The Human Brain Project: Responsible Brain Research for the Benefit of Society

Salles, Arleen; Bjaalie, Jan G.; Evers, Kathinka; Farisco, Michele; Fothergill, B. Tyr; Guerrero, Manuel Alejandro; Maslen, Hannah; Muller, Jeffrey; Prescott, Tony J.; Stahl, Bernd Carsten; Walter, Henrik; Zilles, Karl; Amunts, Katrin

doi:10.1016/j.neuron.2019.01.005

Cited by 49 publications

(38 citation statements)

References 9 publications

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“…Additionally, human values and ethics often meet at the heart of a large variety of research, ranging from management [127,128] to sustainability [129,130], globalization [131,132] or neuroethics [133,134]. Thus, building on this relationship can open the doors for numerous, diverse and innovative research possibilities.…”

Section: Discussionmentioning

confidence: 99%

From Values to Behavior: Proposition of an Integrating Model

Aride

Pàmies

2019

Sustainability

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Human values are at the heart of our lives. We all hold a set of values that influence our actions. The protection of the natural environment is no exception to this rule. That is why the study of human values is key to reaching the imperative of sustainability. In this conceptual paper, we aim to (1) integrate the key theories and models explaining the influence of human values on behavior and to (2) identify factors that might have previously been overlooked. Drawing from a range of disciplines, this article proposes an integrated model mapping the influence of human values on behavior. It also puts forward the concept of consequences as an emerging factor that could play an important role in this relationship. Recommendations are to extend the research to an empirical investigation of the model and to develop the definition of the concept of consequences and the role they play in the influence of values on consumer behavior.

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Section: Discussionmentioning

confidence: 99%

From Values to Behavior: Proposition of an Integrating Model

Aride

Pàmies

2019

Sustainability

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“…There is an urgent need for standardized, usable, dataprotection-policy-compliant solutions for sensitive data sharing which are capable of integrating and analyzing health data from different sources, organizations, and potentially from different research disciplines. These aspects are subject to ongoing discussions and debates in the EOSC initiative [35]; for instance, progress has been made in the Human Brain Project (HBP) through its Ethics and Society sub-project in collaboration with the project platforms [36,37]. Other examples of data sharing that are compliant with data protection policies can be found in the recent literature [38][39][40][41][42][43][44][45].…”

Section: Existing Ethics and Legal Frameworkmentioning

confidence: 99%

“…36 Lynkeus, Via Livenza, 00198 Rome, Italy. 37 Public Policy Consultant, Rome, Italy. 38 European Clinical Research Infrastructure Network, Heinrich-Heine-Universität, Düsseldorf, Germany.…”

Section: Acknowledgementsmentioning

confidence: 99%

Towards a European health research and innovation cloud (HRIC)

Aarestrup

Albeyatti

Armitage³

et al. 2020

Genome Med

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The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

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“…More recently, the Human Brain Project (HBP), funded in 2012 by the European Commission to build an open e-infrastructure, started to provide supervised/unsupervised machine learning (ML) tools for neuroinformatics, brain simulation, neuromorphic computing, and medical informatics. Within this overarching project, the Medical Informatic Platform (MIP) was developed to allow hospitals and research centers to share medical data (20). Notably, patient records in European hospitals represent an enormous source of data (21) that are waiting to be processed using data mining algorithms and mathematical models to extract meaningful and potentially hidden information.…”

Section: Introductionmentioning

confidence: 99%

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

et al. 2020

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Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC Redolfi et al. MIP Pilot Study identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.

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The Human Brain Project: Responsible Brain Research for the Benefit of Society

Abstract: Recognizing that its research may raise various ethical, social, and philosophical issues, the HBP has made the identification, examination, and management of those issues a top priority. The Ethics and Society subproject is part of the core research project.

Cited by 49 publications

References 9 publications

From Values to Behavior: Proposition of an Integrating Model

From Values to Behavior: Proposition of an Integrating Model

Towards a European health research and innovation cloud (HRIC)

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

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