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Lock C, Tan NSM, Long IJ, Keong NC. Neuroimaging data repositories and AI-driven healthcare-Global aspirations vs. ethical considerations in machine learning models of neurological disease. Front Artif Intell 2024; 6:1286266. [PMID: 38440234 PMCID: PMC10910099 DOI: 10.3389/frai.2023.1286266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 12/27/2023] [Indexed: 03/06/2024] Open
Abstract
Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias-ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term "AI ethics." Further work is required to expand the approach proposed here into a reproducible strategy.
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Affiliation(s)
- Christine Lock
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Nicole Si Min Tan
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Ian James Long
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Nicole C. Keong
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
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2
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Blixhavn CH, Reiten I, Kleven H, Øvsthus M, Yates SC, Schlegel U, Puchades MA, Schmid O, Bjaalie JG, Bjerke IE, Leergaard TB. The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases. Front Neuroinform 2024; 18:1284107. [PMID: 38421771 PMCID: PMC10884250 DOI: 10.3389/fninf.2024.1284107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.
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Affiliation(s)
- Camilla H. Blixhavn
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ingrid Reiten
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Heidi Kleven
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Martin Øvsthus
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Sharon C. Yates
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ulrike Schlegel
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maja A. Puchades
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | | | - Jan G. Bjaalie
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ingvild E. Bjerke
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Trygve B. Leergaard
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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3
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Patel D, Shetty S, Acha C, Pantoja IEM, Zhao A, George D, Gracias DH. Microinstrumentation for Brain Organoids. Adv Healthc Mater 2024:e2302456. [PMID: 38217546 DOI: 10.1002/adhm.202302456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 12/10/2023] [Indexed: 01/15/2024]
Abstract
Brain organoids are three-dimensional aggregates of self-organized differentiated stem cells that mimic the structure and function of human brain regions. Organoids bridge the gaps between conventional drug screening models such as planar mammalian cell culture, animal studies, and clinical trials. They can revolutionize the fields of developmental biology, neuroscience, toxicology, and computer engineering. Conventional microinstrumentation for conventional cellular engineering, such as planar microfluidic chips; microelectrode arrays (MEAs); and optical, magnetic, and acoustic techniques, has limitations when applied to three-dimensional (3D) organoids, primarily due to their limits with inherently two-dimensional geometry and interfacing. Hence, there is an urgent need to develop new instrumentation compatible with live cell culture techniques and with scalable 3D formats relevant to organoids. This review discusses conventional planar approaches and emerging 3D microinstrumentation necessary for advanced organoid-machine interfaces. Specifically, this article surveys recently developed microinstrumentation, including 3D printed and curved microfluidics, 3D and fast-scan optical techniques, buckling and self-folding MEAs, 3D interfaces for electrochemical measurements, and 3D spatially controllable magnetic and acoustic technologies relevant to two-way information transfer with brain organoids. This article highlights key challenges that must be addressed for robust organoid culture and reliable 3D spatiotemporal information transfer.
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Affiliation(s)
- Devan Patel
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Saniya Shetty
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Chris Acha
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Itzy E Morales Pantoja
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Alice Zhao
- Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Derosh George
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - David H Gracias
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Chemistry, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, 21218, USA
- Sidney Kimmel Comprehensive Cancer Center (SKCCC), Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for MicroPhysiological Systems (MPS), Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
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4
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Tüshaus J, Sakhteman A, Lechner S, The M, Mucha E, Krisp C, Schlegel J, Delbridge C, Kuster B. A region-resolved proteomic map of the human brain enabled by high-throughput proteomics. EMBO J 2023; 42:e114665. [PMID: 37916885 PMCID: PMC10690467 DOI: 10.15252/embj.2023114665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023] Open
Abstract
Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).
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Affiliation(s)
- Johanna Tüshaus
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Amirhossein Sakhteman
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Severin Lechner
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Matthew The
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Eike Mucha
- Bruker Daltonics GmbH & Co. KGBremenGermany
| | | | - Jürgen Schlegel
- Department of Neuropathology, Klinikum Rechts der ISAR, School of MedicineTechnical University MunichMunichGermany
| | - Claire Delbridge
- Department of Neuropathology, Klinikum Rechts der ISAR, School of MedicineTechnical University MunichMunichGermany
| | - Bernhard Kuster
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
- German Cancer Consortium (DKTK), Munich SiteHeidelbergGermany
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5
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Frégnac Y. Flagship Afterthoughts: Could the Human Brain Project (HBP) Have Done Better? eNeuro 2023; 10:ENEURO.0428-23.2023. [PMID: 37963651 PMCID: PMC10646882 DOI: 10.1523/eneuro.0428-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/16/2023] Open
Affiliation(s)
- Yves Frégnac
- UNIC-NeuroPSI, University Paris-Saclay, 91190 Gif-sur-Yvette, France
- Cognitive Sciences at Ecole Polytechnique, 91120 Palaiseau, France
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6
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Martinez CS, Cuadra MB, Jorge J. BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development. Neuroimage 2023; 273:120074. [PMID: 37004826 DOI: 10.1016/j.neuroimage.2023.120074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/16/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
The benefits, opportunities and growing availability of ultra-high field magnetic resonance imaging (MRI) for humans have prompted an expansion in research and development efforts towards increasingly more advanced high-resolution imaging techniques. To maximize their effectiveness, these efforts need to be supported by powerful computational simulation platforms that can adequately reproduce the biophysical characteristics of MRI, with high spatial resolution. In this work, we have sought to address this need by developing a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI properties that affect image generation. This phantom, termed BigBrain-MR, was generated from the publicly available BigBrain histological dataset and lower-resolution in-vivo 7T-MRI data, using a newly-developed image processing framework that allows mapping the general properties of the latter into the fine anatomical scale of the former. Overall, the mapping framework was found to be effective and robust, yielding a diverse range of realistic "in-vivo-like" MRI contrasts and maps at 100-µm resolution. BigBrain-MR was then tested in three imaging applications (motion effects and interpolation, super-resolution imaging, and parallel imaging reconstruction) to investigate its properties, value and validity as a simulation platform. The results consistently showed that BigBrain-MR can closely approximate the behavior of real in-vivo data, more realistically and with more extensive features than a more classic option such as the Shepp-Logan phantom. Its flexibility in simulating different contrast mechanisms and artifacts may also prove valuable for educational applications. BigBrain-MR is therefore deemed a favorable choice to support methodological development and demonstration in brain MRI, and has been made freely available to the community.
