Children’s Feedback Preferences in Response to an Experimentally Manipulated Peer Evaluation Outcome: The Role of Depressive Symptoms

The present study examined the linkage between pre-adolescent children’s depressive symptoms and their preferences for receiving positive vs. negative feedback subsequent to being faced with an experimentally manipulated peer evaluation outcome in real time. Participants (n = 142) ages 10 to 13, played a computer contest based on the television show Survivor and were randomized to either a peer rejection (i.e., receiving the lowest total ‘likeability’ score from a group of peer-judges), a peer success (i.e., receiving the highest score), or a control peer evaluation condition. Children’s self-reported feedback preferences were then assessed. Results revealed that participants assigned to the negative evaluation outcome, relative to either the success or the control outcome, showed a significantly higher subsequent preference for negatively tuned feedback. Contrary to previous work and predictions derived from self-verification theory, children higher in depressive symptoms were only more likely to prefer negative feedback in response to the negative peer evaluation outcome. These effects for depression were not accounted for by either state mood at baseline or mood change in response to the feedback manipulation.

Depression is a leading cause of disability across the life span (Murray & Lopez, 1996). Whereas the point prevalence of major depressive disorder (MDD) in school-age children is relatively modest (i.e., between 1 and 3 percent; Cohen et al., 1993), epidemiological community surveys of childhood disorders suggest that 10 to 20 percent of school-aged children in the general population experience periods of elevated depressive symptoms, including depressed mood (e.g., Compas, 1997; Hammen & Rudolph, 1996). Several studies have shown that moderate levels of depression are associated with significant impairment in school and peer functioning (e.g., Nolen-Hoeksema, Girgus, & Seligman, 1992; Petersen, Sarigiani, & Kennedy, 1991), and may persist for years in some children (Nolen-Hoeksema et al., 1992). Moreover, children displaying moderate levels of depressive symptoms are at high risk to suffer from severe, recurrent major depression later in life (e.g., Costello, Angold, & Keeler, 1999; Lewinsohn, Rohde, Klein, & Seeley, 1999). Taken together, although moderate levels of depressive symptoms may not meet DSM-IV-TR (APA, 2000) criteria for MDD, they are of significant concern.
Several theorists have argued that feedback seeking may be implicated in the maintenance and/or exacerbation of dysphoric states, including the development of full-blown depression. For instance, Coyne (1976) has asserted that people high in depression systematically alienate their relationship partners through excessive reassurance seeking. This behavior is particularly likely to elicit negative reactions in others—including rejection, raising the risk of becoming even more depressed—when people’s feedback seeking reflects their suspicion that they will not receive such reassurance (e.g., constantly asking their partners if they still love them).
Whereas Coyne suggested that depressed people merely inadvertently create the social conditions that make them suffer, the more recently advanced self-verification theory (e.g., Swann, 1990; Swann, Rentfrow, & Guinn, 2002) posits that people high in depression seek out negative social feedback more actively, as a means of verifying their negative self-conceptions. Specifically, according to self-verification theory (e.g., Swann, 1983; Swann, Pelham, & Krull, 1989; Swann, Rentfrow, & Guinn, 2002) individuals are drawn to feedback that confirms their self-conceptions because it tends to buttress their feelings of existential security (“epistemic” concerns) and/or because they want their social interactions to unfold smoothly, and thus work to ensure that others do not form appraisals of them that are overly positive or overly negative (“pragmatic” concerns).
Negative self-views including worthlessness, repulsiveness, and excessive self-criticism figure prominently in depression (e.g., Beck, 1967; Ellis, 1977). Consistent with self-verification theory, several studies among adults have provided evidence to suggest that people higher in depression are more inclined to actively seek out (or prefer) negative social feedback, as well as interaction partners likely to provide them with such unfavorable feedback. For instance, Swann and colleagues observed that depressed college students, relative to controls, preferred friends who evaluated them negatively, were more inclined to seek negative feedback from their roommates, and preferentially solicited unfavorable feedback from their dating partners (Swann, Wenzlaff, Krull, & Pelham, 1992). Moreover, when presented with self-discrepant (i.e., positive) feedback, adults high on depressive symptoms appeared to attempt to restore their sense of worthlessness by subsequently seeking feedback about their weaknesses from a different evaluator; whereas adults low on depressive symptoms were more likely to solicit feedback about their strengths (Swann, Wenzlaff, & Tafarodi, 1992). Finally, participants higher on depressive symptoms, relative to controls, were more likely to choose interacting with someone who appraised them negatively over the opportunity to avoid the encounter (Swann et al., 1992).
Contrary to research with adults, the investigation of how depression in children is associated with feedback seeking preferences is still in its infancy. Joiner and colleagues (Joiner, Katz, & Lew, 1997) examined the linkage between depression and feedback seeking in a sample of inpatient youths. They found that children who were more depressed showed a greater preference for receiving feedback of a negative nature, based on responses to a questionnaire assessing feedback preferences in four distinct self-relevant domains (i.e., social, intellectual, athletic, and physical attractiveness). As noted by the authors, an important limitation of this study was the unknown predictive validity of their feedback preference questionnaire. Hence, it is unclear to what extent scores on this measure converge with participants’ actual preference for negative feedback in the face of actually occurring events (see Joiner et al., 1997).
In the only study to examine feedback seeking in children using a community sample, Cassidy and colleagues used a bogus feedback paradigm to examine children’s actual feedback preferences (Cassidy, Ziv, Mehta, & Feeney, 2003). Specifically, participants provided personal information about themselves by completing a series of questionnaires. They were led to believe that their responses would be shown to peers from a different school who would use this information to answer specific questions of an evaluative nature about each participant. Next, participants were provided the opportunity to select some of the alleged answers to these questions, knowing that some answers would include negative feedback and others would include positive feedback. In line with findings for adults and children diagnosed with depression, children displaying higher depressive symptoms were more likely to display a relative preference for negative information about themselves. A similar pattern of findings consistent with self-verification theory was observed for the linkage between feedback preferences and global self-representations, as indexed by level of general self-esteem.
An important contribution of the Cassidy et al. study is that it provides evidence to suggest that the tendency for depressed individuals to prefer rather negative feedback extends to moderately depressed children and adolescents, as indexed by level of self-reported depressive symptoms. However, the study is limited by its relatively narrow scope of inquiry with regard to the role of depressive symptoms. First, the potential role of state mood was not examined. Whereas self-verification theory claims that interest in negative feedback is motivated by negative self-views, an alternative explanation for the linkage between elevated depressive symptoms and more negative feedback preferences is that due to high levels of depressed state mood, people displaying elevated depressive symptoms are prone to be drawn to all that is negative, including unfavorable appraisals from others (e.g., Beck, 1967; Beck, Rush, Shaw, & Emery, 1979). Moreover, enhancing state mood with adults by providing them with experimentally manipulated favorable evaluations has been shown to increase levels of subsequent positive feedback seeking (Mischel, Ebbesen, & Zeiss, 1973).
A second limitation of the Casssidy et al. study is that the valence of the evaluative feedback was not manipulated. Beck has asserted that depressed people seek unfavourable feedback only insofar as their negative self-views have been activated by a stressful negative event. Work conducted by Swann and colleagues (Swann et al., 1992) has shown that adults high in depressive symptoms respond quite differently with respect to their feedback preferences in the face of positive, negative, or neutral evaluations. Specifically, these individuals responded to positive—i.e., self-discrepant—evaluations with a more pronounced preference for negatively tuned feedback, relative to a control condition; whereas they were not more inclined to seek negative feedback subsequent to receiving unfavorable—i.e., self-congruent—evaluations.
The present study examined the linkage in pre-adolescent children between depressive symptoms and feedback preferences, in response to a salient and ego-involving emotion-eliciting event. Specifically, we examined these linkages in response to both a positive and a negative peer evaluation outcome. We chose peer evaluation as the emotion provocation stimulus because peer praise and peer rejection are common emotion-eliciting events in childhood (Coie, 1990). Moreover, ample evidence suggests that rejection ranks among the most aversive of human experiences and is associated with marked negative affect (e.g., Baumeister & Tice, 1990; Leary, Tambor, Terdal, & Downs, 1995). Finally, peer rejection figures prominently in the development and/or maintenance of several forms of psychopathology, including depression (e.g., Nolan, Flynn, & Garber, 2003). The reason for our focus on pre-adolescent children is twofold. First, the most salient content of self-representations among pre-adolescents is one’s social appeal and social skills/attributes that influence interactions with others (Harter, 1998). Second, in this age group approximtely 50% of children’s social activities involve peers (Grusec & Lytton, 1988; Rubin, Bukowski, & Parker, 1998), and peers as a reference group are of central importance in these children’s lives (e.g., Hay, Payne, & Chadwick, 2004; Hartup, 1996).
Toward this aim, we devised an experimental peer evaluation manipulation based on the television show Survivor. In brief, participants were led to believe that they were playing an Internet version of Survivor against four same-sex contestants of comparable age (all of them were computerized bogus contestants) from four different schools in the same area. They were informed that a team of 16 same-age peer judges consisting of eight boys and eight girls would evaluate all participants. Specifically, each juror would give them a score between 0 and 100, with higher scores reflecting higher levels of perceived likeability. Moreover, the jurors would indicate what they liked most and what they disliked most about each contestant. Participants were randomized to one of the following three peer evaluation feedback conditions: (a) success feedback (i.e., having obtained the highest total score), (b) failure feedback (i.e., having obtained the lowest total score), or (c) control feedback (i.e., having obtained neither the highest nor the lowest score). These feedback conditions were designed to induce mood improvement, mood worsening, and no mood change, respectively. Subsequent to receiving the bogus peer evaluation feedback outcome, children’s feedback preferences were assessed. The assessment of state mood at pre- and post-feedback afforded the possibility to examine the linkages between participants’ level of depressive symptoms, feedback valence, (changes in) state mood, and subsequent feedback preferences. To increase the specificity of our findings for depressive symptoms, we also examined the potential role of children’s peer-nominated social standing in affecting their feedback preferences.
