C International Psychogeriatric Association 2015 International Psychogeriatrics (2016), 28:2, 291–301  doi:10.1017/S1041610215001556

Disturbance of attention network functions in Chinese healthy older adults: an intra-individual perspective ...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................

Hanna Lu, Ada W. T. Fung, Sandra S. M. Chan and Linda C. W. Lam Department of Psychiatry, the Chinese University of Hong Kong, G/F, Multi-Centre, Tai Po Hospital, Hong Kong, SAR China

ABSTRACT

Background: Intra-individual variability (IIV) and the change of attentional functions have been reported to be susceptible to both healthy ageing and pathological ageing. The current study aimed to evaluate the IIV of attention and the age-related effect on alerting, orienting, and executive control in cognitively healthy older adults. Method: We evaluated 145 Chinese older adults (age range of 65–80 years, mean age of 72.41 years) with a comprehensive neuropsychological battery and the Attention network test (ANT). A two-step strategy of analytical methods was used: Firstly, the IIV of older adults was evaluated by the intraindividual coefficient of variation of reaction time (ICV-RT). The correlation between ICV-RT and age was used to evaluate the necessity of subgrouping. Further, the comparisons of ANT performance among three age groups were performed with processing speed adjusted. Results: Person’s correlation revealed significant positive correlations between age and IIV (r = 0.185, p = 0.032), age and executive control (r = 0.253, p = 0.003). Furthermore, one-way ANOVA comparisons among three age groups revealed a significant age-related disturbance on executive control (F = 4.55, p = 0.01), in which oldest group (group with age >75 years) showed less efficient executive control than young-old (group with age 65–70 years) (Conventional score, p = 0.012; Ratio score, p = 0.020). Conclusion: Advancing age has an effect on both IIV and executive attention in cognitively healthy older adults, suggesting that the disturbance of executive attention is a sensitive indicator to reflect healthy ageing. Its significance to predict further deterioration should be carefully evaluated with prospective studies. Key words: healthy ageing, attention network, intra-individual variability, executive control

Introduction According to the latest World Alzheimer Report, 44 million adults now suffer from dementia worldwide, and the projected number will double the current brunt of disease by 2,030, even trebling the existing number by 2,050 (Alzheimer’s Association, 2014). Because there is no effective treatment to halt the disease process and treatment outcomes are unfavourable when the disease progresses to a later stage, it is crucial to identify the specific cognitive characteristics and markers that differentiate cognitive ageing from conditions associated with high risk of progressive neurodegeneration. The potential clinical implications of these markers may help to lay platforms for early intervention. Among Correspondence should be addressed to: Linda C. W. Lam, MD, Department of Psychiatry, The Chinese University of Hong Kong, G/F Multicenter, Tai Po Hospital, Tai Po, Hong Kong. Phone: +(852) 2607-6027; Fax: +(852) 26675464. Email: [email protected]. Received 15 Apr 2015; revision requested 8 May 2015; revised version received 20 Aug 2015; accepted 26 Aug 2015. First published online 28 September 2015.

all the cognitive domains, complex attention plays a fundamental role (Grady, 2008) and serves as a main diagnostic item of mild neurocognitive disorders (MND) in Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM5) (American Psychiatric Association, 2013), which includes selective attention, sustained attention, and processing speed (Blazer, 2013). Hence, the composite role of complex attention as a sensitive marker for healthy ageing warrants exploration. Based on attention network theory, attention is described as a set of integrated processes with differentiated functions, which includes alerting, orienting, and executive control (Petersen and Posner, 2012b; Posner and Petersen, 1990). Alerting provides the capacity to achieve and maintain vigilance to an impending stimulus, and has been associated with functioning in frontal lobe, parietal lobe and thalamus. Orienting is defined as the selection of relevant stimuli to attend to and the shifting of attention. The brain regions involved in