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Herrmann H. What's next for responsible artificial intelligence: a way forward through responsible innovation. Heliyon 2023; 9:e14379. [PMID: 36967876 PMCID: PMC10036946 DOI: 10.1016/j.heliyon.2023.e14379] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/29/2023] Open
Abstract
Industry is adopting artificial intelligence (AI) at a rapid pace and a growing number of countries have declared national AI strategies. However, several spectacular AI failures have led to ethical concerns about responsibility in AI development and use, which gave rise to the emerging field of responsible AI (RAI). The field of responsible innovation (RI) has a longer history and evolved toward a framework for the entire research, development, and innovation life cycle. However, this research demonstrates that the uptake of RI by RAI has been slow. RAI has been developing independently, with three times the number of publications than RI. The objective and knowledge contribution of this research was to understand how RAI has been developing independently from RI and contribute to how RI could be leveraged toward the progression of RAI in a causal loop diagram. It is concluded that stakeholder engagement of citizens from diverse cultures across the Global North and South is a policy leverage point for moving the RI adoption by RAI toward global best practice. A role-specific recommendation for policy makers is made to deploy modes of engaging with the Global South with more urgency to avoid the risk of harming vulnerable populations. As an additional methodological contribution, this study employs a novel method, systematic science mapping, which combines systematic literature reviews with science mapping. This new method enabled the discovery of an emerging 'axis of adoption' of RI by RAI around the thematic areas of ethics, governance, stakeholder engagement, and sustainability. 828 Scopus articles were mapped for RI and 2489 articles were mapped for RAI. The research presented here is by any measure the largest systematic literature review of both fields to date and the only cross-disciplinary review from a methodological perspective.
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8
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Hashemi M, Vattikonda AN, Jha J, Sip V, Woodman MM, Bartolomei F, Jirsa VK. Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators. Neural Netw 2023; 163:178-194. [PMID: 37060871 DOI: 10.1016/j.neunet.2023.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.
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9
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Cobb EAW, Petroccione MA, Scimemi A. NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON. Sci Rep 2023; 13:464. [PMID: 36627356 PMCID: PMC9832141 DOI: 10.1038/s41598-022-27302-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
One of the fundamental goals in neuroscience is to determine how the brain processes information and ultimately controls the execution of complex behaviors. Over the past four decades, there has been a steady growth in our knowledge of the morphological and functional diversity of neurons, the building blocks of the brain. These cells clearly differ not only for their anatomy and ion channel distribution, but also for the type, strength, location, and temporal pattern of activity of the many synaptic inputs they receive. Compartmental modeling programs like NEURON have become widely used in the neuroscience community to address a broad range of research questions, including how neurons integrate synaptic inputs and propagate information through complex neural networks. One of the main strengths of NEURON is its ability to incorporate user-defined information about the realistic morphology and biophysical properties of different cell types. Although the graphical user interface of the program can be used to run initial exploratory simulations, introducing a stochastic representation of synaptic weights, locations and activation times typically requires users to develop their own codes, a task that can be overwhelming for some beginner users. Here we describe NRN-EZ, an interactive application that allows users to specify complex patterns of synaptic input activity that can be integrated as part of NEURON simulations. Through its graphical user interface, NRN-EZ aims to ease the learning curve to run computational models in NEURON, for users that do not necessarily have a computer science background.
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Affiliation(s)
- Evan A. W. Cobb
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA ,grid.265850.c0000 0001 2151 7947Department of Computer Science, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Maurice A. Petroccione
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Annalisa Scimemi
- Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY, 12222-0100, USA.
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10
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Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit. Commun Biol 2022; 5:1240. [PMCID: PMC9663576 DOI: 10.1038/s42003-022-04213-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/02/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractThe cerebellar network is renowned for its regular architecture that has inspired foundational computational theories. However, the relationship between circuit structure, function and dynamics remains elusive. To tackle the issue, we developed an advanced computational modeling framework that allows us to reconstruct and simulate the structure and function of the mouse cerebellar cortex using morphologically realistic multi-compartmental neuron models. The cerebellar connectome is generated through appropriate connection rules, unifying a collection of scattered experimental data into a coherent construct and providing a new model-based ground-truth about circuit organization. Naturalistic background and sensory-burst stimulation are used for functional validation against recordings in vivo, monitoring the impact of cellular mechanisms on signal propagation, inhibitory control, and long-term synaptic plasticity. Our simulations show how mossy fibers entrain the local neuronal microcircuit, boosting the formation of columns of activity travelling from the granular to the molecular layer providing a new resource for the investigation of local microcircuit computation and of the neural correlates of behavior.
