Resting cerebral blood flow, attention, and aging

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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m

w w w. e l s e v i e r. c o m / l o c a t e / b r a i n r e s

Research Report

Resting cerebral blood flow, attention, and aging Katja Bertsch a,⁎, Dirk Hagemann b , Michael Hermes b , Christof Walter c , Robina Khan a , Ewald Naumann a a

Department of Psychology, University of Trier, Germany Institute of Psychology, University of Heidelberg, Germany c Department of Radiology and Neuroradiology, Hospital of the Barmherzigen Brüder, Trier, Germany b

A R T I C LE I N FO

AB S T R A C T

Article history:

Aging is accompanied by a decline of fluid cognitive functions, e.g., a slowing of information

Accepted 25 February 2009

processing, working memory, and division of attention. This is at least partly due to

Available online 6 March 2009

structural and functional changes in the aging brain. Although a decrement of resting cerebral blood flow (CBF) has been positively associated with cognitive functions in patients

Keywords:

with brain diseases, studies with healthy participants have revealed inconsistent results.

Continuous arterial spin labeling

Therefore, we investigated the relation between resting cerebral blood flow and cognitive

Brain perfusion

functions (tonic and phasic alertness, selective and divided attention) in two samples of

Cognitive performance

healthy young and older participants. We found higher resting CBF and better cognitive

Attention

performances in the young than in the older sample. In addition, resting CBF was inversely

Neural efficiency

correlated with selective attention in the young and with tonic alertness in the elderly participants. This finding is discussed with regard to the neural efficiency hypothesis of human intelligence. © 2009 Elsevier B.V. All rights reserved.

1.

Introduction

It is well-known that cognitive functions decline with increasing age. Moreover, these changes occur together with a reduced cerebral blood flow (CBF) in old age. Does this imply that individuals with greater CBF also show better cognitive functions? The present paper reports on an empirical study that aimed at this research question. To provide the relevant background, we first review some of the literature on age-related differences in cognitive function and CBF, and then present some studies on the association between both variables.

1.1.

Age-related differences in cognitive functions and CBF

In healthy elderly individuals the age-related decrements in cognitive functions foremost comprise fluid or process

based functions, i.e., a slowing of the speed of information processing (Salthouse, 1985, 1996), deficits in the division of attention (McDowd and Shaw, 2000), in dual-task performance (Hawkins et al., 1992), and in sensory and motor functions (Park, 2000). These decreases are at least partly due to deteriorating changes of the aging central nervous system. Aging is accompanied by an increasing atrophy of the gray and white brain matter (Raz, 2000), an enhanced cell loss, a widespread shrinkage of brain tissue as well as a degeneration of dentritic branches and synaptic connections (Reuter-Lorenz, 2000), a change in neurotransmitter concentration (Nyberg and Bäckman, 2004), and a decrease in cerebral oxygen and glucose metabolism (Marchal et al., 1992). The dorsolateral prefrontal cortex, which plays a central role in working memory and attention (Posner, 2004; Raz, 2000), as well as the hippocampus and parts of the

⁎ Corresponding author. Fax: +49 651 201 3956. E-mail address: [email protected] (K. Bertsch). 0006-8993/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2009.02.053

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cerebellum are especially affected by those structural and functional changes in the aging brain (Nyberg and Bäckman, 2004; Raz, 2004). Another variable that changes with age is CBF. Several studies have shown a continuous decrease in resting CBF in the whole gray matter during adulthood (Devous et al., 1986; Frackowiak et al., 1980; Hagstadius and Risberg, 1989; Leenders et al., 1990; Parkes et al., 2004; Rodriguez et al., 1991; Rodriguez et al., 1988), although others found only regional age-related decrements in resting CBF (Marchal et al., 1992; Martin et al., 1991; Van Laere et al., 2001). In addition, the reported extent of the age-related differences in resting CBF varies between studies, and their comparability is restricted due to different measurement methods and spatial resolutions as well as differences of sample characteristics such as the participants' age, and physical and mental fitness. Since a constant delivery and transport of glucose and oxygen is crucial for an optimal functioning of the brain (Gusnard and Raichle, 2001), the investigation of the age-related decrease in CBF remains important. Therefore, one aim of the present study was to investigate differences in resting CBF as well as in cognitive functions between healthy young and elderly individuals.

1.2.

Relationship between cognitive functions and CBF

In addition to age-related decrements in resting CBF and cognitive functions, the question arises if and how these two parameters are related in individuals of different age groups. So far, several clinical studies with patients suffering from brain diseases, i.e., Alzheimer's disease, senile dementia, or Huntington's disease, have found both severe decrements in cognitive functions and a reduced resting CBF or cerebral glucose utilization rate in patients compared with agematched healthy counterparts (Alexander et al., 1997; Berent et al., 1988; Butler et al., 1983; Ferris et al., 1980; Meyer et al., 1988). These reductions were directly related to the severity of the dementia (Butler et al., 1983). In addition, the patients' cognitive performance was positively associated with their glucose utilization rate (Ferris et al., 1980) or resting CBF (Meyer et al., 1988). However, a negative correlation between resting CBF and cognitive test performance was found in children suffering from sickle cell anemia, a disease that includes decreased neuropsychological functions and intelligence (Strouse et al., 2006). Unfortunately, only two of the studies reported a relationship between cognitive functions and resting CBF or glucose metabolism rate in a healthy control group. Contrary to the results in the patient samples, these studies found a null (Meyer et al., 1988) or even negative correlation (Berent et al., 1988) between resting cerebral blood flow or glucose metabolism and cognitive functions in healthy participants. These findings are complemented by the few studies with healthy individuals that have examined the association between resting cerebral metabolism or blood flow and cognitive functions. Duara et al. (1984) found no association between cognitive functions and resting cerebral glucose metabolism in healthy male participants between 21 and 83 years. Interestingly, cerebral glucose metabolism

did not correlate with the participants' age in this study. In a longitudinal study, Rogers et al. (1990) observed a continuous decrease in resting CBF over a period of four years in physically inactive elderly participants (62 to 70 years), while there was no such decrease in age-matched active participants. The physically inactive participants also showed significantly lower cognitive test performance after four years than their active counterparts. However, the correlation between cognitive performance and resting CBF was not reported in this study. Rabbitt et al. (2006) performed a study with elderly participants between 62 and 85 years, and reported that resting carotid and basilar blood flow was negatively related to the test scores in several cognitive tasks, especially tasks involving information processing speed. In this study, resting cerebral blood flow accounted for up to 36% of the age-related variance in the speed tests.

