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.

Application of deep learning in fMRI-based human brain parcellation: a review

“…The publicly available UK Biobank dataset [21], for instance, collects hundreds of thousands of genetic and behavioral data as well as tens of thousands of multimodal brain imaging. In the current big data era [20] (see review for public neuroimaging datasets [19,100]), how to fully utilize multimodal information is also a significant challenge. Multimodal analysis techniques based on deep learning have made great progress [101], in which techniques such as reinforcement learning [102] and multimodal generalized foundation models [103,104] can be combined with brain parcellations to achieve better performance via the fusion of complementation information across multimodal.…”

“…With the progressive development of neuroimaging technology and large-scale databases, parcellation research has come into a 'big data' era [18][19][20]. For instance, UK Biobank [21], the largest brain imaging database, currently has more than 40 000 fMRI samples, and each sample contains more than 100 000 voxels and 490 time points.…”

Editorial: Big Data analytics to advance stroke and cerebrovascular disease: a tool to bridge translational and clinical research

“…For example, Li Y. et al demonstrated that CT imaging features and markers of small vessel disease are predictive of the presence of >10 cerebral microbleeds on MRI. More research is needed before Big Data analysis such as artificial intelligence and machine learning can be more ubiquitously applied to clinical care ( 2 – 6 ).…”

“…Big Data analytics is a rapidly evolving field and there are important considerations and pauses that should be factored into data interpretation and application. It is important to be aware of biases that may be present in datasets as a result of patient recruitment ( 1 – 6 ). Even within large datasets, there may be unknown missing confounders.…”

“…Even within large datasets, there may be unknown missing confounders. It is important to consider validation of results in different datasets ( 1 – 6 ).…”