Researchers pave way for adaptive DBS treatment after identifying OCD-associated brain signals

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In an effort to improve treatment for obsessive-compulsive disorder (OCD), a team of researchers has—for the first time, according to a Brown University (Providence, USA) announcement—recorded electrical signals in the human brain associated with fluctuations in OCD symptoms over an extended period while they went about their daily living at home. This could be an important step in making deep brain stimulation (DBS) responsive to everyday changes in OCD symptoms, the researchers claim.

DBS, a technique that involves precisely placing small electrodes in the brain to deliver mild electrical pulses, is effective in treating more than half of the OCD patients for whom other therapies, such as traditional physical or drug treatments, have failed. However, one limitation is that DBS is unable to adjust to moment-to-moment changes in OCD symptoms—which are impacted by the patient’s physical and social environment.

By adjusting the intensity of stimulation in response to real-time signals recorded in the brain, adaptive DBS has the potential to be more effective than traditional DBS therapy and also reduce unwanted side-effects.

“OCD is a disorder in which symptom severity is highly variable over time and can be elicited by triggers in the environment,” said David Borton, an associate professor of biomedical engineering at Brown University, a biomedical engineer at the US Department of Veterans Affairs Center for Neurorestoration and Neurotechnology, and a senior author of this new research.

“A DBS system that can adjust stimulation intensity in response to symptoms may provide more relief and fewer side-effects for patients. But, in order to enable that technology, we must first identify the biomarkers in the brain associated with OCD symptoms, and that is what we are working to do in this study.”

For the experimental study—the details of which are published in Nature Medicine—the research team recruited five participants with severe OCD who were eligible for DBS treatment. Each participant was then implanted with an investigational DBS device (Medtronic) capable of both delivering stimulation and recording native electrical brain signals. Using the sensing capabilities of the hardware, the team gathered brain-signal data from participants in both clinical settings and at home as they went about daily activities.

Alongside these data, the team also collected a suite of behavioural biomarkers. In the clinical setting, these included facial expression and body movement. Using computer vision and machine learning, they discovered that the behavioural features were associated with changes in internal brain states. At home, they measured participants’ self-reports of OCD symptom intensity as well as biometric data—heart rate and general activity levels—recorded by a smart watch and paired smartphone application (Rune Labs). All of those behavioural measures were then time-synched to the brain-sensing data, enabling the researchers to look for correlations between the two.

“This is the first time brain signals from participants with neuropsychiatric illness have been recorded chronically at home alongside relevant behavioural measures,” said Nicole Provenza, the Brown University biomedical engineering PhD graduate who led this research. “Using these brain signals, we may be able to differentiate between when someone is experiencing OCD symptoms, and when they are not, and this technique made it possible to record this diversity of behaviour and brain activity.”

Provenza’s analysis of the data showed that the technique did pick out brain-signal patterns potentially linked to OCD symptom fluctuation. While more work needs to be done across a larger cohort, this initial study shows that this technique is a promising way forward in confirming candidate biomarkers of OCD, as per the release from Brown University.

Once those biomarkers are positively identified, they could then be used in an adaptive DBS system, the release continues. Currently, DBS systems employ a constant level of stimulation, which can be adjusted by a clinician at clinical visits. Adaptive DBS systems, in contrast, would stimulate and record brain activity and behaviour continuously without the need for these visits. When the system detects signals associated with an increase or decrease in symptom severity, it could ramp up or tone down stimulation levels correspondingly—potentially improve the effectiveness of DBS therapy while reducing associated side-effects.

“In addition to advancing DBS therapy for cases of severe and treatment resistant OCD, this study has the potential for improving our understanding of the underlying neurocircuitry of the disorder,” said Wayne Goodman (Baylor College of Medicine, Houston, USA). “This deepened understanding may allow us to identify new anatomic targets for treatment that may be amenable to novel interventions that are less invasive than DBS.”

Work on this line of research is ongoing, the release adds, and the team hopes to expand the number of participants to capture more of that variability. They also seek to identify a fuller set of OCD biomarkers that could be used to guide device-makers towards creating adaptive DBS systems.

“Our goal is to understand what those brain recordings are telling us and to train the device to recognise certain patterns associated with specific symptoms,” said Sameer Sheth, also of Baylor College of Medicine. “The better we understand the neural signatures of health and disease, the greater our chances of using DBS to successfully treat challenging brain disorders like OCD.”


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