Late-life Depression

     Late Life depression or recurrent major depressive disorder, is a mental health condition where repeated episodes of depression happen across a person’s lifespan. Life long depression happens in cycles that can last anywhere from a week to years. The risk of repeated episodes are high even after a person may feel better for a period of time. Each episode increases the risk of future episodes that creates a cycle that can be difficult to come out of, even with effective treatment. For most individuals, depression mainly appears in adolescence or early adulthood and it could be triggered by major life stressors or trauma. 

Dr. Ajilore and his team are researching which clinical, cognitive, and social factors could be involved in relapse in older adults who have been diagnosed with late-life depression. The study “Reconsidering Remission in Recurrent Late-life Depression: Clinical Presentation and Phenotypic Predictors of Relapse Following Successful Antidepressant Treatment” researches how often a group of participants who were diagnosed with late-life depression had a relapse over a period of two years. Participants were being checked on every two months by conducting interviews or testing. Dr. Ajilore’s hypothesis was that in older adults with late-life depression who are at risk of relapse over a two-year period could be associated with life stress, social support, and other depression symptoms even during remission periods. He also compared the individuals with late-life depression to healthy controls by measuring their constant depressive symptoms, medical history, social scene, and memory. The results show that 44% of participants with late-life depression have a relapse within two years. Individuals with late-life depression have higher residual depressive symptoms, they have more medical issues such as obesity, disability, or pain, they have low social aspects in their lives, and that they are less likely to perform well cognitively. 

George S. Alexopoulos is researching a similar aspect of late-life depression by finding out the mechanisms of late life depression. In his article, “Mechanisms and Treatment of late-life depression” he too mentions that medical illness is a factor of risk of late-life depression. He claims that stress responses can lead to depression because of the dysfunction that is happening in control networks. He compares how different responses to stress and the predisposing factors both lead to diagnosing late life depression by looking at it mechanistically. Even though they are separate hypotheses, at times they can overlap and create a different mechanism. 

In conclusion, both of these articles express the different factors that go into diagnosing late-life depression. These studies show that late-life depression is not easily diagnosable or treatable, but it can help us understand how and which factors could contribute to it. 

References: 

Alexopoulos, G.S. Mechanisms and treatment of late-life depression. Transl Psychiatry 9, 188 (2019). https://doi.org/10.1038/s41398-019-0514-6

Taylor WD et al (2024). Reconsidering remission in recurrent late-life depression: clinical presentation and phenotypic predictors of relapse following successful antidepressant treatment. Psychological Medicine 54, 4896–4907. https:// doi.org/10.1017/S0033291724003246

The Enduring Effects of Late-Life Depression and Future Intervention Possibilities

 The manifestations of mental illness in the brain can be seen in various ways, including on brain structure, function, and chemistry. Recent research has shown that mental illness can result from problems in interneuron communication, affecting crucial brain structures such as the prefrontal cortex, amygdala, and hippocampus, which act as emotion, stress, and memory centers in the brain. Specifically, depression, or major depressive disorder is a common and serious medical illness characterized by persistent sadness and loss of interest or pleasure in activities, heavily impacting the way an individual thinks, acts, and perceives the world. Nearly three in ten adults have been diagnosed with depression at some point in their lives, and many individuals suffer from late-life depression, or LLD, which is characterized by repeated recurrent depressive episodes even with maintenance treatment. Most experts believe that depression can be attributed to a combination of biological, social, and psychological factors, and it can be difficult to pinpoint the exact origin of depression among this complex interplay of factors and events. Studies have begun to research, however, what specific factors can distinguish those with depression from those without depression. How can our knowledge of neuron interaction, social and environmental factors, and current depression intervention further our understanding of depression and how it can affect an individual’s brain chemistry? 

