Play Behavior and Attachment in Toddlers with Autism

Play helps to develop social skills. Children with autism show deviances in their play behavior that may be associated with delays in their social development. In this study, we investigated manipulative, functional and symbolic play behavior of toddlers with and without autism (mean age: 26.45, SD 5.63). The results showed that the quality of interaction between the child and the caregiver was related to the development of play behavior. In particular, security of attachment was related to better play behavior. When the developmental level of the child is taken into account, the attachment relationship of the child with the caregiver at this young age is a better predictor of the level of play behavior than the child's disorder.
Introduction
Play is important in the development of a child because it allows children to learn and practice new skills in safe and supportive conditions (Boucher 1999). In play children have the opportunity to develop not only motor skills but also cognitive and social skills (e.g. Pellegrini and Smith 1998).
Play shows developmental steps; cognitive development is reflected in, in order, manipulative, functional and symbolic or representational play. First children handle toys in oral and manipulative ways by feeling, licking, sniffing, turning them around, throwing them away, etc. This manipulation creates opportunities to learn about different objects, relations, and about ways to interact and influence the direct environment (Gibson 1988; Piaget 1962; Ruff 1984; Williams 2003). Functional play develops at approximately 14 months of age (Bretherton 1984), and is defined by Ungerer and Sigman (1981) as ‘the appropriate use of an object or the conventional association of two or more objects, such as a spoon to feed the doll, or placing a teacup on a saucer’. The child assigns a function to an object that it contains in daily life, even when an object is miniaturized. Around 24 months of age, symbolic play emerges, although it is difficult to define the point at which play becomes truly ‘symbolic’ (Jarrold et al. 1993). Symbolic play is considered a higher level of play, because it involves pretence, whereas pretence is not necessarily present in functional play.
The social part in play development starts with the step from the child’s playing by itself to noticing the play of others. This social aspect develops further by participating in the play of others, which creates the opportunity to deal with ‘interference’ of others and to develop cooperation skills. Play forms also the context for learning about trust, negotiation and compromise, and with these skills the child has the opportunity to form and maintain friendships (Jordan 2003).
The quality of the relationship with the parent may have an impact on motivational aspects of play behavior as well as on the quality of play. A secure relationship with a trusted attachment figure optimizes the opportunity for the child to explore the environment under safe and supportive conditions (Ainsworth 1978; Bowlby 1982). Indeed it has been found that children with secure attachment relationships display more sophisticated, complex and diverse play during interaction with their mother and during solitary play (e.g., Bornstein et al. 1996; Fiese 1990; Haight and Miller 1992; O’Connell and Bretherton 1984; Slade 1987; Tamis-LeMonda et al. 2002).
Play Behavior and Autism
One of the core deficits in autism is a severe deficit in social behavior. In children with Pervasive Developmental Disorders (PDD) this is demonstrated in play behavior at various levels. Deviations in play behavior can be detected in the first year of life (Ungerer and Sigman 1981; Van Berckelaer-Onnes 2003) and continue through all phases of play development.
The first phase of play development, which involves exploratory/manipulative behavior of objects, is in children with autism characterized by a number of unusual features. They tend to restrict their play to a limited selection of objects (Van Berckelaer-Onnes 2003), or even an isolated part of an object (Freeman et al. 1979). They prefer proximal senses of touch and taste above visual exploration (Williams 2003) and can become intensely preoccupied for long periods of time with non-variable visual examination of just one object (Freeman et al. 1979), or non-play (Ruff 1984), which impairs further development of play (Van Berckelaer-Onnes 2003).
Although several studies reported children with autism to produce the same number of functional acts under spontaneous as well as structured conditions (e.g. Baron-Cohen 1987; Van Berckelaer-Onnes 1994; Charman 1997; Lewis and Boucher 1988; Libby et al. 1998; Williams et al. 2001), it has also been found that children with autism spend significantly less time playing functionally than controls (Lewis and Boucher 1988; Jarrold et al. 1996; Sigman and Ungerer 1984), show lower levels of appropriate object use (Freeman et al. 1984), less variety in their functional play (Sigman and Ungerer 1984), more repetition (Atlas 1990; Williams et al. 2001) and fewer functional acts (e.g. Mundy et al. 1990; Sigman and Ungerer 1984; Ungerer and Sigman 1981).
Children with autism spectrum disorders (ASD) may experience particularly difficulties in symbolic play. When symbolic play is performed, their play behavior may be more like ‘learned routine’ rather than spontaneous play (Williams et al. 2001). The lack of this particular type of play in the behavioral repertoire of children with autism does however not necessarily imply a specific impairment in their symbolic abilities. It might reflect a more general cognitive or social deficit associated with autism impinging on the whole range of play development (Jarrold et al. 1993).
This Study
Play behavior in children with autism has been studied before, under various circumstances and on different levels. However, most studies involved subjects older than 42 months of age. The control groups in these studies were mainly subjects matched on mental age (MA), which created substantial differences in chronological age. In this study we investigated play behavior of children with and without ASD, but also of atypically and typically developing controls under the age of 36 months. Several domains of play behavior were analyzed to investigate differences in play behavior between clinical and non-clinical children, and between clinical children with and without ASD. Observing play behavior at this young age provides the opportunity to detect whether the basic play skills of children with ASD are disturbed, or whether the differences appear at a later age when higher levels of play are expected to be shown. We expected the ASD children to lag behind in their level of play behavior already from their first years of life.
Play behavior in children with ASD was also examined in relation to attachment quality. We expected that ASD children with secure attachment relationships would be more playfully engaged and socially involved compared to insecurely attached children with the same disorder. Furthermore, as disorganized attachment is the most insecure type of attachment, it was expected that disorganized children would show more delay in ‘social’ play behavior compared to children without disorganized attachment.
