The Longitudinal Assessment of Neuropsychiatric Symptoms in Mild Cognitive Impairment and Alzheimer’s disease and their Association with White Matter Hyperintensities in the National Alzheimer’s Coordinating Center’s Uniform Data Set
Bibliographic record
Abstract
ABSTRACT Introduction Neuropsychiatric symptoms (NPS) are common in all dementias, including those with Alzheimer’s disease (AD). NPS contribute to patients’ distress, caregiver burden, and can lead to institutionalization. White matter hyperintensities (WMH) are a common finding on MRI usually indicative of cerebrovascular disease and have been associated with certain NPS. The aim of this study was two-fold. Firstly, we assessed the relationship between WMH load and NPS severity in MCI due to AD (MCI-AD) and AD. Secondly, we assessed the ability of WMH to predict the development and progression of NPS in these participants. Data was obtained from the National Alzheimer’s Coordinating Center. Methods WMH were obtained from baseline MRIs and quantified using an automated segmentation technique. NPS were measured using the Neuropsychiatric Inventory (NPI). Mixed effect models and correlations were used to determine the relationship between WMH load and NPS severity scores. Results Cross-sectional analysis showed no significant association between NPS and WMH at baseline. Longitudinal mixed effect models, however, revealed a significant relationship between increase in NPI total scores and baseline WMH load (p=0.014). There was also a significant relationship between increase in irritability severity scores over time and baseline WMH load (p= 0.009). Trends were observed for a relationship between increase in agitation severity scores and baseline WMH load (p=0.058). No other NPS severity scores were significantly associated with baseline WMH load. The correlation plot analysis showed that baseline whole brain WMH predicted change in future NPI total scores (r=0.169, p=0.008). Baseline whole brain WMH also predicted change in future agitation severity scores (r= 0.165, p= 0.009). The temporal lobe WMH (r=0.169, p=0.008) and frontal lobe WMH (r=0.153, p=0.016) contributed most to this this change. Conclusion Irritability and agitation are common NPS and very distressful to patients and caregivers. Our findings of an increase in irritability severity over time as well as higher agitation severity scores at follow-up in participants with MCI-AD and AD with increased WMH loads have important implications for treatment, arguing for aggressive treatment of vascular risk factors in patients with MCI-AD and AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".