In sum, the present study aimed to address the following specific research questions: (a) Does the type (valence) of peer feedback influence children’s subsequent feedback preferences? (b) Are the effects of feedback valence moderated by children’s level of depressive symptoms? (c) Do differences in state mood at baseline account for these potential effects of depression? and (d) Are the effects of feedback valence and depressive symptoms on subsequent feedback seeking mediated by changes in mood from pre to post-feedback?
We hypothesized that children assigned to receive a positive peer evaluation outcome would be more likely to prefer subsequent favorable feedback than those assigned to the neutral peer feedback condition; whereas children assigned to receive a negative peer evaluation outcome would not be more likely to prefer subsequent negative feedback. This set of predictions was based on findings with adults suggesting that positively valenced feedback leads to a subsequent preference for positively tuned feedback (Mischel et al., 1973), whereas Swann and colleagues (Swann et al., 1992) found that negative feedback exerted no effect on adults’ subsequent preference for negative feedback. We also hypothesized that level of depressive symptoms would qualify the relationship between our peer feedback manipulation and children’s subsequent feedback preferences, such that children higher on depressive symptoms would be more likely to respond to positive—i.e., self-discrepant—evaluations with a preference for negatively tuned feedback, relative to the control condition. Finally, we hypothesized that the effects of the peer feedback manipulation on subsequent feedback preferences would be mediated by changes in state mood from pre- to post-feedback.
Method
Participants
Participants were 142 children (73 boys, 69 girls) enrolled in 5th and 6th grade classes from two public elementary schools in the Netherlands. They were predominantly Caucasian (92.9%), ranged in age from 10 to 13 years (M = 11.2, SD = .66) and were predominantly from a middle-class SES background. For the initial sample of 185 children, classroom teachers sent parent permission letters home with children. Of the 165 letters returned, 142 parents (86.1%) gave their consent for their children to participate in the study, and 23 (13.9%) declined. We also obtained verbal permission to perform the study from the principal of the school and each child’s teacher. Children were informed that they could discontinue their participation at any time.
Procedure
In the first of two sessions, approximately one week apart, participants were administered the Children Depression Inventory (CDI, Kovacs, 1981) in their regular classrooms during school hours. Moreover, participants’ social standing in their peer group was assessed via a commonly used procedure, which asks children to indicate whom among their classmates they like most and whom they like least (e.g., Newcomb, Bukowski, & Pattee, 1993). During administration of the measures the classroom teacher remained in the room. A research assistant read the directions aloud and children were encouraged to ask for help if they had questions or encountered problems completing the questionnaires. At the end of the first session, which lasted approximately 30 min, children were informed that later that week they would participate in a computer-contest. The second session was carried out in a quiet room on the school grounds. Participants were told that their class was selected to take part in an Internet computer-contest called ‘Survivor.’ In reality, the contest was a computer program written in Visual Basic designed to present the illusion of playing on-line with four other children.
Survivor contest
Upon arrival, the participant was seated in front of a laptop computer equipped with a web-cam to have their photo taken. Participants were told that their picture would allow all the contestants to see what each of the other children looked like. Prior to beginning (Time 1), participants completed a computer-administered baseline mood measure, i.e., a one-item mood scale ranging from 1 (extremely negative) to 10 (extremely positive). In an attempt to add both to the credibility and the attractiveness of the contest, the opening bars of the hit ‘Survivor’ (produced by the band “Destiny’s Child”) were played at the start of the game. In addition, an eye-catching logo of the American TV-show appeared on the computer display.
The objective and rules of the contest were presented on screen. Participants were encouraged to read the information, which was pre-tested on comprehensibility for children in this age-range, carefully in their own pace and click “continue” to progress to the next screen. Participants were informed that they would be playing against four same-sex contestants of comparable age (all of them were computerized fictitious co-players) from four different schools in the same area, and that all participants would be evaluated by a jury consisting of 16 members, eight boys and eight girls. Specifically, participants were explained that each judge would give them a score between 0 and 100, with higher scores reflecting higher levels of perceived likeability.
After receiving this information, the computer displayed a screen announcing that in a moment pictures and names of all 16 judges would be presented one-at-a-time. The children whose pictures appeared were child actors from two different modeling agencies in the Netherlands. In a pilot study, 31 adults rated the attractiveness of 43 children’s faces, (20 boys, 23 girls) on a scale ranging from 1 (extremely unattractive) to 10 (extremely attractive). For the purposes of the present study, we selected the eight children rated highest (four boys, M = 7.9, and four girls, M = 8.1) and the eight children rated lowest (four boys, M = 5.2, and four girls, M = 5.1). Following the viewing of these children, participants were directed through a series of screens in which they were asked to answer a series of questions that would give the members of the jury and the other contestants information about them. Participants responded to questions about their favorite musical group, hobbies, future occupation, things they liked and disliked about themselves, a number of character traits (e.g., sense of humor, agreeableness, intelligence, trustworthiness), how they got along with other children, and their academic performance. Most of the questions were in a multiple-choice format but some (e.g., “what is your favorite musical group?”) required an open-ended response. In view of the potential risk that children would respond to personal questions in a socially desirable fashion, instructions emphasized the importance of responding to questions honestly. To further minimize response bias, most questions were worded such that the “best” or “optimal” answer was unclear (e.g., “what is your favorite leisure activity”?). Participants were informed on screen that their picture (previously taken by a web camera) along with the biographical information from their answers to the personal questions would be transmitted over the Internet and viewed by the judges who would then give them a ‘likeability’ score ranging from 0 to 100. Moreover, children were informed that the judges would also indicate what they liked most and what they disliked most of each participant (e.g., “(s)he doesn’t seem to be a nice person,” “(s)he is witty,” “(s)he is unattractive,” “(s)he seems fun to hang out with”).
Subsequent to answering all the biographical questions, participants were informed that pictures and descriptions of each of the other contestants would be presented one-at-a-time for review. Upon clicking “continue,” the picture of the first bogus co-player was displayed together with his or her self-description. The latter consisted of the alleged answers to the same questions that the participant had answered earlier. To enhance credibility of the bogus co-players, actual self-descriptions were taken from those of same-age children participating in another study. These participants gave their explicit consent to have this information viewed by other children, provided that the alleged self-description profiles would contain randomly combined personal information from at least three different children. Participants progressed through the game examining each of the profiles at their own pace.
Following the participant scrutinizing the last profile, a message appeared on the screen indicating that the computer would now for every player add the scores from the judges to determine which player had received the highest total score and which player had received the lowest total score. After a 5 s waiting period, the names of the players with the highest and the lowest score appeared in capital letters on the screen. In the success condition, the name of the participant was displayed as having obtained the highest total score; one randomly chosen alleged co-player’s name appeared as having obtained the lowest total score. Conversely, in the failure condition the name of the participant was displayed as having obtained the lowest total score, while one alleged co-player’s name appeared as having obtained the highest total score. In the control condition, the participant received neither the highest nor the lowest score.
Five seconds after receiving feedback (Time 2), participants were re-administered the mood measure via computer. Instructions emphasized the importance of rating how they felt ‘right now.’ Subsequent to completing this measure, participants were administered the feedback measure which assessed preferences for obtaining feedback from jurors that had evaluated them favorably versus jurors that had evaluated them unfavorably. Subsequently, a computer screen appeared announcing that children would now participate in another study. The participant was then accompanied to an adjacent room where a female research assistant debriefed him or her thoroughly.
Debriefing
Each child was thoroughly debriefed with the aim of removing any lingering effects of the false rejection feedback while participating in the Survivor contest. During the debriefing, the child was informed that the judges, the co-players and the received feedback were entirely fictitious and that this deception was a necessary part of the procedure. At this point it should be noted that in our previous work, more than 100 participants were assigned to receive Survivor-administered peer rejection feedback (Reijntjes et al., 2006), including children with elevated depressive symptoms. Interviews with all participants, both immediately post-feedback as well as at one-week follow-up, indicated that the peer failure experience was not too emotionally upsetting. For instance, when asked, none of the participants made mention of any feelings of regret with regard to participation and none reported any objections to the procedure. On the contrary, several children—spontaneously—reported to consider the rejection experience an unnerving but useful experience, for themselves and/or their peers. Still, during the investigation a registered clinical psychologist was available when needed. Moreover, before being dismissed children were interviewed at length about a recent positive social experience in which they felt positive and/or were successful. In so doing, we aimed to maximize the remedy for children showing a strong emotional response to the peer feedback.
Towards the end of the debriefing, participants were encouraged to ask questions or voice their concerns. All children reported that they understood the purposes of the research, as well as the necessity of having been deceived. The credibility of the deception manipulation was also assessed during the debriefing by asking each participant whether they had believed that they were playing against other children. With no exception, participants indicated that they believed that the game was genuine. Finally, all participants reported that prior to participating they had not talked with classmates about Survivor. At the conclusion of the debriefing, participants were urged to observe complete secrecy by not talking with their classmates about Survivor until all the other children had participated. To increase adherence to this instruction, children were asked to sign a non-disclosure agreement and were then provided a choice of one of several possible small gifts for playing the game (e.g., a small tape recorder, a gift certificate worth about 3 dollars).
