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Brain scan predicts effectiveness of spinal cord surgery

A 10-minute brain scan could determine whether patients with chronic pain will respond to spinal cord stimulation therapy, potentially avoiding unnecessary invasive procedures for those unlikely to benefit, according to a new Kobe University study.

Published in British Journal of Anaesthesia | Estimated reading time: 4 minutes

For those suffering from intractable chronic pain, spinal cord stimulation offers a glimmer of hope when all other treatments have failed. However, this surgical procedure – which involves implanting electrical leads into the spine – doesn’t work for everyone. Currently, patients must undergo a risky trial period lasting up to two weeks to determine if the treatment will be effective.

Now, researchers at Kobe University have discovered that functional magnetic resonance imaging (fMRI) scans can predict treatment success before any surgical intervention. The study, led by anesthesiologist Ueno Kyohei, examined 29 patients with various forms of chronic pain.

“A big issue is that the procedure is effective for some but not for other patients, and which is the case is usually evaluated in a short trial of a few days to two weeks prior to permanent implantation,” explains Ueno. “Although this trial is short, it is still an invasive and risky procedure.”

The research team’s findings point to a specific relationship between two brain networks – the default mode network, involved in self-related thought, and the salience network, which regulates attention and stimulus response. The weaker the connection between certain regions of these networks, the better patients responded to spinal cord stimulation therapy.

Of the 29 study participants, 14 responded positively to the treatment while 15 did not. The brain scan method showed impressive accuracy in predicting these outcomes, with 71% sensitivity and 87% specificity. This level of predictive power could significantly improve patient care by helping doctors and patients make more informed decisions about pursuing the treatment.

While the diversity of pain conditions in the study participants created some analytical challenges, it also suggests broader applicability of the findings. “From a clinical perspective, the ability to predict outcomes for patients with various conditions may provide significant utility,” the researchers note in their paper.

Looking ahead, Ueno and his team are investigating how different patterns of spinal cord stimulation affect specific brain regions. “We are just at the beginning of this research,” says Ueno, “but our main goal is to use functional brain imaging as a biomarker for spinal cord stimulation therapy to identify the optimal treatment for each patient in the future.”

Glossary

Functional magnetic resonance imaging (fMRI)
A brain scanning technique that measures brain activity by detecting changes in blood flow, showing which parts of the brain are active during specific tasks or states.
Default mode network
A network of connected brain regions that is active when we are not focused on the external environment, playing a key role in self-reflection and chronic pain.
Spinal cord stimulation
A surgical procedure that involves implanting electrical leads along the spinal cord to modify pain signals before they reach the brain.

Test Your Knowledge

What is the primary advantage of using fMRI scanning before spinal cord stimulation?

It can predict treatment effectiveness without requiring an invasive trial procedure, potentially saving patients from unnecessary surgical risks.

How many participants were included in the study, and what was the response rate?

The study included 29 participants, with 14 responding positively to the treatment and 15 not responding.

What specific brain network connection was associated with better treatment outcomes?

Patients with weaker connections between regions of the default mode network and salience network showed better responses to the treatment.

What were the sensitivity and specificity rates of the brain scan prediction method?

The method showed 71% sensitivity and 87% specificity in predicting treatment outcomes.


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