INS NYC 2024 Program

Poster	Poster Session 05 Program Schedule 02/15/2024 02:30 pm - 03:45 pm Room: Shubert Complex (Posters 1-60) Poster Session 05: Neuropsychiatry \| Addiction/Dependence \| Stress/Coping \| Emotional/Social Processes Final Abstract #37 Bridging Neuropsychiatric Symptoms and Cognitive Performance in Alzheimer’s Disease: A Network Analysis Grace Goodwin, University of Nevada, Las Vegas, Las Vegas, United States Jeffrey Cummings, Chambers-Grundy Center for Transformative Neuroscience; University of Nevada, Las Vegas, Las Vegas, United States Samantha John, University of Nevada, Las Vegas, Las Vegas, United States Category: Dementia (Alzheimer's Disease) Keyword 1: dementia - Alzheimer's disease Keyword 2: neuropsychiatry Keyword 3: cognitive functioning Objective: Alzheimer’s disease (AD) is characterized by co-occurring cognitive decline and neuropsychiatric symptoms (NPS). However, minimal research identifies which symptoms and deficits interact and influence one another. Network analysis models bi-directional co-occurrence among and between symptom clusters, and network centrality metrics, (i.e., bridge strength) identify symptoms that directly connect symptom clusters. In treatment simulations, removing bridge symptoms prevents overall connectivity, or “contagion”, across clusters. We utilized the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) to identify variables responsible for co-occurrence between NPS severity and neurocognitive performance. Findings advance mechanistic understanding of symptom co-occurrence in AD and suggest novel clinical trial outcomes. Participants and Methods: The identified sample includes symptomatic and cognitively impaired older adults from the NACC UDS (all versions). The Neuropsychiatric Inventory-Questionnaire (NPI-Q) severity scores and neuropsychological summary scores (Animals, Vegetables, Trail Making Test Part A and B, WAIS-R Digit Symbol, Boston Naming Test/Multilingual Naming Test, Logical Memory/Craft Story-21, Digit span/Number Span Test) were analyzed. We identified participants according to: age 50+; cognitive status of mild cognitive impairment (MCI) or dementia; AD as primary or contributing cause of observed impairment; at least one NPI-Q item endorsed; neuropsychological data were valid, and participants were not missing 5 or more items/summary scores. The final sample (n=11,752) consisted of older adults (M_age=73.69, SD_age=9.2; 47.2% male, 52.8% female) who predominantly identified as non-Hispanic white (75.4% NHW, 10.5% non-Hispanic Black, 8% other, 5.8% Hispanic white, .3% Hispanic Black). Most of the sample met criteria for dementia (74.4% dementia, 25.6% MCI) and AD was the presumed primary etiology in 94.3%. The network was estimated from NPI-Q severity scores and neuropsychological summary scores, where nodes represent item or summary scores and edges represent partial correlations between them. Graphical LASSO regularization was used to shrink weak or false positive edges. Nodes were assigned a priori to either the NPS or the cognitive community, and bridge centrality values were calculated for each node. Bridge strength quantifies each node’s inter-community connections and is calculated by taking the sum of the absolute value of all edges that exist between a given node and all other nodes from the opposing community. Edge-weight accuracy, bridge centrality stability, and difference tests were estimated. Results: The network (M=.023) was densely connected within and between communities (Fig 1). Edge weights and bridge strength (CS(cor=.7)=.75) were stable and interpretable. Logical Memory/Craft Story-21, hallucinations, delusions, and apathy/indifference had the highest bridge strength. Among the neurocognitive nodes, Logical Memory/Craft Story-21 had the greatest bridge strength, indicating it had many connections to NPI-Q nodes (negative associations with delusions, apathy/indifference, motor disturbance; positive associations with irritability/lability and nighttime behaviors). Among NPI-Q nodes, hallucinations, delusions, and apathy/indifference had the greatest bridge strength, implying they had many connections to neurocognitive nodes (generally negative). Conclusions: Performance on narrative memory had the greatest influence on NPS, while severity of psychotic symptoms and apathy/indifference had the greatest influence on neurocognitive performance. Interventions targeting these may minimize symptom co-occurrence and overall syndrome burden. Future work will compare network structures between distinct neurodegenerative conditions.

