Can detection and prediction models for Alzheimer's Disease be applied to Prodromal Parkinson's Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures

Abstract

Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

Keywords: digital neuro signatures; prediagnostic Parkinson's disease; prodromal Parkinson's disease.

Figure 1.. Flowchart showing the overall dataset structure and the prelim study purpose.

Figure 2.. The motoric functioning tasks in the Altoida test.

These are executed one after another. Using their index finger of their dominant hand, from left to right, the task is to 1) draw a circle, 2) draw a square, 3) draw a rotated W shape within 7 seconds, 4) draw as many circles as possible within 7 seconds, 5) tap the highlighted buttons (left, right, left, right, etc.) 6) tap the highlighted button as fast as possible, the buttons highlight at random.

Figure 3.. Illustration of the Augmented Reality (AR) task in the Altoida test.

During the AR test, the subject is asked to place and find three virtual objects in the room. To do so, the subject is required to walk around the room holding a tablet or smartphone device in front of him/her. While doing so, the camera of the device records the environment and displays it back to the user on the screen, augmented with virtual objects (in this illustration, a teddy bear). The user needs to place the objects on flat surfaces and later recall their position by walking back to that location.

Figure 4.. Feature importance of the Prodromal-PD classifier.

A) The top twenty feature groups according to the SHAP method. Each bar represents the summed SHAP value of the features in that feature group. B) A feature value SHAP distribution plot for the top five contributing features. Subject specific SHAP values were computed for each datapoint in the classifier training data. For each feature, we then plot for each datapoint a dot with the feature value of that datapoint, with the dot color coded by the relative feature value. The position of each dot on the SHAP value x-axis represents the magnitude and the direction of the contribution of that specific feature value of that specific datapoint towards classifying as female (-1) or male (+1). Acronyms in the plots are Augmented Reality (AR), Fast Fourier Transform (FFT), SHapley Additive exPlanations (SHAP), Accelerometer (ACC), variance (var), first part of a single test (1st) or second part of a single test (2nd).