Abstract

Some older individuals seem to use compensatory mechanisms to maintain high-level performance when submitted to cognitive tasks. However, whether and how these mechanisms affect fronto-striatal activity has never been explored. The purpose of this study was to investigate how aging affects brain patterns during the performance of a lexical analog of the Wisconsin Card Sorting Task, which has been shown to strongly depend on fronto-striatal activity. In the present study, both younger and older individuals revealed significant fronto-striatal loop activity associated with planning and execution of set-shifts, though age-related striatal activity reduction was observed. Most importantly, while the younger group showed the involvement of a “cognitive loop” during the receiving negative feedback period (which indicates that a set-shift will be required to perform the following trial) and the involvement of a “motor loop” during the matching after negative feedback period (when the set-shift must be performed), older participants showed significant activation of both loops during the matching after negative feedback period only. These findings are in agreement with the “load-shift” model postulated by Velanova et al. (Velanova K, Lustig C, Jacoby LL, Buckner RL. 2007. Evidence for frontally mediated controlled processing differences in older adults. Cereb Cortex. 17:1033–1046.) and indicate that the model is not limited to memory retrieval but also applies to executive processes relying on fronto-striatal regions.

Introduction

Aging is not always correlated with significant cognitive decline, functional neuroimaging studies have indeed reported that some elderly people could perform almost as well as younger ones on cognitive tasks (for review, see Ansado et al. 2009). These individuals often showed bilateralization of activation (Cabeza 2002; Reuter-Lorenz 2002) as well as intrahemispheric reorganization of activation, mainly from the occipitotemporal to the frontal cortex (Grady et al. 1994, 2005; Madden et al. 1997; Reuter-Lorenz et al. 2000; Cabeza 2004), an observation referred by Dennis and Cabeza (2008) as the posterior–anterior shift in aging (PASA) phenomenon. Furthermore, since overrecruitment in the elderly has been shown in high performing individuals (people with substantial cognitive reserve [CR]), it has been suggested that this additional cerebral network recruitment represents a form of plasticity which may serve as neural compensation for age-related loss in brain structure. This could explain why language performance, for example, does not decline proportionally to age-related brain atrophy (Park et al. 2002). Indeed, several functional neuroimaging studies have shown increased bilateral activity in high performing older individuals when compared with younger participants during naming (Wierenga et al. 2008) and verb generation tasks (Persson et al. 2004). Obler et al. (2010) have even shown anatomical evidence (using structural magnetic resonance imaging and diffusion tensor imaging) for increased reliance on right-hemisphere regions mainly in the peri-Sylvian and the midfrontal areas in older individuals with high naming skills.

Beside neural compensation, CR may also rely on another mechanism, known as neural reserve (for a review, see Stern 2009), which is the utilization by the elderly of preexisting brain networks that are more efficient and less susceptible to age-induced disruption. Both mechanisms are not mutually exclusive and may actually coexist. Indeed, in a study by Ansado et al. (unpublished data), older individuals performing a 2 level load condition visual letter-matching task showed greater prefrontal cortex (PFC) activity in both low- and high-load conditions as well as similar parietal activity to the younger ones in the high-load condition, indicating the use of both neural compensation (frontal activity) and neural reserve (parietal activity) to cope with increasing task demands.

Recently, Velanova et al. (2007) have also suggested the existence of another age-related compensatory mechanism that consists of a shift from early to late selection processing during memory retrieval (the load-shift model). In other words, using the concepts of Rugg and Wilding (2000), who divided memory retrieval into 3 entities: retrieval orientation (anticipation of retrieval demands), retrieval effort (access of information), and postretrieval monitoring (evaluation of the appropriateness of the recollected information), Velanova et al. (2007) hypothesized that older participants would rely more on retrieval effort and postretrieval monitoring and less on retrieval orientation than younger ones. Indeed, their results showed that older adults presented increased and delayed recruitment of frontal regions compared with the younger ones during demanding retrieval. They theorized that this strategy shift could underlie the retention of high-level cognitive function in some older individuals but at the expense of less flexible and slower performance on demanding cognitive tasks.

Although older adults have been shown to perform more poorly than younger ones on many neuropsychological tasks (traditionally used to access executive functions) such as the Wisconsin Card Sorting Task (WCST) (Parkin and Walter 1992; Kramer et al. 1994) and the color-word Stroop task (Brink and McDowd 1999), most age-related functional neuroimaging experiments have focused primarily on memory, perception, and language processes not on executive functions per se. These latter functions may be loosely defined as the collection of processes involved in planning, cognitive flexibility, rule acquisition, initiation of appropriate actions, and inhibition of inappropriate ones, as well as execution of novel actions (Stuss and Knight 2002). A possible explanation for such a lack of functional neuroimaging studies on executive processes and aging could be that behavioral findings are themselves quite inconsistent. Indeed, several studies have either shown that the age-related decline in performance would disappear if nonexecutive components (e.g., motor-speed) were considered (Fristoe et al. 1997; Parkin and Java 1999) or found no age-related performance decline at all (Boone et al. 1990). Furthermore, the separation between different executive processes can also be somewhat challenging. Nonetheless, some, as Hampshire et al. (2008), successfully managed to use functional magnetic resonance imaging (fMRI) to investigate how aging affected executive function. They arrived at the conclusion that there was indeed some age-related loss in efficient problem solving associated with decreased activity in the ventrolateral PFC and posterior parietal cortex, as well as in the dorsolateral PFC at a very old age.

The majority of functional neuroimaging experiments on age-related changes associated with executive processing, such as the one of Hampshire et al. (2008), have largely focused on cognitive decline and alterations in cortical patterns of activity. Yet, the idea of executive processing related compensatory mechanisms was not explored nor was the effect of aging on the patterns of activity in subcortical areas such as the basal ganglia. However, it has been shown in neuropsychological studies with Parkinson’s (PD) and Huntington’s (HD) disease patients that the basal ganglia contributes to executive functions, such as planning and set-shifting (Owen et al. 1996; Dubois and Pillon 1997). Among the different striatal nuclei, the caudate nucleus is traditionally thought to play a greater role in executive processing, whereas the putamen seems more associated with motor activities. Results from our laboratory have suggested that the caudate nucleus and the putamen are, respectively, important in the planning and execution of self-generated novel actions (Monchi et al. 2006). Furthermore, structural degradation has been reported to occur in the striatum with aging (Wang et al. 2009). Indeed, the caudate nucleus and the putamen are known to have dense dopaminergic innervations which, combined with strong evidence for age-related loss in presynaptic and postsynaptic dopamine markers (D1 and D2 receptor densities) and fronto-striatal atrophy, would support a striatal activity decrease associated with aging (Bäckman et al. 2006).

Monchi et al. (2001) previously used an electronic version of the WCST to study fronto-striatal involvement in executive processes in young healthy adults. During task performance, participants were asked to match test cards with reference ones according to the color, shape, or number of stimuli on the cards. If they received the instruction that their matching was correct (positive feedback), participants had to continue matching according to the same rule as in the previous trial. On the other hand, if they were instructed that their matching was incorrect (negative feedback), they needed to choose a different rule (plan a set-shift) and then execute their matching according to the new rule they had chosen. With this experiment, Monchi et al. (2001) showed the participation of 2 different cortico-striatal loops associated with the task: the planning of a set-shift (recorded during negative feedback periods) involved a cognitive loop that includes area 47/12 of the midventrolateral PFC, the caudate nucleus, and the thalamus, whereas the execution of a set-shift (recorded during matching following negative feedback periods) involved the posterior PFC and the putamen. These results were replicated in a more recent study by Nagano-Saito et al. (2008).

