Search the site...

ORGANIZATION FOR HUMAN BRAIN MAPPING
  • BLOG
  • Tutorials
  • Media
  • Contributors
  • OHBM WEBSITE
  • BLOG
  • Tutorials
  • Media
  • Contributors
  • OHBM WEBSITE

OHBM 2017 Abstract Highlight: Deep Learning Reveals Brain Features associated with Preterm Birth and Perinatal Risk Factors

9/6/2017

0 Comments

 

Q & A with Manuel Hinojosa-Rodriguez

BY BRENDAN E. DEPUE 
Infants and children with a history of preterm birth (PB) and with perinatal risk factors (PRF) for brain injury may exhibit structural brain abnormalities. For example, they may exhibit grey matter (GM) lesions that could impair motor or cognitive functions. However, MRI identification of these potential GM abnormalities in infants and children is very challenging and not often employed in clinical practice. Researchers have therefore devised machine learning algorithms to identify such structural abnormalities. To better understand these new tools, I got together with Manuel Hinojosa-Rodriguez currently at the Universidad Nacional Autonoma de Mexico, who collaborates with the University of Southern California.

Brendan Depue (BD): If you had to explain to your non-neuroscientist friends what your study is about, what would you say?

Manuel Hinojosa-Rodriguez (MHR): Machine learning is an artificial intelligence technique that allows the computer to identify (“learn”) the relationship between given information (brain measures) and a clinical diagnosis, without explicitly being programmed to do so. Deep learning, a type of machine learning, uses neural networks that include highly complicated structures generated by examining data and then subsequently, may achieve accurate prediction of clinical diagnosis based on certain brain features. According to our results, neural networks and deep learning are promising methods for identifying relationships between brain structure and medical conditions that affect early neurodevelopment of the preterm babies.
Picture
Manuel Hinojosa-Rodriguez, MD, Autonomous National University of Mexico.

​BD: Please briefly explain why machine learning is important for translational neuroimaging analyses.

MHR: Briefly, machine learning is an “artificial intelligence” technique that enables the computer to automatically identify “specific mathematical patterns”, which belong to distinctive conditions. Therefore, machine learning may potentiate our abilities to distinguish different medical conditions in neuroimaging analysis. Based on neurological features — which are informative for the machine to provide accurate prediction — this will hopefully enhance or even deepen our understanding of specific medical conditions.

BD: How does deep learning compare to other machine learning algorithms?
MHR: Deep learning is a technique in machine learning that enables the use of “neural-network” models, which contain a significant number of processing layers. Within each layer, there exists varied numbers of “neurons” and “connections”. Each individual neuron is a function that may include a distinctive mathematical operation, which defines how the neuron can be activated/deactivated; each connection may vary in the amount of information passed between neurons of different layers. Therefore, deep learning in neural networks may allow us to build a mathematical model that defines the relationship between input predictors and output medical conditions that is far more complex than ones initiated from other machine learning algorithms. Accordingly, deep learning may help us identify more subtle or intrinsic patterns in nature.

BD: Why do you think your abstract was selected as newsworthy? What is the appeal of your abstract to a broad audience?
MHR:
Until now, neuroimaging detection of subtle perinatal brain injury has been complicated, because conventional magnetic resonance imaging (MRI) in clinical environments does not allow us to detect microscopic lesions and does not offer relevant information about the etiology or perinatal risk factors suffered by the patient. However, by using neural networks and deep learning, it is hopefully possible to identify relationships between brain structure and medical conditions which affect early neurodevelopment. Our results reveal that brain features of preterm children can be associated with certain perinatal pathologies and/or risk factors for perinatal brain injury (see paper here).

BD: Given these findings, what are your next (research) steps going to be?
MHR:
The next step in this project will be to examine the correlation between clinical MRI (per grades of severity) and our results. We are very interested in the accurate diagnosis of subtle brain pathologies and early prediction of motor and cognitive disabilities using conventional and advanced MRI.
Picture
Figure 1. Brain regions used in determining possible perinatal risk factors.
0 Comments

Your comment will be posted after it is approved.


Leave a Reply.

    BLOG HOME

    ​TUTORIALS

    ​MEDIA

    ​contributors

    ​OHBM WEBSITE

    ​

    OHBM OnDemand 
    ​Education Platform


    RSS Feed

    Archives

    January 2024
    December 2023
    November 2023
    October 2023
    September 2023
    August 2023
    July 2023
    June 2023
    May 2023
    April 2023
    March 2023
    January 2023
    December 2022
    October 2022
    September 2022
    August 2022
    July 2022
    June 2022
    May 2022
    April 2022
    March 2022
    January 2022
    December 2021
    November 2021
    October 2021
    September 2021
    August 2021
    July 2021
    June 2021
    May 2021
    April 2021
    March 2021
    February 2021
    January 2021
    December 2020
    November 2020
    October 2020
    September 2020
    June 2020
    May 2020
    April 2020
    March 2020
    February 2020
    January 2020
    December 2019
    November 2019
    October 2019
    September 2019
    August 2019
    July 2019
    June 2019
    May 2019
    April 2019
    March 2019
    February 2019
    January 2019
    December 2018
    November 2018
    October 2018
    August 2018
    July 2018
    June 2018
    May 2018
    April 2018
    March 2018
    February 2018
    January 2018
    December 2017
    November 2017
    October 2017
    September 2017
    August 2017
    July 2017
    June 2017
    May 2017
    April 2017
    March 2017
    February 2017
    January 2017
    December 2016
    November 2016
    October 2016
    September 2016
    August 2016
    July 2016
    June 2016
    May 2016
    April 2016

stay connected with ohbm!


become a member

Telephone

952-646-2029

Email ohbm

EMAIL BLOG TEAM
Header image created by Thiebaut de Schotten & Batrancourt  
www.brainconnectivitybehaviour.eu