Pruessmann, 2019). Essentially, the major difference between the two forms is that fluids, such as the cerebrospinal fluid, appear brighter on a T2 weighted image, and dense tissue segments such as cysts appears brighter on a T1 image. Recently, researchers have started to notice that PD can actually be detected on simple T2 weighted MRI scans. In fact, two major main methods for the detection of PD on MRI scans have been documented. Firstly, it has been reported that if there is any noticeable atrophy in the dopaminergic regions of the brain such as the diencephalon, mesencephalon, and the olfactory bulb, then a PD diagnosis is likely (Chen, Zhu, Liu,

Liu, Yuan, Zhang, et al., 2020). The amount of atrophy is most commonly found by looking at the total volumes of the brain regions. However, because MRI scans are 2D images, volumes of structures cannot be accurately found without first constructing a 3D model of the brain. Suraparaju 7

To solve this issue, a computer-aided morphometric technique known as voxel-based morphometry (VBM) can be applied to the MRI scans in order to extract the objective amount of atrophy present in certain areas of the brain (Chen et al., 2020). In essence, VBM involves a voxel wise comparison of regional gray matter density that can be seen on an MRI scan between two patients. For clarification, the value for density in VBM techniques refer to the relative amount of gray matter, not the density of cell packaging (number of cells per unit volume of a ganglion). The procedure consists of two major steps: spatially normalizing and segmenting high-resolution MRI scans and then performing volumetric analysis on the image segments. Spatial normalization involves warping all the gray matter images present in the MRI file into the same stereotaxic space. A stereotaxic space can be thought of as an analogue to the 3D cartesian plane in that it consists of three coordinate sets that allows for the precise location of brain sections as described by each coordinate point (Ashburner & Friston, 2009).

Once the scans are spatially normalized, voxel wise parametric statistical tests are performed, which compare the smoothed gray matter images from the groups using statistical parametric mapping.

Ultimately, the relative level of atrophy in the brain can be successfully extracted from these 2- dimensional MRI segments in order to produce a PD diagnosis. Using VBM is not only an extremely objective diagnostic method, but it also allows for great amounts of predictability because of the fact that it is entirely computer aided. This topic will be further discussed in the next section.

Predictive Models – An Overview

Statistical models vs. Machine Learning Methods

Two of the most common forms of predictive models are statistical analyses and machine learning (ML) methods. Some examples of statistical analysis include univariate/multivariate functions, time series and panel data, and linear regression models. All of these models deal with a family of probability distributions, and predicting future data points (Firth, n.d.) While statistical models offer a wide range of applications, they are, for the most part, static. They do not have the ability to evolve and Suraparaju 8 change their decisions based on newly presented variables. Machine learning algorithms, on the other hand, excel at evolution. They are able to alter their decisions in real time, with little to no interference from an outside physician. For example, most statistical models would not be able to account for the fact that different individuals have different brain sizes. This might falsely diagnose a patient with PD because they less over amount of brain tissue, when in reality, the reduced brain matter is actually normal for that patient. ML models, on the other hand, can be trained to adjust their decisions based on the individual brain matter in real time since the model is constantly learning. To further rectify this idea, researchers compared the overall effectiveness of machine learning models when classifying existing brain atrophy to the results show by statistical models such as Gaussian process regression and partial least squares regression. The study concluded that the machine learning model was able to achieve a success rate of 96%, whereas the Gaussian process regression and partial least squares regression were only able to achieve an accuracy rates of 79% and 82% respectively (Zhang, Tian,

Chen, Ma, Li, Guo, 2019). Hence, machine learning models are generally considered to be better when performing predictive analyses such as the ones described here.

Accuracy Evaluation

In order to analyze the effectiveness of the machine learning model at predictively diagnosing

Parkinson’s Disease, a receiver operating characteristic curve (ROC) can be graphed. This curve is based on two parameters: the true positive rates (TPR) and the false positive rates (FPR) as obtained from the tests. The value for TPR is the amount of test data that the model was accurate in classifying, and the FPR value is the amount of test data that the model failed to classify. The ROC curve plots TPR vs. FPR at different classification thresholds. Lowering the classification threshold classifies more items as positive, which increases both False Positives and True Positives. Once the ROC curve is plotted, the area under the curve (AUC) needs to be measured in order to get an accurate sense of the model’s performance. This is because the AUC is the probability that the model ranks a random Suraparaju 9 positive result more highly than a random negative result. Hence, calculating the AUC will give a numeric value that represents the effectiveness of the machine learning algorithm (Lasko, Bhagwat, Zou,

Ohno-Machod, 2005)

One of the simplest ways to calculate the AUC is by employing the empirical method, which is a non-calculus technique. Essentially, two data points on the ROC are first connected to form a straight line, and then the estimated area of the space under that line is calculated using the trapezoidal rule. The distance between the two chosen data points is extremely small, which means this process essentially sums up the area of large amounts of trapezoids in order to find the total area under the curve (Lasko et al. 2005). In mathematical terms, this can be thought of as finding the area presented under a non- parametric comparison function. For example, if d1, d2, d3, all the way up until dnD are to represent the test values for PD patients, and h1, h2, h3 all the way until hnH represent the test values for healthy patients, then a comparison function for this scenario can be written as the expression shown in figure 3.

Figure 3: Definition of the comparison function used to calculate the area under an ROC

Lasko, T. A., Bhagwat, J. G., Zou, K. H., & Ohno-Machado, L. (2005). The use of receiver operating

characteristic curves in biomedical informatics. Journal of Biomedical Informatics, 38 (5), 404–415.

https://doi.org/10.1016/j.jbi.2005.02.008

This function can then help calculate the AUC since the estimated area under the ROC can be thought of as the average value of the comparison function for all possible pairs of diseased vs. healthy subjects.

Refer to figure 4 in order to see in this in mathematical terms (Lasko et al., 2005). Suraparaju 10

Figure 4: Final equation to calculate the area under an ROC curve

Lasko, T. A., Bhagwat, J. G., Zou, K. H., & Ohno-Machado, L. (2005). The use of receiver operating characteristic

curves in biomedical informatics. Journal of Biomedical Informatics, 38(5), 404–415.

https://doi.org/10.1016/j.jbi.2005.02.008

Essentially, the expression above describes the sum of the areas of the large amounts of trapezoids discussed earlier. Hence, this method can be used to calculate the area under the curve for a receiver operating characteristic curve.

Conclusion

From the existing research in the field, it is clear that there still is not a predictive diagnostic method that will detect the presence of Parkinson’s Disease before a patient starts to exhibit symptoms.

The overall aim of this project is to contribute to this area and develop a machine learning algorithm that can analyze patient MRI scans and detect PD before the disease starts its destructive course. A set consisting of MRI scans from both PD patients as well as healthy controls will be used as the main dataset. Then, voxel-based morphometry techniques will be applied to each MRI scan in order to create a three-dimensional model of the patient’s brain. Finally, data regarding tissue atrophy and surface morphometry will be fed to a machine learning algorithm that utilizes past cases of PD in order to make a prediction as to whether or not the patient depicted in the particular MRI scan will develop the disease in the future. From this research project, a novel machine learning algorithm that has the potential to become the industry standard for diagnosing Parkinson’s can be developed.

Suraparaju 11

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