Motivation and emotion/Book/2022/Neuroimaging and mood disorders
How can neuroimaging assist in diagnosing and treating mood disorders?
Neuroimaging offers a window into the biological components of mental health, which has increased interest in objectively diagnosing and treating mental health conditions, such as mood disorders (Lai et al., 2021). Mood disorders are burdensome on lives and society, relying on self-reported symptoms for diagnosis, which can lead to inaccurate diagnoses or ineffective treatment. Neuroimaging research has significantly contributed to identifying unique brain differences in mood disorders compared to healthy individuals (Chen et al., 2020). This research provides more information about how these conditions develop, offering ways for health professionals to diagnose patients more accurately (Merenstein & Bennet, 2022). These findings can also help researchers to find new clinical treatments and psychiatric medications for mood disorders (Chen et al., 2020).
This chapter explores the background of mood disorder treatment and diagnosis, neuroimaging’s use in mood disorder research, and the hunt to identify unique brain differences in mood disorders. It discusses how neuroimaging can help with mood disorder diagnosis and treatment, why healthcare providers do not currently use it in clinical settings, and how this issue could change.
Mood disorders, also known as affective disorders, are a broad term for two types of mental illnesses: bipolar and depressive disorders. These disorders are two of the most common mental illnesses worldwide and a leading cause of disability (Chen et al., 2020). Bipolar disorder is marked by abnormally high or low moods that interfere with thinking and impair daily functioning (Marwaha et al., 2018). Depressive disorders are characterised by extended periods of sad, empty, irritable or pleasureless mood in combination with physical and mental problems resulting in impaired daily functioning (Rakofsky & Rapaport, 2018).
The challenges in treating mood disordersEdit
Decades of substantial research have sought to treat mood disorders effectively (Kalin, 2020). Based on this research, the current standard treatment for mood disorders is a combination of psychiatric medication and psychotherapy (Sekhon et al., 2022). Current approaches cannot accurately predict how individuals will respond to medication. Trial and error is often used until patients find a treatment with an ideal balance of side effects and benefits. This process can have adverse side effects that disrupt patients’ lives and worsen the illness, and sometimes no beneficial medication is found (Howes et al., 2022). These challenges present a need for new treatments for mood disorders according to individual needs.
Did you know?
Treatment resistance in mood disorders produces major economic and healthcare burdens (Diaz et al., 2022). Within Australia, finding a way to successfully treat bipolar could save as much as $1.3 billion dollars per year (Harper, 2017).
The need for improved methods of diagnosing mood disordersEdit
Currently, there is no objective or biological way to diagnose mood disorders. Instead, mood disorder diagnosis is based primarily on self-reported thoughts, feelings, and behaviour patterns according to the diagnostic criteria in the Diagnostic Statistics Manual or the International Classification of Diseases (Kalin, 2020). Many experts claim that the current psychiatric diagnosis systems have flaws that can lead to incorrect diagnoses and poor treatment (Henderson, 2020). For example, diagnoses often rely on self-reported symptoms, and multiple mental illnesses can show similar symptoms, making it challenging to provide a correct diagnosis (Sekhon et al., 2022). Further, it is difficult to accurately predict mood disorder diagnoses before their onset or how the condition may progress over time. With the challenges in diagnosing mood disorders correctly, there is a need for new diagnostic tools.
Section Quiz 1
Watch this video!
Neuroimaging and mood disordersEdit
Neuroimaging is a growing field that has revolutionised neuroscience research since the 1990s, enabling the non-invasive study of live patients and providing science with a richer understanding of how brain functions relate to human behaviour (Lai et al., 2021). Neuroimaging applies various technologies to see the function and structure of the central and peripheral nervous system, including the brain (Chen et al., 2020. For example, techniques such as functional magnetic resonance imaging (fMRI), computer tomography (CT) scanning and single-photon emission computed tomography (SPECT) are commonly used in psychological research (Merenstein & Bennett, 2020). Within psychological research, neuroimaging has become a primary tool for identifying brain activity that correlates to many psychological phenomena and to better understand conditions like mood disorders (Moran & Zaki, 2013).
