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The Role of Physical Fitness in the Neurocognitive Performance of Task Switching in Older Persons with Mild Cognitive Impairment

Abstract

Although elderly people with amnestic mild cognitive impairment (aMCI) have been found to show impaired behavioral performance in task switching, no research has yet explored the electrophysiological mechanisms and the potential correlation between physical fitness and neurocognitive (i.e., behavioral and electrophysiological) performance in aMCI. The present study was thus aimed to examine whether there are differences in electrophysiological (i.e., event-related potential) performance between aMCI participants and controls when performing a task-switching paradigm, and to investigate the role of physical fitness in the relationship between neurocognitive performance and aMCI. Sixty participants were classified into aMCI (n = 30) and control (n = 30) groups, and performed a task-switching paradigm with concomitant electrophysiological recording, as well as underwent senior functional physical fitness tests. The aMCI group showed comparable scores on most parts of the physical fitness tests, but reduced lower body flexibility and VO2max as compared to the control group. When performing the task-switching paradigm, the aMCI group showed slower reaction times in the heterogeneous condition and larger global switching costs, although no significant difference was observed in accuracy rates between the two groups. In addition, the aMCI group showed significantly prolonged P3 latencies in the homogeneous and heterogeneous conditions, and a smaller P3 amplitude only in the heterogeneous condition. The level of cardiorespiratory fitness was significantly correlated with P3 amplitude in the aMCI group, particularly in the heterogeneous condition of the task-switching paradigm. These results show that the aMCI group exhibited abnormalities in their neurocognitive performance when performing the task-switching paradigm and such a deficit was likely associated with reduced cardiorespiratory fitness, which was shown to be the important predictor of neurocognitive performance.

INTRODUCTION

Mild cognitive impairment (MCI) is a clinical syndrome, with the most common feature among elderly sufferers who subsequently progress to Alzheimer’s disease (AD) dementia being memory problems [1–4]. This condition might be associated with early neuropathological aging [5, 6], possibly caused by AD, vascular dementia, traumatic brain injuries, metabolic dysregulation, or psychiatric diseases [7, 8]. Although individuals with MCI do not yet fulfill the diagnostic criteria for AD/dementia, the high-risk rate of MCI developing to dementia within one year is estimated at about between 10 to 54% [4, 9–13], and that of developing to AD at about 10–15% per year, representing approximately a 10-fold increased risk of AD relative to individuals without memory impairments [4, 12]. MCI is thus proposed as a transitional stage from normal aging to dementia [14, 15], and is regarded as an important risk factor for AD due to its possibly similar neuropathological characteristics [16]. Factors to postpone or prevent the progression of MCI thus need to be explored, because there is currently no curative treatment for dementia.

Although some well-characterized biomarkers (e.g., tau protein and amyloid-β42 in cerebrospinal fluid) and sophisticated neuroimaging instruments [e.g., magnetic resonance imaging (MRI) and fluorodeoxyglucose-positron emission tomography (FDG-PET)] have been shown to reliably detect early AD pathology [17], there are still some significant limitations to these approaches, such as low to moderate sensitivity and specificity of preclinical measurements, high costs, invasiveness, need of radioactive tracers, and the absence of standardization and clear diagnostic cutoff values [17–19]. Several reports over the past decade described the potential diagnostic importance of electrophysiological markers of cognitive decline in patients with MCI and the preclinical stage of AD, as obtained by analysis of the electroencephalography-derived event-related potentials (ERPs) [20–29]. For example, the ERP P2, N2, and P3 components are recognized as effective electrophysiological indices in the early stage of MCI diagnosis [20–23], as MCI subjects have prolonged P2, N2, and P3 latencies, and reduced P3 amplitudes, compared to healthy elderly controls [20, 24–26]. Even specific to the MCI subgroups [e.g., amnestic MCI (aMCI)], these ERP components may also be useful for observing the differences in cognitive processes from those of the healthy elderly [20, 24, 27, 28], and to distinguish the different MCI subtypes (e.g., aMCI versus non-aMCI) [29]. ERP components may thus be sensitive enough to identify elderly patients with early cognitive decline or disease progression to MCI and/or AD.

Executive functioning is a potential mediator of age-related cognitive decline in normal adults, particularly in the fronto-parietal network involved in working memory [30–32]. There is a growing body of research that supports the presence of an early executive functioning deficit in persons with aMCI [21, 29, 33–35]. Task switching is an executive function that is needed to master a wide array of daily tasks, and thus is suitable for examining age-related effects on the abilities to represent, maintain, and update contextual information in working memory [36]. In the current study a task-switching paradigm which requires the subjects to maintain multiple task sets in the working memory, and makes greater demands on executive control processes [37], was used to assess executive function. Recently, Belleville et al. [33] used a task-switching paradigm involving either conceptual or spatial switching to assess the differences in the executive functioning (e.g., working memory and executive control) between aMCI and AD individuals, and found that switching deficits are selective in both groups. That is, as compared to healthy controls, patients with AD showed both greater global switching costs (i.e., the ability to maintain the two task sets which the participant needs to alternate between within the working memory) and local switching costs (i.e., the ability to reconfigure one’s actions from trial to trial, according to the relevant task sets), although the elderly subjects with aMCI only exhibited a significant discrepancy in global but not in local switching costs, supporting the notion that the patterns (i.e., the different deficit mechanisms involved in task switching) across conditions are coherent, with there being a continuum between MCI and AD [33].

