Despite countless attempts, scientists have still not been able to identify any concrete biological cause or reliable biomarker for any mental disorder. Why might this be the case?

The rise and fall of the biomedical model for psychiatry

Before all else, it will be conducive to outline the rise of such an approach and why it has been the dominant force in psychiatry in the last 3 decades.

The story starts with the rise of the biomedical model of disease. With origins in the early 19th century, the idea of diseases having specific etiology was derived from the germ theory of disease. This theory, proposed by Louis Pasteur, suggested that diseases could be brought upon by pathogens that invade the body. Pasteur began to lay the foundation of knowledge necessary to power the biomedical model (Deacon, 2013). As we gained more knowledge about the body works, we could create direct links of causation from pathogen to disease. The emergence of biological causes of disease was pivotal to treating illness and was quickly adopted throughout the 20th century.

Naturally, the biomedical model was extended such that these methods would work in parallel for mental illnesses and we coined them as diseases of the brain. In the 1930s and 1940s, the widespread and fervent use of somatic therapies such as electroconvulsive therapy, lobotomy, and insulin coma therapy instilled hope that mental disorders could be cured (Deacon, 2013).

The 1950s marked the beginning of psychiatry's psychopharmacological revolution with the accidental discovery of compounds that dampened symptoms of various mental illnesses such as psychosis, depression, mania, anxiety, and hyperactivity (Deacon, 2013). As a result, theories on chemical imbalances as the cause of mental disorders emerged. The biomedical model emphasizes that there is no distinction between mental and physical diseases and that both can be treated via pharmacological intervention.

The biomedical model quickly took the Western medical world by storm and is now the most dominant practice in healthcare systems (Deacon, 2013). An adherent to the biomedical model of mental illness would suggest there psychatric disorders are the direct result of excesses or deficiencies of neurotransmitters, genetic abnormalities, or alterations in brain structure.

Functional neuroimaging aims to measure physiological changes in the brain and map them to mental states. Physiological variables which are measured include but are not limited to, metabolic activity, blood flow, magnetic fields, and receptor-ligand bonding. We have learned a considerable amount about the mechanisms of the brain, yet our knowledge about the neurobiology of the brain has not contributed much to improving the practice of mental health care.

Thomas Insel, the former director of the National Institute of Mental Health (NIMH), even said that after 13 years of subsidizing $20 billion worth of research on the neuroscience and genetics of mental disorder, he did not “think that [the NIMH] moved the needle in reducing suicide, reducing hospitalizations, improving recovery for the tens of millions of people who have mental illness,” (Rogers, 2017). This statement highlights the misguided paradigm that neuropsychiatry has been following and demonstrates the need for change.

With scaffolding from the biomedical model, major depressive disorder (further referred to as depression for simplicity) has been reduced to anything from chemical imbalances, regional brain abnormalities, or circuit-level connectivity abnormalities. This is not to say that these patterns of brain activity play no role in mental illness, but rather meant to highlight the overemphasis of these factors over others. It is perfectly reasonable to want to pin down the correlates of such a disorder. We may want to synthesize drugs for treatment or identify biomarkers of depression. While biomarkers have the potential to improve diagnostic accuracy and treatment outcomes in depression, their current limitations suggest that a more nuanced approach is necessary.

By examining the philosophical underpinnings of biomarker research and analyzing empirical studies on the reliability and validity of the biomedical model, this paper argues that a more comprehensive understanding of depression requires taking into account the complex interplay between biological, psychological, and social factors. Biological research must be viewed as just one tool among many in achieving this goal. I will make the case that we should be suspicious of pharmacological interventions as a means to treat the causes of mental illness. Instead, I suggest we shift towards a more nuanced perspective, accounting for both social and psychological factors, along with biological influences.

The failures of the biomedical model: explanatory reductionism, biomarkers, antidepressants

While the biomedical model has the potential to improve diagnostic accuracy and treatment outcomes, it is limited by several factors. The biomedical model states that only physiological changes in the brain can be seen as ultimate causes of mental illnesses. Any external factors contributing to brain changes are seen as not relevant or unaddressable in the study of the etiology of the disease.