1.3.

The present study

In summary, robust age-related decrements have been found for cognitive functions and resting CBF in healthy individuals. However, these effects are reported only if both variables are analyzed separately. When the association between brain physiology and cognitive functions is examined, the results of different studies are inconsistent. While the results of most of the patient studies indicate a positive relationship between resting CBF and cognitive functions, the findings of the few studies with healthy participants are controversy. In addition, the samples of these studies often consisted only of elderly individuals and therefore, give no evidence about similarities or differences in the relation between measurements of resting brain activity and cognitive functions between different age groups. The aim of the present study was (1) to investigate age differences in resting cerebral blood flow and basal cognitive functions in a healthy population and (2) to assess the association between resting CBF and cognitive performance in different age groups. Resting cerebral blood flow and cognitive performances were measured in two samples: the first sample consisted of healthy young adults (20– 30 years) while the second sample included healthy older adults (60–70 years). This design allows the investigation of age-related similarities and differences in the relation between cognitive performance and resting CBF. In addition, resting CBF was measured twice and non-invasively with continuous arterial spin labeling (CASL) magnetic resonance (MR) imaging. We used three neuropsychological subtests to measure four different facets of attentional performance because intact attention is one of the basic requirements to accomplish every day life as it underlies all kinds of cognitive processes (e.g. perception, memory, orientation, and problem solving) and previous studies have shown agerelated decrements in those functions (McDowd and Shaw, 2000). All participants underwent the same measurement procedure. According to the results of previous studies, we expected an age-related difference between the two samples in resting CBF with greater CBF values in the young than in the older participants. We hypothesized age differences in the per-

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formance of the cognitive tasks with faster reactions in the student sample. Within the two age groups, we expected an association between resting CBF and the cognitive test performance.

2.

Results

2.1.

Age differences

2.1.1.

Age differences in CBF

Table 1 shows the mean CBF averaged over all CBF acquisitions of both measurement occasions for the young and older sample. The young adults showed greater resting CBF than the older adults in the whole gray matter as well as in all regions of interest (ROIs). In both samples, CBF values increased from anterior to posterior brain areas with greatest values in the occipital cortex, and CBF in all areas was slightly greater in the right than in the left hemisphere. Beyond this, in both samples the CBF for the whole gray matter was greater in female than in male participants. The age group (young adults, older adults) by sex (male, female) by lobes (frontal, temporal, parietal, occipital) by hemisphere (left, right) ANOVA of CBF statistically confirmed the overall difference in CBF values between the two samples since the main effect of age group was significant, F(1,52) = 4.16, p = .047, ω2 = .08. The young adults had significantly greater CBF values (whole gray matter: 71.8 ± 12.0 ml/100 g/min) than the older adults (whole gray matter: 67.3 ± 6.2 ml/100 g/min) in all brain areas. In addition, there was a significant main effect of sex, F(1,52) = 18.27, p = .000, ω2 = .32, with greater CBF values in females (76.3 ± 10.7 ml/ 100 g/min) than in males (64.4 ± 6.5 ml/100 g/min). These main effects were qualified by a significant interaction between age group and sex, F(1,52) = 5.10, p = .028, ω2 = .10.

Table 1 – Mean CBF ± one standard deviation, for the young and the older sample.

Gray matter a Frontal lobes - Left - Right Temporal lobes - Left - Right Parietal lobes - Left - Right Occipital lobes - Left - Right

Young adults

Older adults

M ± SD

M ± SD

71.8 ± 12.0 68.8 ± 11.5 68.1 ± 11.5 69.5 ± 11.7 749 ± 13.0 74.2 ± 13.0 75.6 ± 13.5 73.0 ± 13.2 72.2 ± 13.1 73.8 ± 13.6 79.4 ± 14.0 77.9 ± 14.3 80.8 ± 13.9

67.3 ± 6.2 63.8 ± 4.4 63.4 ± 4.3 64.3 ± 4.9 69.9 ± 6.6 69.4 ± 6.3 70.4 ± 7.4 68.4 ± 9.2 67.8 ± 8.7 68.9 ± 9.9 73.5 ± 11.0 73.1 ± 11.1 73.9 ± 11.6

CBF was measured in units of ml/100 g/min. The mean CBF represents the average over both measurement occasions. a Gray matter includes all lobes, the cingulate cortex, the insula, and subcortical nuclei.

Fig. 1 – Mean CBF (±1 standard error) separately for age group and sex.

There was greater CBF in the young than in the elderly women, whereas no such age-related effect was present in the men (see Fig. 1). Additionally, there was a significant main effect of lobes, F(3,156) = 60.19, p = .000, ω2 = .45. Post hoc tests revealed significant differences of CBF values between all lobes except for the temporal and parietal lobes. The differences were greatest between CBF values in the frontal (67.2 ± 10.0 ml/100 g/min) and occipital lobes (77.5 ± 13.3 ml/100 g/min). This main effect was qualified by a significant interaction between lobes and sex, F(3,156) = 7.50, p = .000, ω2 = .08, with smallest sex differences of CBF values in the frontal lobes and greatest sex differences in the occipital lobes (see Fig. 2). Within the female participants, the differences of CBF values between all lobes were significant except for the comparison between CBF values in the temporal and parietal lobes. Within the male participants, however, CBF only differed significantly between the frontal and the occipital lobes. Finally, a main effect of hemisphere, F(1,52) = 11.79, p = .001, ω2 = .13, indicated significantly greater CBF values in the right (73.2 ±

Fig. 2 – Mean CBF (±1 standard error) separately for lobes and sex.

