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Healthy Aging

Posted Jun 30, 2012 Updated Apr 10, 2023
By Xiaojing Long PhD , Weiqi Liao MPH , Chunxiang Jiang MS , Dong Liang PhD , Bensheng Qiu PhD , Lijuan Zhang MD
10 min read

Rationale and Objectives

Morphologic changes of the human brain during healthy aging provide useful reference knowledge for age-related brain disorders. The aim of this study was to explore age-related global and regional morphological changes of healthy adult brains.

Materials and Methods

T1-weighted magnetic resonance images covering the entire brain were acquired for 314 subjects. Image processing of registration, segmentation, and surface construction were performed to calculate the volumes of the cerebrum, cerebellum, brain stem, lateral ventricle, and subcortical nuclei, as well as the surface area, mean curvature index, cortical thickness of the cerebral cortex, and subjacent white matter volume using FreeSurfer software. Mean values of each morphologic index were calculated and plotted against age group for sectional analysis. Regression analysis was conducted using SPSS to investigate the age effects on global and regional volumes of human brain.

Results

Overall global and regional volume loss was observed for the entire brain during healthy aging. Moderate atrophy was observed in subcortical gray matter structures, including the thalamus ( R 2 = 0.476, P < .001), nucleus accumbens ( R 2 = 0.525, P < .001), pallidum ( R 2 = 0.461, P < .001), and putamen ( R 2 = 0.533, P < .001). The volume of hippocampus showed a slight increase by 40 years of age, followed by a relatively faster decline after the age of 50 years ( R 2 = 0.486, P < .001). Surface area and mean curvature were less affected by aging relative to cortical thickness and subjacent white matter volume. Significant cortical thinning was mainly found in the parietal ( R 2 = 0.553, P < .001) and insula regions ( R 2 = 0.405, P < .001).

Conclusions

Morphologic alterations of human brain manifested regional heterogeneity in the scenario of general volume loss during healthy aging. The age effect on the hippocampus demonstrated a unique evolution. These findings provide informative reference knowledge that may help in identifying and differentiating pathologic aging and other neurologic disorders.

Delineating changes of normal aging provides useful knowledge about the deviating courses of age-related brain disorders . There is accumulating evidence from magnetic resonance imaging studies showing age-associated brain volume loss as well as ventricular expansion using either manual drawing of regions of interest or automated or semiautomated whole-brain analysis, such as voxel-based morphometry . These techniques also detected nonuniform regional aging patterns across the entire brain . Frontal and temporal cortices have been shown to be the most affected with advancing age, while the primary sensory (especially visual) cortices were observed to be largely preserved . Frontal volume decrease is consistent with evidence of age-related decline in cognitive function, such as working memory, cognitive control, and processing speed . Studies on medial temporal lobe which plays an important role in episodic memory, have shown that atrophy of this region predicts future memory decline in healthy aging .

Age-associated white matter loss was also reported to be related with myelin degeneration . Guttmann et al found that significant reduction in cortical white matter occurs across the adult life span, whereas pronounced decrease in total gray matter is absent. Other investigators have demonstrated a greater longitudinal rate of reduction for white matter than for gray matter . Brickman et al observed different regional shrinkage from young to old age, whereby variations in white matter of the frontal lobe were of the largest magnitude, followed by those of the temporal lobe.

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Materials and methods

Subjects

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Figure 1, Representative axial T1-weighted magnetic resonance images of subjects aged 24 (i), 46 (ii), 64 (iii), and 86 (iv) years. Volume loss of brain tissue and enlargement of the cerebral spinal fluid space become prominent as aging progresses.

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Image Processing

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Figure 2, Image processing. (a) The entire brain was divided into cerebrum, cerebellum, and brain stem. The cerebrum was further parcellated into the cerebral cortex, white matter, ventricles, and subcortical nuclei. (b) Surface construction. (Upper right) The gray matter (GM)–cerebrospinal fluid (CSF) interface (pial surface) of the left hemisphere. (Bottom right) The GM–white matter (WM) interface (white surface) of the left hemisphere. (c) Gyrus-based cortical parcellation. (Left) Lateral view of parcellated left hemisphere. (Right) Medial view of parcellated left hemisphere. A, anterior; L, left; P, posterior; R, right.

Figure 3, Variate calculation. (a) Calculation of surface area. As the surfaces were tessellated with triangles, the surface area of a region of interest (ROI) was measured as the sum of the areas of all triangles belonging to this region. (b) Calculation of cortical thickness. Thickness was calculated as the mean distance between the pial surface and the white surface. The corresponding triangle vertexes on the pial surface and the white surface have the same thickness value. (c) Calculation of curvature index. Circles were drawn tangent to the surface at each triangle vertex. The curvature index on each vertex was estimated as the inverse of the radius of the circle. (d) The calculation of subjacent white matter (WM) volume. The volume of subjacent WM was computed by multiplying the size of voxel and the number of voxels in the corresponding region.

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Statistical Analysis

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Results

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Table 1

Summary of Demographic Information of the Subjects

Age Group (y) Count (Men/Women) Mean Age (Range) (y) ≤29 136 (60/76) 22.24 (18–29) 30–39 16 (11/5) 33.38 (30–39) 40–49 31 (10/21) 45.58 (40–49) 50–59 33 (11/22) 54.36 (50–59) 60–69 25 (7/18) 64.88 (60–69) 70–79 36 (10/26) 73.50 (70–78) 80–89 29 (7/22) 84.07 (80–89) ≥90 8 (1/7) 91.00 (90–94)

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Figure 4, Overall volume loss during aging was observed in the entire brain, with an increase of cerebrospinal fluid partition in the lateral ventricles.

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Figure 5, Age effect on volume changes of subcortical nuclei. An overall nonuniform age-related volume loss was observed, with moderate reduction in the thalamus, hippocampus, and putamen and mild decline in the nucleus accumbens, caudate, amygdala, and pallidum.

Figure 6, Data fitted to the quadratic model showed that volume loss of subcortical gray matter was significantly age dependent ( P < .001). The volume of the hippocampus did not change or slightly increased before 50 years of age, followed by a decline later in life.

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Figure 7, Regional cortical thickness declined heterogeneously from 20 to 90 years of age, with a faster drop in the parietal and insula regions and a slower decrease in the cingulate and temporal lobe.

Figure 8, Quadratic fitting model manifested the best-fitting robustness and showed that the volume declines of lobar cortices were significantly age dependent.

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Figure 9, Surface area changes were mild in all cortical regions. The regression analysis (not shown) confirmed this observation (maximum R 2 = 0.267).

Figure 10, Lobar volume loss of cortical white matter was mild in all regions, confirmed by regression analysis (not shown; maximum R 2 = 0.25).

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Discussion

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Conclusions

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Academic Radiology, Volume 19, Issue 7
Journals General Radiology
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