The biomedical model also assumes that any models of psychopathology can be modelled using causal terms (i.e. A→B→C, therefore if we manipulate A, we will see downstream effects on C).

Let's consider a simple example of the relationship between exercise and weight loss. According to the biomedical model, the relationship can be modelled as A→B→C, where A represents exercise, B represents changes in metabolism, and C represents weight loss. However, this model leaves no room for context-dependent events such as individual differences in metabolism, dietary factors, or environmental factors. By applying reductionism to this complex system, we may overlook important interactions and relationships that contribute to weight loss. To gain a deeper understanding of how exercise and weight loss are related, we must consider not only the individual components of the system (i.e., exercise and metabolism), but also their interactions and relationships with other factors that may influence weight loss.

Reductionism is not uncommon in the sciences and can be advantageous for simpler models, but when we apply it to complex systems that consist of interdependent components (such as the brain and its manifestations of illness), its limitations become apparent. In order to understand how a complex system works, it is necessary to study not only the individual parts of the system, but also their relationships and interactions. Only by examining the relationships and interactions between the parts of a complex system can we gain a deeper understanding of how the system as a whole operates.

Despite advancing our understanding of the body and disease, the biomedical model has vastly oversimplified the complex systems of mental disorders. First, not all lower-level explanations are superior to higher-level explanations in understanding complex systems. For example, the human heartbeat is the result of multiple properties interacting through feedback loops, rather than a central control unit. Secondly, while biomarkers can help identify mental health issues such as rumination, it is the content of the ruminative thoughts that drives the associated feelings and behaviours, rather than its biological origins. Furthermore, more perplexing traits such as ruminations are likely to have multiple realizations, meaning that there may be different patterns in brain activation observed across individuals and even within the same individual (Fried, 2022).

The field of diagnostic biomarker research for mental illnesses is rampant. A biomarker is defined by the FDA-NIH Biomarker Working Group as the following:

a characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions'

— US Food and Drug Administration, 2020

Diagnostic biomarkers in psychiatry aim to create empirically based findings that can identify and predict the presence of a disorder (Henderson et al., 2020). These biomarkers are inextricably connected to the diagnostic criteria outlined by the Diagnostic and Statistical Manual (DSM), the standard tool for psychiatric classification (Aftab & Sharma, 2021). The diagnostic criteria and constructs have been widely criticized and lack validation through research findings, failing to demonstrate distinct disease processes as anticipated (Aftab & Sharma, 2021).

Research instead suggests that a dimensional perspective of distress could be more valuable than a categorical one, particularly given the high rates of comorbidity among those who are experiencing distress (Aftab & Sharma, 2021). As such, the limitations of current diagnostic biomarkers are highlighted and questioned by these critiques of existing classification systems (Aftab & Sharma, 2021).

Despite the fact that the rigid and operationalized criteria of the DSM appear to poorly reflect the complexity of mental illnesses, they continue to define the objectives of biomarker studies (Kendler, 2016). The notion that mental illnesses can fit into categories of “natural kinds”, or have biological signatures is philosophically flawed and assumes that our definitions (i.e. Major Depressive Disorder) have an epistemological foundation (Fried, 2017a). There are more than 280 scales used to measure the severity of depression, and these scales are only weakly correlated (Fried, 2017b; Santor et al., 2006). Additionally, the diagnostic criteria in the DSM-V of depression paint a confusing picture of the phenotype for depression. Of the criteria, four of them account for opposite symptoms (e.g. insomnia vs hypersomnia; weight gain vs weight loss; appetite gain vs loss; psychomotor agitation vs retardation) and make very little sense from a psychometric perspective (Kendler, 2016). By equating anticorrelated items, variance in the phenotype of depression greatly increases.