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11
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Redolfi A, Archetti D, De Francesco S, Crema C, Tagliavini F, Lodi R, Ghidoni R, Gandini Wheeler-Kingshott CAM, Alexander DC, D'Angelo E. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci 2022. [PMID: 36310103 DOI: 10.1111/ejn.15854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/15/2022]
Abstract
Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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12
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Chen W, Carel T, Awile O, Cantarutti N, Castiglioni G, Cattabiani A, Del Marmol B, Hepburn I, King JG, Kotsalos C, Kumbhar P, Lallouette J, Melchior S, Schürmann F, De Schutter E. STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale. Front Neuroinform 2022; 16:883742. [DOI: 10.3389/fninf.2022.883742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel implementations, inadequate. This paper introduces a new generation of the STochastic Engine for Pathway Simulation (STEPS) project (http://steps.sourceforge.net/), denominated STEPS 4.0, and its core components which have been designed for improved scalability, performance, and memory efficiency. STEPS 4.0 aims to enable novel scientific studies of macroscopic systems such as whole cells while capturing their nanoscale details. This class of models is out of reach for serial solvers due to the vast quantity of computation in such detailed models, and also out of reach for naive parallel solvers due to the large memory footprint. Based on a distributed mesh solution, we introduce a new parallel stochastic reaction-diffusion solver and a deterministic membrane potential solver in STEPS 4.0. The distributed mesh, together with improved data layout and algorithm designs, significantly reduces the memory footprint of parallel simulations in STEPS 4.0. This enables massively parallel simulations on modern HPC clusters and overcomes the limitations of the previous parallel STEPS implementation. Current and future improvements to the solver are not sustainable without following proper software engineering principles. For this reason, we also give an overview of how the STEPS codebase and the development environment have been updated to follow modern software development practices. We benchmark performance improvement and memory footprint on three published models with different complexities, from a simple spatial stochastic reaction-diffusion model, to a more complex one that is coupled to a deterministic membrane potential solver to simulate the calcium burst activity of a Purkinje neuron. Simulation results of these models suggest that the new solution dramatically reduces the per-core memory consumption by more than a factor of 30, while maintaining similar or better performance and scalability.
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13
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Olikkal P, Pei D, Adali T, Banerjee N, Vinjamuri R. Data Fusion-Based Musculoskeletal Synergies in the Grasping Hand. SENSORS (BASEL, SWITZERLAND) 2022; 22:7417. [PMID: 36236515 PMCID: PMC9570582 DOI: 10.3390/s22197417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
The hypothesis that the central nervous system (CNS) makes use of synergies or movement primitives in achieving simple to complex movements has inspired the investigation of different types of synergies. Kinematic and muscle synergies have been extensively studied in the literature, but only a few studies have compared and combined both types of synergies during the control and coordination of the human hand. In this paper, synergies were extracted first independently (called kinematic and muscle synergies) and then combined through data fusion (called musculoskeletal synergies) from 26 activities of daily living in 22 individuals using principal component analysis (PCA) and independent component analysis (ICA). By a weighted linear combination of musculoskeletal synergies, the recorded kinematics and the recorded muscle activities were reconstructed. The performances of musculoskeletal synergies in reconstructing the movements were compared to the synergies reported previously in the literature by us and others. The results indicate that the musculoskeletal synergies performed better than the synergies extracted without fusion. We attribute this improvement in performance to the musculoskeletal synergies that were generated on the basis of the cross-information between muscle and kinematic activities. Moreover, the synergies extracted using ICA performed better than the synergies extracted using PCA. These musculoskeletal synergies can possibly improve the capabilities of the current methodologies used to control high dimensional prosthetics and exoskeletons.
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14
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Niso G, Botvinik-Nezer R, Appelhoff S, De La Vega A, Esteban O, Etzel JA, Finc K, Ganz M, Gau R, Halchenko YO, Herholz P, Karakuzu A, Keator DB, Markiewicz CJ, Maumet C, Pernet CR, Pestilli F, Queder N, Schmitt T, Sójka W, Wagner AS, Whitaker KJ, Rieger JW. Open and reproducible neuroimaging: From study inception to publication. Neuroimage 2022; 263:119623. [PMID: 36100172 PMCID: PMC10008521 DOI: 10.1016/j.neuroimage.2022.119623] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/17/2022] [Accepted: 09/09/2022] [Indexed: 10/31/2022] Open
Abstract
Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid, Madrid and CIBER-BBN, Spain; Instituto Cajal, CSIC, Madrid, Spain.
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | | | - Oscar Esteban
- Dept. of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Psychology, Stanford University, Stanford, CA, USA
| | - Joset A Etzel
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Rémi Gau
- Institute of Psychology, Université catholique de Louvain, Louvain la Neuve, Belgium
| | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Peer Herholz
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada
| | - Agah Karakuzu
- Biomedical Engineering Institute, Polytechnique Montréal, Montréal, Quebec, Canada; Montréal Heart Institute, Montréal, Quebec, Canada
| | - David B Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | | | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm - IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Cyril R Pernet
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Franco Pestilli
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - Nazek Queder
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada; Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Tina Schmitt
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany
| | - Weronika Sójka
- Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University, Toruń, Poland
| | - Adina S Wagner
- Institute for Neuroscience and Medicine, Research Centre Juelich, Germany
| | | | - Jochem W Rieger
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany; Department of Psychology, Carl-von-Ossietzky Universität, Oldenburg, Germany.