In a later experiment, Simard et al. (2010) developed a lexical version of the WCST, the Wisconsin Word-Sorting Task (WWST), and scanned young healthy participants during task performance using fMRI. The principles governing the WWST were exactly the same as the pictogram WCST version, but instead of matching cards, participants were asked to match test words with reference words according to semantic categorization, syllable rhyme, or syllable onset. In the study, Simard et al. (2010) showed the involvement of the cognitive loop during negative feedback periods and the involvement of the motor loop during the following matching periods. These results (similar to the ones of Monchi et al. 2001) seem to indicate that fronto-striatal loops contribute to the same executive processes regardless of whether they are applied to visual or language domains.

The aim of the present study was to explore how the patterns of activity observed in the cognitive and motor fronto-striatal loops during the performance of the WWST (Simard et al. 2010) changed with aging. We hypothesized that, as for young participants, the elderly would show fronto-striatal loop activity associated with planning and execution of set-shifts but with reduced striatal recruitment due to an age-related striatal degradation (Wang et al. 2009). We also expected to see some neural compensation such as the PASA phenomenon to occur. This hypothesis was based on the fact that our older participants were expected to be high performing individuals, since they were all active professionals. Indeed, it has been shown that the PFC tends to be under activated in older individuals whose performance becomes impaired both in TEP (Reuter-Lorenz and Cappell 2008) and fMRI studies (Hampshire et al. 2008).

We predicted that unlike the younger group, the older one would recruit similar frontal regions for both positive and negative trials as both types of trials should require compensatory mechanisms involving the frontal cortex, whereas only the negative trials would require frontal involvement for the younger participants. Finally and most importantly, we also wished to investigate whether the processes suggested by the load-shift model and proposed in the context of memory retrieval (Velanova et al. 2007) also occurred in tasks involving executive processes. In the present study, this would be reflected by a shift of activity from the planning period (feedback) to the execution period (matching) of a trial.

Materials and Methods

Subjects

Twenty-four French-speaking right-handed subjects (14 [group 1] whose mean age was 26 years old [range from 18 to 35, standard deviation {SD}: 5; 6 males, 8 females] and 10 [group 2] whose mean age was 62 years old [range from 55 to 75, SD: 8; 6 males, 4 females]) with no personal or familial history of neurological or psychiatric disorder participated in this study. The age difference between the 2 groups proved to be very significant (t = 13.44, P < 0.001). The 2 groups were matched for level of education. Data from the young participants have been previously reported by Simard et al. (2010). Handedness was assessed by the Edinburgh Handedness Inventory. All candidates gave written informed consent to the protocol, which had been reviewed and approved by the research ethics committee of the Regroupement Neuroimagerie Québec (CMER-RNQ). This committee follows the guidelines of the Tri-Council Policy Statement of Canada, the civil code of Quebec, the Declaration of Helsinki, and the code of Nuremberg.

The WWST developed by Simard et al. (2010) was administered, using a stimulus presentation software (Media Control Function; Digivox, Montréal, Canada). The WWST is a lexical analog of the computerized WCST used by Monchi et al. (2001), in which French words are used instead of the usual pictogram cards. A strict correspondence regarding the stimuli, the rules, and the number of exemplars was established between the WWST and the original WCST. Specifically, the 3 classification rules of the original task (i.e., classification according to color, shape, and number of visual stimuli) were replaced by 3 lexical ones: one semantic and 2 phonological rules namely syllable onset (attack) and syllable rhyme.

Throughout the task, 4 fixed reference words (bateau [ship], araignée [spider], cadran [clock], and poivron [pepper]) were presented in a row at the top of the screen: whereas a test word was shown in the middle of the screen below the reference row (Fig. 1). During scanning, we projected the computer display onto a mirror in the MRI scanner. Each trial, participants had to match the test word with one of the reference words based on one the following rules: 1) semantic categorization, 2) syllable rhyme, or 3) syllable onset. Word selection was performed by pressing the appropriate button of a magnetic resonance imaging compatible response box held with the right hand by the participant: the left button moved a cursor under the reference card from left to right, and the selection was made by pressing the right button. Every participant had to find the proper classification rule and apply it based on the feedback he/she received following each selection. A change in the screen brightness indicated to the subject whether the answer was correct (bright screen) or not (dark screen). After 6 consecutive correct trials, the rule changed without warning and the participant had to discover the new appropriate rule.

Figure 1.

An example of a typical trial of the WWST. In this example, the participant is presented with the word “poignée” (handle) as a test stimulus. Matching according to semantics would require selection of the word “cadran” (frame), according to rhyme syllable would require selection of the word “arraignée” (spider) and according to onset syllable the word “poivron” (pepper) (from Simard et al. 2010).

Figure 1.

An example of a typical trial of the WWST. In this example, the participant is presented with the word “poignée” (handle) as a test stimulus. Matching according to semantics would require selection of the word “cadran” (frame), according to rhyme syllable would require selection of the word “arraignée” (spider) and according to onset syllable the word “poivron” (pepper) (from Simard et al. 2010).

As in the original WCST, there were 4 matching possibilities for each one of the categories in the WWST: 4 semantic categories: transportation, animals, objects, and vegetables; 4 phonological onset syllables: “ba,” “a,” “ca,” and “poi” and 4 phonological rhyme syllables: “au,” “é,” “an,” and “on.” The words have been all carefully chosen so they could have the same phonological syllabic structure according to the French lexical database “lexique 3” (New et al. 2004) http://www.lexique.org/ and be considered concrete according to the concreteness scale of Bonin et al. (2003). Word length ranged from 4 to 9 letters and the number of syllables from 2 to 3. Also, they were all selected out of over the 135 000 words contained in the French lexical database lexique 3 (New et al. 2004). First, the onset and rhyme syllables in which there were the most words were selected. Then, from this selection, the words that shared the same onset and rhyme syllables were picked and matched. And finally, from this later selection, the words that shared the same semantic category were chosen.

The same periods that were identified for the WCST version of Monchi et al. (2001) were defined for the present lexical equivalent. The WWST trials contained 2 types of periods: a matching period and a feedback period. The matching period started with the presentation of a new test word and continued until reference word selection. The length of this period varied from trial to trial depending on subject’s response time. Matching was followed by a feedback period, which lasted 2.3 s and started as soon as a selection was made. This period ended with the presentation of the next test word on the screen initiating a new trial. Thus, inside those 2 periods, 4 different experimental events were defined: event 1, receiving negative feedback indicated by a dark screen and informing the subject that the selection was incorrect and therefore that a shift was required (the need to plan a set-shift); event 2, matching following negative feedback, which was the execution of the first match following negative feedback (the execution of a set-shift); event 3, receiving positive feedback indicated by a bright screen informing the subject that the current classification rule was the correct one (the need to maintain the same rule as in the previous trial); and event 4, matching following positive feedback, which was the execution of matching according to the current rule. A control condition was added in which the test word was the same as one of the 4 reference words, and the participant was asked to match the test word to its reference twin. In this condition, 2 other event periods were defined: event 5, control feedback in which the brightness of the screen did not change and event 6, control matching.