Poster

02/15/2024
02:30 pm - 03:45 pm
Room: Shubert Complex (Posters 1-60)

Poster Session 05: Neuropsychiatry | Addiction/Dependence | Stress/Coping | Emotional/Social Processes

Final Abstract #37

Bridging Neuropsychiatric Symptoms and Cognitive Performance in Alzheimer’s Disease: A Network Analysis

Grace Goodwin, University of Nevada, Las Vegas, Las Vegas, United States
Jeffrey Cummings, Chambers-Grundy Center for Transformative Neuroscience; University of Nevada, Las Vegas, Las Vegas, United States
Samantha John, University of Nevada, Las Vegas, Las Vegas, United States

Category: Dementia (Alzheimer's Disease)

Keyword 1: dementia - Alzheimer's disease
Keyword 2: neuropsychiatry
Keyword 3: cognitive functioning

Objective:

Alzheimer’s disease (AD) is characterized by co-occurring cognitive decline and neuropsychiatric symptoms (NPS). However, minimal research identifies which symptoms and deficits interact and influence one another. Network analysis models bi-directional co-occurrence among and between symptom clusters, and network centrality metrics, (i.e., bridge strength) identify symptoms that directly connect symptom clusters. In treatment simulations, removing bridge symptoms prevents overall connectivity, or “contagion”, across clusters. We utilized the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) to identify variables responsible for co-occurrence between NPS severity and neurocognitive performance. Findings advance mechanistic understanding of symptom co-occurrence in AD and suggest novel clinical trial outcomes.

Participants and Methods:

The identified sample includes symptomatic and cognitively impaired older adults from the NACC UDS (all versions). The Neuropsychiatric Inventory-Questionnaire (NPI-Q) severity scores and neuropsychological summary scores (Animals, Vegetables, Trail Making Test Part A and B, WAIS-R Digit Symbol, Boston Naming Test/Multilingual Naming Test, Logical Memory/Craft Story-21, Digit span/Number Span Test) were analyzed. We identified participants according to: age 50+; cognitive status of mild cognitive impairment (MCI) or dementia; AD as primary or contributing cause of observed impairment; at least one NPI-Q item endorsed; neuropsychological data were valid, and participants were not missing 5 or more items/summary scores. The final sample (n=11,752) consisted of older adults (M_age=73.69, SD_age=9.2; 47.2% male, 52.8% female) who predominantly identified as non-Hispanic white (75.4% NHW, 10.5% non-Hispanic Black, 8% other, 5.8% Hispanic white, .3% Hispanic Black). Most of the sample met criteria for dementia (74.4% dementia, 25.6% MCI) and AD was the presumed primary etiology in 94.3%.

The network was estimated from NPI-Q severity scores and neuropsychological summary scores, where nodes represent item or summary scores and edges represent partial correlations between them. Graphical LASSO regularization was used to shrink weak or false positive edges. Nodes were assigned a priori to either the NPS or the cognitive community, and bridge centrality values were calculated for each node. Bridge strength quantifies each node’s inter-community connections and is calculated by taking the sum of the absolute value of all edges that exist between a given node and all other nodes from the opposing community. Edge-weight accuracy, bridge centrality stability, and difference tests were estimated.

Results:

The network (M=.023) was densely connected within and between communities (Fig 1). Edge weights and bridge strength (CS(cor=.7)=.75) were stable and interpretable. Logical Memory/Craft Story-21, hallucinations, delusions, and apathy/indifference had the highest bridge strength. Among the neurocognitive nodes, Logical Memory/Craft Story-21 had the greatest bridge strength, indicating it had many connections to NPI-Q nodes (negative associations with delusions, apathy/indifference, motor disturbance; positive associations with irritability/lability and nighttime behaviors). Among NPI-Q nodes, hallucinations, delusions, and apathy/indifference had the greatest bridge strength, implying they had many connections to neurocognitive nodes (generally negative).

Conclusions:

Performance on narrative memory had the greatest influence on NPS, while severity of psychotic symptoms and apathy/indifference had the greatest influence on neurocognitive performance. Interventions targeting these may minimize symptom co-occurrence and overall syndrome burden. Future work will compare network structures between distinct neurodegenerative conditions.