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orienting are the superior parietal lobe, temporal parietal junction, frontal eye fields, and superior colliculus (Posner, 2014). Executive control refers to a variety of abilities ranging from planning, manipulating, and the inhibition of behavioral responses. It involves conflict resolution and control over decision-making, error detection, and response inhibition. The brain regions involved in this final and highest order of attentional process are the anterior cingulate cortex (ACC) and lateral ventral prefrontal cortex (VLPFC) along with the basal ganglia (Matsumoto and Tanaka, 2004). The ANT is used to quantitatively and simultaneously evaluate alerting, orienting, and executive control (Fan et al., 2002a), and has been widely used across various participants (Rueda et al., 2004; Dennis et al., 2008; Mullane et al., 2011; Weaver et al., 2013). As to its application in the study of ageing, a handful of studies focusing on healthy adults with different age ranges have reported some consistent and inconsistent results. For instance, compared to young adults, a diminished alerting effect during conflict resolution has been found in healthy older adults (FernandezDuque and Black, 2006; Jennings et al., 2007). Later, Gamboz also found that alerting was significantly reduced in old age, while orienting and executive control between young and older adults were equivalent (Gamboz et al., 2010). When comes to a relatively large sample across the adulthood, a significant age-related decline has been found on executive control (Waszak et al., 2010; Zhou et al., 2011). Moreover, the negative correlation between age and executive control is still significant within older adults. In a relatively large sample of older adults, the age differences have been found in three attention network functions. Besides, the old-old adults (80+ years old) have demonstrated worse performance on executive control than the youngold adults (70–79 years old) (Mahoney et al., 2010). Given the evidence summarized above, it seems that age has an effect on attention network functions, particularly on executive control function. However, two things should be noted may contribute to the inconsistent results between studies: The first is slowing processing speed: processing speed is the basic component of information processing, and is conceived as a general resource related to higher-level cognition (Salthouse, 1996). Moreover, slowing processing speed is strongly tied to healthy ageing (Hedden and Gabrieli, 2004). Except for Mahoney et al.’s study (Mahoney et al., 2010), the previous comparisons of ANT performance did not adjust for processing speed. Considering ANT scores are calculated by the subtractions of reaction time under specific experimental conditions, an immediate concern is

whether the worse performance of ANT is due to the slowing RT? The second is increased IIV: IIV represents the facet of within-person variability, and is regarded as a measure of moment-tomoment fluctuations in an individual’s performance (Martin and Hofer, 2004; Hilborn et al., 2009; Shin et al., 2013). Throughout the adulthood, IIV is a stable individual characteristic that appears to progressively increase with chronological age, especially with older age (Bielak et al., 2014). Studies have confirmed that IIV measured by processing speed could differentiate young adults from older adults and has been served as a sensitive biomarker of ageing (MacDonald et al., 2009). As the spectrum of cognitive abilities has become diversified in old age, it should be noted that except for the slowing processing speed, the higher within-person variability might cause mixed results as well (Nelson and Dannefer, 1992; Christensen, 2001). Indeed, one recent study has reported that increased IIV is correlated with the decline of cognitive performance (Kennedy et al., 2013). Therefore, it may lead to the concern that whether the age difference of cognitive function was an artifact associated with increased IIV? Considering together, these findings provide the critical insight to investigate the possible influence of within-person variability on cognitive functions in older adults. Thus, in this study we aimed to detect the disturbance of attention network functions in healthy Chinese older adults, with both processing speed and within-person variability considered. To achieve these objectives, we proposed a two-step strategy based on two analytical methods. In the first step, the older adults were considered as one group. The relationship of age and IIV would provide evidence to evaluate the necessity of subgrouping. In the second step, we would compare the ANT performance among different age groups, with processing speed considered. We hypothesized that: (1) The within-person variability would increase with advancing age; (2) The adults with older age would demonstrate poorer performance on ANT, especially under the conditions of high demand on executive control.

Methods Study participants A total of 145 right-handed Chinese older adults aged from 65 to 80 years (mean age 72.41 years) participated in this study. All participants were recruited from another cohort study that aims to establish a detailed characterization of cognitive and healthy profiles of Chinese older adults and to determine the diagnostic markers for preclinical

