J Physiol 562.2 (2005) pp 605–615 605 The influence of normal human ageing on automatic movements Tao Wu and Mark Hallett Human Motor Control Section, Medical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA There is evidence that aged normal subjects have more difficulty in achieving automaticity than young subjects. The underlying central neural mechanism for this phenomenon is unclear. In the present study, functional magnetic resonance imaging (fMRI) was used to investigate the effect of normal ageing on automaticity. Aged healthy subjects were asked to practice self-initiated, self-paced, memorized sequential finger movements with different complexity until they could perform the tasks automatically. Automaticity was evaluated by having subjects perform a secondary task simultaneously with the sequential movements. Although it took more time, most aged subjects eventually performed the tasks automatically at the same level as the young subjects. Functional MRI results showed that, for both groups, sequential movements activated similar brain regions before and after automaticity was achieved. No additional activity was observed in the automatic condition. While performing automatic movements, aged subjects had greater activity in the bilateral anterior lobe of cerebellum, premotor area, parietal cortex, left prefrontal cortex, anterior cingulate, caudate nucleus and thalamus, and recruited more areas, including the pre-supplementary motor area and the bilateral posterior lobe of cerebellum, compared to young subjects. These results indicate that most healthy aged subjects can perform some complex motor tasks automatically. However, aged subjects appear to require more brain activity to perform automatically at the same level as young subjects. This appears to be the main reason why aged subjects have more difficulty in achieving automaticity. (Resubmitted 24 September 2004; accepted after revision 26 October 2004; first published online 28 October 2004) Corresponding author M. Hallett: Building 10, Room 5 N226, 10 Center Drive MSC 1428, Bethesda, MD 20892-1428, USA. Email: [email protected]Normal ageing is not only characterized by a decline of memory, perception and cognition (Mark & Rugg, 1998; Grady, 2000), but is also accompanied by progressive slowness and impaired motor ability (Sailer et al. 2000; Calautti et al. 2001; Mattay et al. 2002). Some functional imaging studies found more activation in aged subjects in several brain areas compared to young subjects while performing the same simple motor tasks (Calautti et al. 2001; Mattay et al. 2002; Hutchinson et al. 2002; Ward & Frackowiak, 2003). In contrast, a study on motor sequence learning found no ageing-related reorganization (Daselaar et al. 2003). These results suggest that normal ageing may have different effects in various motor tasks. However, most previous functional imaging experiments employed simple tasks, and the influence of ageing on more complex motor behaviours has been only rarely studied. A general characteristic of the motor system is that people can perform some learned movements automatically. Such movements are performed without attention being clearly directed towards the details of the movement (Bernstein, 1967). For example, musicians can perform music accurately while holding a conversation. For young subjects, after a period of training even some complex tasks can be executed automatically (Wu et al. 2004). It has been speculated that aged adults have more difficulty achieving automaticity than younger subjects (Rogers et al. 1994). However, it is unclear whether healthy aged subjects can achieve full automaticity after sufficient practice. Moreover, if aged subjects can achieve automaticity, it is not clear whether there would be a difference in the pattern of brain activation. In the present study, we used several sequential movement tasks with different complexity to investigate the influence of normal ageing on automaticity. To evaluate whether automaticity was achieved, we applied a dual task protocol (Wu et al. 2004). The evidence that a task has become automatic can be proven by the fact C The Physiological Society 2004. No claim made to original US government works. DOI: 10.1113/jphysiol.2004.076042
Transcript
J Physiol 562.2 (2005) pp 605–615 605
The influence of normal human ageing on automaticmovements
Tao Wu and Mark Hallett
Human Motor Control Section, Medical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health,Bethesda, MD, USA
There is evidence that aged normal subjects have more difficulty in achieving automaticitythan young subjects. The underlying central neural mechanism for this phenomenon is unclear.In the present study, functional magnetic resonance imaging (fMRI) was used to investigatethe effect of normal ageing on automaticity. Aged healthy subjects were asked to practiceself-initiated, self-paced, memorized sequential finger movements with different complexityuntil they could perform the tasks automatically. Automaticity was evaluated by having subjectsperform a secondary task simultaneously with the sequential movements. Although it took moretime, most aged subjects eventually performed the tasks automatically at the same level as theyoung subjects. Functional MRI results showed that, for both groups, sequential movementsactivated similar brain regions before and after automaticity was achieved. No additionalactivity was observed in the automatic condition. While performing automatic movements,aged subjects had greater activity in the bilateral anterior lobe of cerebellum, premotor area,parietal cortex, left prefrontal cortex, anterior cingulate, caudate nucleus and thalamus, andrecruited more areas, including the pre-supplementary motor area and the bilateral posteriorlobe of cerebellum, compared to young subjects. These results indicate that most healthy agedsubjects can perform some complex motor tasks automatically. However, aged subjects appearto require more brain activity to perform automatically at the same level as young subjects.This appears to be the main reason why aged subjects have more difficulty in achievingautomaticity.