Another problem which stems from the flawed diagnostic criteria of the DSM is that patients who have been diagnosed with the same psychiatric illness often exhibit varied clinical presentations (Kendler, 2016). This, coupled with an incomplete understanding of the neurochemical changes associated with mental disorders, limits the efficacy of current pharmacological treatments. Historically, as psychiatry shifted from psychodynamic approaches to biological approaches to defining mental illness, pharmaceutical companies attempted to promote biological psychiatry by financing those who supported the biological approach (Sharfstein, 2008).

The financial interests of Big Pharma come before public health, resulting in the advancement of the biomedical model as the primary paradigm to diagnose and treat depression. The biomedical paradigm has been driven by a powerful combination of ideological, political, and economic forces.

As a result, rates of psychiatric medication use have skyrocketed, to the point where some have coined it the “Pharmageddon” (Healy, 2012). There is a wealth of evidence that now suggests that depression has evolved from being a temporary, sporadic issue into a chronic and persistent condition due to the abundant availability of antidepressants. One study from the Netherlands looked at relapse rates after 10 years and found that 76% of individuals who were not prescribed antidepressants recovered without relapsing, compared to only 50% of those who were prescribed antidepressants (van Weel-Baumgarten et al., 2000). Goldberg et al., (1998) looked at patients across 15 cities who were identified by their primary care physicians as having depression and subsequently prescribed antidepressants. At the 1-year checkpoint, individuals on antidepressants had twice the likelihood of still experiencing depression as opposed to patients who were not diagnosed or treated, despite having nearly identical baseline depression scores (Goldberg et al, 1998). Further, long-term use of antidepressants has been associated with higher risks of coronary heart disease, cerebrovascular disease, and all-cause mortality (Bansal et al., 2022). Moreover, an NIMH-funded study assessed outcomes of medicated and non-medicated depressed individuals and found that medicated individuals were 3 times more likely to experience cessation of their principal social role and 7 times more likely to have been completely incapacitated (Akiskal et al., 1995). Even further, a sample of nearly 10,000 depressed patients in Canada found that medicated individuals reported being depressed for 19 weeks per year, whereas unmedicated individuals only reported 11 weeks per year (Patten, 2004). It is clear that antidepressants have been overprescribed and exert unwanted consequences on depression and health, so why is it that they have been the primary treatment option for depressed individuals over the past few decades (Winerman, 2017)?

Using antidepressants to treat depression stems from the theory that there is an inherent chemical imbalance in the depressed brain. Specifically, proponents of the chemical imbalance theory suggested that depression arises from low levels of serotonin. The serotonin hypothesis of depression was first suggested in the 1960s and sparked the design of antidepressants such as selective serotonin reuptake inhibitors (SSRIs) (Moncrieff et al., 2023). This long-held theory was directly opposed by Joanna Moncrieff and colleagues, who conducted a large-scale systematic review of the serotonin hypothesis of depression. Moncrieff et al. (2023) concluded that the theory is not empirically supported and the data is highly inconsistent. Curiously, there was evidence to suggest that long-term use of antidepressants can even cause serotonin levels to decrease (Moncrieff et al., 2023).

While the biomedical model has been crucial to the development of the healthcare system, its application to mental healthcare has been somewhat suboptimal. Millions of individuals have been prescribed antidepressants, without any ontological reality of their efficacy (Winerman, 2017). Research continues to look for biomarkers of depression without the context of any external factors. By assuming that changes in brain structure or activity are the ultimate causes of the symptoms of mental illness, the biomedical model gives a reductionist explanation of mental illness.

The application of the biomedical model to mental health care has been deemed unsatisfactory due to several factors. The biomedical paradigm tends to neglect the many dimensions of mental health concerns, favouring a restricted focus on biological ones. Such dimensions include social, psychological, and cultural variables. The biomedical model of mental illness fails to recognize one of the greatest forces influencing this epidemic, that being the sociopolitical climate in which humans exist. This limited focus may lead to a symptom management approach that does not adequately address the underlying causes of mental health issues. Furthermore, the biomedical model pathologizes mental health conditions as individual problems that should be treated with medical interventions, failing to consider broader environmental and societal factors that contribute to mental health concerns. Additionally, the stigma and shame associated with mental health issues can prevent individuals from seeking help, a factor that can be worsened by societal pressure to prioritize productivity and success over well-being. A comprehensive and holistic approach is needed that acknowledges the complex interplay of biological, social, psychological, and cultural factors in mental health care.

