New Study Uses fMRI and Machine Learning to Explore Brain Function

Researchers at MIT have recently showcased a groundbreaking map of the cerebral cortex’s functional structure. This map, developed with functional magnetic resonance imaging (fMRI), reveals 24 distinct neural networks involved in sensory and cognitive processing, observed as participants watched various movie clips. This innovative approach aims to deepen understanding of brain functions and lay the groundwork for future research into neural complexities.

Mapping Brain Networks with fMRI and Machine LearningMachine Learning to Explore Brain Function" />
Study: Neuroscientists create a comprehensive map of the cerebral cortex. Image Credit: Fer Gregory/Shutterstock.com

Advancements in Neuroimaging Technology

Understanding the functional architecture of the human brain has been a major challenge in neuroscience. Recently, functional magnetic resonance imaging (fMRI) has become a valuable tool in this field for the non-invasive study of brain activity.

fMRI measures brain activity indirectly by detecting changes in blood flow, known as the blood-oxygen-level-dependent (BOLD) response. When neurons are active, blood flow increases in those regions, creating measurable changes in MRI signals. This technique allows researchers to visualize real-time brain activity, aiding the mapping of networks associated with sensory processing, cognition, and behavior.

Traditionally, fMRI studies have been reliant on controlled tasks to isolate specific brain functions. However, this method may not fully represent the intricate network interactions present in natural settings.

Exploring Brain Function through Naturalistic Stimuli

In this study, the authors used a naturalistic approach where participants watched one hour of different movie clips while undergoing fMRI scans with a high-resolution 7-Tesla MRI scanner. This technique enabled the capture of brain activity patterns that reflect the complex interactions between cognitive and sensory processes as participants processed dynamic visual narratives.

Using data from the Human Connectome Project, which included brain scans from 176 individuals, the researchers applied a machine-learning algorithm to analyze activity across brain regions during the movie-watching task. This approach provided high-precision mapping of functional networks, revealing nuanced interactions and identifying regions of heightened activity tied to specific cognitive functions.

Functional Architecture of the Cerebral Cortex

The study identified 24 distinct neural networks within the cerebral cortex. Several networks aligned with previously known sensory-processing regions, such as the visual and auditory cortices, reinforcing their established roles in processing sensory input from the movie stimuli.

Other networks were linked to higher-order cognitive functions, such as language processing, social interaction analysis, and semantic information extraction. The 7-Tesla scanner’s spatial resolution offered more precise boundaries between networks than earlier studies.

Notably, the study uncovered novel networks, including a prefrontal cortex network responsive to visual scenes in the movie clips, suggesting a previously underappreciated role for this region in visual scene processing. Additionally, three networks were identified as "executive control" networks, which exhibited increased activity during transitions between clips. These networks demonstrated a dynamic “push-pull” relationship with domain-specific networks, indicating they help regulate cognitive resources depending on the complexity and ambiguity of stimuli.

Expanding the Understanding of Brain Function

This research provides a comprehensive understanding of the cerebral cortex's functional organization. The naturalistic movie-watching paradigm was highly effective in activating a wide range of cortical regions, offering a more complete view of network interactions than previous methods. These findings set a valuable foundation for future studies aiming to explore the specific roles and interactions of these networks in detail.

By analyzing brain function during natural tasks, researchers hope to refine diagnostic tools for cognitive disorders and improve therapies for conditions like autism, schizophrenia, and other neurodevelopmental disorders. Ongoing studies are anticipated, which will be used to investigate specific networks involved in social processing and visual scene analysis, using targeted experimental designs to test focused hypotheses.

Conclusion

This study represents an exciting step forward in understanding brain function. By combining naturalistic movie viewing with high-resolution fMRI and machine learning, researchers have mapped 24 distinct brain networks with remarkable detail.

This comprehensive map not only confirms previous findings but also shows novel networks and interactions, providing a solid foundation for future research into the complex interplay of cognitive and sensory processes.

As neuroscience progresses, these findings offer a valuable foundation for exploring how the cerebral cortex manages sensory and cognitive information, potentially unlocking fresh insights into the intricate processes behind human cognition and behavior.

Journal Reference

Rajimehr, R., & et al. Neuroscientists create a comprehensive map of the cerebral cortex. Published on: MIT News Website, Accessed on November 6, 2024. https://news.mit.edu/2024/neuroscientists-create-comprehensive-map-cerebral-cortex-1106

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Osama, Muhammad. (2024, November 12). New Study Uses fMRI and Machine Learning to Explore Brain Function. AZoRobotics. Retrieved on November 22, 2024 from https://www.azorobotics.com/News.aspx?newsID=15458.

  • MLA

    Osama, Muhammad. "New Study Uses fMRI and Machine Learning to Explore Brain Function". AZoRobotics. 22 November 2024. <https://www.azorobotics.com/News.aspx?newsID=15458>.

  • Chicago

    Osama, Muhammad. "New Study Uses fMRI and Machine Learning to Explore Brain Function". AZoRobotics. https://www.azorobotics.com/News.aspx?newsID=15458. (accessed November 22, 2024).

  • Harvard

    Osama, Muhammad. 2024. New Study Uses fMRI and Machine Learning to Explore Brain Function. AZoRobotics, viewed 22 November 2024, https://www.azorobotics.com/News.aspx?newsID=15458.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.