The evolution of Big Data in neuroscience and neurology

Abstract

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary information: The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Keywords: Addiction; Alzheimer’s; Artificial Intelligence; Big data; Brain Stimulation; Depression; Neurology; Neuroscience; Pain; Stroke.

Fig. 1

Evolution of data types [21]. The evolution of Data types in the development of Computational Neuroscience can be traced from Golgi and Ramón y Cajal’s structural data descriptions of the neuron in the nineteenth century [22]; to Hodgkin, Huxley, and Ecceles’s biophysical data characterization of the “all-or-none” action potential during the early to mid-twentieth century [23]; to McCulloch and Pitts’ work on the use of ‘the "all-or-none" character of nervous activity’ to model neural networks descriptive of fundamentals of nervous system [24]. Similarly, Connectomics’ Data evolution [25] can be traced from Galen’s early dissection studies [26], to Wernicke’s and Broca’s postulations on structure and function [27], to imaging of the nervous system [28, 29], and brain atlases (e.g., Brodmann, Talairach) and databases [30, 31] into the Big Data field that is today as characterized by the Human Connectome Project [32] and massive whole brain connectome models [7, 33]. Behavioral Neuroscience and Neurology can be tracked from early brain injury studies [34] to stimulation and surgical studies [35, 36], to Big Data assessments in cognition and behavior [37]. All these fields are prime examples of the transformative impact of the Big Data revolution on Neuroscience and Neurology sub-fields

Fig. 2

The 5 V’s. While the 5 V’s of Big Data (“Volume, Variety, Velocity, Veracity, and Value”) are clearly found in certain fields (e.g., social media) there are many "Big Data" Neuroscience and Neurology projects where categories are not explored or are underexplored. Many self-described “Big Data” studies are limited to Volume and/or Variety. Furthermore, most “Big Data” clinical trial speeds move at the variable pace of patient recruitment which can pale in comparison to the speeds of Big Data Velocity in the finance and social media spaces. “Big Data” acquisition and processing times are also sporadically detailed in the fields. Finally, there is not an accepted definition of data Veracity as it pertains to healthcare (e.g., error, bias, incompleteness, inconsistency) and Veracity can be assessed on multiple levels (e.g., from data harmonization techniques to limitations in experimental methods used in studies)

Fig. 3

Cumulative number of papers on Big Data over time for different areas, as per Pubmed. The panels illustrate when Big Data started to impact the area and allow a comparison across areas As graphs were simply created by using the keywords “Big Data” AND “area”, with "area" being “Parkinson’s Disease”, “Addiction”, etc. as opposed to using multiple keywords that may be used to describe each field, actual numbers are likely to be underestimated

Fig. 4

High Performance Computing solutions for modeling brain stimulation dosing have been explored for well over a decade. The above figure is adapted from [183], where Sinusoidal Steady State Solutions of the electromagnetic fields during TMS and DBS were determined from MRI derived Finite Element Models based on frequency specific tissue electromagnetic properties of head and brain tissue. The sinusoidal steady state solutions were then transformed into the time domain to rebuild the transient solution for the stimulation dose in the targeted brain tissues. These solutions were then coupled with single cell conductance-based models of human motor neurons to explore the electrophysiological response to stimulation. Today, high resolution patient specific models are being developed (see below), implementing more complicated biophysical modeling (e.g., coupled electromechanical field models) and are being explored as part of large heterogenous data sets (e.g., clinical, imaging, and movement kinematic) to optimize/tune therapy

Fig. 5

Schematic of our suite under development for delivering personalized treatments based on a Big Data infrastructure, whereby multimodal data sets (e.g., imaging, biophysical field-tissue interaction models, clinical, biospecimen data) can be coupled to deliver personalized brain stimulation-based treatments in a diverse and expansive patient cohort. Each integrated step can be computationally intensive (e.g., see Fig. 4 for simplified dosing example for exemplary electromagnetic brain stimulation devices)