Machine Learning in Neuroimaging for Diagnosis of Major Depressive Disorder
DOI:
https://doi.org/10.47611/jsrhs.v11i4.3857Keywords:
machine learning, neuroimaging, major depressive disorderAbstract
Is machine learning an effective diagnostic tool to identify the presence of major depressive disorder in a patient? Major depressive disorder (MDD) is a common mood disorder, distinguished by prolong emotional distress and dysfunction in normal activities, yet its current diagnosis still depends on subjective interpretation of clinical interviews and self-reported symptoms. Not only are these prevalent methods susceptible to inconsistent diagnoses, the lack of clear boundaries in present diagnostic criteria to evaluate MDD also further magnifies the need for objective diagnostic tools in clinical environment. One of those methods explored by psychologists is the application of machine learning in neuroimaging techniques. Due to its analytical prowess, machine learning has become popular in psychological research to understand and discover biomarkers in illnesses. This literature review aims to evaluate the effectiveness of different machine learning models and neuroimaging in classifying MDD patients before discussing some of the major setbacks faced by researchers and the suggested solutions for overcoming these barriers to clinical application.
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References or Bibliography
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596
Bromet, E., Andrade, L. H., Hwang, I., Sampson, N. A., Alonso, J., de Girolamo, G., de Graaf, R., Demyttenaere, K., Hu, C., Iwata, N., Karam, A. N., Kaur, J., Kostyuchenko, S., Lépine, J.-P., Levinson, D., Matschinger, H., Mora, M. E., Browne, M. O., Posada-Villa, J., … Kessler, R. C. (2011). Cross-national epidemiology of DSM-IV major depressive episode. BMC Medicine, 9(1). https://doi.org/10.1186/1741-7015-9-90
Vos, T., Barber, R. M., Bell, B., Bertozzi-Villa, A., Biryukov, S., Bolliger, I., Charlson, F., Davis, A., Degenhardt, L., Dicker, D., Duan, L., Erskine, H., Feigin, V. L., Ferrari, A. J., Fitzmaurice, C., Fleming, T., Graetz, N., Guinovart, C., Haagsma, J., … Murray, C. J. L. (2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: A systematic analysis for the global burden of disease study 2013. The Lancet, 386(9995), 743–800. https://doi.org/10.1016/s0140-6736(15)60692-4
Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: review and meta-analysis. The American journal of psychiatry, 157(10), 1552–1562. https://doi.org/10.1176/appi.ajp.157.10.1552
Kendler, K. S., Gatz, M., Gardner, C. O., & Pedersen, N. L. (2006). A Swedish national twin study of lifetime major depression. The American journal of psychiatry, 163(1), 109–114. https://doi.org/10.1176/appi.ajp.163.1.109
Li, M., D'Arcy, C., & Meng, X. (2016). Maltreatment in childhood substantially increases the risk of adult depression and anxiety in prospective cohort studies: systematic review, meta-analysis, and proportional attributable fractions. Psychological medicine, 46(4), 717–730. https://doi.org/10.1017/S0033291715002743
Pandya, M., Altinay, M., Malone, D. A., Jr, & Anand, A. (2012). Where in the brain is depression?. Current psychiatry reports, 14(6), 634–642. https://doi.org/10.1007/s11920-012-0322-7
Smith, K. M., Renshaw, P. F., & Bilello, J. (2013). The diagnosis of depression: current and emerging methods. Comprehensive psychiatry, 54(1), 1–6. https://doi.org/10.1016/j.comppsych.2012.06.006
Uher, R., Payne, J. L., Pavlova, B., & Perlis, R. H. (2013). Major depressive disorder in DSM-5: Implications for clinical practice and research of changes from DSM-IV. Depression and Anxiety, 31(6), 459–471. https://doi.org/10.1002/da.22217
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery & Psychiatry, 23(1), 56–62. https://doi.org/10.1136/jnnp.23.1.56
Kovacs, M., Obrosky, S., & George, C. (2016). The course of major depressive disorder from childhood to young adulthood: Recovery and recurrence in a longitudinal observational study. Journal of affective disorders, 203, 374–381. https://doi.org/10.1016/j.jad.2016.05.042
Cuijpers, P., van Straten, A., Andersson, G., & van Oppen, P. (2008). Psychotherapy for depression in adults: A meta-analysis of comparative outcome studies. Journal of Consulting and Clinical Psychology, 76(6), 909–922. https://doi.org/10.1037/a0013075
Shelton C. I. (2004). Long-term management of major depressive disorder: are differences among antidepressant treatments meaningful?. The Journal of clinical psychiatry, 65 Suppl 17, 29–33.
Dunlop, B. W., & Mayberg, H. S. (2017). Neuroimaging Advances for Depression. Cerebrum: the Dana forum on brain science, 2017, cer-16-17.
