Exploring synergies: Advancing neuroscience with machine learning

Comprehensive Summary

Machine learning has revolutionized the field of neuroscience throughout the years. Although the field of neuroscience receives data from functional MRIs (fMRIs), electroencephalographies (EEGs), and Local Field Potentials (LFPs), it is difficult for neuroscientists to analyze this voluminous amount of data in real time. Machine learning provides the tools for physicians and neuroscientists to analyze connected brain areas and activity by overcoming challenges such as latency reduction and scalability. Being able to analyze the neural data can deepen our understanding of certain neurological diseases. State-Space models (SSMs) are MLs commonly used in neuroscience to illustrate the neural data and make it easier to interpret. Gaussian Processes (GPs) use a Bayesian framework to understand latent states and the non-linear connections among brain regions. Recurrent neural networks (RNNs), which include long-short term memory (LSTM) networks and gate recurrent units (GRU), help analyze neural dynamics. The authors of this article applied these MLs to fMRI data from 35 individuals with major depressive disorder. Neural data was collected when the patients were in different cognitive states: Anticipation and Feedback. The responsive expressions of the patients were recorded as happy, sad, or neutral. Using both continuous and discrete latent representations, the models successfully compress high-dimensional fMRI data into interpretable low-dimensional states that help scientists understand neural dynamics. GP-based connectivity models analyze the neural interactions, and IVA-based analysis highlights the functional networks while maintaining the individual variability. Overall, the study combines multiple probabilistic models to help analyze neural data and further the knowledge of how the brain is connected.

Outcomes and Implications

Machine learning has helped develop the field of neuroscience and healthcare by allowing physicians to understand the complications in certain neurodegenerative diseases. Use of this vital technology will allow physicians to find better, targeted treatment for their patients and allow better clinical outcomes. Furthermore, this research can be furthered using other MLs not explicitly used in this study, such as deep learning models. It is also important, however, to recognize that complex neural systems will continue to inspire new machine learning methods; neuroscience and machine learning are interdependent on one another.