White matter hyperintensities and impaired choice stepping reaction time in older people

Neurobiology of Aging 33 (2012) 1177–1185 www.elsevier.com/locate/neuaging

White matter hyperintensities and impaired choice stepping reaction time in older people Jacqueline J. Zhenga, Kim Delbaerea,b,c, Jacqueline C.T. Closea,d, Perminder S. Sachdeve,f, Wei Wene,f, Stephen R. Lorda,* b

a Falls and Balance Research Group, Neuroscience Research Australia, University of New South Wales, Sydney, Australia Department of Experimental-Clinical and Health Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Belgium c Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine Health Sciences, Ghent University, Belgium d Department of Geriatric Medicine, Prince of Wales Clinical School, University of New South Wales, Sydney, Australia e School of Psychiatry, University of New South Wales, Sydney, Australia f Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, Australia

Received 26 May, 2010; received in revised form 7 December 2010; accepted 12 December 2010

Abstract White matter hyperintensities (WMHs) are associated with impaired mobility in older people, but no studies have identified neuropsychological and sensorimotor mediating factors for this association. Our objective was to determine whether neuropsychological and sensorimotor functions mediate the association of WMHs and choice stepping reaction time (CSRT) under standard and dual-task conditions using structural equation modeling. Two hundred fifty-four older community dwellers (77.8 ⫾ 4.5 years) underwent structural magnetic resonance imaging, CSRT tests, neuropsychological and sensorimotor assessments. WMH volumes were quantified using an automated method. WMH volumes were significantly associated with neuropsychological tests and dual task CSRT performance. All neuropsychological and sensorimotor variables were also significantly associated with standard and dual task CSRT. The structural equation modeling revealed that impaired sensorimotor function was the only factor influencing impaired stepping performances in the standard condition. In the dual task condition, the association between WMHs and CSRT was mediated through slowed cognitive processing and not through reduced sensorimotor functioning. The conclusion was that WMHs are associated with slowed performance on a dual task CSRT task and this relationship is explained primarily by underlying neuropsychological impairments. © 2012 Elsevier Inc. All rights reserved. Keywords: WMHs; Magnetic resonance imaging; Aged; Accidental falls; Choice stepping reaction Time; Muscle strength; Postural balance; Cognition

1. Introduction White matter hyperintensities (WMHs) are abnormal signals in the white matter observed on T2 weighted magnetic resonance imaging (MRI). They are very common in older adults, having been reported in as many as 95% of people aged 60 years and above (Longstreth et al., 1996; Wen and Sachdev, 2004a). While these signals are non-specific and can have multiple etiologies, epidemiological, clinical,

* Corresponding author. Tel.: 61 2 9399 1061; fax: 61 2 93991005. E-mail address: [email protected] (S.R. Lord). 0197-4580/$ – see front matter © 2012 Elsevier Inc. All rights reserved. 10.1016/j.neurobiolaging.2010.12.009

pathological and experimental evidence collectively suggest that WMHs seen in otherwise healthy older people may have an ischemic origin (Enzinger et al., 2006; Fazekas et al., 1993; Pantoni and Garcia, 1997; Sachdev et al., 2008; Wen and Sachdev, 2004b). Falls are also common in older adults. About 1 in 3 older people living in the community suffers a fall at least once a year and many suffer multiple falls (Lord et al., 2007). One recent prospective cohort study found that severe degree of WMHs was a significant risk factor for falls (Srikanth et al., 2008) and there is considerable evidence that the severity of WMHs is associated with both gait and balance impairments (Baezner et al., 2008; Blahak et al., 2009; Novak et

1178

J.J. Zheng et al. / Neurobiology of Aging 33 (2012) 1177–1185

al., 2009; Silbert et al., 2008; Soumare et al., 2009; Srikanth et al., 2008; Sullivan et al., 2009). WMH volumes in the frontal region are also associated with impaired executive function including attention and visuospatial memory (Au et al., 2006; Bunce et al., 2007; Gunning-Dixon and Raz, 2003). Adequate executive functioning may be important for avoiding falls (Holtzer et al., 2007), particularly in situations that require sufficient working memory and attention allocated to walking while undertaking a secondary task for older people (Tideiksaar, 1996; Toulotte et al., 2006). During everyday activities including walking in a complex environment, being able to rapidly take a voluntary step can be crucial for maintaining balance while negotiating unexpected obstacles. One previous study has reported pilot findings with regard to associations between WMHs and stepping initiation time during an auditory-cued stepping reaction time test (Sparto et al., 2008). These investigators found that in 8 participants, increases in WMHs were associated with greater voluntary step initiation time with 1 foot and that this relationship was particularly strong when the stepping task involved a choice of anterior or lateral step. In previous studies we have found that a functional test of stepping performance— choice stepping reaction time (CSRT)—is a good predictor of falls (George et al., 2007; Lord and Fitzpatrick, 2001; Pijnappels et al., 2009). The CSRT test requires participants to step with either foot onto targets that are illuminated randomly, and thus body weight and balance transfers are similar to the step responses required to avoid many falls, particularly those that result from late visual detection of hazards and unexpected changes in the gait path. CSRT when undertaking a secondary task is increased significantly in older people at high risk of falls (George et al., 2007; Lord and Fitzpatrick, 2001) suggesting that this paradigm may be particularly appropriate for investigating associations and interactions between WMHs, attention and balance in older people. We hypothesized that stepping performance depends on the integrity of the neural network. In addition to balance and muscle strength, stepping performance may also be determined by the individual’s capacity to set task priority in a dual task situation, which is an essential component of executive function. Therefore, we additionally hypothesized that the relationship between WMHs and stepping performances may be mediated through both cognitive and physical functioning. The objective of the current study was to investigate the relationship between WMHs and both standard CSRT and CSRT with a dual task in a large sample of older community living people. We explored how such relationships could be mediated by physiological and cognitive factors using structural equation modeling.