Late-life depression can be characterized by a variety of residual psychiatric symptoms across different clinical-rated and self-reported symptom domains, including greater medical comorbidity and disability, higher BMI, personality trait differences, poorer social support, greater perceived stress, and lower cognitive function performance (Ajilore, 2024). Dr. Olu Ajilore et al studies factors that may predict relapse in older adults with late-life depression who have achieved remission through antidepressant therapy, finding that approximately 44% of remitted LLD participants experienced a relapse. These patients, when being studied in relation to the comparison participants, were seen to show higher residual depressive symptoms, increased rumination, greater medical comorbidity, and executive dysfunction. Dr. Ajilore’s work is fundamental in emphasizing that despite the goals of antidepressant treatment to lower an individual’s severity in symptoms as much as possible, relapse happens frequently, contributing to the idea that future research remains incredibly important in understanding the phenotypic and neuroimaging differences that may influence the likelihood of depressive symptoms and relapse. Research in those with late-life depression has displayed how a vast number of outside factors can come into effect. Higher treatment intensity, lower social support, and increased life stress can be seen as significant predictors of relapse, contributing to suggestions for potential targets for interventions in preventing relapse in late-life depression beyond antidepressants, and encouraging deeper research into the few baseline differences that separate relapsers from non-relapsers.  

Studies performed by researchers such as Dr. Ajilore have made a vast difference in the shift of classifying depression from a short, self-limiting condition to a condition that may occur over a longer time frame and have a recurrent or chronic course for an individual. In their studies, Dr. Gregory E. Simon et. al addresses the importance of sustained treatment strategies to achieve and maintain remission in individuals with major depressive disorder, as it is a condition with a high risk of relapse and recurrence. Evidence has indicated that ongoing treatment combined with both antidepressant medication and psychotherapy results in greater symptom improvement than with psychotherapy alone or medication alone (Simon, 2024). Research into the effectiveness of treatment planning has helped to reveal the necessity of including depression severity, patient preferences, treatment availability, patient concerns, and current life stressors. Clinical trials also serve to demonstrate the effects of psychotherapy, including cognitive behavioral therapy, behavioral activation, interpersonal therapy, and short-term psychodynamic psychotherapy, and participants have found these specific psychotherapies similarly more efficacious than usual care without psychotherapy to a small effect. Dr. Simon’s review is important in defining the effectiveness of first-line depression treatment, broadening beyond novel treatment to combinations of new forms of psychotherapy and a range of antidepressants. 

The study of mental illness and depression continues to make waves and further scientific innovation within research on the brain. Exploring the necessity for long-term treatment is critical for achieving sustained wellness, emphasizing that remission alone does not equate to full recovery for many conditions that impact an individual’s brain chemistry and neural pathways. While acute treatment may alleviate the most severe symptoms, late-life depression proves to be a complex, multi-layered condition that must continue to be studied. Ongoing treatment strategies such as maintenance pharmacotherapy and evidence-based psychotherapy are important in creating a comprehensive, individualized approach to depression, promoting enduring recovery and greater functional and life outcomes for those affected. 

References 

Taylor, W. D., Butters, M. A., Elson, D., Szymkowicz, S. M., Jennette, K., Baker, K., Renfro, B., Georgaras, A., Krafty, R., Andreescu, C., & Ajilore, O. (2025). Reconsidering remission in recurrent late-life depression: Clinical presentation and phenotypic predictors of relapse following successful antidepressant treatment. Psychological Medicine, 54(16), 1–12. https://doi.org/10.1017/S0033291724003246​:contentReference[oaicite:1]{index=1}

Shelton, R. C., & Hollon, S. D. (2012). The long-term management of major depressive disorders. Focus, 10(4), 434–446. https://doi.org/10.1176/appi.focus.10.4.434​:contentReference[oaicite:3]{index=3}

 

 

How Much Can Regret Impact Your Wallet?

 In Dr. Sweis’ talk about forms of regret, the main factor being observed where the levels of cortisol that goes through the CREB molecular mediators. This specific mediator regulates responses to pertain to activating rewarding or stressing behaviors in the nucleus accumbens (NAc) and the medial prefrontal cortex (mPFC). While transcription factors that come from CREB are the same, the individual regions account for two different behaviors: the NAc will produce stress response and the mPFC will produce a behavior that allows for resilience in the face of stress. While these tests were conducted on mice, they are model animals that present a high genetic similarity to humans; therefore, making them suitable subjects for the behavioral experiment. Even though the stimulus for the feelings of regret was finding food in a maze with a time limit, there are many applications to this kind of research in daily human activities. Everyday financial decisions are some of the most common ways in which regret and risk tolerance can be observed, whether for a large purchase like a house or car, or deciding whether or not a side dish at a good restaurant is worth spending money for. “Neuroeconomics: Making Big and Small Decisions” by  Michael W. Richardson on Brainfact.org explores the growing field of neuroeconomics through the context of hormonal and neurophysiological stimulation from various financial institutions.