If not biomedical, then what?

So, what would an alternative approach look like? In 1977, George Engel, a psychiatrist, posited the biopsychosocial model as an alternative to the dominant biomedical model in psychiatry, proposing a holistic approach that considers the complex interplay between biological, psychological, and social factors in the etiology and treatment of mental illness.

The biopsychosocial model is a comprehensive approach to understanding human health and illness that considers biological, psychological, and social factors as interrelated influences. This model proposes that a person's health and well-being are not solely determined by biological factors, but are also influenced by psychological and social factors, such as lifestyle, stress, social support, culture, and socioeconomic status (Engel, 1977).

In the context of depression, we are now becoming more aware of how psychological and social factors can affect symptomatology. Depression can arise from interactions between physical systems that are not confined to the brain, such as interactions between an individual and their family, community, level of education, socioeconomic status, etc.

According to the biopsychosocial model, depression would be viewed as a complex condition that arises from a combination of biological, psychological, and social factors. Biological factors that may contribute to depression include imbalances in neurotransmitters, genetics, and hormonal changes. Psychological factors may include negative thought patterns, low self-esteem, and a history of trauma or abuse. Social factors may include a lack of social support, stressful life events, and economic hardship.

Treatment according to the biopsychosocial model would be tailored to address all of these factors, rather than focusing exclusively on medication. Treatment may involve a combination of medication, therapy (such as cognitive-behavioural therapy or interpersonal therapy), lifestyle modifications (such as exercise or diet), and social support (such as joining a support group or building a stronger social network). Research has found that pharmacotherapy-psychotherapy combinations are more effective than either in isolation (Frank et al., 2005). Improvements to the environment of communities can also improve mental health. By creating green spaces out of vacant lots in low-income neighbourhoods, researchers were able to reduce depression by 42% and feelings of worthlessness by 51% (South et al., 2018).

By taking a holistic approach, the biopsychosocial model allows healthcare providers to consider the patient’s life experience when making a diagnosis and developing a treatment plan. For instance, clinicians can take into account a patient’s past traumatic experiences or social network when moving forwards with a treatment option. Medication may be prescribed to address biological factors but could be paired with therapy to address psychological and social factors. The biopsychosocial model may also offer more accurate diagnoses by explaining symptoms of depression that arise from external factors, such as the loss of a job or stressors in a relationship, rather than a chemical imbalance in the brain. The idea that the etiology of their depressive symptoms may lie outside of the body is not accepted in the biomedical model and is the more nuanced approach of the biopsychosocial model. Although mental illnesses undoubtedly have a basis in the workings of the human body, they are intrinsically involved with the social and psychological world.

In recent years, contemporary psychiatrists and researchers have begun to recognize the importance of external variables on mental illness and shifted towards the biopsychosocial paradigm (Bracken et al., 2012). Despite many advocates of the model, the biopsychosocial model has not fully lived up to its initial promise and has faced criticisms and challenges in its implementation. Many psychiatrists would even claim that the biopsychosocial model does not have its own ontology and does not exist in modern practice, although some may insist they do (Moore, 2022). Psychiatry’s assertion of their use of the biopsychosocial model has become a dogma.

Although I have presented a convincing case for the biopsychosocial model over the biomedical model, it is important to note that critics have condemned the biopsychosocial model’s lack of specificity and philosophical coherence (Ghaemi, 2009; O’Leary, 2021). Additionally, the biopsychosocial model has a significant drawback in that it fails to provide an explanation for the causal interaction among the biopsychosocial processes. There is no inherent tripartite causation assumed by the biopsychosocial model, meaning dysfunction could arise from any combination of biological, psychological, or social factors. Such a model puts the onus on the clinician to probe where the problems may lie. Despite such criticisms, the biopsychosocial model can be clinically useful and it has been demonstrated that psychiatry resident trainees were not just committed to one model (ex. biological, cognitive, behavioural, psychodynamic, social realist, social constructionist, nihilist, spiritualist) for interpreting and treating mental illnesses (Harland et al., 2009).

