CT measurement of suprasellar cistern predicts rate of cognitive decline in Alzheimer's disease.

Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Journal of the International Neuropsychological Society (Impact Factor: 2.96). 03/1996; 2(2):89-95. DOI: 10.1017/S135561770000093X
Source: PubMed


Previous studies reveal significant relationships between some quantitative computed tomography (CT) measures and level of cognitive functioning in patients with Alzheimer's disease (AD). This study was designed to determine whether measurements from CT scans of AD patients could predict future rates of decline in cognitive function. Subjects were 8 men and 19 women diagnosed with probable AD. CT measures included bifrontal ratio, bicaudate ratio, and areas of lateral ventricles, third ventricle, and suprasellar cistern (SSC). Measures of cognitive and adaptive functioning were obtained at the time of the scan and on follow-up. Of the CT measures, the SSCR (SSC corrected for intracranial area) was the most highly correlated with Mini-Mental State Exam (MMSE) score and other cognitive measures at the time of the scan. Follow-up data were obtained for those 20 individuals who were mildly to moderately demented at the time of the scan (MMSE > or = 10). Rate of change was calculated for each neuropsychological measure. The SSCR correlated significantly with rate of change for MMSE and other measures of cognitive and adaptive functioning. This study demonstrates that CT measurement of the SSC can predict the subsequent rate of neurocognitive decline in AD patients.

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    ABSTRACT: Functional imaging of the brain can aid in the diagnosis of Alzheimer’s disease. Tc-HMPAO SPECT is widely available and relatively inexpensive to use. Combined with computer-based analysis of images, SPECT is a powerful tool in detecting decreases in brain perfusion caused by Alzheimer’s disease. However, analysis can falsely elevate the perfusion of normal areas and diminish the perfusion of atrophic areas in the Alzheimer’s brain when used with conventional scaling methods. In this paper, we present a technique for scaling images that overcomes the problems associated with conventional scaling methods. Our technique was successful in eliminating or attenuating the false increases in perfusion shown in probable Alzheimer’s patients in over 90% of cases (n=17), and in enhancing the sensitivity of detection of degenerative changes by Statistical Parametric Mapping.
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