Older people with MCI or dementia have been shown to have lower physical fitness [38] and worse executive functioning [33], and the vast majority of related research has demonstrated that better physical fitness or higher levels of physical activity (PA) will benefit executive functions in the elderly. For example, elderly subjects with higher cardiorespiratory fitness or PA showed better performance on a wide variety of cognitive tasks, such as task-switching paradigms [37, 39, 40], and greater strength due to long-term resistance exercise has also been shown to have positive effects on various aspects of cognitive performance in the elderly, such as on executive control, memory, and attention [41–45]. Elderly subjects with MCI who regularly participated in long-term multicomponent exercise interventions (including aerobic exercise, muscle strength training, and postural balance retraining) showed improved logical memory with regard to immediate recall, maintained general cognitive functioning, and reduced whole brain cortical atrophy [46, 47], possibly modulated by reduced cholesterol and increased brain-derived neurotrophic factor (BDNF) [46]. These findings suggest that better physical fitness may protect against cognitive aging. Recently, Baker et al. [48] examined the effects of aerobic exercise on aMCI, and found that six months of high-intensity (75–85% of heart rate reserve) exercise intervention had sex-specific effects on cognition. That is, aerobic exercise improved performance on multiple tests of executive functions in aMCI women, but only improved Trail Making Test B performance in aMCI men. Since physical fitness is strongly related to cognitive performance in the elderly, as noted above, and individuals with MCI show lower physical fitness compared to healthy subjects [38], the executive functioning deficits found in individuals with MCI could be associated with poorer physical fitness.

Different ERP components are evoked simultaneously when individuals perform a visual task-switch paradigm, such as the P2 to the target for a unique component of cognitive control in relation to task-set activation or the switching cost [49–51], and P3 for the task-set updating processes and attentional resources allocated to the updating of working memory [37, 52, 53]. With regard to the relationship between physical fitness and electrophysiological indices, the ERP P2 and P3 components are sensitive to improvements in physical fitness in the elderly. Ozakaya et al. [44] found that after a strength or endurance exercise intervention the latencies of the P2 and P3 components shortened significantly in the elderly subjects, while the amplitude of the P2 component increased significantly. In addition, previous studies also demonstrated that, among the elderly, the ERP P2 and P3 components induced by the task-switching paradigm are associated with physical activity levels or cardiorespiratory fitness, with greater amplitudes found in individuals with higher levels of physical activity or greater aerobic fitness [37, 40]. Both P2 and P3 can thus serve in the current study as electrophysiological ERP indices to examine the effects of healthy compared to early pathological aging on task switching, and to understand the relationship between physical fitness and MCI.

Previous research has demonstrated that individuals with aMCI showed impaired behavioral (e.g., global switching cost) performance on task switching [33]. However, to the best of our knowledge, no research has yet been conducted to examine the potential electrophysiological mechanisms involved in task switching in relation to the premature pathological aging seen with aMCI. The first purpose of this study was thus aimed at investigating whether there are differences in electrophysiological performance between aMCI participants and controls when performing a task switching paradigm. Most importantly, although it has been well established in a growing number of clinical studies that individuals with aMCI exhibit lower neurocognitive (i.e., behavioral and electrophysiological) performance, as mentioned above, to date the potential correlation between physical fitness and neurocognitive performance in MCI has not yet been investigated. The second purpose of this study was thus to examine the relationship between the components of physical fitness and neurocognitive performance during task switching in aMCI subjects. Based on the findings of a previous study, which indicated reduced behavioral performance in individuals with aMCI [33], and given that the executive control processes have been shown to be strongly affected by physical fitness among healthy older and aMCI adults in previous research [37, 40, 42, 45, 48], it is thus reasonable to expect that (1) the elderly with aMCI would have poorer behavioral and electrophysiological performances when performing the task switching paradigm; and (2) the elderly with aMCI would show poorer physical fitness relative to controls, which could play an important role in the relationship between aMCI and neurocognitive performance. We believe that this study will provide the first experimental evidence from integrating behavioral, electrophysiological and physical fitness data, and thus clarify the associations between neurocognitive performance and physical fitness, with the results having implications regarding non-pharmacological interventions for the elderly with aMCI.

MATERIALS AND METHODS

Participants

Sixty older adults aged 60–85 years participated in the current study and were categorized into amnestic MCI (aMCI group, n = 30, 12 males) and healthy older (control group, n = 30, 14 males) groups. The inclusion criteria for the control group were as follows: (1) Mini-Mental State Examination (MMSE) scores between 24 and 30; (2) a Clinical Dementia Rating (CDR) of 0; (3) non-depressed [Beck Depression Inventory, 2nd edition (BDI-II) scores < 13]; (4) non-MCI; and (5) not suffering from dementia. The older adults with aMCI were recruited through the Alzheimer’s Disease Research Center, National Cheng Kung University Hospital, using a standardized clinical protocol. The clinical criteria for participants with aMCI were as follows: (1) subjective memory complaints confirmed by family members; (2) MMSE scores > 24; (3) objective memory impairment for age; (4) a CDR of 0.5; (5) absence of significant levels of impairment in other cognitive domains (e.g., language, attention, abstraction, and orientation), as assessed by the Cognitive Abilities Screening Instrument (CASI) [54]; (6) largely intact functional activities of daily living; (7) an absence of dementia; (8) no brain abnormalities (e.g., stroke and malignant brain tumors) via structural MRI scans; and (9) non-depressed [2, 3, 8, 12, 55, 56]. Computed tomography (CT) for structural brain examinations and single photon emission computed tomography (SPECT) for functional examinations were performed when more information was needed to confirm the diagnosis. All participants were right-handed according to the Edinburgh Handedness Inventory [57], had normal or corrected-to-normal vision, and no history of other significant neurological disorders, current psychiatric illnesses, significant cerebrovascular disease, musculoskeletal impairment, substance abuse or addiction, or anti-dementia medicine. The subjects’ demographic characteristics are summarized in Table 1. Written informed consent, as approved by the local Ethics Committee of the institution in which the study was performed, was obtained from all the participants.