2. Methods 2.1. Participants Two hundred fifty-four people (122 men, 48%) aged 70 to 90 years participated in a cohort study. Participants were those who consented to undergo both an MRI and falls risk assessment from a cohort of 1037 community-dwelling men and women living in eastern Sydney as part of the Sydney Memory and Ageing Study (SMAS, January 2006 to October 2007) (Sachdev et al., 2010). Exclusion criteria were neurological, cardiovascular or major musculoskeletal impairments (determined at a baseline physical assessment) that precluded participants from walking 20 meters without a walking aid, cognitive impairment determined by a score of ⬍ 24 on the Mini Mental State Examination (MMSE) (Folstein et al., 1975), and having a pacemaker or other metallic implants. The Human Studies Ethics Committee at the University of New South Wales approved the study, and informed consent was obtained from all participants. 2.2. Measures At baseline, all participants underwent an extensive assessment of medical, physical, and cognitive measures by trained research assistants. A complete medical history was recorded during a face-to-face interview including the presence of medical conditions, medication use and falls history. Levels of disability were assessed using the 12-item World Health Organization Disability Assessment Schedule (WHODAS II, total score range 12– 48) (2001). Quality of life was assessed using the 20-item AQOL II (score range 0 –100) (Hawthorne et al., 1999). 2.2.1. Choice stepping reaction time test The CSRT device consisted of a wooden platform (0.84 ⫻ 0.74 m) that contained 6 rectangular panels (30 ⫻ 15 cm), 2 base panels to stand on, one light panel in front of each foot and one light panel to the side of each foot (Lord and Fitzpatrick, 2001). One light panel per trial was illuminated and participants were instructed to step on to the illuminated panel as quickly as possible, using the left foot only for the 2 left panels (front and side) and the right foot only for the 2 right panels. Each light panel contained a pressure switch to determine the time of foot contact. After 4 practice trials, 20 trials were conducted with 5 trials per panel. The time in ms between stimulus presentation and completion of the correct step was recorded as CSRT. The participants performed the choice stepping task under 2 conditions, once as a standard choice stepping reaction time test (CSRT) and once with an additional task (DTCSRT). The order of both conditions was randomized. The additional “visuospatial star movement” working memory task involved participants envisaging 3 boxes side by side labeled A, B, and C (George et al., 2007). Participants were then asked to visualize a star located in one of the boxes making 3 movements. They were told the starting box of the

J.J. Zheng et al. / Neurobiology of Aging 33 (2012) 1177–1185

star and the direction of the 3 movements, i.e., left or right. Performance was recorded as the number of errors made in identifying the finishing position of the star. 2.2.2. Neuropsychological function Simple attention was tested using the Trail making Test (Part A), which requires participants to draw lines connecting numbers (e.g., 1-2-3). Complex attention was tested using the Trail making Test (Part B), which requires participants to draw lines connecting a number of circles alternating between letters and numbers (e.g., 1-A-2-B) (Reitan and Wolfson, 1993). The difference between the 2 parts was calculated to remove the speed element from the test evaluation, leaving an estimate of executive function. Visuomotor speed and short-term visual memory were assessed using the Digit Symbol Substitution Test (DSST), which requires participants to copy symbols that are paired with numbers within a 120-second time limit (Wechsler, 1997). Complex visuomotor coordination was assessed using the Grooved Pegboard Test (GPT), which is a manual dexterity test requiring participants to place pegs with a notch on 1 side in one of 25 holes with varying notch position slots (Klove, 1963). 2.2.3. Sensorimotor function The physiological profile assessment (PPA) (Lord et al., 2003) contains 5 tests and was used to assess falls risk. Visual contrast sensitivity was measured using the Melbourne Edge Test. Proprioception was measured using a lower limb-matching task, where errors in degrees are recorded using a protractor inscribed on a vertical clear acrylic sheet placed between the legs. Quadriceps strength was assessed isometrically in the dominant leg while participants were seated with the hip and knee flexed to 90 degrees. Standard reaction time was measured using a light stimulus and a finger-press as the response. Postural sway (path length) was measured using a sway meter recording displacements of the body at the level of the pelvis, while participants stood on a foam rubber mat with eyes open. In multivariate models, weighted contributions from these 5 variables provide a falls risk score that can predict those at risk of falling with 75% accuracy in community settings (Lord et al., 2003). The coordinated stability test (CoStab) assessed participants’ ability to adjust body position in a steady and coordinated way while placing them at or near the limits of their base of support (Lord et al., 1996). 2.3. MRI acquisition and image analysis MRI acquisitions were performed on a Philips 3 Tesla Intera Quasar scanner (Philips Medical Systems, Best, the Netherlands) located at Neuroscience Research Australia, Sydney. Positioning in the scanner was based on a scout midsagittal scan to locate the anterior to posterior commissural plane. For reasons outside the control of the researchers, the original scanner was replaced by a similar 3 Tesla Philips