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11.6 ml/100 g/min) than in the left (71.6 ± 11.2 ml/100 g/min) hemisphere.1

2.1.2.

Age differences in attention task performance

The mean reaction times in the four attention tasks are presented in Table 2 for both samples. In general, the young adults reacted faster than the older adults in all cognitive tasks. In both samples, reactions on the more simple attention tasks (tonic and phasic alertness) were faster than on the more complex tasks (selective and divided attention). The age group (young adults, older adults) by sex (male, female) by attention task (tonic alertness, phasic alertness, selective attention, divided attention) ANOVA of reaction times statistically confirmed this main effect of age, F(1,52)= 10.33, p = .002, ω2 = .14, with faster overall reactions in the young (409.6 ± 31.5 ms) than in the older sample (462.4 ± 48.9 ms). Moreover, we found a significant main effect of attention task, F(3,156) = 856.12, p = .000, ω2 = .92, with significantly shorter reaction times in tonic and phasic alertness than in selective and divided attention tasks. Further on, we found a significant interaction between attention task and age group, F(3,156) = 14.17, p = .000, ω2 = .15. Although the young adults reacted faster than the older adults in all attention tasks, this age difference was greatest in selective attention (see Table 2).2 A visual inspection of Table 2 also suggests that there may be age-related differences in the variances of the reaction time measures, with greater variance in the older than in the young sample. Confirming this observation, this age-related difference was significant for the tonic alertness task, F(17, 37) = 4.93, p = .000, ω2 = .54, the phasic alertness task, F(17, 37) = 2.07, p = .032, ω2 = .25, and the divided attention task, F(17, 37) = 2.67, p = .006, ω2 = .34. No significant difference occurred for the selective attention task.

2.2. Cerebral blood flow and attention task performance within the two samples In order to investigate the relation between resting CBF and cognitive performance within the two age groups, we performed four-way ANOVAs with repeated measurements on

1

Table 2 – Mean reaction times ± one standard deviation for the young and the older sample.

The two age groups differed in size, i.e., 38 young and 18 older adults participated in this study. Therefore the same group comparison was conducted with N = 18 randomly selected young participants in order to avoid different sample sizes between the young and older sample. The results of this analysis are comparable to those including all participants of the young sample with significant main effects of age group, F(1,32) = 8.37, p = .007, ω2 = .17, and sex, F(1,32) = 25.52, p = .000, ω2 = .41, as well as a significant interaction between age group and sex, F(1,32) = 7.54, p = .01, ω2 = .18. Also, significant main effects of lobes F(3,96) = 30.47, p = .000, ω2 = .38, and hemisphere F(1,32) = 9.31, p = .005, ω2 = .10, as well as a significant interaction between lobes and sex F(3,96) = 4.96, p = .008, ω2 = .07, were found. 2 Again, a second group comparison was conducted with N = 18 randomly selected young participants in order to avoid different sample sizes between the young and older sample. As in the analysis which included all participants of the young sample, we found significant main effects of age group, F(1,32) = 6.03, p = .02, ω2 = .09, and attention task, F(3,96) = 527.32, p = .000, ω2 = .92, as well as a significant interaction between age group and attention task, F(3,96) = 11.13, p = .000, ω2 = .17.

Overall reaction time Tonic alertness a Phasic alertness b Selective attention c Divided attention d

Young adults

Older adults

M ± SD

M ± SD

409.6 ± 31.5 229.7 ± 19.9 228.0 ± 24.8 507.3 ± 55.6 673.4 ± 61.2

462.4 ± 48.9 274.7 ± 44.3 262.0 ± 35.6 591.4 ± 68.3 721.7 ± 93.5

Reaction time was measured in units of ms. a Choice reaction time task without fore-signal. b Choice reaction time task with fore-signal. c Donder's choice reaction time task with two target and three nontarget signals. d Cross-modal dual-task test.

the factors lobes and hemisphere, the between-subject factor sex, and the continuous between-subject factor attention task performance (tonic alertness, phasic alertness, selective attention, or divided attention) separately for the two samples. Only the effects that involve the attention task performance factor are reported here.

2.2.1.

Young adults

In the young sample, the ANOVAs revealed a main effect of selective attention, F(1,32) = 9.82, p = .004, ω2 = .19. Bivariate correlations showed a significantly positive relation between the reaction times in the subtest of selective attention and CBF in all ROIs (.33 ≤ r ≤ .47, ps < .05), which indicates faster reactions — and thus better performance in selective attention — in participants with lower CBF values. Besides a significant interaction between lobes, hemisphere, and divided attention, F(3,96) = 2.79, p = .045, ω2 = .02, there was no further main effect of any of the attention task performances or interactions between task performance and CBF. Bivariate correlations between divided attention and the CBF values were close to zero (− .07 ≤ r ≤ .16, ps > .05). The correlations between resting CBF and the reaction times in selective and divided attention are presented for the young adults in Table 3, which additionally presents the data separately for men and women. There were stronger associations between CBF and selective attention in all ROIs for the male compared to the female participants. These findings were complemented by a voxel-based analysis of the significant ROI results, using simple regression models with resting CBF in the voxels as dependent variable and the attention task performance as predictor. The results of this analysis are presented in Fig. 3A. Although there was a slight right temporal accentuation, the results indicate that the performance in selective attention in the young sample is associated with the global resting CBF in the whole gray matter and not specifically with CBF in certain regions or clusters.

2.2.2.