Experimental procedure

The participants were required to make two visits to the cognitive neurophysiology laboratory. On the first visit, the research assistant explained the experimental procedure, and asked the participants to complete a medical history and demographic questionnaire, MMSE, DBI II, a handedness inventory, social participation questionnaire [58], and an informed consent form. Working memory span was estimated by the digit span component of the Wechsler-IV Adult intelligence test [59]. All of the participants’ heights and weights were also measured to calculate their body mass indexes (BMI). Two certified fitness instructors then completed all assessments of functional physical fitness.

On the second visit in the same week, after the participants arrived at the acoustically shielded laboratory with dimmed lights, the experimenter explained the procedure regarding the cognitive test. Initially, each participant was asked to sit in an adjustable chair in front of a 17” computer monitor, the display of which was driven by an IBM-compatible personal computer with a stimulation system (Neuroscan Ltd., EI Paso, USA). The electrocap and electro-oculographic (EOG) electrodes were then attached to the participants’ heads and faces for the cognitive test.

Cognitive task— task-switching paradigm

The task-switching paradigm employed in the present study was adapted from one previously used by Hillman et al. [37] with older adults. The task switching paradigm included two tasks (low/high and odd/even tasks) that used the same numeric stimuli (See Fig. 1). The white stimuli (digits 1–9, excluding 5) were presented focally in the center of the computer screen on a black background after counting down in reverse order, 3, 2, and 1. The participants needed to judge whether the digit was greater or less than five (low/high task), or whether it was odd or even (odd/even task), when the digit was surrounded by a solid or dashed square, respectively. The task-switching paradigm involved two homogeneous conditions and one heterogeneous condition, and consisted of six blocks of stimuli, with a brief rest period in the middle of each block. The first two blocks were the homogeneous conditions in which only one task (i.e., less/more than 5 or odd/even task) with 64 trials was performed, and these were counterbalanced across participants. The blocks 3–6 were the heterogeneous conditions with 256 trials (64 trials×4 blocks) in which the two equiprobable task sets (i.e., less/more than 5 and odd/even tasks) were alternated randomly, with seven consecutive trials as the maximum number that were performed repeatedly for each task. The participants were thus required to switch between two tasks on some trials and repeatedly perform the same task over trials in other cases in the heterogeneous block. The participants were asked to use their left index finger to press the X button on the keyboard when the digit was less than five or odd. In contrast, when the digit was greater than five or even, the participants were asked to use their right index finger to press the M button. The participants were instructed to press the response button as quickly and accurately as possible. Digits were presented on the screen for 200 ms, with a 2000 ms response-stimulus interval. Participants were given the task instructions, and each block (e.g., single-task as well as task-switch trials) was preceded by a series of practices before the formal test to familiarize the participants with the rules.

Senior functional physical fitness assessment

In order to assess the participants’ physical fitness, they completed the Senior Functional Physical Fitness (SFPF) test [60], which includes (1) the Back Scratch test and Chair Sit-and-Reach test, which assess upper body and lower body flexibility, respectively, (distance in centimeters between the third fingertip of each hand, and fingertips and toes, respectively); (2) the Arm Curl test, which assesses arm muscle strength endurance (specifically of the biceps) (number of repetitions in 30 s); (3) 8-Foot Up-and-Go test, which assesses physical agility and dynamic balance (time to rise, walk eight feet and return to the chair, the best of two timed trials); and (4) the Chair Stand test, which assesses lower body strength (number of repetitions in 30 s). With regard to cardiorespiratory fitness, the Rockport Fitness Walking Test was used to estimate VO2max [61], in which the participants were required to walk one mile as quickly as possible, during which their heart rate was continuously recorded using a Polar heart rate monitor (RX800CX, Finland).

Electrophysiological recording and analysis

Electroencephalographic activity was recorded using an elastic electrode cap (Quik-Cap, Compumedics Neuroscan, Inc., El Paso, TX) and Ag/AgCl sintered electrodes placed at 18 scalp sites (F7, F8, F3, F4, Fz, T3, T4, C3, C4, Cz, T5, T6, P3, P4, Pz, O1, O2, and Oz), based on the International 10–20 System. Scalp locations were referred to linked mastoid electrodes. A ground electrode was placed on the mid-forehead on the Quik-Cap. The adhesive ocular electrodes placed on the supero-lateral right canthus and infero-lateral to the left eye connected to the system reference to obtain horizontal and vertical electrooculograms (i.e., HEOG and VEOG) activity for eye movements. The electrode impedance was kept below 5 kΩ. The EEG signals were recorded with an A/D rate of 500 Hz/channel, a band-pass filter of 0.1–50 Hz, and a 60 Hz notch filter. These data were continuously written for off-line analysis using SCAN4.3 analysis software and amplified by a SynAmps amplifier (Compumedics Neuroscan, Inc., El Paso, USA). The epochs for averaging were 1200 ms, including 200 ms of pre-stimulus baseline. All trials with response errors and blink artifacts (i.e., VEOG, HEOG, and electromyogram exceeding ± 100μV) were excluded offline. The remaining effective ERP data was averaged across epochs according to different trial types.

As seen in Fig. 2, the effects of task switching on the P2 and P3 components were clearly visible in the present study. Since the cognitive task relies on a set of separate components implicating a fronto-to-parietal network of brain areas [33], the two stimulus-locked ERP components in the three electrodes (Fz, Cz, and Pz) were thus identified in artifact-free trials followed by correct responses, and analyzed in the current work. Two types of ERP variables (i.e., amplitudes and latencies) were measured. P2 and P3 were defined as the largest positive-going peak within 150–300 ms and 300–650 ms latency windows, respectively. Latencies were defined as the time point of the maximal amplitude within the latency window for every participant, and the results were equivalent for the ERP elicited by all conditions and participants.