The importance of neurological brain biomarkers in mood disordersEdit
A primary focus of neuroimaging studies exploring mood disorders is identifying precision brain biomarkers in the condition (Chen at al., 2020). Precision neurological biomarkers are objectively measurable changes in the brain’s structure and function compared to healthy people (Lai et al., 2021). Finding these biomarkers in people with mood disorders may provide evidence for the condition’s cause and effects and how to diagnose and treat them more accurately (Chen at al.,, 2020; Merenstein & Bennett, 2022). Consequently, neuroimaging may be able to assist the diagnosis and treatment of mood disorders by identifying mood disorder brain biomarkers.
Did you know?
The 'human circulation balance,' developed by Angelo Mosso in the 1880s is regarded as the earliest neuroimaging technique ever. Angelo used this technique to explore the redistribution of blood during emotional and intellectual activity with the hopes of better understanding how the brain works (Sandrome et al., 2012).
Research shows brain differences in mood disordersEdit
Attempts to identify precision neurological biomarkers for mood disorders using neuroimaging have yielded promising results. Compared to healthy groups, multiple studies identify structural and functional brain changes in people with mood disorders that may contribute to the conditions’ emotional, thought, and physical symptoms (Merenstein & Bennet, 2022). For example, studies have revealed that patients with mood disorders have, on average, smaller than typical grey and white matter volumes in specific regions of the brain and larger than average volumes in others (Schmaal et al., 2020; Lai et al., 2021). In addition, neuroimaging studies have discovered potential risk factors for the onset of mood disorders, including childhood maltreatment and traumatic brain injury (Henderson et al., 2020).
This research shows neuroimaging’s ability to identify critical structural and functional differences in the brain as well as risk factors, which could guide future diagnosis and treatment of mood disorders. However, it is still unclear whether the identified brain changes are the cause or effect of the condition. Furthermore, it is unclear how brain structure might be affected by multiple mental illnesses, the age the disease starts, and the effects of medications (Lai et al., 2021; Merenstein & Bennett, 2022).
Section Quiz 2
Watch this video!
The pressing question of what causes mood disorders is intricate, with multiple parts and no agreed-upon answer (Kalin, 2020). Neuroimaging research on mood disorders has sought to understand their causes better; however, these findings have been inconclusive (Rakofsky & Rapaport, 2018; Kalin, 2020). One helpful direction of neuroimaging research is to help support or disprove hypotheses on the causes of mood disorders. Furthering our understanding of what causes mood disorders may help identify pre-disease precision neurological biomarkers, and this research could help guide methods to diagnose mood disorders before they develop .
Hypothalamic-pituitary-adrenal axis dysfunctionEdit
The Hypothalamic-Pituitary-Adrenal (HPA) axis is a biological system crucial to the body’s stress response (Sheng et al., 2021). Dysfunction of the HPA axis is linked to various diseases, including bipolar disorder (Murri et al., 2016). Neuroimaging studies have shown how HPA axis dysfunction, as demonstrated by abnormally high cortisol levels, influence similar structural and functional brain changes to bipolar disorder (Valli et al., 2016). However, it is unclear whether HPA axis dysfunction is a cause or effect of the illness (Murri et al., 2016).
The neurotrophic hypothesisEdit
The neurotrophic hypothesis suggests that a decline in neurotrophins, molecules that maintain and repair the nervous system, negatively impacts the brain's structure and function, causing depressive disorders (Skaper, 2018). Neuroimaging research has supported this hypothesis by connecting the reduced volume and functionality of brain regions that typically have high levels of neurotrophins in healthy populations to people with depressive disorders (Levy et al., 2018). This research shows how neuroimaging can reveal potential neurological mechanisms for depressive disorders, but it is still unclear whether reduced neurotrophins cause or effect the condition.
The monoamine hypothesisEdit
According to the monoamine hypothesis, decreased monoamine neurotransmitters in the central nervous system cause depressive symptoms (Rakofsky & Rapaport, 2018). Monoamine neurotransmitters are chemicals in the brain, such as serotonin, dopamine and norepinephrine, that are crucial for the brain’s function (Swallow et al., 2016). A neuroimaging study by Kaufman and colleagues (2016) found depressed patients to have dysfunction in brain areas associated with reduced monoamine levels. However, many experts challenge this hypothesis as research conducted after patient death showed that healthy persons and individuals with major depressive disorder (MDD) had the same levels of monoamines in their brains (Rakofsky & Rapaport, 2018). As a result, the evidence suggests that reduced monoamine levels are only a minor part of how depressive disorders work.
