Summary: A new study combined neuroimaging with machine learning to reveal cellular properties in different brain regions. The approach and findings, researchers report, could potentially be used to asses the treatment of neurological disorders and develop new therapies.
An inter-disciplinary research team led by scientists from the National University of Singapore (NUS) has successfully employed machine learning to uncover new insights into the cellular architecture of the human brain.
The team demonstrated an approach that automatically estimates parameters of the brain using data collected from functional magnetic resonance imaging (fMRI), enabling neuroscientists to infer the cellular properties of different brain regions without probing the brain using surgical means. This approach could potentially be used to assess treatment of neurological disorders, and to develop new therapies.
“The underlying pathways of many diseases occur at the cellular level, and many pharmaceuticals operate at the microscale level. 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 non-invasively,” said team leader Assistant Professor Thomas Yeo, who is from the Singapore Institute for Neurotechnology (SINAPSE) at NUS, and the A*STAR-NUS Clinical Imaging Research Centre (CIRC).
The new study, conducted in collaboration with researchers from the Netherlands and Spain was first reported online in scientific journal Science Advances on 9 January 2019.
Unravelling the complexity of the human brain
The brain is the most intricate organ of the human body, and it is made up of 100 billion nerve cells that are in turn connected to around 1,000 others. Any damage or disease affecting even the smallest part of the brain could lead to severe impairment.
Currently, most human brain studies are limited to non-invasive approaches, such as magnetic resonance imaging (MRI). This limits the examination of the human brain at the cellular level, which may offer novel insights into the development, and potential treatment, of various neurological diseases.
Different research teams around the world have harnessed biophysical modelling to bridge this gap between non-invasive imaging and cellular understanding of the human brain. The biophysical brain models could be used to simulate brain activity, enabling neuroscientists to gain insights into the brain. However, many of these models rely on overly simplistic assumptions, such as, all brain regions have the same cellular properties, which scientists have known to be false for more than 100 years.
Constructing virtual brain models
Asst Prof Yeo and his team worked with researchers from Universitat Pompeu Fabra, Universitat Barcelona and University Medical Center Utrecht to analyse imaging data from 452 participants of the Human Connectome Project. Departing from previous modelling work, they allowed each brain region to have distinct cellular properties and exploited machine learning algorithms to automatically estimate the model parameters.
“Our approach achieves a much better fit with real data. Furthermore, we discovered that the micro-scale model parameters estimated by the machine learning algorithm reflect how the brain processes information,” said Dr Peng Wang, who is the first author of the paper, and had conducted the study when he was a postdoctoral researcher in Asst Prof Yeo’s team.
The research team found that brain regions involved in sensory perception, such as vision, hearing and touch, exhibit cellular properties opposite from brain regions involved in internal thought and memories. The spatial pattern of the human brain’s cellular architecture closely reflects how the brain hierarchically processes information from the surroundings. This form of hierarchical processing is a key feature of both the human brain and recent advances in artificial intelligence.
“Our study suggests that the processing hierarchy of the brain is supported by micro-scale differentiation among its regions, which may provide further clues for breakthroughs in artificial intelligence,” said Asst Prof Yeo, who is also with the Department of Electrical and Computer Engineering at the NUS Faculty of Engineering.
Moving forward, the NUS team plans to apply their approach to examine the brain data of individual participants, to better understand how individual variation in the brain’s cellular architecture may relate to differences in cognitive abilities. The team hopes that these latest results can be a step towards the development of individualised treatment plans with specific drugs or brain stimulation strategies.
About this neuroscience research article
Source: Carolyn Fong – NUS Publisher: Organized by NeuroscienceNews.com. Image Source: NeuroscienceNews.com image is in the public domain. Original Research: Open access research for “Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain” by Peng Wang, Ru Kong, Xiaolu Kong, Raphaël Liégeois, Csaba Orban, Gustavo Deco, Martijn P. van den Heuvel and B.T. Thomas Yeo in Science Advances. Published January 10 2019. doi:10.1126/sciadv.aat7854
Cite This NeuroscienceNews.com Article
[cbtabs][cbtab title=”MLA”]NUS “Harnessing Machine Learning to Uncover New Insights Into the Brain.” NeuroscienceNews. NeuroscienceNews, 10 January 2019. <https://neurosciencenews.com/machine-learning-brain-insights-10487/>.[/cbtab][cbtab title=”APA”]NUS(2019, January 10). Harnessing Machine Learning to Uncover New Insights Into the Brain. NeuroscienceNews. Retrieved January 10, 2019 from https://neurosciencenews.com/machine-learning-brain-insights-10487/[/cbtab][cbtab title=”Chicago”]NUS “Harnessing Machine Learning to Uncover New Insights Into the Brain.” https://neurosciencenews.com/machine-learning-brain-insights-10487/ (accessed January 10, 2019).[/cbtab][/cbtabs]
Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain
We considered a large-scale dynamical circuit model of human cerebral cortex with region-specific microscale properties. The model was inverted using a stochastic optimization approach, yielding markedly better fit to new, out-of-sample resting functional magnetic resonance imaging (fMRI) data. Without assuming the existence of a hierarchy, the estimated model parameters revealed a large-scale cortical gradient. At one end, sensorimotor regions had strong recurrent connections and excitatory subcortical inputs, consistent with localized processing of external stimuli. At the opposing end, default network regions had weak recurrent connections and excitatory subcortical inputs, consistent with their role in internal thought. Furthermore, recurrent connection strength and subcortical inputs provided complementary information for differentiating the levels of the hierarchy, with only the former showing strong associations with other macroscale and microscale proxies of cortical hierarchies (meta-analysis of cognitive functions, principal resting fMRI gradient, myelin, and laminar-specific neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in the dynamic resting brain.