Attention network function in healthy older adults

dementia. Potential participants 65 and older, identified from the communities in Hong Kong, were first contacted and invited to participate by telephone. The eligible participants were scheduled for a 1.5-hour in-person interview at the research center. During the on-site interview, the participants received comprehensive assessments, including cognitive, psychological, medical, everyday activity as well as attention network functions. The inclusion criteria were as follows: (1) aged 65 years and older; (2) no significant cognitive impairment (presented with Clinical dementia rating scale (CDR) score equal to 0 and Cantonese version of Mini-Mental State Examination (CMMSE) score of larger than 28; (3) absence of mood disorder, sleep disorder, and psychiatric disorders. Exclusion criteria were as follows: (1) cognitive impairment based on CMMSE score of less that 24; (2) mild or major neurocognitive disorders as defined by DSM5 (American Psychiatric Association, 2013). Ethics approval Ethics approval was obtained from the Joint Chinese University of Hong Kong — New territories East Cluster Clinical Research Ethics Committee (The Joint CUHK-NTECCREC). Written informed consent from all participants was obtained before assessment. Cognitive assessment and clinical evaluation A standardized neuropsychological battery was used to evaluate the global cognition and major domains of cognition (Lam et al., 2008). The battery was administered by trained research assistants and the scores were calculated by two independent raters. Cornell scale for depression in dementia (CSDD) is used to assess the depression symptom, with a cut-off of five (global score) (Schreiner et al., 2003). Chinese version of the Pittsburgh sleep quality index (PSQI) is used to evaluate the sleep problem, with a cut-off of eight (total score) (Beck et al., 2004). Activity of daily living scale (ADL) is used to assess the everyday functioning (Lam et al., 2009). CDR, CMMSE, Alzheimer’s Disease Assessment Scale (ADAS), Montreal Cognitive Assessment Hong Kong version (HK MoCA) (Chu et al., 2000) are used as a global measure of cognitive function. Delayed recall of words: the number of recalled words is used as the performance of episodic memory. Trail making tests (TMT): the participants connect either numbers (A), or numbers and letters in an alternate fashion (B) as fast as possible; the time of completing in seconds is used as a measure of attention (TMT-A) and a measure of general executive function (TMT-

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B). Digit span tests: the participants repeat the numbers they heard, either forward or backward; the number of correct repeated numbers is used as a measure of attention (DSF) and a measure of working memory (DSB). All the measures were conducted with Chinese instructions. Attention network test (ANT) PARADIGM AND PROCEDURE

We used the standard ANT paradigm developed by Fan Jin and his team (Fan et al., 2002a) (Download from: https://www.sacklerinstitute.org/ cornell/assays_and_tools/ant/jin.fan/), and ran the paradigm on E-Prime 2.0 (Psychology Software Tools, Pittsburgh, PA). Within ANT paradigm, there are four types of cue: no cue, center cue, double cue, and spatial cue; and three types of flanker: neutral, congruent, and incongruent. In a given trial, a central cross-fixation point presents for 400 to 1,600 ms (randomized), subsequently is replaced for 100 ms by one of four warning cues. The target, a central arrow could appear above or below the cross-fixation and was surrounded by two flankers on each side (Figure 1). None of the participants had practiced the ANT previously. All participants were seated approximately 53 cm from the computer screen with corrected vision by their glasses if needed. Before the test, participants were instructed to play the practice trials of ANT and required to decide whether a central arrow pointed to left or right, and press a left button if the central arrow was pointing to left, or right button if it was pointing to right. Following the illustration, all participants were instructed to respond as rapidly and as accurately as possible to the direction of the flanker stimulus by clicking the left or right button. After completing 24 practice trials with accuracy feedback, all participants would take part in three test blocks without feedback, which contained 288 trials in total. There was a short break between each of the test block. EVALUATION OF ATTENTION NETWORK FUNCTIONS

There are two indices used to evaluate the performance of ANT, which are accuracy and reaction time (RT). Accuracy is the degree of correctness of making a decision and is also used for the quality control of ANT data. An accuracy of experimental condition less than 70% is considered as unqualified data (Fan et al., 2002a). RT, measured in milliseconds (ms), refers to the processing speed for a defined stimulus, and is also used to assess the within-person variability (Dixon et al., 2007; Wojtowicz et al., 2014).

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Figure 1. (Colour online) The schematic diagram of attention network test (ANT).

MEASURE OF INTRA-INDIVIDUAL VARIABILITY

As described above, IIV represents the withinperson fluctuation of performance in a repeated task. The ANT consists of 288 trials for collecting response speed and is suitable for measuring IIV. To adjust the effect of slowing processing speed, ICVRT (Stuss et al., 2003; Phillips et al., 2013) is used to evaluate the IIV enumerated with the formula: ICV-RT = (standard deviation of processing speed / mean of processing speed) × 100 (Tractenberg and Pietrzak, 2011). MEASURES OF ATTENTION NETWORK FUNCTIONS