All subjects participated in one fMRI session. Each scanning session contained 4 functional runs; each of one was made up of 4 task blocks. Each block consisted of 3 experimental (corresponding to each 1 of the 3 rules) and one control condition presented in a pseudorandom fashion. Just before the scanning session began, subjects were fully trained on the task using a personal computer. They practiced until their performance reached a plateau and with less than 6% of perseverative and nonperseverative errors. Finally, prior to training, participants were also familiarized with the test word list in order to verify that they knew all the words being used and could classify each one within 1 of the 4 semantic categories.

fMRI Scanning

Participants were scanned at the Unité de Neuroimagerie Fonctionnelle of the Institut de gériatrie de Montréal using a 3T Siemens TIM MRI scanner (Siemens AG, Erlangen, Germany). Each scanning session began with a high-resolution T1-weighted 3D volume acquisition for anatomical localization (voxel size, 1 × 1 × 1 mm3), followed by acquisitions of echoplanar $T2*$-weighted images with blood oxygen level–dependent (BOLD) contrast (time echo, 30 ms; Flip Angle:, 90°). Functional images were acquired in 4 runs containing 210 volumes each acquired every 2.5 s. Volumes contained 36 slices with a matrix size 64 × 64 pixels (voxel size, 3.5 × 3.5 × 3.5 mm3). The stimulus presentation and the scanning were synchronized at the beginning of each run.

Data Analysis

The fMRI data was analyzed following the same method as in our previous studies (Monchi et al. 2001, 2004, 2006, 2007; Simard et al. 2010) and made use of the fMRIstat software developed by Worsley et al. (2002). The first 3 frames in each run were discarded. Images from each run were first realigned to the fourth frame for motion correction and smoothed using a 6-mm full-width half-maximum isotropic Gaussian kernel. The statistical analysis of the fMRI data was based on a linear model with correlated errors. The design matrix of the linear model was first convolved with a difference of 2 gamma hemodynamic response functions timed to coincide with the acquisition of each slice. The correlation structure was modeled as an autoregressive process. At each voxel, the autocorrelation parameter was estimated from the least squares residuals, after a bias correction for correlation induced by the linear model. The autocorrelation parameter was first regularized by spatial smoothing and was then used to “whiten” the data and the design matrix. The linear model was reestimated using least squares on the whitened data to produce estimates of effects and their standard errors. The resulting effects and standard effect files were then spatially normalized by nonlinear transformation into the MNI 305 standard proportional stereotaxic space, which is based on that of Talairach and Tournoux (1988), using the algorithm of Collins et al. (1994). Anatomical images were also normalized to the same space using the same transformation. In a second step, runs and subjects were combined using a mixed effects linear model for the data taken from the previous analysis. A random effects analysis was performed by first estimating the ratio of the random effects variance to the fixed effects variance, then regularizing this ratio by spatial smoothing with a Gaussian filter. Intergroup analyses were performed by direct comparisons using the effects and SDs files of all individuals from both groups. The amount of smoothing was chosen to achieve 100 effective degrees of freedom (Worsley et al. 2002; Worsley 2005). Statistical maps were thresholded at P < 0.05 correcting for multiple comparisons using the minimum between a Bonferroni correction and random field theory in the single and intergroup analysis. This yields a threshold of t > 4.70 for a single voxel or a cluster size > 534 mm3 for a significance assessed on the special extent of contiguous voxel (Friston et al. 1995). Peaks within the basal ganglia, thalamus, and PFC that were observed in our previous studies using the WCST in young healthy adults (Monchi et al. 2001) were considered predicted and are reported at a significance of P < 0.001 uncorrected (indicated by an asterisk [*] in the tables).

Six contrasts were generated for statistical analysis by subtracting the appropriate control period trials from that of the experimental event periods: 1) receiving negative feedback minus control feedback; 2) matching following negative feedback minus control matching; 3) receiving positive feedback minus control feedback; 4) matching following positive feedback minus control matching; 5) receiving negative feedback minus receiving positive feedback; and 6) matching following negative feedback minus matching following positive feedback.

Behavioral data (errors and reaction times) were also collected, and intergroup analyses were performed using SPSS 15.0 for Windows. A comparison between the 2 groups for each matching condition: 1) control matching, 2) matching following positive feedback, and 3) matching following negative feedback was performed using T-Tests (one for each condition).

The same procedure was used to analyze errors: 1) set-loss errors (the participant changes of classification rule after having correctly applied it at least 3 times), 2) perseverative errors (incorrect and repetitive, more than twice, and use of the same classification rule following negative feedback), and 3) control errors (incorrect classification during control trials); as well as incorrect classifications after a change in rule (related to the search for a correct rule). Note that these incorrect classifications are not considered errors because subjects could not know the new classification rule on the first attempt after a set-shift.

Results

Behavioral Performance

On average, in the younger group, control matching lasted 1286 ms (±154 ms), matching following positive feedback lasted 1628 ms (±183 ms), and matching following negative feedback lasted 1990 ms (±187 ms).

In the older group, control matching lasted 1794 ms (±293 ms), matching following positive feedback lasted 2295 ms (±525 ms), and matching following negative feedback lasted 2775 ms (±465 ms).

Matching following positive feedback proved to be significantly longer in the older group than in the younger one (t = 3.845, P = 0.003), so was matching following negative feedback (t = 5.107, P < 0.001) as well as control matching (t = 5.001, P < 0.001).

The younger candidates made on average 0.06% perseverative errors and 2.38% set-loss errors per experimental classification, as well as 0.05% errors per control classification. They also made an average of 13.42% incorrect classifications per experimental classification.

The elderly made on average 0.11% perseverative errors and 4.15% set-loss errors per experimental classification and 0.04% errors per control classification. They also made an average of 14.29% incorrect classifications per experimental classification.

The number of set-loss errors proved to be significantly larger in the older group than in the younger one (t = 2.383, P = 0.042). However, this number remained very low in both groups, indicating that both our younger and older individuals were high performing participants. No other comparison between the 2 groups was significantly different (perseverative errors: t = 2.001, P = 0.072; control errors: t = 0.520, P = 0.608; and incorrect classifications after a change in the rule: t = 1.619, P = 0.120).

fMRI Results

For each group, we compared the average BOLD signal obtained during the receiving and matching periods according to semantics, onset, and rhyme (combined) with the BOLD signal obtained during the corresponding periods in the control trials. We also performed intergroup analysis.

As predicted, these analyses revealed, in both groups, the involvement of 2 different corticostriatal loops during the performance of the WWST: one composed of the midventrolateral PFC (area 47/12), the caudate nucleus, and the thalamus and another composed of the posterior frontal cortex and the putamen. However, the 2 loops were not activated during the same periods for the young and the elderly. Indeed, in younger adults, the analyses showed the involvement of the first frontostriatal loop in the receiving negative feedback versus control feedback contrast and the second one in the matching following negative feedback versus matching following positive feedback contrast, whereas, in the elderly, both loops were significantly activated only during the matching following negative feedback versus control matching contrast (Table 6).

Due to these differences in frontostriatal loop activation timing, we computed 2 other contrasts in order to compare the overall frontostriatal activity between the 2 groups. In these contrasts, feedback and matching periods were combined, in order to look at trials as a whole, that is, (7) (receiving negative feedback plus matching following negative feedback) minus (control feedback plus control matching) or put more simply (whole negative trial vs. whole control trial) and (8) (receiving positive feedback plus matching following positive feedback) minus (control feedback plus control matching) or put more simply (whole positive trial vs. whole control trial). Only intergroup analyses are reported for these 2 last contrasts since intragroup analyses showed exactly the same activated regions as the corresponding receiving and matching periods put together.

As predicted, this further analysis revealed significantly reduced caudate nucleus activity in the older compared with the younger group in the negative events (Table 4). They also showed increased frontopolar area activity in older adults compared with younger ones in both the negative and positive event periods (Tables 4 and 5).

Only significant activations in the frontal, striatal and thalamic regions for the younger adults, older adults, and intergroup comparisons are reported in the text below and the tables. The complete results for the younger group can be found in Simard et al. (2010).

