Elsevier

NeuroImage

Volume 32, Issue 4, 1 October 2006, Pages 1891-1904
NeuroImage

Aging: Compensation or maturation?

https://doi.org/10.1016/j.neuroimage.2006.05.005Get rights and content

Abstract

Neuroimaging studies of healthy aging often reveal differences in neural activation patterns between young and elderly groups for episodic memory tasks, even though there are no differences in behavioral performance. One explanation typically offered is that the elderly compensate for their memory deficiencies through the recruitment of additional prefrontal regions. The present study of healthy aging compared magnetoencephalographic (MEG) timecourses localized to specific cortical regions in two groups of subjects (20–29 years and ≥65 years) during a visual delayed-match-to-sample (DMS) task. MR morphometrics and neuropsychological test results were also examined with the hope of providing insight into the nature of the age-related differences. The behavioral results indicated no differences in performance between young and elderly groups. Although there was a main effect of age on the latency of the initial peak in primary/secondary visual cortex, these longer latencies were not correlated with the performance of elderly on the DMS task. The lateral occipital gyrus (LOG) revealed qualitatively different patterns of activity for the two age groups corroborated by neuropsychological test results. Morphometric results for the young versus elderly groups revealed less white (WM) and gray matter (GM) volumes in the frontal lobes of the elderly. When a group of middle-aged subjects (33–43 years) was included in the morphometric analyses, the middle-aged subjects revealed statistically greater WM volumes in frontal and parietal cortex suggesting immature WM tracts in the young. Perhaps our elderly utilized a different strategy compared to the young due to the different brain maturation levels of these groups.

Introduction

The study of age-related changes in cognition is challenging since normal aging reflects a delicate balance between basic biological–genetic factors and social–cultural factors (Shimamura et al., 1995, Hedden and Gabrieli, 2004). Physiological viewpoints describe aging as an inevitable loss of tissues and functional reserves (Bellamy, 1997). But these views cannot explain how elder professors, for example, can perform equally well or better than young on the recall of prose passages (Shimamura et al., 1995) or how task instruction itself can abolish differences in memory performance between young and elderly (Rahhal et al., 2001). The preservation of performance in some elderly is often attributed to the use of strategies which compensate for memory decline (Cabeza et al., 2002, Grady et al., 2006). Cabeza et al. (2002), for example, suggest that older adults who perform as well or better than young adults reveal less asymmetric prefrontal cortex activity which may help compensate for age-related neurocognitive decline. This view emphasizes that effective strategies evolve to counteract the “series of deleterious changes” that occur during aging. But, recent anatomical evidence shows that brain maturation (e.g., myelination) also continues throughout adulthood. For example, postmortem studies reveal that myelination in the hippocampus increases into the 5th and 6th decades of life (Benes et al., 1994). Currently, there is no clear description of the underlying mechanisms of strategies which seem to change as a function of aging. The present study attempts to characterize the strategies used by young and elderly groups during a nonverbal working memory task by correlating physiological measures with performance, neuropsychological, and anatomical data and then relates these results to developmental trends in anatomy and cognition.

One critical issue for studies of development and aging is the extent to which the functional neuroanatomy underlying task performance differs between various age groups. Poldrack, 2000, Schlaggar et al., 2002 note that many functional neuroimaging studies do not take into consideration the plastic nature of the nervous system that is altered on the basis of development and experience throughout the life span. For instance, while total brain volume does not change much during adulthood due to the fixed volume of the cranium, dynamic processes associated with maturation and decline of white matter (WM), gray matter (GM), myelination, and synaptogenesis continue throughout the life span. Each of these processes has different rates of maturation and degeneration in different regions of the brain. The gradual maturation of fiber tracts (e.g., increase in diameter and myelination of axons in the fronto-temporal pathway) during late childhood and adolescence, for example, supports the concurrent maturation of motor and speech functions (Paus et al., 1999). Similarly, WM fractional anisotropy (FA), a measure of fiber direction, correlated significantly with reading within a left temporo-parietal region, a region well-associated with the development of fluent reading skill (Beaulieu et al., 2005). These studies suggest that more efficient neural processing, due to more myelination, could be critical for learning and memory (Beaulieu et al., 2005). In general, recent anatomical evidence emphasizes that the timecourse of human brain development is a long process of specialization shaped by postnatal experience. Bartzokis (2002) suggests that the temporal extent of brain development/maturation extends into middle age when maximal WM volume and myelination are reached in frontal lobes and association areas, regions necessary for higher cognitive functions. Therefore, it is important that studies of aging take into consideration healthy maturational changes that occur during adulthood as well as the degenerative changes.