Method
Diagnostic Assessments
This study was part of a study on early screening for autism. For further details regarding recruiting see Dietz et al. (2006), and Swinkels et al. (2006). The children were recruited between the age of 14 and 36 months, based on social developmental delay, but final psychiatric diagnosis was obtained at 42 months. Psychiatric examinations included a series of six visits that were scheduled within a period of 5 weeks. At each weekly visit, the social and communicative behavior of the child was observed in a small group of young children and their parents. The assessments included a standardized parental interview, developmental history, and the Vineland Social-Emotional Early Childhood Scales (Sparrow et al. 1997); the Autism Diagnostic Interview—Revised (ADI—R; Lord et al. 1994); standardized behavior observation (Autism Diagnostic Observation Schedule ADOS-G; DiLavore et al. 2000), and pediatric examination and medical work-up. On the basis of all available information, and on the basis of clinical judgment, a diagnosis was given by an experienced child psychiatrist. The inter-rater reliability for two diagnostic categories; ASD or other than ASD was calculated. Agreement among three child psychiatrists (HvE, JB, ED) was reached in 92% of 38 cases. Agreement corrected for chance was 0.74 (Cohen’s Kappa). Agreement for all diagnostic categories was reached in 79% of 38 cases. Agreement corrected for chance was 0.67 (Cohen’s Kappa). Diagnostic discrepancies were resolved at a consensus meeting. If appropriate, children and their parents were offered “care as usual”.
The cognitive level of the child was measured with the Mullen Scales of Early Learning (Mullen 1995).
Diagnostic Groups
Forty-one clinical children participated in this study. At the age of 42 months they were classified with AD (n = 12; mean age 30.25 months (SD 4.81) and developmental level 51.17 (SD 4.06)), PDD-NOS (n = 11; mean age 27.73 months (SD 7.42) and developmental level 71.36 (SD 15.98)), MR (n = 10; mean age 26.50 months (SD 5.38) and developmental level 55.10 (SD 4.09)) or LD (n = 8; mean age 27.75 months (SD 5.68) and developmental level 83.63 (SD 8.48)). No difference in age was detected between the clinical groups, but developmental level was significantly different, F(3, 40) = 23.44, p < .01.
Besides the clinical diagnosis, the groups of children were also divided into the group ASD, including children with AD and PDD-NOS (n = 23, mean age 29.04 months (SD = 6.18) and a mean developmental level 60.83 (SD = 15.19)). The other group (non-ASD) contained children with the developmental disorders MR and LD (n = 18, mean age 27.06 months (SD = 5.38), developmental level 67.78 (SD = 15.85)). No differences in age and developmental level were detected between clinical children with and without ASD.
We included two control groups. One control group contained children who were referred to the hospital due to doubt about the development (AC (Atypical Controls), n = 16). Clinical investigation, however, showed that these children were free from clinical diagnoses. The other control group recruited through well-baby clinics contained typically developing children (NC (normal controls), n = 16). Based on parental reports and observations of the psychologists, these children were free from any child psychiatric disorder.
The group of children with atypical development was younger (AC; M = 20.50, SD = 3.03) compared to the typical developing control group (NC; M = 28.00, SD = 1.75), t = −8.57, p < .01. Although the children in the atypical control group were not showing severe developmental delays, their overall developmental level was lower (AC; M = 85.00, SD = 10.46) compared to the typically developing control group (NC; M = 98.44, SD = 12.18) t = −3.35, p < .01. However, no significant differences were detected in the play behavior of the both control groups.
Descriptive characteristics of the children are presented in Table 1.
Table 1Characteristics of the various diagnostic groupsADPDDNOSMRLDACNCM (SD)M (SD)M (SD)M (SD)M (SD)M (SD)Total12111081616Boy/Girl8/48/38/28/013/36/10Age30.25 (4.81)27.73 (7.42)26.50 (5.38)27.75 (5.68)20.50 (3.03)28.00 (1.75)Dev. level51.17 (4.06)71.36 (15.98)55.10 (4.09)83.63 (8.48)85.00 (10.46)98.44 (12.18)Aut. Symp.36.25 (5.99)21.55 (14.19)23.30 (9.86)7.38 (2.97)11.13 (6.85)Security of Attachment−1.96 (3.03)1.06 (3.34)−1.28 (3.57)2.19 (2.52)1.54 (2.32)2.17 (1.86)Disorg. of Attachment5.17 (2.95)2.82 (2.64)4.00 (3.04)2.00 (2.33)2.40 (2.41)1.50 (1.10)Attachment classification    Insecure-avoidant212000    Secure3627613    Insecure-ambivalent022023    Disorganized723120
Measures
Strange Situation Procedure
Ainsworth (Ainsworth et al. 1978) developed the SSP to observe the attachment behavior of the child towards the mother in a standardized and stressful laboratory setting. The SSP was coded by two trained observers (SS & MBK), who were blind for the diagnoses of the children. Agreement for the four attachment classifications (n = 28) corrected for chance was .74 (Cohen’s Kappa). Besides the attachment classifications, we also used the simplified Richters et al. (1988) algorithm to compute continuous scores for attachment security (Van IJzendoorn and Kroonenberg 1990) on the basis of the interactive SSP scale scores for proximity seeking, contact maintaining, resistance and avoidance. Disorganization was coded using the Main and Solomon (1990) 9-point rating scale for disorganized/disoriented attachment.
