Neuropsychiatric symptoms (NPS) affect almost all patients with dementia and are a major focus of study and treatment. Accurate assessment of NPS through valid, sensitive and reliable measures is crucial. Although current NPS measures have many strengths, they also have some limitations (e.g. acquisition of data is limited to informants or caregivers as respondents, limited depth of items specific to moderate dementia). Therefore, we developed a revised version of the NPI, known as the NPI-C. The NPI-C includes expanded domains and items, and a clinician-rating methodology. This study evaluated the reliability and convergent validity of the NPI-C at ten international sites (seven languages). Face validity for 78 new items was obtained through a Delphi panel. A total of 128 dyads (caregivers/patients) from three severity categories of dementia (mild = 58, moderate = 49, severe = 21) were interviewed separately by two trained raters using two rating methods: the original NPI interview and a clinician-rated method. Rater 1 also administered four additional, established measures: the Apathy Evaluation Scale, the Brief Psychiatric Rating Scale, the Cohen-Mansfield Agitation Index, and the Cornell Scale for Depression in Dementia. Intraclass correlations were used to determine inter-rater reliability. Pearson correlations between the four relevant NPI-C domains and their corresponding outside measures were used for convergent validity. Inter-rater reliability was strong for most items. Convergent validity was moderate (apathy and agitation) to strong (hallucinations and delusions; agitation and aberrant vocalization; and depression) for clinician ratings in NPI-C domains. Overall, the NPI-C shows promise as a versatile tool which can accurately measure NPS and which uses a uniform scale system to facilitate data comparisons across studies.
1 December 2007 in Dementia and Geriatric Cognitive Disorders
Background/Aims: The aim of this study was to determine the consistency of neuropsychiatric subsyndromes of the Neuropsychiatric Inventory across several clinical and demographic subgroups (e.g. dementia subtypes, dementia severity, medication use, age and gender) in a large sample of outpatients with dementia. Methods: Cross-sectional data of 2,808 patients with dementia from 12 centres from the European Alzheimer’s Disease Consortium were collected. Principal component analysis was used for factor analysis. Subanalyses were performed for dementia subtypes, dementia severity, medication use, age and gender. Results: The results showed the relatively consistent presence of the 4 neuropsychiatric subsyndromes ‘hyperactivity’, ‘psychosis’, ‘affective symptoms’ and ‘apathy’ across the subanalyses. The factor structure was not dependent on dementia subtypes, age and gender but was dependent on dementia severity and cholinesterase use. The factors hyperactivity and affective symptoms were present in all subanalyses, but the presence of the factors apathy and psychosis was dependent on use of cholinesterase inhibitors and dementia severity, respectively. Conclusion: The present study provided evidence of the relative consistency of neuropsychiatric subsyndromes across dementia subtypes, age and gender, thereby stressing the importance of thinking about neuropsychiatric subsyndromes instead of separate symptoms. However, the subsyndromes apathy and psychosis were dependent on use of cholinesterase inhibitors and dementia severity.
This project aimed to investigate the metabolic basis for resilience to neurodegeneration (cognitive reserve) in highly educated patients with prodromal Alzheimer disease (AD). Methods: Sixty-four patients with amnestic mild cognitive impairment who later converted to AD dementia during follow-up, and 90 controls, underwent brain 18F-FDG PET. Both groups were divided into a poorly educated subgroup (42 controls and 36 prodromal AD patients) and a highly educated subgroup (48 controls and 28 prodromal AD patients). Brain metabolism was first compared between education-matched groups of patients and controls. Then, metabolism was compared between highly and poorly educated prodromal AD patients in both directions to identify regions of high education-related metabolic depression and compensation. The clusters of significant depression and compensation were further used as volumetric regions of interest (ROIs) in a brain interregional correlation analysis in each prodromal AD subgroup to explore metabolic connectivity. All analyses were performed by means of SPM8 (P < 0.001 uncorrected at peak level, P < 0.05 false discovery rate–corrected at cluster level; age, sex, Mini-Mental State Examination score, and center as nuisance). Results: Highly educated prodromal AD patients showed more severe hypometabolism than poorly educated prodromal AD patients in the left inferior and middle temporal gyri and the left middle occipital gyrus (ROI depression). Conversely, they showed relative hypermetabolism in the right inferior, middle, and superior frontal gyri (ROI compensation). The sites of compensation, mainly corresponding to the right dorsolateral prefrontal cortex (DLFC), showed wide metabolic correlations with several cortical areas in both hemispheres (frontotemporal cortex, parahippocampal gyrus, and precuneus) in highly educated prodromal AD patients but not in poorly educated prodromal AD patients. To provide evidence on whether these metabolic correlations represent preservation of the physiologic networks of highly educated control subjects (neural reserve) or rather the recruitment of alternative networks (neural compensation), or a combination of the two, we performed metabolic connectivity analysis of the DLFC in highly educated controls as well. The correlation sites of right DLFC partly overlapped those of highly educated prodromal AD patients but were less extended. Conclusion: The present findings suggest that highly educated prodromal AD patients can cope better with the disease thanks to neural reserve but also to the recruitment of compensatory neural networks in which the right DLFC plays a key role.
29 June 2017 in European Journal of Nuclear Medicine and Molecular Imaging