Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-26T03:00:11.214Z Has data issue: false hasContentIssue false
  • English
  • Français

Network analysis of impulse dyscontrol in mild cognitive impairment and subjective cognitive decline

Published online by Cambridge University Press:  15 February 2021

T. Saari Open the ORCID record for T. Saari [Opens in a new window] ,
E. E. Smith  and
Z. Ismail
T. Saari*
Affiliation:
Department of Neurology, University of Eastern Finland, Kuopio, Finland
E. E. Smith
Affiliation:
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
Z. Ismail
Affiliation:
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
*
Correspondence should be addressed to: T. Saari, University of Eastern Finland, Yliopistonranta 1B, FIN-70210Kuopio, Finland. Phone: +358 50 325 9130; Fax: 358 17 163539. Email: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

To investigate conditional dependence relationships of impulse dyscontrol symptoms in mild cognitive impairment (MCI) and subjective cognitive decline (SCD).

Design:

A prospective, observational study.

Participants:

Two hundred and thirty-five patients with MCI (n = 159) or SCD (n = 76) from the Prospective Study for Persons with Memory Symptoms dataset.

Measurements:

Items of the Mild Behavioral Impairment Checklist impulse dyscontrol subscale.

Results:

Stubbornness/rigidity, agitation/aggressiveness, and argumentativeness were frequent and the most central symptoms in the network. Impulsivity, the fourth most central symptom in the network, served as the bridge between these common symptoms and less central and rare symptoms.

Conclusions:

Impulse dyscontrol in at-risk states for dementia is characterized by closely connected symptoms of irritability, agitation, and rigidity. Compulsions and difficulties in regulating rewarding behaviors are relatively isolated symptoms.