Modern approaches to understanding the causes of mood disorders follow the biopsychosocial model, which acknowledges that several inherited, psychological, and socio-environmental factors impact mood disorder emergence (Sekhon & Gupta, 2022). However, most neuroimaging studies do not expand current psychological understandings of how mood disorders develop and persist, such as Beck’s cognitive triad (Henderson, 2020; Moskow et al., 2022). Consequently, some psychologists doubt how well neuroimaging can fully capture all the psychological and environmental factors that affect mood disorders. (Henderson, 2020).
Section Quiz 3
How can neuroimaging help mood disorder treatment?Edit
The high prevalence of treatment-resistant mood disorders and the undesirable side effects of psychiatric medication presents a need to develop new treatments (Carvalho & McIntyre, 2015). Recent years have seen a sharp decline in new mood disorder medication, partly due to the lack of well-established neurological targets (Howes et al., 2022). Researchers may be ignoring neuroimaging’s full potential in mood disorder treatment research, as many studies still rely on other biological measures, such as focusing on pharmacokinetics (Medhi et al., 2014). Neuroimaging could be a valuable way to help treat mood disorders by finding new treatments, better understanding and enhancing existing medication and offering the potential for individualised treatment plans.
Identifying new treatmentsEdit
Neuroimaging research has identified possible pharmacological and surgical treatment therapies, most notably target locations for deep brain stimulation (Merenstein & Bennet, 2022). This procedure involves implanting electrodes in the brain to send electrical pulses to impaired areas, which neuroimaging has made possible by identifying brain abnormalities (Sui et al., 2020). Deep brain stimulation has successfully treated some depressive disorders by targeting areas of the brain thought to produce the effects of the condition. This treatment’s promising potential may provide a side-effect-free method of treating depressive disorders. However, due to the risk of worsening elevated moods, studies do not recommend deep brain stimulation for treating bipolar disorder (Graat et al., 2020). This research shows the potential for neuroimaging to identify new methods of treating mood disorders.
Understanding and enhancing existing treatmentsEdit
Neuroimaging studies have played a key role in research developments seeking to understand psychiatric medication benefits. Multiple studies have explored common pharmacological treatments for mood disorders’ unique effects on the brain, which may further science’s understanding of treatment-resistant populations (Merenstein & Bennett, 2022). Notably, neuroimaging aided the development of the cognitive neurophysiological model of antidepressant action. This model identifies common antidepressant medication’s biological impact, suggesting that the medication positively influences the cognitive processing of emotional information rather than directly influencing mood (Zghoul et al., 2019). These findings and similar information may provide a way to monitor and accurately predict treatment outcomes (Medhi et al., 2014). Accurately identifying medication responses could eventually inform individualised treatment options for mood disorders, removing the need for patients to try multiple medications before finding an ideal option (Godlewska, 2020).
Section Quiz 4
Watch this video!
How can neuroimaging help mood disorder diagnosis?Edit
Initial speculation around neuroimaging’s clinical use in diagnosing mental illness provoked great interest in the scientific community (Merenstein & Bennett, 2022). Many experts have hoped that neuroimaging would be the tool required in psychiatry and psychology to provide an objective diagnosis of mental illness, removing the need for a ‘less accurate’ behavioural diagnosis. However, the American Psychiatric Association states that neuroimaging presently provides no benefit to diagnosing and treating any mental illness (Henderson et al., 2020). As such, healthcare providers limit neuroimaging’s clinical use in helping diagnose mood disorders to ruling out biological conditions that may mimic mental disorders, such as cerebrovascular disease, neoplasm, and hematoma (Chen et al., 2020). Although this application of neuroimaging to mood disorders is beneficial, it does not achieve many researchers’ ambitions for the technology. Despite identifying neurological biomarkers for mood disorders, healthcare providers do not apply these findings in treating and diagnosing mood disorders due to three crucial barriers: a lack of clinical sensitivity, specificity, and standardisation (Rachofsky & Rapaport, 2018; Henderson et al., 2020). Understanding and finding solutions to these challenges are necessary for neuroimaging to aid in diagnosing mood disorders.
Sensitivity is the ability of a diagnostic method to precisely pinpoint the disease or condition under investigation (Swift et al., 2020). Although healthcare providers diagnose individuals, psychiatric neuroimaging studies currently analyse and combine group data averages (Lai et al., 2021). Consequently, the present neuroimaging findings cannot accurately represent individuals’ high degree of neurological variability (Merenstein & Bennett, 2022). Specificity is the degree to which a medical or psychological examination can rule out individuals who do not have the ailment (Swift et al., 2020). Diversity in how neuroimaging results are interpreted in research limits specificity (Chen et al., 2020). This is due to the high variability of individual brain function and structure and the inability to discriminate between various mental disorders due to similar neurological patterns (Lai et al., 2021).
