Abstract
The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.
Highlights
IntroductionFunctional neuroimaging techniques, including functional magnetic resonance imaging (fMRI) acquired both during task (tfMRI) and resting-state (rsfMRI), electroencephalography (EEG), magnetoencephalography (MEG), and other modali-
Functional neuroimaging techniques, including functional magnetic resonance imaging acquired both during task and resting-state, electroencephalography (EEG), magnetoencephalography (MEG), and other modali-a ORCID: 0000-0002-9851-6376. b ORCID: 0000-0002-8432-7056. c ORCID: 0000-0002-9651-5820
In the special issue ‘‘Dealing with Data” of Science, Akil et al [7] addressed the need for neuroinformatics system to support the analytics on neuroimaging big data and enable coordination across multicentered efforts under the Neuroscience Information Framework (NIF)
Summary
Functional neuroimaging techniques, including functional magnetic resonance imaging (fMRI) acquired both during task (tfMRI) and resting-state (rsfMRI), electroencephalography (EEG), magnetoencephalography (MEG), and other modali-. In the special issue ‘‘Dealing with Data” of Science, Akil et al [7] addressed the need for neuroinformatics system (as a ‘‘prelude to new discoveries”) to support the analytics on neuroimaging big data and enable coordination across multicentered efforts under the Neuroscience Information Framework (NIF). They proposed the concept of both macroscopic (e.g., MRI and behavioral data) and microscopic (function and structure of individual neuron cells) connectomes to achieve the goal of deciphering the ‘‘neural choreography” of the brain at multiple scales spatiotemporally. In the later sections we will review the community perspectives and efforts on addressing these three challenges
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
