The Contribution of the Cerebello-thalamo-cortical Circuit to the Pathology of Non-dopaminergic Responsive Parkinson\u27s Disease Symptoms

It has been well established that motor symptoms in Parkinson’s disease (PD) are primarily associated with dopaminergic degeneration in the basal ganglia. However, symptoms which respond poorly to dopaminergic replacement, such as tremor, gait, and balance deficits, point to an alternative pathology to dysfunction of the basal ganglia. Over-activity of the cerebellum has been demonstrated in PD, however it is not entirely clear how the cerebellum might be affecting motor symptoms. A lack of consensus exists regarding how cerebellar over-activity might be influencing PD tremor, and whether resting and postural tremor are differentially influenced by cerebellar dysfunction. It is also unclear how cerebellar over-activity might be affecting gait and balance deficits in PD, even though the cerebellum is an important subcortical structure for the control of gait and balance. Thus, the aim of the current thesis was to assess how cerebellar over-activity may be influencing symptoms which respond poorly to dopamine replacement in PD by inhibiting cerebellar activity using repetitive transcranial magnetic stimulation (rTMS). Additionally, a direct comparison was made between the effects of stimulation targeted to the medial versus lateral cerebellum with the aim to localize the effect of cerebellar over-activity. Fifty PD participants were randomly assigned to receive stimulation over either the medial cerebellum (n=20), lateral cerebellum (n=20) or sham stimulation (n=10). 900 pulses at 1Hz were delivered at an intensity of 120% resting motor threshold determined from the first dorsal interosseous muscle representation in the primary motor cortex. Tremor was assessed quantitatively using a wireless finger accelerometer to record tremor. Balance was measured with objective, computerized protocols: modified clinical test of sensory integration and balance (m-CTSIB) and postural stability testing (PST). Spatiotemporal gait parameters were measured quantitatively during self-paced walking. All assessments were performed before and after either real or sham stimulation. Resting tremor frequency was reduced in tremor-dominant individuals, regardless of whether stimulation was applied over the medial (p=0.024) or lateral (p=0.033) cerebellum, but not in the sham group. Additionally, inhibition of the cerebellum did not result in modulation of gait and balance outcome measures. Hence, dysfunction of the cerebellum may be a contributing factor to resting tremor, but not gait and balance deficits in PD. Importantly, the improvements in resting tremor occurred without detriment to gait or balance, demonstrating the therapeutic potential of this stimulation protocol. Low frequency rTMS over the medial or lateral cerebellum provides promise of an alternative treatment for resting tremor in PD, a symptom that is poorly responsive to dopaminergic replacement

Big Data Needs Big Governance: Best Practices From Brain-CODE, the Ontario-Brain Institute’s Neuroinformatics Platform

The Ontario Brain Institute (OBI) has begun to catalyze scientific discovery in the field of neuroscience through its large-scale informatics platform, known as Brain-CODE. The platform supports the capture, storage, federation, sharing, and analysis of different data types across several brain disorders. Underlying the platform is a robust and scalable data governance structure which allows for the flexibility to advance scientific understanding, while protecting the privacy of research participants. Recognizing the value of an open science approach to enabling discovery, the governance structure was designed not only to support collaborative research programs, but also to support open science by making all data open and accessible in the future. OBI’s rigorous approach to data sharing maintains the accessibility of research data for big discoveries without compromising privacy and security. Taking a Privacy by Design approach to both data sharing and development of the platform has allowed OBI to establish some best practices related to large-scale data sharing within Canada. The aim of this report is to highlight these best practices and develop a key open resource which may be referenced during the development of similar open science initiatives

Designing and Implementing a Privacy Preserving Record Linkage Protocol

Introduction The Ontario Brain Institute has developed Brain-CODE, an informatics platform, to support the acquisition, storage, management and analysis of multi-modal data. The standardized research data within Brain-CODE spans several brain disorders, allowing for integrative analyses, while also providing the opportunity to leverage existing clinical administrative data holdings through external linkages. Objectives and Approach Within Ontario, the majority of individuals who access the healthcare system have a unique identifier, the Ontario Health Insurance Plan (OHIP) number. The OHIP number can facilitate linkages with administrative data holdings, such as those at the Institute for Clinical Evaluative Sciences (ICES). Given that OBI is not permitted under Ontario’s privacy legislation to hold OHIP numbers, identifiers for consented participants are encrypted using a public key mechanism upon entry into Brain-CODE, where the private key is inaccessible. To facilitate linkages involving OHIP numbers between Brain-CODE and ICES, Brain-CODE Link software was co-developed by members of the Indoc Consortium. Results Brain-CODE Link allows a deterministic linkage between encrypted identifiers (OHIP numbers), without revealing participant identity. The same homomorphic encryption algorithm applied to identifiers upon entry to Brain-CODE, is applied to relevant identifiers within ICES data holdings. Encrypted identifiers from Brain-CODE are securely transferred to ICES, where a comparison computation calculates differences between the encrypted sets. These differences are sent to a semi-trusted third party, who has no access to the original data, to decrypt the differences using the private key. A zero difference indicates a set of matching identifiers. One of the main challenges during testing and development of Brain-CODE Link was ensuring the software was capable of scaling to a population level, performing a large number of comparisons, in a computationally efficient manner. Conclusion/Implications Ongoing pilot projects within the areas of epilepsy, neurodevelopment disorders, and neurodegeneration will be the first examples of linkages between Brain-CODE and ICES. Brain-CODE Link has successfully performed several billion test comparisons, indicating its suitability to function as a scalable privacy preserving record linkage to support comprehensive analyses

Validating a novel deterministic privacy-preserving record linkage between administrative & clinical data: applications in stroke research