Standardisation is a consistent test administration process between studies to ensure that researchers measure and test under the same circumstances (Tate & Panteghini, 2007). Within neuroimaging studies of mental illnesses, there is no consensus ensuring uniformity in research methods (Rakofsky & Rapaport, 2018). This makes it hard to put together and compare the results of different neuroimaging studies on the same topic because the researchers may use different measurements and come to different conclusions (Merenstein & Bennett, 2022). These three limitations highlight the primary barriers restricting neuroimaging's potential to help diagnose mood disorders.
Section Quiz 5
The future of neuroimaging and mood disordersEdit
Currently, problems with sensitivity, specificity, and standardisation make it hard to use clinical neuroimaging widely to diagnose and treat mood disorders. However, research is presently exploring ways to overcome the technology’s limitations. Combining neuroimaging with machine learning, which uses algorithms to find patterns in a dataset, could be a method for neuroimaging to become sufficiently sensitive and specific (Davatzkios, 2019). Additionally, a standardised diagnostic approach combining genetic, physiological and neuroimaging analysis techniques may provide a method for accurate and objective mental illness diagnosis (Thompson et al., 2020).
The use of machine learning has grown since 2010, finding widespread applications in research and commercial enterprise. Applying machine learning to neuroimaging mental diseases could address the issues of data interpretation variability and provide a means to precisely subcategorise complicated brain presentations (Merenstein & Bennet, 2022). Researchers in multiple studies that use machine learning to compare vast amounts of neuroimaging research has reported successful differentiation of biomarkers in different psychiatric conditions relative to healthy populations (Merenstein & Bennet, 2022). However, the reliable use of machine learning in neuroimaging is still in its early stages and not without limitations (Davatzikos, 2019). For instance, the lack of standardisation between neuroimaging studies makes it difficult to compare the findings accurately (Davatzikos, 2019). Consequently, further standardised research and development are needed for machine learning to improve neuroimaging's clinical potential for diagnosing and treating mood disorders.
Did you know?
Machine learning technology is a multi-billion dollar industry, and its observable in many areas of modern life, including what shows Netflix recommends, personalised advertisement on social media and which matches dating apps like hinge offer their customers (Mukherjee, 2021; Pajkovic, 2022).
Multiple biomarker diagnosisEdit
It is doubtful that any single neuroimaging biomarker will provide enough specificity and sensitivity to justify neuroimaging's use in mood disorder diagnosis (Godlewska, 2020). Instead, a standardised clinical approach that combines neuroimaging biomarkers of mental disorders with other known biomarkers, such as neuroinflammatory and genetic indicators, may be able to find both shared and unique brain changes across mental disorders. The Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium is a research group exploring this idea and applying machine learning to mood disorder diagnosis and treatment (Schmaal et al., 2020; Thompson et al., 2020). Through their work and similar research, neuroimaging biomarkers combined with multiple biological biomarkers may help diagnose and treat mood disorders in an objective and targeted way, revolutionising modern psychology and psychiatry.
Section Quiz 6
Watch this video!
Mood disorders are debilitating and prevalent health issues needing new diagnoses and treatment methods due to limitations, such as the risk of a wrong diagnosis or treatment resistance. Neuroimaging research has tried to find accurate and objective measures of mood disorders by finding precision neurological biomarkers, distinct brain changes influenced by the condition. Finding neurological biomarkers for mood disorders may help neuroimaging assist in diagnosing and treating the condition. People with mood disorders have been found to have more than one pattern of abnormal brain structure and function. These changes may help explain aspects of mood disorder's symptoms and identify causal risk factors, but it is unclear if the identified brain changes cause or affect mood disorders.
The search for precise biomarkers has provided helpful evidence for influential theories about what causes mood disorders. For example, neuroimaging has helped demonstrate the HPA axis dysregulation hypothesis, the neurotrophic hypothesis and supported the monoamine theory of how mood disorders start. However, neuroimaging technology may not be able to identify all the factors influencing the cause of mood disorders, such as how people think. Consequently, some experts question neuroimaging's usefulness in psychological research.