            One hormone that has a strong effect towards making riskier decisions is testosterone, as there is a positive correlation between the stockbrokers that present higher levels of testosterone and the willingness to invest large sums of money in the stocks of their choosing. On the other hand, higher levels of cortisol showed a correlation to making less risky investments based with the same amount of money. While correlation is not causation, this can point to other key factors that are in the hormonal pathway of testosterone being the cause for an increase in tolerance for uncertainty. Furthermore, Oxytocin also plays a large role when making decisions. More specifically, when the choosing a course of action with another person present, a spike in oxytocin levels generates higher feelings of trust and confidence while decreasing those of stress. Oxytocin is a key hormone that is expressed from the hypothalamus, the cerebral region that also oversees expression of dopamine: the hormone that directs mood.

            Lastly, the more physiological evaluation of risk taking is mentioned in the article: the effects of gambling and the risk tolerance that comes with it. Often times in high-roller games, people are willing to back down from losing their winnings because they no longer feel that the potential reward is worth putting what they have in jeopardy. The insular cortex sees stimulation when players gauge whether or not to continue betting.

            In conclusion, while economics is largely numerical, psychology and neuroscience have started to gain a foothold in the financial sector, as more and more neuroscientific findings open up new ways to interpret good or bad decision for humans in financial contexts.

 

Source: https://www.brainfacts.org/neuroscience-in-society/law-economics-and-ethics/2018/neuroeconomics-making-big-and-small-decisions

Psychedelics' Plastic Potential

 

In recent years, the image surrounding psychedelic drugs has morphed into a somewhat positive one. Conversations surrounding drugs in general have seemed to become more complex, medicine and the general public considering each drug’s unique affects on the human nervous system and chemical properties—properties that might lend to possible therapeutic applications. Psychedelics are a class of drug that seem to be highlighted in this conversation, their potential healing ability implicated particularly in psychiatric disorders like Generalized Anxiety Disorder, Bipolar Disorder, and even schizophrenia.

            Grieco et al (2022) explores the potential therapeutic implications of psychedelics, hinting at their facility to drive neural plasticity in some areas of the brain. They discuss certain unmet clinical needs and the current state of translation to the clinic for psychedelics. Psilocybin, LSD, and ketamine function as “psychoplastogens”—compounds that induce long-lasting neural plasticity after just one or a few doses. This mirrors the circuit-level changes described in the Puranik study, where manipulating glutamatergic inputs from the laterodorsal tegmental nucleus (LDTg) to the ventral tegmental area (VTA) blocked cocaine-induced behavioral and dopamine responses. Both studies underscore the significance of glutamatergic signaling in reshaping neural pathways linked to reward and addiction. These compounds primarily act through serotonin 2A receptors (5-HT2ARs) and influence downstream pathways such as BDNF (brain-derived neurotrophic factor) signaling and glutamate release, both of which are critical for synaptic remodeling.

Psychedelics can also increase dendritic spine density, and enhance synaptic strength, especially in brain regions like the prefrontal cortex. The question of whether these changes underlie improvements in mood, personality traits (like openness), and behavior following psychedelic therapy is considered in this study.

In Puranik et al (2022) a maladaptive plasticity is seen in cocaine sensitization, as described, where repeated drug exposure leads to hyperactivity in circuits like the LDTg-VTA pathway. Since sensitization involves lasting changes in these circuits, psychedelics may help reverse or rewire these maladaptive changes by reintroducing a more adaptive form of neuroplasticity, potentially offering a method to desensitize overactive reward pathways and mitigate addiction-related behaviors.

It is interesting to note the ways the current culture’s attitude towards psychedelics has altered, still, it is important to consult the vast literature on systems neuroscience to evaluate the true value of this possible healing ability.