Data processing and statistical analysis

With respect to the behavioral performance, the subjects’ reaction times (RT) and accuracy rates (percentage correct) were measured and analyzed. Individual trials with RTs shorter than 100 ms and longer than 2400 ms were excluded from further analyses. Two types of switching cost were determined by the RT performance: (1) the global switching cost was determined by subtracting the mean RT between homogeneous and heterogeneous conditions; and (2) the local switching cost was determined by subtracting the mean RT between switching and non-switching trials during the heterogeneous condition. No participant exceeded a 30% error rate.

Three trial types were subjected to behavioral (e.g., accuracy rate and RTs) and electrophysiological (i.e., ERP P2 and P3 latencies and amplitudes) statistical analyses: one (i.e., pure trial) during the homogeneous condition, and two (i.e., non-switching and switching trials) during the heterogeneous condition. The results for the behavioral and electrophysiological performances were separately analyzed using a mixed design, factorial, and repeated-measures analysis of variance (RM ANOVA). With regard to the behavioral performance of the task switching paradigm, Group (aMCI versus Control) was the between-subjects factor, and Trial Type (pure, switching, and non-switching) was the within-subject factor, with the accuracy rates and mean RTs of accepted trials serving as the dependent variables. For the ERP P2 and P3 measures, Group (aMCI versus Control) was also the between-subjects factor, and Trial Type (pure versus switching versus non-switching) and Electrode (Fz versus Cz versus Pz) were the within-subjects variables. The homogeneity and normality of variance assumptions were confirmed by the Levene and Lilliefors test of Kolmogorov-Smirnov test, respectively. When the RM ANOVAs revealed significant effects due to the factors and their interactions, posterior comparisons of the mean values were carried out by paired multiple comparisons (adjusted using the Bonferroni correction). When appropriate, the Greenhouse-Geisser (G-G) procedure was applied to correct the degrees of freedom. More specifically, the G-G was used whenever a major violation of the sphericity assumption was detected in repeated measures ANOVA, with more than two degrees of freedom. The effect size (i.e., partial η2: ηp2) is also reported to complement the use of significance testing, with the following conventions adopted to determine the magnitude of the mean effect size: <0.08 (small effect size), between 0.08 to 0.14 (medium effect size), and > 0.14 (large effect size). Since the correction factor reduces the degrees of freedom of the usual F-test, and often results in non-integer values, only the corrected probability values and degrees of freedom are reported. In addition, the associations between the physical fitness scores and behavioral (e.g., accuracy rate and RTs across three trials, and both global and local switching costs) and electrophysiological (e.g., P2 and P3 amplitudes and latencies) performances were further investigated across groups using Pearson’s r product moment correlation coefficient. A value of p < 0.05 was considered to be significant. The stepwise regression analyses were performed to determine the best predictors of MMSE and P3 amplitude. The models selected with P-value threshold criteria contained BDI-II, VO2max, BMI, age, gender, social participation, and education level (i.e., independent variables), Akaike Information Criterion was used for the best model selection. All statistical analyses were performed with the IBM SPSS Statistics package v.19 for Windows and with JMP ver. 4.0.4 (Academic).

RESULTS

Demographic characteristics

As shown in Table 1, the aMCI and control groups had a comparable age range. A chi-square analysis failed to reveal any significant gender distribution difference between the two groups. The two groups were also matched at the group level on height, weight, BMI, systolic and diastolic pressure (all ps > 0.05). The years of education, social participation, and depression (all ps > 0.05) also revealed non-significant differences across the two groups. Although the MMSE scores were above 24 in the two groups, the aMCI group showed a significant difference when compared to the control group.

In terms of the SFPF Test, the Chair Sit-and-Reach test showed significant differences between aMCI and control groups, revealing that healthy older adults showed better lower body flexibility than older adults with aMCI. In addition, cardiorespiratory fitness (i.e., VO2max) also exhibited a significant difference between the two groups, demonstrating that the control group had significantly larger maximal oxygen uptake than the aMCI group.

Behavioral performance

Accuracy rate

RM–ANOVA for the accuracy rates highlighted a significant main effect of Trial Type, with a distinct “pure” (93.1%) > “non-switching” (90.7%) > “switching” (89.0%) situation for the two groups. Neither significant main effects of Group [F(1,58) = 0.20, p = 0.660] nor significant interactions between Group and Trial [F(2,116) = 0.12, p = 0.888] were obtained.

Reaction time (RT)

As illustrated in Fig. 2, RM-ANOVA for the RT data revealed a significant main effect of Trial Type [F(2,116) = 275.95, p < 0.001, ηp2 = 0.83], with the following values: Switching (1252.93 ms) >non-switching (1101.93 ms) >pure (561.28 ms). There were also significant main effects of Group [F(1,58) = 6.13, p = 0.016, ηp2 = 0.10] and a Group by Trial Type [F(2,116) = 5.90, p = 0.004, ηp2 = 0.09] interaction. Post hoc analyses indicated that the aMCI group responded slower in the non-switch (aMCI versus control: 1184.79 ± 222.29 versus 1019.06 ± 268.31 ms, p = 0.012) and switching trials (aMCI versus control: 1356.88 ± 341.35 versus 1148.97 ± 322.95 ms, p = 0.019) than the control group.