Play Behavior
Play behavior was observed according to protocol. The child and the mother were left in the room for ten minutes. The mother was instructed not to stimulate the child to play but to join when the child asked for cooperation in this free play situation. All children received the same set of toys.
Videotapes were coded by means of ‘The Observer’ (Noldus 1991) using an ethogram developed to analyze the play behavior of 2-year-old children, who cannot speak or speak very little. The behaviors mouthing, manipulative play, exploration, functional play, representational play 1 and 2, container play, grouping and stacking 1 and 2 were defined by Largo and Howard (1979). Doll directed play and relational play were derived from Williams et al. (2001). Fenson et al. (1976) provided descriptions of the behaviors symbolic acts and banging. From Leslie’s (1987) definitions of symbolic play, only subject substitution was used.
The various play behaviors were categorized in manipulative, functional or symbolic play, and these variables were used in the analyses. The amount of time that a child spent actually playing was calculated as the percentage of time the child played of the total time of the play session. The amounts of time the child performed manipulative, functional and/or symbolic play, were calculated as percentages of the time spent playing. When the child did not play, it would typically show other behaviors such as sitting passively. Reliability among the three coders for play behavior was based on 50% of the videotapes. Agreement was reached in 92% of 38 cases. Mean agreement corrected for chance was 0.74 (Cohen’s Kappa).
The variable ‘level of play’ was calculated based on the three different levels of play behavior; manipulative, functional and symbolic play. Durations of the three kinds of play behavior were included in the calculation with the following formula: ((1 x duration of manipulative play) + (2 x duration of functional play) + (3 x duration of symbolic play)) / total duration of play. Differential weights were thus assigned to the social and cognitive levels of play. The variable ‘change toys’ was based on the frequency per minute the child initiated play with another toy. The preference of toys was measured by calculating which toys were preferred most during play.
Correlations between play behavior, child characteristics and attachment related variables are presented in Table 2.
Table 2Correlations between play, characteristics of children and attachment related variablesAge Developmental levelAttachment security Attachment disorganizationManipulative playFunctional playSymbolic playLevel of playDuration of playNumber of autistic characteristics.23−.66**−.47**−.41**.28*−.20−.19−.23−.08Age in months–−.21.01−.00−.02.06.03.08.07Developmental level–.43**−.44**−.33**.30*.21.35**.19Attachment security–−.49**−.00.08.09.14.14Attachment disorganization–.05−.23−.09−.30*−.30*Manipulative play–−.59*−.15−.38**.08Functional play–−.20.78**.66**Symbolic play–.37**.10Level of play–.87***Correlation is significant at the .05 level**Correlation is significant at the .01 level
Analyses
Play Behavior and Clinical Diagnoses
No gender differences were found for the variables ‘duration of play’, ‘manipulative’, ‘functional’ and ‘symbolic’ play, the ‘overall level of play’ and ‘change toys’. Analyses started with an overall analysis (ANOVA) for all groups, taking differences in developmental level and age into account. Next, differences between the clinical groups with and without ASD were analysed. Preference for toys was investigated using ANOVA for the whole sample, taking differences in developmental level and age into account, and for clinical children with and without ASD.
Contribution of Attachment
To examine whether security of attachment and attachment disorganization contributed to differences in play behavior and preference for toys, analyses with attachment were performed overall, in clinical children and in the group of children with ASD. Play behaviors and preference of toys of children with and without secure attachment, and children with and without disorganized attachment were analyzed.
Results
Duration of Play Behavior
No differences were detected for duration of play time after controlling for difference in age and developmental level (F(5, 72) = 1.87, p = .11). Also, no differences were detected between clinical children with and without ASD (t = −1.09, p = .29). Mean values for the play variables are presented in Table 3.
Table 3Duration, level and type of play, and change of toys of the various diagnostic groupsDuration of play (%time)Level of playChanging toys (freq/min)nM (SD)M (SD)M (SD)AD 1257.32 (22.24)15.66 (7.29).04 (.01)PDDNOS 1168.19 (12.61)20.00 (5.05).05 (.02)MR 1070.20 (10.73)18.47 (5.03).05 (.02)LD 866.38 (22.45)20.96 (8.74).04 (.02)AC 1669.50 (15.03)21.47 (5.89).05 (.02)NC 1677.19 (15.67)24.00 (5.34).05 (.02)Manipulative playFunctional playSymbolic playnM (SD)M (SD)M (SD)AD 1222.86 (20.68)32.28 (20.62)2.19 (7.35)PDDNOS 1124.90 (18.32)34.74 (19.55)8.54 (12.43)MR 1032.38 (18.65)35.01 (21.32)2.81 (5.08)LD 817.91 (10.05)37.53 (26.57)10.93 (10.99)AC 1614.66 (9.33)50.36 (20.48)4.49 (7.45)NC 1615.62 (14.95)56.33 (21.61)5.25 (7.55)
Manipulative, Functional and Symbolic Play
Overall analysis showed no differences for percentage of time actually spent on manipulative (F(5, 72) = 1.00, p = .43), functional (F(5, 72) = 2.08, p = .08), or symbolic play (F(5, 72) = 1.32, p = .27) when differences in age and developmental level were taken into account. Mean values of the percentage of time for the three different forms of play are presented in Table 3. Neither were differences detected for manipulative play between clinical children with and without ASD (t = −.38, p = .71), for functional play between clinical children with and without ASD (t = −.39, p = .70), and for symbolic play between clinical children with and without ASD (t = −.39, p = .70), when differences in age and developmental level were taken into account. Mean values of the percentage of time for the three different forms of play for clinical children with and without ASD are presented in Table 4.