References:

Grieco, S. F., Castrén, E., Knudsen, G. M., Kwan, A. C., Olson, D. E., Zuo, Y., Holmes, T. C., & Xu, X. (2022). Psychedelics and Neural Plasticity: Therapeutic Implications. The Journal of Neuroscience, 42(45), 8439–8449. https://doi.org/10.1523/jneurosci.1121-22.2022

Puranik, A., Buie, N., Arizanovska, D., Vezina, P., & Steidl, S. (2022). Glutamate inputs from the laterodorsal tegmental nucleus to the ventral tegmental area are essential for the induction of cocaine sensitization in male mice. Psychopharmacology, 239(10), 3263–3276. https://doi.org/10.1007/s00213-022-06209-2

The Comorbidities of Late Life Depression

  

Comorbidities of Late-life Depression: An Increased Risk of Dementia

              The paper “Reconsidering remission in recurrent late-life depression: clinical presentation and phenotypic predictors of relapse following successful antidepressant treatment” (Taylor et al., 2025) covers a study that focused on participant’s susceptibility to relapse after a period of remission, over the course of a two-year timespan.

              Late-life depression specifically refers to repeated recurrent depressive episodes and is typically seen in adults of a more advanced age. Some of its major symptoms are increased disability, poor or impaired cognitive function and an increased risk of dementia.  Even with regular treatment, these patients remain at a high risk of relapse – between 35% and 45% of patients – which is thought to only worsen the already negative effects of the symptoms on patients.

              Although patients with late-life depression do not necessarily experience a more rapid decline than those who have never been depressed, they were shown to perform worse at baseline cognitive functions than individuals with either early-onset depression or no depression at all (Ly et al., 2021). This suggests that individuals with late-onset depression might have a lower threshold for developing dementia. The increase in symptom severity associated with multiple remissions of late-life depression may also explain the more rapid decline in verbal skills and delayed memory ability. Considering that recurrence risk is substantially influenced by an individual’s environmental and social factors – and that recurrence in people with late-life depression has been associated with lower levels of instrumental social support – it is not entirely difficult to see why their baseline is significantly lower than individuals in other test groups.

 

References

Taylor WD et al (2024). Reconsidering remission in recurrent late-life depression: clinical presentation and phenotypic predictors of relapse following successful antidepressant treatment. Psychological Medicine 54, 4896–4907. https://doi.org/10.1017/S0033291724003246

Ly, M., Karim, H.T., Becker, J.T. et al. Late-life depression and increased risk of dementia: a longitudinal cohort study. Transl Psychiatry 11, 147 (2021). https://doi.org/10.1038/s41398-021-01269-y

 

 

Depression Prevelance and Relapse

     A speaker we had this semester, Olu Ajilore, presented his work and study about late-life depression (LLD) and the phenotypic predictors of relapsing into that. The specific study we looked at was titled “Reconsidering remission in recurrent late-life depression: clinical presentation and phenotypic predictors of relapse following successful antidepressant treatment.” Him and his colleagues looked at people suffering from late-life depression and the relapses and recurrences they fell into even after consistent treatment and maintenance. It was a longitudinal study that included 135 remitted LLD participants and 69 comparison participants that were used as a control group. All participants were clinically assessed every two months for two years while receiving typical treatment using antidepressants. By doing this, they discovered that sixty (44%) LLD participants experienced a relapse over the two-year period. They also found that, by comparing LLD participants and the control group,  residual depressive symptom severity, rumination, executive dysfunction, and medical comorbidity significantly predicted LLD classification. 

In a another scientific study titled “Trends in U.S. Depression Prevalence from 2015 to 2020: The Widening Treatment Gap,” Lisa Dierker and her colleagues pulled data from the 2015-2020 National Survey on Drug Use and Health and found that from 2015-2020, there was an increase in depression without a corresponding increase in treatment. They also found that in 2020, past-12 month depression was widespread in nearly 1 in 10 Americans and nearly 1 in 5 in adolescents and young adults. This was attributed to the COVID-19 pandemic that forced almost all Americans into quarantine.