In terms of RT switching costs, the aMCI group exhibited a larger global switching cost than the control group [aMCI versus control: 709.41 ± 240.70 versus 525.44 ± 264.71 ms, t(58) = 2.82, p = 0.007]. However, there was no significant difference for the local switching cost between the two groups [aMCI versus control: 138.62 ± 152.11 versus 129.91 ± 99.21 ms, t(58) = 0.26, p = 0.794].

Electrophysiological indexes

P2 latency

There was only a significant effect of Trial Type [F(2,116) = 22.31, p < 0.001, ηp2 = 0.28] on the P2 latency, indicating that P2 latency in the pure trial (168.82 ms) showed significantly earlier than in the non-switching (180.96 ms) and switching (182.52 ms) trials. No significant difference was observed between the two groups in the P2 latency, without any other main effect or interaction. (See Fig. 3.)

P2 amplitude

There was only a significant effect of Electrode [F(2,116) = 88.73, p < 0.001, ηp2 = 0.61] on the P2 amplitude, indicating that the P2 amplitude at the Pz site (7.25μV) was significantly smaller than those at the Fz (10.29μV) and Cz (10.25μV) sites. No significant difference was observed between the two groups in the P2 amplitude, without any other main effect or interaction.

P3 latency

There was a significant effect of Electrode [F(2, 116) = 4.22, p = 0.017, ηp2 = 0.07] on the P3 latency, indicating that P3 latency at the Fz site (479.29 ms) was significantly longer than that at the Pz site (459.91 ms). There were also significant main effects of Group [F(1,58) = 21.82, p < 0.001, ηp2 = 0.27] and a Group by Trial Type [F(2,116) = 3.31, p = 0.040, ηp2 = 0.05] interaction. Post hoc analyses indicated that mean P3 latencies were significantly longer in the aMCI group than in the control group in pure (aMCI versus control: 503.11 ± 47.06 versus 435.17 ± 38.35 ms, p < 0.001), non-switching (aMCI versus control: 487.19 ± 53.91 versus 451.14 ± 50.63 ms, p = 0.010), and switching (aMCI versus control: 495.01 ± 53.51 versus 453.60 ± 54.41 ms, p = 0.004) trials.

P3 amplitude

There was a significant main effect of Electrode [F(2,116) = 5.54, p = 0.005, ηp2 = 0.09] on the P3 amplitude, indicating that the P3 amplitude at the Pz site (9.14μV) was significantly larger than those at the Fz (8.14μV) and Cz (8.37μV) sites. Moreover, the Electrode by Trial Type interaction reached significance [F(4,232) = 4.58, p = 0.001, ηp2 = 0.07]. Repeated-contrasts, which compare electrode differences between trials, indicated that the interaction was due to a larger increase in P3 amplitude in non-switching rather than pure trials from the Cz to the Pz electrode [F(2,177) = 4.32, p = 0.015]. There were also significant main effects of Group [F(1,58) = 10.43, p = 0.002, ηp2 = 0.15] and a Group by Trial Type [F(2,116) = 4.71, p = 0.011, ηp2 = 0.08] interaction. Post hoc analyses indicated that mean P3 amplitudes were significantly smaller in the aMCI group than in the control group in non-switching (aMCI versus control: 7.10 ± 2.83 versus 9.35 ± 5.31μV, p = 0.046) and switching (aMCI versus control: 6.28 ± 3.15 versus 10.48 ± 3.46μV, p < 0.001) trials.

Correlation and regression analyses

Among all the scores of the SFPF and cardiorespiratory tests, only VO2max across two groups showed significant correlations with global switching

cost (r = –0.26, p = 0.048) and P3 amplitudes across trials (r = 0.35, p = 0.006). The correlation between VO2max and P3 latency (r = –0.24, p = 0.065) approached significance (See Fig. 4). Other functional fitness scores did not show any pattern of significant correlations with behavioral responses and ERP performance across the two groups. For the aMCI group, P3 amplitude across trials (r = 0.38, p = 0.036), in the non-switching (r = 0.37, p = 0.043) and switching (r = 0.42, p = 0.020) trials, was also significantly correlated with the VO2max. (See Fig. 5) However, there were no significant correlations between VO2max and behavioral responses or ERP performance in the control group. Furthermore, the regression analysis showed that VO2max (p = 0.002) and education level (p = 0.025) were independent predictors of MMSE, explaining more than 28% of its variability. Importantly, VO2max was the sole predictor of P3 amplitude in the model, accounting for more than 18% of its variability (p = 0.002).

DISCUSSION

The current study investigated the electrophysiological mechanisms of task switching in the elderly with and without aMCI syndrome to extend the body of research exploring the behavioral performance of aMCI [33], and also examined the correlations between physical fitness and neurocognitive performance. We found that although the elderly subjects with aMCI, as compared to the controls, showed comparable scores on most of functional fitness tests, accuracy rates, and P2 performance when performing the task-switching paradigm, they exhibited worse body flexibility, cardiorespiratory fitness (i.e., VO2max), slower RTs and smaller P3 amplitudes in the heterogeneous condition (i.e., non-switch and switching trials), a larger global switching cost, and longer P3 latencies in homogeneous and heterogeneous conditions. The VO2max scores were significantly correlated with the global switching costs and P3 amplitudes, with a trend toward a negative association with P3 latency in both groups. For the aMCI group, cardiorespiratory fitness was significantly correlated with P3 amplitude, particularly in the heterogeneous condition.