Receiving Negative Feedback

When receiving negative feedback was compared with the control feedback (Table 1 and Fig. 2), significant activations were observed bilaterally in the frontopolar cortex (area 10), the middorsolateral PFC (areas 9/46, 46), the midventrolateral PFC (area 47/12), and the supplementary motor cortex (area 6). There was also increased activity in the left hemisphere in the anterior cingulate cortex (area 32), the posterior PFC (junction or areas 6,8, and 44), and the lateral premotor cortex (area 6). Furthermore, the thalamus and the caudate nucleus were also significantly activated.

Table 1

Receiving negative feedback (event 1) minus control feedback (event 5)

 Young Old Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size Frontopolar cortex (area 10) Frontopolar cortex (area 10) Left −38 54 14 4.13 1344 Left −42 54 2 4.56 3520 Right 34 60 12 3.85 648 −30 64 12 4.08 SC Anterior cingulate cortex (area 32) Right 38 58 8 4.13 1984 Left −8 34 26 3.88 5000 Middorsolateral PFC (area 9) Midventrolateral PFC (area 47/12) Left −44 12 34 4.97 3004 Left −30 26 −2 6.04 >10 000 Right 40 24 30 4.39 1103 Right 32 26 0 6.49 4503 Middorsolateral PFC (areas 9, 46, and 9/46) Posterior PFC (junction of 6,8, and 44) Left −52 26 30 4.44 4105 Left −40 28 52 5.04 2600 Right 52 24 36 3.58* 312 Right 42 26 50 4.99 1300 Posterior PFC (junction of 6,8, and 44) 42 6 34 4.01 SC Left −48 6 38 6.46 4204 Supplementary motor cortex (area 6) −36 18 26 4.86 SC Supplementary motor cortex (area 6) Left −4 20 48 5.90 1000 Left −4 14 54 4.86 5000 Right 6 28 46 3.84 568 Right 8 16 50 4.39 5000 Lateral premotor cortex (area 6) Lateral premotor cortex (area 6) Left −50 0 54 4.39 4145 Left −36 2 66 5.58 2501 −40 0 40 4.16 SC Right 34 22 66 4.138 1005 Caudate nucleus (head) Left −14 22 −2 6.3 >10 000 Right 16 20 0 6.11 4400 Thalamus Left −8 −14 8 4.79 >10 000 Right 8 −11 3 3.99 >10 000 Midbrain Left −4 −28 −4 5.79 >10 000 −6 −14 0 4.75 SC Right 6 −24 −4 5.72 >10 000 6 −16 −12 5.52 SC Young versus old Old versus young Midventrolateral PFC (area 47/12) — Right 32 26 0 4.71 6600 Caudate ucleus (head) Left −18 −16 6 6.23 3650 Right 20 12 −2 6.06 3300 Putamen Left −18 16 −6 6.23 3650 Right 20 12 −2 6.06 3300 Thalamus Left −8 −14 4 3.71 600 Right 8 −16 6 4.08 648
 Young Old Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size Frontopolar cortex (area 10) Frontopolar cortex (area 10) Left −38 54 14 4.13 1344 Left −42 54 2 4.56 3520 Right 34 60 12 3.85 648 −30 64 12 4.08 SC Anterior cingulate cortex (area 32) Right 38 58 8 4.13 1984 Left −8 34 26 3.88 5000 Middorsolateral PFC (area 9) Midventrolateral PFC (area 47/12) Left −44 12 34 4.97 3004 Left −30 26 −2 6.04 >10 000 Right 40 24 30 4.39 1103 Right 32 26 0 6.49 4503 Middorsolateral PFC (areas 9, 46, and 9/46) Posterior PFC (junction of 6,8, and 44) Left −52 26 30 4.44 4105 Left −40 28 52 5.04 2600 Right 52 24 36 3.58* 312 Right 42 26 50 4.99 1300 Posterior PFC (junction of 6,8, and 44) 42 6 34 4.01 SC Left −48 6 38 6.46 4204 Supplementary motor cortex (area 6) −36 18 26 4.86 SC Supplementary motor cortex (area 6) Left −4 20 48 5.90 1000 Left −4 14 54 4.86 5000 Right 6 28 46 3.84 568 Right 8 16 50 4.39 5000 Lateral premotor cortex (area 6) Lateral premotor cortex (area 6) Left −50 0 54 4.39 4145 Left −36 2 66 5.58 2501 −40 0 40 4.16 SC Right 34 22 66 4.138 1005 Caudate nucleus (head) Left −14 22 −2 6.3 >10 000 Right 16 20 0 6.11 4400 Thalamus Left −8 −14 8 4.79 >10 000 Right 8 −11 3 3.99 >10 000 Midbrain Left −4 −28 −4 5.79 >10 000 −6 −14 0 4.75 SC Right 6 −24 −4 5.72 >10 000 6 −16 −12 5.52 SC Young versus old Old versus young Midventrolateral PFC (area 47/12) — Right 32 26 0 4.71 6600 Caudate ucleus (head) Left −18 −16 6 6.23 3650 Right 20 12 −2 6.06 3300 Putamen Left −18 16 −6 6.23 3650 Right 20 12 −2 6.06 3300 Thalamus Left −8 −14 4 3.71 600 Right 8 −16 6 4.08 648

Note: SC, same cluster as preceding peak; VS, intergroup comparison. Average BOLD signal in the first group is significantly greater than in the second one.

Figure 2.

Location of frontal and striatal peaks during receiving negative versus control feedback. The younger group (cf. left) shows the activation of a corticostriatal loop composed of the midventrolateral PFC (area 47/12), the caudate nucleus, and the thalamus, whereas the older group (cf. center) shows increased activity in the frontopolar cortex (area 10). When compared with the older group (cf. right), the younger one continues to show increased activity in the corticostriatal loop. The anatomical MRI images are the average of the T1 acquisitions of the 14 younger subjects (cf. left), the 10 older subjects (cf. center), and all 24 subjects (cf. right) transformed into stereotaxic space. The color scale represents the T statistic. Z values correspond to the coordinate of the axial plane.

Figure 2.

Location of frontal and striatal peaks during receiving negative versus control feedback. The younger group (cf. left) shows the activation of a corticostriatal loop composed of the midventrolateral PFC (area 47/12), the caudate nucleus, and the thalamus, whereas the older group (cf. center) shows increased activity in the frontopolar cortex (area 10). When compared with the older group (cf. right), the younger one continues to show increased activity in the corticostriatal loop. The anatomical MRI images are the average of the T1 acquisitions of the 14 younger subjects (cf. left), the 10 older subjects (cf. center), and all 24 subjects (cf. right) transformed into stereotaxic space. The color scale represents the T statistic. Z values correspond to the coordinate of the axial plane.

When receiving negative feedback was compared with receiving positive feedback, the same pattern of activation was shown except for the posterior PFC (junction or areas 6,8, and 44), which proved to be significantly activated both in the left and the right hemisphere.

In the older group (Table 1 and Fig. 2), when receiving negative feedback was compared with the control feedback, there were bilateral significant activations in the frontopolar cortex (area 10), the middorsolateral PFC (area 9), the posterior PFC (junction of areas 6, 8, and 44), the supplementary motor cortex (area 6), and the lateral premotor cortex (area 6).

When receiving negative feedback was compared with the receiving positive feedback, there was only significant left activation in the frontopolar cortex (area 10) and the middorsolateral PFC (area 9/46).

Interestingly, no significantly increased activity was found in the midventrolateral PFC or basal ganglia in either of the contrasts.

Intergroup comparison.

When receiving negative feedback was compared with control feedback, significant activation was found in the younger participants versus the older ones in the right midventrolateral PFC (area 47/12). There was also bilateral significantly increased activity in the thalamus, the putamen, and the caudate nucleus (Table 1 and Fig. 2).

