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AI activates neuroscience to map the brain’s terra incognita
By Rohitashva Agrawal  |  Jan 25, 2024
AI activates neuroscience to map the brain’s terra incognita
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One of the most consequential developments taking place today is the AI-driven transformation of neuroscience, which is helping medical professionals uncover new insights and discover more about neural structures and the brain, as Harvard Med Fellow Rohit Agrawal, MD, elaborates.

BANGALORE, INDIA - The ever-deepening intertwining of artificial intelligence (AI) and neuroscience is ushering in a new dawn of understanding neural structures and their functions.

The synergy between these disciplines is uncovering many novel insights into brain functionality, which in turn aids in disease diagnosis and fosters therapeutic advancements for patients. 

AI is further speeding up neuroscience's research and development process in multiple ways, e.g., by enabling quicker analysis of large neuroimaging datasets - which was previously cumbersome and time-consuming.1 AI-enabled results are also identifying interesting patterns and trends in these datasets, and the tech is particularly relevant in data-intensive domains such as cognitive and computational neuroscience, where the automated processing of real-time cognition analysis by AI generates intriguing insights almost impossible for humans to discover on their own.2 As ongoing newer developments impact change in human behavioral patterns, AI is also playing a significant role in behavioral neuroscience in the real-time monitoring of complex behaviors, in both experimental and commercial settings.3

AI is making its presence felt today in almost all domains of neuroscience in some capacity and is doing so in many ways, some of which are outlined in this article:

Diagnostic precision

When applied to neuroimaging data like magnetic resonance imaging (MRI) or electroencephalograms (EEG), AI-driven diagnostic tools enable neurologists to learn about subtle precursors or etiological reasons for neurological disorders. Such algorithms are trained on vast datasets to recognize patterns associated with difficult-to-diagnose diseases. To understand how this works, one can look at AI-based applications like NeuroQuant, used to quantify brain structure

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