Older adults

In the older sample, the ANOVAs revealed a significant main effect of tonic alertness, F(1,12) = 5.17, p = .042, ω2 = .19. There was a positive relationship between reaction times in the tonic alertness task and CBF in all ROIs (.39 ≤ r ≤ .55, ps ≤ .11) except for the frontal lobes. Participants with lower CBF values

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Table 3 – Correlations between CBF in various ROIs (separately for lobes and hemispheres) and reaction times in selective and divided attention for the young sample. Selective attention a

Gray matter Frontal lobes - Left - Right Temporal lobes - Left - Right Parietal lobes - Left - Right Occipital lobes - Left - Right

Divided attention b

Males

Females

All participants

Males

Females

All prticipants

.65(⁎⁎) .61(⁎⁎) .58(⁎⁎) .60(⁎⁎) .70(⁎⁎) .70(⁎⁎) .68(⁎⁎) .65(⁎⁎) .71(⁎⁎) .56(⁎) .53(⁎) .54(⁎) .51(⁎)

.32 .17 .16 .18 .32 .31 .33 .27 .28 .26 .46(⁎) .46(⁎) .45

.44(⁎⁎) .35(⁎) .33(⁎) .37(⁎) .46(⁎⁎) .43(⁎⁎) .47(⁎⁎) .40(⁎) .41(⁎) .39(⁎) .47(⁎⁎) .47(⁎⁎) .47(⁎⁎)

.18 .04 .00 .06 .32 .38 .27 .17 .26 .08 .15 .18 .11

.11 −.03 −.08 .01 .09 .07 .11 .04 .06 .02 .30 .33 .26

.07 − .03 − .07 .00 .11 .10 .11 .03 .06 .01 .14 .16 .12

CBF was measured in units of ml/100 g/min and reaction time was measured in units of ms. A positive correlation between the reaction time in an attention task and CBF indicates a negative association between the performance in this task and blood flow. ⁎The correlation is significant at the .05 level (2-tailed); ⁎⁎the correlation is significant at the .01 level (2-tailed). a Donder's choice reaction time task with two target and three non-target signals. b Cross-modal dual-task test.

reacted faster, and thus showed a better performance, in tonic alertness. The correlations were statistically significant between tonic alertness and the CBF in the temporal (r = .47, p < .05) and occipital (r = .54, p < .05) lobes and lowest in the frontal lobes (r = .12, p > .05). The low correlations between tonic alertness and CBF in the frontal lobes could partly be due to the low stability of the CBF measures in this region (see Experimental procedures section). There were no further main effects of attention task performance or interactions between the performance and resting CBF. The correlations between resting CBF and the reaction times in tonic alertness are shown for the older adults in Table 4, which additionally presents the data separately for men and women. There were

greater associations between CBF and tonic alertness in the female than the male participants. These findings were again complemented by a voxel-based analysis of the significant ROI results. The results of this analysis are presented in Fig. 3B and suggest that the performance in tonic alertness in the older sample is associated with the global resting CBF in the whole gray matter and not specifically with CBF in certain regions or clusters.

2.2.3.

Additional analyses

Taken together, the present findings suggest that there were substantial associations between CBF and selective attention in the younger sample, and between CBF and tonic alertness in the

Fig. 3 – (A) Regions of significant correlations between CBF and selective attention in the young sample (uncorrected p < .05, cluster size > 10 voxels, N = 38) and (B) correlations between CBF and tonic alertness in the older sample (uncorrected p < .05, cluster size > 10 voxels, N = 18).

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Table 4 – Correlations between CBF in various ROIs (separately for lobes and hemispheres) and reaction times in tonic alertness for of the older sample.

the TAP subtests with regard to their association with CBF at least in the sample of younger participants.

Tonic alertness a

Gray matter Frontal lobes - Left - Right Temporal lobes - Left - Right Parietal lobes - Left - Right Occipital lobes - Left - Right

Males

Females

All participants

−.06 −.64 −.55 −.61 .10 .01 .17 .07 .08 .07 .28 .35 .20

.63 .50 .63 .35 .68(⁎) .66 .67(⁎) .52 .54 .51 .59 .56 .57

.42 .12 .19 .05 .47(⁎) .39 .51(⁎) .40 .40 .39 .54(⁎) .55(⁎) .50(⁎)

CBF was measured in units of ml/100 g/min and reaction time was measured in units of ms. A positive correlation between the reaction time in an attention task and CBF indicates a negative association between the performance in this task and blood flow. ⁎The correlation is significant at the .05 level (2-tailed). a Choice reaction time task without fore-signal.

older sample. Two additional analyses investigated how specific these relationships are for the particular attention tasks. For the first analysis, we calculated the mean z-standardized reaction time of the four attention tasks and used it as index for overall information processing speed. Multiple regression analyses were performed separately for the two samples in order to investigate whether the global CBF in the whole gray matter, age, and sex could significantly predict overall information processing speed. These analyses revealed that neither global CBF in the whole gray matter, nor age or sex were significant predictors for overall information processing speed in the young, R2 = .02, F(3,34) = .24, p = .866, all β < .20, p > .40, and in the elderly sample, R2 = .24, F(3,14) = 1.44, p = .273, all β < .39, p > .150. More specifically, the correlation between global CBF and the index of overall information processing speed was not significant in the younger sample, r = .09, p = .601, and it was not significant in the older sample, r = .24, p = .346. These findings suggest that this index of overall information processing speed is less sensitive for CBF relationships than the four TAP subtest measures. In the second analysis, we used a correlation pattern analysis of the relationships between CBF and the performance in the four attention tasks in order to test the equality of these four associations (e.g., Steiger 1980). In the younger sample, the correlation between global CBF in the gray matter and the reaction times was significantly greater in the selective attention task (r = .44) than in the tonic alertness task (r = − .12; difference p = .001), than in the phasic alertness task (r = − .13; difference p = .000), and compared to the divided attention task (r = .07; difference p = .020). In the older sample, however, there were no significant differences for the correlations between global CBF in the gray matter and the reaction times in the four attention tasks, although the difference approached significance when comparing the tonic alertness task (r = .42) with selective attention task (r = .08; difference p = .062). These findings suggest that there are significant differences between

3.