(Resubmitted 24 September 2004; accepted after revision 26 October 2004; first published online 28 October 2004)Corresponding author M. Hallett: Building 10, Room 5 N226, 10 Center Drive MSC 1428, Bethesda, MD 20892-1428,USA. Email: [email protected]
Normal ageing is not only characterized by a decline ofmemory, perception and cognition (Mark & Rugg, 1998;Grady, 2000), but is also accompanied by progressiveslowness and impaired motor ability (Sailer et al. 2000;Calautti et al. 2001; Mattay et al. 2002). Some functionalimaging studies found more activation in aged subjectsin several brain areas compared to young subjects whileperforming the same simple motor tasks (Calautti et al.2001; Mattay et al. 2002; Hutchinson et al. 2002; Ward& Frackowiak, 2003). In contrast, a study on motorsequence learning found no ageing-related reorganization(Daselaar et al. 2003). These results suggest that normalageing may have different effects in various motor tasks.However, most previous functional imaging experimentsemployed simple tasks, and the influence of ageingon more complex motor behaviours has been onlyrarely studied.
A general characteristic of the motor system isthat people can perform some learned movements
automatically. Such movements are performed withoutattention being clearly directed towards the details of themovement (Bernstein, 1967). For example, musicians canperform music accurately while holding a conversation.For young subjects, after a period of training even somecomplex tasks can be executed automatically (Wu et al.2004). It has been speculated that aged adults have moredifficulty achieving automaticity than younger subjects(Rogers et al. 1994). However, it is unclear whetherhealthy aged subjects can achieve full automaticity aftersufficient practice. Moreover, if aged subjects can achieveautomaticity, it is not clear whether there would be adifference in the pattern of brain activation.
In the present study, we used several sequentialmovement tasks with different complexity to investigatethe influence of normal ageing on automaticity. Toevaluate whether automaticity was achieved, we applieda dual task protocol (Wu et al. 2004). The evidence thata task has become automatic can be proven by the fact
that a secondary task can be performed simultaneouslywith minimal or no interference (Passingham, 1996). Thefirst part of the dual task required subjects to performsome self-initiated, self-paced, memorized sequentialfinger tapping movements. The secondary part was aletter-counting task in which subjects were asked toidentify the number of times a target letter from asequence of letters was seen.
We employed functional magnetic resonance imaging(fMRI) to observe the related brain activity. A recentneuroimaging study on normal young subjects by ourgroup revealed that most of the motor network participatesin executing automatic movements and some areas,such as the bilateral cerebellum, anterior supplementarymotor area (SMA), cingulate cortex, left caudate nucleus,bilateral premotor cortex, bilateral parietal cortex anddorsal lateral prefrontal cortex become less activated asmovements become more automatic. In addition, we didnot find any area that was more activated for automaticmovements (Wu et al. 2004).
The objective of this study was to compareperformance and brain activation related to the process ofautomaticity in aged and young normal individuals.We hypothesized that the aged subjects require morebrain network recruitment than the young subjects toexecute automatic movements. We also made additionalobservations on the influence of normal ageing onperformance of dual tasks.
Methods
Subjects
We studied 14 healthy aged subjects. Two subjects wereexcluded because they did not achieve automaticityafter training. The remaining 12 subjects ranged in agefrom 57 to 73 years old (mean 61.8 years), and includedeight males and four females. We also investigated12 sex-matched young subjects, aged 23–38 years (mean30.5 years). The results from these young subjects werepreviously reported (Wu et al. 2004). The subjectswere all right-handed (laterality index was 0.8–1.0)as measured by the Edinburgh Inventory. The agedgroup was also administered the Mini-Mental StateExam (MMSE). The MMSE score was 30 in allsubjects. They reported no history of neurologicalillness or psychiatric history. No subjects were takingany medications that could affect brain excitability.For all subjects, no significant pathological changewas found with standard T1- and T2-weighted MRI,although there were changes due to normal ageing. Theexperiments were performed according to the Declarationof Helsinki and were approved by the InstitutionalReview Board. All subjects gave their written informedconsent for the study.