When receiving negative feedback was compared with receiving positive feedback, greater left activation was found in the younger adults versus the older ones in the middorsolateral PFC (area 9), the posterior PFC (areas 44), and the lateral premotor cortex (area 6), as well as right activation in the supplementary motor cortex (area 6). Also, bilateral activation was recorded in the caudate nucleus and left activation in the putamen.

On the other hand, the older group showed only increased left activity in the frontopolar cortex (area 10) compared with the younger one when receiving negative feedback was compared with receiving positive feedback; and no increased activity at all when receiving negative feedback was compared with control feedback (Table 1).

Matching Following Negative Feedback

When matching following negative feedback was compared with control matching in the younger individuals (Table 2 and Fig. 3), BOLD signal was significantly greater bilaterally in the frontopolar cortex (area 10), the middorsolateral PFC (areas 9 and 9/46), the midventrolateral PFC (areas 45 and 47/12), the posterior PFC (junction of areas 6, 8, and 44), the supplementary motor cortex (area 6), and the lateral premotor cortex (area 6). Significant activation was also found in the right hemisphere in the anterior cingulate cortex (area 32).

Table 2

Matching following negative feedback (event 2) minus control matching (event 6)

 Young Old Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size Frontopolar cortex (area 10) Frontopolar cortex (area 10) Left −36 56 16 5.16 >10 000 Left −30 56 12 5.55 >10 000 Right 30 56 18 5.19 >10 000 Right 38 58 14 5.06 4088 Anterior cingulate cortex (area 32) Midventrolateral PFC (areas 45, 47/12) Right 10 30 28 5.69 >10 000 Left −30 28 2 6.99 >10 000 Midventrolateral PFC (areas 45, 47/12) Right 32 26 0 6.11 6606 Left −30 28 6 7.12 >10 000 Middorsolateral PFC (areas 9, 46, and 9/46) −44 28 16 4.58 SC Right 34 28 0 5.27 4448 Left −46 24 32 6.84 >10 000 56 16 10 3.63 SC Right 42 20 32 3.97 740 Middorsolateral PFC (areas 9, 46, and 9/46) Posterior PFC (junction of 6,8, and 44) Left −48 28 32 4.74 >10 000 Left −46 10 48 4.48 >10 000 Right 42 36 26 5.26 >10 000 −48 14 12 4.38 SC Posterior PFC (junction of 6,8, and 44) Right 22 14 52 4.83 1890 Left −50 6 44 5.00 >10 000 34 14 28 3.67 742 −50 14 16 3.85 SC Supplementary motor cortex (area 6) Right 46 20 42 4.17 >10 000 Left −4 20 48 5.97 9004 Supplementary motor cortex (area 6) Right 8 24 46 5.08 8080 Left −6 −4 68 4.52 >10 000 Lateral premotor cortex (area 6) Right 2 −4 68 4.52 SC Lateral premotor cortex (area 6) Left −28 2 60 3.45 >10 000 Left −44 2 40 5.34 >10 000 Right 26 6 56 5.4 1862 −28 0 5 5.26 SC Caudate nucleus (head) Right 28 0 52 6.92 >10 000 Left −14 16 6 4.55 1376 Right 16 16 2 3.5* 488 Putamen Left −20 2 2 3.46* 124 Right 22 −2 10 4.03 3000 Thalamus Left −8 −10 4 4.89 1002 Right 12 −10 10 5.25 2002 Midbrain Left −6 −12 −6 4.68 1000 Right 6 −14 −8 4.96 998 Young versus old Old versus young Supplementary motor cortex (area 6) Frontopolar cortex (area 10) Left −2 8 56 4.46 1712 Left −2 58 −4 3.61 656 Lateral premotor cortex (area 6) Right 2 60 −4 3.70 656 Right 32 0 56 4.24 856 Midventrolateral PFC (area 47/12) Left −32 36 −10 3.98* 200 Caudate nucleus (head) Left −16 14 −4 4.51 656 Putamen Left −16 14 10 4.34 600 Right 16 12 −6 4.48 1168
 Young Old Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size Frontopolar cortex (area 10) Frontopolar cortex (area 10) Left −36 56 16 5.16 >10 000 Left −30 56 12 5.55 >10 000 Right 30 56 18 5.19 >10 000 Right 38 58 14 5.06 4088 Anterior cingulate cortex (area 32) Midventrolateral PFC (areas 45, 47/12) Right 10 30 28 5.69 >10 000 Left −30 28 2 6.99 >10 000 Midventrolateral PFC (areas 45, 47/12) Right 32 26 0 6.11 6606 Left −30 28 6 7.12 >10 000 Middorsolateral PFC (areas 9, 46, and 9/46) −44 28 16 4.58 SC Right 34 28 0 5.27 4448 Left −46 24 32 6.84 >10 000 56 16 10 3.63 SC Right 42 20 32 3.97 740 Middorsolateral PFC (areas 9, 46, and 9/46) Posterior PFC (junction of 6,8, and 44) Left −48 28 32 4.74 >10 000 Left −46 10 48 4.48 >10 000 Right 42 36 26 5.26 >10 000 −48 14 12 4.38 SC Posterior PFC (junction of 6,8, and 44) Right 22 14 52 4.83 1890 Left −50 6 44 5.00 >10 000 34 14 28 3.67 742 −50 14 16 3.85 SC Supplementary motor cortex (area 6) Right 46 20 42 4.17 >10 000 Left −4 20 48 5.97 9004 Supplementary motor cortex (area 6) Right 8 24 46 5.08 8080 Left −6 −4 68 4.52 >10 000 Lateral premotor cortex (area 6) Right 2 −4 68 4.52 SC Lateral premotor cortex (area 6) Left −28 2 60 3.45 >10 000 Left −44 2 40 5.34 >10 000 Right 26 6 56 5.4 1862 −28 0 5 5.26 SC Caudate nucleus (head) Right 28 0 52 6.92 >10 000 Left −14 16 6 4.55 1376 Right 16 16 2 3.5* 488 Putamen Left −20 2 2 3.46* 124 Right 22 −2 10 4.03 3000 Thalamus Left −8 −10 4 4.89 1002 Right 12 −10 10 5.25 2002 Midbrain Left −6 −12 −6 4.68 1000 Right 6 −14 −8 4.96 998 Young versus old Old versus young Supplementary motor cortex (area 6) Frontopolar cortex (area 10) Left −2 8 56 4.46 1712 Left −2 58 −4 3.61 656 Lateral premotor cortex (area 6) Right 2 60 −4 3.70 656 Right 32 0 56 4.24 856 Midventrolateral PFC (area 47/12) Left −32 36 −10 3.98* 200 Caudate nucleus (head) Left −16 14 −4 4.51 656 Putamen Left −16 14 10 4.34 600 Right 16 12 −6 4.48 1168

Note: SC, same cluster as preceding peak; VS, intergroup comparison. Average BOLD signal in the first group is significantly greater than in the second one.

Figure 3.

Location of frontal and striatal peaks during matching following receiving negative feedback versus control matching. The younger group (cf. left) shows increased activity in midventrolateral PFC (area 47/12) and the frontopolar cortex (area 10), whereas the older group (cf. center) shows increased activity in the frontopolar cortex (area 10), in a corticostriatal loop composed of the midventrolateral PFC (area 47/12), the caudate nucleus, the thalamus, and the putamen (which, with the posterior PFC, makes up another corticostriatal loop). When compared with the younger group (cf. right), the older one continues to show increased activity in the frontopolar cortex (area 10) and the ventrolateral PFC (area 47/12), as well as the caudate nucleus and the putamen (not shown in the figure). The anatomical MRI images are the average of the T1 acquisitions of the 14 younger subjects (cf. left), the 10 older subjects (cf. center), and all 24 subjects (cf. right) transformed into stereotaxic space. The color scale represents the T statistic. Z values correspond to the coordinate of the axial plane.