Table 4Play behaviors of children with and without ASDDuration of play (%time)Level of playChanging toys (freq/min)nM (SD)M (SD)M (SD)ASD 2362.52 (18.72)17.74 (6.56).04 (.02)Non-ASD 1868.50 (16.50)19.58 (6.82).04 (.02)Manipulative play (%time)Functional play (%time)Symbolic play (%time)nM (SD)M (SD)M (SD)ASD 2323.84 (19.17)33.46 (19.96)5.23 (10.38)Non-ASD 1825.95 (16.75)36.13 (23.09)6.42 (8.98)
Level of Play
Level of play, taking differences in developmental level and age into account, did not show any differences between the different groups (F(5, 72) = 1.20, p = .32). Mean values of the level of play are presented in Table 3. Moreover, no differences were detected between clinical children with and without ASD either (t = −.87, p = .39), see Table 4.
Preference for Toys and Change of toys
Analyses were performed for the duration of play that the children were involved with the toys ‘car’, ‘doll’, ‘puzzle’, ‘daily utensils’, bricks’, ‘book’ and ‘ball’, to analyze whether there were any differences in preference for toys. Again, differences in age and developmental level were taken into account. An overall difference was found for playing with ‘daily utensils’ F(5, 72) = 2.53, p = .04. Children with AD spent significant less time playing with daily utensils. Overall differences were also found for reading a book, F(5, 72) = 2.61, p = .03 and playing with a puzzle F(5, 72) = 3.11, p = .01. No differences were detected between children with and without ASD for the time spent playing with daily utensils and playing with a puzzle. However, children with ASD spent significantly less time (M = 2.12, SD = 4.60) reading a book compared to clinical children without ASD (M = 13.11, SD = 17.62), t = −2.88, p < .01.
No differences were detected for changing toys in the overall group, taking differences in age and developmental level into account F(5, 72) = .47, p = .80. Mean values of the frequency of changing toys are presented in Table 3. There were also no differences between the clinical children with and without ASD (t = .37, p = .72), see Table 4.
Quality of Attachment and Play
Children with a secure attachment showed higher levels of play (M = 21.74, SD = 6.32) compared to children without secure attachment (M = 18.24, SD = 6.61), t = −2.18, p = .03. However, because of the higher percentages of secure attachment in children without a clinical disorder, the difference was also tested in the group of children with clinical diagnoses. In the clinical group children with secure attachment (M = 20.88, SD = 7.02) showed significantly higher levels of play than children without a secure attachment relationship (M = 16.35, SD = 5.73) as well, t = −2.20, p = .04.
Children with ASD who were securely attached spent more time playing symbolic play compared to children with ASD without a secure attachment (t = −2.37, p = .03). Level of play was also higher in children with ASD with a secure attachment relationship compared to children with ASD without a secure attachment relationship (t = −3.27, p < .01) (Fig. 1). Moreover, children with ASD with a secure attachment relationship spent more time actually playing compared to children with ASD without a secure attachment relationship (t = −2.74, p = .01). The differences within the group of children with ASD with and without secure attachment relationships remained significant after taking differences in age and developmental level into account; for symbolic play F (1, 23) = 4.47, p = .05, for level of play F (1, 23) = 8.88, p < .01, and for duration of play F (1, 23) = 7.19, p = .01, see Table 5.
Fig. 1Secure and disorganized attachment and mean level of play in children with ASDTable 5Play behaviors of children with ASD with and without secure and disorganized attachmentDuration of play (%time)Level of playChanging toys (freq/min)nM (SD)M (SD)M (SD)ASD-nonB 1454.99 (20.16)a*14.78 (5.65)a*.04 (.02)ASD-B 974.22 (7.09)a*22.33 (5.25)a*.05 (.02)ASD-nonD 1470.74 (12.46)b*20.24 (5.38)b* .05 (.02)ASD-D 949.73 (20.22)b*13.84 (6.58)b*.04 (.01)Manipulative playFunctional playSymbolic playnM (SD)M (SD)M (SD)ASD-nonB 1422.77 (18.59)30.74 (19.82)1.48 (4.40)a**ASD-B 925.50 (21.08)37.67 (19.87)11.05 (14.22)a**ASD-nonD 1427.40 (21.07)35.98 (16.78)7.36 (12.31)ASD-D 918.30 (15.25)29.53 (24.09)1.90 (5.44)aASD-B versus ASD non-BbASD-D versus ASD non-D*p < .05, **p < .01
Disorganized Attachment Relationship and Play
Children with a disorganized attachment classification showed lower levels of play (M = 16.39, SD = 6.83) than children without a disorganized attachment classification (M = 21.32, SD = 6.20), t = 2.51, p = .02. Within the clinical group no difference was shown for level of play of children with and without disorganized attachment. However, children without a disorganized attachment relationship spent more time playing (M = 69.29, SD = 15.67) compared to children with a disorganized attachment classification (M = 55.50, SD = 19.28) t = 2.25, p = .04.
Children with ASD with disorganized attachment showed lower levels of playing than children with ASD without disorganized attachment (t = 2.44, p = .03), see Fig. 1. This difference remained significant after taking differences in age and developmental level into account, F (1, 23) = 5.29, p = .03. Moreover, children with ASD without disorganized attachment spent more time playing compared to children with ASD with disorganized attachment (t = 11.94, p = .02). Again, the difference remained significant after taking differences in age and developmental level into account, F (1, 23) = 9.40, p < .01 (see Table 5).
Children with and without secure or disorganized attachment relationships did not differ on preference for toys.
