News | Magnetic Resonance Imaging (MRI) | December 23, 2021

The new machine-learning based model can forecast a person’s aptitude for neurofeedback training treatments with a high generalization ability

The high generalization ability of the new neurofeedback (NF) aptitude prediction model developed by scientists from NAIST Japan offers a quick, simple and non-invasive method to screen candidates in clinical settings for whom fMRI-NF training would be most beneficial. Image courtesy of Nara Institute of Science and Technology

The high generalization ability of the new neurofeedback (NF) aptitude prediction model developed by scientists from NAIST Japan offers a quick, simple and non-invasive method to screen candidates in clinical settings for whom fMRI-NF training would be most beneficial. Image courtesy of Nara Institute of Science and Technology


December 23, 2021 — Advancements in medical science have allowed the treatment of psychiatric disorders like major depressive disorder (MDD) with functional magnetic resonance imaging neurofeedback (fMRI-NF) training. fMRI-NF training is a type of treatment that provides a non-invasive way to control and reinforce brain functions in patients with mental disorders through the use of real-time fMRI monitoring. However, the effectiveness of the treatment is not universal – it is influenced by a parameter called neurofeedback (NF) aptitude.

NF aptitude refers to an individual’s capacity to respond to NF training by displaying changes in brain activity. But NF aptitude varies from individual to individual. Thus, predicting a patient’s NF aptitude becomes important not only for the success of fMRI-NF training, but also to reduce the physical and economic burden on the patient and healthcare system. Thus far, NF aptitude prediction models have focused on specific target regions in the brain, where the NF training was focused. Now, in a new study published in NeuroImage, a group of Japanese scientists, led by Junichiro Yoshimoto from Nara Institute of Science and Technology, Japan, have successfully developed a mathematical model for the prediction of NF aptitude with a high generalization ability.

Speaking about their research, Yoshimoto said, “We applied machine learning, which is an offshoot of artificial intelligence (AI) technology, on data obtained from heathy individuals and patients with major depressive disorder to successfully develop a mathematical model that can predict individual fMRI-NF training aptitude, based on their pre-recorded brain activity at the resting state.”

To arrive at the model, the scientists first studied fMRI images of healthy patients and patients with MDD before fMRI-NF training. They then used these images to calculate the resting state functional connectivity (FC), which describes the correlated or anti-correlated activities in different areas of the brain. They then applied a technique called ‘partial least squares regression’ (PLS) to transform the FC patterns into participants’ NF aptitude. Furthermore, they determined which FCs were most effective for predicting NF aptitude.

They found that the PLS model could be generalized to the independent dataset from other institutes, i.e., it could successfully predict the NF aptitude of individuals based solely on resting-state fMRI scanning. They also found that a part of the brain called the posterior cingulate cortex was the functional hub among the brain regions, suggesting that it plays a major role in NF aptitude. “We believe that our research will help fMRI-NF training become more popular as a non-invasive treatment with minimal side effects for patients with mental health disorders,” concludes Yoshimoto.

Even though the study focused on MDD, the generalizability of the model developed in this study ensures that it can be applied to different neuropsychological disorders, providing hope to patients suffering from mental illnesses and neurological disorders.

For more information: http://www.naist.jp/en/

Related fMRI content:

Functional MRI Provides Encouraging Results in Unresponsive COVID-19 Patient

New MRI Technique Captures Brain Changes in Near-real Time

Machine Learning Uncovers New Insights Into Human Brain Through fMRI

 


Related Content

Webinar | Information Technology

Postpandemic staffing shortages and increased volumes require radiologists to do more with less, exacerbating burnout ...

Time January 30, 2023
arrow
News | Artificial Intelligence

January 26, 2023 — MedCognetics, Inc., an Artificial Intelligence (AI) software firm, announced that it has been awarded ...

Time January 26, 2023
arrow
News | Artificial Intelligence

Artificial intelligence (AI) is playing a growing role in all our lives and has shown promise in addressing some of the ...

Time January 26, 2023
arrow
News | Digital Pathology

January 25, 2023 — mTuitive, Inc. and PathPresenter Corporation announced a new partnership to deliver an enhanced ...

Time January 25, 2023
arrow
News | Magnetic Resonance Imaging (MRI)

January 25, 2023 — On November 11th, 2022 at the Southern Hills Hospital in Las Vegas, USA, Robotic Spine Surgeon Dr ...

Time January 25, 2023
arrow
News | Breast Imaging

January 24, 2023 — For patients with cancer, lengthy delays in treatment can decrease their chances of survival. In an ...

Time January 24, 2023
arrow
News | Digital Radiography (DR)

January 24, 2023 — Carestream Health was awarded 20 new patents in 2022 for global advances in artificial intelligence ...

Time January 24, 2023
arrow
News | Radiology Imaging

January 23, 2023 — Canon Medical Systems has released a new eBook featuring its new medical imaging roadshow. This new ...

Time January 23, 2023
arrow
News | Artificial Intelligence

January 20, 2023 — Artificial intelligence (AI) can reconstruct coarsely-sampled, rapid magnetic resonance imaging (MRI) ...

Time January 20, 2023
arrow
News | RSNA

January 19, 2023 — The Radiological Society of North America (RSNA) is launching a new artificial intelligence (AI) ...

Time January 19, 2023
arrow
Subscribe Now