Figure 3.

Location of frontal and striatal peaks during matching following receiving negative feedback versus control matching. The younger group (cf. left) shows increased activity in midventrolateral PFC (area 47/12) and the frontopolar cortex (area 10), whereas the older group (cf. center) shows increased activity in the frontopolar cortex (area 10), in a corticostriatal loop composed of the midventrolateral PFC (area 47/12), the caudate nucleus, the thalamus, and the putamen (which, with the posterior PFC, makes up another corticostriatal loop). When compared with the younger group (cf. right), the older one continues to show increased activity in the frontopolar cortex (area 10) and the ventrolateral PFC (area 47/12), as well as the caudate nucleus and the putamen (not shown in the figure). The anatomical MRI images are the average of the T1 acquisitions of the 14 younger subjects (cf. left), the 10 older subjects (cf. center), and all 24 subjects (cf. right) transformed into stereotaxic space. The color scale represents the T statistic. Z values correspond to the coordinate of the axial plane.

When matching following negative feedback was compared with matching following positive feedback (Table 3), the same cortical pattern of activation was observed except for the fact that the anterior cingulate cortex (area 32) proved to be bilaterally activated and the posterior PFC (junction or areas 6, 8, and 44) showed only increased activation in the right hemisphere. However, this later contrast also showed significantly increased activation in subcortical regions, namely in the right thalamus and in the putamen bilaterally.

Table 3

Matching following negative feedback (event 2) minus matching following positive feedback (event 4)

 Young Old Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size Frontopolar cortex (area 10) Frontopolar cortex (area 10) Left −34 58 14 6.88 >10 000 Left −34 60 16 5.62 9650 Right 30 60 0 6.38 >10 000 −38 52 4 5.53 SC Anterior cingulate cortex (area 32) Right 34 56 8 4.86 7936 Left −4 42 20 5.42 >10 000 Middorsolateral PFC (areas 9, 46, 9/46) Right 2 34 18 4.64 >10 000 Left −40 36 26 5.02 8560 Midventrolateral PFC (areas 45, 47/12) −44 46 4 5.12 SC Left −44 18 −6 4.9 5360 Right 40 38 36 4.58 9752 Right 46 18 −8 5.44 >10 000 Midventrolateral PFC (area 47/12) 54 20 16 4.12 5000 Left −30 28 0 4.80 1968 Middorsolateral PFC (areas 9, 46, 9/46) −48 26 −8 4.10 SC Left −36 44 24 4.35 >10 000 Right 44 38 −8 4.17 752 −42 28 36 6.01 SC Posterior PFC (junction of 6, 8, 44) Right 40 48 24 5.09 >10 000 Left −48 20 38 6.8 >10 000 44 34 40 5.64 SC −48 16 10 3.66 888 Posterior PFC (junction of 6, 8, 44) Right 40 30 46 5.78 >10 000 Right 42 12 38 6.50 >10 000 Supplementary motor cortex (area 6) Supplementary motor cortex (area 6) Left −6 36 38 5.1 9654 Left −2 28 42 8.17 >10 000 Right 6 26 50 5.86 >10 000 Right 2 30 44 8.66 >10 000 Lateral premotor cortex (area 6) Lateral premotor cortex (area 6) Left −46 12 50 5.34 8620 Left −18 10 74 4.18 >10 000 Right 36 18 60 6.17 >10 000 Right 30 8 62 4.97 >10 000 Putamen Left −30 6 6 3.44 152 Right 34 2 2 4.26 5000 Young versus old Old versus young Putamen — Left −30 0 6 3.37* 200
 Young Old Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size Frontopolar cortex (area 10) Frontopolar cortex (area 10) Left −34 58 14 6.88 >10 000 Left −34 60 16 5.62 9650 Right 30 60 0 6.38 >10 000 −38 52 4 5.53 SC Anterior cingulate cortex (area 32) Right 34 56 8 4.86 7936 Left −4 42 20 5.42 >10 000 Middorsolateral PFC (areas 9, 46, 9/46) Right 2 34 18 4.64 >10 000 Left −40 36 26 5.02 8560 Midventrolateral PFC (areas 45, 47/12) −44 46 4 5.12 SC Left −44 18 −6 4.9 5360 Right 40 38 36 4.58 9752 Right 46 18 −8 5.44 >10 000 Midventrolateral PFC (area 47/12) 54 20 16 4.12 5000 Left −30 28 0 4.80 1968 Middorsolateral PFC (areas 9, 46, 9/46) −48 26 −8 4.10 SC Left −36 44 24 4.35 >10 000 Right 44 38 −8 4.17 752 −42 28 36 6.01 SC Posterior PFC (junction of 6, 8, 44) Right 40 48 24 5.09 >10 000 Left −48 20 38 6.8 >10 000 44 34 40 5.64 SC −48 16 10 3.66 888 Posterior PFC (junction of 6, 8, 44) Right 40 30 46 5.78 >10 000 Right 42 12 38 6.50 >10 000 Supplementary motor cortex (area 6) Supplementary motor cortex (area 6) Left −6 36 38 5.1 9654 Left −2 28 42 8.17 >10 000 Right 6 26 50 5.86 >10 000 Right 2 30 44 8.66 >10 000 Lateral premotor cortex (area 6) Lateral premotor cortex (area 6) Left −46 12 50 5.34 8620 Left −18 10 74 4.18 >10 000 Right 36 18 60 6.17 >10 000 Right 30 8 62 4.97 >10 000 Putamen Left −30 6 6 3.44 152 Right 34 2 2 4.26 5000 Young versus old Old versus young Putamen — Left −30 0 6 3.37* 200

Note: SC, same cluster as preceding peak; VS, intergroup comparison. Average BOLD signal in the first group is significantly greater than in the second one.

In the older group, when matching following negative feedback was compared with control matching or matching following positive feedback, significantly increased activity was found bilaterally in the frontopolar cortex (area 10), the middorsolateral PFC (areas 9 and 46), the midventrolateral PFC (areas 45 and 47/12), the posterior PFC (junction of areas 6, 8, and 44), the supplementary motor cortex (area 6), and the lateral premotor cortex (area 6) (Tables 2 and 3 and Fig. 3).

However, significant subcortical activation in the caudate nucleus, putamen, and thalamus was only found when matching following negative feedback was compared with control matching (Table 2 and Fig. 3).

Intergroup comparison.

Greater activation was found in the younger adults compared with the elder ones in the left supplementary motor cortex (area 6) and the right lateral premotor cortex (area 6) when matching following negative feedback was compared with control matching (Table 2), as well as in the left putamen when matching following negative feedback was compared with matching following positive feedback (Table 3).

On the other hand, the older group showed significantly increased activity compared with the younger group bilaterally in the frontopolar cortex (area 10) and the putamen, as well as in the left midventrolateral PFC (area 47/12), and the left caudate nucleus when matching following negative feedback was compared with control matching (Table 2 and Fig. 3), but no increased activity at all when matching following negative feedback was compared with matching following positive feedback (Table 3).

Receiving Positive Feedback

When receiving positive feedback was compared with control feedback, there was no significant activation in the PFC or in the basal ganglia.

In the older group, there were bilateral significant BOLD signal increases in the posterior PFC (area 8) but in no other region of the PFC or the basal ganglia.

Intergroup comparison.

There was significant bilateral increased activation in the caudate nucleus in young adults compared with the older ones when receiving positive feedback was compared with control feedback. On the other hand, no significant activation was found in the PFC or the basal ganglia when comparing the older group with the younger one.

