Effects of age on volumes of cortex, white matter and subcortical structures
Introduction
Several studies have examined the effects of age on different brain volumes, among them total gray or cortical gray matter [3], [5], [12], [16], [17], [22], [23], [25], [28], [29], [35], [36], white matter [5], [12], [14], [23], [28], [36], hippocampus [17], [21], [22], [35], amygdala [17], [21], thalamus [36], [36], pons [20], [24], [26], [36], caudate nucleus [17], [19], [27], putamen [27], globus pallidus [27], cerebellum [8], [17], [24], [26], [32], and ventricular spaces [27], [21], [28]. While semi-automated techniques for the quantification of global gray and white matter are often used (e.g. [5], [12]), exact measurements of specific subcortical structures are typically obtained by manually tracing their boundaries in MR images. This requires high technical and neuroanatomical skills, and is quite time consuming. Thus, morphometric reports are usually limited to one or a few such structures. Knowledge of the relative age decline of different brain volumes is, therefore, limited by the use of different samples, scanning protocols and volumetric techniques.
The present study utilizes automatic labeling of all of the above mentioned brain structures in an adult lifespan sample. The results of manual labeling using the validated techniques of the Center for Morphometric Analysis [4], [11], [18], [32] are used to automatically extract the information required for automating the segmentation procedure [9]. The effects of age is investigated in, and compared across, cortical gray matter, cerebral white matter, hippocampus, amygdala, thalamus, the accumbens area, caudate, putamen, pallidum, the brainstem, cerebellar cortex, cerebellar white matter, the lateral ventricle, the inferior lateral ventricle, and the 3rd and 4th ventricle. The nature of each measure's relationship with age is investigated with respect to linear, quadratic, and cubic components.
In the following, some previous findings on the presently studied structures’ relationship with age are briefly discussed. Many studies have given valuable information on these topics. However, the present study is targeted at comparison of several structures within the same pool of participants, more than the study of different structures in isolation. Thus, the discussion below is designed to provide a brief background only and is not intended to be exhaustive with respect to previous relevant literature.
There is consensus that gray matter is reduced with age [3], [5], [12], [16], [17], [22], [23], [25], [28], [29], [35], [36], and this reduction seems to start at a very early point in life [5]. On average, there appears to be somewhat greater gray volume loss in the cortex than in subcortical structures [17], and studies of the latter vary in the extent of age differences found. In one of the few studies comparing age effects on a number of different structures, Jernigan et al. [17] found no relationship between age and the volumes of amygdala and thalamus, modest age-related volume loss in the caudate nucleus and nucleus accumbens, and evidence for markedly greater hippocampal reduction, with accelerated loss relative to the cerebral cortex. No age decline was found in the lenticular nucleus. In another study comparing age decline in the caudate, putamen, and pallidum, a decrease of equal magnitude was found for the caudate and putamen cross-sectionally as well as longitudinally, while age decline in the pallidum was only found by longitudinal measurement [27]. Absence of age decline in the thalamus [16], as well as significant age decline in the caudate nucleus (e.g. [16], [19]) and hippocampus (e.g. [21], [22]) have been observed in other samples too. However, there is also conflicting evidence regarding age effects on subcortical structures. For instance, contrary to the above, thalamic age decline has been reported [25], [26], [37], one large-scale study [21] found that the amygdala is significantly reduced with age, and one study with a somewhat younger male sample [35] found no hippocampal age decline. Further, in a very large-scale study [12], preservation relative to other gray matter areas was found in the amygdala and hippocampus. Differences such as these may at least partly be attributed to varying sample characteristics – in particular, whether the full age range is sampled, including persons above 70 years and older.
Reports on the relationship between age and white matter volume seem less consistent than those on gray matter. Some studies have reported no age changes in white matter volume [3], [12], [16], [23], [36], while others have found that total white matter volume [14], [17] is reduced with age. There have also been findings of white matter decline in parts of the brain [12], [25], [30]. As for gray matter structures, samples of varying ages may be a reason for the discrepant findings, and studies including the oldest participants tend to report an age-related decline. One such recent study [5] found white matter decrease only from 70 years of age onwards. This is in accordance with another study [17], in which it was also found that despite its later onset, white matter loss was more rapid, and ultimately exceeded that of gray matter. As for gray matter, results indicate somewhat less age-related loss in deep subcortical regions than in the cerebral lobes [17], and several studies have reported no age reduction of the pons [20], [24], [26], [36].
Cerebellar volume also seems to decline with age (e.g. [17], [24], [26], [34], though discrepant findings have been reported also here [8]. When cerebellar gray and white matter have been measured separately, age effects have been observed only for gray matter in one study [34], but age reductions in both, comparable to the decline found in cerebral gray and white matter, have recently been reported [17].
Age-related volume loss is typically seen in the form of increased CSF spaces, and expansion of ventricular spaces has been found in healthy elderly persons [17], [21], [28]. In a study comparing age effects on cortical and subcortical gray and white matter as well as CSF compartments [17], the greatest age effect was seen in the form of increased cortical sulcal, cerebral ventricular, and cerebellar CSF. No significant difference was observed in rate of change across the different compartments.
Section snippets
Sample
Volunteers were recruited by advertisements placed on campus and in local newspapers. Participants were required to be right-handed, feel well and healthy, have normal or corrected to normal vision, not use a hearing aid, and not suffer from diseases or conditions known to affect central nervous system functioning (e.g. hypothyroidism, multiple sclerosis, Parkinson's disease, stroke, head injury). Those satisfying these criteria were further screened for health problems and cognitive problems
Relationships between age and brain measures
The number of voxels in each raw volume measured is shown in Table 2. The regression equations, F and p values for all the structures predicted by age, as well as the R2 change when age2 was included as a multiple regressor are shown in Table 3. When introducing age3 as a third regressor, additional unique variance (p < .05) was explained in only one structure, the putamen, for which R2 increased from .283 to .333. The regression plots for all gray, subcortical, and white matter volumes are shown
Discussion
The present results indicate significant age differences in all neuroanatomical volumes, with the exception of pallidum, which followed the same trend, but showed marginally non-significant differences. Age differences were also observed in the lateral, inferior lateral, and 3rd ventricle. A similar trend was observed for the 4th ventricle, but this was marginally insignificant. In sum, the data for these 16 brain measures suggest almost globally smaller neuroanatomical volumes and larger CSF
Acknowledgements
Support for this research was provided by the Norwegian Research Council, the Institute of Psychology at the University of Oslo, the National Institutes of Health (R01-NS39581, R01-RR16594, P41-RR14075, and R01-RR13609), the Mental Illness and Neuroscience Discovery (MIND) Institute, and in part by the Biomedical Informatics Research Network Project (BIRN, http://www.nbirn.net), which is funded by the National Center for Research Resources at the National Institutes of Health (NCRR BIRN
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