Methods
All procedures were identical to those of our previouspaper (Wu et al. 2004) and are only briefly describedhere. Subjects were asked to perform two sequencesof right finger tapping, referred to as sequence-4 andsequence-12, based on the number of movementsin each unit of the sequence. ‘Sequence-4’ was1–3–4–2, in which 1, 2, 3 and 4 refer to the index,middle, ring and little fingers, respectively. ‘Sequence-12’ was 1–4–3–2–2–4–1–3–4–1–2–3. Both sequenceswere executed at 0.5 Hz. Automaticity was evaluated byhaving subjects perform a visual letter-counting tasksimultaneously with these sequential movements.For the letter-counting task, letter sequencesconsisting of a random series of the letters A, G,L and O were presented on a screen and subjectswere asked to identify the number of times theysaw a specified target letter. All sequential movementswere self initiated and self paced. Before the first scan, allsubjects practised until they could move at the requiredrate. They briefly practiced each sequential movement.In addition, subjects were given enough practice trials toensure that they could perform the visual letter-countingtasks correctly with no difficulty. After scanning, subjectswere asked to report the number of target letters. After thefirst scan, subjects practised these tasks until they couldperform sequence-4 and sequence-12 from memory10 times in a row without error, as well as the dual tasksaccurately.
Control experiment
In order to explain whether the age-related change in brainactivity was due to difference of strength, a control studywas carried out. All aged and young subjects were askedto tap their left index finger at a frequency of 1 Hz. Thistapping task was a self-paced task and was well trainedbefore the fMRI scan.
Functional MRI experiments
T2∗ time constant-sensitive functional images wereobtained using a whole-body 1.5 T magnetic resonanceimaging (MRI) scanner (Signa, General Electric,Milwaukee, WI, USA) and a standard head coil.Subjects lay supine in the MR scanner with a responsedevice fixed to their hands. The response device had fivebuttons, corresponding to the index, middle, ring andlittle fingers of the right hand; and the index finger ofthe left hand and was used to record finger movements.The subjects viewed visual signals on a screen througha mirror built into the head coil. We used an echoplanar imaging (EPI) gradient echo sequence (21 slices,echo time (TE) = 30 ms, repetition time (TR) = 2500 ms,flip angle = 90 deg, field of view (FOV) = 22 × 22 cm,matrix = 64 × 64) to obtain functional images. A
time-course series of 100 images per slice was acquired foreach trial, in an off–on cycle protocol of rest and activation.Each scanning session lasted 4 min.
Functional MRI scans were acquired both before andafter the subjects achieved automaticity. Two conditionswere contained in each scanning session and were definedas the ‘rest’ and ‘active’ conditions. Each conditionlasted 25 s and was repeated five times in a session.In the rest condition, subjects were asked to relaxand focus on the screen in front of them. The activecondition in each session contained one of the sixsingle or dual tasks: sequence-4, sequence-12, lettercounting, left hand tapping, dual task of sequence-4 andletter counting, and dual task of sequence-12 and lettercounting. No feedback was provided during scanning totell subjects whether their finger movements were corrector incorrect.
Behavioural data analysis
Each subject’s performance for each task was recorded.Errors were used to evaluate whether these taskswere performed automatically after training. Only theperformances achieving high accuracy in both single anddual tasks were considered automatic. The performanceof each task of the aged group was compared to theyoung subjects (two-sample t test, P < 0.05). Within eachgroup, the difference in performance before and afterautomaticity was achieved, and between single anddual task, was calculated (repeated-measures ANOVA,P < 0.05). In addition, the performance betweensequence-12 and sequence-4 was compared (two-samplet test, P < 0.05).
Imaging data analysis
Image analysis was performed with SPM 99 software(Wellcome Institute of Cognitive Neurology, London,UK). Functional images were aligned to the first imageof each session for motion correction. After spatialnormalization, all images were resampled into voxelsthat were 2 × 2 × 2 mm in size. Images were alsosmoothed with a Gaussian filter of 6 mm full-width athalf-maximum (FWHM). Both first- and second-levelanalyses were performed. In the first-level analysis, datawere analysed for each single subject separately on avoxel-by-voxel basis using the principles of the generallinear model extended to allow the analysis of fMRI dataas a time series. The data were modelled using a fixedeffect boxcar design, convolved with a haemodynamicresponse function chosen to represent the relationshipbetween neuronal activation and blood flow changes. Themodel had the same on–off frequency as the alternationfrequency of the active and rest conditions, and wasconstructed for analysis of task-dependent activation,identical for all subjects and for all conditions. A
contrast representing the effect of the active conditioncompared with the rest condition was defined andcontrast images were calculated individually foreach condition. In addition, a contrast representing‘deactivation’, which means more voxel intensityduring rest condition than during active condition,was also calculated. These contrast images were usedin the second-level analysis for random effects. Forthe within-group analysis, a one-sample t test modelwas used to identify the brain activity before andafter training for each condition (P < 0.001, withoutcorrection for multiple comparisons). A student’spaired t test model was used to compare the pretrainingresults with the post-training results for each condition(P < 0.001, uncorrected). In addition, one-way ANOVAwas used to compare the results of dual tasks and singletasks (P < 0.001, uncorrected). For between-groupcomparisons, a two-sample t test model (P < 0.001,uncorrected) was used to explore the difference betweenaged and young subjects after training. We chose thisthreshold because it is often more informative and mayshow a trend towards increased activation, althoughnot reaching the more conservative corrected statisticalthreshold. Locations of activated areas for differentconditions were displayed by superimposing them on theMontreal Neurological Institute (MNI) template.