1179

scanner (Achieva Quasar Dual scanner) in the late stage of the study. Nevertheless, as subject recruitment was random, no systematic sampling bias was likely due to the scanner change. Acquisition parameters for both scanners for T1weighted structural and fluid attenuated inversion recovery (FLAIR) MRI scans were also identical. The first 210 MR images were acquired on the original scanner and the remaining 44 MRI scans were performed on the replacement scanner. The T1-weighted structural scan had repetition time (TR) ⫽ 6.39 ms, echo time (TE) ⫽ 2.9 ms; flip angle ⫽ 8°; matrix size ⫽ 256 ⫻ 256; field of view (FOV) ⫽ 256 ⫻ 256 ⫻ 190 mm; slice thickness ⫽ 1 mm with no gap between, yielding 1 ⫻ 1 ⫻ 1 mm3 isotropic voxels. T2 weighted FLAIR sequence was acquired with TR ⫽ 10,000 ms, TE ⫽ 110 ms, TI (inversion time) ⫽ 2800 ms; matrix size ⫽ 512 ⫻ 512; slice thickness ⫽ 3.5 mm with no gap between slices, yielding spatial resolution of 0.488 ⫻ 0.488 ⫻ 3.5 mm3/voxel. Participants scanned with the 2 different scanners were compared on social, demographic and imaging parameters, and there were no significant differences in sex, years of education, age. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF) volumes and total intracranial cavity volume (ICV) of the whole brain were not significantly different between the scans of participants scanned in 2 different scanners after controlling for age, education and sex. We analyzed the scans of 5 healthy participants who were scanned in both scanners within 2 months, and no significant scanner difference was found. Nevertheless, a binary variable of scanner was included in the statistical analysis as an additional covariate to minimize the possible scanner effect. WMHs were delineated from coronal plane FLAIR and T1-weighted 3-dimensional structural image scans by using a computer algorithm (Fig. 1). The automatic qualification of WMHs was carried out in the following steps (Wen et al., 2009): (1) preprocessing to prepare the images for the WMHs classification and analysis (coregistration, segmentation, removal of non-brain tissue and intensity correction); (2) initial detection of candidate WMH clusters employing a parametric method; (3) removal of false WMHs and classification of WMHs clusters into either deep WMHs or periventricular WMHs by kNN classification, which included extended periventricular “rims” and frontal and occipital “caps”. Small caps and pencil-thin rims in periventricular region were not taken as WMHs for this analysis as they were considered a normative finding in older people. Both automated and manual procedures were used to remove false WMHs such as hyperintense signals caused by partial-voluming effects in the ventricles of FLAIR image, or tissues such as tumors or blood. In addition, we also excluded hyperintense regions if they were less than 3 voxels in size. Lacunar infarcts were not considered in this study. To avoid misclassification, all the scans were independently and blindly rated by one of the authors (JZ) using the modified rating methods of Fazekas et al. (Fazekas et al., 1987) on a 0 –2 scale to examine deep white matter (ab-

1180

J.J. Zheng et al. / Neurobiology of Aging 33 (2012) 1177–1185

Fig. 1. Automated WMH segmentation results using kNN classification (a) WMHs are shown on FLAIR sequence MRI of 1 subject at the level of ventricle. (b) detected WMH clusters are superimposed on the FLAIR MRI slice of the same subject.

sence/punctate, beginning confluence, and large confluent) and periventricular regions (pencil-thin lining, smooth “halo” and extensive irregular lesions) separately. Pearson correlations were carried out and results are r ⫽ 0.742 (p ⬍ 0.0001) between deep WMHs visual rating score and deep WMH volumes; and r ⫽ 0.783 (p ⬍ 0.0001) between periventricular WMHs visual rating score and periventricular WMH volumes. 2.4. Statistical analyses Statistical analyses were performed using SPSS (version 17.0) in conjunction with Analysis of Moment Structures (AMOS 7.0) Graphics. Multivariate normality and linearity were evaluated by examining the normality, linearity, and homoscedasticity of the individual variables and residuals. After logarithmic transformations of standard CSRT, DTCSRT, WMHs, TMT, GPT, and CoStab, assumptions regarding multivariate normality were met and all parametric statistics were performed with the transformed data. Bivariate correlations between variables were calculated using Pearson’s correlations. Structural equation modeling in AMOS was then performed to examine the relationships between WMHs, standard CSRT and DT-CSRT and to evaluate our hypotheses on the mediating role of the physiological and cognitive parameters. Structural equation modeling is an optimal statistical technique for testing these hypotheses as it can evaluate a priori models, identify mediators, and elucidate direct and indirect paths between the 2 endpoints. We constructed a path model based on our hypothesis and on significant correlations. Three latent variables (WMHs, neuropsychological function and sensorimotor function) were created from the model indicators (deep WMHs and periventricular WMHs for the WMHs, Trail making Tests, DSST, and GPT for neuropsychological

function; and PPA and CoStab for the sensorimotor function). Indicators for each latent variable were strongly intercorrelated. Goodness-of-fit of the models was examined by chi square (␹2), Goodness-of-Fit Index (GFI), and Root Mean Square Error of Approximation (RMSEA) (Byrne, 2004). Chi square should not be significant as it investigates lack of fit, resulting from overidentifying restrictions placed on a model (Byrne, 2004). GFI should be high (⬎ 0.90) as it assesses the extent to which the model provides a better fit compared with no model at all (Byrne, 2004). RMSEA should be small (⬍ 0.08) as it estimates lack of fit in a model compared with a perfect model (Hu and Bentler, 1999). Finally, model trimming (Kline, 1998) was used to systematically remove associations that were not significant in the hypothesized model.

