Data Modeling
Mathematical and Computational Data Modeling
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Volume 1 Number 1 2020
RESEARCH ARTICLE (Open Access)
Brain-Tech Fusion: A Survey-Based Assessment of Depression Severity and the Case for Integrating Neuromodulatory Interventions in Treatment-Resistant Care
Kamruzzaman Mithu 1*, Khandahar A. Mamun 1
Data Modeling 1 (1) 1-9 https://doi.org/10.25163/data.1110844
Submitted: 16 April 2020 Revised: 03 June 2020 Accepted: 12 June 2020 Published: 14 June 2020
Abstract
Depression remains one of the more stubborn contributors to global disease burden, and its severity — not just its presence — often determines whether standard care is enough. This study set out, somewhat modestly, to estimate the distribution of depressive symptom severity within a community sample and to explore whether a subset of respondents showed signs consistent with progression toward schizophrenia-spectrum presentations. A 29-item screening survey was administered to approximately 300 respondents, with a separate online survey examining depression-to-psychosis transition risk. Severity scores were used to classify respondents into normal, moderate, and severe categories. Of the sample, 6.3% met criteria for severe depression, a subgroup at elevated risk for both suicidality and psychotic progression; a related subset also showed patterns consistent with this progression. These findings, while descriptive rather than diagnostic, echo established evidence that treatment-resistant depression often requires escalating intervention beyond first-line medication, and they lend renewed relevance to brain-based options — electroconvulsive therapy, transcranial magnetic stimulation, and, in rare cases, neurosurgical approaches — each carrying its own balance of efficacy and risk. Taken together, the results suggest that even a modest screening effort can surface a clinically meaningful subgroup, one that stands to benefit from earlier identification and a clearer pathway toward the fuller spectrum of available treatments.
Keywords: Depression severity; Treatment-resistant depression; Neuromodulation; Electroconvulsive therapy; Schizophrenia-spectrum progression
References
Ferrari, A. J., Somerville, A. J., Baxter, A. J., et al. (2013). Global variation in the prevalence and incidence of major depressive disorder: A systematic review of the epidemiological literature. Psychological Medicine, 43(3), 471–481. https://doi.org/10.1017/S0033291712001511
Kessler, R. C., Chiu, W. T., Demler, O., Merikangas, K. R., & Walters, E. E. (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6), 617–627. https://doi.org/10.1001/archpsyc.62.6.617
World Health Organization. (2008). The global burden of disease: 2004 update. World Health Organization.
Murray, C. J. L., Vos, T., Lozano, R., et al. (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2197–2223. https://doi.org/10.1016/S0140-6736(12)61689-4
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., et al. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: A STAR*D report. American Journal of Psychiatry, 163(11), 1905–1917. https://doi.org/10.1176/ajp.2006.163.11.1905
Kho, K. H., van Vreeswijk, M. F., Simpson, S., & Zwinderman, A. H. (2003). A meta-analysis of electroconvulsive therapy efficacy in depression. Journal of ECT, 19(3), 139–147. https://doi.org/10.1097/00124509-200309000-00005
Prudic, J., Sackeim, H. A., & Devanand, D. P. (1990). Medication resistance and clinical response to electroconvulsive therapy. Psychiatry Research, 31(3), 287–296. https://doi.org/10.1016/0165-1781(90)90098-P
Devanand, D. P., Sackeim, H. A., & Prudic, J. (1991). Electroconvulsive therapy in the treatment-resistant patient. Psychiatric Clinics of North America, 14(4), 905–923.
Sackeim, H. A., Prudic, J., Fuller, R., Keilp, J., Lavori, P. W., & Olfson, M. (2007). The cognitive effects of electroconvulsive therapy in community settings. Neuropsychopharmacology, 32(1), 244–254. https://doi.org/10.1038/sj.npp.1301180
Sackeim, H. A. (2000). Memory and ECT: From polarization to reconciliation. Journal of ECT, 16(2), 87–96. https://doi.org/10.1097/00124509-200006000-00001
Donahue, A. B. (2000). Electroconvulsive therapy and memory loss: A personal journey. Journal of ECT, 16(2), 133–143. https://doi.org/10.1097/00124509-200006000-00005
Zervas, I. M., Calev, A., Jandorf, L., et al. (1993). Age-dependent effects of electroconvulsive therapy on memory. Convulsive Therapy, 9(1), 39–42.
Tokutsu, Y., Umene-Nakano, W., Shinkai, T., et al. (2013). Follow-up study on electroconvulsive therapy in treatment-resistant depressed patients after remission: A chart review. Clinical Psychopharmacology and Neuroscience, 11(1), 34–38. https://doi.org/10.9758/cpn.2013.11.1.34
Nordenskjöld, A., von Knorring, L., & Engström, I. (2011). Predictors of time to relapse/recurrence after electroconvulsive therapy in patients with major depressive disorder: A population-based cohort study. Depression Research and Treatment, 2011, 470985. https://doi.org/10.1155/2011/470985
Patel, S. R., Aronson, J. P., Sheth, S. A., & Eskandar, E. N. (2013). Lesion procedures in psychiatric neurosurgery. World Neurosurgery, 80(3–4 Suppl.), S3.e9–S3.e16. https://doi.org/10.1016/j.wneu.2012.11.038
Dashti, S. R., Baharvahdat, H., Spetzler, R. F., et al. (2008). Operative intracranial infection following craniotomy. Neurosurgical Focus, 24(6), E10. https://doi.org/10.3171/FOC/2008/24/6/E10
Ochsner, K. N., Kosslyn, S. M., Cosgrove, G. R., et al. (2001). Deficits in visual cognition and attention following bilateral anterior cingulotomy. Neuropsychologia, 39(3), 219–230. https://doi.org/10.1016/S0028-3932(00)00114-7
Rossi, S., Hallett, M., Rossini, P. M., & Pascual-Leone, A. (2009). Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clinical Neurophysiology, 120(12), 2008–2039. https://doi.org/10.1016/j.clinph.2009.08.016
Medical devices; neurological devices; classification of repetitive transcranial magnetic stimulation system. Final rule. (2011). Federal Register, 76(145), 44489–44491.
Xie, J., Chen, J., & Wei, Q. (2013). Repetitive transcranial magnetic stimulation versus electroconvulsive therapy for major depression: A meta-analysis of stimulus parameter effects. Neurological Research, 35(10), 1084–1091. https://doi.org/10.1179/1743132813Y.0000000245
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