Supercomputing in the Study and Stimulation of the Brain

2021 ◽  
pp. 290-300
Author(s):  
Laura Dipietro ◽  
Seth Elkin-Frankston ◽  
Ciro Ramos-Estebanez ◽  
Timothy Wagner
Keyword(s):  
High Performance ◽  
Neurological Diseases ◽  
Data Sets ◽  
Imaging Data ◽  
Building Simulations ◽  
History Of ◽  
Brain Imaging Data ◽  
Evolution Of Science ◽  
Computational Systems ◽  
The Brain

The history of neuroscience has tracked with the evolution of science and technology. Today, neuroscience's trajectory is heavily dependent on computational systems and the availability of high-performance computing (HPC), which are becoming indispensable for building simulations of the brain, coping with high computational demands of analysis of brain imaging data sets, and developing treatments for neurological diseases. This chapter will briefly review the current and potential future use of supercomputers in neuroscience.

scholarly journals Analysis of task-based functional MRI data preprocessed with fMRIPrep

2019 ◽  
Author(s):  
Oscar Esteban ◽  
Rastko Ciric ◽  
Karolina Finc ◽  
Ross Blair ◽  
Christopher J. Markiewicz ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Brain Activity ◽  
Imaging Data ◽  
Modeling And Analysis ◽  
Manual Intervention ◽  
Standard Tool ◽  
History Of ◽  
Brain Imaging Data ◽  
Many Sources ◽  
The Brain

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure (BIDS) to standardize both the input datasets —MRI data as stored by the scanner— and the outputs —data ready for modeling and analysis—, fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

scholarly journals The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data

2020 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Modern Science ◽  
Imaging Data ◽  
General Data Protection Regulation ◽  
Data User ◽  
History Of ◽  
General Data ◽  
Brain Imaging Data ◽  
The Brain ◽  
The Eu

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavour is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants’ privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU General Data Protection Regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.

scholarly journals High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Johanna Wagner ◽  
Ramon Martinez-Cancino ◽  
Arnaud Delorme ◽  
Scott Makeig ◽  
Teodoro Solis-Escalante ◽  
...  
Keyword(s):  
Pressure Sensors ◽  
Step Length ◽  
High Density ◽  
Imaging Data ◽  
Joint Angles ◽  
Body Imaging ◽  
Cortical Dynamics ◽  
Brain Imaging Data ◽  
Auditory Cueing ◽  
The Brain

Abstract In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.

scholarly journals Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC

GigaScience ◽  
2016 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
Daniel Clark ◽  
Krzysztof J. Gorgolewski ◽  
R. Cameron Craddock
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
The Brain

scholarly journals Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Rayus Kuplicki ◽  
James Touthang ◽  
Obada Al Zoubi ◽  
Ahmad Mayeli ◽  
Masaya Misaki ◽  
...  
Keyword(s):  
Data Management ◽  
Large Scale ◽  
Laboratory Data ◽  
Imaging Data ◽  
Common Data Elements ◽  
Level Data ◽  
Reproducible Analysis ◽  
Brain Imaging Data ◽  
Data Elements ◽  
The Brain

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

scholarly journals Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Dimitris A. Pinotsis ◽  
Earl K. Miller
Keyword(s):  
Computational Models ◽  
Multiple Scales ◽  
Neurological Diseases ◽  
Cortical Layer ◽  
Statistical Decision Theory ◽  
Imaging Data ◽  
Cortical Layers ◽  
Statistical Decision ◽  
Non Invasive ◽  
Brain Imaging Data

AbstractNeural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject’s hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.

scholarly journals The modular and integrative functional architecture of the human brain

2015 ◽  
Vol 112 (49) ◽  
pp. E6798-E6807 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
B. T. Thomas Yeo ◽  
Mark D’Esposito
Keyword(s):  
Human Brain ◽  
Cognitive Functions ◽  
Topic Model ◽  
Brain Activity ◽  
Modular Function ◽  
Imaging Data ◽  
Functional Architecture ◽  
Brain Imaging Data ◽  
The Brain

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules’ processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author–topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network’s modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules’ functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain’s modular yet integrated implementation of cognitive functions.

scholarly journals Probing Brain Activation Patterns by Dissociating Semantics and Syntax in Sentences

2020 ◽  
Vol 34 (05) ◽  
pp. 9201-9208
Author(s):  
Shaonan Wang ◽  
Jiajun Zhang ◽  
Nan Lin ◽  
Chengqing Zong
Keyword(s):  
Brain Regions ◽  
Feature Representation ◽  
Semantic Feature ◽  
Imaging Data ◽  
Activation Patterns ◽  
Syntactic Feature ◽  
Syntactic Information ◽  
Brain Imaging Data ◽  
The Right ◽  
The Brain

The relation between semantics and syntax and where they are represented in the neural level has been extensively debated in neurosciences. Existing methods use manually designed stimuli to distinguish semantic and syntactic information in a sentence that may not generalize beyond the experimental setting. This paper proposes an alternative framework to study the brain representation of semantics and syntax. Specifically, we embed the highly-controlled stimuli as objective functions in learning sentence representations and propose a disentangled feature representation model (DFRM) to extract semantic and syntactic information in sentences. This model can generate one semantic and one syntactic vector for each sentence. Then we associate these disentangled feature vectors with brain imaging data to explore brain representation of semantics and syntax. Results have shown that semantic feature is represented more robustly than syntactic feature across the brain including the default-mode, frontoparietal, visual networks, etc.. The brain representations of semantics and syntax are largely overlapped, but there are brain regions only sensitive to one of them. For instance, several frontal and temporal regions are specific to the semantic feature; parts of the right superior frontal and right inferior parietal gyrus are specific to the syntactic feature.

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