Reconciling the literature: The future of mental health care

If the biopsychosocial model is a myth, how can we best move forwards? With vague guidelines about the execution of the biopsychosocial model, it has come to somewhat of a halt in its implementation. Some have suggested a reformulated version of the biopsychosocial must be devised in order to truly enact change.

One philosopher from the Netherlands has proposed a shift to an enactive view of depression that may benefit our mental health care system (de Haan, 2021). Enactivism is a movement which focuses on the relationship between the mind, body, and world as a system, and emphasizes the embodied nature of an organism in its environment. When we apply enactivism to psychiatric disorders, they can be viewed as emergent phenomena that arise from complex interactions between the individual, their environment, and the social context. This perspective emphasizes the importance of considering the person as a whole and the dynamic interactions between the person and their surroundings, rather than solely focusing on isolated symptoms or neurobiological abnormalities (Aftab & Nielsen, 2021).

With an enactive perspective, the distinctions made in the biopsychosocial model between the “bio”, “psycho” and “social” become more blurred, while the interactions between the three domains become more obvious and prominent. Along with the enactive approach to psychiatry, Sanneke de Haan introduced the concepts of local-to-global and global-to-local causation (dde Haan, 2021). To expand, an example of local-to-global causation could be any small physiological change that thereby influences mood. On the other hand, global-to-local causation may occur after a particularly perceptive therapy session that calms the individual, thereby exerting effects at the physiological level, perhaps changing neurotransmitter levels, for example. This is all to say that within the complex system of one individual, both local and global processes occur at different timescales (de Haan, 2021). Both physiological and experiential domains overlap, as they coexist within the same system. The biopsychosocial model breaks down these domains into distinct elements, rather than how they may relate to one another, which is heavily emphasized in the enactive perspective (de Haan, 2021).

Another potential path for research that is being explored is to move the diagnostic criteria away from the system of DSM, which is traditionally rooted in the biomedical model. The most prominent approach is the Research Domain Criteria (RDoC). The RDoC is a research framework developed by the NIMH that is now attempting to create a new taxonomy of mental disorders (Insel et al., 2010) Initially, the RDoC took an explanatory reductionist approach, but has evolved into a more unifying program that aims to integrate biological, psychological, environmental, and developmental factors which contribute to mental health systems. The RDoC seeks to uncover and comprehend the fundamental aspects of mental functioning that are disturbed in mental disease, in contrast to the standard diagnostic approach, which categorizes mental illnesses primarily on symptom criteria.

One alternative framework is the Hierarchical Taxonomy of Psychopathology (HiTOP). HiTOP is a classification scheme that aims to group mental disorders into hierarchically arranged dimensions or spectra of psychopathology based on empirical evidence of their associations with genetics, neural circuits, and other biological factors (Kotov et al., 2017). The HiTOP model acknowledges the richness and heterogeneity of mental diseases and aims to capture this complexity by identifying many dimensions or spectra of psychopathology that go beyond conventional diagnostic classifications. This opens the possibility of understanding transdiagnostic symptoms. Such a framework aims to move away from the categorical diagnoses which put mental disorders into discrete boxes, and instead attempt to group human functioning into 5 different domains (i.e., positive valence systems, negative valence systems, cognitive systems, social systems, and arousal/regulatory systems) (Kotov et al., 2017).

