Speed of processing and executive functioning
As discussed previously, some indication of a relation between white matter integrity and cognition derives from research on ischemic lesions of white matter, which have demonstrated that increasing WMH volume is associated with decreasing performance on tests of elementary perceptual speed (Rabbitt et al., 2007
; Raz et al., 2007
; D. M. van den Heuvel et al., 2006
). One general principle that emerges from DTI studies is that, independently of age effects, decreasing integrity of normal appearing white matter is associated with worse performance on tests that rely on processing speed and executive functioning. This relation has been confirmed with both voxelwise (Turken et al., 2008
), and tractography-based (Correia et al., 2008
) DTI techniques. The relation between white matter integrity and speed also extends to tasks such as lexical decision (word/nonword discrimination), which rely on the retrieval of semantic memory information (Gold et al., 2007
Although most measures of processing speed are defined on the basis of manual reaction times and thus have a significant motor component, the white matter-speed relation appears to hold for information processing stages that precede the motor response. Gold et al. (2007)
found that, in younger adults, decreased white matter integrity (FA) in language regions of the left hemisphere (inferior frontal and inferior parietal) was correlated with slower lexical decisions, but that a corresponding effect was not evident in regions mediating visual/motor processing (optic radiation and posterior limb of the internal capsule, bilaterally). Similarly, data from combined imaging modalities of DTI and magnetoencephalography (MEG) suggest that the correlation between white matter integrity and the latency of peak visual responses during an eye movement task, in younger adults, holds at an early stage of information processing, within the first 120 ms of saccade initiation (Stufflebeam et al., 2008
Vernooij et al. (2009)
provided a particularly strong source of support for the relation between white matter integrity and processing speed in a population-based sample of 860 older adults 61-92 years (Rotterdam Study). These authors conducted regression analyses of whole-brain DTI variables and cognitive performance that statistically controlled the relative volume of both normal appearing white matter (i.e., atrophy effects) and white matter lesions. A variety of cognitive measures yielded composite measures of information processing speed, motor speed, memory, and executive functioning (e.g., response fluency and inhibition). Declining FA of normal appearing white matter was related significantly to declines in both motor speed and processing speed, but only the latter relation held for mean FA when lesion volume was controlled. In addition, both axial and radial diffusivity exhibited more widespread relation, than mean FA, to the cognitive measures. Both diffusivity measures were correlated significantly with information processing speed and executive functioning, with lesion volume controlled (i.e., higher diffusivity associated with worse performance). None of the DTI variables was related to memory performance. Vernooij et al. also used age as a covariate in their analyses, however, and thus the observed correlations between white matter integrity and processing speed were independent of age-related variability in processing speed.
Aging, white matter integrity, and cognition: Interpretive issues
The DTI studies reviewed to this point lead to two conclusions. First, age-related differences occur both in the overall measures of white matter integrity (e.g., FA) and in component measures (e.g., axial and radial diffusivity), especially in prefrontal regions. Second, independently of age, variation in white matter integrity is correlated with cognitive performance, particularly in tests relying on speed of information processing and executive functioning. An independent question is whether there are interactive effects in the aging-white matter and cognition-white matter trends. Although individual research studies bearing on this question have been accumulating rapidly, several issues should be considered in interpreting the results.
The most fundamental question to be addressed is the causal role of white matter integrity in age-related differences in cognitive performance. Specifically, to what extent is age-related variability in cognitive performance shared with age-related differences in white matter integrity? To date, however, few studies have conceptually framed the research in this manner. Individual studies have addressed a variety of different, though related, issues that do not always map directly onto this fundamental question. For example, the demonstration of a correlation between white matter integrity and some cognitive measure, for older adults, does not necessarily imply that white matter integrity is associated with the age-related variance in the cognitive measure. Similarly, if age groups are analyzed separately, and a correlation between white matter integrity and cognition is statistically significant for older adults but not for younger adults, this pattern, by itself, does not necessarily imply that the correlation differs significantly between the age groups. The correlation may be slightly above threshold for older adults, but slightly below threshold for younger adults. A direct comparison of the age groups, or some test of the age-related difference in the relation between the DTI and cognitive measures, is necessary.