Measures
State mood
Similar to previous work among children (Reijntjes, Stegge, Meerum Terwogt, Kamphuis, & Telch, 2006) and adults (e.g., Gross, 1998), children rated their current feeling state using a one-item global mood index, ranging from 1 (extremely negative) to 10 (extremely positive). This Likert-scale was administered to assess participants’ state mood at baseline (pre-feedback), and their changes in state mood from pre- to post-feedback. The mean score at baseline was 7.96 (SD = 1.36). Scores did not differ as a function of age, gender, or their interaction.
Children Depression inventory (CDI; Kovacs, 1981)
The CDI is a 27-item self-report measure designed to assess the social, behavioral, and affective symptoms of depression in children. Each item consists of three sentences that describe a symptom of depression in increasing degrees of severity. The respondent chooses the sentence that best describes him or her during the past week. Each item set is scored from 0 (symptom absent) to 2 (symptom is present always or most of the time). The CDI has adequate discriminant and convergent validity, test-retest reliability, and internal consistency (Saylor, Finch, Spirito, & Bennett, 1984). Coefficient alpha in the present sample, using the Dutch version of the instrument (Braet & Timbremont, 2002), was .80. Total scores ranged from 0 to 23; the median is 6. The top quartile (i.e., 24.6% of the participants) obtained a score of 12 or higher. Eleven participants (7.7%) obtained a score of 20 or higher, which is recommended as a cut-off point for general screening (Kovacs, 1992). Mean scores of this sample (M = 8.03, SD = 6.59) were similar to those previously reported (Reijntjes et al., 2006; Smucker, Craighead, Craighead, & Green, 1986) and did not differ as a function of age, gender, or their interaction.
Table 1Means and standard deviations of baseline measures by conditionFeedback conditionSuccess (n = 47)Neutral (n = 47)Failure (n = 45)MeasureMSDMSDMSDDepressive symptoms 7.837.18 8.346.78 8.116.00Social acceptance 3.7227.47 3.9027.63 3.0226.75Age (months)134.628.22134.178.11132.387.52
Social standing in the peer group
Participants completed a widely used nomination-based sociometric questionnaire, in which they identified the three classroom peers whom they liked most and the three classroom peers they disliked most (Newcomb et al., 1993). Children who declined participation in the present study were included in the classroom lists, however the data collected on these children were not used. From these nomination data, two continuous scores for each participating child were computed. These scores included: (a) a measure of preference, by dividing the number of times each participant was nominated for the ‘like most’ question by the total number of participating children in the class, and (b) a measure of rejection, by dividing the number of times each participant was nominated for the ‘like least’ question by the total number of participating children in the class. Our measure of social acceptance (i.e., social standing in the peer group) was computed by subtracting the measure of rejection from the measure of preference. This figure was then multiplied by 100, yielding scores ranging from—84.6 to 61.5 (M = 3.56; SD = 27.10). These scores did not differ as a function of gender, age, or their interaction. For the majority of participants (58%), a positive social acceptance score was observed. Based on the widely used taxonomy for sociometric classification (Newcomb et al., 1993), the number of children that were classified as “popular,” “rejected,” “neglected,” “controversial,” and “average” amounted to 26, 26, 24, 8, and 58, respectively.
Self- reported feedback preference
Subsequent to receiving the bogus peer evaluation outcome, participants responded to the measure assessing the valence of feedback preferences (i.e., positive versus negative). The specific dichotomous probe included: ‘Which judges would you prefer to get feedback from, those that hold a positive view on you or those that hold a negative view on you’?
Results
Preliminary analyses
Exploratory analyses revealed a departure from symmetry for the distribution of the CDI, as evidenced by a skewness value of 5.84. We therefore performed a square root transformation on the scores, which was successful in producing a distribution that was no longer skewed (skewness value is .73). This transformed variable (CDIt) was used in all analyses reported below. Participants reporting higher CDIt scores displayed lower levels of state mood at baseline (r = −.26, p < .01). Moreover, higher CDIt scores were negatively associated with peer nominated social acceptance ratings (r = −.21, p < .02).
Equivalence of the experimental groups
Children were matched on age and gender, and then randomly assigned to one of the three experimental conditions. Means and standard deviations for Time 1 measures are presented in Table 1. To confirm that the randomization procedure resulted in comparable groups, baseline differences on continuous measures were examined using one-way ANOVAs. Results revealed no significant between-group differences.
Change in state mood: Effects of the feedback manipulation
Means and standard deviations for state mood scores at Time 1 (baseline) and Time 2 (immediately post-feedback) are presented in Table 2. Two a priori repeated measures ANOVA’s were performed to examine the magnitude and direction of emotional reactivity elicited by success feedback and failure feedback, each relative to the control (neutral) feedback condition. In the first planned contrast, state mood scores at Time 1 and Time 2 served as the dependent variables. Condition (Success vs. Neutral feedback) served as the between subjects factor, and Assessment phase (Pre-feedback vs. Post-feedback) served as the within subject repeated measures factor. Interaction effects were followed up with simple main effects analyses, comparing Time 1 vs. Time 2 scores for each feedback condition separately. In the second analysis, the planned contrast comparing Failure vs. Neutral feedback was tested using the same analytic approach as that outlined above for the Success vs. Neutral feedback contrast.
Table 2Means and standard deviations for state mood at baseline (Time 1) and immediately post feedback (Time 2) by conditionFeedback conditionSuccess (n = 46)Neutral (n = 45)Failure (n = 42)MSDMSDMSDTime 17.831.528.031.088.021.47Time 28.831.258.191.196.842.05Note. Due to technical failures, data were not collected for 6 participants (1 randomized to the success condition, 2 to the neutral condition, and 3 to the failure condition).
Effects for success feedback
The analysis comparing Time 1 vs. Time 2 scores yielded a significant effect for Time, F(1, 89) = 34.70, p < .001, which was qualified by a significant Time by Condition interaction, F(1, 89) = 18.53, p < .001, η2 = .17. Subsequent simple effect analyses for both conditions separately revealed a significant increase in state mood in the success condition, F(1, 45) = 61.79, p < .001, η2 = .58, but no significant change in state mood in the neutral condition, p > .30. Taken together, these findings show that the success feedback condition elicited change in state mood in the expected positive direction, whereas the control feedback condition elicited no emotional reactivity.
Effects for failure feedback
The analysis comparing Time 1 vs. Time 2 scores yielded a main effect for Time, F(1, 85) = 12.84, p < .001, which was qualified by a Time by Condition interaction, F(1, 85) = 21.08, p < .001, η2 = .20. Subsequent simple effect analyses for both conditions separately revealed a significant decrease in state mood in the failure condition, F(1, 42) = 20.83, p < .001, η2 = .34, but no significant change in state mood in the neutral condition, p > .30 (see above). These findings indicate that the failure feedback condition elicited change in state mood in the expected negative direction.
Do depressive symptoms, social acceptance, or gender moderate change in state mood?
The potential moderating effects of depressive symptoms, social acceptance, and gender on affective change were examined using the regression approach outlined by Aiken and West (1991). Separate hierarchical regression analyses were performed for the success feedback condition and the failure feedback condition, each relative to the control (neutral) feedback condition. In Step 1 of the first analysis, state mood score at Time 2 was regressed on state mood score at Time 1. In Step 2, feedback condition, gender (both dummy coded as 0 or 1), social acceptance score (centered), and CDIt score (centered) were entered. In Step 3, all three two-way interactions were entered, including the CDIt by Condition interaction term. Finally, the three-way interactions (e.g., Condition by Gender by CDIt) were entered in Step 4.
Moderator effects for success feedback
Change in mood from pre to post-feedback was significantly predicted by feedback condition (see above). However, neither depressive symptoms, social acceptance score, nor gender moderated the relationship between feedback condition and mood change, as evidenced by non-significant interaction effects (p’s > .25).
Moderator effects for failure feedback
A similar analytic strategy as reported above for success vs. neutral feedback was employed. Results revealed that change in mood from pre to post-feedback was significantly predicted by feedback condition (see above). Again, none of the three within-person variables examined moderated the relationship between feedback condition and mood change, as evidenced by non-significant interaction effects (p’s > .25).
Self-reported feedback preferences: Effects of the feedback manipulation
Self-reported feedback preferences for each of the three conditions are presented in Table 3. Results are reported for the total sample and for the top and bottom third as indexed by children’s score on the CDI. Similar to the analyses reported above, we examined feedback preferences in response to success feedback and failure feedback, each relative to the control (neutral) feedback condition.Table 3Feedback preferences across conditions broken down by CDI statusFeedback conditionSuccess (n = 47)Neutral (n = 47)Failure (n = 45)PreferredPreferredPreferredPreferredPreferredPreferredCDI statuspositive (%)negative (%)positive (%)negative (%)positive (%)negative (%)Low (Bottom third) CDI<594.55.582.417.683.316.7High (Top third) CDI>1088.211.893.76.335.764.3Total sample91.58.591.58.566.733.3
Effects of success feedback
As displayed in Table 3, there was no significant effect for Condition. Binominal tests showed that in both the success and the neutral condition children preferred significantly more positive feedback than expected by chance (p’s < .001).
Effects of failure feedback
A Fisher-Exact test revealed a significant effect for condition, p < .004. This finding indicates that children randomized to receive a negative peer evaluation outcome, relative to controls, were significantly more inclined to subsequently prefer negatively tuned feedback. However, a binominal test revealed that children in the failure condition still preferred significantly more positive feedback than expected by chance, p < .04.
Do depressive symptoms, social acceptance, or gender moderate feedback preferences?
