| Paper authors: | Ruiyang Ge, Daniel M. Blumberger, Jonathan Downar, Zafiris J. Daskalakis, Adam A. Dipinto, Joseph C.W. Tham, Raymond Lam, Fidel Vila-Rodriguez |
| Year of paper publication: | 2017 |
| Post authors: | Caleb Pozdnikoff, Lisa Ridgway, Fidel Vila-Rodriguez |
| View the research article: | Ge et al. (2017) Abnormal functional connectivity within resting-state networks is related to rTMS-based therapy effects of treatment resistant depression: A pilot study |
Introduction
Major depressive disorder is a widespread mental health condition, and a significant number of individuals develop treatment-resistant depression (TRD), meaning they don’t get better with standard treatments. In fact, up to 50% of patients do not respond to standard treatments for depression.
While repetitive transcranial magnetic stimulation (rTMS) is an effective therapy for TRD, it’s difficult to predict which patients will benefit most. To address this, finding biological markers (biomarkers) that can predict treatment response is a crucial goal for providing personalized care. This study used resting-state functional magnetic resonance imaging (fMRI) to explore brain activity patterns, aiming to discover neuroimaging biomarkers that can distinguish patients who will respond to rTMS from those who won’t.
To compare the patterns of activity in people’s brains, a measurement called functional connectivity was used. Functional connectivity measures the amount of coordination between brain regions. Researchers can tell if regions are coordinated if they are active at the same time, or silent at the same time.
This research aims to develop tools that may predict which TRD patients will respond to rTMS therapy before patients start treatment, enabling doctors to provide more personalized and effective care.
Methods
The participants in this study included 18 people with TRD, 11 of whom responded to rTMS (responders) and 7 who did not respond (non-responders). This group had to have tried, and not benefited from, at least one antidepressant in the past. There were also 21 “healthy controls” (people without depression or other illnesses) that were used as a comparison group.
As mentioned in several of our recent articles, brain activity was measured using fMRI. Each person lies down in a tube-like scanner. This scanner uses a very strong magnet and is able to detect differences in blood and oxygen flow and usage in our brains. This gives us information about what brain areas are working at any given time. For an additional video that explains how MRIs and fMRIs work, check out this link: https://youtu.be/4UOeBM5BwdY?si=qVcXgGJy2kDmiqSj.
Regions of the brain can work in a coordinated way with each other to perform specific functions (see here for our recent articles related to brain networks). Regions that are coordinated in this way are called networks. One network that is relevant in depression is the salience network (SN). The SN is related to regulating thoughts, behaviours, and emotional states. Another network involved in depression is the default mode network (DMN). The DMN is involved in introspection and rumination (repetitive thinking of negative emotions or thoughts).
Regions in the SN:
- The dorsal anterior cingulate cortex (dACC) acts like a “fire alarm” for important things, helping your brain decide what to pay attention to, whether it’s from inside you or the outside world.
- The left insula helps you feel and be aware of your body’s internal sensations and emotions.
Regions in the DMN:
- The anterior cingulate cortex (ACC) and ventromedial prefrontal cortex (VMPFC): These areas are primarily involved in thinking about yourself, your feelings, and making decisions related to them.
The study compared functional connectivity of brain regions while resting between rTMS responders, rTMS non-responders, and “healthy controls”. The goal was to find biomarkers using brain imaging that could predict if an individual patient would respond to rTMS therapy for TRD.
Results
- Before starting rTMS treatment, the study looked at brain activity patterns in people with severe depression. They found clear differences between rTMS responders and rTMS non-responders
- Responders to rTMS had stronger connections in specific brain areas before starting treatment
- These areas are part of the Default Mode Network (DMN) (involved in rumination) and the Salience Network (SN) (involved in regulating thoughts, behaviours, and emotions)
- The most important areas for predicting who would get better were the anterior cingulate cortex/ventromedial prefrontal cortex (ACC/VMPFC) and the dorsal anterior cingulate cortex (dACC) and left insula.
- By looking at the connectivity in these brain areas, researchers could predict with very high accuracy (up to 100% chance of correctly identifying a responder and up to 82% chance of correctly identifying a non-responder) who would benefit from rTMS
Conclusion
This study showed that specific patterns of brain activity in resting networks (like the DMN and SN) could help predict which patients with treatment-resistant depression will respond to rTMS treatment. This ability to predict treatment success more accurately means doctors could select the best, most personalized treatment for each patient. However, larger studies are needed to confirm these findings before they can be routinely used to guide individual treatment decisions.