Results
Task performance
The accuracy of sequential finger movements and visualletter counting for single sequential movements anddual tasks across all aged and young subjects is shownin Table 1. Two aged subjects performed the dual taskof sequence-4 and letter counting with high accuracybut could not perform sequence-12 and letter countingcorrectly after extensive training, which suggests thatthey could not achieve automaticity in performingsequence-12. Therefore, all their data were excluded.Before training, both groups committed errors withsequential finger movements in performing all sequentialmovements and dual tasks. The wrong finger taps of thesequences were consistent across the five blocks during thefirst scanning session. In both the aged and young groups,there were more finger movement errors in performingsequence-12 than in performing sequence-4 (two-samplet test, P < 0.05), and in performing dual tasks than inperforming single tasks (ANOVA, P < 0.05). In addition,more errors were found when performing the dual taskof sequence-12 and letter counting than when performingthe dual task of sequence-4 and letter counting (ANOVA,P < 0.05). Aged subjects made significantly more errorsthan young subjects while performing dual tasks (ANOVA,P < 0.05). They also had more errors than young subjectsin performing either sequence-4 or sequence-12, although
Values are given as means ± S.D. for percentage of errors. The results of the dual task of sequential movements and visual lettercounting are given as errors of finger movements/errors of letter counting.
the difference was not statistically significant (two-samplet test, P > 0.05).
Aged subjects needed significantly more time thanyoung subjects to achieve automaticity (4.7 ± 1.0 versus3.0 ± 0.7 h). After training, the performance of bothaged and young groups was significantly improved(repeated-measures ANOVA, P < 0.05). They couldperform single and dual tasks correctly. Moreover,both groups reported that they could execute thetasks without paying attention to the sequential fingermovements and did not feel difficulty any more.There were no more errors in performing sequence-12than in performing sequence-4, or in performingdual tasks than in performing single tasks in eachgroup, and there was no significant difference betweenthe two groups.
Additionally, there was no between- or within-groupdifference for the rate of sequential movements. Before andafter training, the rates of aged subjects were 0.54 ± 0.07
Figure 1. Brain areas activated during performance ofautomatic movementsBrain regions activated during performing sequence-12 at theautomatic stage in aged subjects. Results were thresholded atP < 0.001 (uncorrected).
and 0.52 ± 0.06 Hz, respectively, while the rates of youngsubjects were 0.55 ± 0.04 and 0.52 ± 0.03 Hz, respectively.However, during practice aged subjects reported moredifficulty than young subjects in acquiring the requiredrate.
Within-group analysis of brain activity whileperforming single tasks
Before training, for aged subjects the performancesof sequence-4 and sequence-12 were associated withactivations in the left primary sensorimotor cortex,bilateral premotor areas, bilateral parietal cortex,bilateral inferior frontal gyrus, bilateral dorsal lateralprefrontal cortex, supplementary motor area(SMA)-proper, pre-SMA, anterior cingulate motorcortex, basal ganglia, bilateral insular cortex and bilateralcerebellum. After training, the pattern of brain activity wassimilar to that before training and no additional activationwas observed for either sequence-4 or sequence-12 (Fig. 1).These results were similar to those for young subjects(Wu et al. 2004). In aged subjects, after training therewas less activation in the bilateral premotor area, bilateralsuperior and inferior parietal lobes and pre-SMAcompared to the before-training stage (P < 0.001,uncorrected; Fig. 2). There was less activity in bilateralcerebellum, bilateral premotor area, bilateral superiorand inferior parietal lobes, left dorsal lateral prefrontalcortex, pre-SMA, anterior cingulate motor cortexand left caudate nucleus in the young group aftertraining (Wu et al. 2004).
