Emotions have been demonstrated to have significant meanings for human being: they help signal danger in the environment (LeDoux, 2012), enhance attention (Villemure & Schweinhardt, 2010), foster social relationships (Tully, Lincoln, Liyanage-Don, & Hooker, 2014), alter behaviors (McNaughton & Corr, 2009) etc. The researcher has been comparing the function of mood to a thermostat which constantly monitors the discrepancy between individuals’ current state (current temperature) and their “desired subjective state” (setpoint) (Larsen, 2000, p. 132). Therefore, having the ability to “sense” the discrepancy and reduce it either actively or passively is critical for mental health.
When emotions don’t work the way they should, people often suffer from a variety of symptoms which researchers, clinicians, and physicians often refer to as affective disorders or mood disorders. Though prevalence rates can be highly variable, general population studies have shown that 1-year prevalence for major depressive disorder, dysthymic disorder, and bipolar I disorder were 4.1 per 100, 2.0 per 100 and 0.72 per 100, respectively (Waraich et al., 2004). Epidemiological studies show that major depression is common among adolescents with 25% prevalence by the end of adolescence (R. Kessler, S. Avenevoli, & K. Ries Merikangas, 2001). For anxiety disorders, up to 33.7% of the population are affected during their lifetime (Bandelow & Michaelis, 2015).
I would like to extend the research paper: Resting-state functional MRI in depression unmasks
increased connectivity between networks via the dorsal nexus by Sheline, Price, Yan, and Mintun published in 2010. Though not specifically stated, the research question of the paper is: what is the intrinsic brain connections in major depression? (Sheline, Prince, Yan, and Mintun, 2010, p11020). In the paper, the authors compared three different brain networks -- the cognitive control network, default mode network, and affective network between depressed subjects and healthy control (Sheline, Prince, Yan, and Mintun, 2010, p11020). They used a neuroimaging technique -- functional MRI (fMRI) to measure the resting state functional connectivity (Sheline, Prince, Yan, and Minttun, 2010, p11020). Participants include 18 individuals with major depression in which 11 are males and 7 are females, and 17 controls in which 5 are males and 12 are females. The data set is the fMRI data of the participants. The authors performed a functional connectivity analysis of resting-state activity (Sheline, Prince, Yan, and Minttun, 2010, p11023). The result shows that depressed subjects have increased connectivity in the bilateral dorsal medial prefrontal cortex region (Sheline, Prince, Yan, and Minttun, 2010, p11020).
I would like to extend this study to include a population with anxiety disorders including General Anxiety Disorders, panic disorder, social anxiety disorder, and specific phobias. The reason why I include this population is that depression has a high comorbidity with anxiety disorders in the clinical and sub-clinical population. There have been efforts made to understand the similarities and differences between depression and anxiety, but the neurocorrelates underlying these two clusters of diagnosis remain unclear. Therefore I would select 20 depression participants, 20 anxiety participants, and 20 healthy controls and perform a similar data analysis procedure. Ideally, it would be 10 males and 10 females in each group. To select the seed regions, I would perform a systemic literature review to identify the regions in past research that associated with depression and anxiety.
Reference:
Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus
Yvette I. Sheline, Joseph L. Price, Zhizi Yan, Mark A. Mintun
Proceedings of the National Academy of Sciences Jun 2010, 107 (24) 11020-11025; DOI: 10.1073/pnas.1000446107
Akiskal, H.S., & Akiskal, K. (1992). Cyclothymic, hyperthymic, and depressive temperaments as subaffective variants of mood disorders. American Psychiatric Press Preview of Psychiatry, 11, 43–62. Retrieved from https://psycnet.apa.org/record/2000-00339-003
Alloy, L. B., Abramson, L. Y., Walshaw, P. D., & Neeren, A. M. (2006). Cognitive Vulnerability to Unipolar and Bipolar Mood Disorders. Journal of Social and Clinical Psychology, 25(7), 726–754. https://doi.org/10.1521/jscp.2006.25.7.726
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Association.