In addition, although both the cognitive and DTI measures may vary with age, this pattern is not sufficient to infer that white matter integrity has a direct influence on age-related effects in cognitive performance. To infer a mediating or causal role, the DTI measures should have a significant relation to the measures of cognition that is independent of age, and the age-related variance in the cognitive measure should be attenuated by including the DTI measures in the regression model predicting cognition from age (Baron & Kenny, 1986
; Lindenberger & Pötter, 1998
; Salthouse, 1992
Related to these methodological concerns is the issue of identifying specific cognitive effects in aging. Although the general trend of DTI studies suggests that the pronounced influence of white matter integrity is related to processing speed and executive functioning (Correia et al., 2008
; Stufflebeam et al., 2008
; Turken et al., 2008
; Vernooij et al., 2009
), it is not clear whether age-related differences in these aspects of cognitive functioning are entirely separable at the behavioral level. Some behavioral studies suggest that age-related differences in executive functioning are empirically separable from speed-dependent differences (Keys & White, 2000
; Rodriguez-Aranda & Sundet, 2006
), and the anterior-posterior gradient that has been noted in DTI studies of aging and white matter integrity (Davis et al., 2009
; Madden et al., 2009
; Sullivan & Pfefferbaum, 2006
) is consistent with the assumption of an identifiable role of executive functioning. Elementary measures of information processing speed, however, share age-related variance with a wide variety of cognitive tasks (Madden, 2001
; Salthouse, 1996
; Salthouse & Madden, 2007
), including executive functioning (Salthouse et al., 2003
), and neuropsychological tests that are assumed to measure frontal lobe functioning correlate highly with non-frontal tests (Salthouse et al., 1996
). Thus, interpreting age-related variation in white matter integrity in terms of the anterior-posterior gradient and executive functioning may be a useful starting point for DTI research but ultimately is not likely to account for all of the findings (Bennett et al., in 2009
; Greenwood, 2000
; Kennedy & Raz, 2009
; Madden et al., 2009
Finally, the potential role of health-related variables, in the interaction of age-related change in the brain and cognition, is not well understood. As noted previously, the relation of DTI measures to cognitive performance is influenced by the presence of WMH (Vernooij et al., 2009
), and WMH tend to increase as a function of both age and cardiovascular risk (e.g., hypertension). Thus, variation in DTI measures of white matter integrity, across age groups, is likely influenced by WMH and cardiovascular health.
Nitkunan et al. (2008)
, for example, examined the relation between white matter integrity measures from DTI and brain biochemistry from magnetic resonance spectroscopy (MRS), within a single ROI (white matter of the centrum semiovale). These authors examined cerebrovascular status by comparing three groups of older adults: individuals with cerebral small vessel disease (SVD), hypertensive individuals without a history of stroke, and normotensive controls. Within each group, the DTI measures correlated significantly with MRS measures indicating axonal loss/dysfunction, and the magnitude of the correlations was generally graded with the severity of cerebrovascular disease, with the hypertensive group intermediate between the normotensive and SVD groups. The Nitkunan et al. analyses, however, did not focus specifically on the age-related variation in white matter integrity; age-related effects were covaried. Vernooij et al. (2008)
, examined the age-related effects and found that, in their Rotterdam Study participants, few age-related differences in FA remained significant once white matter atrophy and WMH lesion load were controlled statistically. Vernooij et al. concluded that age-related differences in white matter integrity reflect a pathophysiologic process rather than aging per se.
Alternatively, cardiovascular status and WMH volume may alter the normal relation between adult age and white matter integrity rather than entirely subsuming the relation between age and white matter integrity. Correia et al. (2008)
derived several quantitative metrics of white matter integrity, based on deterministic tractography, and compared individuals with known vascular white matter injury (40-79 years) to a control group of demographically similar, healthy individuals (44-84 years). Metrics calculated from whole-brain data indicated significantly lower white matter integrity for the vascular group than for the controls. In addition, the metrics exhibited significant age-related decline for the healthy group but not for the vascular group, suggesting that subcortical vascular disease disrupted the normal variation in white matter integrity with age.
Aging, white matter integrity, and cognition: Empirical findings
The results of DTI studies, regarding cognitive correlates of age-related differences in white matter integrity, have yielded a complex pattern. But the data generally incorporate the trends that we have noted for 1) the relation between aging and white matter (anterior-posterior gradient); and 2) the relation between cognition and white matter (correlation with speed and executive functioning).