The potential moderating effects of depressive symptoms, social acceptance, and gender on feedback preferences were examined using logistic regression analyses. In these analyses feedback preference (positive vs. negative) served as the binary outcome measure. In Step 1 of the first analysis, state mood at baseline (centered) was entered. In Step 2, feedback condition (success vs. control), gender (both dummy coded as 0 or 1), social acceptance score (centered), and CDIt score (centered) were entered. In Step 3, the two-way interaction terms were entered, including the CDIt by Condition interaction term. Finally, the three-way interaction terms (e.g., Condition by Gender by CDIt) were entered in Step 4. An identical analytic strategy as reported above was used for the failure vs. neutral contrast.
Moderator effects for success feedback
Feedback preferences were not predicted by either state mood at baseline, gender, condition (see above), CDIt, or social acceptance score. Moreover, all interaction terms, including the interaction between CDIt and condition were not significant (p’s > .20).
Moderator effects for failure feedback
Feedback preferences were not predicted by state mood at baseline, gender, or social acceptance score. However, we observed a significant effect for condition (see above). Moreover, our findings yielded a significant effect for the interaction between CDIt and Condition (p < .05). Subsequent analyses revealed that children higher in depressive symptoms were significantly more likely to prefer negative feedback subsequent to the negative peer evaluation outcome (relative odds = 2.44, 95% CI = 1.14 to 5.22, p < .02). In contrast, no significant linkage between depressive symptoms and feedback preferences emerged in the control condition (p > .30).1
Are the effects of feedback valence on feedback preferences mediated by changes in state mood from pre- to post-feedback?
The proposed mediation model depicted below was examined following Baron and Kenny (1986), who asserted that a variable functions as a mediator when it meets the following three conditions: (a) variations in the level of the independent variable account for variations in the presumed mediator (i.e., change in mood; path A); (b) variation in the level of the presumed mediator account for variations in the dependent variable (i.e., feedback preferences, path B); and (c) after controlling for paths A and B, a previously significant relation between the independent and dependent variables (i.e., path C) is no longer significant, with the strongest demonstration of mediation when path C becomes zero (Fig. 1).Fig. 1Mediational model of feedback preferences
Our analyses revealed that the second condition for mediation was not met, as evidenced by a non-significant relation between changes in state mood and feedback preferences. Taken together, our findings show that neither state mood at baseline (see above), nor changes in affect from pre- to post-feedback were significantly associated with children’s feedback preferences.
Discussion
The present study sought to advance our knowledge on how sub-clinical depression in pre-adolescent children is associated with self-reported feedback preferences. To our knowledge, this is the first study to investigate this linkage in response to an experimentally manipulated, ecologically relevant emotion-eliciting event in real time. In so doing, we included both a positive and a negative peer feedback manipulation, along with the assessment of the roles of state mood at baseline, changes in affect from pre- to post-feedback, and actual standing in the peer group. Data on participants’ immediate changes in state mood in response to the feedback manipulation revealed that the Survivor contest was successful in eliciting differential affective reactions in the expected direction as a function of feedback valence. Moreover, our debriefing interviews revealed that children were involved in the activity and that none of the participants reported being aware that the feedback they received was bogus. Taken together, these data suggest that the Survivor contest was successful in achieving its major objective of providing a credible and ecologically relevant emotion-eliciting event.
Several interesting findings emerged with respect to the examination of feedback preferences subsequent to receiving a positive (success) and a negative (failure) peer evaluation outcome, each relative to the control feedback condition. For instance, contrary to findings reported by Mischel and colleagues (1973), feedback preferences did not differ as a function of receiving a favorable versus a neutral control peer evaluation outcome. In contrast, relative to the control condition, the negative peer evaluation outcome lead to a significant increase in negatively tuned feedback preferences. However, it should be noted that the negative evaluation outcome merely induced a relative preference for negative feedback, as evidenced by the fact that in all three conditions children sought significantly more positive feedback than expected by chance.
What might account for the finding that the effects of manipulated feedback on subsequent feedback preferences in the present study are at odds with those observed in prior work? One possible explanation may involve the (single and joint) effects of differences in the domain of feedback seeking, the age of the participants, and the specific measures that were employed. For instance, Mischel et al. (1973) provided adult participants with feedback on an intellectual task, and observed that a favorable feedback outcome increased subsequent positive feedback seeking; whereas in the present study pre-adolescent children received feedback on a social task. Another explanation may be that our manipulated peer evaluation outcome differs from the types of manipulations used in other studies examining feedback preferences. It may be that the manipulation employed in the present study may have been more personally relevant (i.e., ego-involving), and hence elicited a different pattern of feedback preferences relative to exposure to less personally relevant feedback (e.g., impressions evaluators had allegedly formed on the basis of participants’ displayed nonverbal behavior while delivering a speech; see Swann et al., 1992). Specifically, it seems likely that participants in the present study were more concerned and/or distressed after being rejected based on their personal and physical attributes, relative to participants receiving a negative evaluation based on their non-verbal behavior. Consequently, they may have displayed an increased interest in (preference for) negative feedback, which might be considered more diagnostic and informative on learning why the judges viewed them negatively.
Our findings on the linkages between the valence of the manipulated feedback outcome and subsequent feedback preferences were not consistent with previous work among adults. In a similar vein, our results on the relationship between depressive symptoms and feedback preferences qualify the findings reported by Cassidy et al. (2003). Specifically, in the present study the predictive role of depressive symptoms on subsequent feedback preferences emerged only for children in the negative feedback condition, such that children with higher levels of depressive symptoms displayed a significantly stronger preference for negative feedback. This depression effect was not observed in either the success or control conditions. Interestingly, across conditions we found that children’s actual standing in the peer group was not associated with feedback preferences. In this context, it should be noted that in the Joiner et al. (1997) study it was also observed that interest in negative feedback evidenced symptom specificity to depression.
What might account for the observed effects of depressive symptoms on children’s feedback preferences in response to the negative peer evaluation outcome? We considered the possibility that the observed effects were due to the higher levels of depressed mood observed at baseline. However, our findings indicated that this was not the case. Moreover, the observed linkage between depressive symptoms and feedback preferences cannot be explained by change in affect, given that change in affect from pre- to post-feedback did not mediate the single or joint effects of feedback valence and depressive symptoms on feedback preferences. These findings converge with the Joiner et al. (1997) study showing that interest in negative feedback among depressed inpatient children was not associated with the emotional features of depression. We also considered the possibility that self-verification processes might account for the observed linkage between depressive symptoms and feedback preferences. However, our finding that the linkage was present in the failure condition but not in the success or neutral conditions is not in line with self-verification theory, which would predict depression effects across all three conditions. Rather, our findings are more consistent with the formulation put forth by Beck (1967), asserting that depressed (dysphoric) people seek negative feedback only insofar as their negative self-views have been activated by a potent stressful negative event.
Possible implications of our findings deserve mention. The greater tendency for children high in depressive symptoms to seek out negative peer feedback in response to negative peer experiences might contribute to further negative evaluations from peers, and thus serve to maintain or exacerbate depressive symptoms in a cycle similar to that described by Coyne (1976). In line with this reasoning, considerable work has shown both concurrent and prospective linkages between heightened depressive symptoms (depression) in children and peer rejection (Panak & Garber, 1992), decreased peer interactions (Axelson et al., 2003), and less secure peer attachment (Armsden & Greenberg, 1987; Armsden, McCauley, Greenberg, Burke, & Mitchell, 1990). Moreover, in a recent longitudinal study Borelli and Prinstein (2006) observed that a more negative feedback seeking style at baseline was positively associated with more negative perceptions of best friendship criticism at one-year follow-up, and positively associated with increases in depressive symptoms over time. However, this linkage was only found for girls.
Several features of the present study deserve further comment. First, only self-reported feedback preferences were assessed, with no attention given to feedback seeking behavior. Future work should examine children’s feedback seeking when they are free to choose how to divide their time between different sources of feedback information. Second, because all significant results are based on children’s self-report, it is possible that our findings are partly due to shared method variance. Third, our findings are based on a community sample of children, rather than a clinical sample with a diagnosed mood disorder. Children’s mean scores on the CDI suggest that depressive symptoms were relatively modest in magnitude. It is therefore an empirical question to what extent our findings can be generalized to children who meet criteria for major depressive disorder. Finally, this investigation focused only on children’s feedback preferences in response to peer evaluation. While peer evaluation has the benefit of being both a potent elicitor of positive and negative affect as well as being ecologically sound, we should not assume that our findings are generalizeable to other relevant domains such as academic failure, interpersonal conflict, and loss or separation.
Notwithstanding these limitations, the results of the present study extend our limited knowledge base on children’s feedback preferences in several ways. First, our findings suggest that children respond quite differently with respect to their feedback preferences subsequent to positive or negative evaluations. Second, in line with the Joiner et al. (1997) study, our findings contradict the view that the baseline feeling state of children high in depressive symptoms or manipulations of affect may be involved in governing their preference for more unfavorable feedback. Third, our findings provide evidence to suggest that children displaying elevated depressive symptoms may prefer favorable feedback so long as their negative self-views are not accessed by a potent negative stressful event.