Discussion
Our findings highlight the importance of attachment in the development of play of children with autism and other developmental disorders. Attachment quality explained play behavior regardless of the clinical status of the children. Taking developmental level of the child into account, we found that children with a secure attachment relationship spent more time playing. They also showed a higher level of play and more symbolic play behavior. Children with a disorganized attachment relationship spent less time playing, and within the group of children with ASD disorganized attachment was related to lower levels of play.
Our earliest understanding about the world and our own actions may have a social rather that a cognitive origin (Hobson 2002; Jordan 2003; Vygotsky 1978). Social deficits belong to the core deficits of children with autism. Nevertheless, children with autism are able to develop a secure attachment relationship with the primary caregiver (Naber et al. 2007; Rutgers et al. 2004), which contributes to better play outcomes in children with autism. We indeed found that children with secure attachment relationships showed more exploration and higher levels of play, whereas children with disorganized attachment showed less exploration, even after controlling for developmental level. Especially in children with autism the quality of attachment relationship was associated with the development of ‘social’ play.
Unexpectedly, for duration of play, level of play or changing toys no differences were detected between children with and without ASD, or even between children with and without a developmental disorder, after controlling for developmental level. Nevertheless, similar to Williams et al. (2001), we found that children with autism preferred toys that were based on ‘simpler’ play behavior. Children with autism did not prefer daily utensils or books to play with.
Several explanations may account for the absence of a difference in most play variables. First, we used a free play situation with the mother. Although the parents were instructed only to follow the child leads and not to structure the setting, the presence of the parent may have motivated the child to continue playing. Second, the lower levels of play behavior typical for this young age period may still be within the reach of children with autism. At a later age, when the children are expected to show symbolic play behavior at higher levels, differences in the amount or quality of symbolic play may emerge. Third, play behavior in children with autism may show delays compared to other children (Beyer and Gammeltoft 2000; Howlin 1986; Lord 1984; Lord and Magill 1989; Wolfberg 1999), but as Jarrold (2003) pointed out, the absence of pretend/symbolic play in children with autism may result from assessing individuals who are (mentally) too young to be expected to show pretend/symbolic play. In the current young age group few children showed much symbolic play, and no difference emerged. Fourth, we used ethological measures to observe play behavior in an objective manner, but the context and coherence or patterning of the behavior was not taken into account. A more holistic approach with global ratings of play behavior may uncover differences between children with and without autism.
Limitations of the Study
Although studies of play behavior are required at an early age to get more insight into its development, the young age of the children is also a limitation because symbolic play was not yet in reach of many subjects in the current study. However, retrospective parental reports and screening studies mention differences in play behavior already at this young age. Longitudinal studies are needed to follow the development of these children’s play across time to see whether differences in play behavior arise at a later stage, and to examine whether the effects of a positive attachment relationship are lasting.
Conclusions and Future Research
As pointed out in a review of Jarrold et al. (1993), studies that indicate a lack of symbolic play behavior in children with autism cannot be seen as convincing proof of the inability of the child to produce this type of play, because MA matched controls would be needed. In our study, we matched the clinical groups both on chronological and on MA. Due to this matching we were able to compare play behavior of children with and without autism. We found no differences in play between children with and without ASD. This may be due to the mental and chronological age of the toddlers included in our study; the lower levels of play behavior typical for this age period may still be within the reach of children with autism. At a later age, children with autism may start to lag behind which might be shown in delayed and infrequent occurrence of symbolic play. We hope to follow-up the current sample to test this interpretation.
What we did find were striking differences between children with and without secure or disorganized attachment relationships. The quality of the parent-child relationship appears to contribute substantially to the development of play in young children regardless of their autistic symptoms. Intervention studies based on play behavior have shown to positively contribute to the development of play in children with ASD during the intervention period. However, no long-term effects have been documented. Our findings show the importance of attachment and suggest that interventions focusing on the improvement of play behavior of children with autism should also focus on enhancing the quality of the attachment relationship. The early intervention study with PDD children by Mahoney and Perales (2005) explored this approach in stimulating cognitive, communicative and socio-emotional functioning. Attachment-based video-feedback intervention has been proven to be effective in typically developing children (Bakermans-Kranenburg et al. 2003; Juffer et al. 2007) and this approach might not only in the short run but also long-term lead to improvement of quality of attachment as well as level of play behavior in children with autism.

play behavior
attachment
autism
developmental disorder


Higher Sensitivity of Human Auditory Nerve Fibers to Positive Electrical Currents

Most contemporary cochlear implants (CIs) stimulate the auditory nerve with trains of amplitude-modulated, symmetric biphasic pulses. Although both polarities of a pulse can depolarize the nerve fibers and generate action potentials, it remains unknown which of the two (positive or negative) phases has the stronger effect. Understanding the effects of pulse polarity will help to optimize the stimulation protocols and to deliver the most relevant information to the implant listeners. Animal experiments have shown that cathodic (negative) current flows are more effective than anodic (positive) ones in eliciting neural responses, and this finding has motivated the development of novel speech-processing algorithms. In this study, we show electrophysiologically and psychophysically that the human auditory system exhibits the opposite pattern, being more sensitive to anodic stimulation. We measured electrically evoked compound action potentials in CI listeners for phase-separated pulses, allowing us to tease out the responses to each of the two opposite-polarity phases. At an equal stimulus level, the anodic phase yielded the larger response. Furthermore, a measure of psychophysical masking patterns revealed that this polarity difference was still present at higher levels of the auditory system and was therefore not solely due to antidromic propagation of the neural response. This finding may relate to a particular orientation of the nerve fibers relative to the electrode or to a substantial degeneration and demyelination of the peripheral processes. Potential applications to improve CI speech-processing strategies are discussed.