Physical fitness is positively related to the ability to perform activities of daily living in older people with MCI and dementia [62]. Although MCI syndrome is defined as greater than expected cognitive declines based on education and age, and older adults with MCI compared to age-matched controls exhibit impairments in complex activities of daily living [63, 64], MCI has often been shown to have no significant effects on daily activities [1, 23]. Since the item scores of the SFPF test are associated with independent functioning in the elderly [60], the present study’s comparable scores on the SFPF test in aMCI and control groups, except for lower body flexibility, indicate that older adults with aMCI might still have sufficient capacities to show intact overall daily functioning and maintain their usual daily activities. However, the current findings are somewhat inconsistent with Hesseberg et al. [38], which reported that older people with MCI or dementia showed lower scores on all of the SFPF tests, except the Arm-curl test, as compared to the normative data. This lack of consistency may be attributable to excess variation resulting from the differences in the participants’ characteristics (aMCI group versus MCI and early dementia group) and age ranges (an average of 68.23 versus 78.8 years old) in the current and previous studies. Although the aMCI and control groups had comparable levels of social participation, the present and previous physical fitness test findings also suggested that if the progress of MCI is not prevented in a timely manner then elderly subjects with this condition would gradually show much poorer physical fitness with aging, and this would impair their complex activities of daily living, decrease social engagement, and lead to further age-related cognitive declines or even dementia [65].

Previous studies have suggested that elderly people with MCI have lower executive functioning compared to age-matched controls, leading to less efficient inhibitory control and working memory [21, 33]. In the present study the accuracy rates and RTs in the homogeneous condition for the aMCI subjects did not differ significantly from those of the controls, a result that is in agreement with Belleville et al.’s [33] findings, indicating that people with aMCI have no particular problem in managing such tasks in isolation. The changes in RT performance were not accompanied by any changes in the accuracy rate in this study, demonstrating that the difference in RTs between aMCI and control groups was not due to any trade-off between speed and accuracy, and truly reflected the central cognitive process efficiency in the two groups. Although the aMCI and control groups showed comparable accuracy rates, the former responded significantly slower in the heterogeneous condition of the task switching paradigm compared to the homogeneous condition, suggesting that they only displayed a generalized reduction in the time efficiency of the central processing of cognitive functions with regard to maintaining two tasks in the working memory.

Additionally, in the current study the aMCI subjects showed a larger global switching cost compared to the healthy controls, but a comparable local switching cost, as seen in Belleville et al. [33], suggesting that elderly people with aMCI demonstrate deficits in the maintenance of potentially relevant task sets within working memory, but still preserve the capacity to reconfigure new action sets (i.e., activate the currently relevant task set and deactivate the previous task set). Indeed, previous studies have also shown that AD patients are impaired in clinical tests (e.g., the Trail Making B or the Wisconsin card sorting test) [66, 67], which could be explained as reflecting deficient switching capacities. Interestingly, the deficit in concurrent maintenance of two relevant task sets active in the working memory can be improved upon practice in the elderly with MCI but not in AD patients [33], implying that this early cognitive decline in aMCI seems to be remediable. Additionally, it is worth noting that Belleville et al. [33] demonstrated that AD patients showed larger global and local switching costs when performing the task-switching paradigm. Coupled with these previous aMCI and AD results, the findings in the current study could reflect differential switching capacity deficits from aMCI to the early phase of AD, and suggest that the different switching costs during task switching might reflect mechanisms that have differential sensitivity to aMCI and AD.

Although older adults need to meet more processing demands compared to younger adults during maintenance and retrieval of two task sets concurrently held in working memory when performing a task-switching paradigm task [68, 69], and the older adults with MCI showed prolonged P2 latency as compared to the healthy controls when performing a highly working memory demanding two-back task [24], in the present study we observed no difference in P2 latency or amplitude between aMCI and control groups, suggesting that neural mechanisms of cognitive control with regard to a shift of target modality, task-set activation [49, 51], and cue-task retrieval processes [70] is not compromised in aMCI. However, the ERP P2 component is also associated with a component of the switching cost [50]. As mentioned above, the aMCI and control groups showed different global switching costs but roughly the same local switching costs. The partially contradictory results of behavioral and electrophysiological performances are difficult to account for. One possible explanation is that the stimulus-dependent neural processes may not contribute to RT switching costs in the elderly, since a previous study found that the switching sensitivity of the P2 component was not related to the RTs in any kind of switching costs when the elderly subjects performed the task switching paradigm [40].

P3 latency and amplitude are related to the early conscious processes involved in memory and attention control [71]. The elderly with aMCI showed longer P3 latencies in homogenous and heterogeneous conditions in the current study compared to the controls, suggesting decreased efficacy of perceptual/central processing time with regard to performing a cognitive task requiring frequent updating and switching of task sets [53]. Previous research reported that the P3 latency was stable and sensitive in cognitive evaluations of MCI patients and those with conversion from MCI to AD [23]. Indeed, a number of experimental studies also demonstrated prolonged P3 latency in the elderly with aMCI [21, 72] and MCI [73–75] when performing the visual and auditory oddball tasks. Therefore, in support of these earlier findings, the prolonged P3 latency found in the current paper seems to be a neuropathological characteristic of MCI.

The P3 amplitude induced by the task-switching paradigm reflects task-set updating processes and attentional allocation to the focal stimuli in the service of updating the working memory [37, 52, 53]. Elderly individuals have been demonstrated to show difficulties in reallocating attentional resources [76] and suffer from age-related disturbances in the task-set updating process [77]. The P3 amplitude was significantly smaller in the heterogeneous condition in the aMCI group than in the control group in the present study, suggesting that the cognitive processing related to tasks requiring stimulus perception and identification, task-set and memory updating, response conflict detection and monitoring processing [52, 53] was compromised in the subjects with aMCI. Given the sensitivity of P3 measures (e.g., prolonged P3 latency and decreased P3 amplitude) to a subset of processes reflected by behavioral performance (i.e., longer RTs and global switching cost), as observed in the present study, these findings might reflect reduced perceptual/central as well as response-related neural processing in the elderly with aMCI when they need to perform a challenging task involving multiple components of executive control (e.g., task switching). In addition, although task-switching capacity gradually becomes worse with increasing age [36, 37], it deteriorates significantly with aMCI [33].

