School of Medicine
58 Treatment Prediction of Depression from a Neuro-Motor Response Perspective
Jasmine Jacobo and Vincent Koppelmans (Psychiatry)
Faculty Mentor: Vincent Koppelmans (Psychiatry, University of Utah)
There are over 350 million people suffering from major depressive disorder (MDD) worldwide. Roughly half of all these patients are resistant to first line antidepressants [2]. While nearly all existing research in MDD has focused on cognitive and emotional domains, the research being conducted at University of Utah research park is investigating depression amongst individuals motor function. With the data collected from the investigation we can find what treatment is best for the individual going further into diagnosis.The lab has hypothesized that depression amongst adults affects their motor skills overtime and thus will lead to better approaches to treatment for mental disorders. Depression is a common and serious medical illness that negatively affects mood, and one’s actions. “Depression causes feelings of sadness and/or a loss of interest in activities you once enjoyed. It can lead to a variety of emotional and physical problems and can decrease your ability to function at work and at home” [4]. The interplay of body and mind seems relevant during the development of cognitive decline and dementia. The measurement of gait speed may improve the detection of prodromal dementia and cognitive impairment in individuals with and without initial cognitive deficits [1]. We propose to build models based on motor composite scores that reflect performance across all motor domains. Based on our pilot data of with MDD and control subjects, we want to detect a significant difference in the following motor measures: 1) grip strength of the dominant and non dominant hand; 2) spiral tracing, 3) 4-meter walk test; and 4) errors made during the walking while-talking test. In order to test whether these variables are significant, we will furthermore analyze if motor behavioral and neural measures measured while depressed patients are in their treatment phase are predictive of time-to-remission using statistical analysis. As for our future research, by integrating such neurobiological measures (MRI), we will be able to gain important insight into MDD etiology and the role of motor dysfunction in MDD [3]. This will also enable us to develop a better understanding as to whether motor dysfunction among various MDD subtypes is transient, permanent, and the degree to which it can be used as a valid biomarker.
References:
[1] Grande G, Triolo F, Nuara A, Welmer AK, Fratiglioni L, Vetrano DL. Measuring gait speed to better identify prodromal dementia. Exp Gerontol. 2019 Sep;124:110625. doi: 10.1016/ j.exger.2019.05.014. Epub 2019 Jun 4. PMID: 31173841
[2] “Major Depression.” National Institute of Mental Health, U.S. Department of Health and Human Services, https://www.nimh.nih.gov/health/statistics/major-depression.
[3] “Depression (Major Depressive Disorder).” Mayo Clinic, Mayo Foundation for Medical Education and Research, 3 Feb. 2018, https://www.mayoclinic.org/diseases-conditions/ depression/diagnosis-treatment/drc-20356013.
[4] American Psychiatric Association. “What Is Depression?” What Is Depression?, https:// www.psychiatry.org/patients-families/depression/what-is-depression.