feedback preferences
peers
depressive symptoms
pre-adolescent children
sociometric status


MR imaging of osteochondral grafts and autologous chondrocyte implantation

Surgical articular cartilage repair therapies for cartilage defects such as osteochondral autograft transfer, autologous chondrocyte implantation (ACI) or matrix associated autologous chondrocyte transplantation (MACT) are becoming more common. MRI has become the method of choice for non-invasive follow-up of patients after cartilage repair surgery. It should be performed with cartilage sensitive sequences, including fat-suppressed proton density-weighted T2 fast spin-echo (PD/T2-FSE) and three-dimensional gradient-echo (3D GRE) sequences, which provide good signal-to-noise and contrast-to-noise ratios. A thorough magnetic resonance (MR)-based assessment of cartilage repair tissue includes evaluations of defect filling, the surface and structure of repair tissue, the signal intensity of repair tissue and the subchondral bone status. Furthermore, in osteochondral autografts surface congruity, osseous incorporation and the donor site should be assessed. High spatial resolution is mandatory and can be achieved either by using a surface coil with a 1.5-T scanner or with a knee coil at 3 T; it is particularly important for assessing graft morphology and integration. Moreover, MR imaging facilitates assessment of complications including periosteal hypertrophy, delamination, adhesions, surface incongruence and reactive changes such as effusions and synovitis. Ongoing developments include isotropic 3D sequences, for improved morphological analysis, and in vivo biochemical imaging such as dGEMRIC, T2 mapping and diffusion-weighted imaging, which make functional analysis of cartilage possible.
Introduction
Articular cartilage injuries are one of the most common types of injuries seen in orthopaedic practice. In a retrospective review of 31,510 knee arthroscopies, the incidence of chondral lesions was 63% [1]. Full-thickness articular cartilage lesions with exposed bone were found in 20% of patients, with 5% of these occurring in patients less than 40 years old [1].
The treatment of articular cartilage damage remains a challenge because cartilage has a limited capacity for spontaneous repair after a traumatic insult or degenerative joint disease [2]. Joint surface defects that exceed a critical size heal poorly and usually lead to osteoarthritis. As a result, several therapeutic strategies have been developed to restore articular cartilage and produce a durable repair. These methods may be arthroscopic or open surgical techniques and include marrow-stimulation techniques, osteochondral grafting, and chondrocyte implantation technique [3, 4].
As the use of articular cartilage repair techniques has become more widespread, techniques for imaging articular cartilage have become increasingly important. Arthroscopy is unsuitable for routine follow-up due to its invasive nature and associated risks. Conventional radiography does not allow direct visualisation of cartilage and arthrography combined with conventional radiography or computer tomography only provides information related to the cartilage surface [5]. By contrast, through the use of appropriate magnetic resonance imaging (MRI) techniques it is possible to evaluate the biochemical and biomechanical status of cartilage in addition to cartilage morphology. These benefits make MRI a powerful tool for the initial diagnosis and subsequent post-operative monitoring of cartilage lesions and cartilage repair tissue.
Articular cartilage repair techniques
Marrow stimulation
There are several established marrow stimulation techniques, including abrasion arthroplasty, subchondral drilling and microfracture. The principle of marrow stimulation techniques is to abrade (abrasion arthroplasty) or pierce (drilling and microfracture) the subchondral bone at the base of the cartilage defect, causing controlled bleeding and clot formation in the cartilage defect, which leads to the subsequent formation of fibrocartilage. The newly formed fibrocartilage may fill the defect and give relief of symptoms, but it lacks the structural, biomechanical and biochemical properties to sustain normal joint function in the long term [6]. Therefore, there is an increasing interest in osteochondral autografts and autologous chondrocyte implantation (ACI) therapies.
Osteochondral grafting
Osteochondral autograft transplantation is currently considered as the only surgical technique that provides and retrains proper hyaline articular cartilage [7]. Cylinders of autologous bone with their associated overlying hyaline cartilage are harvested from relatively non-weight-bearing areas of the knee and transferred into similarly sized holes created within the articular defect to be treated [8]. This technique is used most frequently at the knee and ankle joints [9, 10]. The main clinical indications for autologous osteochondral transplantation are focal cartilage defects, osteochondritis dissecans and osteonecroses. Autologous osteochondral transplantation is recommended for cartilage defects up to 3–4 cm2 [8].
Autologous chondrocyte implantation (ACI)
ACI is currently considered the treatment of choice in young patients with a symptomatic full-thickness cartilage defect between 2 and 12 cm2 on the femoral condyle [11]. The classical ACI was first described in the mid-1990s and is a two-stage procedure. In the first stage, chondrocytes are harvested and cultured. In the second stage, a periosteal patch, usually harvested from the tibia, is sewn over the defect and the proliferated and differentiated cells are injected beneath the patch in order to fill the cartilage defect [12–14]. ACI provides significant and long-term benefits for patients in terms of diminished pain and improved function [15].
Most complications are directly related to the periosteal graft and a revision arthroscopy rate between 4.8% and 60% has been described due to the problems with the periosteal flap [15, 16]. Early problems (<6 months) include periosteal graft detachment and delamination, and the most common late complication is periosteal hypertrophy. Delamination has been reported in 14% of patients undergoing ACI [17, 18]. Periosteal hypertrophy, has been reported to cause symptoms in 10–26% of the cases [11, 15, 19, 20].
Matrix-associated ACT (MACT)
The necessity of a periosteal flap with classical ACI and the complications associated with this periosteal flap have led to the development of biomaterials as carrier for chondrocyte cells. The use of three-dimensional (3D) scaffolds has been shown to favour the maintenance of a chondrocyte-differentiated phenotype [21]. Thus efforts are now focused toward a tissue-engineering approach, which combines laboratory-grown cells with appropriate 3D biocompatible scaffolds.
Second generation ACI
The second generation of ACI technique use a bioengineered bi-layer collagen membrane rather than a periosteal flap [22, 23]. The use of a collagen membrane simplifies the surgical procedure and reduces overall surgical morbidity. Furthermore, the problem of periosteal hypertrophy can be avoided.
Third generation of ACI
Further technological advances have led to the third generation of ACI, which use biomaterials seeded with chondrocytes as carriers and scaffolds for cell growth. These “all-in-one” grafts do not need a periosteal cover or fixing stitches and can be trimmed to exactly fit the cartilage defect. The advantages of these new techniques are their technical simplicity, shorter operating time and the possibility to perform the surgery via a mini-arthrotomy or arthroscopy [23–26].
MR techniques for cartilage and cartilage repair imaging
Appropriate combinations of MRI techniques allow objective non-invasive measures of the properties of the grafted regions after biological cartilage repair, facilitating the longitudinal follow-up and evaluation of the repair tissue. MRI is currently the standard method for cartilage evaluation as it allows morphological assessment of the cartilage surface, thickness, volume and subchondral bone [27–33]. Additionally MRI techniques can be used to evaluate the biochemical and biomechanical status of articular cartilage. MRI is therefore ideal for the evaluation of the morphologic status of cartilage defects and the repair tissue throughout the post-operative period [34–37].
MR evaluation of cartilage repair can be performed using the same acquisition techniques as used for native cartilage, as recommended by the International Cartilage Repair Society [29, 35]. The most commonly used MRI techniques are intermediate-weighted fast spin-echo (FSE), and 3D fat-suppressed gradient-echo (GRE) acquisition [27–29, 31–35]. Fast spin-echo (FSE) imaging combines heavy T2 weighting, magnetization transfer effects and relative preservation of high signal intensity in the marrow fat, so that the subchondral bone exhibits high signal intensity. In FSE imaging, cartilage appears dark against bright synovial fluid and there is consecutive high contrast between joint fluid and cartilage, and cartilage and bone marrow [40]. Collagen fibres with a highly regular structure, in particular near the bone-cartilage interface, tend to immobilize water molecules and promote dipolar interactions between their protons, thus accelerating T2 relaxation. T2-weighted FSE sequences are therefore useful for both the detection of surface and matrix damage assessed by intrachondral signal abnormalities. Proton density-weighted T2 FSE is of intermediate signal adjacent to the low signal subcortical bony plate, which allows to better visualise intrachondral abnormalities and lesions near to the cortical bone; whereas with T2-weighted FSE cartilage has a low signal intensity, which gives a high contrast to bright synovial fluid, resulting in better delineation of cartilage surface defects. The T2-weighted FSE sequence is relatively insensitive to magnetic susceptibility artefacts, which is advantageous in patients who have undergone previous surgery of the joint. FSE sequences are advocated because of the possibility of acquiring high-resolution images in a relatively short scan time and are normally included in the standard MRI protocol for the knee joint for detection of meniscal and ligamentous lesions [28, 31]. An exciting new development is a 3D FSE sequence [sampling perfection with application optimised contrasts using different flip angle evolutions (SPACE)] with isotropic voxels, which allows multi-planar reformatting in any plane without loss of resolution.
The advantage of fat-suppressed 3D spoiled gradient echo sequences is the relatively high signal intensity of articular cartilage in contrast to low signal intensity from the adjacent fat-suppressed tissue. Three-dimensional acquisitions yield images with higher resolution and contrast-to-noise ratio than 2D acquisitions. Fat-suppressed, 3D spoiled gradient echo imaging is easy to perform, widely available and, contrary to other cartilage imaging techniques, such as magnetization transfer imaging, it requires no post-processing of data and avoids misregistration artefacts [27, 31–33, 35, 38]. The 3D data set can be reformatted in any plane, allowing 3D visualisation and volume measurements [27, 30, 39]. New isotropic 3D gradient echo sequences such as DESS (double-echo steady-state), true FISP (fast imaging in steady state precession), balanced FFE (fast field echo), VIBE (volume interpolated breath-hold examination) and MEDIC (multi-echo data image combination), with a voxel size down to 0.5 mm3, at 3 T, and also at 1.5 T with a high gradient strength, have been developed and seem to be very promising for cartilage imaging. However, the usefulness of these techniques is yet to be validated in clinical studies. Caution should be exercised as a flare phenomenon in the double-echo steady-state sequence has been shown to sometimes cause a spurious wavy appearance of a normal graft or cartilage surface [41, 42]. Furthermore, there are limitations in the sensitivity of the fast low angle shot (FLASH) and double-echo steady-state sequences for depicting cartilage abnormalities and opinions vary regarding which sequence is superior for cartilage imaging [42–44].