3. Results 3.1. Sample characteristics Table 1 shows the demographic, falls history, health and medical characteristics of the SMAS sample included and not included in this study. Participants not included were younger and took fewer medications including antidepressants. The mean age of participants included was 77.8 years (SD 4.5) and 132 (52%) participants were women. The mean scores of the WHODAS, AQOL and MMSE were 17.9 (SD 5.6), 89.8 (SD 8.2) and 28.1 (SD 1.4) respectively. The most common medical conditions were arthritis (58.5%) and hypercholesterolemia (57.9%), and hypertension (56.5%). 39.8 percent of the participants used more than 5 medications and 37.8% of the participants reported one or more falls in the previous 18 months. WMHs were present in all of our participants with the mean volume for total WMHs of 8.65 ml (SD 13.24; range 0.03–135.10 ml).

J.J. Zheng et al. / Neurobiology of Aging 33 (2012) 1177–1185 Table 1 Demographic, fall risk, fall history, health and medical characteristics of the Memory and Aging sample included and not included in this study

Age (years), mean (SD) Female gender, % Body Mass Index mean (SD) Education (years), mean (SD) World Health Organization Disability Assessment Schedule score, mean (SD) Assessment of quality of life score, mean (SD) Mini mental status examination score, mean (SD) One or more falls in last 18 mo, % Chronic medical conditions# Hypertension, % Diabetes, % Hypercholesterolemia, % Arthritis, % Ever diagnosed with depression, % Cerebrovascular conditions, % Cardiovascular conditions, % Respiratory conditions, % Endocrine conditions, % Neurological conditions, % Medication use Five plus medications, % Antidepressants, %

Included (n ⫽ 254)

Not-included (n ⫽ 783)

77.8 (4.5) 52.0% 27.1 (4.3) 11.5 (3.4) 17.9 (6.0)

79.2 (4.9)** 56.3% 27.1 (4.6) 11.6 (3.5) 18.4 (6.4)

89.8 (8.2)

88.9 (9.2)

28.1 (1.4)

27.9 (1.6)

37.8%

37.7%

56.5% 13.0% 57.9% 58.5% 14.2% 7.5% 28.0% 15.0% 28.7% 30.3%

62.3% 11.9% 61.1% 53.4% 16.2% 10.5% 31.5% 13.4% 30.3% 26.7%

39.8% 5.1%

47.4%* 11.0%**

Comparisons between included and not-included participants using t-test for continuous variables and chi-square for categorical variables: * p ⬍ 0.05, ** p ⬍ 0.01. # Medical conditions surveyed were medical practitioner diagnosed; cerebrovascular conditions include stroke, and transient ischemic attack; cardiovascular conditions include heart attack, angina, arrhythmias rather than hypertension, dyslipidemia; respiratory conditions includes asthma, emphysema, and COPD; endocrine conditions include thyroid, hypoglycemic and osteoporosis; and neurological conditions include Parkinson’s disease, epilepsy, head injuries, migraines and hydrocephalus.

The mean volume of WMHs was greater in the deep subcortical region than in the periventricular region, ⫺5.56 ml (SD 10.8; range 0.02–119.80 ml) and 3.05 ml (SD 3.09; range 0 –17.50 mL) respectively. 3.2. Test measures descriptive statistics and intercorrelations Table 2 presents means, standard deviations and correlations between test measures. The means and SDs of WMH volumes before and after the upgrade of the MRI scanner were 9.38 ml (SD 14.45) and 9.10 ml (SD 14.76) respectively (tx ⫽ 0.95, p ⫽ 0.34). All cognitive, sensorimotor and balance variables were significantly associated with CSRT and DT-CSRT. Total WMHs and deep WMHs were significantly associated with DSST, Trail making Tests and DTCSRT, and periventricular WMHs were only significantly associated with DT-CSRT.

1181

3.3. Structural equation models As WMHs were not significantly associated with standard CSRT, the first path model (Fig. 2, Model A) investigated the relationships only between neuropsychological functioning, sensorimotor functioning and standard CSRT (Fig. 2, Model A). The model showed that there was no direct effect of neuropsychological function on standard CSRT (rc ⫽ 0.19, p ⫽ 0.150), but rather indirectly, i.e., mediated by sensorimotor function. Model trimming removed the direct path (represented by the dashed lines in Fig. 2, Model B). The overall fit of the model was confirmed by the chi-square (␹2 ⫽ 11.60, d.f. ⫽ 7, p ⫽ 0.114), GFI (0.984), AGFI (0.952), RMSEA (0.052) and CFI (0.982) statistics. The model explained 21% of the variance in standard CSRT. The second model (Fig. 2, Model B) investigated the relationship between WMHs and dual task CSRT. This model showed that there was a significant effect of WMHs on neuropsychological functioning, but no significant effect on sensorimotor functioning. Neuropsychological functioning had a significant effect on DT-CSRT (rc ⫽ 0.06) and sensorimotor functioning. There were no significant association between sensorimotor functioning and DT-CSRT or between WMHs and DT-CSRT, indicating the relationship between WMHs and DT-CSRT is mediated entirely by neuropsychological function. After trimming 3 non-significant paths, the overall fit of the model was confirmed with ␹2 ⫽ 23.82 (d.f. ⫽ 18, p ⫽ 0.161), GFI (0.975), AGFI (0.950), RMSEA (0.037) and CFI (0.988) statistics. The model explained 29% of variance in DT-CSRT. 4. Discussion This study investigated the associations between WMHs and measures of stepping performance and the mediating roles of neuropsychological and physiological factors in these associations. The findings suggest that greater volumes of WMHs are not associated with impaired CSRT under a standard condition. In contrast, in the condition requiring a dual task, WMH volumes were associated with CSRT, and this relationship was mediated primarily through impaired neuropsychological functioning. The direct impact of sensorimotor function on standard CSRT reinforces the important roles of muscle strength, vision and balance in stepping performance (George et al., 2007; Lord and Fitzpatrick, 2001), while the indirect neuropsychological path on standard CSRT indicates that slow information processing speed and reduced attention capacity also contributes to stepping impairments through limiting postural control (Bernard-Demanze et al., 2009). Our study supports previous findings that neuropsychological function becomes more important during balance tests requiring a secondary task (George et al., 2007; Tideiksaar, 1996; Woollacott and Shumway-Cook, 2002). Our path model for DT-CSRT suggests that the sensorimotor/balance