Between-group analysis of brain activity whileperforming single tasks
Areas more activated in the aged group at theafter-training stage. Compared to young subjects, at theafter-training stage, aged subjects had greater activationin the bilateral cerebellum (anterior lobe), bilateralpremotor area, bilateral parietal cortex, left dorsallateral prefrontal cortex, anterior cingulate motor cortex,right caudate nucleus, thalamus and occipital cortexduring performance of sequence-4 and sequence-12.
Figure 2. Brain areas less activated in theautomatic stageBrain areas more activated at the pretraining stagethan at the automatic stage during the performanceof sequence-12 in aged subjects. Results werethresholded at P < 0.001 (uncorrected) and renderedover a standard anatomical brain.
Furthermore, the pre-SMA and bilateral posterior lobeof cerebellum, areas that were not activated in the youngsubjects any more at this stage, were still recruited in theaged subjects (Fig. 3 and Table 2).
Areas less activated in the aged group. There was lessactivation in the left primary sensorimotor cortex (SM1)in the aged subjects than in the young subjects whileperforming sequence-4 and sequence-12 after training(Fig. 4). In contrast, in the control study (left index fingertapping), neither right nor left SM1 was less activated inthe aged subjects compared to the young group.
Figure 3. Brain areas more activated inaged subjectsBrain areas more activated in aged subjectsthan in young subjects during automaticexecution of sequence-12 (P < 0.001,uncorrected).
Deactivations. The comparison of brain deactivationduring execution of sequence-4 and sequence-12 at theafter-training stage between the two groups is shownin Fig. 5. Young subjects had more deactivation in theprefrontal, anterior cingulate and precuneus areascompared to aged subjects.
Within-group analysis of brain activity whileperforming dual tasks
Before training, for both groups, the performance ofthe dual task of sequence-4 and letter counting or
Table 2. Brain areas more activated in aged subjects than in young subjects whileperforming sequence-12 at the automatic stage
Cluster size Activated areas x y z Z-value
398 R Caudate nucleus 2 2 9 6.281023 L Cerebellum, anterior lobe −4 −56 −4 6.16
L Cerebellum, posterior lobe −20 −67 −27 5.80597 L Cingulate −8 29 4 6.12156 R Parietal cortex 30 −53 54 6.08339 L Pre-SMA −2 14 51 6.06529 L Premotor area −39 2 48 5.9329 R Premotor area 20 12 55 5.80128 R Thalamus 14 −6 −1 5.7871 L Parietal cortex −50 −40 44 5.67107 R Prefrontal cortex 42 25 −6 5.61221 R Cerebellum, posterior lobe 37 −63 −20 5.57
R Cerebellum, anterior lobe 32 −56 −27 5.3829 L Precuneus −6 −63 58 5.49115 R Occipital cortex 6 −85 15 5.45140 L Prefrontal cortex −2 41 46 5.34
The coordinates are given as stereotaxic coordinates referring to the atlas of Talairachand Tournoux. Cluster size is the number of voxels. All areas were significant at P < 0.001(uncorrected). Abbreviations: L, left; R, right; SMA, supplementary motor area; Z-value,Z-statistic value.
sequence-12 and letter counting was associated withactivations of the left primary sensorimotor cortex,bilateral premotor areas, bilateral parietal cortex, bilateralinferior frontal gyrus, bilateral dorsal lateral prefrontalcortex, SMA-proper, pre-SMA, cingulate cortex, basalganglia, bilateral insular cortex, bilateral cerebellum andoccipital cortex. Aged subjects also activated bilateralprecuneus. After training, no additional activation wasobserved for either group. Similar to single tasks, aftertraining there was less activation in the bilateral premotorarea, bilateral parietal cortex and pre-SMA comparedto the before-training stage in aged subjects. In youngsubjects, there was less activity in bilateral cerebellum,bilateral premotor area, bilateral parietal cortex, leftdorsal lateral prefrontal cortex, pre-SMA, anteriorcingulate motor cortex and left caudate nucleus. In agedsubjects, additional activity was found in the bilateralprecuneus during performance of dual tasks comparedto the component tasks (one-way ANOVA, P < 0.001,uncorrected; Fig. 6). In young subjects, there was no brainarea additionally activated for dual tasks.
Between-group analysis of brain activity whileperforming dual tasks
At the after-training stage, aged subjects had greateractivation in the bilateral cerebellum, bilateralpremotor area, bilateral parietal cortex, bilateralprecuneus, left dorsal lateral prefrontal cortex, pre-SMA,anterior cingulate motor cortex, caudate nucleus,thalamus and occipital cortex compared to young subjectsduring performance of dual tasks of sequence-4 and
letter counting or sequence-12 and letter counting. Inaddition, we found less activation in the left SM1 inthe aged subjects compared to the young subjects whileperforming dual tasks.