Discussion

The purpose of the present study was to investigate (1) agerelated differences in resting CBF and cognitive functions, and (2) the relationship between individual differences of resting CBF and cognitive functions in healthy young and elderly individuals. We measured resting CBF with CASL MR imaging and cognitive performance in four attention tasks in two samples consisting of young and older adults.

3.1.

Age differences

3.1.1.

Cerebral blood flow

There was a significant difference between the two age groups in resting CBF, with lower CBF values in the entire gray matter of the older sample. This is in line with the findings of a previous CASL study (Parkes et al., 2004) and several studies measuring resting CBF with positron emission tomography (PET) (Frackowiak et al., 1980; Leenders et al., 1990) and Single Photon Emission Computed Tomography (SPECT) (Devous et al., 1986; Hagstadius and Risberg, 1989; Matsuda et al., 1984; Melamed et al., 1980; Rodriguez et al., 1988, 1991; Slosman et al., 2001). This effect could be explained by deteriorated cardiovascular functioning in the older sample. Aging is accompanied with a decline in several parameters of cardiovascular functioning (e.g., maximal heart rate and cardiac output) and cardiorespiratory fitness (i.e., aerobic capacity), even in the absence of coronary disease (Brandfonbrener et al., 1955; Dustman et al., 1994; Luisada et al., 1980). As a consequence, the blood supply of organs such as the brain becomes less sufficient with increasing age.

3.1.2.

Attention task performance

In the present sample, there was a clear difference between the two age groups in the attention task performance, with worse performances (slower reactions) in the older sample. The difference was greatest for selective attention. Age differences have also been reported in the test manual (Zimmermann and Fimm, 2002) and in previous studies, which demonstrated a heavy decline in fluid cognitive functions. In particular, agerelated decline has been reported for the speed of information processing (Salthouse, 1985, 1996), deficits in the division of attention (McDowd and Shaw, 2000), dual-task performance (Hawkins et al., 1992), and in sensory and motor functions (Park, 2000). Since we primarily observed slower reaction times in the older sample, this might indicate a general age-related slowing of information processing.

3.2. CBF and attention task performance within the two samples The new and interesting finding of this study is a negative relationship between resting CBF measured with CASL MR imaging and the attention task performance in two healthy samples of different age. In the young sample, we observed a significant negative relation between resting CBF in all cortical

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regions and the performance in selective attention. However, these participants showed no association between CBF and tonic and phasic alertness as well as divided attention. In the older sample, resting CBF in all cortical regions was negatively correlated with the performance in tonic alertness, but not with the performance in phasic alertness and selective and divided attention. The inverse relationship between resting CBF and the performance in a cognitive task is consistent with the results of studies with healthy participants (e.g., Berent et al., 1988) and children suffering from sickle cell anemia (Strouse et al., 2006). In addition, a negative relation between resting brain activity and intelligence has also been found in several EEG studies (Doppelmayr et al., 2002; Jaušovec, 1996; Jaušovec and Jaušovec, 2001; Schmid et al., 2002). These results are often discussed in the context of the neural efficiency hypothesis, which is based on the evidence of various EEG and PET studies that revealed a lower and spatially more localized brain activity in above-average intelligent individuals (Haier et al., 1988; Jaušovec, 1996, 1998, 2000; Jaušovec and Jaušovec, 2000). According to the neural efficiency hypothesis, intelligence is not so much a function of how hard a brain works, but rather of how efficiently it works (Haier et al., 1988). Because there are strong and positive associations between various measures of intelligence and attention (e.g., Cornelius et al., 1983; Cowan et al., 2006; Schweizer and Moosbrugger, 2004), the present finding of a negative relationship between attention task performance and CBF renders further support for the neural efficiency hypothesis. It is intriguing that we specifically found associations between resting CBF and selective attention in the young adults, and associations between CBF and tonic alertness in the older adults. As for the young sample, a possible reason might be the very small variance in reaction times of tonic and phasic alertness, which indicates very good test performances of all young participants. According to Salthouse (1991), speed differences are small within a homogeneous sample of young adults, but speed decreases with age and individual differences in speed may increasingly account for cognitive differences. This is in line with a significantly greater variance in most of the attention tasks in the older compared to the young sample, especially in tonic alertness and has been reported by others (Rabbitt et al., 2004). Moreover, it has been suggested that the performance in selective and divided attention tasks is largely dependent on executive functions, which are especially associated with regions of the prefrontal cortex (Posner, 2004; Raz, 2000). As resting cerebral blood flow in the frontal cortex was very unstable over a period of seven weeks in our elderly participants, this might be a reason for the lacking association between CBF and the performance in selective or divided attention in the older sample. Furthermore, it should be kept in mind that the older sample only consisted of 18 participants. As the four attention tasks shared some common variance as indicated by the positive intercorrelations and the mediocre internal consistencies in both samples, we additionally analyzed whether global CBF in the whole gray matter, sex, and age were associated with overall information processing speed. However, no such association could be found in the young and in the older sample, which suggests that the

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relationships between CBF and attention task performance has been rather specific for the different attention tasks. Another interesting result of the present study is that the relations between attention task performance and resting CBF were not localized to a particular lobe, e.g., to the frontal lobe, but were found in all lobes of the brain. One possible reason for this could be a great inter-individual variability in large vessel velocity. This could cause variability in the labeling efficiency and thus to relations between global CBF and cognitive functions. This would be in line with a relationship between blood flow in the carotid and basilar artery and various cognitive test scores reported by Rabbitt et al. (2006). In addition, tonic and phasic alertness seems to be regulated by a wide network including, especially right hemispheric, parts of the frontal and parietal cortex as well as brain-stem and talamic nuclei (Sturm and Willmes, 2001). The wide outspread of this network could be a reason for the association of tonic alertness with resting CBF in all cortical regions. Moreover, previous studies (Haier et al., 1988, 1992; Strouse et al., 2006) have also reported a negative relation between cognitive functions and resting CBF as well as glucose metabolism rate for the whole brain.