Indirect MR arthrography has been advocated in the evaluation of ACI [20, 29]. The indirect technique is performed by intravenous administration of contrast agent followed by a period of joint exercise, which leads to uptake of contrast agent by the synovial tissue and subsequent diffusion into the joint cavity. Indirect MR arthrography can be particularly helpful in differentiating delamination of the base of the graft from normal high signal intensity repair tissue in the immediate postoperative period [20, 29]. Thus, indirect MR arthrography may play a role in the evaluation of cartilage repair techniques in the early post-operative period.
Quantitative MRI of articular cartilage
Quantitative geometric measurements of cartilage parameters, such as cartilage thickness and volume, have been suggested as sensitive image based biomarkers for detecting and monitoring cartilage degeneration in osteoarthritis [45]. This has been possible due to the development of higher field magnets and stronger gradients systems as well as a parallel improvement in image analysis and processing techniques. New techniques allow rapid fully automated generation of accurate 3D reconstructions of articular cartilage layers [46]. Three-dimensional reconstructions can be generated from pre- and post-operative MR examinations and spatially registered to facilitate longitudinal comparisons of the cartilage repair site. This area holds a lot of promise for cartilage repair surgery planning; with on-going work to co-register biochemical and biomechanical sequences, such as dGEMRIC, with high-resolution 3D sequences.
High-resolution MRI
Most MRI studies on articular cartilage have tried to optimise pulse sequences that accentuate the contrast-to-noise ratio for cartilage, but less attention has been paid to image resolution.
Rubenstein et al. [47] demonstrated that the image resolution of standard MR sequences is insufficient to detect fraying of the articular surface of cartilage. It has also been reported that the smooth articular surface of healthy cartilage is indistinguishable from early superficial degenerative changes [27, 31–33]. Therefore, MRI of cartilage repair tissue must be performed at a sufficiently high resolution to detect early surface changes.
In order to obtain a sufficient resolution for cartilage repair imaging a 1.5-T MR scanner with a high performance gradient system and a dedicated extremity coil (quadrature/phased array coil) is a minimum requirement. Three-Tesla clinical MR systems are becoming more widespread and can generate images with both a high signal-to-noise ratio and a high resolution.
In our experience with cartilage repair patients, the signal-to-noise ratio issue arising with standard field MR units can be partially resolved by using surface coils and optimising the pulse sequence to increase resolution for a given imaging time. Surface coils provide a high signal-to-noise ratio allowing a small field of view and high matrix size to be used resulting in an in-plane resolution of 0.234 mm2 acquired in a clinically acceptable time. However, the use of a surface coil is limited to the evaluation of one knee compartment; since cartilage repair is usually restricted to one compartment, this rarely poses a problem.
Using this approach an appropriate assessment of the cartilage repair tissue can be made. Moreover, this approach formed the basis for the definition of pertinent variables for describing articular cartilage repair tissue following biological cartilage repair [48].
MR evaluation of the biochemical and biomechanical status of cartilage
Various tissue parameters which may be evaluated by MRI reflect the biomechanical properties of articular cartilage. The T1 and T2 relaxation times and apparent diffusion constants change during the cultivation of cartilage implants [49], indicating that the biomechanical properties of cartilage implants change as the graft matures. Furthermore, the spatial distribution of the T2 relaxation time can be used for in vivo monitoring of the biomechanical properties, pathological changes or aging of various cartilage layers [50, 51].
Glycosaminoglycans (GAG) are the main source of fixed charge density (FCD) in cartilage and changes in GAG concentration are one of the features of cartilage graft maturation. Intravenously administered gadolinium diethylenetriamine pentaacetate anion (Gd-DTPA2−), equilibrates in inverse relation to the FCD, which is in turn directly related to the GAG concentration. Therefore, T1 which is determined by the Gd-DTPA2− concentration becomes a specific measure of tissue GAG concentration. The delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) technique can be considered the method of choice for visualisation and quantitative evaluation of proteoglycan in articular cartilage [52, 53]. The dGEMRIC technique is feasible at 3 T [54] and is useful for evaluating cartilage repair [55].
In vitro studies show that the dGEMRIC index has a good correlation with cartilage biochemical properties [56]. However, in vivo studies of the dGEMRIC index in ACI grafted tissue are not so clearly correlated with stiffness of the repair tissue. The differences are presumably due to differences in the collagen content and architecture of the repair tissue. Therefore, the both GAG (dGEMRIC) and collagen (T2 mapping) imaging techniques are required when evaluating the biomechanical status of cartilage repair tissue.
MR classification systems of cartilage implants
For the long-term follow-up, evaluation and classification of cartilage repair tissue clinical scores in addition to morphological and biochemical evaluation of biopsies taken during control arthroscopies have previously been used [37, 57].
For optimal use of MRI in the evaluation of cartilage repair tissue, a simple evaluation and a point scoring system that allows efficient statistical data analysis is necessary. A few different classification systems for the description of articular cartilage repair tissue have been proposed [37, 48] (personal correspondence with Dr C. Winalski, Brigham and Womens Hospital, Boston, Mass., USA); however, early systems have certain limitations and deficiencies that can potentially lead to confusion.
Roberts et al. [37] used four parameters to assess cartilage repair on MR images: surface integrity and contour, cartilage signal in graft region, cartilage thickness and changes in underlying bone. A score is obtained by summing the values of the four parameters; scores range from 0, no repair, to a maximum of 4, complete repair. Unfortunately the system only assesses each parameter as normal or abnormal and provides no assessment of the graft integration, degree of defect fill or the presence of adhesions.
The magnetic resonance observation of cartilage repair tissue (MOCART) scoring system, defined by our working group and shown in Table 1 [48], was designed to systematically record only those observations that can be most accurately and reproducibly determined and to avoid the use of ambiguous terms [48]. The MOCART system has been shown to be reliable and has excellent inter-observer reproducibility for the defined variables [58]. The system is very helpful for the longitudinal follow-up of cartilage repair patients [58], and facilitates prospective multi-centre studies comparing different cartilage repair techniques.
Table 1The MOCART scoring systemVariableClassesDegree of defect repair and defect fillingComplete (on a level with adjacent cartilage)Hypertrophy (over the level of the adjacent cartilage)Incomplete (under the level of the adjacent cartilage; underfilling)        >50% of the adjacent cartilage        <50% of the adjacent cartilageSubchondral bone exposed (complete delamination or dislocation and/or loose body)Integration to border zoneComplete (complete integration with adjacent cartilage)Incomplete (incomplete integration with adjacent cartilage)demarcating border visible (split-like)Defect visible  <50% of the length of the repair tissue        >50% of the length of the repair tissueSurface of the repair tissueSurface intact (lamina splendens intact)Surface damaged (fibrillations, fissures and ulcerations)        <50% of repair tissue depth        >50% of repair tissue depth or total degenerationStructure of the repair tissueHomogeneousInhomogeneous or cleft formationSignal intensity of the repair tissueDual T2-FSE        Isointense        Moderately hyperintense        Markedly hyperintense3D GE-FS        Isointense        Moderately hypointense        Markedly hypointenseSubchondral laminaIntactNot intactSubchondral boneIntactOedema, granulation tissue, cysts, sclerosisAdhesionsNoYesEffusionNo effusionEffusion
MR findings following osteochondral autografting
Due to the invasive nature of arthroscopy, MRI has become the most important tool for the follow-up of patients with osteochondral autografts. There have been a limited number of studies focusing on the morphologic assessment of osteochondral grafts during clinical follow up. With the exception of the study by Link et al. [59], these studies have evaluated only relatively small numbers of patients [60, 61].
An MRI evaluation of osteochondral grafts should include: the number and size of the grafts; bone and cartilage integration, the cartilage surface contour; the contour of the cartilage bone interface; an assessment of the signal in the graft, the adjacent bone marrow and at the donor site; details of any soft tissue abnormalities and an assessment of the contrast enhancement patterns.
Cartilage and bone integration and congruity
When assessing integration and surface congruity following osteochondral grafting cartilage and bone should be considered separately. With respct to the cartilage, Link et al. [59] found only 15% of patients had an incongruity of the cartilage-cartilage interface. Furthermore, no substantial defects or irregularities of the cartilage overlying the bony cylinders were seen on MRI (Fig. 1). Lastly, gaps between cartilage plugs and between cartilage plugs and adjacent native cartilage were rarely visualised in this study [59] (Fig. 2).
Fig. 1Normal cartilage integration of osteochondral autografts in the weight bearing region of the femoral condyle in a patient 2 years after osteochondral autograftsFig. 2Cystic cavities in the osteochondral graft area with fissure-like gap (arrow) between cartilage caps of the grafts in a patient 2 years after osteochondral autografts. Both signs are associated with a poor prognosis
Sanders et al. [61] examined 21 patients between 1 and 22 months following osteochondral transplantation using a series of five dynamic contrast-enhanced sequences at 1-min intervals post contrast. Their findings were very similar to Link et al. [59] in terms of cartilage-cartilage interface incongruity.
In a study to evaluate the use of indirect MR arthrography in examinations of osteochondral graft patients Herber et al. [60] found that indirect MR arthrography helped to identify a persistent fissure gap between the implanted cartilage and native cartilage. They showed that the grafts were well seated and the cartilage-cartilage interface demonstrated a smooth contour in the majority of cases. Moreover, the authors felt that indirect MR arthrography was superior to unenhanced imaging in the assessment of the cartilage surface. Their results highlight a potential role for indirect MR arthrography in challenging cases.
