scholarly journals Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging

2016 ◽  
Author(s):  
Simon R. Schultz ◽  
Caroline S. Copeland ◽  
Amanda J. Foust ◽  
Peter Quicke ◽  
Renaud Schuck
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Neural Circuit ◽  
Functional Neuroimaging ◽  
Imaging Techniques ◽  
Region Of Interest ◽  
Calcium Transient ◽  
Two Photon ◽  
Analysis Tools ◽  
Pattern Size

AbstractRecent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem ofscanningactive circuits; and the prospect ofscanlessmicroscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotem-poral patterns of neural activity, that scale well with pattern size.

The role of functional magnetic resonance imaging in brain surgery

2010 ◽  
Vol 28 (2) ◽  
pp. E4 ◽  
Author(s):  
Geert-Jan Rutten ◽  
Nick F. Ramsey
Keyword(s):  
Mr Imaging ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Imaging Techniques ◽  
Convincing Evidence ◽  
Brain Surgery ◽  
Wada Test ◽  
Functional Mr Imaging ◽  
Large Scale Networks

New functional neuroimaging techniques are changing our understanding of the human brain, and there is now convincing evidence to move away from the classic and clinical static concepts of functional topography. In a modern neurocognitive view, functions are thought to be represented in dynamic large-scale networks. The authors review the current (limited) role of functional MR imaging in brain surgery and the possibilities of new functional MR imaging techniques for research and neurosurgical practice. A critique of current clinical gold standard techniques (electrocortical stimulation and the Wada test) is given.

scholarly journals Graph-Based Method for Anomaly Detection in Functional Brain Network using Variational Autoencoder

2019 ◽  
Author(s):  
Jalal Mirakhorli ◽  
Mojgan Mirakhorli
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Evaluation Criteria ◽  
Region Of Interest ◽  
Brain Network ◽  
Convolutional Network ◽  
Large Scale Data ◽  
Functional Brain Network ◽  
Variational Autoencoder ◽  
Brain Connections

AbstractFunctional neuroimaging techniques using resting-state functional MRI (rs-fMRI) have accelerated progress in brain disorders and dysfunction studies. Since, there are the slight differences between healthy and disorder brains, investigation in the complex topology of human brain functional networks is difficult and complicated task with the growth of evaluation criteria. Recently, graph theory and deep learning applications have spread widely to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns. Irregular graph analysis has been widely applied in many brain recognition domains, these applications might involve both node-centric and graph-centric tasks. In this paper, we discuss about individual Variational Autoencoder and Graph Convolutional Network (GCN) for the region of interest identification areas of brain which do not have normal connection when apply certain tasks. Here, we identified a framework of Graph Auto-Encoder (GAE) with hyper sphere distributer for functional data analysis in brain imaging studies that is underlying non-Euclidean structure, in learning of strong rigid graphs among large scale data. In addition, we distinguish the possible mode correlations in abnormal brain connections.

scholarly journals Neuroscience Cloud Analysis As a Service

2020 ◽  
Author(s):  
Taiga Abe ◽  
Ian Kinsella ◽  
Shreya Saxena ◽  
Liam Paninski ◽  
John P. Cunningham
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Large Scale ◽  
Ease Of Use ◽  
Cutting Edge ◽  
Analysis Tools ◽  
Large Scale Computing ◽  
Cloud Computation ◽  
Computational Resources ◽  
Cloud Analysis

AbstractA major goal of computational neuroscience is to develop powerful analysis tools that operate on large datasets. These methods provide an essential toolset to unlock scientific insights from new experiments. Unfortunately, a major obstacle currently impedes progress: while existing analysis methods are frequently shared as open source software, the infrastructure needed to deploy these methods – at scale, reproducibly, cheaply, and quickly – remains totally inaccessible to all but a minority of expert users. As a result, many users can not fully exploit these tools, due to constrained computational resources (limited or costly compute hardware) and/or mismatches in expertise (experimentalists vs. large-scale computing experts). In this work we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully-managed infrastructure platform, based on modern large-scale computing advances, that makes state-of-the-art data analysis tools accessible to the neuroscience community. We offer NeuroCAAS as an open source service with a drag-and-drop interface, entirely removing the burden of infrastructure expertise, purchasing, maintenance, and deployment. NeuroCAAS is enabled by three key contributions. First, NeuroCAAS cleanly separates tool implementation from usage, allowing cutting-edge methods to be served directly to the end user with no need to read or install any analysis software. Second, NeuroCAAS automatically scales as needed, providing reliable, highly elastic computational resources that are more efficient than personal or lab-supported hardware, without management overhead. Finally, we show that many popular data analysis tools offered through NeuroCAAS outperform typical analysis solutions (in terms of speed and cost) while improving ease of use and maintenance, dispelling the myth that cloud compute is prohibitively expensive and technically inaccessible. By removing barriers to fast, efficient cloud computation, NeuroCAAS can dramatically accelerate both the dissemination and the effective use of cutting-edge analysis tools for neuroscientific discovery.

scholarly journals Toward a Global and Reproducible Science for Brain Imaging in Neurotrauma: The ENIGMA Adult Moderate/Severe Traumatic Brain Injury Working Group

2019 ◽  
Author(s):  
Alexander Olsen ◽  
Talin Babikian ◽  
Erin D. Bigler ◽  
Karen Caeyenberghs ◽  
Virginia Conde ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Data Analysis ◽  
Brain Injury ◽  
Working Group ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Small Sample ◽  
Mortality And Morbidity ◽  
Neuroimaging Data

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with nonimaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for largescale neuroimaging data analysis. In this consensus statement we outline the working group’s short-term, intermediate, and long-term goals.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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