3.3.

Limitations

Before strong conclusions can be drawn, three limitations of the present study may be noted. First, all participants in the young sample were students of the University of Trier, while the participants of the older sample consisted of senior citizens with various educational backgrounds. All participants of the older sample had at least nine years of education, most of them had completed an apprenticeship, which had been the conventional way of job training at that time, and only five had an academic degree. Thus, the age-related differences in the present study may at least in part be due to differences in educational level. However, the group differences in the present study are consistent with a larger literature, which suggests that they are not only due to differences in education. Moreover, the relationships between attention task performance and CBF were found within each sample and thus are not an effect of differences between the samples. Second, we used the same transit time for the young and the older participants in order to compare between the two groups. Also, the same labeling efficiency was assumed for all participants. However, transit time and labeling efficiency might differ between young and older individuals due to differences in blood velocity in the major arteries, which could result in a longer transit time and a less efficient labeling in the older group. Nevertheless, age-related differences in resting CBF have been reported by other studies with different measurement techniques (i.e., PET, SPECT). Furthermore, the relationship between resting CBF and cognitive functions were analyzed in separate analyses for the two groups and therefore cannot be affected by potential differences in transit time or labeling efficiency between the groups. Third, the correlation pattern analysis of the associations between CBF and performance in the four attention tasks revealed a significant specificity of this relationship for the selective attention task in the younger participants, whereas no significant specificity of this relationship emerged in the

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older participants. The latter null finding might be due to the relatively small size of the older sample (N = 18), which renders not sufficient statistical test power to detect the descriptively found differences in the size of the associations as statistically significant. Future research may investigate the specificity of these associations in larger samples.

3.4.

Conclusion

In summary, the present study showed age-related differences in resting CBF and the performance in four attention tasks and indicated that attention task performance is related to resting CBF. Moreover, the results of this study indicate that the relationship between resting CBF and attention is complex, task specific, and age dependent. Future studies with larger sample sizes are necessary to further investigate the task specificity and the reasons for it. The complex relations between CBF and cognitive task performance are difficult to interpret but emphasize the problem of generalizing evidence found in young adults on elderly individuals. Therefore, the use of mixed age or solely elderly samples is necessary to examine cognitive functions and possible neurophysiological correlates in that age group.

4.

Experimental procedures

4.1.

Participants

We investigated cognitive functions and resting CBF in two samples: Sample 1 (young adults) consisted of 38 young adults of the University of Trier (19 female and 19 male, mean age = 24.5 years, SD = 2.3 years, range = 20–29 years). Sample 2 (older adults) consisted of 18 elderly participants (9 female and 9 male, mean age = 64 years, SD = 2.4 years, range = 60–68 years). All participants underwent a screening interview to determine if they were suitable for MR imaging. After a complete description of the study, written informed consent was obtained for all participants. Exclusion criteria included left-handedness, assessed by a German version of the Edinburgh Handedness Inventory (Oldfield, 1971), cerebrovascular diseases or psychiatric disorders, a resting blood pressure higher than 140:90 mmHg, regular medication (besides contraceptives and anti-hypertensive drugs) as well as any chronic disease.3 The study was approved by the local ethics committee. Participation was compensated with €100 (approximately US$150).

3

Hypertension is a prevalent condition in aging and is known to affect CBF (Beason-Held et al., 2007). Therefore, hypertension might be a confounding variable in the present study. However, we only included participants with a blood pressure below 140:90 mmHg. Some of the older participants took anti-hypertensive medication to control for hypertension. Blood pressure did not significantly differ between participants who took antihypertensive medication and those without medication. There were also no significant differences between male and female participants in blood pressure and blood pressure was not significantly related to resting CBF.

4.2.

Experimental design and procedure

The experimental procedure was identical for both samples. Each participant took part in three measurement sessions: two resting CBF measurements with CASL magnetic resonance imaging (MRI) and one neuropsychological assessment. The order was identical for all participants and started with an MRI measurement followed by the neuropsychological testing after three to four weeks, and by a second MRI measurement seven weeks after the first MRI session. The two MRI measurements were necessary to estimate the reproducibility of resting CBF as measured with CASL (Hermes et al., 2007).

4.3.

Imaging data acquisition

A detailed description of the imaging procedure, image acquisition and processing for the present study is reported by Hermes et al. (2007). In short, in the first measurement occasion, four consecutive baselines (each lasting 5 min 42 s) were conducted in a well-lit scanning room in order to measure resting CBF. Two baselines were acquired with eyes open and two were administered with eyes closed. The same randomly assigned, counterbalanced orders were used for the eyes-open (O) and eyes-closed (C) conditions of the resting baselines (O–C–C–O and C–O–O–C) in both measurement occasions. Each of the four baseline CBF measurements consisted of 40 CASL acquisitions, which result in 160 pairs of label and control images (Alsop and Detre, 1996). In order to control for neurological abnormalities, a T 2 weighted image was administered after the CASL measurement. The same measurement protocol was used at the second measurement occasion except of leaving out the T2weighted sequence. Imaging was conducted in a clinical 1.5 T scanner (Interna, Philips Medical Systems, Best, The Netherlands) with a send/ receive coil provided by the manufacturer. After a triplanar planning sequence (scan duration: 2 min 22 s), an anatomical T1weighted sequence (fast field echo, field of view [FOV] = 256 × 192 mm, matrix = 256 × 256, slice thickness= 1 mm, TR/ TE=11.9 ms/3.3 m, scan duration: 13 min 22 s) was acquired, which was followed by the CBF measurements. A continuous arterial spin labeling (CASL) technique was used for resting CBF measurements. Thirteen slices covering the whole brain were acquired from inferior to superior in an interleaved order using a single-shot spin echo (EPI) sequence (field of view [FOV]=230 mm, matrix 64 × 63, slice thickness = 8 mm with a 1 mm gap, bandwidth=78.4 kHz, flip angle=90°, TR/TE=4125 ms/42 ms) and reconstructed to an in-plane resolution of 1.8×1.8 mm. The labeling plane was placed 60 mm beneath the center of the imaging slices (labeling duration = 2.2 s, labeling amplitude=35 mG, labeling gradient=0.25 G/cm). The postlabeling delay varied from 0.8 to 1.8 s because each slice was acquired at a slightly different time. An amplitude-modulated version of the CASL technique was used to control for magnetization transfer effects (Alsop and Detre, 1998).