Discussion
Effect of ageing on automaticity
Before training, both groups made errors and the agedsubjects made more errors than the young subjects. Twoaged subjects failed to achieve automaticity, and the otheraged subjects needed more training time than youngsubjects. These results demonstrate that aged subjects hadgreater difficulty than young subjects in achieving auto-maticity (Rogers et al. 1994). However, extensive practicewas beneficial not only for young subjects, but also foraged subjects. Eventually most aged subjects significantlyimproved their performance and could execute thesecomplex sequential movements automatically at the samelevel as the young subjects.
Aged subjects had a similar pattern of cortical activityfor both the before- and after-training conditions. Aftertraining, however, there was less activity compared to thebefore-training condition. No brain area was specificallymore activated in the automatic stage. These findings aresimilar to the results of young subjects and support ourprevious observation that no additional areas are activatedspecifically for automaticity in a self-initiated memorizedsequential movement (Wu et al. 2004).
To perform sequential movements automatically, agedsubjects had more activity than young subjects in thebilateral cerebellum, bilateral premotor area, bilateral
Figure 4. Brain areas more activated in youngsubjectsBrain regions more activated in young subjects thanin aged subjects during performance ofsequence-12 after training (P < 0.001,uncorrected).
parietal cortex, caudate nucleus, pre-SMA, anteriorcingulate motor cortex, thalamus, left dorsal lateralprefrontal cortex and occipital cortex; and less activationin the left (contralateral) SM1. Our finding of increasedneural network activation for aged subjects is similar toprevious observations on age-related changes in cognitiveand motor circuitry (Sailer et al. 2000; Calautti et al.2001; Hutchinson et al. 2002; Mattay et al. 2002; Ward& Frackowiak, 2003). In our study, greater activation inaged subjects was found in more brain areas comparedto those studies, which must be attributed to the morecomplex nature of our protocol. It is well known thatmore complex movements require greater brain activation(Sadato et al. 1996; Catalan et al. 1998; Wu et al. 2004).A previous study on motor sequence learning (Daselaaret al. 2003) found no age-related difference. The possiblereason for this difference from our study might beascribed to the use of different protocols. In the presentstudy, we used self-initiated, self-paced, memorized
Figure 5. Brain areas more deactivated in young subjectsBrain regions more deactivated in young subjects compared to agedsubjects during automatic execution of sequence-12 (P < 0.001,uncorrected).
sequence movements, whereas Daselaar and colleaguesused an externally triggered, implicitly learned motor task.
There are several factors that may account forthe observed age-related differences while performingautomatic movements. One is the difference in the taskperformance. In our study, accuracy and rate at theautomatic stage were the same for both groups. Wedid not measure other features, such as strength andvelocity, which are known to decline with ageing (Smithet al. 1999). Previous studies have shown that decreasedforce is associated with less brain activation in areassuch as the contralateral SM1 and SMA (Dettmers et al.1995; Cramer et al. 2002). We found less activity in thecontralateral SM1 in aged subjects, perhaps due to lessvigorous movement. However, there was no differencein activity in the contralateral (right) SM1 betweenthe two groups in the control experiment (left handtapping). Hutchinson et al. (2002) found similar results
Figure 6. Brain area specifically activated in dual tasks in agedsubjectsBrain area more activated in the dual task of sequence-12 and lettercounting compared to single component tasks in aged subjects(one-way ANOVA, P < 0.001, uncorrected).
and suggested that the difference in SM1 could not beexplained by the difference of strength. Most importantly,we found more activity in extensive areas in the agedsubjects. Therefore, although a subtle difference in motorperformance might exist, the differences noted betweenaged and young subjects during execution of automaticmovements cannot be attributed to task performance.
Age-related differences in the haemodynamic couplingof signal change between the two groups may alsocontribute to the differences in the brain activity. Previousinvestigations found some decreases, but no increases inthe blood oxygen level dependent (BOLD) signal responsein aged subjects as a result of altered haemodynamiccoupling (Ross et al. 1997; D’Esposito et al. 1999). It hasbeen suggested that if, in aged subjects, less activationin some brain regions is accompanied by more activityin other regions, as in our study, it is unlikely thatregional variations in the haemodynamic coupling ofneural activity to imaging signal would account for theage-related differences in brain activation (D’Esposito et al.1999).
