Consumption of ultraprocessed food and risk of depression
JAMA Network Open
Peer-Reviewed PublicationAbout The Study: The findings of this study suggest that greater ultraprocessed food (UPF; i.e., energy-dense, palatable, and ready-to-eat items) intake, particularly artificial sweeteners and artificially sweetened beverages, is associated with increased risk of depression. Although the mechanism associating UPF to depression is unknown, recent experimental data suggests that artificial sweeteners elicit purinergic transmission in the brain, which may be involved in the etiopathogenesis of depression.
Authors: Raaj S. Mehta, M.D., M.P.H., and Andrew T. Chan, M.D., M.P.H., of Massachusetts General Hospital and Harvard Medical School in Boston, are corresponding authors.
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(doi:10.1001/jamanetworkopen.2023.34770)
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About JAMA Network Open: JAMA Network Open is an online-only open access general medical journal from the JAMA Network. On weekdays, the journal publishes peer-reviewed clinical research and commentary in more than 40 medical and health subject areas. Every article is free online from the day of publication.
JOURNAL
JAMA Network Open
Researchers discover biomarker for tracking depression recovery
New deep brain stimulation device coupled with powerful AI may improve therapy for treatment-resistant depression
Using a novel deep brain stimulation (DBS) device capable of recording brain signals, researchers have identified a pattern of brain activity or “biomarker” related to clinical signs of recovery from treatment-resistant depression. The findings from this small study are an important step towards using brain data to understand a patient’s response to DBS treatment. The study was published in Nature and supported by the National Institutes of Health’s Brain Research Through Advancing Innovative Neurotechnologies® Initiative, or The BRAIN Initiative®.
Although the approach is still experimental, clinical research shows that DBS can be used safely and effectively to treat cases of depression in which symptoms have not improved with antidepressant medications, referred to as treatment-resistant depression. People receiving DBS undergo surgery to have a thin metal electrode implanted into specific brain areas to deliver electrical impulses that modulate brain activity. How exactly DBS improves symptoms in people with depression is not well-understood, which has made it difficult for researchers to objectively track patients’ response to treatment and adjust as needed.
The small study enrolled 10 adults with treatment-resistant depression, all of whom underwent DBS therapy for six months. Each participant received the same stimulation dose to begin and then stimulation levels were increased once or twice. Later, researchers used artificial intelligence (AI) tools to analyze collected brain data from six patients and observed a common brain activity signature or biomarker that correlated with patients self-reporting feeling symptoms of depression or stable as they recovered. In one patient, researchers identified the biomarker and were retrospectively able to predict that a patient would fall back into a major depressive episode four weeks before clinical interviews showed they were at risk of a relapse occurring.
“This study demonstrates how new technology and a data-driven approach can refine DBS therapy for severe depression, which can be debilitating,” said John Ngai, Ph.D., director of the BRAIN Initiative. “It’s this type of collaborative work made possible by the BRAIN Initiative that moves promising therapies closer to clinical use.”
In the study, patients received DBS targeting the subcallosal cingulate cortex (SCC), a brain region that regulates emotional behavior and is involved in feelings of sadness. DBS of the SCC is an emerging therapy that can provide stable, long-term relief from depressive symptoms for years. However, using DBS to treat depression remains challenging because each patient’s path to stable recovery looks different. Clinicians also must rely on subjective self-reports from patient interviews and psychiatric rating scales to track symptoms, which can fluctuate over time. This makes it hard to distinguish between normal mood variations and more serious situations requiring a tweak in stimulation. In addition, changes in symptoms in response to DBS can take weeks or months to occur, making it difficult to tell how well the therapy is working.
“This biomarker suggests that brain signals can be used to help understand a patient’s response to DBS treatment and adjust the treatment accordingly,” said Joshua A. Gordon, M.D., Ph.D., director of NIH’s National Institute of Mental Health. “The findings mark a major advance in translating a therapy into practice.”
The patients in the study responded well to DBS therapy; after six months, 90% showed a significant improvement in depression symptoms, and 70% were in remission or no longer depressed. This high response rate was a unique opportunity to look back and examine how each patient’s brain responded differently to the stimulation during treatment.
Christopher Rozell, Ph.D., Julian T. Hightower Chair and professor of electrical and computer engineering at Georgia Tech in Atlanta, and his colleagues used a technique called explainable artificial intelligence to understand these subtle changes in brain activity. The algorithm used brain data to distinguish between depressive versus stable recovery states and was able to explain what activity changes in the brain were the main drivers of this transition. Importantly, the biomarker also distinguished between normal day-to-day transient mood changes and sustained worsening symptoms. This algorithm could provide clinicians with an early warning signal that a patient is moving toward a highly depressive state and requires a DBS adjustment and extra clinical care.