Two scoring methods were performed to evaluate the attention network functions: (1) Conventional score: the score of each attention function is defined as a subtraction of the RT between one condition and the appropriate reference condition, resulting in scores for the attention network functions (Fan et al., 2002a; Galvao-carmona and Izquierdo, 2014; Wang et al., 2014). Alerting is calculated by subtracting the mean RT of the condition with center cue from the

mean RT of the condition with no cue. Orienting is calculated by subtracting the mean RT of the conditions with spatial cue from the mean RT of the condition with center cue. Executive control is calculated by subtracting the mean RT of the condition with congruent flanker from the mean RT of the condition with incongruent flanker. The formulae of conventional scoring are as follows: Alerting = RTno-cue – RTcenter-cue Orienting = RTcenter-cue – RTspatial-cue Executive control (conflict) = RTincongruent – RTcongruent (2) Ratio score: to adjust the effect of slowing processing speed, the ratio score is equal to the conventional score divided by the RT of reference condition (Westlye et al., 2011; Yin et al., 2012; Galvao-carmona and Izquierdo, 2014). The formulae of ratio scoring are as follows: Alerting ratio = (RTno-cue – RTcenter-cue )/RTcenter-cue Orienting ratio = (RTcenter-cue – RTspatial-cue )/RTspatial-cue Executive control ratio = (RTincongruent – RTcongruent )/RTcongruent

Attention network function in healthy older adults STATISTICAL ANALYSES In the first step: median values of RT averaged across blocks for each condition were used to avoid the influence of outliers. The scores of attention network components were calculated using the formulae described above. Person’s correlation coefficients were performed to examine the relationships between age, ICV-RT, and ANT indices, controlled for the effects of gender, education, and processing speed. Linear regression was used to estimate the relationship between age, ICV-RT, and ANT indices. Significance levels were set at p values less than 0.05. In the second step: The differences of demographics and cognitive performance and ANT among different age groups were tested either with χ2 test for categorical variables or with oneway analysis of variance (ANOVA) for continuous variables. When appropriate, the Tukey method was used to do the post hoc multiple comparisons. The effect size was evaluated using the partial eta squared (η 2 ). Person’s correlation coefficients were used to measure the associations of ANT indices and cognitive functions. Significance levels were set at p values less than 0.05. The quality checking, accuracy, RT, and standard deviation of RT were calculated by E-Data Aid embedded in EPrime 2.0. The χ2 test, one-way ANOVA, Pearson’s correlation, and linear regression were performed by IBM SPSS Statistics 20.

Results Quality control and ANT performance According to the quality control standard suggested by Fan Jin (Fan et al., 2002a), an accuracy of ANT less than 70% indicates that the participate does not well understand how to perform the ANT. Thus, under this circumstances, the ANT indices, including processing speed, and attentional components calculated by subtractions of RTs, are considered unqualified as well. To evaluate the attention network functions with caution, eight participants were excluded from the dataset. Thus, a final sample of 137 healthy older adults was used for further analysis. The group-level performance of ANT was as follows: accuracy was higher than 99%; the processing speed was 687.49 ± 105.97 ms; the ICV-RT was 19.99 ± 4.57 ms; alerting score was 3.95 ± 31.88; orienting score was −19.76 ± 28.65; executive control score was 59.86 ± 45.86. Age-related change of attention network functions Person’s correlation analysis revealed that the alerting score (r = 0.011, p = 0.895) and orienting

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score (r = 0.019, p = 0.828) did not correlate with age. A significant positive correlation was found between the executive control score (r = 0.253, p = 0.003) and age, which indicates adults with older age spend more time to detect the conflict and make a decision. Linear regression demonstrated that age (controlled for gender, education) was the significant factor determining the executive control (β = 0.252, t = 3.01, p = 0.003). As to the within-person variability, a significant relationship was found between age and the ICVRT (r = 0.185, p = 0.032), which indicates that adults with older age demonstrate the greater within-person fluctuation of performance, even in the context of healthy ageing. This result indicates that the heterogeneity of attentional performance within older adults has become noticeable. Thus, in the second step, we divided 137 healthy older adults into subgroups. Investigations among three age groups DEMOGRAPHICS AND COGNITIVE PERFORMANCE