Since effective brain function requires integration of information from segregated brain regions and thus depends on the structural properties of the connecting pathways, including axon diameter and thickness of the insulating myelin sheet, the ability for localized regions to work together as large-scale neural networks is likely to change during learning and development as well (Poldrack, 2000, Ben Bashat et al., 2005). Therefore, differing rates of maturation of brain regions and their connections affect the development of specific skills and consequent strategies adopted. For example, Schlaggar et al. (2002) discuss their visual word processing data by noting that left extrastriate regions become specialized in children as they become literate while maturation of left frontal cortex, believed to be related to the development of control processes, occurs later. In the absence of a mature left frontal region, a child's brain adopts a strategy for processing visual verbal stimuli that includes greater use of left extrastriate cortex while adults utilize both regions. Although the left frontal lobe of children may appear mature in a general sense (i.e., GM volume, it is accessible), the experience of incorporating the processing resources of this region into a strategy for performing visual verbal tasks may be lacking at this age. In essence, the functional neuroanatomy underlying controlled lexical task performance is still developing during early school years, and possibly continues to develop or change as a function of experience into adulthood.

A second critical issue for studies examining age-related changes concerns differences in performance measures (Poldrack, 2000). Most investigators agree that it is good to have similar performance between age groups in order to be certain that differences seen in the functional imaging data are attributable to age differences per se rather than other factors such as increased effort. One remedy offered by Poldrack for performance confounds is to constrain performance to remain roughly constant or equate difficulty between conditions. One approach employs the simplest possible version of the task so both groups can perform at a high level while engaged in the cognitive processes of interest. Indeed, most of the neuroimaging studies of normal aging show differences in neural activation patterns between young and elderly groups (e.g., under-activation, over-activation), when there are no differences in behavioral performance (McIntosh et al., 1999, Grady et al., 2003, Morcom et al., 2003), and the results are interpreted within a compensatory framework (Grady and Craik, 2000, Cabeza et al., 2002, Reuter-Lorenz, 2002). But, as Nielson et al. (2002) point out, what needs to be determined for any compensation view is whether there is direct benefit to the additional activity (i.e., increase in amplitude or recruitment of additional structures). Because a brain region is active during the performance of a task does not demonstrate that the region is necessary for performance of the task (Poldrack, 2000). Therefore, neuroimaging methods should be combined with other techniques in order to shed light on whether a compensation-based strategy best explains the results and/or a maturation-based strategy underlies the development of an effective cognitive strategy. The finding that WM maturation continues until ∼45 years, beyond the typical age of subjects labeled as young (e.g., 20–29 years), implies that additional changes may have occurred between the age groups being compared that are not due solely to brain deterioration (and hence compensation).

