White matter integrity and reaction time intraindividual variability in healthy aging and early-stage Alzheimer disease

Abstract

Aging and early-stage Alzheimer disease (AD) have been shown to be associated with increased RT intraindividual variability (IIV, as reflected by the coefficient of variation) and an exaggeration of the slow tail of the reaction time (RT) distribution in attentional control tasks, based on ex-Gaussian analyses. The current study examined associations between white matter volume, IIV, and ex-Gaussian RT distribution parameters in cognitively normal aging and early-stage AD. Three RT attention tasks (Stroop, Simon, and a consonant-vowel odd-even switching task) in conjunction with MRI-based measures of cerebral and regional white matter volume were obtained in 133 cognitively normal and 33 early-stage AD individuals. Larger volumes were associated with less IIV and less slowing in the tail of the RT distribution, and larger cerebral and inferior parietal white matter volumes were associated with faster modal reaction time. Collectively, these results support a role of white matter integrity in IIV and distributional skewing, and are consistent with the hypothesis that IIV and RT distributional skewing are sensitive to breakdowns in executive control processes in normal and pathological aging.

Introduction

An important challenge in the cognitive neuroscience of aging involves understanding the associations between cognitive and structural changes in the aging brain (Raz, 2004, Raz and Rodrigue, 2006) and distinguishing these changes in the context of normal and pathological aging (Johnson, Storandt, Morris, & Galvin, 2009). Recent work suggests that examination of white matter integrity and potentially more sensitive metrics of cognitive performance beyond mean reaction time may facilitate understanding of brain-behavior associations in aging (Gunning-Dixon and Raz, 2000, Johnson et al., 2010, MacDonald et al., 2009, MacDonald et al., 2006). The present paper focuses on the association between regional white matter volume and both cognitive intraindividual variability (IIV) and reaction time distribution parameters in cognitively normal and pathological aging.

White matter integrity has been shown to be compromised in both healthy aging and early-stage Alzheimer disease (AD; Gunning-Dixon et al., 2009, Johnson et al., 2010). First, consider the evidence regarding healthy aging. This work has revealed more anterior declines in white matter in healthy aging that may accelerate with time (e.g., Bartzokis et al., 2003, Gunning-Dixon et al., 2009, Head et al., 2004, Salat et al., 2009). Furthermore, altered regulation of functional activity in frontal regions may be tied to white matter integrity in aging (see Grady, 2008, for a review). Additionally, the importance of white matter integrity is revealed in its association with reduced functional connectivity of brain regions in aging. For example, Andrews-Hanna et al. (2007) reported a positive association between reduced functional connectivity and reduced white matter integrity, even after controlling for the influence of age. Associations between white matter integrity and cognitive performance have also been reported in the aging literature (Gunning-Dixon et al., 2009, Raz et al., 2007, Raz et al., 2008).

Turning to pathological aging, there may be a differential vulnerability in more posterior regions in AD (Head et al., 2004, Kavcic et al., 2008, Salat et al., 2009). Recently, Bartzokis (2009) has suggested that AD may begin with compromised myelin. This is supported by evidence demonstrating strong links between hippocampal atrophy, an early region of decline in AD, and atrophy of critical white matter regions (Villain et al., 2010; but see Bai et al., 2009). Furthermore, frontal, temporal, corpus callosal, and parietal white matter integrity have been associated with memory and executive function in AD (Amar et al., 1996, Anstey et al., 2007, Huang and Auchus, 2007, Kavcic et al., 2008).

Although Rabbitt (1966) early on noted the importance of reaction time (RT) variability in the study of aging, there has been a burgeoning interest in this topic in the recent literature (see Hultsch, Strauss, Hunter, & MacDonald, 2008, for a review of variability in aging). Typically, examinations of cognitive functioning tend to rely on mean RT, a measure of central tendency that assumes stability within individuals or groups. Treating the mean as the dependent measure relegates RT variability to random noise, ignoring potentially systematic variations that may lead to a better understanding of cognitive and neurobiological processes. For purposes of the present paper, we focus on inconsistency, a type of IIV indicating fluctuations in RT task performance over very short intervals (i.e., variability on a trial-to-trial level). One needs to be concerned that the variability is above and beyond the increase in overall mean performance, since there is a strong relation between mean performance and SDs. Thus, researchers have turned to the coefficient of variation (CoV; SD/mean), which is emerging as a standard measure of inconsistency, due to its low bias from mean reaction time, relatively easy calculation, and high association with other measures of inconsistency (Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000).