Introduction
Polarity sensitivity of auditory nerve fibers has been extensively studied in animal and computational models using both monophasic and biphasic stimuli. Responses to monophasic cathodic pulses show, in general, lower thresholds and longer latencies than anodic responses (Miller et al. 1998, 1999a, 2004). Similarly, the most effective phase of sinusoidal stimuli was shown to be the cathodic phase in single-fiber recordings from the cat (Hartmann et al. 1984). However, polarity sensitivity may also depend on the electrode position relative to the fibers and on the nerve morphology (Ranck 1975; McIntyre and Grill 1999; Rubinstein 1993; Rattay 1999; Rattay et al. 2001a). If so, then, the findings obtained in the vast majority of animal experiments may not apply to human cochlear implant (CI) listeners (Miller et al. 1998).
In human CI stimulation, charge-balanced stimuli are required to prevent damaging electrochemical reactions (Brummer and Turner 1977). Consequently, the effect of absolute polarity, which is usually studied using monophasic pulses, remains largely unknown in humans. Asymmetric pulses, where the second phase is lower in amplitude and longer in duration than the first, have provided a way to study polarity sensitivity in CI users. In such pulses, the contribution of the short, high-amplitude phase is increased at the expense of the long, low-amplitude one while maintaining the charge-balancing requirement. We recently showed that inverting the polarity of an asymmetric pulse stimulus yielded differences in most comfortable levels (MCLs) of CI users, giving lower MCLs when the short, high-amplitude phase was anodic than when it was cathodic (Macherey et al. 2006). Because short pulses require less charge to evoke the same loudness as longer ones (at least for phase durations less than approximately 500 μs; Moon et al. 1993; Macherey et al. 2006), it is possible that loudness was determined primarily by the higher-amplitude phase of our asymmetric pulses. This result, then, suggests that the human auditory nerve might be more sensitive to anodic stimulation—the opposite to what one expects from animal measures of auditory-nerve activity (Hartmann et al. 1984; Miller et al. 1998, 1999a, 2004). The following series of experiments directly test this hypothesis in eight CI users, using both electrophysiological and psychophysical measures. The experimental design relies on the characteristics of the auditory nerve fibers which, after a stimulation pulse, undergo a period of absolute refractoriness, lasting approximately 400 μs, followed by a progressive recovery (Stypulkowski and van den Honert 1984; Miller et al. 2001; Morsnowski et al. 2006). Experiment 1 provides an insight into the morphology of anodic and cathodic phase responses. Experiments 2 and 3 measure the masking effectiveness of each individual polarity phase using electrophysiological and psychophysical masking, respectively.
General methods
Subjects
Eight users of the CII or HiRes 90k CIs, manufactured by Advanced Bionics, participated in this series of electrophysiological (experiment 1 and 2) and psychophysical (experiment 3) experiments. The subjects’ biographical data are shown in Table 1. Testing was approved by the Cambridge and Leuven Local Research Ethics Committees and informed consent was obtained from all subjects.
TABLE 1Biographical data of the subjects, including age, duration of deafness (DD) and duration of CI use (CI), in years and etiologySubjectAge (years)DD (years)CI (years)EtiologyS15544GeneticS25113MeningitisS370<23Unknown suddenS43127Unknown progressiveS566>61Unknown progressiveS66032Unknown progressiveS761183UnknownS85214Meningitis
Electrically evoked compound action potential recordings and psychophysical testing
Most contemporary CI systems allow the direct recording of the auditory nerve response, known as the electrically evoked compound action potential (ECAP). This is a noninvasive technique that consists of recording the intracochlear potential evolution after a stimulation pulse. To minimize electrical artifacts, we used the “masker–probe paradigm” (Brown et al. 1990), which relies on the fact that the neural response to a “probe” pulse is reduced by an immediately preceding “masker.” By presenting the probe and masker either alone or in combination, it is possible to derive the neural response to the probe with the artifact “subtracted out” (cf. Fig. 1). In experiments 1 and 2, ECAPs were measured using several combinations of masker and probe. In all cases, masker and probe were presented in monopolar mode, on an electrode located in the middle of the array (electrode 9) with reference to the case (extracochlear) electrode of the implant. The responses were recorded using electrode 7 (approximately 2 mm apical to electrode 9) with reference to the ring ground electrode for S5 and the case electrode for all other subjects and were averaged over 128 sweeps. In experiment 3, psychophysical masking patterns were measured using the APEX experimental platform and the BEDCS software (cf. Macherey et al. 2006; Laneau et al. 2005). Masker and probe were also presented in monopolar mode, the masker on electrode 9 and the probe on an adjacent electrode (8 or 10).
FIG. 1.Description of the masker–probe paradigm for artifact cancellation (Brown et al. 1990). Four traces are recorded: masker alone (1), masker and probe (2), probe alone (3), and zero stimulation (4) to correct for any DC offset. This method is based on the assumption that the nerve fibers are in their absolute refractory state when the probe is presented after the masker (2). The subtraction (1) + (3) −(2) − (4) therefore yields the probe neural response with the artifact removed.
Experiments
Experiment 1: ECAP responses to anodic and cathodic phases
Rationale and methods
In experiment 1, we recorded ECAPs from seven subjects (S1–S7). The masker was a cathodic-first, symmetric biphasic pulse with a 32-μs phase duration presented at a comfortable level, determined subjectively for each subject before testing. The probes were either anodic-first (Fig. 2A) or cathodic-first (Fig. 2B) asymmetric waveforms with a phase separation of 2.9 ms. The first (short) phase of the pulse was 32-μs long, and the second (long) phase was eight times longer and lower in amplitude. The 2.9-ms phase separation present in the probe allowed us to measure the neural response to the first phase independently of the second. The two probes had the same level (between most comfortable and comfortably loud) and were each presented 500 μs after the end of the masker. Additionally, to differentiate neural responses from imperfectly cancelled stimulation artifacts, we recorded, in four of the subjects (S3–S6), the responses to several probe levels, spanning the entire electrical dynamic range.