1182

J.J. Zheng et al. / Neurobiology of Aging 33 (2012) 1177–1185

Table 2 Correlations between WMHs, neuropsychological variables, sensorimotor variables and CSRT Mean (SD)

Neuropsychological function

8.65 (13.24) 5.56 (10.82) 3.05 (3.09) 49.55 (12.34) 73.67 (53.20) 108.90 (32.80) 14.23 (12.51) 0.78 (0.91) 993.17 (196.92)

⫺0.13* ⫺0.15* ⫺0.08 1.000 — — — — —

4 1. 2. 3. 4. 5. 6. 7. 8. 9.

WMHs total (mL) WMHs deep subtotal(mL) WMHs periventricular subtotal (mL) DSST (total numbers correct) Trail making test (B-A) (s) Grooved pegboard test (s) Coordinated stability (errors) Physiological profile assessment (sd) Standard choice stepping reaction Time (ms) 10. DT-choice stepping reaction Time (ms)

1768.5 (791.5)

—

Sensorimotor/balance function

Choice stepping performance

5

6

7

8

9

0.17** 0.20** 0.11 ⫺0.44** 1.000 — — — —

0.08 0.08 0.08 ⫺0.52** 0.33** 1.000 — — —

0.11 0.11 0.10 ⫺0.23** 0.13 0.35** 1.000 — —

0.10 0.11 0.10 ⫺0.35** 0.24** 0.30** 0.36** 1.000 —

0.02 0.02 0.08 ⫺0.31** 0.19** 0.26** 0.18** 0.30** 1.000

—

—

—

—

—

10 0.15* 0.17* 0.16* ⫺0.38** 0.35** 0.36** 0.21** 0.24** 0.43** 1.000

All the WMHs measures were adjusted for intracranial volumes in statistical analysis. Low scores in DSST and high scores in all other variables indicate impaired performance. Significant correlations are indicated: * p ⬍ 0.05, ** p ⱕ 0.01. All variables that significantly correlated with choice stepping performances were included in the initial path analysis model.

mediating path is inconsequential compared with the neuropsychological mediating path. This is in accordance with functional neuroimaging studies indicating that dual-task performance results in increased activity in the frontoparietal cortex which is involved in attention and executive functioning (Klingberg, 2000; Linortner et al., 2010; Stelzel et al., 2009; Szameitat et al., 2002; Venkatraman et al., 2010). A direct relationship between WMHs and neuropsychological function, especially executive function, has been

found in several population-based studies (Au et al., 2006; de Groot et al., 2000; Mosley et al., 2005; Soderlund et al., 2006). White matter pathways are an integral component of the frontal-subcortical circuits that interconnect various cortical areas related to frontal-executive function, including attention, information processing speed and planning (Inzitari et al., 2007; Junque et al., 1990). The relatively small amount of variance of neuropsychological function explained by WMHs in our model is in line with results from

Fig. 2. The structural equation models of the test measures. The estimated standardized coefficients shown for the direct and indirect effects are provided on each arrow and explained variances are provided in bold above each variable. Dashed lines indicated associations that were not significant (p ⬎ 0.05) and were therefore removed from the hypothesized model to obtain the final model. Latent variables are represented with ovals and observed variable are represented with rectangles.

J.J. Zheng et al. / Neurobiology of Aging 33 (2012) 1177–1185

other studies (Frisoni et al., 2007), suggesting a subtle but detectable relationship with a threshold effect above which WMH severity plays an important role in reduced performance (Wright et al., 2008). Previous studies have also shown significant associations between WMHs and balance and lower extremity function (Baloh et al., 2003; Moscufo et al., 2009; Novak et al., 2009; Sullivan et al., 2009). Results from the structural equation modeling suggest that WMHs mainly contribute to slow dual task stepping performance through impairment of cognitive pathways. This is consistent with previous studies that have reported significant associations between measures of executive function and gait and postural control in older people (Springer et al., 2006; Woollacott and Shumway-Cook, 2002). The integrity of the frontal-subcortical neuronal circuits, responsible for executive function (RorizCruz et al., 2007), could also have a direct impact on gait function (Soumare et al., 2009), however this was not supported by our data. Strengths of the study include the fully automated measures of WMHs, the computerized measure of stepping, and an extensive battery of cognitive and physiological measures administered to a large community-based sample. Our study also has certain limitations. Firstly, although the demographic, health and medical characteristics of the participants included in the study were similar to those in the SMAS cohort as a whole, we acknowledge that the exclusion of some participants who were unable to undergo MRI could potentially influence the representativeness of our sample. Secondly, we did not include lacunar infarcts in our model which could potentially have additional effects on stepping performance. It is also possible that the MRI scanner upgrade part way through recruitment may have influenced the findings. However, no significant difference in mean and standard deviation for WMH volumes obtained from 2 scanners was found and additional analyses using separately calculated Z-scores before and after the upgrade of the scanner (data not presented) revealed very similar bivariate associations among the variables and did not alter the outcomes of our structural equation models. Thirdly, the cross sectional design does not allow us to make firm conclusions about causal relationships between variables. Previous studies have found that the decline in gait speed and neuropsychological function over time is associated with the greater WMH volume at baseline assessments with a threshold effect (Silbert et al., 2008; Soumare et al., 2009), but further longitudinal research is warranted to confirm these findings. The study findings have some implications for falls prevention strategies. Minimizing the risk factors for WMHs, such as treating high blood pressure (Dufouil et al., 2005), remains crucial for the overall physiological and cognitive health of older people and may potentially play a role in the reduction of falls. Furthermore, cognitive training under the dual-task conditions as a part of balance rehabilitation pro-

