A study in rats analyzed neural rhythm in the prefrontal cortex and hippocampus with machine learning techniques. The results could guide future personalized treatments for psychiatric disorders (image: Freepik)
Published on 03/13/2023
By Maria Fernanda Ziegler | Agência FAPESP – Researchers have used machine learning combined with electroencephalography (EEG) – which produces a graph of electrical activity in the brain using electrodes attached to the scalp – to find patterns in the brain activity of rats submitted to stress and show how the patterns can serve as a basis for predicting which animals are most resistant to adversities.
The study was conducted at the University of São Paulo’s Ribeirão Preto Medical School (FMRP-USP) with support from FAPESP. The results, reported in an article published in the Journal of Neuroscience, provide a biomarker of resistance to stress and can be used in future to guide the treatment of psychiatric patients.
“Our sophisticated approach based on machine learning enabled us to identify patterns of neural activity in the prefrontal cortex and hippocampus, and to find out which animals were stress-resistant. A future possibility would be to use neural rhythmicity to predict which individuals could respond more positively to stress,” said João Pereira Leite, last author of the article. Leite is a professor in FMRP-USP’s Department of Neuroscience and Behavioral Sciences.
The experimental model chosen for the study, widely used in psychiatric research on stress, consisted of submitting rats’ paws to moderate shocks, from which they could escape by jumping over a low wall. A second group of rats received the same number of shocks with the same intensity and duration but were not offered an escape route. A third group acted as control and did not receive any shocks.
Most of the animals that received uncontrollable shocks failed to escape adversities presented later, even when these new shocks were escapable. “This phenomenon is well understood for the experimental model in question and is known as learned helplessness. The animals that experience a first exposure to controllable shocks tend to become more resistant to stress situations in future. This is currently known as learned resistance,” said Danilo Benette Marques, a researcher at FMRP-USP and first author of the article.
During the experiments, the researchers recorded the electrical activity in the animals’ prefrontal cortex and hippocampus. These brain regions have been associated with the effects of stress and depression in previous studies.
The results were analyzed using machine learning, a branch of artificial intelligence in which analysis of big data is a basis for automation of analytical model building. The algorithm learns from the data to identify patterns or make decisions.
Neural rhythm of resilience
“We were able to conduct an extensive investigation of brain activity during stress and discover neural oscillations that distinguished resistant from helpless animals,” Marques explained. “The interesting point is that these oscillations in the brain’s electrical activity could be verified by EEG [which is non-invasive] and could help orient personalized treatment for depression, anxiety and post-traumatic stress disorder.”
The researchers observed in the resistant animals a rise in oscillations at frequencies in the range of 4 hertz-10 hertz, known as theta (θ) oscillations or theta rhythm. “What’s involved here isn’t a larger amount of neural activity but synchronicity in the same frequency range. Brain activity tends to be irregular and lacking in a clear pattern, but when cognitive or behavioral activity is ongoing, you see a periodic pattern with very clear oscillation that can last seconds. This is theta rhythm,” said Rafael Naime Ruggiero, second author of the article and co-leader of the study, during which he had a postdoctoral scholarship from FAPESP.
According to Ruggiero, the phenomenon is neither more nor less than synchronization of a set of neurons. “What’s observed in neural oscillations generally is that an input makes the neurons depolarize [and deflects the wave], after which the pattern becomes regular again. The wave [or frequency] rises and falls periodically,” he said.
Understanding these rhythmic patterns in brain activity can contribute to the treatment of psychiatric patients, Pereira Leite noted. Stress and trauma are risk factors for the development of mental disorders such as generalized anxiety, severe depression and post-traumatic stress disorder (PTSD).
In a more recent study, reported in a preprint posted to the bioRxiv platform and not yet peer-reviewed, the researchers showed that synchronous oscillations at theta frequencies in several brain regions are simultaneously involved in coping with stress, whether via shock avoidance or in terms of positive behavior such as reward seeking.
According to the study, the positive links between brain activity and the behavioral patterns associated with resilience could form the basis for novel therapeutic approaches. “The results could contribute to the development of non-pharmacological strategies for treating these patients, such as neuromodulation, in which certain brain regions are stimulated so as to increase brain-wide theta synchrony and potentially obtain a more positive outcome than can be achieved with psychiatric medications,” Pereira Leite said.
Furthermore, a deeper understanding of the workings of the prefrontal cortex and hippocampus can help develop more specific treatments. “In the case of antidepressants, for example, the medication is taken orally and acts on synapses and neurotransmitters throughout the brain. The study showed that both resistance and helplessness involve specific brain circuits and dynamics but not the brain as a whole. It’s possible to identify brain regions and interactions that will be very important to guide the development of novel psychiatric treatments with greater efficacy and fewer side effects. For example, medications wouldn’t need to affect serotonin throughout the brain,” Pereira Leite said.
The article “Prediction of learned resistance or helplessness by hippocampal-prefrontal cortical network activity during stress” is at: www.jneurosci.org/content/42/1/81.