Azari, Nina P. and Pettigrew, Karen D. and Schapiro, Mark B. and Haxby, James V. and Grady, Cheryl L. and Pietrini, Pietro and Salerno, Judith A. and Heston, L. L. and Rapoport, Stanley I. and Horwitz, Barry Early Detection of Alzheimer's Disease: A Statistical Approach Using Positron Emission Tomographic Data. Journal of Cerebral Blood Flow & Metabolism, 13 (3). pp. 438-447. ISSN 0271-678X (1993)
Full text not available from this repository.Abstract
Correlational analysis of regional cerebral glucose metabolism (rCMRglc) obtained by high-resolution positron emission tomography (PET) has demonstrated reduced neocortical rCMRglc interactions in mildly/moderately demented patients with probable Alzheimer's disease (AD). Thus, identification of individual differences in patterns of rCMRglc interactions may be important for the early detection of AD, particularly among individuals at greater risk for developing AD (e.g., those with a family history of AD). Recently, a statistical procedure, using multiple regression and discriminant analysis, was developed to assess individual differences in patterns of rCMRglc interdependencies. We applied this new statistical procedure to resting rCMRglc PET data from mildly/moderately demented patients with probable AD and age/sex-matched controls. The aims of the study were to identify a discriminant function that would (a) distinguish patients from controls and (b) identify an AD pattern in an individual at risk for AD with isolated memory impairment whose initial PET scan showed minor abnormalities, but whose second scan showed parietal hypometabolism, coincident with further cognitive decline. Two discriminant functions, reflecting interactions involving regions most involved in reduced correlations in probable AD, correctly classified 87 of the patients and controls, and successfully identified the first scan of the at-risk individual as AD (probability >0.70). The results suggest that this statistical approach may be useful for the early detection of AD.
| Item Type: | Article |
|---|---|
| Identification Number: | https://doi.org/10.1038/jcbfm.1993.58 |
| Additional Information: | Free fulltext on publisher's site |
| Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
| Research Area: | Computer Science and Applications |
| Depositing User: | Caterina Tangheroni |
| Date Deposited: | 23 Mar 2016 13:56 |
| Last Modified: | 05 Apr 2016 12:29 |
| URI: | http://eprints.imtlucca.it/id/eprint/3332 |
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