1183

grams (Silsupadol et al., 2009) may have the potential to improve the ability to execute a protective step. Cognitive training may also have a direct therapeutic effect by increasing the capacity to better tolerate brain pathology (Brickman et al., 2009). In conclusion, this study explored the relationship between WMHs and choice stepping performance in older people using structural equation modeling. The findings suggest that increased WMHs are associated with stepping responses under dual task conditions and that this relationship is mediated primarily by reduced neuropsychological functioning. These findings provide useful insights into the possible mechanisms underlying WMHs and stepping impairments that predispose older people to falls. Disclosure statement The Physiological Profile Assessment (NeuRA FallScreen) is commercially available through Neuroscience Research Australia. The study was approved by the Human Studies Ethics Committee at the University of New South Wales, and informed consent was obtained from all participants. Acknowledgments This research was conducted as part of a study on understanding Fear of Falling and Risk-taking in Older People, which has been funded by an Australian NHMRC grant (no. 400941). Professor Lord is currently a NHMRC Senior Principal Research Fellow. The participants in this study were drawn from the Sydney Memory and Ageing Study of the Brain and Ageing Program, School of Psychiatry, UNSW, funded by a NHMRC Program Grant (no. 350833) to Professors P. Sachdev, H. Brodaty and G. Andrews. We also acknowledge Nicky Kochan, Mellissa Slavin, Simone Reppermund, Kristan Kang, Julian Trollor and John Crawford for their contributions to the study. References Au, R., Massaro, J.M., Wolf, P.A., Young, M.E., Beiser, A., Seshadri, S., D’Agostino, R.B., DeCarli, C., 2006. Association of white matter hyperintensity volume with decreased cognitive functioning: the Framingham Heart Study. Arch. Neurol. 63, 246 –250. Baezner, H., Blahak, C., Poggesi, A., Pantoni, L., Inzitari, D., Chabriat, H., Erkinjuntti, T., Fazekas, F., Ferro, J.M., Langhorne, P., O’Brien, J., Scheltens, P., Visser, M.C., Wahlund, L.O., Waldemar, G., Wallin, A., Hennerici, M.G., Group, L.S. 2008. Association of gait and balance disorders with age-related white matter changes: the LADIS study. Neurology 70(12), 935– 42. Baloh, R.W., Ying, S.H., Jacobson, K.M., 2003. A Longitudinal Study of Gait and Balance Dysfunction in Normal Older People. Arch. Neurol. 60, 835– 839. Bernard-Demanze, L., Dumitrescu, M., Jimeno, P., Borel, L., Lacour, M., 2009. Age-related changes in posture control are differentially affected by postural and cognitive task complexity. Curr. Aging Sci. 2, 139 – 149.

1184

J.J. Zheng et al. / Neurobiology of Aging 33 (2012) 1177–1185

Blahak, C., Baezner, H., Pantoni, L., Poggesi, A., Chabriat, H., Erkinjuntti, T., Fazekas, F., Ferro, J.M., Langhorne, P., O’Brien, J., Visser, M.C., Wahlund, L.O., Waldemar, G., Wallin, A., Inzitari, D., Hennerici, M.G., Group, L.S., 2009. Deep frontal and periventricular age related white matter changes but not basal ganglia and infratentorial hyperintensities are associated with falls: cross sectional results from the LADIS study. J Neurol Neurosurg Psychiatry 80(6), 608 –13. Brickman, A.M., Siedlecki, K.L., Muraskin, J., Manly, J.J., Luchsinger, J.A., Yeung, L.K., Brown, T.R., Decarli, C., Stern, Y., 2009. White matter hyperintensities and cognition: Testing the reserve hypothesis. Neurobiol. Aging 2009 Nov 17. [Epub ahead of print]. Bunce, D., Anstey, K.J., Christensen, H., Dear, K., Wen, W., Sachdev, P., 2007. White matter hyperintensities and within-person variability in community-dwelling adults aged 60 – 64 years. Neuropsychologia 45, 2009 –2015. Byrne, B.M., 2004. Testing for multigroup invariance using AMOS graphics: a road less travelled. Struct. Equation Model. 11, 272–300. de Groot, J.C., de Leeuw, F.E., Oudkerk, M., van Gijn, J., Hofman, A., Jolles, J., Breteler, M.M., 2000. Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Ann. Neurol. 47, 145– 151. Dufouil, C., Chalmers, J., Coskun, O., Besancon, V., Bousser, M.G., Guillon, P., MacMahon, S., Mazoyer, B., Neal, B., Woodward, M., Tzourio-Mazoyer, N., Tzourio, C., 2005. Effects of blood pressure lowering on cerebral white matter hyperintensities in patients with stroke: the PROGRESS (Perindopril Protection Against Recurrent Stroke Study) Magnetic Resonance Imaging Substudy. Circulation 112, 1644 –1650. Enzinger, C., Smith, S., Fazekas, F., Drevin, G., Ropele, S., Nichols, T., Behrens, T., Schmidt, R., Matthews, P.M., 2006. Lesion probability maps of white matter hyperintensities in elderly individuals: results of the Austrian stroke prevention study. J. Neurol. 253, 1064 –1070. Fazekas, F., Chawluk, J.B., Alavi, A., Hurtig, H.I., Zimmerman, R.A., 1987. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am. J. Roentgenol. 149, 351–356. Fazekas, F., Kleinert, R., Offenbacher, H., Schmidt, R., Kleinert, G., Payer, F., Radner, H., Lechner, H., 1993. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 43, 1683–1689. Folstein, M.F., Folstein, S.E., McHugh, P.R., 1975. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189 –198. Frisoni, G.B., Galluzzi, S., Pantoni, L., Filippi, M., 2007. The effect of white matter lesions on cognition in the elderly--small but detectable. Nat. Clin. Pract. Neurol. 3, 620 – 627. Gunning-Dixon, F.M., Raz, N., 2003. Neuroanatomical correlates of selected executive functions in middle-aged and older adults: a prospective MRI study. Neuropsychologia 41, 1929 –1941. Hawthorne, G., Richardson, J., Osborne, R., 1999. The Assessment of Quality of Life (AQoL) instrument: a psychometric measure of healthrelated quality of life. Qual. Life Res. 8, 209 –224. Holtzer, R., Friedman, R., Lipton, R.B., Katz, M., Xue, X., Verghese, J., 2007. The relationship between specific cognitive functions and falls in aging. Neuropsychology 21, 540 –548. Hu, L.T., Bentler, P.M., 1999. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Struct. Equation Model. 6, 1–55. Inzitari, M., Pozzi, C., Rinaldi, L.A., Masotti, G., Marchionni, N., Di Bari, M., 2007. Cognitive and functional impairment in hypertensive brain microangiopathy. J. Neurol. Sci. 257, 166 –173. Junque, C., Pujol, J., Vendrell, P., Bruna, O., Jodar, M., Ribas, J.C., Vinas, J., Capdevila, A., Marti-Vilalta, J.L., 1990. Leuko-araiosis on magnetic resonance imaging and speed of mental processing. Arch. Neurol. 47, 151–156. Kline, R., 1998. Principles and Practice of Structural Equation Modelling. Guildford Publishing Group, New York.