Through finding precise biomarkers for mood disorders, sites for deep brain stimulation implants have been found that may be able to treat depressive disorders. Neuroimaging studies exploring current mood disorder medicines have furthered our understanding of how they work. One notable example of this is the cognitive neurophysiological model of antidepressant action. These results could help produce new medicines and make individualised treatment plans, leading to better treatment outcomes. Despite what we have learnt from neuroimaging mood disorders, it is presently only used in mood disorder diagnosis to rule out other diseases with similar symptoms to mood disorders, such as neurodegenerative disorders. This is because current neuroimaging technology cannot always distinguish between different brain problems or correctly diagnose people with a wide range of mental illnesses. There is also no standard way to collect and interpret data from neuroimaging studies of mood disorders, making it hard to compare the results of different studies.
Research is striving to overcome the limitations of the current technology. Combining neuroimaging results with machine learning and a standard diagnostic strategy that combines genetic, physiological, and neuroimaging analytic approaches could also be a way to diagnose mental disorders accurately and objectively. The combination of these two strategies is presently underway with the ENIGMA project, which seeks to overcome neuroimaging's current clinical limitations. Overall, neuroimaging has promising future potential for diagnosing and treating mood disorders.
- Affective Disorders (Book chapter, 2021)
Chen, R., Cui, Z., Capitão, L., Wang, G., Satterthwaite, T. D., & Harmer, C. (2020). Precision biomarkers for mood disorders based on brain imaging. BMJ, m3618. https://doi.org/10.1136/bmj.m3618
Davatzikos C. (2019). Machine learning in neuroimaging: Progress and challenges. NeuroImage, 197, 652–656. https://doi.org/10.1016/j.neuroimage.2018.10.003
Godlewska, B. R. (2020). Neuroimaging as a tool for individualised treatment choice in depression: The past, the present and the future. Current Behavioral Neuroscience Reports, 7(1), 32–39. https://doi.org/10.1007/s40473-020-00198-2
Graat, I., van Rooijen, G., Mocking, R., Vulink, N., de Koning, P., Schuurman, R., & Denys, D. (2020). Is deep brain stimulation effective and safe for patients with obsessive compulsive disorder and comorbid bipolar disorder?. Journal of affective disorders, 264, 69–75. https://doi.org/10.1016/j.jad.2019.11.152
Howes, O.D., Thase, M.E. & Pillinger, T. Treatment resistance in psychiatry: state of the art and new directions. Mol Psychiatry 27, 58–72 (2022). https://doi.org/10.1038/s41380-021-01200-3
Kalin, N. H. (2020). Advances in understanding and treating mood disorders. American Journal of Psychiatry, 177(8), 647–650. https://doi.org/10.1176/appi.ajp.2020.20060877
Kaufman, J., DeLorenzo, C., Choudhury, S., & Parsey, R. V. (2016). The 5-HT1A receptor in major depressive disorder. European Neuropsychopharmacology, 26(3), 397–410. https://doi.org/10.1016/j.euroneuro.2015.12.039
Lai, C.-H., Kim, Y.-K., & Radua, J. (2021). Editorial: Neuroimaging Biomarkers in Mood and Anxiety Disorders. Frontiers in Psychiatry, 12. https://doi.org/10.3389/fpsyt.2021.773034
Medhi, B., Misra, S., Avti, P. K., Kumar, P., Kumar, H., & Singh, B. (2014). Role of neuroimaging in drug development. Reviews in the Neurosciences, 25(5). https://doi.org/10.1515/revneuro-2014-0031
Merenstein, J. L., & Bennett, I. J. (2022). Neuroimaging studies of Mental Disorders. Reference Module in Neuroscience and Biobehavioral Psychology. https://doi.org/10.1016/b978-0-323-91497-0.00030-8
Moskow, D. M., Barthel, A. L., Hayes, S. C., & Hofmann, S. G. (2022). A Process-Based Approach to Cognitive Behavioral Therapy. Comprehensive Clinical Psychology, 16–33. https://doi.org/10.1016/b978-0-12-818697-8.00183-7