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15
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D'Angelo E, Jirsa V. The quest for multiscale brain modeling. Trends Neurosci 2022; 45:777-790. [PMID: 35906100 DOI: 10.1016/j.tins.2022.06.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/20/2022] [Accepted: 06/21/2022] [Indexed: 01/07/2023]
Abstract
Addressing the multiscale organization of the brain, which is fundamental to the dynamic repertoire of the organ, remains challenging. In principle, it should be possible to model neurons and synapses in detail and then connect them into large neuronal assemblies to explain the relationship between microscopic phenomena, large-scale brain functions, and behavior. It is more difficult to infer neuronal functions from ensemble measurements such as those currently obtained with brain activity recordings. In this article we consider theories and strategies for combining bottom-up models, generated from principles of neuronal biophysics, with top-down models based on ensemble representations of network activity and on functional principles. These integrative approaches are hoped to provide effective multiscale simulations in virtual brains and neurorobots, and pave the way to future applications in medicine and information technologies.
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Affiliation(s)
- Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, and Brain Connectivity Center, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy.
| | - Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale (INSERM) Unité 1106, Centre National de la Recherche Scientifique (CNRS), and University of Aix-Marseille, Marseille, France
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16
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Awile O, Kumbhar P, Cornu N, Dura-Bernal S, King JG, Lupton O, Magkanaris I, McDougal RA, Newton AJH, Pereira F, Săvulescu A, Carnevale NT, Lytton WW, Hines ML, Schürmann F. Modernizing the NEURON Simulator for Sustainability, Portability, and Performance. Front Neuroinform 2022; 16:884046. [PMID: 35832575 PMCID: PMC9272742 DOI: 10.3389/fninf.2022.884046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/26/2022] [Indexed: 12/25/2022] Open
Abstract
The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.
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Affiliation(s)
- Omar Awile
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Nicolas Cornu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Salvador Dura-Bernal
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - James Gonzalo King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Olli Lupton
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Ioannis Magkanaris
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Yale Center for Medical Informatics, Yale University, New Haven, CT, United States
| | - Adam J. H. Newton
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
| | - Fernando Pereira
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Alexandru Săvulescu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - William W. Lytton
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Michael L. Hines
- Department of Neuroscience, Yale University, New Haven, CT, United States
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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17
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Rivière D, Leprince Y, Labra N, Vindas N, Foubet O, Cagna B, Loh KK, Hopkins W, Balzeau A, Mancip M, Lebenberg J, Cointepas Y, Coulon O, Mangin JF. Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy. Front Neuroinform 2022; 16:803934. [PMID: 35311005 PMCID: PMC8928460 DOI: 10.3389/fninf.2022.803934] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/17/2022] [Indexed: 11/14/2022] Open
Abstract
Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.
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Affiliation(s)
- Denis Rivière
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Yann Leprince
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Nicole Labra
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
| | - Nabil Vindas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Ophélie Foubet
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Bastien Cagna
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Kep Kee Loh
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - William Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, United States
| | - Antoine Balzeau
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
- Department of African Zoology, Royal Museum for Central Africa, Tervuren, Belgium
| | - Martial Mancip
- Maison de la Simulation, CNRS, CEA Saclay, Gif-sur-Yvette, France
| | - Jessica Lebenberg
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- Université de Paris, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Yann Cointepas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Olivier Coulon
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
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18
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Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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19
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Eke DO, Bernard A, Bjaalie JG, Chavarriaga R, Hanakawa T, Hannan AJ, Hill SL, Martone ME, McMahon A, Ruebel O, Crook S, Thiels E, Pestilli F. International data governance for neuroscience. Neuron 2022; 110:600-612. [PMID: 34914921 PMCID: PMC8857067 DOI: 10.1016/j.neuron.2021.11.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/16/2021] [Accepted: 11/15/2021] [Indexed: 12/19/2022]
Abstract
As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively called data governance. Whereas neuroscience data transcend borders, data governance is typically constrained within geopolitical boundaries. An international data governance framework and accompanying infrastructure can assist investigators, institutions, data repositories, and funders with navigating disparate policies. Here, we propose principles and operational considerations for how data governance in neuroscience can be navigated at an international scale and highlight gaps, challenges, and opportunities in a global brain data ecosystem. We consider how to approach data governance in a way that balances data protection requirements and the need for open science, so as to promote international collaboration through federated constructs such as the International Brain Initiative (IBI).
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Affiliation(s)
- Damian O Eke
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, UK; Human Brain Project
| | | | | | - Ricardo Chavarriaga
- Center for Artificial Intelligence, School of Engineering, Zurich University of Applied Sciences, Zurich, Switzerland
| | | | - Anthony J Hannan
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Sean L Hill
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | | | - Oliver Ruebel
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
| | - Edda Thiels
- National Science Foundation, Alexandria, VA, USA
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, and Institute for Neuroscience, University of Texas, Austin, TX, USA.