O'Sullivan et al. (2001)
reported one of the initial correlations between DTI and cognitive data for older adults. These authors used relatively broad ROIs (whole-brain white matter divided into anterior, middle, and posterior regions) and correlated mean diffusivity and FA with several psychometric tests of cognitive function. Correlations performed within the older adult group indicated that mean diffusivity in the anterior ROI was correlated with a neuropsychological measure of executive functioning (Trail Making), whereas FA in the middle region was correlated with verbal fluency. These analyses, however, were conducted only within the older group, and the effect of age was covaried. O'Sullivan et al. suggested that the age-related decline in white matter integrity would lead to a disconnection of neural networks necessary for efficient cognitive performance. This concept of disconnection has been discussed widely as an explanatory construct for age-related cognitive decline, although whether the relevant neurobiological mechanism is demyelination, axonal loss, macrostructural organization, or a combination of these, is being investigated currently (Andrews-Hanna et al., 2007
; Bartzokis, 2004
; Bartzokis et al., 2004
; Charlton et al., 2006
; Charlton et al., 2008
; Damoiseaux, et al., 2009
Contemporaneously with the O'Sullivan et al. (2001)
report, Sullivan et al. (2001)
confirmed that white matter integrity was correlated with a behavioral measure, for 51 participants across a wide range of 23-79 years. A sensory/motor task requiring interhemispheric transfer (alternating finger tapping) correlated positively with mean FA, whereas a baseline version of the task (unimanual finger tapping) did not. Two aspects of the Sullivan et al. findings are particularly relevant: First, the positive correlation between FA and alternating finger tapping held within posterior ROIs (splenium and parietal pericallosal), whereas the age-related decline in mean FA was greater for more anterior regions. Second, in a regression model containing both age and FA, from the posterior ROIs, predicting finger tapping output, FA remained a significant predictor whereas age did not. This finding suggests that white matter integrity may have a more direct influence than age on this form of sensory/motor performance.
Madden et al. (2004)
reported a similar pattern in an ROI analysis of 16 younger and 16 older adults. Mean reaction time in a choice response task (visual oddball) correlated with splenium FA for younger adults, but with FA within the anterior limb of the internal capsule for older adults. Rather than comparing the effectiveness of age and FA as predictors, Madden et al. took the approach of testing the age group difference in the correlation between mean reaction time and FA. This comparison, illustrated in , was significant, which provided the first direct test of an age-related difference in the correlation between white matter integrity and a behavioral measure of cognitive performance. In addition, as in the Sullivan et al. (2001)
study, those regions exhibiting significant correlations with behavioral measures were not those exhibiting the most pronounced age-related decline in mean FA.
Figure 5 Relation between choice reaction time and FA, for younger and older adults. ALC = anterior limb of internal capsule. Figure modified from Madden et al. (2004). © Neuroimage and Elsevier, 2004.
Several subsequent studies have also focused specifically on age-related differences in the relation between white matter integrity and cognition. In their tractography study of younger and older adults, Davis et al. (2009)
used a regression model that included the interaction between age group and FA as a predictor of each of several neuropsychological tests. These authors found that several tracts exhibited a stronger correlation between FA and cognitive performance for older adults than for younger adults. Specifically, older adults' increasing FA within frontal regions (genu, uncinate fasciculus) was correlated with better performance on several tests of executive functioning (e.g., spatial working memory, set shifting), whereas the corresponding effect within older adults' more posterior brain regions (e.g., splenium, inferior longitudinal fasciculus) reflected increasing FA and better performance on visual memory tests. Further, when the components of FA were distinguished, the correlations were nearly entirely due to radial diffusivity rather than axial diffusivity.
Similarly, Kennedy and Raz (2009)
, in their ROI analysis, also used regression models that tested for age-related differences in the relation between FA and several composite measures of cognitive performance. The regional variation in the correlations, however, was somewhat different than that reported by Davis et al. (2009)
. In the Kennedy and Raz data, age-related decline in white matter integrity within more anterior brain regions was associated with decreased processing speed and working memory, whereas decline within more posterior brain regions was associated with reduced inhibition and task switching. Kennedy et al. emphasized that the targeted cognitive domains, though differentiated into speed, executive functioning, and several forms of memory, still relied on widely distributed white matter pathways, in which disruption can have wide-reaching effects.
