News | Artificial Intelligence | December 11, 2019

This deep learning approach could also have applications for other neurological conditions, according to researchers

Schematic diagram of the proposed multichannel deep neural network model analyzing multiscale functional brain connectome for a classification task. rsfMRI = resting-state functional MRI.

Schematic diagram of the proposed multichannel deep neural network model analyzing multiscale functional brain connectome for a classification task. rsfMRI = resting-state functional MRI. Graphic courtesy of the Radiological Society of North America.


December 11, 2019 — Deep learning, a type of artificial intelligence, can boost the power of magnetic resonance imaging (MRI) in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Researchers said the approach could also have applications for other neurological conditions.

The human brain is a complex set of networks. Advances in functional MRI, a type of imaging that measures brain activity by detecting changes in blood flow, have helped with the mapping of connections within and between brain networks. This comprehensive brain map is referred to as the connectome.

Increasingly, the connectome is regarded as key to understanding brain disorders like ADHD, a condition that makes it difficult for a person to pay attention and control restless behavior.

According to the National Survey of Children's Health, approximately 9.4 percent of U.S. children, ages 2 to 17 years (6.1 million) in 2016 have been diagnosed with ADHD. The disorder cannot yet be definitively diagnosed in an individual child with a single test or medical imaging exam. Instead, ADHD diagnosis is based on a series of symptoms and behavior-based tests.

Brain MRI has a potential role in diagnosis, as research suggests that ADHD results from some type of breakdown or disruption in the connectome. The connectome is constructed from spatial regions across the MR image known as parcellations. Brain parcellations can be defined based on anatomical criteria, functional criteria, or both. The brain can be studied at different scales based on different brain parcellations.

Prior studies have focused on the so-called single-scale approach, where the connectome is constructed based on only one parcellation. For the new study, researchers from the University of Cincinnati College of Medicine and Cincinnati Children's Hospital Medical Center took a more comprehensive view. They developed a multi-scale method, which used multiple connectome maps based on multiple parcellations.

To build the deep learning model, the researchers used data from the NeuroBureau ADHD-200 dataset. The model used the multi-scale brain connectome data from the project's 973 participants along with relevant personal characteristics, such as gender and IQ.

The multi-scale approach improved ADHD detection performance significantly over the use of a single-scale method.

"Our results emphasize the predictive power of the brain connectome," said study senior author Lili He, Ph.D., from the Cincinnati Children's Hospital Medical Center. "The constructed brain functional connectome that spans multiple scales provides supplementary information for the depicting of networks across the entire brain."

By improving diagnostic accuracy, deep-learning-aided MRI-based diagnosis could be critical in implementing early interventions for ADHD patients. Approximately 5 percent of American pre-school and school-aged children have been diagnosed with ADHD. These children and adolescents face a high risk of failing in academic study and building social relationships, which can result in financial hardship for families and create a tremendous burden on society.

The approach also has potential beyond ADHD, according to He.

"This model can be generalized to other neurological deficiencies," she said. "We already use it to predict cognitive deficiency in pre-term infants. We scan them soon after birth to predict neurodevelopmental outcomes at two years of age."

In the future, the researchers expect to see the deep learning model improve as it is exposed to larger neuroimaging datasets. They also hope to better understand the specific breakdowns or disruptions in the connectome identified by the model that are associated with ADHD.

For more information: www.RadiologyInfo.org


Related Content

News | Breast Imaging

May 13, 2025 — In one of the larger studies of its kind, researchers have identified six breast texture patterns that ...

Time May 16, 2025
arrow
News | Computed Tomography (CT)

May 15, 2025 — GE HealthCare has launched CleaRecon DL, technology powered by a deep-learning algorithm, to improve the ...

Time May 15, 2025
arrow
News | Radiation Oncology

May 2, 2025 — GE HealthCare has announced an intended expansion of its radiation oncology portfolio as well as the ...

Time May 03, 2025
arrow
News | Cardiac Imaging

April 30, 2025 – Viz.ai, the leader in AI-powered disease detection and intelligent care coordination, has launched Viz ...

Time May 02, 2025
arrow
News | X-Ray

May 01, 2025 — Researchers from the Rajpurkar Lab in the Department of Biomedical Informatics at Harvard Medical School ...

Time May 01, 2025
arrow
News | Lung Imaging

April, 15, 2025 — Optellum has entered an agreement with Bristol Myers Squibb to leverage AI in early diagnosis and ...

Time April 17, 2025
arrow
News | Pediatric Imaging

April 10, 2025 — Cincinnati Children’s and GE HealthCare will form a strategic research program focused on driving ...

Time April 10, 2025
arrow
News | Breast Imaging

March 20, 2025 — GE HealthCare has launched Invenia Automated Breast Ultrasound (ABUS) Premium, the latest 3D ultrasound ...

Time March 21, 2025
arrow
News | Computed Tomography (CT)

Feb. 25, 2025 —Stratasys Ltd. and Siemens Healthineers recently presented the results of a joint research effort that ...

Time March 04, 2025
arrow
News | Artificial Intelligence

Feb. 4, 2025 — Riverain Technologies recently announced it expanded across eight countries in 2024 and added nearly 50 ...

Time February 04, 2025
arrow
Subscribe Now