There is accumulating evidence indicating that aging is associated with increased IIV (Hultsch et al., 2002, Hultsch et al., 2008, Nesselroade and Salthouse, 2004), with increased variability linked to poorer cognitive performance (Hultsch & MacDonald, 2004). The increased variability appears to be particularly strong in more complex, attention-demanding tasks (Bielak et al., 2010, Lövdén et al., 2007, MacDonald et al., 2003). Rabbitt, Osman, and Moore (2001) have argued that higher IIV indicates less effective behavioral and attentional control that may occur via increasingly inefficient neural and network activation, and this perspective is supported by neurocomputational models (Li et al., 2006a, Li et al., 2006b). IIV has proven to be a useful marker of cognitive change, and can reliably distinguish between younger and older adults, even after correcting for age-related changes in central tendency (Bielak et al., 2010, Hultsch et al., 2008). Interestingly, Duchek et al. (2009) and Dixon et al. (2007) reported that the CoV from cognitive attention tasks can also discriminate between healthy and pathological aging, and can even identify cognitively normal individuals at risk for developing dementia (i.e., carriers of an Apolipoprotein ɛ4 allele). Indeed, Duchek et al. (2009) suggested that the increased IIV may reflect impaired attentional control and executive functioning.

There is currently limited research on the association between white matter integrity and RT inconsistency, although the studies that have been reported have indicated significant associations. Higher IIV, as measured by standard deviation, has been linked to smaller cerebral white matter (Walhovd & Fjell, 2007) as well as prefrontal, temporal and parietal white matter volumes (Ullén, Forsman, Blom, Karabanov, & Madison, 2008), and greater prefrontal white matter hyperintensities in healthy adults (Bunce et al., 2007). Associations have also been reported between volumetric estimates of the corpus callosum and IIV in individuals with mild cognitive disorders (Anstey et al., 2007). However, to our knowledge, this earlier research has not been extended to older adults. Only one past study (Walhovd & Fjell, 2007) has examined adults beyond 64 years of age and this study focused on total cerebral white matter volume, constraining generalizations about the relationships among aging, variability, and white matter integrity. Furthermore, none of the previous studies have examined this relationship in early-stage AD. Finally, previous studies have tended to use non-demanding cognitive tasks, such as visual oddball tasks (Walhovd & Fjell, 2007), simple RT tasks (Anstey et al., 2007), or isochronous tapping (Ullén et al., 2008). In the present study, the use of standard attentional control tasks as a basis for the calculation of RT IIV allowed us to draw more robust conclusions regarding the association between variability, control systems, and white matter integrity in older adults.

The importance of white matter integrity becomes increasingly important in light of recent work on brain networks. For example, connectivity in the default network, a group of frontal and parietal brain regions that become active and spontaneously correlate in the absence of an explicit task (Raichle et al., 2001), has been central in a number of recent studies of aging and early stage AD. Indeed, both aging and AD are associated with alterations of the default network (Andrews-Hanna et al., 2007, Bai et al., 2008, Damoiseaux et al., 2008, Grecius et al., 2004). Typically, the default network is anticorrelated with attentional networks, where one network is suppressed while the other is active. Kelly, Uddin, Biswal, Castellanos, and Milham (2008) reported that dysregulation of default and executive networks (i.e., lack of a strong anticorrelation) is associated with increased IIV. A breakdown in white matter integrity may disrupt both within and cross network correlations, thereby producing the observed relation reported in the Kelly et al. (2008) study.

