The duration of depression may influence how severely the disease alters the brain
Researchers from the universities of São Paulo and Oxford show that the duration of the disorder and severity of symptoms are associated with differences in functional brain connections, which may pave the way for personalized treatments
Depression affects about 5.8% of the Brazilian population and presents a wide range of symptoms, intensities, and durations. A study published in Scientific Reports involving patients with major depressive disorder demonstrated that the severity of symptoms, as measured by the Hamilton Depression Scale, and the length of time a person remains depressed (chronicity), are both associated with changes in brain function.
For the study, researchers from the University of São Paulo (USP) in Brazil and the University of Oxford in the United Kingdom analyzed brain images from 46 patients with major depression. The results suggest that the duration of depressive episodes is associated with differences in functional brain connectivity. However, they do not allow for the differentiation or diagnosis of individual cases on their own.
“Major depression can alter brain function compared to people without the disorder. In this study, however, we identified that chronic patients [those with depression for more than 24 months] and non-chronic patients exhibit distinct patterns of connection between two important functional networks: the Central Executive Network [CEN], focused on executive control, and the Default Mode Network [DMN], associated with introspective thoughts and self-reflection,” says Tamires Zanão, a FAPESP fellowship recipient and first author of the study.
The researcher explains that the CEN and the DMN are two major brain systems with complementary functions. The CEN involves brain regions such as the dorsolateral prefrontal cortex and the parietal cortex. It is associated with the executive control required for goal-oriented tasks. The DMN, on the other hand, is diffuse and includes areas such as the medial prefrontal cortex, the precuneus, and the hippocampus. It is related to internal processes such as self-reflection, autobiographical memory, and spontaneous thoughts. Due to its complexity, the DMN is often divided into subnetworks, such as those involving the precuneus.
“More detailed models of brain network organization suggest that the precuneus doesn’t act solely within the DMN, but also connects to subnetworks related to the CEN. For that reason, that region is considered a point of integration between different brain systems, functioning as a sort of ‘bridge’ between processes oriented toward the ‘internal world’ and cognitive control,” she says.
Typically, both networks exhibit coordinated dynamics with the participation of the salience network, which is involved in switching focus between the external environment and internal processes. In depression, however, these dynamics may be altered, which has been associated with symptoms such as rumination and difficulty concentrating. “That misalignment between those networks may favor the predominance of introspective and self-referential thoughts, often with a negative bias. That helps explain why people with depression tend to get stuck in negative thoughts and have difficulty directing their attention to the environment when necessary,” Zanão explained to Agência FAPESP.
In the study, the researchers observed that the duration of depression appears to be associated with the dynamics of these two networks. In people with more recent episodes, the functional connection between the CEN and precuneal regions in the DMN decreases as symptoms worsen. In contrast, patients with long-term depression exhibited an opposite pattern: the greater the severity, the stronger the connectivity between these networks.
Other studies using large population databases, such as the UK Biobank, have demonstrated a positive correlation between CEN activity and precuneus regions in the DMN in healthy individuals. In the USP study conducted with the University of Oxford, although there was no direct comparison with healthy individuals, patients with non-chronic depression, especially those with fewer symptoms, exhibited connectivity patterns closer to the typical profile.
“The results are consistent with the hypothesis that changes in brain connectivity in depression may evolve over time. Previous research indicates that, in initial episodes, there may be a reduction in connectivity between certain networks, while in recurrent or more prolonged cases, changes in functional connectivity emerge,” Zanão explains.
Another finding from the study indicated an association between the severity of depressive symptoms and gray matter volume in two specific brain regions: the anterior cingulate cortex and the right dorsolateral prefrontal cortex. These regions have both been extensively linked to the disorder in previous studies.
“The anterior cingulate cortex, for example, has connections with areas involved in both emotional regulation and cognitive functions, playing an important role in integrating emotion and thought. Changes in that region have often been associated with the intensity of depressive symptoms. Although, in some contexts, greater gray matter volume is interpreted as indicative of better cognitive functioning, the results in the literature on depression are mixed. Previous studies have reported both reductions and increases in volume in those regions. Part of that discrepancy may be related to the use of antidepressants, as there’s evidence that medication can influence structural measures of the brain,” she states.
Because the current study included patients who were not taking antidepressants at the time of analysis, the authors suggest that the findings may more accurately reflect changes associated with depression itself rather than the effects of treatment.
Evidence from techniques such as tomography and transcranial magnetic stimulation suggests that depression may involve an imbalance in prefrontal cortex activity, with lower activity on the left side and higher activity on the right.
“According to that hypothesis, the left hemisphere would be more closely related to processing positive emotions, while the right hemisphere would be more involved with negative emotions. In this context, the finding of increased volume in the right dorsolateral prefrontal cortex observed in the study is consistent with that theoretical model, although its functional significance isn’t yet fully understood,” she says.
The researcher explains that these findings could aid in developing more personalized depression treatments in the future.
“Although guided by clinical evidence, the choice of treatment for depression still involves adjustments over time, as the response can vary from person to person. Studies like this one contribute to advancing our understanding of the disease, but more data is needed before this information can guide individualized clinical decisions,” she adds.
