Linking Computational Models to Experimental Data with the Brain Operation DataBase (BODB)

ABSTRACTBeing able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

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Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽

10.3390/antiox10020229 ◽

2021 ◽

Vol 10 (2) ◽

pp. 229

Author(s):

JunHyuk Woo ◽

Hyesun Cho ◽

YunHee Seol ◽

Soon Ho Kim ◽

Chanhyeok Park ◽

...

Keyword(s):

Oxidative Stress ◽

Mitochondrial Dysfunction ◽

Computational Models ◽

Neuronal Damage ◽

Living Organism ◽

The Body ◽

Mitochondrial Functions ◽

A Cell ◽

The Brain ◽

And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

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Inferring brain-computational mechanisms with models of activity measurements

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2016.0278 ◽

2016 ◽

Vol 371 (1705) ◽

pp. 20160278 ◽

Cited By ~ 30

Author(s):

Nikolaus Kriegeskorte ◽

Jörn Diedrichsen

Keyword(s):

Computational Models ◽

Brain Activity ◽

Visual Object ◽

Visual Object Recognition ◽

Summary Statistics ◽

Activity Data ◽

Individual Measurement ◽

Activity Measurements ◽

The Brain ◽

The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

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Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2019.0765 ◽

2021 ◽

Vol 376 (1821) ◽

pp. 20190765 ◽

Cited By ~ 2

Author(s):

Giovanni Pezzulo ◽

Joshua LaPalme ◽

Fallon Durant ◽

Michael Levin

Keyword(s):

Large Scale ◽

Computational Models ◽

State Of The Art ◽

Memory Representation ◽

Theme Issue ◽

Evolutionary Innovation ◽

New Interpretation ◽

The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

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AN ATHEROSCLEROTIC CORONARY ARTERY PHANTOM FOR PARTICLE IMAGE VELOCIMETRY

Proceedings of the Canadian Engineering Education Association (CEEA) ◽

10.24908/pceea.v0i0.3985 ◽

2011 ◽

Cited By ~ 1

Author(s):

Jean Brunette ◽

Rosaire Mongrain ◽

Adrian Ranga ◽

...

Keyword(s):

Experimental Data ◽

Particle Image Velocimetry ◽

Heart Attack ◽

Computational Models ◽

Particle Image ◽

Flow Analysis ◽

Novel Technique ◽

Image Velocimetry ◽

Inflammatory Processes ◽

Injection Molded

Myocardial infarction, also known as a heart attack, is the single leading cause of death in North America. It results from the rupture of an atherosclerotic plaque, which occurs in response to both mechanical stress and inflammatory processes. In order to validate computational models of atherosclerotic coronary arteries, a novel technique for molding realistic compliant phantom featuring injection-molded inclusions and multiple layers has been developed. This transparent phantom allows for particle image velocimetry (PIV) flow analysis and can supply experimental data to validate computational fluid dynamics algorithms and hypothesis.

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Estimating complexity of spike-wave discharges with largest Lyapunov exponent in computational models and experimental data

AIMS Biophysics ◽

10.3934/biophy.2020006 ◽

2020 ◽

Vol 7 (2) ◽

pp. 65-75

Author(s):

T. M. Medvedeva ◽

◽

A. K. Lüttjohann ◽

M. V. Sysoeva ◽

G. van Luijtelaar ◽

...

Keyword(s):

Experimental Data ◽

Lyapunov Exponent ◽

Computational Models ◽

Largest Lyapunov Exponent ◽

Spike Wave Discharges ◽

Spike Wave

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Distributed Code for Semantic Relations Predicts Neural Similarity

10.1101/596726 ◽

2019 ◽

Author(s):

Jeffrey N. Chiang ◽

Yujia Peng ◽

Hongjing Lu ◽

Keith J. Holyoak ◽

Martin M. Monti

Keyword(s):

Computational Models ◽

Semantic Network ◽

Building Blocks ◽

Semantic Relations ◽

Human Cognition ◽

Sequential Presentation ◽

Specific Content ◽

Distributed Representations ◽

Relational Similarity ◽

The Brain

AbstractThe ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.SignificanceRelations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain.

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Learning protein–DNA interaction landscapes by integrating experimental data through computational models

Bioinformatics ◽

10.1093/bioinformatics/btu408 ◽

2014 ◽

Vol 30 (20) ◽

pp. 2868-2874 ◽

Cited By ~ 6

Author(s):

Jianling Zhong ◽

Todd Wasson ◽

Alexander J. Hartemink

Keyword(s):

Experimental Data ◽

Computational Models ◽

Dna Interaction

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COVID-19 Beyond the Lungs

10.4018/978-1-7998-8225-1.ch007 ◽

2022 ◽

pp. 109-126

Author(s):

Omar El Hiba ◽

Hicham Chatoui ◽

Nadia Zouhairi ◽

Lahoucine Bahi ◽

Lhoussaine Ammouta ◽

...

Keyword(s):

Experimental Data ◽

Nervous System ◽

Clinical Symptoms ◽

Case Reports ◽

The World ◽

Acute Pneumonia ◽

The Brain ◽

Cardinal Feature ◽

Shed Light

Since December 2019, the world has been shaken by the spread of a highly pathogen virus, causing severe acute respiratory syndrome (SARS-Cov2), which emerged in Wuhan, China. SARS-Cov2 is known to cause acute pneumonia: the cardinal feature of coronavirus disease 2019 (COVID-19). Clinical features of the disease include respiratory distress, loss of spontaneous breathing, and sometimes neurologic signs such as headache and nausea and anosmia, leading to suppose a possible involvement of the nervous system as a potential target of SARS-CoV2. The chapter will shed light on the recent clinical and experimental data sustaining the involvement of the nervous system in the pathophysiology of COVID-19, based on several case reports and experimental data reporting the possible transmission of SARS-CoV2 throughout the peripheral nerves to the brain cardiorespiratory centers. Thus, understanding the role of the nervous system in the course of clinical symptoms of COVID-19 is important in determining the appropriate therapeutic approach to combat the disease.

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Peri-personal space as an interface for self-environment interaction

The World at Our Fingertips ◽

10.1093/oso/9780198851738.003.0002 ◽

2021 ◽

pp. 17-46

Author(s):

Jean-Paul Noel ◽

Tommaso Bertoni ◽

Andrea Serino

Keyword(s):

Motor System ◽

Computational Models ◽

Multisensory Processing ◽

Critical Role ◽

Personal Space ◽

Environment Interaction ◽

Specific System ◽

Physical Interactions ◽

Psychophysical Studies ◽

The Brain

The brain has developed a specific system to encode the space closely surrounding our body, our peri-personal space (PPS). This space is the theatre where all physical interactions with objects in the environment occur, and thus is postulated to play a critical role in both approaching and defensive behaviour. Here, we first describe the classic neurophysiological findings that have led researchers to conceive of PPS as a multisensory-motor interface. This historical perspective is given to clarify what properties are strictly related to PPS encoding, and what characteristics bear out or are related to PPS. Then, in an effort to uncover gaps in knowledge that often go unnoticed, we critically examine the association between PPS and i) multisensory processing, and ii) the motor system—its strongest allies. We do not mean to say that PPS isn’t multisensory-motor, simply to pinpoint current research shortcomings. Subsequently, we detail more recent psychophysical studies, highlighting the extreme plasticity of PPS, and its putative role in bodily self-consciousness and social cognition. Lastly, we briefly discuss computational models of PPS. Throughout the chapter, we particularly attempt to emphasize open areas of investigation. By critically evaluating past findings, many of them our own, we hope to provide a forward-looking perspective on the study of PPS.

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