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20
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Brain simulation as a cloud service: The Virtual Brain on EBRAINS. Neuroimage 2022; 251:118973. [DOI: 10.1016/j.neuroimage.2022.118973] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 12/18/2022] Open
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21
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Mikulan E, Russo S, Zauli FM, d'Orio P, Parmigiani S, Favaro J, Knight W, Squarza S, Perri P, Cardinale F, Avanzini P, Pigorini A. A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation. Hum Brain Mapp 2021; 42:5523-5534. [PMID: 34520074 PMCID: PMC8559469 DOI: 10.1002/hbm.25639] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 01/03/2023] Open
Abstract
Deidentifying MRIs constitutes an imperative challenge, as it aims at precluding the possibility of re-identification of a research subject or patient, but at the same time it should preserve as much geometrical information as possible, in order to maximize data reusability and to facilitate interoperability. Although several deidentification methods exist, no comprehensive and comparative evaluation of deidentification performance has been carried out across them. Moreover, the possible ways these methods can compromise subsequent analysis has not been exhaustively tested. To tackle these issues, we developed AnonyMI, a novel MRI deidentification method, implemented as a user-friendly 3D Slicer plugin-in, which aims at providing a balance between identity protection and geometrical preservation. To test these features, we performed two series of analyses on which we compared AnonyMI to other two state-of-the-art methods, to evaluate, at the same time, how efficient they are at deidentifying MRIs and how much they affect subsequent analyses, with particular emphasis on source localization procedures. Our results show that all three methods significantly reduce the re-identification risk but AnonyMI provides the best geometrical conservation. Notably, it also offers several technical advantages such as a user-friendly interface, multiple input-output capabilities, the possibility of being tailored to specific needs, batch processing and efficient visualization for quality assurance.
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Affiliation(s)
- Ezequiel Mikulan
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
| | - Simone Russo
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
| | - Flavia Maria Zauli
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
| | - Piergiorgio d'Orio
- "Claudio Munari" Epilepsy and Parkinson Surgery Center, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Sara Parmigiani
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
| | - Jacopo Favaro
- Department of Women's and Children's Health, Pediatric Neurology and Neurophysiology Unit, University of Padua, Padua, Italy
| | - William Knight
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, UK
| | - Silvia Squarza
- Department of Neuroradiology, "Claudio Munari" Epilepsy and Parkinson Surgery Center, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Pierluigi Perri
- Dipartimento di Scienze Giuridiche Cesare Beccaria, Università degli Studi di Milano, Milano, Italy
| | - Francesco Cardinale
- "Claudio Munari" Epilepsy and Parkinson Surgery Center, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Pietro Avanzini
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma, Italy
| | - Andrea Pigorini
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
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22
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A Novel Perspective for Examining and Comparing Real and Virtual Test Tasks Performed by the Dominant and Non-Dominant Hand in Healthy Adults. Symmetry (Basel) 2021. [DOI: 10.3390/sym13101810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study presents a novel perspective for the study of functional lateralization in a virtual reality environment. In the model study of handedness, the recognition of the dominant and non-dominant hand in real and virtual conditions was assessed using selected tests, such as a real light exposure test of Piórkowski’s apparatus and classical clinical tests, as well as virtual test tasks, in healthy adults. Statistically significant differences between the dominant and non-dominant hand were observed for tests carried out both in classical conditions and the virtual environment. The results and findings of other studies suggest that the virtual reality approach is a very promising and sensitive tool in the research on functional asymmetries in healthy and disease for motor skills and cognition processes.
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23
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Morales H. Current and Future Challenges of Functional MRI and Diffusion Tractography in the Surgical Setting: From Eloquent Brain Mapping to Neural Plasticity. Semin Ultrasound CT MR 2021; 42:474-489. [PMID: 34537116 DOI: 10.1053/j.sult.2021.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Decades ago, Spetzler (1986) and Sawaya (1998) provided a rough brain segmentation of the eloquent areas of the brain, aimed to help surgical decisions in cases of vascular malformations and tumors, respectively. Currently in clinical use, their criteria are in need of revision. Defining functions (eg, sensorimotor, language and visual) that should be preserved during surgery seems a straightforward task. In practice, locating the specific areas that could cause a permanent vs transient deficit is not an easy task. This is particularly true for the associative cortex and cognitive domains such as language. The old model, with Broca's and Wernicke's areas at the forefront, has been superseded by a dual-stream model of parallel language processing; named ventral and dorsal pathways. This complicated network of cortical hubs and subcortical white matter pathways needing preservation during surgery is a work in progress. Preserving not only cortical regions but most importantly preserving the connections, or white matter fiber bundles, of core regions in the brain is the new paradigm. For instance, the arcuate fascicululs and inferior fronto-occipital fasciculus are key components of the dorsal and ventral language pathways, respectively; and their damage result in permanent language deficits. Interestedly, the damage of the temporal portions of these bundles -where there is a crossroad with other multiple bundles-, appears to be more important (permanent) than the damage of the frontal portions - where plasticity and contralateral activation could help. Although intraoperative direct cortical and subcortical stimulation have contributed largely, advanced MR techniques such as functional MRI (fMRI) and diffusion tractography (DT), are at the epi-center of our current understanding. Nevertheless, these techniques posse important challenges: such as neurovascular uncoupling or venous bias on fMRI; and appropriate anatomical validation or accurate representation of crossing fibers on DT. These limitations should be well understood and taken into account in clinical practice. Unifying multidisciplinary research and clinical efforts is desirable, so these techniques could contribute more efficiently not only to locate eloquent areas but to improve outcomes and our understanding of neural plasticity. Finally, although there are constant anatomical and functional regions at the individual level, there is a known variability at the inter-individual level. This concept should strengthen the importance of a personalized approach when evaluating these regions on fMRI and DT. It should strengthen the importance of personalized treatments as well, aimed to meet tailored needs and expectations.
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Affiliation(s)
- Humberto Morales
- Section of Neuroradiology, University of Cincinnati Medical Center, Cincinnati, OH.