Regarding the bony integration, Link et al. [59] reported that poor bony integration of the osteochondral cylinder may be suggested by the presence of cystic cavities with fluid-like signal intensity and/or a persistent oedema-like signal within the subchondral bone.
Cartilage and bone signal intensity
From 105 osteochondral cylinders in 55 patients (51%) Link et al. [59] reported bone marrow signal intensities consistent with oedema (hypointensity on T1-weighted images and hyperintensity on the fat-suppressed T2-weighted or PD-weighted images), during the first 12 months post-operatively (Fig. 3). During the 12–24 month period this dropped to 17%. They found cystic changes in the osseous component of the graft in four of 99 cylinders. Furthermore, eight cylinders showed no or partial enhancement after contrast agent was administered and was reported as necrosis. The T2 signal intensities varied depending on whether the osteonecrosis caused a fibro/sclerotic or cystic degeneration. This limits the usefulness of T2-weighted images for diagnosing graft osteonecrosis. Interestingly, only two of the six patients who showed signs of osteonecrosis of one or more cylinders had associated clinical abnormalities [59]. These osteonecroses of the graft cylinders did not lead to collapse of the bone or pathological changes of the cartilage that could be visualised by the MRI. Since cartilage derives its nutrition almost exclusively from the synovial membrane, this may provide an explanation. By comparison, Sanders et al. [61] and Hangody and Fules [9] reported no cases of osteochondral necrosis in their patient groups.
Fig. 3Coronal STIR images 12 weeks (a), 52 weeks (b) and 2 years (c) after osteochondral transplantation show gradual resolution of severe bone marrow oedema in and around the grafts as bony incorporation occurs
When the cartilage layer of the osteochondral cylinder was examined, the cartilage signal intensity of the graft was similar to the surrounding cartilage in the vast majority of cases, 86% [59]. These findings are supported by Sanders et al. [61], who found that the graft cartilage signal intensity was similar to adjacent cartilage in most cases. Additionally they found similar rates of graft and perifocal graft oedema.
Graft and adjacent bone
Subchondral bone marrow oedema is often present in the early post-operative phase but usually resolves as the graft incorporates into the subchondral bone. A normal fatty marrow signal is seen within and around the plugs when solid bony incorporation occurs (Fig. 4). Herber et al. [60] examined ten patients at 3, 6 and 12 months using indirect MR arthrography and noted a high rate of early post-operative subchondral marrow oedema, which settled in most cases by 12 months.
Fig. 4a, bSagittal T1-weighted SE images of the normal development of autologous osteochondral transplants. a Marked oedema in and around the osteochondral plugs at the recipient site 12 weeks (arrows) after surgery and b bony incorporation of the grafts with fatty bone marrow in and around the grafts (arrows) and filling of the donor site with cancellous bone after 2 years
Normal findings
The results of these earlier follow-up studies have helped to define normal and abnormal MR findings following osteochondral autografting and help in the identification of possible complications.
“Normal” MR findings associated with after osteochondral autografting include bone marrow oedema in and around the grafts in approximately 50% of the subjects during the first 12 months, with a gradual reduction thereafter. However persistent oedema may be seen in a small number of cases for up to 3 years post-operatively. Joint effusion and synovitis appear to follow a similar trend. Incongruities at the bone-bone interface occur frequently, while incongruities at the cartilage-cartilage interface occur in approximately 15% of grafts. The sometimes substantial incongruities of the bone-bone interface should not be interpreted as an abnormal finding or complication. Differences occur because the cartilage thickness of the donor site commonly differs in thickness from the implant site, but the cylinder is inserted to a depth to ensure a smooth cartilage surface.
Abnormal findings
Complications which can be determined by MRI include graft loosening or migration, incongruencies of the cartilage-cartilage interface, significant gaps between osteochondral plugs and adjacent native cartilage and partial or complete necroses of the grafts (Figs. 5, 6). However, it should be noted that over time fibrocartilagenous tissue fills the gaps between osteochondral plugs and adjacent native cartilage thereby improving surface congruity.
Fig. 5Incongruity at the cartilage-cartilage interface on a medial femoral condyle 12 months after osteochondral autograftingFig. 6Osteonecrosis of the osteochondral autograft 2 years after surgery shown by marked hypointense signal alteration of the graft on T1-weighted SE image in the sagittal plane (arrows)
Future studies in this field should provide better understanding of the pathophysiology of transplanted hyaline cartilage and its function, which is important for the long-term prognosis of these patients.
MR findings following ACI and MACT
Assessment and interpretation of MR examinations for ACI and MACT patients should be performed in a systematic fashion. Careful attention should be paid to the degree of defect filling, the integration of the graft to adjacent cartilage and underlying bone, the graft’s internal structure and surface, its signal intensity and any changes in the subchondral bone. Last but not least, the presence of adhesions to the graft or joint effusion should be evaluated.
Defect filling
One of the major goals of ACI and MACT is to ensure the graft cartilage has the same thickness as the adjacent native cartilage in order to restore the smooth contour of the articular cartilage surface (Fig. 7). When evaluating defect fill MRI has been shown to be effective in detecting cases of incomplete filling, either focally or globally [20, 29, 34, 36].
Fig. 7A sagittal FSE image of a stable cartilage implant at 2 years after MACT surgery shows complete filling of the defect (arrows mark the borders of the implant)
Longitudinal MRI follow-up of ACI patients has shown that a consistent volume of repair tissue can be visualised after 3 months, which remains stable for at least 2 years post-operatively [62]. Tins et al. [63] demonstrated that 63% of patients in their series had normal cartilage thickness compared with native articular cartilage. Roberts et al. [37] also found a similar percentage of grafts to have normal cartilage thickness compared with adjacent native cartilage. In a different study with a 2-year MR follow-up [64], complete filling of the defect by repair tissue was seen in 65.2% of patients.
Graft hypertrophy is often asymptomatic, but may produce pain and catching. It usually occurs between 3 and 7 months and has been reported to complicate between 10% and 39% of cases [15, 18, 65]. Graft hypertrophy is seen on MRI as the ACI graft protruding above the level of the native articular cartilage and may involve part or the full width of the graft (Fig. 8). It is important to note that hypertrophy of grafts close to the intercondylar notch may cause impingement on the anterior cruciate ligament. Treatment consists of arthroscopic debridement of the hypertrophied tissue.
Fig. 8Severe hypertrophy of cartilage implant 104 weeks after MACT surgery on a sagittal T2-weighted FSE image
The reported incidence of graft hypertrophy following MACT is lower than with the classical ACI with periosteal flap technique [22, 66]. We found an incidence of 20% graft hypertrophy following MACT surgery [64], which is similar to the hypertrophy rate following classical ACI surgery [18, 67]. Interestingly, however, in three out of four cases the graft hypertrophy resolved, with the cartilage thickness returning to the level of adjacent cartilage within one year. This was hypothesised to be due to increasing weight bearing during rehabilitation and subsequent remodelling of the repair site.
Henderson et al. [19] analysed 81 lesions in 58 knees 12 months after ACI and reported that MRI showed 81.6% of the lesions had normal or nearly normal cartilage at the site of repair. These investigators analysed four categories, including the filling and the signal intensity of the repair site, as well as the presence and severity of bone-marrow oedema and effusion. A complete fill of the repair site was found in 79%, 13.6% had >50% filling, and 2.5% had <50% filling. By comparison, we found filling to the level of adjacent cartilage in 65.2% and underfilling in 17.4% in a 2-year follow-up of MACT patients using Hyalograft C [64].
Integration
The interface between ACI and native cartilage should be indiscernible, a fluid-like split, in particular a broad split or one that extends beneath the base of the ACI has been described as pathological [20, 34, 36] (Fig. 9). Poor graft integration can be identified on high-resolution sequences by fluid signal clefts or ill-defined high signal intensity at the interface between the graft tissue and native cartilage [15, 18, 20, 34, 36, 67].
Fig. 9A split-like integration defect, seen on a sagittal FSE image, 52 weeks after surgery. Arrows mark the borders of the implant, the right-hand arrow points to the defect
The clinical importance of a full-thickness fissure or a broader gap, suggesting incomplete integration, is still unclear. Potentially, a gap between the native cartilage and the edge of the graft may act as a focal point for cartilage wear [36]. We found integration to be complete in 18 out of 23 knees after 2 years [64]. One patient had incomplete integration with a split-like fissure between the graft and adjacent cartilage. Four patients showed incomplete integration with a slightly broader gap between the graft and adjacent cartilage (17.4%).
Poor integration of the ACI repair tissue to the bone or to the adjacent native cartilage may result in delamination of the graft from the underlying bone (Fig. 10). On MRI, a delaminated graft may appear as a loose body in the joint if it has dislocated, or if still in situ at the repair site, a thin rim of fluid between the base of the graft and the subchondral bone plate, resembling a cartilage flap, may be seen [20, 34]. Clinically, patients may complain of pain, swelling or locking. This complication may occur in between 5% and 14% of cases [15, 18].
Fig. 10A sagittal FSE image of incomplete delamination of the repair tissue 24 weeks after MACT surgery. Fluid partially demarcates the bone interface
Structure and surface
The articular surface of the ACI or MACT site should appear smooth and be continuous with the adjacent native cartilage. Irregularities of the graft surface on MRI have been described previously and seem to be relatively common [19, 20, 34, 68, 69]. This is in keeping with findings during follow-up arthroscopy [70].
In some patients, we have seen a transition from an initially irregular surface to a regular smooth surface over time (Fig. 11). We believe this may represent continuous organisation of the graft as it matures. In contrast, the development of surface defects over time should be considered as abnormal [62].