Bandelow, B., & Michaelis, S. (2015). Epidemiology of anxiety disorders in the 21st century. Dialogues in Clinical Neuroscience, 17(3), 327–335. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/26487813
Chae, D. H., Nuru-Jeter, A. M., Lincoln, K. D., & Jacob Arriola, K. R. (2012). Racial Discrimination, Mood Disorders, and Cardiovascular Disease Among Black Americans. Annals of Epidemiology, 22(2), 104–111. https://doi.org/10.1016/J.ANNEPIDEM.2011.10.009
Cicchetti, D., & Sroufe, L. A. (2000). The past as prologue to the future: the times, they’ve been a-changin’. Development and Psychopathology, 12(3), 255–264. https://doi.org/10.1017/S0954579400003011
Craddock, N., & Jones, I. (1999). Genetics of bipolar disorder. Journal of Medical Genetics, 36(8), 585–594. https://doi.org/10.1136/JMG.36.8.585
Duman, R. S., & Monteggia, L. M. (2006). A Neurotrophic Model for Stress-Related Mood Disorders. Biological Psychiatry, 59(12), 1116–1127. https://doi.org/10.1016/J.BIOPSYCH.2006.02.013
Kessler, R. C., Avenevoli, S., & Ries Merikangas, K. (2001). Mood disorders in children and adolescents: an epidemiologic perspective. Biological Psychiatry, 49(12), 1002–1014. https://doi.org/10.1016/S0006-3223(01)01129-5
Larsen, R. J. (2000). Toward a Science of Mood Regulation. Psychological Inquiry, 11(3), 129–141. https://doi.org/10.1207/S15327965PLI1103_01
LeDoux, J. E. (2012). Evolution of human emotion, 431–442. https://doi.org/10.1016/b978-0-444-53860-4.00021-0
Lewis, A. J. (1934). Melancholia: A Historical Review. Journal of Mental Science, 80(328), 1–42. https://doi.org/10.1192/bjp.80.328.1
McNaughton, N., & Corr, P. J. (2009). Central Theories of Motivation and Emotion. Handbook of Neuroscience for the Behavioral Sciences, (1953), 710–730. https://doi.org/10.1002/9780470478509.neubb002036
Patel, V., & Arthur, K. (2003). Poverty and common mental disorders in developing countries. Bulletin of the World Health Organization, 81, 609–615. Retrieved from https://www.scielosp.org/scielo.php?pid=S0042-96862003000800011&script=sci_arttext&tlng=pt
Tully, L. M., Lincoln, S. H., Liyanage-Don, N., & Hooker, C. I. (2014). Impaired cognitive control mediates the relationship between cortical thickness of the superior frontal gyrus and role functioning in schizophrenia. Schizophrenia Research, 152(2–3), 358–364. https://doi.org/10.1016/j.schres.2013.12.005
Villemure, C., & Schweinhardt, P. (2010). Supraspinal pain processing: Distinct roles of emotion and attention. Neuroscientist, 16(3), 276–284. https://doi.org/10.1177/1073858409359200
Waraich, P., Goldner, E. M., Somers, J. M., & Hsu, L. (2004). Prevalence and Incidence Studies of Mood Disorders: A Systematic Review of the Literature. The Canadian Journal of Psychiatry, 49(2), 124–138. https://doi.org/10.1177/070674370404900208
Emotions have been demonstrated to have significant meanings for human being: they help signal danger in the environment (LeDoux, 2012), enhance attention (Villemure & Schweinhardt, 2010), foster social relationships (Tully, Lincoln, Liyanage-Don, & Hooker, 2014), alter behaviors (McNaughton & Corr, 2009) etc. The researcher has been comparing the function of mood to a thermostat which constantly monitors the discrepancy between individuals’ current state (current temperature) and their “desired subjective state” (setpoint) (Larsen, 2000, p. 132). Therefore, having the ability to “sense” the discrepancy and reduce it either actively or passively is critical for mental health.
When emotions don’t work the way they should, people often suffer from a variety of symptoms which researchers, clinicians, and physicians often refer to as affective disorders or mood disorders. Though prevalence rates can be highly variable, general population studies have shown that 1-year prevalence for major depressive disorder, dysthymic disorder, and bipolar I disorder were 4.1 per 100, 2.0 per 100 and 0.72 per 100, respectively (Waraich et al., 2004). Epidemiological studies show that major depression is common among adolescents with 25% prevalence by the end of adolescence (R. Kessler, S. Avenevoli, & K. Ries Merikangas, 2001). For anxiety disorders, up to 33.7% of the population are affected during their lifetime (Bandelow & Michaelis, 2015).
I would like to extend the research paper: Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus by Sheline, Price, Yan, and Mintun published in 2010. Though not specifically stated, the research question of the paper is: what is the intrinsic brain connections in major depression? (Sheline, Prince, Yan, and Mintun, 2010, p11020). In the paper, the authors compared three different brain networks -- the cognitive control network, default mode network, and affective network between depressed subjects and healthy control (Sheline, Prince, Yan, and Mintun, 2010, p11020). They used a neuroimaging technique -- functional MRI (fMRI) to measure the resting state functional connectivity (Sheline, Prince, Yan, and Minttun, 2010, p11020). Participants include 18 individuals with major depression in which 11 are males and 7 are females, and 17 controls in which 5 are males and 12 are females. The data set is the fMRI data of the participants. The authors performed a functional connectivity analysis of resting-state activity (Sheline, Prince, Yan, and Minttun, 2010, p11023). The result shows that depressed subjects have increased connectivity in the bilateral dorsal medial prefrontal cortex region (Sheline, Prince, Yan, and Minttun, 2010, p11020).