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24
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Morris K, Nami M, Bolanos JF, Lobo MA, Sadri-Naini M, Fiallos J, Sanchez GE, Bustos T, Chintam N, Amaya M, Strand SE, Mayuku-Dore A, Sakibova I, Biso GMN, DeFilippis A, Bravo D, Tarhan N, Claussen C, Mercado A, Braun S, Yuge L, Okabe S, Taghizadeh-Hesary F, Kotliar K, Sadowsky C, Chandra PS, Tripathi M, Katsaros V, Mehling B, Noroozian M, Abbasioun K, Amirjamshidi A, Hossein-Zadeh GA, Naraghi F, Barzegar M, Asadi-Pooya AA, Sahab-Negah S, Sadeghian S, Fahnestock M, Dilbaz N, Hussain N, Mari Z, Thatcher RW, Sipple D, Sidhu K, Chopra D, Costa F, Spena G, Berger T, Zelinsky D, Wheeler CJ, Ashford JW, Schulte R, Nezami MA, Kloor H, Filler A, Eliashiv DS, Sinha D, DeSalles AAF, Sadanand V, Suchkov S, Green K, Metin B, Hariri R, Cormier J, Yamamoto V, Kateb B. Neuroscience20 (BRAIN20, SPINE20, and MENTAL20) Health Initiative: A Global Consortium Addressing the Human and Economic Burden of Brain, Spine, and Mental Disorders Through Neurotech Innovations and Policies. J Alzheimers Dis 2021; 83:1563-1601. [PMID: 34487051 DOI: 10.3233/jad-215190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Neurological disorders significantly impact the world's economy due to their often chronic and life-threatening nature afflicting individuals which, in turn, creates a global disease burden. The Group of Twenty (G20) member nations, which represent the largest economies globally, should come together to formulate a plan on how to overcome this burden. The Neuroscience-20 (N20) initiative of the Society for Brain Mapping and Therapeutics (SBMT) is at the vanguard of this global collaboration to comprehensively raise awareness about brain, spine, and mental disorders worldwide. This paper aims to provide a comprehensive review of the various brain initiatives worldwide and highlight the need for cooperation and recommend ways to bring down costs associated with the discovery and treatment of neurological disorders. Our systematic search revealed that the cost of neurological and psychiatric disorders to the world economy by 2030 is roughly $16T. The cost to the economy of the United States is $1.5T annually and growing given the impact of COVID-19. We also discovered there is a shortfall of effective collaboration between nations and a lack of resources in developing countries. Current statistical analyses on the cost of neurological disorders to the world economy strongly suggest that there is a great need for investment in neurotechnology and innovation or fast-tracking therapeutics and diagnostics to curb these costs. During the current COVID-19 pandemic, SBMT, through this paper, intends to showcase the importance of worldwide collaborations to reduce the population's economic and health burden, specifically regarding neurological/brain, spine, and mental disorders.
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Affiliation(s)
- Kevin Morris
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Mohammad Nami
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Iran.,Middle East Brain + Initiative, Los Angeles, CA, USA.,Neuroscience Center, Instituto de Investigaciones Científicas Servicios de Alta Tecnología, City of Knowledge, Panama City, Panama
| | - Joe F Bolanos
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Maria A Lobo
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Melody Sadri-Naini
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - John Fiallos
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Gilberto E Sanchez
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Teshia Bustos
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Nikita Chintam
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Marco Amaya
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Susanne E Strand
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Alero Mayuku-Dore
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Indira Sakibova
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Grace Maria Nicole Biso
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Alejandro DeFilippis
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Daniela Bravo
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Nevzat Tarhan
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Middle East Brain + Initiative, Los Angeles, CA, USA.,Department of Psychiatry, Faculty of Medicine, Uskudar University, Istanbul, Turkey
| | - Carsten Claussen
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Fraunhofer-Institute for Translational Research and Pharmacology, Hamburg, Germany
| | - Alejandro Mercado
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Department of Neurosurgery, Hospital Military Regional Mendoza, Mendoza, Argentina
| | | | - Louis Yuge
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Division of Bio-Environment Adaptation Sciences, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan.,Cell Therapy Venture Company, Space Bio-Laboratories, Hiroshima, Japan
| | - Shigeo Okabe
- Brain Medical Science Collaboration Division, RIKEN Center for Brain Science Institution and Department: Cellular Neurobiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Konstantin Kotliar
- Department of Biomedical Engineering, Aachen University of Applied Sciences, Aachen, Germany
| | - Christina Sadowsky
- International Center for Spinal Cord Injury, Kennedy Krieger Institute-Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - P Sarat Chandra
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
| | | | - Vasileios Katsaros
- Department of Advanced Imaging Modalities, MRI Unit, General Anti-Cancer and Oncological Hospital of Athens "St. Savvas", Athens, Greece.,Departments of Neurosurgery and Neurology, National and Kapodistrian University of Athens, Athens, Greece.,Department of Neuroradiology, University College of London, London, UK
| | - Brian Mehling
- T-Neuro Pharma, Inc., Albuquerque, NM, USA.,StemVax LLC, Chesterland, OH, USA
| | - Maryam Noroozian
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Cognitive Neurology and Neuropsychiatry Division, Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran
| | - Kazem Abbasioun
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Department of Neurosurgery, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Amirjamshidi
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Department of Neurosurgery, Tehran University of Medical Sciences, Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- Middle East Brain + Initiative, Los Angeles, CA, USA.,National Brain Mapping Laboratory, Tehran, Iran
| | - Faridedin Naraghi
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Iranian Society for Brain Mapping & Therapeutics, Tehran, Iran
| | - Mojtaba Barzegar