Keywords

impulse control disordersmild cognitive impairmentsubjective cognitive declinemild behavioral impairmentneuropsychiatric symptomsnetwork analysis
Type
Original Research Article
Information
International Psychogeriatrics , Volume 34 , Special Issue 6: Issue Theme: New Studies of Mild Cognitive Impairment , June 2022 , pp. 553 - 562
Copyright
© International Psychogeriatric Association 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albert, M. S. et al. (2011). 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. Alzheimer’s & Dementia, 7, 270–279. DOI: 10.1016/j.jalz.2011.03.008.10.1016/j.jalz.2011.03.008CrossRefGoogle Scholar
Allegri, R. F. et al. (2006). Neuropsychiatric symptoms as a predictor of caregiver burden in Alzheimer’s disease. Neuropsychiatric Disease and Treatment, 2, 105–110.Google ScholarPubMed
Bateman, D. R. et al. (2020). Agitation and impulsivity in mid and late life as possible risk markers for incident dementia. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 6, 1–22.Google ScholarPubMed
Borsboom, D. and Cramer, A. O. J. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121. DOI: 10.1146/annurev-clinpsy-050212-185608.10.1146/annurev-clinpsy-050212-185608CrossRefGoogle Scholar
Bozeat, S. (2000). Which neuropsychiatric and behavioural features distinguish frontal and temporal variants of frontotemporal dementia from Alzheimer’s disease? Journal of Neurology, Neurosurgery & Psychiatry, 69, 178–186. DOI: 10.1136/jnnp.69.2.178.10.1136/jnnp.69.2.178CrossRefGoogle ScholarPubMed
Burger, J. et al. (2020). Bereavement or breakup: differences in networks of depression. Journal of Affective Disorders, 267, 1–8. DOI: 10.1016/j.jad.2020.01.157.10.1016/j.jad.2020.01.157CrossRefGoogle ScholarPubMed
Canevelli, M. et al. (2013). Behavioral and psychological subsyndromes in Alzheimer’s disease using the Neuropsychiatric Inventory: behavioral subsyndromes in Alzheimer’s disease. International Journal of Geriatric Psychiatry, 28, 795–803. DOI: 10.1002/gps.3904 10.1002/gps.3904CrossRefGoogle ScholarPubMed
Choi, S. H. et al. (2000). The Korean version of the Neuropsychiatric Inventory: a scoring tool for neuropsychiatric disturbance in dementia patients. Journal of Korean Medical Science, 15, 609–615.10.3346/jkms.2000.15.6.609CrossRefGoogle ScholarPubMed
Cummings, J. et al. (2015). Agitation in cognitive disorders: International Psychogeriatric Association provisional consensus clinical and research definition. International Psychogeriatrics, 27, 7–17. DOI: 10.1017/S1041610214001963.10.1017/S1041610214001963CrossRefGoogle ScholarPubMed
Cummings, J. L. et al. (1994). The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology, 44, 2308–2308. DOI: 10.1212/WNL.44.12.2308.10.1212/WNL.44.12.2308CrossRefGoogle ScholarPubMed
Dalley, J. W., Everitt, B. J. and Robbins, T. W. (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron, 69, 680–694. DOI: 10.1016/j.neuron.2011.01.020.10.1016/j.neuron.2011.01.020CrossRefGoogle ScholarPubMed
de Mendonça, A. et al. (2004). Frontotemporal mild cognitive impairment. Journal of Alzheimer’s Disease, 6, 1–9. DOI: 10.3233/JAD-2004-6101.10.3233/JAD-2004-6101CrossRefGoogle ScholarPubMed
Epskamp, S. et al. (2012). qgraph : network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, DOI: 10.18637/jss.v048.i04.10.18637/jss.v048.i04CrossRefGoogle Scholar
Epskamp, S., Borsboom, D. and Fried, E. I. (2018a). Estimating psychological networks and their accuracy: a tutorial paper. Behavior Research Methods, 50, 195–212. DOI: 10.3758/s13428-017-0862-1.10.3758/s13428-017-0862-1CrossRefGoogle Scholar
Epskamp, S., Borsboom, D. and Fried, E. I. (2018b). Estimating psychological networks and their accuracy: Supplementary Materials.: 8.Google Scholar
Fineberg, N. A. et al. (2014). New developments in human neurocognition: clinical, genetic, and brain imaging correlates of impulsivity and compulsivity. CNS Spectrums, 19, 69–89. DOI: 10.1017/S1092852913000801.10.1017/S1092852913000801CrossRefGoogle ScholarPubMed
Foygel, R. and Drton, M. (2010). Extended Bayesian Information Criteria for Gaussian Graphical Models. arXiv:1011.6640 [math, stat]. Available at: http://arxiv.org/abs/1011.6640 (accessed 2 April 2020).Google Scholar
Fried, E. I. (2020). Lack of theory building and testing impedes progress in the factor and network literature. Psychological Inquiry, 31, 271–288. DOI: 10.1080/1047840X.2020.1853461.10.1080/1047840X.2020.1853461CrossRefGoogle Scholar
Fried, E. I. and Nesse, R. M. (2015). Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Medicine, 13, DOI: 10.1186/s12916-015-0325-4.10.1186/s12916-015-0325-4CrossRefGoogle ScholarPubMed
Fried, E. I. et al. (2018). Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples. Clinical Psychological Science, 6, 335–351. DOI: 10.1177/2167702617745092.10.1177/2167702617745092CrossRefGoogle ScholarPubMed