I found the similarity in these scientific findings to be very interesting because both of them highlight how prevalent and persistent depression is. There are millions of people across the world suffering from this deadly condition and these two articles really showed how important it is for us as a society to spread awareness about it and especially make treatment more accessible and available to anybody and everybody before it evolves into something much more concerning. In Dierker's work, it showed how depression spiked from 2015-2020, and yet there was no increased supply in treatment options. In Ajilore’s work, it was shown that even with consistent treatment, people still relapsed into depression which points to how complex this condition can be. Overall, both of these studies emphasize the need for more effective, readily available treatment for those suffering from depression and the need for more widespread awareness about it. 

  References

Goodwin, Renee D et al. “Trends in U.S. Depression Prevalence From 2015 to 2020: The Widening Treatment Gap.” American journal of preventive medicinevol. 63,5 (2022): 726-733. doi:10.1016/j.amepre.2022.05.014

Taylor, Warren D. et al. “Reconsidering Remission in Recurrent Late-Life Depression: Clinical Presentation and Phenotypic Predictors of Relapse Following Successful Antidepressant Treatment.” Psychological Medicine 54.16 (2024): 4896–4907. Web.

Tackling the Issues of 3D Object Processing in Autonomous Vehicles

    Autonomous driving vehicles rely on a variety of sources to be able to gather as much information as possible about its surroundings so that it can make decisions quickly. These vehicles especially depend on depth sensors (RGB-D cameras), light detection and ranging (LiDAR) and radio detection and ranging (radar) so that they are able to fully grasp the surroundings. Even with all of this special equipment used, the perception systems of autonomous vehicles are still making errors which could be detrimental in comparison to a human driving the vehicle, and should therefore be properly addressed. 

    In the article: “Beyond the Contour: How 3D Cues Enhance Object Recognition in Humans and Neural Networks”, Cutler and colleagues (2025) analyzed the differences between how humans and artificial neural networks (ANNs) are able to determine what an object is based on different cues. Some of these cues included the outline, textures, shapes, shades and shadows. The researchers were able to determine that although humans are more biased to shapes when detecting objects, they are still able to rely upon the texture cues. They also concluded that ANNs are more texture biased when trying to determine what an object is. Furthermore, when they added shades and shadows to enhance the 3D objects, the researchers found that these were advantageous features to be able to recognize objects for both humans and ANNs (Cutler et al., 2025). Although artificial neural networks are inherently more texture biased, they can be trained enough with other object cues to expand their networks and eliminate these biases. This study demonstrates the importance of continuous training with artificial models so that they are able to perform like humans and eventually be even better than humans. 

        The findings of Cutler et al. (2025) can provide the basis to explain how the networks in autonomous vehicles are gathering information to be properly equipped to fully function without humans. These processing networks still rely upon 2D object detection which is primarily texture biased as previous research has stated. Through significant amounts of training and growth of deep learning (DL), Alaba and Ball (2023) also found that the networks have improved in their performance which demonstrates that it is advantageous to continuously train their artificial models. In the review article: “Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review”, Alaba and Ball (2023) also explained that these autonomous vehicles initially used a two-stage object processing system in which they first analyze the object in its 2D form and then use different techniques to examine its 3D form. It was then proposed that this two-stage process be reduced to a single stage so that these processing networks are more efficient, especially with the new processing network called YOLO (you only look once). Although it became more efficient, this system was less accurate, especially because it was processing a whole scene which can include a variety of objects of many different shapes and sizes and would therefore have to classify everything at once. In more recent times, the YOLO processing system has been further worked on to improve its accuracy to the same level as the two-stage process that was previously used. Overall, this demonstrates that work with artificial models and networks must be ongoing to continue to improve on its accuracy and efficiency, especially in real world contexts like driving. 

References: 

Alaba, S. Y., & Ball, J. E. (2023). Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review. IEEE Sensors Journal, 23(4), 3378–3394. https://doi.org/10.1109/jsen.2023.3235830

Cutler, M., Baumel, L. D., Tocco, J., Friebel, W., Thiruvathukal, G. K., & Baker, N. (2025). Beyond the contour: How 3D cues enhance recognitions in humans and neural networks. Journal of Vision. (Pre-published draft)