Matching Following Positive Feedback

Comparing BOLD signal during matching following positive feedback to control matching yielded significant activation for the younger participants in various regions, namely the right frontopolar cortex (area 10), the left and right middorsolateral PFC (areas 9/46 and 46), the left midventrolateral PFC (areas 45 and 47/12), the left posterior PFC (area 44 and 6, 8, and 44 junction), the left and right supplementary motor cortex (area 6), and the left and right lateral premotor cortex (area 6). No significant activation was found in the basal ganglia.

The older group showed significantly increased activity in the left middorsolateral PFC (areas 9 and 46), the left and right midventrolateral PFC (areas 45 and 47/12), the left posterior PFC (6, 8, and 44 junction), the left and right supplementary motor cortex (junction of areas 6 and 8), and the left and right lateral premotor cortex (area 6). Significantly increased bilateral activity was also found in the thalamus.

Intergroup comparison.

For this comparison, no significantly greater activity was observed in young adults compared with the older ones. However, significant activation was observed in the older group compared with the younger group in the left caudate nucleus and the right putamen.

Whole Negative Trial Versus Whole Control Trial

Intergroup comparison.

When receiving negative feedback and matching following negative feedback were considered as a single event, we observed significantly increased activity in the frontopolar cortex (area 10) bilaterally for the older group compared with the younger one. We also found bilateral significant activation in the caudate nucleus for the younger group compared with the older one (Table 4 and Fig. 4).

Table 4

Whole negative trial (events 1 + 2) minus whole control trial (events 5 + 6)

 Young versus old Old versus young Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size Caudate nucleus Frontopolar cortex (area 10) Left −12 22 0 3.84* 496 Left −2 66 2 5.42 3000 Right 18 24 −2 4.35 624 Right 12 60 −8 4.39 2440
 Young versus old Old versus young Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size Caudate nucleus Frontopolar cortex (area 10) Left −12 22 0 3.84* 496 Left −2 66 2 5.42 3000 Right 18 24 −2 4.35 624 Right 12 60 −8 4.39 2440

Note: VS, intergroup comparison. Average BOLD signal in the first group is significantly greater than in the second one.

Figure 4.

Location of frontal and striatal peaks during receiving negative feedback plus matching following negative feedback versus control feedback plus control matching. When compared with the younger group (cf. left), the older one shows increased activity in the frontopolar cortex (area 10), similar to the PASA phenomenon. When compared with the older group (cf. right), the younger one shows increased caudate nucleus activity. The anatomical MRI images are the average of the T1 acquisitions of all 24 subjects (cf. left and right) transformed into stereotaxic space. The color scale represents the T statistic. Z values corresponds to the coordinate of the axial plane.

Figure 4.

Location of frontal and striatal peaks during receiving negative feedback plus matching following negative feedback versus control feedback plus control matching. When compared with the younger group (cf. left), the older one shows increased activity in the frontopolar cortex (area 10), similar to the PASA phenomenon. When compared with the older group (cf. right), the younger one shows increased caudate nucleus activity. The anatomical MRI images are the average of the T1 acquisitions of all 24 subjects (cf. left and right) transformed into stereotaxic space. The color scale represents the T statistic. Z values corresponds to the coordinate of the axial plane.

Whole Positive Trial Versus Whole Control Trial

Intergroup comparison.

For this contrast, we observed significantly increased activity in the frontopolar cortex (area 10) bilaterally for the older individuals compared with the younger ones. No significant activation in the PFC or the basal ganglia was found in the younger group compared with the older one (Table 5).

Table 5

Whole positive trial (events 3 + 4) minus whole control trial (events 5 + 6)

 Young versus old Old versus young Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size — Frontopolar cortex (area 10) Left −4 66 0 4.38 980 Right 12 58 −10 3.98 808
 Young versus old Old versus young Anatomical area Stereotaxic coordinates t statistic Cluster size Anatomical area Stereotaxic coordinates t statistic Cluster size — Frontopolar cortex (area 10) Left −4 66 0 4.38 980 Right 12 58 −10 3.98 808

Note: VS, intergroup comparison. Average BOLD signal in the first group is significantly greater than in the second one.

Discussion

The primary purpose of this study was to investigate how aging affects 2 different frontostriatal loops involved in the performance of the WWST, namely a cognitive cortico-striatal loop including the midventrolateral PFC (area 47/12), the caudate nucleus and the thalamus involved in the planning of a set-shift, and a motor loop important in the execution of a set-shift that includes the posterior PFC and the putamen. These regions were previously identified in studies using the WCST (Monchi et al. 2001; Nagano-Saito et al. 2008).

Simard et al. (2010) showed that significant activation is required in both of these cortico-striatal loops for set-shifting in young adults, and the present study showed the same for a group of older participants. However, the period events in which the 2 cortico-striatal loops were significantly activated proved to be very different between the 2 groups. Thus, aging influenced the timing of fronto-striatal recruitment, certainly the most important finding of this study. In fact, in younger adults, the analyses showed the involvement of the midventrolateral PFC-caudate loop during the receiving negative feedback period (Tables 1 and 6 and Fig. 2) and the involvement of the posterior PFC-putamen loop during the matching following negative feedback event when compared with the matching following positive feedback event (Tables 3 and 6). Although, in the older participants, they showed that both loops were activated during the matching following negative feedback event and only during this event since no significant activity was observed in the ventrolateral PFC or the caudate nucleus in the receiving negative feedback period (Tables 1, 2, and 6; Figs 2 and 3).

Table 6

Summary of the major results

 VLPFC PPFC CN Pu Th RNFB-CFB Y Y and O Y Y MNFB-CM Y < O Y and O O O O RPFB-CFB O MPFB-CM Y and O Y and O O RNFB-RPFB Y Y Y Y MNFB-MPFB Y and O Y and O Y
 VLPFC PPFC CN Pu Th RNFB-CFB Y Y and O Y Y MNFB-CM Y < O Y and O O O O RPFB-CFB O MPFB-CM Y and O Y and O O RNFB-RPFB Y Y Y Y MNFB-MPFB Y and O Y and O Y

Note: VLPFC, ventrolateral PFC; PPFC, posterior PFC; CN, caudate nucleus; Pu, putamen; Th, thalamus; RNFB-CFB, receiving negative feedback minus control feedback; MNFB-CM, matching following negative feedback minus matching following control feedback; RPFB-CFB, receiving positive feedback minus control feedback; MPFB-CM, matching following positive feedback minus matching following control feedback; RNFB-RPFB, receiving negative feedback minus receiving positive feedback; MNFB-MPFB, matching following negative feedback minus matching following positive feedback; O, significant activation in the older group; Y, significant activation in the younger group, < , implies that both groups showed significant activation but that one showed significantly less activity than the other in the specified region.

It appears that during the performance of the WWST, younger individuals, when confronted to a set-shift, plan during the receiving negative feedback period event and execute the set-shift during the matching event; whereas older individuals tend to plan and execute the set-shift during the matching following negative feedback period only. This observation corroborates the results of Velanova et al. (2007), who found that older individuals, during the performance of memory retrieval tasks, showed delayed (and increased) activation recruitment of cortical frontal regions, suggesting a shift strategy. They postulated that, during memory retrieval, younger individuals may extensively use early-selection processes and thus anticipate retrieval demands (which allows for information filtering before extensive high-level processing), whereas older people tend to rely on late-selection processes to operate on information sequentially (access each past information serially and evaluate its appropriateness). This strategy shift, which they conceptualized in a model called the “load-shift,” may represent an age-related compensatory mechanism that allows older individuals to maintain a high level of cognitive function but at the expense of slower performances. In our experiment and in agreement with this interpretation, older participants proved to be slower than younger ones (they took more time to match the test word with the reference word of their choice) and thus, especially during the matching following negative feedback period event. It should be noted though that slower time responses in the older group could also be attributed to increased neuronal recruitment in the elderly during matching events (Table 6). Indeed, it has been shown, in young individuals, that reaction times tend to increase as the amount of neural activity augments (Just et al. 1996). However, this explanation and the load-shift model are not mutually exclusive and both phenomena likely contribute to the current results.

