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Hippocampal volumes are important predictors for memory function in elderly women.

Ystad MA, Lundervold AJ, Wehling E, Espeseth T, Rootwelt H, Westlye LT, Andersson M, Adolfsdottir S, Geitung JT, Fjell AM, Reinvang I, Lundervold A - BMC Med Imaging (2009)

Bottom Line: Hippocampal volumes were found to decrease with age and a right-larger-than-left hippocampal asymmetry was also found.APOE genotype did not alter the model significantly, and age was only partly influencing the results.APOE genotype did not affect the results in any part of our analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedicine, Neuroinformatics and Image Analysis Laboratory, University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway. martin.ystad@biomed.uib.no

ABSTRACT

Background: Normal aging involves a decline in cognitive function that has been shown to correlate with volumetric change in the hippocampus, and with genetic variability in the APOE-gene. In the present study we utilize 3D MR imaging, genetic analysis and assessment of verbal memory function to investigate relationships between these factors in a sample of 170 healthy volunteers (age range 46-77 years).

Methods: Brain morphometric analysis was performed with the automated segmentation work-flow implemented in FreeSurfer. Genetic analysis of the APOE genotype was determined with polymerase chain reaction (PCR) on DNA from whole-blood. All individuals were subjected to extensive neuropsychological testing, including the California Verbal Learning Test-II (CVLT). To obtain robust and easily interpretable relationships between explanatory variables and verbal memory function we applied the recent method of conditional inference trees in addition to scatterplot matrices and simple pairwise linear least-squares regression analysis.

Results: APOE genotype had no significant impact on the CVLT results (scores on long delay free recall, CVLT-LD) or the ICV-normalized hippocampal volumes. Hippocampal volumes were found to decrease with age and a right-larger-than-left hippocampal asymmetry was also found. These findings are in accordance with previous studies. CVLT-LD score was shown to correlate with hippocampal volume. Multivariate conditional inference analysis showed that gender and left hippocampal volume largely dominated predictive values for CVLT-LD scores in our sample. Left hippocampal volume dominated predictive values for females but not for males. APOE genotype did not alter the model significantly, and age was only partly influencing the results.

Conclusion: Gender and left hippocampal volumes are main predictors for verbal memory function in normal aging. APOE genotype did not affect the results in any part of our analysis.

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Related in: MedlinePlus

Scatterplot matrix between all pairs of variables: age (years), CVLT free recall long delay (number of correct items), intracranial volume (ICV) in mm3, hippocampus volume (ICV-normalized), total volume of cerbral cortex (ICV-normalized), and total volume of lateral ventricles (ICV-normalized). On the diagonal panels estimated probability density are given for each variable, together with measurement value for each observation, the latter in the form of vertical lines along the x-axis. The numerical range for each variable is given along both the horizontal and the vertical borders of the matrix plot. The off-diagnal panels allow for each variable to be compared to any other variable, interchanging the ordinate and the abscissa. For each of these bivariate scatterplots a least square linear regression line is fitted. Elliptic data-concentration contours for the fitted bivariate normal distribution are also plotted. For highly correlated data the elliptic shape is elongated, and for uncorrelated data the shape is circular. The contours are plotted at levels 0.5 and 0.9, i.e. 50% of the data is within the inner ellipse, and 90% within the outer one. (The figure was produced by the car package in R.)
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Figure 3: Scatterplot matrix between all pairs of variables: age (years), CVLT free recall long delay (number of correct items), intracranial volume (ICV) in mm3, hippocampus volume (ICV-normalized), total volume of cerbral cortex (ICV-normalized), and total volume of lateral ventricles (ICV-normalized). On the diagonal panels estimated probability density are given for each variable, together with measurement value for each observation, the latter in the form of vertical lines along the x-axis. The numerical range for each variable is given along both the horizontal and the vertical borders of the matrix plot. The off-diagnal panels allow for each variable to be compared to any other variable, interchanging the ordinate and the abscissa. For each of these bivariate scatterplots a least square linear regression line is fitted. Elliptic data-concentration contours for the fitted bivariate normal distribution are also plotted. For highly correlated data the elliptic shape is elongated, and for uncorrelated data the shape is circular. The contours are plotted at levels 0.5 and 0.9, i.e. 50% of the data is within the inner ellipse, and 90% within the outer one. (The figure was produced by the car package in R.)

Mentions: For descriptive analysis we used matrix scatterplots and simple linear regression models with estimation and plotting of the probability distribution of each variable (Figures 3, 4 and 5). The MATLABĀ® scientific programming package was used for this analysis and graphics. To assess possible complex relationships between morphometric, behavioural, and genetic variables we used the novel technique of conditional inference trees as implemented in R (version 2.7.0) software environment for statistical computations and graphics [51]. This particular kind of recursive conditional inference takes into account the distributional properties of the measures. Severeal covariates are included in the model, and one response variable is defined. The conditional inference model states, that if the -hypothesis of there being independence between any of the covariates and the response cannot be rejected, the variable in question is excluded from further exploration. However, when one variable distinguishes itself by having the strongest association with the response, a split is created with two disjoint sets of the variable in question. The Bonferroni adjusted p-value of the split value is calculated. For each such node, the abovementioned procedure is repeated for each condition until none of the covariates can reject the -hypothesis. From this data we calculated a statistical decision tree as shown in Figures 6a and 6b.


