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Publications

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural Epublish 7 citations

PubMed: 37568209 DOI: 10.1186/s13195-023-01279-6

Network analysis of neuropsychiatric symptoms in Alzheimer's disease.

Alzheimers Res Ther 15(1):135

Authors: Grace J Goodwin, Stacey Moeller, Amy Nguyen, Jeffrey L Cummings, Samantha E John

Affiliations

  • Department of Psychology, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, USA.
  • Department of Brain Health, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, USA.
  • Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, USA.
  • Department of Brain Health, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, USA.

Abstract

BACKGROUND

Neuropsychiatric symptoms due to Alzheimer's disease (AD) and mild cognitive impairment (MCI) can decrease quality of life for patients and increase caregiver burden. Better characterization of neuropsychiatric symptoms and methods of analysis are needed to identify effective treatment targets. The current investigation leveraged the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) to examine the network structure of neuropsychiatric symptoms among symptomatic older adults with cognitive impairment.

METHODS

The network relationships of behavioral symptoms were estimated from Neuropsychiatric Inventory Questionnaire (NPI-Q) data acquired from 12,494 older adults with MCI and AD during their initial visit. Network analysis provides insight into the relationships among sets of symptoms and allows calculation of the strengths of the relationships. Nodes represented individual NPI-Q symptoms and edges represented the pairwise dependency between symptoms. Node centrality was calculated to determine the relative importance of each symptom in the network.

RESULTS

The analysis showed patterns of connectivity among the symptoms of the NPI-Q. The network (M = .28) consisted of mostly positive edges. The strongest edges connected nodes within symptom domain. Disinhibition and agitation/aggression were the most central symptoms in the network. Depression/dysphoria was the most frequently endorsed symptom, but it was not central in the network.

CONCLUSIONS

Neuropsychiatric symptoms in MCI and AD are highly comorbid and mutually reinforcing. The presence of disinhibition and agitation/aggression yielded a higher probability of additional neuropsychiatric symptoms. Interventions targeting these symptoms may lead to greater neuropsychiatric symptom improvement overall. Future work will compare neuropsychiatric symptom networks across dementia etiologies, informant relationships, and ethnic/racial groups, and will explore the utility of network analysis as a means of interrogating treatment effects.

MeSH

Work, Alzheimer Disease, Signs And Symptoms, Aggression, Cognitive Dysfunction

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