Yet another method, developed by Eiko Fried, is a cluster framework for comprehending and classifying mental disorders that places emphasis on the diversity and complexity of mental illnesses. Fried's method holds that mental diseases consist of clusters of symptoms that may be organized into several dimensions of psychopathology, in contrast to standard diagnostic classifications that rely on defined and distinct borders (Fried, 2022). The cluster method is centred on finding symptom clusters that can reveal similarities and differences between various mental disorders, which can then direct the creation of more specialized and efficient therapies (Fried, 2022). Fried's method gives a more flexible and nuanced view of mental disease by uncovering underlying elements that transcend conventional diagnostic classifications, and it has the potential to advance therapeutic research in the area.

The field of psychiatry has been exploring creative frameworks to improve the efficiency with which mental health issues are diagnosed and treated (Fried, 2022; Insel et al., 2010; Kotov et al., 2017; Roef et al., 2022). Although psychiatrists now claim to rely on the biopsychosocial model, there are always opportunities for improvement and innovation. As a result, the field must come to a collective consensus on diagnostic criteria. We must strive to tailor mental health care at the individual level using a holistic approach that encompasses not only the biological components, but also the psychological, social, environmental, and financial components of the human experience.

When we sleep at night, we attempt to rest our minds for the next day. Our brains, however, seem to have other plans. We enter an altered state of reality where you experience enigmas created by our own brains—we call this dreaming. We intuitively know what dreams are, but why do we dream?

Dreams most commonly occur in the stage of sleep called rapid eye movement (REM) sleep, where brain activity remarkably resembles waking brain activity1. REM sleep first originated about 220 million years ago and has been maintained by evolution23. By considering the roles of dreams in early hominids and modern humans, the adaptive processes that shaped this phenomenon provide insight in their purpose. Dreams benefit humans.

Dreaming facilitates higher-level thinking. Following the acquisition of information, dreams benefit memory performance in verbal learning, procedural learning, emotional memory, and spatial navigation45. While dreaming, the brain processes information and consolidates memories from the previous day. Beyond memory consolidation, dreaming enhances creative problem-solving. Dream psychologist Deirdre Barrett describes dreams as “thinking in a different biochemical state”, where an individual can take unconventional approaches to challenges6. During REM sleep, the prefrontal cortex (PFC) becomes significantly less active than other regions. In wakefulness, the PFC is highly active and primarily conducts conventional thought and judgment processes7.

Dampening of the PFC may explain why many have found creative solutions to problems that require out of the box thinking in dreams8. By avoiding conventional thinking, your brain can freely associate concepts which are not usually connected. Furthermore, the secondary visual cortex (V2), associated with imagery, becomes highly active during REM sleep9. V2 activation accounts for how dreams can be useful when visualizing solutions to difficult problems. Visualizing novel solutions promotes creativity and higher-level thinking. Through dreaming, innovation and critical thinking provide benefits to individuals in wakefulness.

Higher-level thinking increases evolutionary fitness. Our unique cognition allows humans to collaborate with one another in fundamentally different ways than other species. Collaborative efforts in higher-level thinking were beneficial to hominids in the environment of evolutionary adaptedness (EEA). The EEA describes situational factors in which an evolved trait adapted10. In the EEA of early humans, those who were able to think in complex manners and problem solve would have greater fitness. As a result, the next generation would benefit from higher-level thinking and promote further evolution of the trait. For example, tool use in the EEA relied on innovation and complex problem-solving. Complex problem-solving requires cognitive abilities such as long-term memory, working memory, and procedural and declarative knowledge11. Hominids able to foster these cognitive resources in complex tool use may have had a competitive advantage throughout evolution. Modern humans went on to evolve skills in social cognition, fundamentally shaped by natural selection and survival of the fittest12. Higher-level thinking in early hominids gradually evolved to reach our current cognitive abilities.

Dreaming promotes human evolution. Dreams may have many fitness-enhancing aspects, with one benefit being the ability to simulate threats within a dream. The threat simulation theory proposed by Antti Revonsuo posited that dreaming simulated threatening events from the EEA and gave a selective advantage to those who were able to do so13. By simulating situations of fear (e.g. predators, natural disasters, interactions with strangers) during sleep, threat perception and avoidance can be rehearsed through priming to prepare and enhance these skills in wakefulness. The amygdala, the region of the brain responsible for the fear response, becomes intensely activated during dreaming14. This biological mechanism would improve the mind’s ability to be alert when awake. In addition, the cognitive benefits provided by dreaming gave early hominids an advantage in the EEA, maintaining by their influence on fitness. The benefits of dreaming in the EEA also extend to the lifestyles of modern humans.