Introduction Research data combined with administrative data provides a robust resource capable of answering unique research questions. However, in cases where personal health data are encrypted, due to ethics requirements or institutional restrictions, traditional methods of deterministic and probabilistic record linkages are not feasible. Instead, privacy-preserving record linkages must be used to protect patients' personal data during data linkage. Objectives To determine the feasibility and validity of a deterministic privacy preserving data linkage protocol using homomorphically encrypted data. Methods Feasibility was measured by the number of records that successfully matched via direct identifiers. Validity was measured by the number of records that matched with multiple indirect identifiers. The threshold for feasibility and validity were both set at 95%. The datasets shared a single, direct identifier (health card number) and multiple indirect identifiers (sex and date of birth). Direct identifiers were encrypted in both datasets and then transferred to a third-party server capable of linking the encrypted identifiers without decrypting individual records. Once linked, the study team used indirect identifiers to verify the accuracy of the linkage in the final dataset. Results With a combination of manual and automated data transfer in a sample of 8,128 individuals, the privacy-preserving data linkage took 36 days to match to a population sample of over 3.2 million records. 99.9% of the records were successfully matched with direct identifiers, and 99.8% successfully matched with multiple indirect identifiers. We deemed the linkage both feasible and valid. Conclusions As combining administrative and research data becomes increasingly common, it is imperative to understand options for linking data when direct linkage is not feasible. The current linkage process ensured the privacy and security of patient data and improved data quality. While the initial implementations required significant computational and human resources, increased automation keeps the requirements within feasible bounds

Additional file 1: of Cerebellar involvement in Parkinson’s disease resting tremor

Summary Analysis Data. (DOCX 13 kb

Big Data, Big Responsibility! Building best-practice privacy strategies into a large-scale neuroinformatics platform

ABSTRACT Objective The Ontario Brain Institute (OBI) has begun to catalyze scientific discovery in the field of neuroscience through its’ large-scale informatics platform, known as Brain-CODE (Centre for Ontario Data Exploration). Brain-CODE manages the acquisition, storage, processing, and analytics of multidimensional data collected from patients with a variety of brain disorders. Our vision is for the platform to act as an informatics catalyst; encouraging multidisciplinary research collaboration, data integration, and innovation in neuroscience research. Brain-CODE’s infrastructure was designed with best-practice privacy strategies built at the forefront to enable secure data capture of sensitive patient information in a manner that abides by government legislation while fostering data sharing and linking opportunities. Approach Privacy and security features have been incorporated into the very foundation of Brain-CODE’s comprehensive guidelines, which are reinforced by our state-of-the-art approaches to keep patient data safe. To ensure clarity for study participants, we have developed standard consent language outlining how sensitive patient data will be collected, entered, de-identified, and shared using Brain-CODE. Moreover, our tiered approach to data accessibility enables the storage of encrypted Ontario Health Card Numbers as well as other patient information, secure long-term storage of de-identified data, and data sharing opportunities by request from third parties following risk-based analysis re-identification techniques. OBI has also established a comprehensive Information Security Policy and Informatics Governance Policies, as well as a carried out a Privacy Impact Assessment and Threat Risk Assessment for Brain-CODE. Results Brain-CODE is proudly named a "Privacy by Design" Ambassador by the Office of the Information and Privacy Commissioner of Ontario, Canada. Moreover, approximately 200 neuroscience researchers and 35 institutions from across Canada have adopted our standard consent language to enable secure data sharing within and across neurological disorders as well as linkage opportunities with national and international databases in a secure environment. Conclusion OBI’s rigorous approach to data sharing in the field of neuroscience maintains the accessibility of research data for big discoveries without compromising patient privacy and security. We believe that Brain-CODE is a powerful and advantageous tool; moving neuroscience research from independent silos to an integrative system approach for improving patient health. OBI’s vision for improved brain health for patients living with neurological disorders paired with Brain-CODE’s best-practice strategies in privacy protection of patient data offer a novel and innovative approach to “big data” initiatives aimed towards improving public health and society world-wide

Additional file 1: of Cerebellar involvement in Parkinson’s disease resting tremor

Summary Analysis Data. (DOCX 13 kb

Brain-CODE: A Secure Neuroinformatics Platform for Management, Federation, Sharing and Analysis of Multi-Dimensional Neuroscience Data

Historically, research databases have existed in isolation with no practical avenue for sharing or pooling medical data into high dimensional datasets that can be efficiently compared across databases. To address this challenge, the Ontario Brain Institute’s “Brain-CODE” is a large-scale neuroinformatics platform designed to support the collection, storage, federation, sharing and analysis of different data types across several brain disorders, as a means to understand common underlying causes of brain dysfunction and develop novel approaches to treatment. By providing researchers access to aggregated datasets that they otherwise could not obtain independently, Brain-CODE incentivizes data sharing and collaboration and facilitates analyses both within and across disorders and across a wide array of data types, including clinical, neuroimaging and molecular. The Brain-CODE system architecture provides the technical capabilities to support (1) consolidated data management to securely capture, monitor and curate data, (2) privacy and security best-practices, and (3) interoperable and extensible systems that support harmonization, integration, and query across diverse data modalities and linkages to external data sources. Brain-CODE currently supports collaborative research networks focused on various brain conditions, including neurodevelopmental disorders, cerebral palsy, neurodegenerative diseases, epilepsy and mood disorders. These programs are generating large volumes of data that are integrated within Brain-CODE to support scientific inquiry and analytics across multiple brain disorders and modalities. By providing access to very large datasets on patients with different brain disorders and enabling linkages to provincial, national and international databases, Brain-CODE will help to generate new hypotheses about the biological bases of brain disorders, and ultimately promote new discoveries to improve patient care