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Intelligent Quantitative Bio-Medical Imaging, Tehran, Iran, and Medical Physics Department, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali A Asadi-Pooya
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.,Jefferson Comprehensive Epilepsy Center, Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Sajad Sahab-Negah
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Neuroscience Research Center, Mashhad University of Medical Sciences, Mashhad Iran.,Shefa Neuroscience Research Center, Khatam Alanbia Hospital, Tehran, Iran
| | - Saeid Sadeghian
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Department of Pediatric Neurology, Golestan Medical, Educational, and Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | | | - Nesrin Dilbaz
- Department of Psychiatry, Faculty of Medicine, Uskudar University, Istanbul, Turkey
| | - Namath Hussain
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Loma Linda University, School of Medicine, Loma Linda, CA, USA
| | - Zoltan Mari
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Robert W Thatcher
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Applied Neuroscience Research Institute, St. Petersburg, FL, USA.,Applied Neuroscience, Inc., St. Petersburg, Fl, USA
| | - Daniel Sipple
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA.,Fraunhofer-Institute for Translational Research and Pharmacology, Hamburg, Germany
| | - Kuldip Sidhu
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA.,CK Cell Technologies Pty Ltd, Norwest, NSW, Australia.,Faculty of Medicine, Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia.,Society for Brain Mapping and Therapeutics-Sydney, Sydney, NSW, Australia
| | | | - Francesco Costa
- IRCCS Humanitas Research Hospital, Milan, Italy.,Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | | | - Ted Berger
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,USC Department of Biomedical Engineering, Los Angeles, CA, USA
| | - Deborah Zelinsky
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,The Mind-Eye Institute, Northbrook, IL, USA
| | - Christopher J Wheeler
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Social Science Research Institute, Tokai University, Shibuya City, Tokyo, Japan
| | - J Wesson Ashford
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Department of Psychiatry & Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Reinhard Schulte
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Loma Linda University, School of Medicine, Loma Linda, CA, USA
| | - M A Nezami
- Sahel Oncology LLC, Newport Beach, CA, USA
| | - Harry Kloor
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Beyond Imagination, Los Angeles, CA, USA
| | - Aaron Filler
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA.,Institute for Nerve Medicine, Santa Monica, CA, USA
| | - Dawn S Eliashiv
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Department of Neurology, UCLA-David Geffen School of Medicine, Los Angeles, CA, USA
| | - Dipen Sinha
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA
| | - Antonio A F DeSalles
- Department of Neurosurgery, UCLA David Geffen School of Medicine, Los Angeles CA, USA.,NeuroSapiens - Rede D'Or São Luiz, Sao Paulo, Brazil.,Society for Brain Mapping and Therapeutics-Brazil, Sao Paulo, Brazil
| | - Venkatraman Sadanand
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Sergey Suchkov
- Applied Neuroscience, Inc., St. Petersburg, Fl, USA.,Society for Brain Mapping and Therapeutics-Russia, Moscow, Russia
| | - Ken Green
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA
| | - Barish Metin
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Department of Psychiatry, Faculty of Medicine, Uskudar University, Istanbul, Turkey
| | - Robert Hariri
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA.,Celularity Corporation, Warren, NJ, USA.,Weill Cornell School of Medicine, Department of Neurosurgery, New York, NY, USA
| | - Jason Cormier
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Blue Horizon International, Hackensack, NJ, USA
| | - Vicky Yamamoto
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, USA.,Brain Mapping Foundation, Los Angeles, CA, USA.,USC Keck School of Medicine, The USC Caruso Department of Otolaryngology-Head and Neck Surgery, Los Angeles, CA, USA.,USC-Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Babak Kateb
- Middle East Brain + Initiative, Los Angeles, CA, USA.,Loma Linda University, School of Medicine, Loma Linda, CA, USA.,National Center for Nanobioelectronics, Los Angeles, CA, USA.,Brain Technology and Innovation Park, Los Angeles, CA, USA
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25
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Deep characterization of individual brain-phenotype relations using a multilevel atlas. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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26
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Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda. Neuroinformatics 2021; 19:685-701. [PMID: 34282528 PMCID: PMC8566446 DOI: 10.1007/s12021-021-09531-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 12/25/2022]
Abstract
Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further ‘curate’ data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.
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27
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McGonigal A, Bartolomei F, Chauvel P. On seizure semiology. Epilepsia 2021; 62:2019-2035. [PMID: 34247399 DOI: 10.1111/epi.16994] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/23/2021] [Accepted: 06/23/2021] [Indexed: 12/30/2022]
Abstract
The clinical expression of seizures represents the main symptomatic burden of epilepsy. Neural mechanisms of semiologic production in epilepsy, especially for complex behaviors, remain poorly known. In a framework of epilepsy as a network rather than as a focal disorder, we can think of semiology as being dynamically produced by a set of interconnected structures, in which specific rhythmic interactions, and not just anatomical localization, are likely to play an important part in clinical expression. This requires a paradigm shift in how we think about seizure organization, including from a presurgical evaluation perspective. Semiology is a key data source, albeit with significant methodological challenges for its use in research, including observer bias and choice of semiologic categories. Better understanding of semiologic categorization and pathophysiological correlates is relevant to seizure classification systems. Advances in knowledge of neural mechanisms as well as anatomic correlates of different semiologic patterns could help improve knowledge of epilepsy networks and potentially contribute to therapeutic innovations.