Klingberg, T., 2000. Limitations in information processing in the human brain: neuroimaging of dual task performance and working memory tasks. Prog. Brain Res. 126, 95–102. Klove, H., 1963. Clinical neuropsychology. In: Forster, F.M., editor. The Medical Clinics of North America. W. B. Saunders, New York, p 1647–1658. Linortner, P., Fazekas, F., Schmidt, R., Ropele, S., Pendl, B., Petrovic, K., Loitfelder, M., Neuper, C., Enzinger, C., 2010. White matter hyperintensities alter functional organization of the motor system. Neurobiol. Aging 2010 Aug 17. [Epub ahead of print]. Longstreth, W.T., Jr, Manolio, T.A., Arnold, A., Burke, G.L., Bryan, N., Jungreis, C.A., Enright, P.L., O’Leary, D., Fried, L., 1996. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 27, 1274 –1282. Lord, S.R., Ward, J.A., Williams, P., 1996. Exercise effect on dynamic stability in older women: a randomized controlled trial. Arch. Phys. Med. Rehabil. 77, 232–236. Lord, S.R., Fitzpatrick, R.C., 2001. Choice stepping reaction time: a composite measure of falls risk in older people. J. Gerontol. A Biol. Sci. Med. Sci. 56, M627–M632. Lord, S.R., Menz, H.B., Tiedemann, A., 2003. A physiological profile approach to falls risk assessment and prevention. Phys. Ther. 83, 237–252. Lord, S.R., Sherrington, C., Menz, H.B., Close, J.C.T, 2007. Falls in Older People: Risk Factors and Strategies for Prevention Cambridge University Press, Cambridge. Moscufo, N., Guttmann, C.R., Meier, D., Csapo, I., Hildenbrand, P.G., Healy, B.C., Schmidt, J.A., Wolfson, L., 2009. Brain regional lesion burden and impaired mobility in the elderly. Neurobiol. Aging 2009 May 8. [Epub ahead of print]. Mosley, T.H., Jr, Knopman, D.S., Catellier, D.J., Bryan, N., Hutchinson, R.G., Grothues, C.A., Folsom, A.R., Cooper, L.S., Burke, G.L., Liao, D., Szklo, M., 2005. Cerebral MRI findings and cognitive functioning: the Atherosclerosis Risk in Communities study. Neurology 64, 2056 – 2062. Novak, V., Haertle, M., Zhao, P., Hu, K., Munshi, M., Novak, P., Abduljalil, A., Alsop, D., 2009. White matter hyperintensities and dynamics of postural control. Magn. Reson. Imaging 27, 752–759. Pantoni, L., Garcia, J.H., 1997. Pathogenesis of leukoaraiosis: a review. Stroke 28, 652– 659. Pijnappels, M., Delbaere, K., Sturnieks, D.L., Lord, S.R., 2009. The association between choice stepping reaction time and falls in older adults—a path analysis model. Age Ageing 39, 99 –104. Reitan, R.M., Wolfson, D., 1993. The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation. 2nd Edition. Neuropsychology Publishing Group, Tucson. Roriz-Cruz, M., Rosset, I., Wada, T., Sakagami, T., Ishine, M., De SaRoriz-Filho, J., Cruz, T.R., Hosseinkhani, M., Rodrigues, R.P., Sudoh, S., Arai, H., Wakatsuki, Y., Souza, A.C., Nakagawa, M., Kita, T., Matsubayashi, K., 2007. Cognitive impairment and frontal-subcortical geriatric syndrome are associated with metabolic syndrome in a strokefree population. Neurobiol. Aging 28, 1723–1736. Sachdev, P., Chen, X., Wen, W., 2008. White matter hyperintensities in mid-adult life. Curr. Opin. Psychiatry 21, 268 –274. Sachdev, P.S., Brodaty, H., Reppermund, S., Kochan, N.A., Trollor, J.N., Draper, B., Slavin, M.J., Crawford, J., Kang, K., Broe, G.A., Mather, K.A., Lux, O., 2010. The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70 –90 years. Int. Psychogeriatr. 22, 1248 –1264. Silbert, L.C., Nelson, C., Howieson, D.B., Moore, M.M., Kaye, J.A., 2008. Impact of white matter hyperintensity volume progression on rate of cognitive and motor decline. Neurology 71, 108 –113. Silsupadol, P., Lugade, V., Shumway-Cook, A., van Donkelaar, P., Chou, L.S., Mayr, U., Woollacott, M.H., 2009. Training-related changes in