As suggested (Hester et al., 2004; Verhaeghen, 2013), we divided the healthy older adults into three groups: age of 65–70 years, age of 71–75 years, and age of >75 years, with an age range of 5 years. As Table 1 shows, the basic demographics in terms of gender and years of education among the groups were similar. The global cognition, episodic memory, and general attention were also comparable. However, a significant difference was found in executive function measure by TMT-B (F (2, 134) = 3.282, p = 0.041). Moreover, the group with age > 75 years showed poorer performance of TMT-B than the group with age of 65–70 years (p = 0.041). ANT PERFORMANCE Accuracy: As Table 2 shows, the accuracy of ANT

among the three age groups was similar. REACTION TIME AND WITHIN-PERSON VARIABILITY

The RT and ICV-RT among the three age groups were similar (Table 3). Although the correlation between age and processing speed did not reach the significance (r = 0.067, p = 0.442), there was a trend that the processing speed was progressively slowed down with advancing age under various cue and flanker conditions. SCORES OF ATTENTION NETWORK FUNCTIONS

There was no age difference of alerting (F (2, 134) = 0.23, p = 0.78) and orienting (F (2, 134) =

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Table 1. Demographics and cognitive profiles among three age groups AGE

65–70 (N = 54)

AGE

71–75 (N = 56)

AGE

>75 (N = 27)

P VALUE

............................................................................................................................................................................................................................................................................................................................

Gender (F/M) Education (years) Systolic BP Diastolic BP CSDD PSQI ADL CDR-SOB CMMSE HK MoCA ADAS Delayed recall Digit span backward (DSB) Trail making test B (time) Trail making test A Digit span forward (DSF)

21/33 8.59 ± 4.26 138.51 ± 20.98 79.85 ± 10.99 0.63 ± 2.12 5.98 ± 3.16 0.990 ± 0.025 0.47 ± 0.63 28.54 ± 1.13 27.46 ± 2.02 4.69 ± 2.19 7.91 ± 1.46 3.65 ± 1.28 57.35 ± 30.94 12.57 ± 5.96 7.78 ± 1.13

27/29 10.05 ± 4.10 140.39 ± 18.90 77.14 ± 10.89 0.11 ± 0.41 6.57 ± 3.83 0.996 ± 0.015 0.44 ± 0.59 28.70 ± 1.21 27.18 ± 1.72 5.17 ± 2.06 7.45 ± 1.31 3.98 ± 1.46 69.40 ± 35.73 12.37 ± 6.03 7.54 ± 1.11

13/14 9.30 ± 4.55 136.67 ± 17.50 76.96 ± 9.64 0.33 ± 0.96 5.30 ± 2.60 0.989 ± 0.036 0.54 ± 0.10 28.74 ± 1.63 27.00 ± 1.73 4.79 ± 1.94 7.56 ± 1.72 3.48 ± 1.09 81.34 ± 61.72 14.64 ± 7.67 7.52 ± 1.16

0.516 0.177 0.704 0.332 0.161 0.261 0.321 0.668 0.605 0.634 0.489 0.265 0.199 0.041 0.283 0.550

Note. Data are raw scores and presented as mean±SD. CSDD = The Cornell Scale for Depression in Dementia; PSQI = Pittsburgh Sleep Quality Index; CDR-SOB = Clinical dementia rating-sum of box; ADL = Activity of daily living scale.

Table 2. Accuracy under different cue/flanker conditions among three age groups AGE

65–70 (N = 54)

AGE

71–75 (N = 56)

AGE

>75 (N = 27)

F

P VALUE

............................................................................................................................................................................................................................................................................................................................

NoCue_Neutral NoCue_Congruent NoCue_Incongruent CenterCue_Neutral CenterCue_Congruent CenterCue_Incongruent DoubleCue_Neutral DoubleCue_Congruent DoubleCue_Incongruent SpatialCue_Neutral SpatialCue_Congruent SpatialCue_Incongruent

0.9891 ± 0.0194 0.9908 ± 0.0132 0.9887 ± 0.0160 0.9893 ± 0.0170 0.9910 ± 0.0118 0.9889 ± 0.0162 0.9896 ± 0.0185 0.9914 ± 0.0122 0.9894 ± 0.0168 0.9889 ± 0.0190 0.9906 ± 0.0132 0.9886 ± 0.0178

0.9879 ± 0.0225 0.9907 ± 0.0193 0.9871 ± 0.0176 0.9853 ± 0.0261 0.9881 ± 0.0227 0.9845 ± 0.0209 0.9884 ± 0.0214 0.9913 ± 0.0178 0.9876 ± 0.0180 0.9858 ± 0.0216 0.9886 ± 0.0179 0.9849 ± 0.0171