Fried, E. I. et al. (2019). Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates. Psychological Medicine, 1–9. DOI: 10.1017/S0033291719002770.Google ScholarPubMed
Funkhouser, C. J. et al. (2020). The replicability and generalizability of internalizing symptom networks across five samples. Journal of Abnormal Psychology, 129, 191–203. DOI: 10.1037/abn0000496.10.1037/abn0000496CrossRefGoogle ScholarPubMed
García-Forero, C. et al. (2009). Disentangling impulsiveness, aggressiveness and impulsive aggression: an empirical approach using self-report measures. Psychiatry Research, 168, 40–49. DOI: 10.1016/j.psychres.2008.04.002.10.1016/j.psychres.2008.04.002CrossRefGoogle ScholarPubMed
Gill, S. et al. (2020). Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data. Journal of Alzheimer’s Disease, 75, 277–288. DOI: 10.3233/JAD-191169.10.3233/JAD-191169CrossRefGoogle ScholarPubMed
Griffiths, A. W. et al. (2019). Validation of the Cohen-Mansfield Agitation Inventory Observational (CMAI-O) tool. International Psychogeriatrics, 1–11. DOI: 10.1017/S1041610219000279.Google ScholarPubMed
Haslbeck, J. M. B. and Waldorp, L. J. (2016). mgm: Structure Estimation for Time-Varying Mixed Graphical Models in high-dimensional Data. aRxiv preprint:1510.06871v2. Available at: http://arxiv.org/abs/1510.06871v2.Google Scholar
Haslbeck, J. M. B. and Waldorp, L. J. (2018). How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods, 50, 853–861. DOI: 10.3758/s13428-017-0910-x.10.3758/s13428-017-0910-xCrossRefGoogle ScholarPubMed
Hu, S. et al. (2019). P1-317: Validation of the Mild Behavioral Impairment Checklist (MBI-C) in a clinic-based sample. Alzheimer’s & Dementia, 15, P365–P365. DOI: 10.1016/j.jalz.2019.06.872.10.1016/j.jalz.2019.06.872CrossRefGoogle Scholar
Ismail, Z. et al. (2008). A functional neuroimaging study of appetite loss in Alzheimer’s disease. Journal of the Neurological Sciences, 271, 97–103. DOI: 10.1016/j.jns.2008.03.023.10.1016/j.jns.2008.03.023CrossRefGoogle ScholarPubMed
Ismail, Z. et al. (2016). Neuropsychiatric symptoms as early manifestations of emergent dementia: provisional diagnostic criteria for mild behavioral impairment. Alzheimer’s & Dementia, 12, 195–202. DOI: 10.1016/j.jalz.2015.05.017.10.1016/j.jalz.2015.05.017CrossRefGoogle ScholarPubMed
Ismail, Z. et al. (2017). The Mild Behavioral Impairment Checklist (MBI-C): A Rating Scale for Neuropsychiatric Symptoms in Pre-Dementia Populations. Journal of Alzheimer’s Disease, 56, 929–938. DOI: 10.3233/JAD-160979.10.3233/JAD-160979CrossRefGoogle ScholarPubMed
Ismail, Z. et al. (2021). Mild Behavioral Impairment and Subjective Cognitive Decline predict Mild Cognitive Impairment. Journal of Alzheimer’s Disease, in press.10.3233/JAD-201184CrossRefGoogle Scholar
Krueger, R. F. et al. (2018). Progress in achieving quantitative classification of psychopathology. World Psychiatry, 17, 282–293. DOI: 10.1002/wps.20566.10.1002/wps.20566CrossRefGoogle ScholarPubMed
Lawton, M. P. and Brody, E. M. (1969). Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9: 179–186.10.1093/geront/9.3_Part_1.179CrossRefGoogle ScholarPubMed
Lyketsos, C. G. et al. (2002). Prevalence of neuropsychiatric symptoms in dementia and mild cognitive impairment: results from the cardiovascular health study. JAMA, 288, 1475. DOI: 10.1001/jama.288.12.1475.10.1001/jama.288.12.1475CrossRefGoogle ScholarPubMed
Marin, R. S., Firinciogullari, S. and Biedrzycki, R. C. (1993). The sources of convergence between measures of apathy and depression. Journal of Affective Disorders, 28, 117–124.10.1016/0165-0327(93)90040-QCrossRefGoogle ScholarPubMed
Moeller, F. G. et al. (2001). Psychiatric Aspects of Impulsivity. American Journal of Psychiatry, 158, 1783–1793. DOI: 10.1176/appi.ajp.158.11.1783.10.1176/appi.ajp.158.11.1783CrossRefGoogle ScholarPubMed
Moheb, N. et al. (2019). Repetitive behaviors in frontotemporal dementia: compulsions or impulsions? The Journal of Neuropsychiatry and Clinical Neurosciences, 31, 132–136. DOI: 10.1176/appi.neuropsych.18060148.10.1176/appi.neuropsych.18060148CrossRefGoogle ScholarPubMed
Morris, C. H., Hope, R. A. and Fairburn, C. G. (1989). Eating habits in dementia: a descriptive study. British Journal of Psychiatry, 154, 801–806. DOI: 10.1192/bjp.154.6.801.10.1192/bjp.154.6.801CrossRefGoogle ScholarPubMed
Nasreddine, Z. S. et al. (2005). The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment. Journal of the American Geriatrics Society, 53, 695–699. DOI: 10.1111/j.1532-5415.2005.53221.x.10.1111/j.1532-5415.2005.53221.xCrossRefGoogle ScholarPubMed
Nyatsanza, S. (2003). A study of stereotypic behaviours in Alzheimer’s disease and frontal and temporal variant frontotemporal dementia. Journal of Neurology, Neurosurgery & Psychiatry, 74, 1398–1402. DOI: 10.1136/jnnp.74.10.1398.10.1136/jnnp.74.10.1398CrossRefGoogle ScholarPubMed