4.4.

Imaging data processing

CASL and T1 images were analyzed offline with the Statistical Parametric Mapping Software (SPM2, Wellcome Department

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of Imaging Neuroscience, London UK, implemented in MATLAB 7, The MathWorks Inc., Natick, MA) as described in Hermes et al. (2007). In short, we first checked the data for gross artifacts. Since the lowest slice showed heavy lowintensity artifacts in all participants, all voxels in this slice were excluded from further data analyses. After this the label and control images were separately realigned in two steps (realignment to the first label/control image and realignment to an average label/control image) to correct for head movements. Then segmentation and normalization of the T1 images to the gray matter Montréal Neurological Institute (MNI) template was administered. Next, label and control images were reoriented, and coregistered to the T1 image and normalized based on the T1 image. After these image registration steps a separate average was calculated for the 320 label and control images of both occasions, which resulted in one pair of label and control images for each participant. CASL images were quantified according to a method devised by Alsop and Detre (1996) and the T1 images were segmented according to Good et al. (2001). The resulting tissue probability maps were converted into dichotomous masks, which were then multiplied with the CBF images resulting in CBF maps for gray and white matter. In a next step several regions of interest (ROIs) were defined based on published templates (Tatu et al., 1998; Tzourio-Mazoyer et al., 2002) and multiplied with the segmented CASL images. Beyond the whole gray matter, several ROIs were chosen according to cerebral lobes (frontal, temporal, parietal, and occipital lobes) and hemispheres (right and left hemisphere) resulting in eight hemisphere × lobe ROIs and one for gray matter. Hermes et al. (2007) have shown good reproducibility of the CASL measures in the young sample.

4.5.

Behavioral data

Three subtests of the Testbatterie zur Aufmerksamkeitsprüfung (TAP) [test battery for attention testing], a German standard neuropsychological test battery (Zimmermann and Fimm, 2002), were used to examine cognitive performances. The TAP is one of the most commonly used computer based test batteries in German clinical neuropsychology as it allows differentiated diagnostics of different attentional functions and thus attentional disorders. The TAP has been constructed based on a multidimensional model of attention, which originates from the work of Posner (Posner and Boies, 1971; Posner and Rafal, 1987) and from several neuropsychological or clinical approaches (Posner and Petersen, 1990; Sturm and Zimmermann, 2000; Van Zomeren and Brouwer, 1994). In particular, the work of Posner and Boies (1971), Posner and Rafal (1987), Van Zomeren and Brouwer (1994), as well as Sturm (Sturm et al., 1999; Sturm and Zimmermann, 2000) suggested that at least four major components of attention may be distinguished based on the location of a brain damage: alertness (phasic and tonic alertness), selective attention, divided attention, and sustained attention. Following this distinction, the TAP comprises subtests that were designed to assess the performance in different components of attention. Previous validation studies used cluster and factor analysis (Becker et al., 1996) and structural equation modeling (Goldhammer et al., 2007) and confirmed that the subtests of the

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TAP measure different components of attention. Although there are no imaging studies that validate the neuropsychological distinctions of the different subtests of the TAP, there are several studies that used similar tasks in imaging experiments. The findings of these studies suggest that the different tasks are associated with different patterns of localized brain activity. In particular, the results of imaging studies (PET and fMRI) with patients and healthy individuals have revealed different neural networks for tonic and phasic alertness (Weis et al., 2000), selective attention, and divided attention (Corbetta et al., 1991; Corbetta et al., 1995; Posner and Raichle, 1994; Sturm et al., 1999; Vohn et al., 2007). The order of the test administration was identical for all participants, starting with a choice reaction time task with and without fore-signal as this is the most simple of the three subtests, followed by a Donder's choice reaction time task with two target and three non-target signals and then a crossmodal dual-task test. All tests took place in a well-lit room and were presented on a 17″ computer screen. The required response consisted of pressing a key, which was placed underneath the participant's right forefinger. Standardized instructions were presented on the monitor before the beginning of each task as well as practice trials to make the participant familiarized with the task.

4.5.1.

Choice reaction time task with and without fore-signal

This test was divided in four sequences each comprising 20 trials: the first and last sequence consisted of a choice reaction time task with a visual stimulus (a white cross), which was always presented in the middle of the (black) computer screen. The participants were instructed to press a button as fast as possible whenever the stimulus appeared. According to the test manual (Zimmermann and Fimm, 2002) and to validation studies (Becker et al., 1996; Goldhammer et al., 2007), the mean reaction time in the 40 trials of this choice reaction time task without fore-signal can be used as a measure of tonic alertness. We therefore tentatively use the term “tonic alertness” to describe the data and results of this task. On the second and third sequence the visual stimulus was preceded by an auditory fore-signal (a tone). Again, the participants' task was to react as fast as possible to the visual stimulus by pressing a button. The mean reaction time in the 40 trials of this choice reaction time task with fore-signal has previously been described as a measure of phasic alertness (Becker et al., 1996; Goldhammer et al., 2007; Zimmermann and Fimm, 2002). Hence, the term “phasic alertness” will preliminarily be used in the present study for the data and results of this task.

