Functional Neuroimaging in the New Era of Big Data

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.

Promoting global collaboration for brain health research

“…15 Thanks to the international collaborative projects, the field of functional neuroimaging has advanced substantially, showing the value of big data science. 16 On the clinical side we have seen the value of harmonisation of variables among relevant studies to promote greater comparability across collaborating research projects. 17 Machine learning and artificial intelligence techniques based on big data are increasingly being used in both understanding and diagnosis of neurological disorders and offer a new model for personalised management.…”

“…The Human Brain Project’s platforms give scientists a single point of access to neuroscientific method, multiomic clinical data, and analysis tools from around the world 15. Thanks to the international collaborative projects, the field of functional neuroimaging has advanced substantially, showing the value of big data science 16. On the clinical side we have seen the value of harmonisation of variables among relevant studies to promote greater comparability across collaborating research projects 17…”

Fisher Scoring for crossed factor linear mixed models

“…For example, in the mass-univariate analyses used in medical imaging, standard practice involves estimating hundreds of thousands of models concurrently. To efficiently perform a mass-univariate analysis within a practical timeframe, the use of vectorized computation which exploits the repetitive nature of simplistic operations to streamline calculation must be employed (Smith and Nichols 2018;Li et al 2019). Unfortunately, many existing LMM tools utilize complex operations, for which vectorized support does not currently exist.…”

Characterization of Brain Iron Deposition Pattern and Its Association With Genetic Risk Factor in Alzheimer’s Disease Using Susceptibility-Weighted Imaging

“…It may also be supportive for the diagnosis of AD, based on both change in white matter tracts ( Douaud et al, 2011 ) and global/local fractional anisotropy (FA) ( Medina et al, 2006 ; Zhang et al, 2007 ). Functional magnetic resonance imaging (fMRI) has also been explored to characterize cognitive and behavior changes caused by AD progression ( Li et al, 2019 ). Previous studies observed that disruption of resting-state functional networks could differentiate MCI/AD with normal controls ( Rombouts et al, 2005 ); so does the decreased activation in cognition-related brain regions measured by memory encoding task ( Machulda et al, 2003 ).…”