Ossenkoppele, R. et al. (2015). The behavioural/dysexecutive variant of Alzheimer’s disease: clinical, neuroimaging and pathological features. Brain, 138, 2732–2749. DOI: 10.1093/brain/awv191.10.1093/brain/awv191CrossRefGoogle ScholarPubMed
Paholpak, P. et al. (2016). Person-based versus generalized impulsivity disinhibition in frontotemporal dementia and Alzheimer disease. Journal of Geriatric Psychiatry and Neurology, 29, 344–351. DOI: 10.1177/0891988716666377.10.1177/0891988716666377CrossRefGoogle ScholarPubMed
Paschali, M. et al. (2018). A systematic evaluation of impulsive–aggressive behavior in psychogeriatric inpatients using the staff observation aggression scale-revision (SOAS-R). International Psychogeriatrics, 30, 61–68. DOI: 10.1017/S1041610217001600.10.1017/S1041610217001600CrossRefGoogle Scholar
R Core Team. (2019). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: https://www.R-project.org/.Google Scholar
Rascovsky, K. et al. (2011). Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain, 134, 2456–2477. DOI: 10.1093/brain/awr179.10.1093/brain/awr179CrossRefGoogle ScholarPubMed
Robbins, T. W. et al. (2012). Neurocognitive endophenotypes of impulsivity and compulsivity: towards dimensional psychiatry. Trends in Cognitive Sciences, 16, 81–91. DOI: 10.1016/j.tics.2011.11.009.10.1016/j.tics.2011.11.009CrossRefGoogle ScholarPubMed
Rosenberg, P. B., Nowrangi, M. A. and Lyketsos, C. G. (2015). Neuropsychiatric symptoms in Alzheimer’s disease: What might be associated brain circuits? Molecular Aspects of Medicine, 43–44, 25–37. DOI: 10.1016/j.mam.2015.05.005.10.1016/j.mam.2015.05.005CrossRefGoogle ScholarPubMed
Ruthirakuhan, M. et al. (2018). Biomarkers of agitation and aggression in Alzheimer’s disease: a systematic review. Alzheimer’s & Dementia, 14, 1344–1376. DOI: 10.1016/j.jalz.2018.04.013.10.1016/j.jalz.2018.04.013CrossRefGoogle ScholarPubMed
Sannemann, L. et al. (2020). Neuropsychiatric symptoms in at-risk groups for AD dementia and their association with worry and AD biomarkers—Results from the DELCODE study. Alzheimer’s Research & Therapy, 12, 131. DOI: 10.1186/s13195-020-00701-7 10.1186/s13195-020-00701-7CrossRefGoogle Scholar
Sano, M. et al. (2018). Identifying better outcome measures to improve treatment of agitation in dementia: a report from the EU/US/CTAD task force. The Journal of Prevention of Alzheimer’s Disease, 5, 98–102.Google ScholarPubMed
Schönbrodt, F. D. and Perugini, M. (2013). At what sample size do correlations stabilize? Journal of Research in Personality, 47, 609–612. DOI: 10.1016/j.jrp.2013.05.009 10.1016/j.jrp.2013.05.009CrossRefGoogle Scholar
Sheikh, F. et al. (2018). Prevalence of mild behavioral impairment in mild cognitive impairment and subjective cognitive decline, and its association with caregiver burden. International Psychogeriatrics, 30, 233–244. DOI: 10.1017/S104161021700151X.10.1017/S104161021700151XCrossRefGoogle ScholarPubMed
Strickland, J. C. and Johnson, M. W. (2020). Rejecting impulsivity as a psychological construct: a theoretical, empirical, and sociocultural argument. Psychological Review. DOI: 10.1037/rev0000263 Google ScholarPubMed
Suhonen, N-M. et al. (2017). The Modified Frontal Behavioral Inventory (FBI-mod) for Patients with Frontotemporal Lobar Degeneration, Alzheimer’s Disease, and Mild Cognitive Impairment. Journal of Alzheimer’s Disease, 56, 1241–1251. DOI: 10.3233/JAD-160983.10.3233/JAD-160983CrossRefGoogle ScholarPubMed
Taragano, F. E. et al. (2009). Mild behavioral impairment and risk of dementia: a prospective cohort study of 358 patients. The Journal of Clinical Psychiatry, 70, 584–592. DOI: 10.4088/JCP.08m04181.10.4088/JCP.08m04181CrossRefGoogle ScholarPubMed
Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.10.1111/j.2517-6161.1996.tb02080.xCrossRefGoogle Scholar
van Bork, R. (2019). Interpreting Psychometric Models. Amsterdam, The Netherlands: University of Amsterdam.10.31237/osf.io/x6a7sCrossRefGoogle Scholar
Welsh, K. A. et al. (1994). The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part V. A normative study of the neuropsychological battery. Neurology, 44, 609–614. DOI: 10.1212/WNL.44.4.609.10.1212/WNL.44.4.609CrossRefGoogle Scholar
Williams, D. R. and Rast, P. (2019). Back to the basics: rethinking partial correlation network methodology. British Journal of Mathematical and Statistical Psychology: bmsp.12173. DOI: 10.1111/bmsp.12173.Google Scholar
Williams, D. R. et al. (2019). On nonregularized estimation of psychological networks. Multivariate Behavioral Research, 54, 719–750. DOI: 10.1080/00273171.2019.1575716.10.1080/00273171.2019.1575716CrossRefGoogle ScholarPubMed
Supplementary material: File

Saari et al. supplementary material

Saari et al. supplementary material

Download Saari et al. supplementary material(File)
File 773.9 KB