Mukherjee, S. (2021, December 23). Machine Learning Is Solving Some Unique Problems Of Online Dating. Analytics India Magazine. https://analyticsindiamag.com/machine-learning-is-solving-some-unique-problems-of-online-dating/ Muneer A. (2016). The Neurobiology of Bipolar Disorder: An Integrated Approach. Chonnam medical journal, 52(1), 18–37. https://doi.org/10.4068/cmj.2016.52.1.18
Niko Pajkovic. (2022). Algorithms and taste-making: Exposing the Netflix Recommender System’s operational logics. Convergence. https://journals.sagepub.com/doi/10.1177/13548565211014464
Rakofsky, J., & Rapaport, M. (2018). Mood Disorders. Continuum (Minneapolis, Minn.), 24(3, BEHAVIORAL NEUROLOGY AND PSYCHIATRY), 804–827. https://doi.org/10.1212/CON.000000000000060
Sandrone, S., Bacigaluppi, M., Galloni, M. R., & Martino, G. (2012). Angelo Mosso (1846–1910). Journal of Neurology, 259(11), 2513–2514. https://doi.org/10.1007/s00415-012-6632-1
Schmaal, L., Pozzi, E., C. Ho, T., van Velzen, L. S., Veer, I. M., Opel, N., Van Someren, E. J., Han, L. K., Aftanas, L., Aleman, A., Baune, B. T., Berger, K., Blanken, T. F., Capitão, L., Couvy-Duchesne, B., R. Cullen, K., Dannlowski, U., Davey, C., Erwin-Grabner, T., … Veltman, D. J. (2020). Enigma MDD: Seven Years of global neuroimaging studies of major depression through Worldwide Data Sharing. Translational Psychiatry, 10(1). https://doi.org/10.1038/s41398-020-0842-6
Sheng, J. A., Bales, N. J., Myers, S. A., Bautista, A. I., Roueinfar, M., Hale, T. M., & Handa, R. J. (2021). The Hypothalamic-Pituitary-Adrenal Axis: Development, Programming Actions of Hormones, and Maternal-Fetal Interactions. Frontiers in Behavioral Neuroscience, 14. https://doi.org/10.3389/fnbeh.2020.601939
Skaper S. D. (2018). Neurotrophic Factors: An Overview. Methods in molecular biology, 1727, 1–17. https://doi.org/10.1007/978-1-4939-7571-6_1
Sekhon, S., & Gupta, V. (2022, May 8). Mood Disorder. Nih.gov; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK558911/
Swift, A., Heale, R. and Twycross, A. (2019) “What are sensitivity and specificity?,” Evidence Based Nursing, 23(1), pp. 2–4. https://doi.org/10.1136/ebnurs-2019-103225
Sui, Y., Tian, Y., Ko, W., Wang, Z., Jia, F., Horn, A., De Ridder, D., Choi, K. S., Bari, A. A., Wang, S., Hamani, C., Baker, K. B., Machado, A. G., Aziz, T. Z., Fonoff, E. T., Kühn, A. A., Bergman, H., Sanger, T., Liu, H., Haber, S. N., … Li, L. (2021). Deep Brain Stimulation Initiative: Toward Innovative Technology, New Disease Indications, and Approaches to Current and Future Clinical Challenges in Neuromodulation Therapy. Frontiers in neurology, 11, 597451. https://doi.org/10.3389/fneur.2020.597451
Tate, J., & Panteghini, M. (2007). Standardisation--the theory and the practice. The Clinical biochemist. Reviews, 28(3), 93–96. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994108/
Valli, I., Crossley, N. A., Day, F., Stone, J., Tognin, S., Mondelli, V., Howes, O., Valmaggia, L., Pariante, C., & McGuire, P. (2016). HPA-axis function and grey matter volume reductions: imaging the diathesis-stress model in individuals at ultra-high risk of psychosis. Translational Psychiatry, 6(5), 797. https://doi.org/10.1038/tp.2016.68
Thompson, P.M., et al., 2020. ENIGMA and global neuroscience: a decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl. Psychiatr., 10(1), 1–28. https://doi.org/10.1038/s41398-020-0705-1
Zghoul, T., Cowen, P. J., & Harmer, C. J. (2019). A perspective: From the serotonin hypothesis to cognitive neuropsychological approaches. The Serotonin System, 95–104. https://doi.org/10.1016/b978-0-12-813323-1.00005-0
- 2-Minute Neuroscience: Neuroimaging (Youtube)
- Crash Course Psychology: Depressive and Bipolar Disorders (Youtube)
- Imaging the brain for precision mental health (Youtube)
- Types of depression (beyondblue.org)
- Simply Psychology: The history of neuroimaging techniques
- TEDEd: What is depression?
- TEDEd: What is bipolar disorder?
- What is machine learning? (Youtube)