0.9879 ± 0.0154 0.9887 ± 0.0150 0.9844 ± 0.0190 0.9876 ± 0.0131 0.9883 ± 0.0129 0.9841 ± 0.0169 0.9885 ± 0.0154 0.9893 ± 0.0156 0.9850 ± 0.0210 0.9873 ± 0.0144 0.9880 ± 0.0144 0.9838 ± 0.0199

0.057 0.176 0.573 0.515 0.438 1.041 0.064 0.195 0.520 0.359 0.346 0.865

0.945 0.839 0.565 0.598 0.646 0.356 0.938 0.823 0.596 0.699 0.708 0.424

Note. Data are raw scores and presented as means±SD. NoCue_Neutral represents the ANT trial with no cue as cue type and neutral as flanker type.

Table 3. ICV-RT and reaction time among three age groups AGE

65–70 (N = 54)

AGE

71–75 (N = 56)

AGE

>75 (N = 27)

F

P

VALUE

............................................................................................................................................................................................................................................................................................................................

ICV-RT NoCue_Neutral NoCue_Congruent NoCue_Incongruent CenterCue_Neutral CenterCue_Congruent CenterCue_Incongruent DoubleCue_Neutral DoubleCue_Congruent DoubleCue_Incongruent SpatialCue_Neutral SpatialCue_Congruent SpatialCue_Incongruent

20.72 ± 7.75 657.36 ± 101.05 664.66 ± 102.07 688.14 ± 103.97 656.49 ± 104.33 663.78 ± 105.33 687.26 ± 107.39 658.67 ± 102.64 665.96 ± 104.08 689.44 ± 106.04 666.46 ± 105.50 673.75 ± 106.86 697.23 ± 109.19

20.55 ± 7.03 665.22 ± 108.91 673.86 ± 113.93 705.66 ± 107.17 662.31 ± 108.27 670.94 ± 113.59 702.74 ± 107.72 667.29 ± 112.93 675.93 ± 118.18 707.73 ± 111.72 671.68 ± 109.64 680.32 ± 115.02 712.12 ± 109.06

22.44 ± 8.23 698.97 ± 102.12 701.24 ± 98.58 740.19 ± 99.09 696.76 ± 96.84 699.03 ± 93.41 737.98 ± 95.27 698.64 ± 102.14 700.91 ± 100.04 739.86 ± 101.07 707.49 ± 97.94 709.76 ± 95.28 748.72 ± 95.99

0.626 1.480 1.076 3.238 1.422 1.014 2.092 1.291 0.923 1.984 1.459 1.039 2.103

0.536 0.231 0.344 0.111 0.245 0.366 0.127 0.279 0.400 0.142 0.236 0.357 0.126

Note. Data are raw scores and presented as means±SD. NoCue_Neutral represents the ANT trial with no cue as cue type and neutral as flanker type. ICV-RT = Intra-individual coefficient of variation (ICV) of Reaction Time.

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Table 4. Scores of attention network functions among three age groups AGE

65–70 (N = 54)

AGE

71–75 (N = 56)

AGE

>75 (N = 27)

P VALUE

............................................................................................................................................................................................................................................................................................................................

1.76 ± 27.28 − 19.94 ± 26.64 46.96 ± 38.41 0.0048 ± 0.0425 − 0.0283 ± 0.0374 0.07 ± 0.06

Alerting Orienting Executive control Alerting ratio Orienting ratio Executive control ratio

5.83 ± 34.71 − 18.75 ± 27.23 63.59 ± 48.54 0.0101 ± 0.0517 − 0.03 ± 0.04 0.10 ± 0.08

4.43 ± 35.12 − 21.47 ± 35.69 77.91 ± 47.94 0.0060 ± 0.5009 − 0.03 ± 0.05 0.12 ± 0.78

0.799 0.922 0.011 0.837 0.964 0.020

Note. Data are raw scores and presented as means±SD.

Table 5. Associations of ANT indices and cognitive functions EXECUTIVE ICV-RT

r

ALERTING

p

r

p

ORIENTING

r

p

CONTROL

r

p

............................................................................................................................................................................................................................................................................................................................