“Nine out of 10 patients in the study got better, providing a perfect opportunity to use a novel technology to track the trajectory of their recovery,” said Helen Mayberg, M.D., director of the Nash Family Center for Advanced Circuit Therapeutics at Icahn Mount Sinai in New York City and co-senior author of the study. “Our goal is to identify an objective, neurological signal to help clinicians decide when, or when not, to make a DBS adjustment.”
“We showed that by using a scalable procedure with single electrodes in the same brain region and informed clinical management, we can get people better,” said Dr. Rozell, co-senior author of the study. “This study also gives us an amazing scientific platform to understand the variation between patients, which is key to treating complex psychiatric disorders like treatment-resistant depression.”
Next, the team analyzed data from MRI brain scans collected from patients before surgery. The results revealed structural and functional abnormalities in the specific brain network targeted by the DBS therapy. More severe white matter deficits were related to longer recovery times.
Researchers also used AI tools to analyze changes in facial expression extracted from videos of participant interviews. In a clinical setting, a patient’s facial expression can reflect the severity of their depression symptoms, a change that psychiatrists likely pick up on in routine clinical evaluations. They found patterns in individual patient expressions that coincided with their transition from illness to stable recovery. This could serve as an additional tool and new behavioral marker to track recovery in DBS therapy. More research is needed to determine whether the video analysis can reliably predict current and future disease states.
Both the observed facial expression changes and anatomical deficits correlated with cognitive states captured by the biomarker, supporting the use of this biomarker in managing DBS therapy for depression.
The research team, including Drs. Mayberg and Rozell, and Patricio Riva-Posse, M.D., at Emory University School of Medicine in Atlanta, is now confirming their findings in a second cohort of patients at Mount Sinai. Future studies will continue to explore the antidepressant effects of DBS by using a next-generation device to study the neural basis of moment-to-moment changes in mood.
According to the research team, this study represents a significant advance in early stage DBS therapy for various mental disorders, including severe depression, obsessive-compulsive disorder, post-traumatic stress disorder, binge eating disorder, and substance use disorder. Other DBS studies have identified brain biomarkers for chronic pain, but using brain data to successfully treat patients is still under development.
The study was supported by the NIH BRAIN Initiative (UH3NS103550), the National Science Foundation, the Hope for Depression Research Foundation, and the Julian T. Hightower Chair at Georgia Tech.
Article:
Alagapan, S, et al. Cingulate dynamics track depression recovery with deep brain stimulation. Nature, September 20, 2023. DOI: 10.1038/s41586-023-06541-3.
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The NIH BRAIN Initiative is managed by 10 Institutes and Centers whose missions and current research portfolios complement the goals of The BRAIN Initiative®: National Center for Complementary and Integrative Health, National Eye Institute, National Institute on Aging, National Institute on Alcohol Abuse and Alcoholism, National Institute of Biomedical Imaging and Bioengineering, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute on Drug Abuse, National Institute on Deafness and other Communication Disorders, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke.
NINDS is the nation’s leading funder of research on the brain and nervous system. The mission of NINDS is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease.
About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.
JOURNAL
Nature
ARTICLE TITLE
Cingulate dynamics track depression recovery with deep brain stimulation
ARTICLE PUBLICATION DATE
20-Sep-2023
Decoding depression: Researchers identify crucial biomarker that tracks recovery from treatment-resistant depression
Harnessing the power of explainable AI, researchers have unveiled the first insights into the complex workings of deep-brain stimulation therapy for treatment-resistant depression
Peer-Reviewed PublicationA team of leading clinicians, engineers, and neuroscientists has made a groundbreaking discovery in the field of treatment-resistant depression. By analyzing the brain activity of patients undergoing deep brain stimulation (DBS), a promising therapy involving implanted electrodes that stimulate the brain, the researchers identified a unique pattern in brain activity that reflects the recovery process in patients with treatment-resistant depression. This pattern, known as a biomarker, serves as a measurable indicator of disease recovery and represents a significant advance in treatment for the most severe and untreatable forms of depression.
The team’s findings, published online in the journal Nature on September 20, offer the first window into the intricate workings and mechanistic effects of DBS on the brain during treatment for severe depression.
DBS involves implanting thin electrodes in a specific brain area to deliver small electrical pulses, similar to a pacemaker. Although DBS has been approved and used for movement disorders such as Parkinson’s disease for many years, it remains experimental for depression. This study is a crucial step toward using objective data collected directly from the brain via the DBS device to inform clinicians about the patient’s response to treatment. This information can help guide adjustments to DBS therapy, tailoring it to each patient’s unique response and optimizing their treatment outcomes.
Now, the researchers have shown it’s possible to monitor that antidepressant effect throughout the course of treatment, offering clinicians a tool somewhat analogous to a blood glucose test for diabetes or blood pressure monitoring for heart disease: a readout of the disease state at any given time. Importantly, it distinguishes between typical day-to-day mood fluctuations and the possibility of an impending relapse of the depressive episode.
