Periodic Reporting for period 1 - ParSyncR (Improving quality of life of Parkinson’s disease patients by Quantitative Cognitive Testing)
Período documentado: 2023-11-01 hasta 2025-04-30
Parkinson’s Disease is the second most common neurodegenerative disease, affecting over 6 million people – a figure expected to grow rapidly with an aging population. While it is generally considered a motor disease, over 90% of all patients develop cognitive symptoms over years of disease progression. Patients, families and clinicians recognized the unmet medical need of treating these cognitive aspects alongside the motor disabilities. However, current tools to assess fine-grained aspects of cognitive changes are lacking, preventing more efficient diagnostics and therapy in this domain.
We aimed to address this by introducing ‘Quantitative Cognitive Testing’ (QCT): high-throughput cognitive tests based on human psychophysics and animal experiments that quantify multiple aspects of learning relevant to neurodegenerative disease, combined with high-precision experimental neurophysiology and machine learning-based data processing. For behavioral assessment, we chose a ‘Stop Signal Reaction Time Task’ (SSRT) that can quantify cognitive parameters including, but not restricted to impulsivity, decision threshold, inhibitory control and motor learning. To efficiently combine this with electroencephalogram (EEG) recordings, we aimed to deliver prototypes of a medical device capable of millisecond precision synchronization of stimuli, behavioral and neurophysiology data. We planned real-world testing of the cognitive task combined with EEG on 15 patients with Parkinson’s Disease recruited in the clinical centers we partnered with for this project. Finally, we aimed to produce a machine learning classification of large-scale brain-behavior data registered in patients with Parkinsons’s Disease, including data collected through this grant.
We aimed to address this by introducing ‘Quantitative Cognitive Testing’ (QCT): high-throughput cognitive tests based on human psychophysics and animal experiments that quantify multiple aspects of learning relevant to neurodegenerative disease, combined with high-precision experimental neurophysiology and machine learning-based data processing. For behavioral assessment, we chose a ‘Stop Signal Reaction Time Task’ (SSRT) that can quantify cognitive parameters including, but not restricted to impulsivity, decision threshold, inhibitory control and motor learning. To efficiently combine this with electroencephalogram (EEG) recordings, we aimed to deliver prototypes of a medical device capable of millisecond precision synchronization of stimuli, behavioral and neurophysiology data. We planned real-world testing of the cognitive task combined with EEG on 15 patients with Parkinson’s Disease recruited in the clinical centers we partnered with for this project. Finally, we aimed to produce a machine learning classification of large-scale brain-behavior data registered in patients with Parkinsons’s Disease, including data collected through this grant.
The first task of this POC grant was to validate our device capable of millisecond precision synchronization of visual stimuli, patient button presses and neurophysiology (e.g. EEG, electromyogram (EMG)) signals (coined SyncR), as well as to validate our SSRT protocol. We ensured full functionality and failsafe operation and manufactured five functional prototypes. While SyncR is protected as a utility model, we sought to broaden IP protection by submitting an international PCT application, according to our IPR plan. Our PCT application has reached the national phase, in which we seek to protect the device in Europe, US and China, covering the largest potential markets. We carried out full market research including SWOT/PESTLE analysis, market assessment, strategy planning, market entry strategy, manufacturing strategy, business planning and cost structure analysis, branding and distribution.
Next, we performed SSRT testing after recruiting n = 15 adult, non-parkinsonian volunteers to improve the task paradigm we developed. The following main questions were posed regarding the suitability of the paradigm. (i) What is the correct strategy for stop signal delay (SSD) adjustments during the task? (ii) What is the optimal proportion of stop trials? (iii) What is optimal trial number to achieve accurate SSRT estimates? Task parameters were determined and optimized based on the behavioral results.
In agreement with our project plan, we performed real-world testing of QCT and SyncR on 15 patients with Parkinson’s Disease, recruited by our neurologist partners at the Neurology Clinic of Semmelweis University. Patients were tested in 10-minute sessions first using their right hand, then using their left hand. The SSRT task was combined with 60-channel EEG recording and EMG recording of lower arm muscles that control finger movements. We found that the SSRT task was capable of quantifying cognitive parameters including inhibitory control, impulsivity and motor learning. The SyncR device was capable of millisecond precision synchronization of visual stimuli, electrophysiology signals and patient reactions in a real-world scenario.