In recent studies, researchers have taken the further step of identifying the age-related variance in cognition and estimating the degree to which age-related variance in DTI is shared with age-related variance in cognition. Madden et al. (2009)
and Gold et al. (2008)
were both concerned with specific components of information processing. Madden et al. used a model of reaction time distributions to distinguish the efficiency of retrieval of semantic information (drift rate), in a word categorization task, from the more peripheral processes of display encoding and response time. In a series of regression analyses, age-related variance in the drift rate measure was attenuated substantially by individual differences in FA within regions of frontoparietal white matter pathways (central genu and splenium-parietal). Using a similar application of regression models, Gold et al. focused on the time required to switch between different tasks (letter or number decisions), rather than on the efficiency of individual decisions. Gold et al. found that FA within a frontoparietal pathway, the superior longitudinal fasciculus of the left hemisphere, was a significant mediator of age-related variance in task switching.
Zahr et al. (2009)
examined domains of cognition: working memory, motor performance, and problem solving, rather than specific information processing components. As in the Madden et al. (2009)
and Gold et al. (2008)
studies, Zahr et al. found that the DTI measures led to significant attenuation of age-related variance in the cognitive measures. The genu and fornix were mediators of all three cognitive domains, whereas age-related differences in motor performance were influenced by a wider range of pathways, including the splenium and uncinate fasciculus in addition to the genu and fornix.
Charlton et al. (2008)
used structural equation modeling to determine whether mean diffusivity within a relatively large ROI (primarily centrum semiovale) was a mediator of the relation between age and several measures of cognitive performance: speed, cognitive flexibility, working memory, and fluid intelligence, for 118 adults 50-90 years of age. Surprisingly, although speed mediated the age-related effects in all of the other cognitive measures, diffusivity was a significant mediator only of the age-related variance in working memory. The authors obtained an identical model using mean FA rather than diffusivity as the DTI mediator.
As a preliminary, semi-quantitative roadmap through the diverse findings of DTI studies on aging and cognition, we have listed in the results of 18 published reports of a statistical relation between one or more DTI variables and a behavioral measure of cognition, with a sample of older adults. We selected these studies from PubMed searches using the terms “white matter integrity AND aging” and “diffusion tensor imaging AND aging AND cognitive.” The selected articles used DTI to assess white matter integrity, examined white matter-cognition effects in healthy older adults, were not review papers, and used regional rather than whole-brain DTI measures. The table values are the mean effect sizes (Rosenthal & DiMatteo, 2001
) for the white matter-cognition relation within a brain region. We have used bold font for effect sizes that are moderate or larger (> .30). Note, however, that these effect sizes refer to the overall effect of the white matter-cognition relation, in studies of older adults, not to the age-related variation in this effect. In addition, the values are likely underestimates of true effects, because nonsignificant findings were assigned an effect size of zero.
Summary of Aging Studies Examining Relationships between Fractional Anisotropy (FA) and Cognitive Functioning
From this table, some support is present for the general trends of the anterior-posterior gradient and prominence of speed and executive functioning that we have discussed previously. These trends do not combine in a regionally definitive manner, however, and considerable variability is evident. The largest mean effect size, for example, is in the cell representing the correlation between composite measures of executive functioning and the genu of the corpus callosum. But several cells of the table representing the posterior regions also contain moderate effects for executive functioning and processing speed. The frontal table-cells include notable effects for other cognitive processes, including recognition memory, word reading, and postural stability.
While recognizing this variability, we would also emphasize two points: First, the vast majority of the table values are positive, reflecting a consistent relation between increasing white matter integrity and better cognitive and sensory/motor performance. Second, the selected studies were uniformly empirical in nature, but still adopted varying approaches to define the cognitive outcome measure. Whereas some studies focused on specific components of information processing (Gold et al., 2008
; Madden et al., 2009
), others used a broad range of tasks to construct composite measures of cognitive domains (Kennedy & Raz, 2009
; Zahr et al., 2009
). As research in this area continues beyond these early stages, a more comprehensive account of the relation between age-related differences in white matter integrity and cognition will emerge. Using composite cognitive measures will be most valuable in investigations based on large sample sizes to establish the reliability and validity of effects, whereas using specific information processing components will be valuable in theory-driven studies that focus on the characteristics of individual neural systems (Paus et al., 2001