J.J. Zheng et al. / Neurobiology of Aging 33 (2012) 1177–1185 dual-task walking performance of elderly persons with balance impairment: a double-blind, randomized controlled trial. Gait Posture 29, 634 – 639. Soderlund, H., Nilsson, L.G., Berger, K., Breteler, M.M., Dufouil, C., Fuhrer, R., Giampaoli, S., Hofman, A., Pajak, A., de Ridder, M., Sans, S., Schmidt, R., Launer, L.J., 2006. Cerebral changes on MRI and cognitive function: the CASCADE study. Neurobiol. Aging 27, 16 –23. Soumare, A., Elbaz, A., Zhu, Y., Maillard, P., Crivello, F., Tavernier, B., Dufouil, C., Mazoyer, B., Tzourio, C., 2009. White matter lesions volume and motor performances in the elderly. Ann. Neurol. 65, 706 –715. Sparto, P.J., Aizenstein, H.J., Vanswearingen, J.M., Rosano, C., Perera, S., Studenski, S.A., Furman, J.M., Redfern, M.S., 2008. Delays in auditory-cued step initiation are related to increased volume of white matter hyperintensities in older adults. Exp. Brain Res. 188, 633– 640. Springer, S., Giladi, N., Peretz, C., Yogev, G., Simon, E.S., Hausdorff, J.M., 2006. Dual-tasking effects on gait variability: the role of aging, falls, and executive function. Mov. Disord. 21, 950 –957. Srikanth, V., Beare, R., Blizzard, L., Phan, T., Stapleton, J., Chen, J., Callisaya, M., Martin, K., Reutens, D., 2008. Cerebral white matter lesions, gait, and the risk of incident falls: a prospective populationbased study. Stroke 40, 175–180. St George, R.J., Fitzpatrick, R.C., Rogers, M.W., Lord, S.R., 2007. Choice stepping response and transfer times: effects of age, fall risk, and secondary tasks. J. Gerontol. A Biol. Sci. Med. Sci. 62, 537–542. Stelzel, C., Brandt, S.A., Schubert, T., 2009. Neural mechanisms of concurrent stimulus processing in dual tasks. Neuroimage 48, 237–248. Sullivan, E.V., Rose, J., Rohlfing, T., Pfefferbaum, A., 2009. Postural sway reduction in aging men and women: relation to brain structure, cognitive status, and stabilizing factors. Neurobiol. Aging 30, 793– 807. Szameitat, A.J., Schubert, T., Muller, K., Von Cramon, D.Y., 2002. Localization of executive functions in dual-task performance with fMRI. J. Cogn. Neurosci. 14, 1184 –1199.

1185

Tideiksaar, R., 1996. Preventing falls: how to identify risk factors, reduce complications. Geriatrics 51, 43– 46, 49 –50, 3, [Quiz, 4 –5]. Toulotte, C., Thevenon, A., Watelain, E., Fabre, C., 2006. Identification of healthy elderly fallers and non-fallers by gait analysis under dual-task conditions. Clin. Rehabil. 20, 269 –276. Venkatraman, V.K., Aizenstein, H., Guralnik, J., Newman, A.B., Glynn, N.W., Taylor, C., Studenski, S., Launer, L., Pahor, M., Williamson, J., Rosano, C., 2010. Executive control function, brain activation and white matter hyperintensities in older adults. Neuroimage 49, 3436 – 3442. Wechsler, D., 1997. Wechsler Adult Intelligence Scale-III. Psychological Corporation, San Antonio. Wen, W., Sachdev, P., 2004a. The topography of white matter hyperintensities on brain MRI in healthy 60 to 64-year-old individuals. Neuroimage 22, 144 –154. Wen, W., Sachdev, P., 2004b. Extent and distribution of white matter hyperintensities in stroke patients: the Sydney Stroke Study. Stroke 35, 2813–2819. Wen, W., Sachdev, P., Li, J.J., Chen, X., Anstey, K.J., 2009. White matter hyperintensities in the forties: their prevalence and topography in an epidemiological sample aged 44 – 48. Hum. Brain Mapp. 30, 1155– 1167. Woollacott, M., Shumway-Cook, A., 2002. Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture 16, 1–14. World Health Organization. Disability Assessment Schedule II (WHODAS II), 2001. Available at: www.who.int/icidh/whodas/index.html. Last accessed date 17 April 2010. Wright, C.B., Festa, J.R., Paik, M.C., Schmiedigen, A., Brown, T.R., Yoshita, M., DeCarli, C., Sacco, R., Stern, Y., 2008. White matter hyperintensities and subclinical infarction: associations with psychomotor speed and cognitive flexibility. Stroke 39, 800 – 805.