The research team, which includes experts from the Georgia Institute of Technology, the Icahn School of Medicine at Mount Sinai, and Emory University School of Medicine, used artificial intelligence (AI) to detect shifts in brain activity that coincided with patients' recovery.
The study, funded by the National Institutes of Health Brain Research Through Advancing Innovative Neurotechnologies ®, or the BRAIN Initiative ®, involved 10 patients with severe treatment-resistant depression, all of whom underwent the DBS procedure at Emory University. The study team used a new DBS device that allowed brain activity to be recorded. Analysis of these brain recordings over six months led to the identification of a common biomarker that changed as each patient recovered from their depression. After six months of DBS therapy, 90 percent of the subjects exhibited a significant improvement in their depression symptoms and 70 percent no longer met the criteria for depression.
The high response rates in this study cohort enabled the researchers to develop algorithms known as "explainable artificial intelligence" that allow humans to understand the decision-making process of AI systems. This technique helped the team identify and understand the unique brain patterns that differentiated a "depressed" brain from a "recovered" brain.
"The use of explainable AI allowed us to identify complex and usable patterns of brain activity that correspond to a depression recovery despite the complex differences in a patient’s recovery,” explained Sankar Alagapan PhD, a Georgia Tech research scientist and lead author of the study. ”This approach enabled us to track the brain’s recovery in a way that was interpretable by the clinical team, making a major advance in the potential for these methods to pioneer new therapies in psychiatry.”
Helen S. Mayberg, MD, co-senior author of the study, led the first experimental trial of subcallosal cingulate cortex (SCC) DBS for treatment-resistant depression patients in 2003, demonstrating that it could have clinical benefit. In 2019, she and the Emory team reported the technique had a sustained and robust antidepressant effect with ongoing treatment over many years for previously treatment-resistant patients.
“This study adds an important new layer to our previous work, providing measurable changes underlying the predictable and sustained antidepressant response seen when patients with treatment-resistant depression are precisely implanted in the SCC region and receive chronic DBS therapy,” said Dr. Mayberg, now Founding Director of the Nash Family Center for Advanced Circuit Therapeutics at Icahn Mount Sinai. “Beyond giving us a neural signal that the treatment has been effective, it appears that this signal can also provide an early warning signal that the patient may require a DBS adjustment in advance of clinical symptoms. This is a game changer for how we might adjust DBS in the future.“
"Understanding and treating disorders of the brain are some of our most pressing grand challenges, but the complexity of the problem means it's beyond the scope of any one discipline to solve,” said Christopher Rozell, PhD, Julian T. Hightower Chair and Professor of Electrical and Computer Engineering at Georgia Tech and co-senior author of the paper. “This research demonstrates the immense power of interdisciplinary collaboration. By bringing together expertise in engineering, neuroscience, and clinical care, we achieved a significant advance toward translating this much-needed therapy into practice, as well as an increased fundamental understanding that can help guide the development of future therapies."
The team's research also confirmed a longstanding subjective observation by psychiatrists: as patients' brains change and their depression eases, their facial expressions also change. The team's AI tools identified patterns in individual facial expressions that corresponded with the transition from a state of illness to stable recovery. These patterns proved more reliable than current clinical rating scales.
In addition, the team used two types of magnetic resonance imaging to identify both structural and functional abnormalities in the brain’s white matter and interconnected regions that form the network targeted by the treatment. They found these irregularities correlate with the time required for patients to recover, with more pronounced deficits in the targeted brain network correlated to a longer time for the treatment to show maximum effectiveness. These observed facial changes and structural deficits provide behavioral and anatomical evidence supporting the relevance of the electrical activity signature or biomarker.
"When we treat patients with depression, we rely on their reports, a clinical interview, and psychiatric rating scales to monitor symptoms. Direct biological signals from our patients' brains will provide a new level of precision and evidence to guide our treatment decisions,” said Patricio Riva-Posse, MD, Associate Professor and Director of the Interventional Psychiatry Service in the Department of Psychiatry and Behavioral Sciences at Emory University School of Medicine, and lead psychiatrist for the study.
Given these initial promising results, the team is now confirming their findings in another completed cohort of patients at Mount Sinai. They are using the next generation of the dual stimulation/sensing DBS system with the aim of translating these findings into the use of a commercially available version of this technology.
Research reported in this press release was supported by the National Institutes of Health BRAIN Initiative under award number UH3NS103550; the National Science Foundation, grant No. CCF-1350954; the Hope for Depression Research Foundation; and the Julian T. Hightower Chair at Georgia Tech. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agency.
https://www.mountsinai.org/profiles/helen-s-mayberg
https://icahn.mssm.edu/research/advanced-circuit-therapeutics
https://ece.gatech.edu/directory/christopher-john-rozell
https://med.emory.edu/directory/profile/?u=PRIVAPO
https://siplab.gatech.edu/rozell.html
https://www.emoryhealthcare.org/centers-programs/treatment-resistant-depression-program/index.html
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