The differences between the two groups duringexecution of automatic movements might also arisefrom a difference in baseline resting activity. Somepositron emission tomography (PET) experiments showedthat aged subjects compared to young subjects havelower resting brain regional cerebral blood flow andglucose consumption in prefrontal areas (Calauttiet al. 2001). With the technique employed in thepresent study, we could not compare the resting neuralactivity between the groups. However, we found more‘deactivation’ in the prefrontal and anterior cingulate areasin young subjects compared to aged subjects (Fig. 5). Thesemore deactivated areas corresponded to some of the moreactivated areas in the aged group (Fig. 3). This suggeststhat the age-related difference in resting metabolic stateand deactivation might influence our results. However,this influence was limited to a few areas and couldnot explain the significant differences in motor networkactivity between the groups.
Some other factors might also have influenced ourresults. For example, some medications may affect brainexcitability. Because all of our aged subjects were healthyand not taking any centrally active medications, thispotential factor could be excluded. Another possible factoris that aged subjects might exhibit heightened levels ofanxiety when attempting to perform the tasks correctly,which might also contribute to the observed differenceof brain activity between the groups. However, our agedsubjects were well trained and all of them were scannedwhile performing tasks correctly without feeling anydifficulty. Moreover, the reduced activity in some regionsand increased activity in others can rule out a significantinfluence of this factor on our results.
If the differences in activation between the two groupscould not be attributed to the above factors, then it shouldreflect a different strategy in the neural network to producethe same performance. We found less activation of thecontralateral SM1 in aged subjects. This contrasts withmost previous studies, in which more activity in thecontralateral SM1 was found in aged subjects (Mattay et al.2002; Ward & Frackowiak, 2003). A possible reason forthis difference might be the different protocols employed.Several studies on visual perception also showed decreasedactivity in the primary visual cortex in aged subjects (Rosset al. 1997; Grady, 2000). It was suggested that less activityis usually related to poorer performance, and aged subjectswho can perform better have more activity (Cabeza et al.2002; Mattay et al. 2002). This explanation is unsuitable forour data because there were no differences in performancebetween the groups. Because the SM1 is more involvedin processing complex sequential movements than simplerepetitive movements (Catalan et al. 1998), presumably,during performance of simple movements, aged subjectscould increase the utilization of SM1 to maintainperformance level (Mattay et al. 2002; Ward &Frackowiak, 2003). For complex movements, theactivation in SM1 also significantly increased in youngsubjects, but in contrast, in aged subjects SM1activity did not increase further. Therefore, SM1 inaged subjects was less activated compared to youngsubjects.
Among the areas additionally activated in agedsubjects, the pre-SMA (Jenkins et al. 2000; Cunningtonet al. 2002), anterior cingulate motor cortex (Frith et al.1991; Jueptner et al. 1997a; Petersen et al. 1998), caudatenucleus (Alexander & Crutcher, 1990; Jueptner et al.1997b), posterior parietal cortex (Deiber et al. 1996) andpremotor cortex (Halsband et al. 1993) have a crucial rolein planning and selecting a motor task. The dorsal lateralprefrontal and anterior cingulate cortices are importantin monitoring task execution (Owen et al. 1996; Jueptneret al. 1997a). The premotor cortex, posterior parietal areasand cerebellum have greater activity as the complexity ofthe movement increases (Sadato et al. 1996; Catalan et al.1998; Wu et al. 2004). In addition, the neocerebellum(Jenkins et al. 1994; Jueptner et al. 1997b), pre-SMA(Sakai et al. 1998; Hikosaka et al. 1999), caudate nucleus(Hikosaka et al. 1999; Nakano, 2000) and dorsal lateralprefrontal cortex (Jenkins et al. 1994; Jueptner et al.1997a,b; Jansma et al. 2001) are important for acquiringnew sequences. It was shown that aged subjects havegreater difficulty in planning and sequencing a motortask than young subjects (Krampe, 2002). Therefore, ourresults suggest that aged subjects must recruit more areasto compensate for the greater difficulty they have inexecuting automatic movements. Even if there is nosubjectively greater behavioural effort compared to the
young subjects, their brain may work harder to performautomatically.
Further support for the idea that the demands differbetween the two groups comes from within-groupcomparison in the before-and after-training stages. Thedifferences in brain activity between the before-andafter-training stage were suggested as being related to theprocess of automaticity (Wu et al. 2004). In the agedgroup, only the pre-SMA, premotor and parietal corticeswere less activated at the automatic stage compared tothe pretraining condition (Fig. 2). In contrast, in theyoung group, less activity was found in the cerebellum,premotor area, parietal cortex, pre-SMA, anteriorcingulate cortex, caudate nucleus and dorsal lateralprefrontal cortex during the automatic stage (Wuet al. 2004). These results suggest that the pattern ofbrain activity of aged subjects in performing automaticmovements is similar to that at the pretraining stage ofyoung subjects and is obviously less efficient.
