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abstract

The role of depression in mild cognitive impairment through the analysis with artificial neural networks

Published: 01 October 2018 Publication History

Editorial Notes

NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the DATA 2018 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.

Abstract

Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between depression and mild cognitive impairment (MCI) is complex. Several authors have found that depression is associated with a subtype of frontal cognitive impairment of possible vascular cause that mostly do not progress to dementia in contrast with other authors that associate depression in MCI as a risk factor for progression to Alzheimer's Disease. To contribute to the understanding of the evolution and prognosis of these two diseases, this study's primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 74 patients classified into two groups: 33 MCI with depression and 41 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy=97%, sensitivity= 96%, specificity=97%). These results provide data in favor of a cognitive frontal profile of patients associated with depression, distinct and distinguishable from other cognitive impairments. Therefore, it should be considered in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment.

References

[1]
De Assis Faria, C.; Alves, H.V.D.; Barbosa, E.N.B.E.; Charchat-Fichman, H. Cogbnitive deficits in older adults with mild cognitive impairment in a two-year follow-up study. Dement. Neuropsychol. 2018, 12, 19--27.
[2]
Gao, Y.; Huang, C.; Zhao, K.; Ma, L.; Qiu, X.; Zhang, L.; Xiu, Y.; Chen, L.; Lu, W.; Huang, C.; et al. Depression as a risk factor for dementia and mild cognitive impairment: A meta-analysis of longitudinal studies. Int. J. Geriatr. Psychiatry 2013, 28, 441--449.
[3]
Diniz, B.S.; Butters, M.A.; Albert, S.M.; Dew, M.A.; Reynolds, C.F., 3rd. Late-life depression and risk of vascular dementia and Alzheimer's disease: Systematic review and meta-analysis of community-based cohort studies. Br. J. Psychiatry 2013, 202, 329--335.
[4]
Makizako, H.; Shimada, H.; Doi, T.; Tsutsumimoto, K.; Hotta, R.; Nakakubo, S.; Makino, K.; Suzuki, T. Comorbid Mild Cognitive Impairment and Depressive Symptoms Predict Future Dementia in Community Older Adults: A 24-Month Follow-Up Longitudinal Study. J. Alzheimers Dis. 2016, 54, 1473--1482.
[5]
Morimoto, S.S.; Kanellopoulos, D.; Manning, K.J.; Alexopoulos, G.S. Diagnosis and treatment of depression and cognitive impairment in late life. Ann. N. Y. Acad. Sci. 2015, 1345, 36--46.
[6]
Sarica, A.; Cerasa, A.; Quattrone, A.; Calhoun, V. Editorial on special issue: Machine learning on MCI. J. Neurosci. Methods 2018, 302, 1--2.
[7]
Peña-Casanova, J.; Quiñones-Ubeda, S.; Quintana-Aparicio, M.; Aguilar, M.; Badenes, D.; Molinuevo, J.L.; Torner, L.; Robles, A.; Barquero, M.S.; Villanueva, C.; et al. Spanish Multicenter Normative Studies (NEURONORMA Project): Norms for verbal span, visuospatial span, letter and number sequencing, trail making test, and symbol digit modalities test. Arch. Clin. Neuropsychol. 2009, 24, 321--341.
[8]
Appollonio, I.; Leone, M.; Isella, V.; Piamarta, F.; Consoli, T.; Villa, M.L.; Forapani, E.; Russo, A.; Nichelli, P. The Frontal Assessment Battery (FAB): Normative values in an Italian population sample. Neurol. Sci. 2005, 26, 108--116.
[9]
Peña-Casanova, J.; Quiñones-Ubeda, S.; Gramunt-Fombuena, N.; Quintana-Aparicio, M.; Aguilar, M.; Badenes, D.; Cerulla, N.; Molinuevo, J.L.; Ruiz, E.; Robles, A.; et al. Spanish Multicenter Normative Studies (NEURONORMA Project): Norms for verbal fluency tests. Arch. Clin. Neuropsychol. 2009, 24, 395--411.
[10]
Benedet, M.J. TAVEC: Test de Aprendizaje Verbal España-Complutense; TEA: Madrid, Spain, 1998; ISBN 9788471745293.
[11]
Peña-Casanova, J.; Gramunt-Fombuena, N.; Quiñones-Úbeda, S.; Sánchez-Benavides, G.; Aguilar, M.; Badenes, D.; Molinuevo, J.L.; Robles, A.; Barquero, M.S.; Payno, M.; et al. Spanish Multicenter Normative Studies (NEURONORMA Project): Norms for the Rey-Osterrieth Complex Figure (Copy and Memory), and Free and Cued Selective Reminding Test. Arch. Clin. Neuropsychol. 2009, 24, 371--393.
[12]
Peña-Casanova, J.; Guardia, J.; Bertran-Serra, I.; Manero, R.M.; Jarne, A. Versión abreviada del test Barcelona (I): Subtests y perfiles normales. Neurologia 1997, 12, 99--111.
[13]
Calero, M.D.; Arnedo, M.L.; Navarro, E.; Ruiz-Pedrosa, M.; Carnero, C. Usefulness of a 15-item version of the Boston Naming Test in neuropsychological assessment of low-educational elders with dementia. J. Gerontol. B Psychol. Sci. Soc. Sci. 2002, 57, P187--P191.
[14]
Guo, L.; Rivero, D.; Pazos, A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 2010, 193, 156--163.
[15]
Møller, M.F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 1993, 6, 525--533.
[16]
Abdi, H.; Willians, L.J. Tukey's Honestly Significant Difference (HSD) Test. In Encyclopedia of Research Design; Sage: Thousand Oaks, CA, USA, 2010; pp. 1--5.
[17]
Salvadori, E.; Dieci, F.; Caffarra, P.; Pantoni, L. Qualitative Evaluation of the Immediate Copy of the Rey-Osterrieth Complex Figure: Comparison between Vascular and Degenerative MCI Patients. Arch. Clin. Neuropsychol. 2018
[18]
Ramirez-Morales, I.; Fernández-Blanco, E.; Rivero, D.; Pazos, A. Automated early detection of drops in commercial egg production using neural networks. Br. Poult. Sci. 2017, 58, 739--747.
[19]
Petersen, R.C.; Caracciolo, B.; Brayne, C.; Gauthier, S.; Jelic, V.; Fratiglioni, L. Mild cognitive impairment: A concept in evolution. J. Intern. Med. 2014, 275, 214--228.
[20]
Albert, M.S.; DeKosky, S.T.; Dickson, D.; Dubois, B.; Feldman, H.H.; Fox, N.C.; Gamst, A.; Holtzman, D.M.; Jagust, W.J.; Petersen, R.C.; et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011, 7, 270--279.
[21]
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; APA Publishing: Washington, DC, USA, 2013.

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      DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems
      October 2018
      274 pages
      ISBN:9781450365369
      DOI:10.1145/3279996
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 01 October 2018

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      1. artificial neural network (ANN)
      2. depression
      3. mild cognitive impairment (MCI)
      4. neuropsychological test

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