Vedashree Meher, M.Sc

Major depressive disorder (MDD) is diagnosed when an individual consistently feels low, has a depressed mood, takes lack of pleasure in activities, experiences feeling of worthlessness, has lack of energy, poor concentration, sleep deprivation, decreased appetite and/or suicidal ideation (Bains & Abdijadid, 2022). To be diagnosed with MDD, individuals have to present with 5 of the above-mentioned symptoms according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). MDD can stem as a result of biological, genetic, environmental and psychosocial factors, such as low socioeconomic status, trauma and substance abuse (Vijayadasan et al., 2021). Certain chronic medical conditions such as diabetes mellitus and rheumatoid arthritis/osteoarthritis may exacerbate the risk of developing MDD (Ryu et al., 2016). Looking into the neurobiological aspects, primary abnormalities in neurotransmitters such as serotonin, norepinephrine and dopamine systems have been reported (Bains & Abdijadid, 2022) and it has been suggested that GABA, glutamate and glycine may play a role in the etiology of depression as well.

Usefulness of clinical imaging for diagnosis of MDD: To improve the accuracy of MDD diagnosis, identifying functional, anatomical, pathophysiological biomarkers and targets is crucial. Towards this, clinical imaging such as MRI and PET have been useful tools.

MRI can be used for studying subtle changes in the blood brain barrier (BBB) associated with various pathologies such as stroke, dementia and Parkinson’s. BBB leakage studied in bipolar patients showed an association between extensive leakage and greater severity of depression/anxiety (Kamintsky et al., 2020). MRI has also shown MDD patients  structural abnormalities in areas involved in regulation of emotional processing (Zhao et al., 2017). Understanding such anatomical abnormalities associated with a disease is important as it provides better insights into understanding potential targets for treatments. 

On the other hand, some studies usingPET have revealed increased norepinephrine (NE) transporter availability in the thalamus and its subregions. This finding may prove beneficial when selecting antidepressants that have affinity for NE transporters (Moriguchi et al., 2017). Another PET study done showed high activation of microglia (Slachta, 2018), a marker of inflammation, in the brain of patients with MDD, which can potentially aid in the development of therapeutics that reduce inflammatory responses and decrease MDD symptoms.

PET/MR hybrid imaging is a powerful tool in the study of depression. For example, it has revealed

Serotonin system abnormalities– 5-HT_1B receptor plays an important role in regulating depressive symptoms. 5-HT_1B receptor antagonists potentially increase levels of extracellular serotonin, thereby exhibiting antidepressant effects. Simultaneous PET/MR has allowed for the assessment of CNS active compounds where this hybrid modality has shown the effects of 5-HT_1B receptorpartial agonist, providing an insight into therapeutic targets for depression (Hansen et al., 2017).

Striatal dopamine deficit. Studies have shown in increase in binding of a dopamine receptor agonist in parts of the brain with a concurrent decrease in connectivity in those same regions (Hamilton et al., 2018). This suggests that combined PET/MRI can give insights into receptor dynamics, adaptions and functions (Sander et al., 2020). Thus, PET/MRI, can be used for investigating the coupling between drug binding and efficacy, relating neurotransmitter release to cognition and disease states, and linking receptor densities to behavioral phenotypes (Sander et al., 2020).

Additionally, combining PET and MRI gives the ability to localize scanning to the brain region to increase patient comfort. This is especially important to keep in mind when treating patients with MDD who are prone to anxiety. This hybrid modality will also allow imaging of specific biomarkers (i.e., tau, gray matter loss, white matter and vascular pathology) with increased accuracy, thereby improving our understanding of the pathophysiology of depression (Emsell et al., 2021). 

References

Bains, N., & Abdijadid, S. (2022). Major Depressive Disorder. In StatPearls. StatPearls Publishing Copyright © 2022, StatPearls Publishing LLC.