FIG. 2.ECAP responses to anodic and cathodic phases. A Masker (thick line) and probe (thin line) used to measure the anodic phase response. B Masker and probe used to measure the cathodic-phase response. C and D ECAPs evoked by the first phases of anodic-first (C) and cathodic-first (D) phase-separated asymmetric probes. The time reference corresponds to the onset of the probe. The second phase is not displayed as it occurs 2.9 ms after the first one. The top panels show an example of a level series obtained with subject S5. The different curves correspond to probe levels ranging from a soft (320 μA) to a comfortably loud level (576 μA) by steps of 64 μA, with the size of the fluctuations increasing monotonically with increasing current level. The other panels show the responses to the highest probe levels for all the other subjects. The N1 peak of the anodic response occurs, on average, 290 μs after the onset of the probe.
Results
An example of a level series is shown in the top panels of Figure 2C (anodic phase) and D (cathodic phase) for S5. The first narrow, short-latency peak observed for both anodic-first and cathodic-first probes almost certainly reflects a residual artifact because, for most subjects (with the exception of S2 and S6), its polarity is dependent on the probe polarity (positive and negative for anodic-first and cathodic-first probes, respectively), and its latency is similar for the two probes. For all subjects, the response to the anodic phase shows a biphasic shape with a first-negative peak N1 followed by a less robust positive peak P2. Only one (S4) of the seven subjects showed a measurable response to the cathodic phase, and only at the highest level of the dynamic range. For this subject, the latency of the response was longer for the cathodic-first (420 μs) than for the anodic-first (270 μs) probe, consistent with reports of animal auditory excitation (Miller et al. 1998, 1999a, 2004). Most of these animal data were, however, obtained using a recording electrode located at the nerve trunk, whereas our data reflect intracochlear recordings. The position of the recording electrode may have an important influence on the response latencies, and we will further consider this parameter in “Discussion.” For now, it is worth noting that the major discrepancy with common animal data was that the amplitude of the response was, in our case, always greater for the anodic phase.
Experiment 2: masking effectiveness of anodic and cathodic phases—ECAP measures
Rationale and methods
The reliability of the masker–probe paradigm strongly depends on the effectiveness of the masker, as the neural response obtained represents the subtraction of the masked response from the unmasked one. Instead of reflecting different neural responses, the anodic/cathodic amplitude difference obtained in experiment 1 may relate to the fact that the biphasic masker did not effectively mask the cathodic phase. This would be expected if, for example, the excitation sites for the biphasic masker and the cathodic probe were the central axon and the peripheral process, respectively. The peripheral process may, in such a case, not be driven in its refractory state by the masker, and the masker–probe paradigm would consequently subtract out the neural response. We performed, in experiment 2, another set of ECAP measures in six subjects (S1–S6) to verify that the anodic phase was also a more effective masker than the cathodic phase. Unlike the previous experiment, we introduced a 2.9-ms phase separation in the symmetric biphasic masker and assumed that the probe would be mainly masked by the masker’s second phase (denoted as the masking phase). We recorded the responses to anodic-first and cathodic-first symmetric biphasic probes with no phase separation in the two conditions, “anodic masking” (Fig. 3A) and “cathodic masking” (Fig. 3B) phase, and also in a third “biphasic masking” (Fig. 3C) condition. This last condition, where the masker was a cathodic-first, symmetric biphasic pulse with no phase separation, was performed for two reasons. First, because this protocol is routinely used in clinics, the morphology of the resulting ECAP response is well documented. It usually consists of an N1–P2 complex (Brown et al. 1990), similar to the response obtained in experiment 1 for the anodic phase. Second, it allowed us to compare the latencies of the probes’ responses. If the anodic phase is the most excitatory phase of a biphasic pulse, we would expect the response to show a longer latency when the anodic phase is the second phase of the probe than when it is the first.
FIG. 3.Combinations of maskers (thick lines) and probes (thin lines) used in experiment 2. A Anodic masking condition. B Cathodic masking condition. C Biphasic masking condition. For each masking condition, two different symmetric biphasic probes were used: an anodic-first and a cathodic-first.
Results
For all masking conditions, a large uncancelled artifact was present at the probe onsets. Figure 4 shows examples of these artifacts obtained with S5 using biphasic cathodic-first (A) and anodic-first (B) probes in the anodic masking condition. The size of this artifact did not depend on the probe level and was still present at subthreshold levels (thick lines of Fig. 4). For means of clarity and because this uncancelled artifact was clearly distinct from the neural response, subsequent figures will only show the neural response part of the recordings. Note that an advantage of the present approach is that it compares the effects of different maskers, whereas the artifact occurred near the onset of the probe.
FIG. 4.Examples of uncancelled artifacts obtained in experiment 2 for S5. ECAP responses to cathodic-first (A) and anodic-first (B) biphasic probes in the anodic-masking condition at subthreshold (thick line) and suprathreshold (thin line) levels.