Fig. 11Gradual integration and normalisation of the graft surface and internal structure after MACT surgery from (a) 12 weeks to (b) 24 weeks on sagittal T2-FSE image
The internal structure of normal hyaline cartilage usually has a trilaminar appearance on most sequences [71]. Although the widths of these signal bands seen on MRI did not correlate with the thickness of histological layers and were affected by chemical shift and the magic angle effect, they represent a homogeneous layering present in normal collagen architecture. Regarding the internal structure of the graft we found only eight out of 23 patients had homogeneous repair tissue at their first follow-up scan. In a further eight patients the repair became homogeneous with signal layering during the follow-up period [62]. This most likely represents a normal maturation process, whereas the reverse process with repair tissue going from a homogeneous to inhomogeneous appearance over time may be considered abnormal.
Signal intensity
In addition to a smooth surface and homogeneous signal layering, the signal intensity of repair tissue should resemble normal hyaline cartilage. The post-operative signal characteristics seen after ACI surgery are clearly affected by the choice of sequence used. In the uncomplicated case, there is a gradual change in the signal characteristics of the repair tissue over time to resemble those of normal articular cartilage [62, 72] (Fig. 12). Typically, when a FSE sequence is used the signal intensity of the ACI/MACT repair tissue steadily decreases with time. By contrast the signal intensity steadily increases over time with fat-suppressed T1-weighted GRE sequences [19, 62, 65].
Fig. 12a–eChanges of implant signal intensity, on PD and T2-FSE images following MACT. a, b Fluid-like signal after 4 weeks; c, d hypointensity at 24 weeks and e isointensity with native hyaline cartilage after 52 weeks
In the early post-operative period (4 weeks) the graft may have a fluid-like appearance, which may be misinterpreted as complete delamination. However, on high-resolution imaging the surface of the implant is seen as a thin dark line, this feature is more commonly seen with classical ACI [34]. Several report show that at approximately 12 months post-operatively the signal intensity of the ACI graft on both FSE and fat-suppressed GRE sequences resembles native hyaline cartilage and can no longer be differentiated from it [19, 20, 34, 68, 69]. By contrast, Brown et al. [17] found that a significant proportion of grafts (69%) remained hyperintense on FSE sequences more than 18 months after surgery.
In clinical follow-up studies, Henderson et al. [19] reported signals identical to the adjacent articular cartilage in 63% of patients following classical ACI grafting and nearly normal signals in 29.6% at 12 months. In our group of MACT patients, we found all grafts evaluated at 24 months were isointense [64]. These differences may be attributable to the different evaluation times and the further maturation of the graft over the additional 12-month period in our study.
Subchondral lamina and bone
The subchondral lamina is normally not violated during the ACI or MACT procedure and therefore should be intact. However, cartilage repair for cases of osteochondritis dissecans or involving partial removal of subchondral sclerosis may be associated with defects of the cortical endplate. An intact cortical endplate which becomes damaged during follow-up may be the result of over-use or recurrent trauma [62]. The bone contour underneath the graft may show the presence of central osteophytes, defects, and irregularities. When central osteophyte formation occurs beneath a graft the surface of the overlying cartilage is usually smooth and level with adjacent cartilage. The importance of central osteophytes has still to be determined [63].
Subchondral oedema commonly occurs at the repair site in the early postoperative period [17, 19, 29, 34, 63, 68], and has been described as part of the normal healing process during the first 3 months following surgery [20, 34]. Abnormal loads transmitted to the bone have also been suggested to account for the marrow oedema [73]. The presence of oedema-like marrow signal beyond 12 months or an increase in intensity of oedema should be considered abnormal and requires close clinical follow-up [20, 34]. Possible reasons for persistent or reappearing subchondral bone marrow oedema include: over-use, an abnormality of the leg axis and new trauma. Conversely, it has been reported that the persistence of oedema-like signal intensity is a sign of yet undetermined importance [34]. For the diagnosis of bone marrow oedema, a fat-suppressed sequence such as STIR or fat-suppressed FSE is required. Cystic changes in the subchondral bone underneath the cartilage implant have been described and indicate a problem with the graft which requires close clinical follow-up [19]. Cyst formation has been associated with oedema-like signal intensity in 10% of cases with these cases also demonstrating a fibrocartilage appearance rather than hyaline-like articular cartilage [63]. In our series of 23 patients who underwent MACT surgery, we found 13% had an oedema-like signal in the subchondral bone at 2 years [64].
A comparison of ACI and MACT patient groups [19, 64] showed the rate of bone marrow oedema and effusion was similar, approximately 40%, with both techniques.
Adhesions
Adhesions are demonstrated on MRI as bands of intermediate to low signal intensity tissue traversing the joint and demonstrating contact to the repair tissue (Fig. 13). Adhesions most commonly connect to the infra-patellar fat pad, suprapatellar pouch and parapatellar recesses [15, 18, 67]. Knee stiffness from intraarticular adhesions requiring arthroscopic release has been reported in up to 10% of ACI patients [15, 67, 73]. Patients who have undergone extensive cartilage repair or have multiple grafts appear to be more at risk [18]. Adhesion to the graft surface may lead to graft tearing or dislocation as the patients activity level increases.
Fig. 13An adhesion seen as a thin band-like structure, running from the tibial articular surface to the cartilage implant on a sagittal T2-FSE image. Arrows mark the borders of the implant
Effusion
Post-operative reactive synovitis should be considered as a possible cause of pain in this patient group. For the evaluation of synovitis intravenous contrast media application is usually required. Synovitis is generally accompanied by effusion and clinical signs of swelling of the affected joint. The majority of cases with synovitis in the early postoperative period resolved during the follow-up, which can be explained by its reactive nature [64]. In addition to the cartilage repair surgery itself other causes of synovitis have to be considered, such as native cartilage defects or meniscal lesions.
Maturation of cartilage repair tissue
Serial follow-up MRI scans of MACT patients show that the cartilage repair is a dynamic process that can be non-invasively monitored [62]. High-resolution MRI examinations at 4, 12, 24, 52 and 104 weeks post-operatively revealed characteristic changes in the repair tissue over time, which we believe represent the normal maturation process of the repair tissue. The most significant features were: first, early filling defects showed progressive filling by 6–12 months (Fig. 14); second, initial graft hypertrophy seen at 3 months resolved by 6 months; third, small surface defects became smooth over time. Finally, signal intensity gradually changes over time from fluid-like appearance in the early post-operative stage to iso-intensity with surrounding native hyaline cartilage by 6–12 months. One may conclude that inverse developments are associated with a poor prognosis.
Fig. 14a, bProgressive defect filling at the repair site. After matrix-based autologous chondrocyte implantation, significant improvement of filling of the defect at the repair site from (a) 4 weeks to (b) 24 weeks post-operatively, depicted on sagittal FSE images
Histology
In a histological analysis of follow-up biopsies after ACI surgery, 57% of patients after ACI demonstrated articular cartilage [74]. However, it is generally hypothesised that continuous remodelling of the graft occurs with the transplant becoming more like hyaline cartilage.
To our knowledge only one study has attempted to correlate MRI findings with ACI graft histological findings [63]. Tins et al. [63] studied 41 patients at 1 year following ACI grafting on the femoral condyle. The histological appearance of the graft was classified into one of the four morphological categories recommended by the ICRS. MRI findings were correlated to the histology findings. The authors found no relationship between any of the MRI features assessed and the histological appearance of the cartilage repair tissue; however, this is unsurprising given the sequences used. Studies of the correlation between histology and the dGEMRIC index and/or T2 maps are a very interesting area for future studies.
Functional outcome
Marlovits et al. [58] recently described the statistical correlation of clinical outcome scores with the radiological variables of the MOCART scoring system. For the variable “filling of the defect” a statistically significant correlation with all KOOS (knee injury and osteoarthritis outcome score) variables and also with the VAS (visual analogue score) was observed. For the variable “integration to border zone” and “surface of the repair tissue” no statistically significant correlation was found. The variable “structure of the repair tissue” showed a statistically significant correlation with the VAS and nearly all KOOS group except for the variable “symptoms”. For the variables “adhesion”, “subchondral lamina”, and “effusion” no statistically significant correlation with the clinical scores was found. In contrast the variable “subchondral bone” showed a statistically significant correlation with the VAS and nearly all KOOS group except for the variable “symptoms”. For the signal intensities a statistically significant correlation was found with the KOOS variables “symptoms”, “sport”, and Activities of daily Living (ADL) function.
Recommendations
From reviewing our studies and those of other centres we recommend that follow-up MR studies should be performed at 3 months and 1 year. The initial imaging at 3 months allows the volume and adherence of repair tissue to be assessed. Imaging at 1 year demonstrates the maturation of the graft and allows complications to be identified both non-invasively and at a sufficiently early stage.
Conclusion
In the near future as the use of clinical high-field (3 Tesla) systems with modern multi-element coil configurations becomes more widespread and new high-resolution isotropic 3D sequences are utilised a further improvement in the morphological analysis of cartilage implants can be expected. Moreover, advanced cartilage imaging techniques which allow the biochemical composition of cartilage to be studied will be possible in vivo (Fig. 15). This is particularly promising for evaluating the maturation of the graft and whether or not hyaline cartilage has developed. The ability to non-invasively assess graft maturity will help to define the optimal postoperative rehabilitation and to detect the early stages of graft failure.
Fig. 15a, bA dGEMRIC image of a matrix-associated ACT 2 years after surgery. a The cartilage layer of the graft shows different T1 values, representing proteoglycan concentration, compared with hyaline cartilage. b a 3D-GRE image of the same patient, which shows morphology of cartilage implant with hypointense signal alteration of the cartilage implant in comparison with normal hyaline cartilage