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Researchers in Singapore use fMRI to better understand human brain on cellular level

By Lecia Bushak, Contributing Reporter

In a new study, researchers have harnessed machine learning through functional magnetic resonance imaging (fMRI) to map out the cellular architecture of the human brain without having to rely on surgical techniques. The study, out of the National University of Singapore (NUS), implies that this approach could be used to help treat neurological disorders — diseases of the brain, spine, and nerves.

“To know what really happens at the innermost levels of the human brain, it is crucial for us to develop methods that can delve into the depths of the brain noninvasively,” Thomas Yeo, lead researcher and assistant professor at the Singapore Institute for Neurotechnology (SINAPSE) at NUS, said in a press release.
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However, many noninvasive techniques used currently, like magnetic resonance imaging (MRI), prevent researchers from studying the brain at a more detailed cellular level. The NUS study aimed to develop an approach to get a more comprehensive map of the brain noninvasively.

“Although patients and their families experience neurological disorders through their devastating symptoms, studies show that each disorder expresses unique features of pathology all the way down at the cellular level,” Yeo told HCB News.

Yeo noted that because pharmacological medications are designed to target pathways at the cellular level, this type of study is “crucial for advancing both our understanding and potential treatment of neurological diseases.” Neurological disorders include Alzheimer’s disease, Parkinson’s disease, and epilepsy.

The human brain is incredibly complex and made up of 100 billion nerve cells that are connected to thousands of others. Using biophysical models, which are simulations of a biological system through mathematical mapping of the physical properties of the system, could help neuroscientists simulate brain activity and learn more about the brain’s complexities.

In the study, the researchers analyzed imaging data from 452 participants involved in the Human Connectome Project. They employed machine learning algorithms to estimate model parameters of each brain region, and found that the “spatial distribution of micro-scale properties of the brain seem to reflect hierarchical processing pathways across large-scale brain regions,” Yeo said. For example, regions involved in sensory perception like vision or hearing showed micro-scale properties that were opposite from brain regions that were involved in internal thought, like remembering what you did last weekend.

As for the next steps, Yeo and his team plan to use this approach to investigate individual brain data and learn more about how differences in the brain’s cellular makeup can result in varying cognitive abilities between people. Yeo also hopes that the study “motivate[s] fellow neuroscientists and clinicians to consider the potential of applying machine learning with biophysical modelling as a way of exploring both healthy brain function and disease mechanisms.”

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