An additional factor that contributes to theobserved age-related difference of brain activity isthe timing-control of movements. Similar to our previousstudy (Wu et al. 2004), we did not use external cues tohelp subjects maintain the rates because the need forattention to follow the pace would weaken the claim forautomaticity. Since the rate of movement has a significanteffect on brain activity (Deiber et al. 1999), before thefMRI scan we gave all subjects sufficient time to practisethe rate. Our results prove that aged subjects can executesimple and regular rates at the same level as young subjects(Krampe, 2002). However, aged subjects reported moredifficulty than young subjects in achieving the requiredrate, which suggests that aged subjects have greaterdifficulty in timing-control (Krampe, 2002). Some brainareas, i.e. cerebellum, pre-SMA, dorsal lateral prefrontalcortex and basal ganglia, are involved in generatingaccurate timing of movement (Kawashima et al. 2000;Dreher & Grafman, 2002). The pre-SMA and dorsal lateralprefrontal cortex are especially important for self-pacedmovements (Wessel et al. 1995; Kawashima et al. 2000).Therefore, increased activity in these areas may be partlydue to the additional brain effort aged subjects used fortiming-control.
The difference in brain activation between the twogroups may also be due to reorganization of a neuralnetwork in response to neurodegeneration. There isa series of changes during ageing, such as cell loss,synaptic degeneration, blood flow reduction and neuro-chemical alteration (Raz, 2000). Extensive studies haverevealed central neural system reorganization followingbrain lesions, amputations and blindness (Cohen et al.1997; Hallett, 2001). It is plausible to assume thatsome reorganization occurs in the ageing brain as aconsequence of normal ageing (Buonomano & Merzenich,1998).
Effect of ageing on dual task
Our knowledge of how normal ageing affects the executionof dual tasks is limited. In our study, at the before-trainingstage, performance of dual tasks by aged subjects wassignificantly worse than young subjects (Table 1). Thisresult supports the previous finding that aged subjectsperform dual tasks more poorly than younger subjects(Rogers et al. 1994; Rubichi et al. 1999; Li et al. 2001).After practice, although two aged subjects still haddifficulty in performing dual tasks correctly, most of themcould execute the dual tasks at the same level as youngsubjects. Our data demonstrate that the ability to performdual tasks is still relatively intact in aged subjects. Agedsubjects had more brain activity than young subjects inperforming dual tasks, which demonstrated that theirbrains needed to work harder to perform the dual tasksat the same level as young subjects. The most remarkabledifference between the two groups was that the bilateralprecuneus was additionally activated in dual tasks in agedsubjects. In contrast, in young subjects no additionalarea was activated in the dual tasks; all areas activated inthe dual task were also activated by one or both of thecomponent tasks. It is still controversial whether thereis a central supervisor (D’Esposito et al. 1995) or not(Passingham, 1996) while performing dual tasks. It is notthe concern of this paper to explore why there was no areaspecifically activated in our young subjects. The function ofprecuneus is poorly understood. The additionally activatedprecuneus suggests that the aged subjects may needmore preparation (Astafiev et al. 2003), working memory(Callicott et al. 1999) and monitoring (Gusnard & Raichle,2001) to execute dual tasks compared to young subjects.They must recruit more brain areas to compensate fortheir difficulty in executing dual tasks. In a previous studyof the ageing effect on dual tasks, Smith et al. (2001)found that the left prefrontal cortex only activated in thedual task in aged subjects. However, in that study, agedsubjects had significantly poorer performance and theprefrontal cortex also specifically activated in youngsubjects with poor performance. In contrast, in our studyall aged and young subjects had good performance aftertraining, which may explain why we did not find thatthe prefrontal cortex additionally activated in dual taskexecution in aged subjects.
In summary, we found that although they have moredifficulty, after extensive practice, most healthy agedsubjects could perform some complex motor tasksautomatically as well as young subjects. Aged subjectshad greater activity in the bilateral anterior lobe ofcerebellum, premotor area, parietal cortex, left prefrontalcortex, anterior cingulate, caudate nucleus and thalamus,and recruited more areas, including the pre-SMA and thebilateral posterior lobe of cerebellum, compared to youngsubjects during performance of automatic movements.
Our results suggest that aged subjects appear to requiremore brain network activity to perform automatically atthe same level as young subjects, which contributes to theirdifficulty in achieving automaticity.
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Acknowledgements
We thank D. G. Schoenberg for skilful editing. T. Wu is supportedby the National Institute of Neurological Disorders and StrokeIntramural Competitive Fellowship.