The brain imaging data from the 46 patients diagnosed with depression are part of a larger clinical trial coordinated by Professor André Brunoni from USP and currently at the University of Texas Southwestern Medical Center. Zanão analyzed the data for this study during his postdoctoral fellowship at the University of Oxford with support from FAPESP. FAPESP also funded the research through projects 12/20911-5, 22/03266-0, and 23/13893-5.
About São Paulo Research Foundation (FAPESP)
The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the State of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can learn more about FAPESP at www.fapesp.br/en and visit FAPESP news agency at www.agencia.fapesp.br/en to keep updated with the latest scientific breakthroughs FAPESP helps achieve through its many programs, awards and research centers. You may also subscribe to FAPESP news agency at http://agencia.fapesp.br/subscribe.
Journal
Scientific Reports
Article Title
Chronicity moderates the impact of severity on central executive-default mode network functional interactions in depression
Machine learning personalizes depression treatment with the help of wearable technology
UC San Diego researchers report substantial decreases in depression symptoms by tailoring behavioral interventions based on data participants collect about their own moods and daily habits.
image:
Senior author Jyoti Mishra, PhD, and first author Jason Nan, PhD. Courtesy of UC San Diego Health Sciences
view moreCredit: UC San Diego Health Sciences
More than 21% of U.S. adults experience depression, greatly impacting their quality of life. Many people with mild‑to‑moderate depression can improve their symptoms by adjusting daily habits like sleep, exercise, diet and social interaction, according to Jyoti Mishra, PhD, associate professor of psychiatry at University of California San Diego School of Medicine. However, because depression is highly variable between people, a one‑size‑fits‑all lifestyle approach isn’t very effective.
In a first-of-its kind study, Mishra and her team developed a machine-learning guided lifestyle coaching program based on data collected via personal devices about participants’ mood and daily habits. They found that participants who implemented the program experienced significant reductions in depressive symptoms after six weeks. The findings offer a promising approach for remotely delivering personalized depression treatment tailored to each individual’s circumstances. The study was published in NPP – Digital Psychiatry and Neuroscience.
During a two-week period, 50 adults with mild‑to‑moderate depression wore a smartwatch that tracked heart rate and exercise levels. In addition, they logged their mood and answered short questions up to four times per day about their quality of sleep, diet, activity level, and how often they talked with friends or family.
The team developed a machine learning model unique to each participant based on this data to discover which lifestyle factors best predicted an individual’s low moods. Then, each participant worked with a health coach to implement an individualized mood augmentation plan, or iMAP.
“Our goal was to figure out the top lifestyle factors driving the depressed state, which would be different for different people, and to find out if by targeting those factors through personalized coaching, people would actually feel better,” said Mishra, who co-directs UC San Diego’s Neural Engineering and Translation Labs (NEATLabs).
For the next six weeks, the participants worked with their coach to implement their iMAPs.
“Every person in the trial was doing different behavioral therapies that are already well-established in the literature depending on their top predictive factor,” said Mishra. “Some people were working on a cognitive behavioral therapy program for insomnia, others on maximizing the kinds of physical activities they were already doing in their daily lives, enhancing social connections, or implementing a healthy mood diet-based intervention.”
After working with the coach via short video calls for six weeks, participants:
- Reported a large reduction in depressive symptoms; 55% of participants no longer had depression after the treatment as measured by the Patient Health Questionnaire-9 (PHQ-9), a standardized depression screening test.
- Reported a 36% drop in anxiety symptoms as measured by the Generalized Anxiety Disorder-7 (GAD-7) screening test.
- Reported significant improvements to their quality of life.
- Scored higher on brief memory and attention tests.
What’s more, the researchers found that the treatment effect persisted during the three months they continued to follow the participants after the intervention ended.
“Clinical trials show that most current interventions only show about a 30% benefit on average in terms of depression remission; here we see a near doubling of that due to targeting the top lifestyle predictive factors with data-driven personalized coaching,” said Mishra.
Mishra thinks the intervention may be more effective because it is a departure from generic recommendations for behavioral health.
“Everybody knows that we should eat healthier diets or try to sleep eight hours or exercise 150 minutes per week and so on,” she said. “But I think personalized insights can be more empowering than these general guidelines because they’re not so overwhelming. When one is in a depressed state, it's not possible to change everything about one's life — you're just trying to survive and function on a day-to-day basis.”
Though the study was small, it provides the first evidence that digital monitoring, machine learning-derived insights and brief, personalized weekly coaching delivered remotely may be a promising integrated approach to address mild-to-moderate depression in large groups of people. A larger, controlled study of this personalized therapeutic approach is needed to validate the findings.
Read the full study here.
Additional co-authors on the study include: Jason Nan, Suzanna Purpura, Satish Jaiswal, Houtan Afshar, Vojislav Maric, James K. Manchanda and Charles T. Taylor at UC San Diego; and Dhakshin Ramanathan at UC San Diego and VA San Diego Medical Center.
The study was funded in part by a seed grant from the Hope for Depression Research Foundation.
Disclosures: Taylor is a paid consultant for Neuphoria Therapeutics (Bionomics), atai Life Sciences and Engrail Therapeutics, and receives payment for editorial work for UpToDate. Other authors declare no competing interests.
Journal
NPP—Digital Psychiatry and Neuroscience
COI Statement
Taylor is a paid consultant for Neuphoria Therapeutics (Bionomics), atai Life Sciences and Engrail Therapeutics, and receives payment for editorial work for UpToDate. Other authors declare no competing interests.