The well-being of future generations depends on dreaming. As the selective pressures of the EEA change, fears that modern humans face adapt to those pressures. Although many of the primitive threats that early humans faced are still present in our EEA, social pressures have introduced a new set of fears for humans. Dreams help with emotion regulation and have applications in psychiatry and psychotherapy. Dreaming can act as a form of “overnight therapy”, promoting adapted emotional responses during wakefulness15 . Moreover, therapeutic interventions involving REM sleep manipulation may benefit individuals with anxiety and mood disorders16. Clinicians may also use dreams to assess clinical change in patients throughout treatment17. Dreams unravel different aspects of the dynamic mind and provide insight on our primitive origins.

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If you made it this far, I hope both sides of your pillow are cold tonight <3

I wrote this paper under the supervision of Dr. Georg Northoff. This was my first exposure to the study of the mind and brain, for which I am very grateful. There are not many people like Dr. Northoff in this field. With degrees in psychiatry, neuroscience and philosophy, he is able to explore consciousness and the self in a comprehensive and interdisciplinary manner. In the time that I was in his lab, there were many lab meetings where we explored the overlap between neuroscientific and neurophilosophical ideas, attempting to unravel the connection between mind and brain.

The main idea of his work is that “spatiotemporal dynamics” govern brain activity, and ultimately bridge the mind and brain:

space-time constructions provide the link or shared feature of neural and mental activities as their “common currency” (Northoff et al., 2020)

I’m not going to lie — sometimes the details of his work are incredibly complex and I have trouble following them. But altogether, his perspective is incredibly nuanced and well-thought out that I have to simply accept that I do not have the capacity to understand the breadth of his work.

Regardless, my project focused on just one subsection of his research, and in this article I will try to summarize it in layman’s terms.

My Project: Rest-Task Switching in Depression

Here, we looked at the rest-task modulation in individuals diagnosed with Major Depressive Disorder (MDD). Rest-task modulation refers to the switching from a rest state (i.e., sitting with your thoughts, letting your mind wander), to a task state (i.e., actively engaging in a cognitively demanding task.

Perhaps contrary to your intuition but when we in these rest states, our brain doesn’t go completely quiet. There is a network in our brain called the default mode network (DMN) that is specifically geared to these periods of wakeful rest. The DMN consists of many different brain regions that become active during rest states. These brain regions are collectively responsible for things like mind-wandering, daydreaming, and self-reflection. Specifically, depressed people are characterized by overactive DMNs.

In this project, I did a review of papers that collected some sort of neuroimaging in individuals with MDD and had imaging data for both rest and task states. Of the 11 studies we found in our literature search, about one third used electroencephalography (EEG) and the rest used functional magnetic resonance imaging (fMRI). Both of these modalities are able to infer regions of function in the brain, but provide different information about that function (see below).

So what did we find?

After months of analysis and writing, the headline finding was that there was in fact decreased rest-task modulation going on in the brains of depressed people. This means that people with MDD have a harder time switching between their rest states (i.e., mind-wandering) to active task states, the rest states can even impacts the ability to effectively be in that task state. Ultimately, people with MDD become “stuck” in this rest state.

The results pointed towards the idea that people with depression have a disbalance of internally- to externally-oriented cognition as measured by spatiotemporal dynamics. In the paper, we outline that this is likely a result of low modulation of task by rest. I pulled the last figure from this article, which succinctly summarizes the result of this study.

If you made it this far, thank you for reading this summary of my first (and only) published paper. Please leave a comment if you have any questions and I’ll do my best to get an answer to you! If you’re interested in reading the whole paper, I’ve linked it above and here.

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lots of love,

anvi