Since more complicated executive control processes appear to be more sensitive to the effects of physical fitness [78, 79], the poorer behavioral and electrophysiological performances in the task-switching paradigm, which requires more extensive executive control processes, in the MCI group could be attributed to poor physical fitness. However, although poorer lower-body flexibility was shown in the aMCI group as compared to the control group in the current study, there was no relationship between lower-body flexibility and neurocognitive performance. To some extent, such a finding receives support from previous research which reported that flexibility was not associated with cognitive functions (e.g., executive functions, set-shift, and working memory) in the elderly with MCI or dementia [38]. In the present study, VO2max was significantly associated with P3 amplitudes and global switching costs, and approached significance in its association with P3 latencies, when the participants performed the task-switching paradigm, suggesting that the efficiency of maintaining multiple task sets in the working memory and the reconfiguration of new action sets are particularly sensitive to cardiorespiratory fitness [78]. Importantly, VO2max was the sole predictor of P3 amplitude in our population, independent on age and gender, while depression score, BMI, and even the education level did not have any predictive value. It is thus plausible to speculate that the expected training-induced changes in VO2max could result in specific changes of neurocognitive functions. Indeed, a previous meta-analysis of the literature on randomized clinical studies of the effects of physical activity on age-related cognition found that aerobic exercise training had a clearly and significantly beneficial effect on a variety of cognitive tasks in the elderly, in particular on those tasks involving executive control processes (e.g., task coordination, working memory, interference control, planning) [78]. The current study extends current knowledge regarding the association between cardiorespiratory fitness and executive control processes to include older people with aMCI. In particular, VO2max was significantly associated with P3 amplitude in the aMCI group, suggesting that cardiorespiratory fitness might modulate, and in some cases potentially reverse, age-related decreases in the capacity of allocating attentional resource to updating the working memory in subjects with aMCI. In agreement with this, Hesseberg et al. [38] also found that aerobic endurance showed a significant association with executive functioning, set-shifting, and working memory in elderly people with MCI or dementia. In addition, a number of prospective studies with fairly large numbers of older participants have found that those who did not regularly participate in aerobic exercise (e.g., walking, running, or jogging) over the past decade had significantly elevated Pittsburgh Compound B, tau and phosphorylated tau (ptau) 181 biomarker values, and the active elderly who met the exercise guidelines set by the American Heart Association had significantly healthier amyloid profiles [80]. Similarly, the elderly with higher aerobic fitness at baseline were less likely to experience cognitive declines, AD, and dementia of any type during five to eight years of follow-up [81–84], suggesting that greater physical fitness could lead to cognitive benefits in the healthy elderly, thus preventing them from suffering from MCI and dementia.

Indeed, Colcombe and Kramer [78] have also demonstrated that physical exercise aimed to improve aerobic fitness is associated with reduced age-related brain tissue atrophy and increased perfusion in regions which support memory processes and executive control. With regard to neurodegenerative diseases (e.g., MCI or AD), several non-human animal studies also found that aerobic exercise could decrease brain amyloid burden and pathogenic phenotypes, facilitate neuronal survivability and function modulated by BDNF, and improve cognitive performance in a transgenic mouse model of AD [85–88]. Recently, Baker et al. [48] found that long-term aerobic exercise could effectively improve multiple tests of executive control (e.g., task switching) for individuals with aMCI, and suggested that such an exercise mode plays a protective role by attenuating progression of cognitive symptoms in aMCI, although the beneficial effects of exercise on cognitive functions were more pronounced for the aMCI women than aMCI men, despite comparable gains in cardiorespiratory fitness. Importantly, the improved cardiorespiratory fitness was associated with improved executive function for aMCI women [48]. These substantial findings suggest that although elderly people with aMCI had declines in executive control processes as a function of neuropathological aging [20, 29, 33–35], they still exhibited cognitive and neural plasticity and their task-switching deficits appear amendable to an intervention based on increased physical activity.

Limitations

While the electrophysiological findings of the present study extend the current knowledge base regarding behavioral performance in the elderly with aMCI when performing the task-switching paradigm, there are the following limitations which mean that the results of this work should be applied with caution. First, this study used a cross-sectional research design which does not allow us to address the issue of directionality or causation. Secondly, although the elderly with aMCI showed significantly prolonged P3 latencies and smaller P3 amplitudes when performing the task-switching paradigm, a meta-analysis performed by Jiang et al. [23] showed that only P3 latency could be an objective and sensitive indicator for disease progression in MCI patients. However, this earlier work included only a few reports exploring P3 amplitude in MCI patients. Therefore, additional research is needed to clarify whether the changes in ERP P3 amplitude elicited by the task-switching paradigm represent a sufficiently sensitive and specific parameter for clinical MCI diagnostics. Third, although there are significant correlations between the electrophysiological performance (i.e., P3 amplitude) and the VO2max in the aMCI group, such a correlative approach cannot sufficiently explain the potential mediating or moderating effects of cardiorespiratory fitness on the electrophysiological problem. Future studies should investigate changes in electrophysiological indices via longitudinal experiments regarding the cardiorespiratory exercise intervention to better understand the potential mechanisms associated with MCI.