I would like to extend this study to include a population with anxiety disorders including General Anxiety Disorders, panic disorder, social anxiety disorder, and specific phobias. The reason why I include this population is that depression has a high comorbidity with anxiety disorders in the clinical and sub-clinical population. There have been efforts made to understand the similarities and differences between depression and anxiety, but the neurocorrelates underlying these two clusters of diagnosis remain unclear. Therefore I would select 20 depression participants, 20 anxiety participants, and 20 healthy controls and perform a similar data analysis procedure. Ideally, it would be 10 males and 10 females in each group. To select the seed regions, I would perform a systemic literature review to identify the regions in past research that associated with depression and anxiety.
Reference: Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus Yvette I. Sheline, Joseph L. Price, Zhizi Yan, Mark A. Mintun Proceedings of the National Academy of Sciences Jun 2010, 107 (24) 11020-11025; DOI: 10.1073/pnas.1000446107
Akiskal, H.S., & Akiskal, K. (1992). Cyclothymic, hyperthymic, and depressive temperaments as subaffective variants of mood disorders. American Psychiatric Press Preview of Psychiatry, 11, 43–62. Retrieved from https://psycnet.apa.org/record/2000-00339-003 Alloy, L. B., Abramson, L. Y., Walshaw, P. D., & Neeren, A. M. (2006). Cognitive Vulnerability to Unipolar and Bipolar Mood Disorders. Journal of Social and Clinical Psychology, 25(7), 726–754. https://doi.org/10.1521/jscp.2006.25.7.726 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Association. Bandelow, B., & Michaelis, S. (2015). Epidemiology of anxiety disorders in the 21st century. Dialogues in Clinical Neuroscience, 17(3), 327–335. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/26487813 Chae, D. H., Nuru-Jeter, A. M., Lincoln, K. D., & Jacob Arriola, K. R. (2012). Racial Discrimination, Mood Disorders, and Cardiovascular Disease Among Black Americans. Annals of Epidemiology, 22(2), 104–111. https://doi.org/10.1016/J.ANNEPIDEM.2011.10.009 Cicchetti, D., & Sroufe, L. A. (2000). The past as prologue to the future: the times, they’ve been a-changin’. Development and Psychopathology, 12(3), 255–264. https://doi.org/10.1017/S0954579400003011 Craddock, N., & Jones, I. (1999). Genetics of bipolar disorder. Journal of Medical Genetics, 36(8), 585–594. https://doi.org/10.1136/JMG.36.8.585 Duman, R. S., & Monteggia, L. M. (2006). A Neurotrophic Model for Stress-Related Mood Disorders. Biological Psychiatry, 59(12), 1116–1127. https://doi.org/10.1016/J.BIOPSYCH.2006.02.013 Kessler, R. C., Avenevoli, S., & Ries Merikangas, K. (2001). Mood disorders in children and adolescents: an epidemiologic perspective. Biological Psychiatry, 49(12), 1002–1014. https://doi.org/10.1016/S0006-3223(01)01129-5 Larsen, R. J. (2000). Toward a Science of Mood Regulation. Psychological Inquiry, 11(3), 129–141. https://doi.org/10.1207/S15327965PLI1103_01 LeDoux, J. E. (2012). Evolution of human emotion, 431–442. https://doi.org/10.1016/b978-0-444-53860-4.00021-0 Lewis, A. J. (1934). Melancholia: A Historical Review. Journal of Mental Science, 80(328), 1–42. https://doi.org/10.1192/bjp.80.328.1 McNaughton, N., & Corr, P. J. (2009). Central Theories of Motivation and Emotion. Handbook of Neuroscience for the Behavioral Sciences, (1953), 710–730. https://doi.org/10.1002/9780470478509.neubb002036 Patel, V., & Arthur, K. (2003). Poverty and common mental disorders in developing countries. Bulletin of the World Health Organization, 81, 609–615. Retrieved from https://www.scielosp.org/scielo.php?pid=S0042-96862003000800011&script=sci_arttext&tlng=pt Tully, L. M., Lincoln, S. H., Liyanage-Don, N., & Hooker, C. I. (2014). Impaired cognitive control mediates the relationship between cortical thickness of the superior frontal gyrus and role functioning in schizophrenia. Schizophrenia Research, 152(2–3), 358–364. https://doi.org/10.1016/j.schres.2013.12.005 Villemure, C., & Schweinhardt, P. (2010). Supraspinal pain processing: Distinct roles of emotion and attention. Neuroscientist, 16(3), 276–284. https://doi.org/10.1177/1073858409359200 Waraich, P., Goldner, E. M., Somers, J. M., & Hsu, L. (2004). Prevalence and Incidence Studies of Mood Disorders: A Systematic Review of the Literature. The Canadian Journal of Psychiatry, 49(2), 124–138. https://doi.org/10.1177/070674370404900208