In addition to standard spectral based EEG analyses, we performed machine learning analysis of EEG recordings from patients with Parkinson’s Disease in three phases. In the first phases, a literature and database search were performed to assess state-of-art AI models and publicly available datasets. Datasets were filtered based on relevance in consultation with a neurology expert. In the second phase, a foundation model for EEG was constructed. Recordings showed clustering by disease labels but not by recording length or by source datasets. These confirm that project-goal-relevant features were encoded in the recording embedding, thus validating our foundation model. In the third phase, we included novel EEG data recorded in course of this POC project. The classifier trained on the public datasets achieved over 90% performance, confirming that EEG collected during the SSRT task carried information on neocortical features relevant to Parkinson’s Disease.
Next, we performed SSRT testing after recruiting n = 15 adult, non-parkinsonian volunteers to improve the task paradigm we developed. The following main questions were posed regarding the suitability of the paradigm. (i) What is the correct strategy for stop signal delay (SSD) adjustments during the task? (ii) What is the optimal proportion of stop trials? (iii) What is optimal trial number to achieve accurate SSRT estimates? Task parameters were determined and optimized based on the behavioral results.
In agreement with our project plan, we performed real-world testing of QCT and SyncR on 15 patients with Parkinson’s Disease, recruited by our neurologist partners at the Neurology Clinic of Semmelweis University. Patients were tested in 10-minute sessions first using their right hand, then using their left hand. The SSRT task was combined with 60-channel EEG recording and EMG recording of lower arm muscles that control finger movements. We found that the SSRT task was capable of quantifying cognitive parameters including inhibitory control, impulsivity and motor learning. The SyncR device was capable of millisecond precision synchronization of visual stimuli, electrophysiology signals and patient reactions in a real-world scenario.
In addition to standard spectral based EEG analyses, we performed machine learning analysis of EEG recordings from patients with Parkinson’s Disease in three phases. In the first phases, a literature and database search were performed to assess state-of-art AI models and publicly available datasets. Datasets were filtered based on relevance in consultation with a neurology expert. In the second phase, a foundation model for EEG was constructed. Recordings showed clustering by disease labels but not by recording length or by source datasets. These confirm that project-goal-relevant features were encoded in the recording embedding, thus validating our foundation model. In the third phase, we included novel EEG data recorded in course of this POC project. The classifier trained on the public datasets achieved over 90% performance, confirming that EEG collected during the SSRT task carried information on neocortical features relevant to Parkinson’s Disease.
During this POC project, we elevated the TRL level of SyncR from TRL4 (component validation in laboratory environment) to TRL7 (demonstrated prototype) specifically in the domain of Parkinson’s Disease. We carried out a full market research to determine further development strategy. The market research concluded that “SyncR is a routine diagnostic tool optimized for assessing patients with Parkinson's Disease, Alzheimer's Disease or dementia, measuring the effectiveness of therapy and making predictions about the patient's condition, supported by AI-based solutions.” The following concrete suggestions were made (selected). 1. Broaden the scope to other dementias beyond Parkinson’s Disease Dementia, most importantly to Alzheimer’s Disease. 2. Add new test panels to allow flexible use in a clinical setting. 3. Continue testing in large sample international multicentric studies. The market research also identified potential future partners and potential funding sources for further development.
A contracted patent and legal office carried out a novelty research before the project. According to the results of the patent search, the sale of the SyncR solution in China and the US necessitates technical delineation from two certain Chinese and US patents. It also concluded that there are currently no published publications in Hungary that would prevent the exploitation of the solution. Therefore, we continued with the Hungarian patent process and also submitted a PCT application, which reached the national phase. In this phase, we are seeking protection in the EU, US and China, which together cover a large fraction of the potential market.
In summary, we developed a device (SyncR) that can improve diagnosis and therapy in the cognitive domain of patients with Parkinson’s Disease. We also carried out software development to provide a cognitive test (QCT) as well as seed analysis of its results. SyncR reached the prototype phase within this POC project, and we are seeking international IP protection. Further developments will depend on both funding and potential interest from industry partners.
A contracted patent and legal office carried out a novelty research before the project. According to the results of the patent search, the sale of the SyncR solution in China and the US necessitates technical delineation from two certain Chinese and US patents. It also concluded that there are currently no published publications in Hungary that would prevent the exploitation of the solution. Therefore, we continued with the Hungarian patent process and also submitted a PCT application, which reached the national phase. In this phase, we are seeking protection in the EU, US and China, which together cover a large fraction of the potential market.
In summary, we developed a device (SyncR) that can improve diagnosis and therapy in the cognitive domain of patients with Parkinson’s Disease. We also carried out software development to provide a cognitive test (QCT) as well as seed analysis of its results. SyncR reached the prototype phase within this POC project, and we are seeking international IP protection. Further developments will depend on both funding and potential interest from industry partners.