There is a rich history showing that the characteristics of RT distributions are sensitive to various aspects of cognition (Luce, 1986, Ratcliff, 1978, Ratcliff, 1979), such as attentional control and/or drift rate in diffusion models (Heathcote et al., 1991, Ratcliff, 1978, Ratcliff, 1979, Spieler et al., 1996, Spieler et al., 2000, Tse et al., 2010). We have chosen to fit the Ex-Gaussian theoretical distribution to capture the characteristics of the RT distributions in the current data. The ex-Gaussian distribution reflects the convolution of a Gaussian and an exponential distribution. There are three parameters obtained from ex-Gaussian analyses: μ and σ, which capture the mean and standard deviation of the Gaussian portion, respectively; and τ, which reflects both the mean of the exponential portion of the distribution. Changes in μ reflect a shifting of the modal portion of the RT distribution, whereas changes in τ typically reflect an increase in the slow tail of the RT distribution. Critically, the algebraic sum of μ and τ equals the mean of the RT distribution, which enables one to make contact with the mean-dominated literature. The Ex-Gaussian distribution has been shown to fit RT data extremely well (Ratcliff, 1979), and has yielded interesting findings on the nature of human cognition (see Balota & Yap, 2011). Interestingly, it is possible to observe two RT distributions with equivalent means, but with opposing distributional parameters that cancel each other out, highlighting the importance of examining the properties of underlying reaction time distributions. For example, consider the congruency effect (the RT difference in naming the color of a congruent word, e.g., RED presented in red, and the color of a neutral word, e.g., DEEP presented in red) in the classic Stroop task. This effect is quite small and sometimes nonsignificant at the level of the mean. However, Heathcote et al. (1991) found that the relatively small congruency effect was due to the opposing influences of a shortening of the modal portion of the distribution (μ) in the congruent condition and a lengthening of the tail (τ) relative to the neutral condition (also see Spieler et al., 2000), both of which would be undetected at the level of the mean.

More recently, distributional analyses have helped to clarify differences between healthy and pathological aging. For example, Tse et al. (2010) recently found that healthy aging had clear effects on both μ and τ in a set of attentional control tasks, whereas early stage AD only had an additional effect on τ. Balota et al. (2010) have recently shown that the slow tail of the RT distribution in the Stroop task may also be useful in predicting convergence from a cognitive normal state to early stage AD across a 12 year longitudinal follow up. Furthermore, τ has been shown to be more strongly associated with working memory measures than μ or σ (Schmiedek et al., 2007, Tse et al., 2010), and thus it is possible that τ may be more related to the white matter integrity that underlies executive/attentional control abilities (see Balota et al., 2010, Breteler et al., 1994, Grieve et al., 2007, Gunning-Dixon et al., 2009, Raz et al., 2008). To our knowledge, no studies have examined the relationship between the parameters of RT distributional analysis and regional volume.

The present study examines (a) the relationship between RT IIV/distributional parameters and white matter volume in targeted brain regions, and (b) whether there are differential behavioral-brain associations between healthy aging and early-stage AD. Specifically, given the evidence of associations between IIV and white matter integrity observed in younger adult samples using less-engaging cognitive tasks (Anstey et al., 2007, Bunce et al., 2007, Ullén et al., 2008, Walhovd and Fjell, 2007), and the disruption of the default mode network in healthy aging and early stage AD (Andrews-Hanna et al., 2007, Damoiseaux et al., 2008), we examined the CoV and RT distributional parameters in attentional control tasks, and the relation between these estimates with cerebral white matter volume as well as targeted volumes in prefrontal and default-mode white matter regions. Moreover, because cognitive variability and distributional skewing may be particularly associated with frontal regions (see MacDonald et al., 2009, MacDonald et al., 2006), those frontal regions important in executive function such as ventral/dorsolateral prefrontal cortex, the superior frontal gyrus, and anterior cingulate may show increased sensitivity to fluctuations in CoV and the exponential estimate from ex-Gaussian analyses. In addition, targeted default-mode regions such as the precuneus, posterior cingulate, and the inferior parietal lobule (see Raichle et al., 2001) may show an association with variability and/or distributional parameters because the anticorrelatons between the default and executive networks has also demonstrated sensitivity to IIV in younger adults (Kelly et al., 2008). Finally, we expected stronger associations between regional white matter volume and cognitive performance in early-stage AD, as there are increasing breakdowns in white matter integrity and IIV, consistent with recent work by Anstey and colleagues (2007), in which they observed a stronger relationship between white matter volume and RT variability in MCI patients.