Therefore, the present study seems to indicate that the “load-shift model,” as postulated by Velanova et al. (2007), is not limited to memory retrieval but may also apply to executive processes relying on cortical frontal regions (such as set-shifting). More interestingly, it shows that age-related delayed neuronal recruitment can be recorded in subcortical regions, such as the striatum and not just in cortical areas. Also, since our older participants seem to wait until the moment they have to execute the task to actually plan their execution, another explanation regarding delayed recruitment may be postulated: with aging, individuals tend not to engage in costly executive processes until these become absolutely necessary. Of course, this latter hypothesis and the load-shift model are not necessarily contradictory and may actually co-occur. In the future, fMRI and electroencephalography experiments using similar cognitive tasks as the WWST or the WCST, but using feedback periods with variable lengths, should be performed in order to further investigate the phenomenon.

One of our major hypothesis was that striatal activity would be significantly reduced in older individuals compared with the young (Wang et al. 2009). Indeed, the caudate nucleus and the putamen are known to have dense dopaminergic innervations which, combined with a strong evidence for an age-related loss in presynaptic and postsynaptic dopamine markers (D1 and D2 receptor densities) and fronto-striatal atrophy, explains the reduction of striatal activity associated with aging (Bäckman et al. 2006). In order to adequately compare young versus elderly fronto-striatal activity, 2 contrasts were computed by combining brain activity during feedback and matching periods of positive and negative trials separately and subtracting the corresponding control brain activity from it. These contrasts were necessary since the 2 groups showed, as previously mentioned, timing differences in striatal activity. As predicted, these analyses confirmed reduced caudate activity in the older group compared with the younger one during negative events (Table 4 and Fig. 4).

The present study also revealed significant age-related increased activity in the fronto-polar cortex (area 10) during both positive and negative trials (Tables 4 and 5 and Fig. 4). This observation is consistent with the neural compensation view of the PASA phenomenon (Dennis and Cabeza 2008) since participants in both our groups were high performing individuals. Indeed, this compensatory model implies that age-related increased prefrontal activity reflects a dynamic reallocation of resources to maintain task performance when brain function goes down (Cabeza 2004; Mattay et al. 2005; Grady et al. 2008; Reuter-Lorenz and Cappell 2008).

Furthermore, it has been proposed that the frontopolar cortex plays a crucial role in the combining of multiple cognitive rules, switching between different subtasks when multitasking and enabling a previously running subtask to be maintained in a pending state for future retrieval and execution upon completion of another ongoing subtask (Koechlin et al. 1999, 2003; Ramnani and Owen 2004). Thus, it is very possible that older individuals, because they tend to operate on information sequentially and delay some executive processes (as previously discussed), need greater frontopolar involvement than younger individuals.

Our study did not show any bilateralization of brain activity in the older group, even though the WWST relies heavily on lexical-retrieval processes. Such an age-related bilateralization could have been excepted since several studies requiring lexical access and retrieval have shown increased recruitment of right-hemisphere regions in high-skilled old individuals (Persson et al. 2004; Wierenga et al. 2008) in both “core” and “supplemental” language regions defined, respectively, by Wingfield and Grossman (2006) as regions necessary for the task performance and regions revealed in healthy adults by neuroimaging studies that are outside the traditional language areas. These findings are consistent with the HAROLD model of Cabeza (2002), which describes age-related hemispheric bilateralization as a compensatory mechanism. A possible reason why our study failed to illustrate similar results relies most certainly on the fact that our younger individuals showed very important bilateral activity themselves (Simard et al. 2010). This would account for why the comparison between the 2 groups did not reveal any age-related hemispheric asymmetry in the present study.

Finally, another of our prediction was that the older group would recruit similar frontal regions for both positive and negative trials as both types of trials should require compensatory mechanisms involving frontal areas, whereas only the negative trials would require important frontal involvement for younger participants. Indeed, when comparing negative versus positive trials for both receiving and matching events (Tables 3 and 6), we saw that there were significantly fewer regional activation differences in the older group than in the younger one. This was especially true for the receiving negative feedback versus receiving positive feedback contrast in which the elderly showed almost no increased activity at all (except in visual regions), whereas the younger group showed much greater activity in the PFC and the basal ganglia. Furthermore, even if these similarities during receiving feedback events seemed to be the result of decreased recruitment in the older group during negative trials, instead of increased recruitment during positive trials, as a consequence of an age-related delayed activation (as discussed above), the same claim cannot be made for matching events. Indeed, during matching events, during which recorded brain activity in the older group was at least as important as in the younger one, the elderly still presented fewer differences between negative and positive trials. The younger group showed increased PFC and putamen activity during negative matching compared with positive matching, whereas in the older group, even if there was still increased prefrontal activity during negative matching, the magnitude and the area extent of the increase was smaller. As well, the older group did not reveal any differences between the 2 types of trials regarding thalamic and basal ganglia activity. These findings corroborate the ones of Ansado et al. (unpublished data), who found that older individuals, during the performance of a 2 level load-condition (low and high) visual letter-matching task, showed important and similar frontal cortex activity in both conditions, an observation they attributed to age-related neural compensation. Our study, though, tends once more to show that these compensatory mechanisms are not limited to fronto-cortical areas but may include the basal ganglia and the thalamus as well, since all these regions are involved in a cognitive cortico-striatal loop (Alexander et al. 1986; Middleton and Strick 2002). Interestingly, regarding parietal areas, a different phenomenon occurs. These regions tend to be more and more recruited in older individuals as cognitive demand increases (Corbetta and Shulman 2002), suggesting that elder adults tend to call upon the neural reserve when neural compensation becomes insufficient to maintain performance. It should be noted that the same observation can be made in our study: the elderly showed increased parietal activity in negative trials compared with positive ones (data not shown).

A limitation of the present study is the fairly small sample size of both our groups, especially the older one. Particularly, since differences in cortical activity (such as decreased dorsolateral PFC activation) have been reported between individuals in their 50s and those in their 70s (Hampshire et al. 2008). Therefore, bigger groups would have allowed for within group age stratification. In the future, similar studies should aim for greater sample sizes to allow for a more comprehensive data analysis.

In summary, the present study suggests that both the young and the elderly show fronto-striatal loop activity associated with planning and execution of set-shifts. The period events, however, in which these loops are activated differ between the 2 groups: older individuals show delayed fronto-striatal activity compared with the young. This finding may be a manifestation of the load-shift model postulated by Velanova et al. (2007). Moreover, we propose that a somewhat different phenomenon may also occur: Older individuals may not engage in costly executive processes until these become absolutely necessary.

Funding

Natural Sciences and Engineering Research Council of Canada (to O.M.) (no. 327518); R.M. received 2 MD-PhD scholarships from the Comité d’Organisation du Programme des Stagiaires d’été de l’Université de Montréal.

The authors would like to thank all the participants, the staff of the Functional Neuroimaging Unit at the CRIUGM, and PCAN laboratory members (namely Thomas Jubault, Kristina Martinu, Cecile Madjar, and Atsuko Nagano) for practical help as well as support. Conflict of Interest: None declared.

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