Finally, the methods utilized to characterize age-related changes in anatomy and physiology have inherent strengths and limitations. Most neuroimaging studies of aging have been conducted using either fMRI or PET. Issues concerning the use of subtraction techniques and associated concerns surrounding resting baselines (Poldrack, 2000); selection of regions of interest (Aizenstein et al., 2004); and confounds associated with effects of aging on neurovascular coupling have been raised previously (Rosen et al., 2002, D'Esposito et al., 2003, Grady et al., 2003). The present study uses magnetoencephalography (MEG) with anatomical MRI (including morphometry). There are two primary advantages in using MEG methods for studies of aging. First, MEG methods are not dependent upon subtraction techniques to examine age-related changes. Within-subject analyses are conducted first to localize the activity and derive the timecourse information, and then timecourse information can be averaged together across subjects from similar cortical locations in order to make group comparisons for each experimental condition. This permits within-group correlations to be conducted between MEG and performance measures. Second, because MEG methods can isolate events in time, it is easier to individually examine encoding, retention during the delay interval, and recognition memory. In addition, MEG, unlike fMRI and PET methods which integrate activity across seconds of time, can capture the nature of changing activity patterns within specific cortical locations, across time. For example, Cabeza et al., 2004, Grady et al., 2006 found reduced occipital activity with age during visual attention and memory tasks and concluded that reduced sensory processing might be a common cause of age differences in cognition. MEG can separate out activity which is primarily sensory-related versus activity in sensory or other regions that are modulated by attention or become sustained due to working memory task demands (Aine et al., 2003). According to our MEG studies, initial sensory-related activity in auditory and somatosensory cortices (∼40 ms and ∼20 ms, respectively) are enhanced in amplitude for the elderly, not decreased (Aine et al., 2005, Kovacevic et al., 2005, Stephen et al., 2006).

The present study was designed to demonstrate age-related neural changes during an explicit episodic nonverbal working memory task while keeping performance at equally high levels for both age groups by using a simple delayed-match-to-sample (DMS) task. Correlational analyses between behavioral performance measures (percent correct and reaction times) and MEG measures will be conducted to determine whether age-related changes in neural activity are related to performance on the task. If age-related MEG activity is related to task performance, then MR-based morphometric and neuropsychological measures should be helpful in understanding the age-related physiological differences obtained. For example, correlating MEG measures from specific brain regions with neuropsychological and morphometric results will allow us to determine if (1) strategy differences (e.g., a cluster of neuropsychological test results correlate with the MEG measures for one group that is different from a cluster of test results for another group) and/or (2) anatomical differences (e.g., increased WM volumes evident for one group) best characterize the age-related neural changes. If age-related anatomical differences exist that suggest positive maturational changes still occur throughout the life span, then we will conclude that an account of aging based solely on characterizing degenerative changes is inadequate. In anticipation of our results, along with the knowledge that the anatomical literature does reveal continued brain maturation through middle age, we examined the developmental literature to determine the types of strategies that tend to evolve naturally as a consequence of development and attempted to account for our presumed strategy differences via these normal maturational processes.

Section snippets

Materials and methods

Twenty-four healthy normal volunteers from two age groups (20–29, ≥65 years) underwent a screening evaluation including a quantitative neurological examination, the Mini Mental State Exam (MMSE), the Physical Self-Maintenance Scale for activities of daily living, the modified Hachinski Ischemic Scale (HIS), and the Geriatric Depression Scale (GDS), in addition to a battery of neuropsychological tests. Subjects were further tested using the Satz–Mogel version of the Wechsler Adult Intelligence

Results

There was no significant effect of Age or Gender in the percentage of correct responses [young females = 87.8% (SEM = 3.0), elderly females = 88.1% (SEM = 3.8), young males = 87.8% (SEM = 4.3), and elderly males = 85.5% (SEM = 5.1)]. The performance levels indicate that this task was not easy for any group of subjects. Reaction times (RTs) revealed a main effect of Gender (F = 4.74, P = 0.04). Females responded more slowly in this task [young females = 742 ms (SEM = 41.5), elderly

Discussion

Our results indicate: (1) no differences in behavioral performance measures between the young and elderly, but significant differences in the MEG and morphometric measures and their correlations with neuropsychological test results and behavioral performance measures; (2) although there was a main effect of age on the latency of the initial peak in primary/secondary cortex and LOG (Fig. 4, see * for medial occipital, MO, responses), consistent with an interpretation of age-related slowing,

Acknowledgments

This work was supported by NIH R01 AG020302-02, VA MERIT Review, the Department of Energy under Award Number DE-FG02-99ER62764 to The MIND Institute, the Research Service at the New Mexico VA Health Care System, the Radiology Department at UNM SOM, and NIH P20 RR15636-03 (Okada, PI). We wish to express our gratitude to Sam Harris, Maria Ashna, and Rebecca Montaño for their image analysis processing work on the project. We are also grateful for the comments provided by the anonymous reviewers.

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