Abstract
The amount of information acquired with functional neuroimaging techniques, particularly fNIRS and fMRI, is rapidly growing and has enormous potential for studying human brain functioning. Therefore, many scientists focus on solving computational neuroimaging and Big Data issues to advance the discipline. However, the main obstacle—the accurate translation of the hemodynamic response (HR) by the investigation of a physiological phenomenon called neurovascular coupling—is still not fully overcome and, more importantly, often overlooked in this context. This article provides a brief and critical overview of significant findings from cellular biology and in vivo brain physiology with a focus on advancing existing HR modelling paradigms. A brief historical timeline of these disciplines of neuroscience is presented for readers to grasp the concept better, and some possible solutions for further scientific discussion are provided.
Highlights
Modern functional neuroimaging methods cover broad spatial and temporal scales (Pouratian et al, 2003) and facilitate the important exploration of the functional organisation of the human brain in health and disease (Liu et al, 2015)
Current trends in neuroimaging and computational neuroscience promote the advanced mathematical modelling of human brain function based on neuroimaging, and the implications associated with the use of Big Data (Hansen et al, 2014) in scientific research and healthcare innovations
The review was designed with a focus on the existing scientific paradigm in human brain functional neuroimaging research, mainly addressing the absence of necessary interactions between different multidisciplinary branches of neuroscience such as cellular biology, human brain physiology and computational modelling (CM)
Summary
Modern functional neuroimaging methods cover broad spatial and temporal scales (Pouratian et al, 2003) and facilitate the important exploration of the functional organisation of the human brain in health and disease (Liu et al, 2015). Some threats arise from fundamental conceptual challenges that remain widely underappreciated within the clinical and neuroimaging communities (Poldrack & Yarkoni, 2016). Current trends in neuroimaging and computational neuroscience promote the advanced mathematical modelling of human brain function based on neuroimaging, and the implications associated with the use of Big Data (Hansen et al, 2014) in scientific research and healthcare innovations. These multidisciplinary interactions between different branches of science are vital for overall scientific progress. Some main conceptual challenges may remain shadowed by massive trends and become a barrier to progress. The ability to assess neural activity in a non-invasive way by
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