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Affiliation(s)
- Aileen McGonigal
- Inserm, INS, Institut de Neurosciences des Systèmes, Aix Marseille Univ, Marseille, France.,Clinical Neurophysiology, APHM, Timone Hospital, Marseille, France
| | - Fabrice Bartolomei
- Inserm, INS, Institut de Neurosciences des Systèmes, Aix Marseille Univ, Marseille, France.,Clinical Neurophysiology, APHM, Timone Hospital, Marseille, France
| | - Patrick Chauvel
- Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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28
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Abstract
Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.
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Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
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29
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Salles A, Farisco M. Of Ethical Frameworks and Neuroethics in Big Neuroscience Projects: A View from the HBP. AJOB Neurosci 2021; 11:167-175. [PMID: 32716744 DOI: 10.1080/21507740.2020.1778116] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The recently published BRAIN 2.0 Neuroethics Report offers a very helpful overview of the possible ethical, social, philosophical, and legal issues raised by neuroscience in the context of BRAIN's research priorities thus contributing to the attempt to develop ethically sound neuroscience. In this article, we turn to a running theme of the document: the need for an ethical framework for the BRAIN Initiative and for further integration of neuroethics and neuroscience. We assess some of the issues raised and provide an explanation of how we have addressed them in the Human Brain Project. We offer our experience in the HBP as a potential contribution to the international debate about neuroethics in the big brain initiatives. Our hope is that among other things, the type of exchange proposed by this AJOB special issue will prove productive in further identifying and discussing the issues and in inspiring appropriate solutions.
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Affiliation(s)
- Arleen Salles
- Uppsala University.,Centro de Investigaciones Filosoficas
| | - Michele Farisco
- Uppsala University.,Biogem, Biology and Molecular Genetics Institute
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30
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Charvériat M, Lafon V, Mouthon F, Zimmer L. Innovative approaches in CNS drug discovery. Therapie 2021; 76:101-109. [DOI: 10.1016/j.therap.2020.12.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 09/04/2020] [Indexed: 12/15/2022]
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31
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Affiliation(s)
- Viktor Jirsa
- Aix Marseille University, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes, Marseille 13005, France
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32
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Groeneboom NE, Yates SC, Puchades MA, Bjaalie JG. Nutil: A Pre- and Post-processing Toolbox for Histological Rodent Brain Section Images. Front Neuroinform 2020; 14:37. [PMID: 32973479 PMCID: PMC7472695 DOI: 10.3389/fninf.2020.00037] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 07/17/2020] [Indexed: 02/01/2023] Open
Abstract
With recent technological advances in microscopy and image acquisition of tissue sections, further developments of tools are required for viewing, transforming, and analyzing the ever-increasing amounts of high-resolution data produced. In the field of neuroscience, histological images of whole rodent brain sections are commonly used for investigating brain connections as well as cellular and molecular organization in the normal and diseased brain, but present a problem for the typical neuroscientist with no or limited programming experience in terms of the pre- and post-processing steps needed for analysis. To meet this need we have designed Nutil, an open access and stand-alone executable software that enables automated transformations, post-processing, and analyses of 2D section images using multi-core processing (OpenMP). The software is written in C++ for efficiency, and provides the user with a clean and easy graphical user interface for specifying the input and output parameters. Nutil currently contains four separate tools: (1) A transformation toolchain named “Transform” that allows for rotation, mirroring and scaling, resizing, and renaming of very large tiled tiff images. (2) “TiffCreator” enables the generation of tiled TIFF images from other image formats such as PNG and JPEG. (3) A “Resize” tool completes the preprocessing toolset and allows downscaling of PNG and JPEG images with output in PNG format. (4) The fourth tool is a post-processing method called “Quantifier” that enables the quantification of segmented objects in the context of regions defined by brain atlas maps generated with the QuickNII software based on a 3D reference atlas (mouse or rat). The output consists of a set of report files, point cloud coordinate files for visualization in reference atlas space, and reference atlas images superimposed with color-coded objects. The Nutil software is made available by the Human Brain Project (https://www.humanbrainproject.eu) at https://www.nitrc.org/projects/nutil/.
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Affiliation(s)
- Nicolaas E Groeneboom
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Sharon C Yates
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maja A Puchades
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan G Bjaalie
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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33
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Brain imaging genomics: influences of genomic variability on the structure and function of the human brain. MED GENET-BERLIN 2020. [DOI: 10.1515/medgen-2020-2007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Brain imaging genomics is an emerging discipline in which genomic and brain imaging data are integrated in order to elucidate the molecular mechanisms that underly brain phenotypes and diseases, including neuropsychiatric disorders. As with all genetic analyses of complex traits and diseases, brain imaging genomics has evolved from small, individual candidate gene investigations towards large, collaborative genome-wide association studies. Recent investigations, mostly population-based, have studied well-powered cohorts comprising tens of thousands of individuals and identified multiple robust associations of single-nucleotide polymorphisms and copy number variants with structural and functional brain phenotypes. Such systematic genomic screens of millions of genetic variants have generated initial insights into the genetic architecture of brain phenotypes and demonstrated that their etiology is polygenic in nature, involving multiple common variants with small effect sizes and rare variants with larger effect sizes. Ongoing international collaborative initiatives are now working to obtain a more complete picture of the underlying biology. As in other complex phenotypes, novel approaches – such as gene–gene interaction, gene–environment interaction, and epigenetic analyses – are being implemented in order to investigate their contribution to the observed phenotypic variability. An important consideration for future research will be the translation of brain imaging genomics findings into clinical practice.
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