Hippocampal volumes are important predictors for memory function in elderly women.

Ystad MA, Lundervold AJ, Wehling E, Espeseth T, Rootwelt H, Westlye LT, Andersson M, Adolfsdottir S, Geitung JT, Fjell AM, Reinvang I, Lundervold A - BMC Med Imaging (2009)

Scatterplot matrix between all pairs of variables: age (years), CVLT free recall long delay (number of correct items), intracranial volume (ICV) in mm3, hippocampus volume (ICV-normalized), total volume of cerbral cortex (ICV-normalized), and total volume of lateral ventricles (ICV-normalized). On the diagonal panels estimated probability density are given for each variable, together with measurement value for each observation, the latter in the form of vertical lines along the x-axis. The numerical range for each variable is given along both the horizontal and the vertical borders of the matrix plot. The off-diagnal panels allow for each variable to be compared to any other variable, interchanging the ordinate and the abscissa. For each of these bivariate scatterplots a least square linear regression line is fitted. Elliptic data-concentration contours for the fitted bivariate normal distribution are also plotted. For highly correlated data the elliptic shape is elongated, and for uncorrelated data the shape is circular. The contours are plotted at levels 0.5 and 0.9, i.e. 50% of the data is within the inner ellipse, and 90% within the outer one. (The figure was produced by the car package in R.)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2743662&req=5

Figure 3: Scatterplot matrix between all pairs of variables: age (years), CVLT free recall long delay (number of correct items), intracranial volume (ICV) in mm3, hippocampus volume (ICV-normalized), total volume of cerbral cortex (ICV-normalized), and total volume of lateral ventricles (ICV-normalized). On the diagonal panels estimated probability density are given for each variable, together with measurement value for each observation, the latter in the form of vertical lines along the x-axis. The numerical range for each variable is given along both the horizontal and the vertical borders of the matrix plot. The off-diagnal panels allow for each variable to be compared to any other variable, interchanging the ordinate and the abscissa. For each of these bivariate scatterplots a least square linear regression line is fitted. Elliptic data-concentration contours for the fitted bivariate normal distribution are also plotted. For highly correlated data the elliptic shape is elongated, and for uncorrelated data the shape is circular. The contours are plotted at levels 0.5 and 0.9, i.e. 50% of the data is within the inner ellipse, and 90% within the outer one. (The figure was produced by the car package in R.)
Mentions: For descriptive analysis we used matrix scatterplots and simple linear regression models with estimation and plotting of the probability distribution of each variable (Figures 3, 4 and 5). The MATLABĀ® scientific programming package was used for this analysis and graphics. To assess possible complex relationships between morphometric, behavioural, and genetic variables we used the novel technique of conditional inference trees as implemented in R (version 2.7.0) software environment for statistical computations and graphics [51]. This particular kind of recursive conditional inference takes into account the distributional properties of the measures. Severeal covariates are included in the model, and one response variable is defined. The conditional inference model states, that if the -hypothesis of there being independence between any of the covariates and the response cannot be rejected, the variable in question is excluded from further exploration. However, when one variable distinguishes itself by having the strongest association with the response, a split is created with two disjoint sets of the variable in question. The Bonferroni adjusted p-value of the split value is calculated. For each such node, the abovementioned procedure is repeated for each condition until none of the covariates can reject the -hypothesis. From this data we calculated a statistical decision tree as shown in Figures 6a and 6b.

Bottom Line: Hippocampal volumes were found to decrease with age and a right-larger-than-left hippocampal asymmetry was also found.APOE genotype did not alter the model significantly, and age was only partly influencing the results.APOE genotype did not affect the results in any part of our analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedicine, Neuroinformatics and Image Analysis Laboratory, University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway. martin.ystad@biomed.uib.no

ABSTRACT

Background: Normal aging involves a decline in cognitive function that has been shown to correlate with volumetric change in the hippocampus, and with genetic variability in the APOE-gene. In the present study we utilize 3D MR imaging, genetic analysis and assessment of verbal memory function to investigate relationships between these factors in a sample of 170 healthy volunteers (age range 46-77 years).

Methods: Brain morphometric analysis was performed with the automated segmentation work-flow implemented in FreeSurfer. Genetic analysis of the APOE genotype was determined with polymerase chain reaction (PCR) on DNA from whole-blood. All individuals were subjected to extensive neuropsychological testing, including the California Verbal Learning Test-II (CVLT). To obtain robust and easily interpretable relationships between explanatory variables and verbal memory function we applied the recent method of conditional inference trees in addition to scatterplot matrices and simple pairwise linear least-squares regression analysis.

Results: APOE genotype had no significant impact on the CVLT results (scores on long delay free recall, CVLT-LD) or the ICV-normalized hippocampal volumes. Hippocampal volumes were found to decrease with age and a right-larger-than-left hippocampal asymmetry was also found. These findings are in accordance with previous studies. CVLT-LD score was shown to correlate with hippocampal volume. Multivariate conditional inference analysis showed that gender and left hippocampal volume largely dominated predictive values for CVLT-LD scores in our sample. Left hippocampal volume dominated predictive values for females but not for males. APOE genotype did not alter the model significantly, and age was only partly influencing the results.

Conclusion: Gender and left hippocampal volumes are main predictors for verbal memory function in normal aging. APOE genotype did not affect the results in any part of our analysis.

Show MeSH
Related in: MedlinePlus