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Behavioral Disorders and Caregivers' Reaction in Taiwanese Patients With Alzheimer's Disease
- Jong-Ling Fuh, Ching-Kuan Liu, Michael S. Mega, Shuu-Jiun Wang, Jeffrey L. Cummings
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- Journal:
- International Psychogeriatrics / Volume 13 / Issue 1 / March 2001
- Published online by Cambridge University Press:
- 10 January 2005, pp. 121-128
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- Article
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Objectives: To evaluate the applicability of the Chinese version of the Neuropsychiatric Inventory Scale (NPI), and to explore the neuropsychiatric manifestations of Taiwanese patients with Alzheimer's disease (AD) and caregiver distress. Method: The Mini-Mental State Examination (MMSE) was administered to 95 patients with AD, and their caregivers were interviewed with the NPI. To assess the test-retest reliability of the Chinese version of the NPI, 86 caregivers underwent a second NPI 3 weeks later. Results: The Cronbach's alpha coefficient of the Chinese version of the NPI was .76. The test-rest reliabilities of frequency, severity, and caregiver burden scores were significantly correlated; overall correlations were .85 for frequency (p < .001), .82 for severity (p < .001), and .79 (p < .001) for distress. Factor analysis was carried out, and three groups, “mood and psychosis,” “psychomotor regulation,” and “social engagement,” were found. Aberrant motor behavior was the most frequently recorded behavior; euphoria was the least. There was no significant correlation between the patient's MMSE and the caregiver distress score, except for aberrant motor activity (r = −.23, p = .03). The symptoms most frequently reported to be severely distressing to caregivers were aberrant motor activity, anxiety, agitation, and delusions. Conclusions: These results indicate that the NPI is a reliable tool to assess behavioral disturbance and caregiver distress in Taiwanese AD patients. These findings also confirm the high prevalence of psychopathology among AD patients and the marked distress produced by many of these behaviors.
8 - Neuroimaging Alzheimer's disease
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- By Arthur W. Toga, Laboratory of Neuro imaging, UCLA School of Medicine, Los Angeles, CA, USA, Michael S. Mega, Laboratory of Neuro imaging, UCLA School of Medicine, Los Angeles, CA, USA, Paul M. Thompson, Laboratory of Neuro imaging, UCLA School of Medicine, Los Angeles, CA, USA
- Edited by Margaret M. Esiri, University of Oxford, Virginia M. -Y. Lee, University of Pennsylvania School of Medicine, John Q. Trojanowski, University of Pennsylvania School of Medicine
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- Book:
- The Neuropathology of Dementia
- Published online:
- 12 October 2009
- Print publication:
- 22 July 2004, pp 128-160
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- Chapter
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Summary
Challenges in population-based brain mapping
Imaging studies of clinical populations continue to uncover new patterns of altered structure and function, and novel algorithms are being applied to relate these patterns to cognitive and genetic parameters. Post-mortem brain maps are also beginning to clarify the molecular substrates of disease.
As imaging studies expand into ever-larger patient populations, population-based brain atlases (Mazziotta et al., 1995; Thompson et al., 2000a,b) offer a powerful framework to synthesize results from disparate imaging studies. These atlases use novel analytical tools to fuse data across subjects, modalities, and time. They detect group-specific features not apparent in individual patients' scans. Once built, these atlases can be stratified into subpopulations to reflect a particular clinical group, such as individuals at genetic risk for AD, patients with mild cognitive impairment (MCI) or different dementia subtypes (frontotemporal dementia/semantic dementia), or patients undergoing different drug treatments. The disease-specific features these atlases resolve can then be linked with demographic factors such as age, gender, handedness, as well as specific clinical or genetic parameters (Mazziotta et al., 1995; Toga & Mazziotta, 1996; Thompson et al., 2001 a–e).
New brain atlases are also being built to incorporate dynamic data (Thompson et al., 2002). Despite the significant challenges in expanding the atlas concept to the time dimension, dynamic brain atlases are beginning to include probabilistic information on growth rates that may assist research into pediatric disorders (Thompson et al., 2000a,b) as well as revealing patterns of degenerative rates in Alzheimer's disease (Fox et al., 1996; Thompson et al., 2001a–e, 2002; Chan et al., 2001). Imaging algorithms are also significantly improving the flexibility of digital brain templates.