CDR-SOB CMMSE HK MoCA ADAS Delayed Recall DSB TMT-B TMT-A DSF

0.007 0.039 − 0.288 0.078 − 0.062 − 0.093 0.191 0.139 − 0.081

0.937 0.654 0.001 0.373 0.479 0.287 0.028 0.112 0.358

0.022 − 0.059 0.060 − 0.055 − 0.007 0.076 0.018 − 0.066 − 0.143

0.804 0.498 0.492 0.530 0.936 0.385 0.838 0.451 0.102

− 0.167 0.131 0.092 0.058 0.028 0.024 0.016 − 0.046 − 0.125

0.055 0.134 0.295 0.505 0.747 0.784 0.858 0.601 0.154

− 0.068 − 0.043 0.033 0.025 0.020 0.027 − 0.122 0.000 0.127

0.437 0.621 0.709 0.780 0.816 0.760 0.164 0.997 0.147

Figure 2. Scores of attention network functions across three age groups.

0.08, p = 0.92) among the three age groups. An age-related decline was found in executive control (Conventional score: F (2, 134) = 4.648, p = 0.011, η 2 = 0.06; Ratio score: F (2, 134) = 3.884, p = 0.023, η 2 = 0.051) (Table 4). Moreover, the group with age > 75 years demonstrated poorer performance of executive control than the group age 65–70 years (Conventional score: p = 0.011; Ratio score: p = 0.020) (Figure 2).

CORRELATIONS BETWEEN ANT INDICES AND COGNITIVE FUNCTIONS

There was no significant correlation between ANT scores and the scores of major cognitive functions (controlled for age, gender, education, and processing speed). As to the within-person variability, there was a negative correlation between ICV-RT and HK MoCA (r = −0.288, p = 0.001, 95% CI [−0.501, −0.125]), and a

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positive correlation between ICV-RT and TMTB (r = 0.191, p = 0.028, 95% CI [0.021, 0.359]). Within ANT indices, no relationship was found between ICV-RT and ANT scores. The results indicated that attention network functions measured by ANT were relatively independent from other cognitive functions. However, the declined cognitive functions might have an effect on the within-person variability, or vice versa.

age differences of executive function might be influenced by the increased IIV, or vice versa. The age differences in IIV seem to have profound effects on many cognitive functions, particularly on executive function. Alternatively, an absence of correlation between the ICV-RT and executive control measured by ANT implicated that the attention network functions measured by ANT seems to be less influenced by the IIV. Therefore, it may provide a more convincing evaluation of the age differences in attention network functions.

Discussion To the best of our knowledge, this is the first work to detect both IIV and attention network functions in community-dwelling Chinese healthy older adults. With advancing age, increased withinperson variability and declined executive control function have been found in current study. Meanwhile, the oldest group (age > 75 years) showed less efficient executive control function than the group with age 65–70 years. These findings suggest that executive control measured by ANT, rather than processing speed, alerting and orienting, is a sensitive marker of healthy ageing, even in the context of relatively normal cognition. Age and intra-individual variability (IIV) Until recently, variability has been considered as an important individual difference measure relevant to understanding age differences in brain function (Garrett et al., 2013). Indeed, inspired by previous attempts (MacDonald et al., 2003; Bunce et al., 2004; Salthouse et al., 2006; Kennedy et al., 2013), our observations demonstrate that the within-person variability increased in old age, and appeared to highlight the necessity of subgrouping in a sample of older adults (Rabbitt et al., 2001; Tractenberg and Pietrzak, 2011; Phillips et al., 2013). The increased within-person variability in old age suggests that older adults have more difficulty on keeping the consistency of performance across the experimental trials. Given the declined brain functions in late adulthood, it is applaudable to assume that withinperson variability maybe affected by the declined executive functions (Bellgrove et al., 2004). As outlined nicely by MacDonald et al. (2009), greater IIV is associated with the reduced functional MRI (fMRI) activities in the regions related to executive functions (MacDonald et al., 2009). A further question emerges from this work is whether poorer executive control and decision-making function in older adults are reflected by within-person variability of neuropsychological measure as well? Interestingly, the positive correlations between age, ICV-RT, and TMT-B supported that the