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J.-P., Frith, C. D., & Frackowiak, R. S. (1994). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2(4), 189–210. https://doi.org/10.1002/hbm.460020402
Davatzikos, C. (2004). Why voxel-based morphometric analysis should be used with great caution when Characterizing Group differences. NeuroImage, 23(1), 17–20. https://doi.org/10.1016/j.neuroimage.2004.05.010
Yoo, K., Rosenberg, M. D., Noble, S., Scheinost, D., Constable, R. T., & Chun, M. M. (2019). Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. NeuroImage, 197, 212–223. https://doi.org/10.1016/j.neuroimage.2019.04.060
Gao, S., Calhoun, V. D., & Sui, J. (2018). Machine learning in major depression: From classification to treatment outcome prediction. CNS neuroscience & therapeutics, 24(11), 1037–1052. https://doi.org/10.1111/cns.13048
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press.
Wernick, M. N., Yang, Y., Brankov, J. G., Yourganov, G., & Strother, S. C. (2010). Machine Learning in Medical Imaging. IEEE signal processing magazine, 27(4), 25–38. https://doi.org/10.1109/MSP.2010.936730
Lao, Z., Shen, D., Xue, Z., Karacali, B., Resnick, S. M., & Davatzikos, C. (2004). Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage, 21(1), 46–57. https://doi.org/10.1016/j.neuroimage.2003.09.027
Patel, M. J., Khalaf, A., & Aizenstein, H. J. (2016). Studying depression using imaging and Machine Learning Methods. NeuroImage: Clinical, 10, 115–123. https://doi.org/10.1016/j.nicl.2015.11.003
Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
Abou-Warda, H., Belal, N. A., El-Sonbaty, Y., & Darwish, S. (2016). A random forest model for Mental Disorders Diagnostic Systems. Advances in Intelligent Systems and Computing, 670–680. https://doi.org/10.1007/978-3-319-48308-5_64
Cacheda, F., Fernandez, D., Novoa, F. J., & Carneiro, V. (2019). Early detection of depression: Social network analysis and Random Forest Techniques. Journal of Medical Internet Research, 21(6). https://doi.org/10.2196/12554
Velazquez, M., & Lee, Y. (2021). Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects. PLOS ONE, 16(4). https://doi.org/10.1371/journal.pone.0244773
Sarica, A., Cerasa, A., & Quattrone, A. (2017). Random Forest algorithm for the classification of neuroimaging data in Alzheimer's disease: A systematic review. Frontiers in Aging Neuroscience, 9. https://doi.org/10.3389/fnagi.2017.00329
Kamarajan, C., Ardekani, B. A., Pandey, A. K., Kinreich, S., Pandey, G., Chorlian, D. B., Meyers, J. L., Zhang, J., Bermudez, E., Stimus, A. T., & Porjesz, B. (2020). Random Forest classification of alcohol use disorder using fmri functional connectivity, neuropsychological functioning, and impulsivity measures. Brain Sciences, 10(2), 115. https://doi.org/10.3390/brainsci10020115
Wade, B. S., Joshi, S. H., Pirnia, T., Leaver, A. M., Woods, R. P., Thompson, P. M., Espinoza, R., & Narr, K. L. (2015). Random Forest Classification of Depression Status Based On Subcortical Brain Morphometry Following Electroconvulsive Therapy. Proceedings. IEEE International Symposium on Biomedical Imaging, 2015, 92–96. https://doi.org/10.1109/ISBI.2015.7163824
Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep Learning in Medical Imaging: General overview. Korean Journal of Radiology, 18(4), 570. https://doi.org/10.3348/kjr.2017.18.4.570
Uyulan, C., Ergüzel, T. T., Unubol, H., Cebi, M., Sayar, G. H., Nezhad Asad, M., & Tarhan, N. (2020). Major depressive disorder classification based on different convolutional neural network models: Deep Learning Approach. Clinical EEG and Neuroscience, 52(1), 38–51. https://doi.org/10.1177/1550059420916634
Safayari, A., & Bolhasani, H. (2021). Depression diagnosis by deep learning using EEG Signals: A systematic review. Medicine in Novel Technology and Devices, 12, 100102. https://doi.org/10.1016/j.medntd.2021.100102
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The Prisma statement. PLoS Medicine, 6(7). https://doi.org/10.1371/journal.pmed.1000097
Ramasubbu, R., Brown, M. R. G., Cortese, F., Gaxiola, I., Goodyear, B., Greenshaw, A. J., Dursun, S. M., & Greiner, R. (2016). Accuracy of automated classification of major depressive disorder as a function of symptom severity. NeuroImage: Clinical, 12, 320–331. https://doi.org/10.1016/j.nicl.2016.07.012
Khodayari-Rostamabad, A., Reilly, J. P., Hasey, G. M., de Bruin, H., & MacCrimmon, D. J. (2013). A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder. Clinical Neurophysiology, 124(10), 1975–1985. https://doi.org/10.1016/j.clinph.2013.04.010
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