Increased communication between the amygdala and hippocampus appear to correlate with symptoms of depression and anxiety, in findings that may have treatment implications, new research suggests.
Combining electroencephalography (EEG) recordings and self-reported mood data with machine learning methods, the researchers discovered an "amygdala–hippocampus subnetwork" that correlates with individual variation in mood.
"Our findings contribute to a deeper understanding of the neural encoding of mood and anxiety and reveal a biomarker that might be useful for diagnosis and treatment of mood and anxiety disorders, in particular, innovative treatments using closed-loop deep brain stimulation," the authors write.
"We found that worsening mood is associated with increased communication between the amygdala and hippocampus, which have long been linked to emotion and memory, respectively," co-senior author Vikaas Sohal, MD, PhD, psychiatrist and neuroscientist at the University of California San Francisco (UCSF), told Medscape Medical News.
"This is very intriguing. We can speculate that maybe during periods of depressed mood, negative emotions in the amygdala trigger the recollection of sad memories or vice versa, but future studies could test this idea," said Sohal.
The study was published online November 8 in Cell.
Unique Dataset
The researchers took advantage of a unique dataset: intracranial EEG recordings of the limbic system collected over several days in 21 patients with epilepsy awaiting brain surgery for seizure localization and simultaneously collected self-reported mood diaries.
In comparing brainwave activity with the mood diaries, 13 of 21 patients showed fluctuations in electrical activity (or communication) at a brainwave frequency in the range of 13-30 cycles/second between the amygdala and hippocampus that correlated with depressed mood. These 13 patients also had significantly higher trait anxiety than the eight patients in whom this amygdala–hippocampus subnetwork was absent.
The results point to a "specific spatiotemporal neural signature that encodes a large portion (roughly 40-50%) of the variation in mood over time. This subnetwork is shared across more than 60% of individuals and is consistently present in individuals with elevated anxiety," the authors write.
"This research is the first step in letting us look at how the brain operates at different frequencies of brain activity, and it opens a lot of research and clinical questions, co-lead author Edward Chang, MD, a neurosurgeon and neuroscientist at UCSF, said in a news release.
"We do not know whether the brain activity that we found is associated with worsening mood, causes worsening mood, or just tells us when mood is low," Sohal told Medscape Medical News.
"If this brain activity (increased communication between the amygdala and hippocampus) causes worsening mood, then it might be possible to look for new treatments that can moderate or reduce excessive communication between these two brain structures," she explained.
"Alternatively, if this brain activity does not cause worsening mood, but is a signal that tells us when mood is low, then it might be possible to monitor this signal in patients with severe depression to determine when their mood is entering a potentially dangerous state. This could be similar to types of pacemakers which monitor heart rhythms to detect periods when the rhythm is dangerously abnormal," Sohal suggested.
New Treatment Avenue?
Commenting on the findings for Medscape Medical News, Conor Liston, MD, PhD, neuroscientist and psychiatrist in the Feil Family Brain & Mind Research Institute at Weill Cornell Medicine in New York City, said the research provides a potential new avenue for treatment.
"The challenge with depression is we just don't understand the neurobiological basis for it in any great detail yet. What this team was able to do was to use machine learning and artificial intelligence-like methods to discover particular patterns of brain activity that seem to be very closely related to a person's mood states," said Liston.
"What's really exciting about this discovery is that it begins to show what kinds of patterns in the brain give rise to low mood and there is the potential to intervene and reverse that, but that is probably many years off," he added.
Helen Mayberg, MD, behavioral neurologist and founding director of the Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai in New York City, said this is "really interesting work, capitalizing on the opportunity afforded by chronic recordings in epilepsy patients."
Mayberg said the findings have implications for "depression, anxiety, and post-traumatic stress disorder with regards to deep brain stimulation and closed-loop strategies."
She said her team will be "reading this closely as we continue to look at our own chronic recordings underway in treatment-resistant depression patients. We can now do chronic long-term recordings from the deep brain stimulation leads. This paper provides a clear strategy for the field," she said.
The research was funded by the Systems-Based Neurotechnology for Emerging Therapies (SUBNETS) program of the Defense Advanced Research Projects Agency (DARPA). Liston and Mayberg have reported no relevant disclosures.
Cell. Published online November 8. 2018. Full text
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Cite this: Unique EEG Signal a Potential Depression, Anxiety Biomarker - Medscape - Nov 12, 2018.
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