Age and attention network functions AGE, ALERTING, AND ORIENTING

Age difference on alerting is quite inconsistent. Some studies (Festa-Martino et al., 2004; Jennings et al., 2007; Zhou et al., 2011) found a decreased alerting effect in older adults. In contrast, an increased alerting effect also was found in old age (Fernandez-Duque and Black, 2006). One thing should be noted is that among these studies, the researchers recruited the adults with a wide age range, such as young adults, middle-age adults, and older adults. However, when focusing on older adults, no significant relationship has been found between age and alerting, exactly in line with the results found by Mahoney et al. (Mahoney et al., 2010). As to orienting, no significant relationship has been found between age and orienting as well, which is consistent with previous attempts (Mahoney et al., 2010; Waszak et al., 2010). AGE AND EXECUTIVE CONTROL

As expected, adults with old age demonstrate poorer performance on ANT, particularly on executive control. Additionally, the group with older age (age >75 years) has less efficient executive control function than the group with younger age (age: 65–70 years). And the results are consistent under both conventional and ratio scoring methods. Given the age-related decline in executive function, it is not surprising that our results are aligned with the studies focusing on late life (Fisk and Sharp, 2004, Mahoney et al., 2010; Zhou et al., 2011). Interestingly, an unexpected result, that is insignificant relationship between age and processing speed encourages us to rethink the coexisting of slowing processing speed and declined executive control in older adults. Based on attention network theory, executive control associated anatomical units are mostly located in prefrontal cortex (PFC) and embedded with dopaminergic networks (Posner, 2012). The PFC is the hub of information processing and decision making, also participates in synthesizing a wide range of external and internal stimuli to exert

Attention network function in healthy older adults

control (van den Heuvel and Sporns, 2013). As “frontal ageing hypothesis” proposed (Greenwood, 2000; Salat, 2011), the close relationship of the PFC and healthy ageing has been explored in a number of studies by means of structural MRI (Raz et al., 1999; Resnick et al., 2003; Yao et al., 2012) and positron emission tomography (PET) (Hazlett et al., 2010). Supporting an association between neuroanatomy and neurotransmitter, the PFC regions are highly related to the executive functions, and with the dominant neuromodulator as dopamine (DA). Besides, this association also plays a key role in healthy ageing process. A body of work have exhibited that through the adulthood, the changes of cognitive functions occur gradually, and are accompanied with the decreasing dopamine D2 receptor (Volkow et al., 1998; Drag et al., 2010). Additionally, a decrease of 5–13% per decade of D2/3R receptor has been found in the extrastriatal brain regions (Kaasinen et al., 2000), especially in the PFC regions (Li et al., 2001). Interestingly, DA is also the dominant neuromodulator of executive control described in attention network theory (Petersen and Posner, 2012b). Taken together with the aforementioned evidence, it is reasonable to infer that the disturbance of executive control in older adults found in this study may enrich the neuropsychological evidence supporting the “frontal ageing hypothesis,” and highlight a sensitive and early sign of healthy ageing before the detectable changes of brain function occurred. It is also important to note that the findings need to be interpreted with its limitation. First, this is a cross-sectional study and the results could not imply ageing effect. Second, only older adults with a relatively restricted age range participate. Third, multiple correlations between ANT indices and the cognitive performance may inflate the rate of false positives. A predefined hypothesis and a confidence interval around a difference without overlap zero may help for declaring the significant results. In the light of the preceding discussion, our findings are aligned with a growing body of evidence that healthy ageing is accompanied by several dynamic changes, such as increased within-person variability, slowing processing speed, and declined executive function (Hedden and Gabrieli, 2004). At this point, we summarize two major thrusts of our results based on ANT: (1) With advancing age, the IIV measured by ICV-RT has become greater, which highlights the necessity of subgrouping in the older participants; and (2) The executive control function, rather than processing speed, alerting, and orienting, is a sensitive marker of healthy ageing, even in the context of relatively normal cognition. Therefore, it would be intriguing to evaluate if

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executive control component would be a sensitive marker to predict cognitive deterioration and the progression to dementia.

Conflict of interest None.

Description of authors’ roles Hanna Lu, Sandra S.M. Chan and Linda C.W. Lam conceived and designed this study. Ada W.T. Fung coordinated the development of this study. Regarding to this paper, Hanna Lu drafted, Ada W.T. Fung, Sandra S.M. Chan and Linda C.W. Lam discussed and agreed the final version.

Acknowledgments This research was supported by Lui Che Woo Institute of Innovation Medicine grant at The Chinese University of Hong Kong. The authors would like to thank all the participants and their relatives that kindly agreed to participate in the study. And we also want to thank the reviewers for their valuable comments and suggestions to improve the quality of the paper.

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