4.5.2. Donder's choice reaction time task with two target and three non-target signals This task demands to press a button whenever one of two visual target patterns is presented on the screen and to ignore the presentation of three distracter patterns. The patterns consisted of white points and horizontal and vertical lines, which were presented in a constant sequence in the center of a black computer screen. The task comprised a total of 20 target trials and 30 non-target trials. The mean reaction time for target patterns has been found to be a valid and reliable measure of selective attention (Becker et al., 1996;

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Goldhammer et al., 2007; Zimmermann and Fimm, 2002). For this reason, the data and results which concern this Donder's reaction time task will provisionally be named “selective attention” in the present study.

4.5.3.

Cross-modal dual-task test

This task required from the participants to attend simultaneously to auditory and visual signals. The visual signals consisted of white crosses, which were presented within a white dot pattern on the black computer screen. The participants had to press a button whenever four crosses formed a square. The auditory task consisted of listening to alternating tones of a low and high pitch. The participants had to press the button (the same button as in the visual task) whenever the alteration was discontinued by a presentation of two tones of the same pitch in a row. The dual-task test comprised the presentation of 100 visual and 200 auditory stimuli, which included 17 visual and 16 auditory target stimuli. The visual and auditory stimuli were presented simultaneously in a constant sequence. According to the test manual (Zimmermann and Fimm, 2002) and the results of validation studies (Becker et al., 1996; Goldhammer et al., 2007), the mean reaction time of the 17 visual and 16 auditory target stimuli indicates the performance in divided attention. Therefore, we tentatively use the term “divided attention” for the data and results of the cross-modal dual-task test in the present study.

4.6.

Behavioral data analysis

The mean reaction times of all valid trials were separately calculated and z-standardized for the four cognitive performance measures (tonic alertness, phasic alertness, selective attention, and divided attention). The mean error rate was 4.2% in the young and 6.3% in the older sample.

4.7.

Statistical analyses

4.7.1. Reliability and stability estimation within the two samples 4.7.1.1. CBF measurement. Stability and estimated reliability of the resting CBF data for the young sample have been reported by Hermes et al. (2007, 2009). In short, the mean CBF values of the two measurement occasions were highly correlated in all ROIs (.70 ≤ rt1t2 ≤ .84), which suggests a high temporal stability after seven weeks. In addition, split-half reliability estimates based on the eyes-open and eyes-closed conditions (.92 ≤ rttc ≤ .98) indicated that the measurement of CBF was very reliable. We additionally estimated the reliability and stability of the CBF measurement for the older sample. The correlations between the two measurement occasions indicated acceptable temporal stability in this sample (.62 ≤ rt1t2 ≤ .80) for the temporal, parietal, and occipital lobes. However, CBF values in the frontal lobes showed a correlation of only .04 between the two measurement occasions. The split-half reliability estimates indicated a reliable measurement of resting CBF in the older sample (.46 ≤ rttc ≤ .95). 4.7.1.2. Attention tasks. Split-half reliability estimates based on the two sequences of tonic alertness and on those

of phasic alertness as well as odd-even reliability estimates for the trials of selective attention and for those of divided attention were acceptable to good for the young (.67 ≤ rttc ≤ .96) and older sample (.81 ≤ rttc ≤ .97). There were positive intercorrelations of reaction times between the four tasks in the younger (.30 ≤ r ≤ .74) and in the older sample (.37 ≤ r ≤ .74). Moreover, the internal consistency of the four attention tasks was mediocre for the young and old sample, with a Cronbach's alpha of .68 and .75, respectively.

4.7.2.

Age differences

To investigate age differences in resting CBF, we performed an analysis of variance (ANOVA) including the four factors age group (young adults, older adults), sex (male, female), lobes (frontal, temporal, parietal, occipital), and hemisphere (left, right) with repeated measurements on the last two factors. To examine age differences in the attention tasks, we performed an age group (young adults, older adults) by sex (male, female) by attention task (tonic alertness, phasic alertness, selective attention, divided attention) ANOVA with repeated measurements on the last factor. Significant effects were further analyzed with Dunn's Multiple Comparison Tests.

4.7.3. Cerebral blood flow and cognitive performance within the two samples We calculated hemisphere (right, left) by lobes (frontal, temporal, parietal, occipital) ANOVAs with repeated measurements and with sex as between-subject factor and the four attention measures (tonic and phasic alertness, selective and divided attention) as continuous between-subject variables separately for the two samples to analyze the association of CBF and cognitive performance. To further study the relation between CBF and cognitive performance, we used Pearson's Product Moment Correlations as post hoc tests. ANOVAs a Huyn–Feldt correction of the degrees of freedom was used where appropriate. In addition to reporting the mean ± standard deviation for significant effects, we calculated Hays' (1974) ω2 as an effect size measure. All statistical analyses were conducted with SPSS for Windows (Version 14.0, SPSS Inc.). In addition, we performed a voxel-based analysis for those associations between CBF and attention task performance that were significant in the ROI analysis. In particular, we used simple regression models with CBF in the voxels as dependent variable and the attention task performance as predictor separately for the two samples (uncorrected p < .05, cluster size > 10 voxels). These analyses were performed with SPM2.

4.7.4.

Additional analyses

The mediocre internal consistencies (Cornbach's alpha ≥ .68) of the performances in the four attention tasks in both samples justified an averaging over the four tasks. Hence, we used the average z-standardized reaction time of all attention tasks as an index for “overall information processing speed”. We then performed multiple regression analyses with overall information processing speed as dependent variable and global CBF in the whole gray matter, age, and sex as predictors separately for the two samples. This was done to investigate whether there is a relation between global CBF and overall information processing speed.

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Acknowledgments The authors are grateful to Manfred Reifer for the help with the data acquisition and to Drs. Hans-Peter Busch and Doris Naumann for support and advice. We also thank Vera Fimm for providing the neuropsychological test material and Stefan Telega, Patrick Rabbitt, and one anonymous reviewer for helpful comments on earlier versions of this manuscript. This study was founded by the Deutsche Forschungsgemeinschaft through grant HA 3044/6-1 to the second author.

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