CONCLUSIONS

There is evidence that patients with early AD show impaired working memory and executive control [89–92], as demonstrated in AD subjects using the task-switching paradigm (e.g., larger local and global switching costs) [33]. In the current study, the elderly with aMCI showed larger global but not local switching costs, slower RTs and poorer electrophysiological performance (i.e., prolonged P3 latencies and smaller P3 amplitudes) in the heterogeneous condition relative to the control group when performing the task-switching paradigm. While older adults progress along a continuum from normal aging to MCI and possibly further to AD later in life, given the findings in both previous research [33] and the present study, gradual dysfunction with regard to task switching (e.g., from only a deficit with regard to global switching cost to both of global and local switching costs) might indicate that aMCI represents an early pathological aging state of AD, and the two disease entities could have similar electrophysiological characteristics, although with differences in severity. In addition, P3 latency and amplitude might represent electrophysiological indices that can be used to identify elderly patients with aMCI when performing the task-switching paradigm. It is important to highlight that cardiorespiratory fitness has positive effects not only on normal aging, but also on age-related neurodegenerative diseases [38, 48, 78, 87, 88]. The current study found that these electrophysiological markers were associated with VO2max in subjects with aMCI. Furthermore, cardiorespiratory fitness appears to be an important predictor of neurocognitive functions, and it is plausible to speculate that training-induced increases in VO2max could result in specific changes in P3 amplitude. A physical exercise intervention leading to improved cardiorespiratory fitness may thus have non-pharmacological remediative effects on the functional integrity of the aMCI patients’ brains and cognition, and further slow or retard the progress of aMCI toward AD.

ACKNOWLEDGMENTS

The authors are grateful to the participants and their family caregivers who gave their precious time to participate in this research and facilitate the work reported here. The research reported in this publication was a Taiwan-Slovak Joint Research Cooperation project, and was supported by the National Science Council in Taiwan under grant number NSC 103-2923-H-006-001-MY3 to Dr. Tsai and Dr. Pai, and by the Slovak Academy of Sciences in Slovakia under grant number SAS/NSC JRP 2013/17 to Dr. Ukropcová and Dr. Ukropec.

Authors’ disclosures available online (http://j-alz.com/manuscript-disclosures/15-1093r2).

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Figures and Tables

Fig.1

Schematic of the task-switching paradigm.

Schematic of the task-switching paradigm.
Fig.2

Grand mean RT data (mean ± SE) for pure trials during the homogeneous condition and non-switching and switching trials during the heterogeneous condition in the amnestic mild cognitive impairment (aMCI) and control groups [*: p < 0.05; the aMCI group responded slower in the heterogeneous condition (i.e., non-switching and switching trials) than the control group].

Grand mean RT data (mean ± SE) for pure trials during the homogeneous condition and non-switching and switching trials during the heterogeneous condition in the amnestic mild cognitive impairment (aMCI) and control groups [*: p < 0.05; the aMCI group responded slower in the heterogeneous condition (i.e., non-switching and switching trials) than the control group].
Fig.3

Grand averaged ERP waveforms in the pure, non-switching, and switching trials for the amnestic mild cognitive impairment (aMCI) and control groups when performing the task-switching paradigm.

Grand averaged ERP waveforms in the pure, non-switching, and switching trials for the amnestic mild cognitive impairment (aMCI) and control groups when performing the task-switching paradigm.
Fig.4

Scatterplots of the relationship between cardiorespiratory fitness and neurocognitive performance (global and local switching costs, and P3 latency and amplitude across two conditions) in the task-switching paradigm in all participants (•: aMCI group; ∘: control group).

Scatterplots of the relationship between cardiorespiratory fitness and neurocognitive performance (global and local switching costs, and P3 latency and amplitude across two conditions) in the task-switching paradigm in all participants (•: aMCI group; ∘: control group).
Fig.5

Scatterplots of the relationship between cardiorespiratory fitness and neurocognitive performance (global and local switching costs, and P3 latency and amplitude across two conditions) in the task-switching paradigm in the amnestic mild cognitive impairment (aMCI) group.

Scatterplots of the relationship between cardiorespiratory fitness and neurocognitive performance (global and local switching costs, and P3 latency and amplitude across two conditions) in the task-switching paradigm in the amnestic mild cognitive impairment (aMCI) group.
Table 1

Demographic characteristics of the amnestic MCI (aMCI) and control groups

aMCI Group (n = 30)Control Group (n = 30)p
Age (years)68.23 ± 5.2566.87 ± 4.380.278
Gender (male/female)12/1814/160.602
Height (cm)159.24 ± 8.18161.96 ± 7.280.179
Weight (kg)60.68 ± 14.3262.10 ± 9.590.653
Body Mass Index (kg/m2)23.67 ± 3.6223.66 ± 3.370.993
Education (years)12.37 ± 2.8512.87 ± 2.600.480
Systolic pressure (mmHg)124.80 ± 19.25123.37 ± 20.750.782
Diastolic pressure (mmHg)73.07 ± 8.1473.33 ± 10.440.912
MMSE*27.60 ± 1.9928.57 ± 1.170.025
BDI-II6.37 ± 4.194.33 ± 3.740.052
Social participation9.47 ± 2.1910.10 ± 2.620.314
Working memory span*19.07 ± 1.6020.63 ± 1.830.001
Grip (kg)26.21 ± 9.7429.03 ± 8.880.247
Back Scratch (cm)–3.02 ± 10.55–3.83 ± 11.810.780
Chair Sit-and-Reach (cm)*3.43 ± 8.678.72 ± 8.840.023
Arm Curl (number)16.63 ± 4.2417.90 ± 4.030.240
8-Foot Up-and-Go (sec)6.30 ± 1.265.94 ± 1.250.268
Chair Stand (sec)16.13 ± 3.7416.40 ± 3.340.772
VO2max (mL/kg/min)*20.85 ± 6.1129.03 ± 6.66<0.001

MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination; BDI, Beck Depression Inventory; *p < 0.05.