Network analysis of neuropsychiatric symptoms in Alzheimer's disease

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.

Keywords: Agitation; Alzheimer’s disease; Dementia; Depression; Disinhibition; MCI; NPI-Q; Network analysis; Neuropsychiatric.

Fig. 1

Participant selection diagram

Fig. 2

Endorsement of NPI-Q Items as present or absent. Note. Percent of sample with presence or absence of individual NPI-Q symptoms based on informant report. Symptom present = 1 or NPI-Q item endorsed. Symptom absent = 0 or NPI-Q item not endorsed. “DEL” = Delusions, “HALL” = hallucinations, “AGIT” = agitation/aggression, “DEPD” = depression/dysphoria, “ANX” = anxiety, “ELAT” = elation/euphoria, “APA” = apathy/indifference, “DISN” = disinhibition, “IRR” = irritability/lability, “MOT” = motor disturbance”, “NITE” nighttime behaviors, “APP” appetite/eating problems

Fig. 3

Network of neuropsychiatric symptoms. Note. The layout of the graph used the Fruchterman-Reingold algorithm. Colors were added manually to highlight statistically derived clusters. Nodes with highest strength centrality and expected influence are outlined in red. “DEL” = Delusions, “HALL” = hallucinations, “AGIT” = agitation/aggression, “DEPD” = depression/dysphoria, “ANX” = anxiety, “ELAT” = elation/euphoria, “APA” = apathy/indifference, “DISN” = disinhibition, “IRR” = irritability/lability, “MOT” = motor disturbance”, “NITE” nighttime behaviors, “APP” appetite/eating problems

Fig. 4

Rank order of node strength and expected influence values. Note. Rank order of node strength (top graph) and expected influence (bottom graph). Nodes are presented in order from highest (top of figure) to lowest strength (bottom of figure). Expected influence values are shown as standardized z-scores