Emsell, L., Laroy, M., Van Cauwenberge, M., Vande Casteele, T., Vansteelandt, K., Van Laere, K., Sunaert, S., Van den Stock, J., Bouckaert, F., & Vandenbulcke, M. (2021). The Leuven late life depression (L3D) study: PET-MRI biomarkers of pathological brain ageing in late-life depression: study protocol. BMC Psychiatry, 21(1), 64. https://doi.org/10.1186/s12888-021-03063-y

Hamilton, J. P., Sacchet, M. D., Hjørnevik, T., Chin, F. T., Shen, B., Kämpe, R., Park, J. H., Knutson, B. D., Williams, L. M., Borg, N., Zaharchuk, G., Camacho, M. C., Mackey, S., Heilig, M., Drevets, W. C., Glover, G. H., Gambhir, S. S., & Gotlib, I. H. (2018). Striatal dopamine deficits predict reductions in striatal functional connectivity in major depression: a concurrent (11)C-raclopride positron emission tomography and functional magnetic resonance imaging investigation. Transl Psychiatry, 8(1), 264. https://doi.org/10.1038/s41398-018-0316-2

Hansen, H. D., Mandeville, J. B., Sander, C. Y., Hooker, J. M., Catana, C., Rosen, B. R., & Knudsen, G. M. (2017). Functional Characterization of 5-HT(1B) Receptor Drugs in Nonhuman Primates Using Simultaneous PET-MR. J Neurosci, 37(44), 10671-10678. https://doi.org/10.1523/jneurosci.1971-17.2017

Kamintsky, L., Cairns, K. A., Veksler, R., Bowen, C., Beyea, S. D., Friedman, A., & Calkin, C. (2020). Blood-brain barrier imaging as a potential biomarker for bipolar disorder progression. NeuroImage: Clinical, 26, 102049. https://doi.org/10.1016/j.nicl.2019.102049

Moriguchi, S., Yamada, M., Takano, H., Nagashima, T., Takahata, K., Yokokawa, K., Ito, T., Ishii, T., Kimura, Y., Zhang, M.-R., Mimura, M., & Suhara, T. (2017). Norepinephrine Transporter in Major Depressive Disorder: A PET Study. American Journal of Psychiatry, 174(1), 36-41. https://doi.org/10.1176/appi.ajp.2016.15101334

Ryu, E., Chamberlain, A. M., Pendegraft, R. S., Petterson, T. M., Bobo, W. V., & Pathak, J. (2016). Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records. BMC Psychiatry, 16, 114. https://doi.org/10.1186/s12888-016-0821-x

Sander, C. Y., Hansen, H. D., & Wey, H.-Y. (2020). Advances in simultaneous PET/MR for imaging neuroreceptor function. Journal of Cerebral Blood Flow & Metabolism, 40(6), 1148-1166. https://doi.org/10.1177/0271678×20910038

Savitz, J. B., Rauch, S. L., & Drevets, W. C. (2013). Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play. Mol Psychiatry, 18(5), 528-539. https://doi.org/10.1038/mp.2013.25

Slachta, A. (2018). PET imaging suggests major depression is a neurodegenerative disease. Innovate Healthcare. https://www.radiologybusiness.com/topics/medical-practice-management/pet-imaging-suggests-major-depression-neurodegenerative-disease

Vijayadasan, J., Raghunathan, D., Rajagopal, S., & Soangra, R. (2021). Assessment of Major Depressive Disorders through Neuroimaging Studies and Their Treatment Methods. International Journal of Biology and Biomedical Engineering, 15, 18-28. https://doi.org/10.46300/91011.2021.15.4

Zhao, K., Liu, H., Yan, R., Hua, L., Chen, Y., Shi, J., Yao, Z., & Lu, Q. (2017). Altered patterns of association between cortical thickness and subcortical volume in patients with first episode major depressive disorder: A structural MRI study. Psychiatry Res Neuroimaging, 260, 16-22. https://doi.org/10.1016/j.pscychresns.2016.12.001