Figure 5A shows the anodic-first (thick line) and cathodic-first (thin line) probe responses in the biphasic masking condition for S1. The response latency of the N1 peak was shorter for the anodic-first probe, consistent with the neural activation being elicited primarily by the anodic phase. All subjects demonstrated this trend with a latency difference ranging from 0 to 80 μs (Fig. 5B), opposite to results obtained in the guinea pig (Klop et al. 2004). A t test performed on the data from the six subjects revealed that this difference was significant (df = 5, p = 0.011). Apart from this latency difference, the responses to anodic-first and cathodic-first probes had similar amplitudes. To further improve artifact cancellation, the two responses were averaged for data analysis.
FIG. 5.Masking effectiveness of anodic and cathodic phases. A Example of ECAP recordings obtained with subject S1 for an anodic-first (thick line) and a cathodic-first (thin line) biphasic probe in the biphasic masking condition. The response latency is shorter for the anodic-first probe. B Latency of the cathodic-first probe response vs. latency of the anodic-first probe response. For each subject, the two probes were presented at an identical level (comfortably loud) and also at the same level as the biphasic masker. C, D, and E Example of ECAPs evoked by a symmetric biphasic probe with no phase separation in the three masker conditions: anodic masking (C), cathodic masking (D), and biphasic masking (E) for subject S1. Each trace corresponds to the average of the anodic-first and cathodic-first biphasic probe responses. In the cathodic masking condition, the neural response was sometimes not clearly measurable. In D, for example, there is no excursion of the trace in the negative portion of the graph. Nevertheless, the recording still shows a voltage increase in the same region where the probe neural response occurs. The amplitude of the probe response was, in this case, assumed to be the magnitude of this increase. F Summary of ECAP amplitudes obtained in the anodic-masking (black bars), cathodic-masking (white bars), and biphasic-masking (gray bars) conditions.
An example of the averaged responses of S1 is illustrated in Figure 5 for anodic masking (C), cathodic masking (D), and biphasic masking (E). For the six subjects tested, the amplitude of the response (difference between N1 and P2) was greater when the masking phase was anodic than when it was cathodic (Fig. 5F). This confirmed the previous finding that the anodic phase is more effective in driving auditory nerve fibers in their refractory state. Furthermore, the probe responses in the anodic masking and biphasic masking conditions were strikingly similar, suggesting that the anodic phase is the effective phase of a symmetric biphasic pulse. This last observation also argues strongly against the responses being influenced by an uncancelled masker artifact. Note that, although the anodic and cathodic maskers had the same level, and differed only in their leading polarity, the level of the biphasic masker was presented, on average, 2.4 dB higher to have a loudness similar to that of the other two maskers. This increase in level reflects the influence of phase separation on loudness reported in previous publications (McKay and Henshall 2003; Carlyon et al. 2005; van Wieringen et al. 2005; Macherey et al. 2006). For S2 and S6, the amplitude is negative in the cathodic masking case because the subjects exhibited an inverted response (with a positive peak). This may mean that the cathodic phase acts as a facilitator and not as a masker for these specific subjects, possibly because of residual potential summation (Miller et al. 1997; De Balthasar et al. 2003).
Experiment 3: masking effectiveness of anodic and cathodic phases—psychophysical measures
Rationale and methods
Because ECAPs in CIs can only be measured intracochlearly and not directly on the nerve trunk, the recordings potentially contain the neural response traveling not only toward the central auditory system (orthodromically) but also in the opposite direction, toward the fibers’ periphery (antidromically; Miller et al. 2004). It is possible that this phenomenon occurred and that the larger ECAP amplitude of the anodic response obtained in experiment 1 did not reflect a larger response propagating toward the central auditory system (Cartee et al. 2005). Although the results of experiment 2 argue against this hypothesis, a perhaps more direct way to tease out this effect is to perform psychophysical measures, which require perceptual judgments from the subjects, thereby reflecting neural responses propagating centrally. The aim of experiment 3 was to compare the masking effectiveness of each polarity phase psychophysically and also to measure the most effective phase of an asymmetric pulse: Is it the short, high-amplitude or the long, low-amplitude one? We used two asymmetric pulse–train stimuli (anodic- and cathodic-first) with a 6.4-ms phase separation as maskers of a biphasic pulse–train probe presented 1 ms either after the first or after the second phase of the masker (Fig. 6A). Contrary to experiments 1 and 2 where we were constrained by the limited duration of the recording window, we used a longer phase separation to minimize refractory effects of one phase of the masker over the other. The masker was a 76-pulse per second pulse train, either anodic-first (conditions 1a and b) or cathodic-first (conditions 2a and b) with a short phase of 22 μs and a long phase eight times longer and lower in amplitude. Masker and probe had a total duration of 400 ms and were temporally interleaved, the masker being presented on electrode 9 and the probe on electrode 8, or, in an additional set of measures, on electrode 10. Four subjects (S1–S3 and S8) were asked to detect the anodic-first biphasic probe in the presence of one of the two maskers, in a three-interval, two-alternative forced-choice task where only one interval contained the probe. Feedback was provided and the level of the probe was adaptively varied in a two-down, one-up adaptive procedure that stopped after eight reversals. For subjects S1–S3, the step size was 32 μA for the first two and 4 μA for the last six reversals. For subject S8, the phase duration of the biphasic probe had to be increased to 43 μs to be able to reach a comfortably loud level, and the step sizes were 64 and 8 μA, respectively, for the first two and the last six reversals. Each estimate was the average value of the last six reversals. We also performed the same four measures with the polarity of the probe reversed (cathodic-first instead of anodic-first). We did this because, in a previous study (Macherey et al. 2006), we reported the presence of a low-amplitude current flow between the two phases of stimuli with relatively long phase separations (similar to the separation used here). The polarity of this current flow is opposite to the polarity of the preceding stimulus phase and is due to the passive discharge of the implant’s DC-blocking capacitors. This current may influence the detection of the probe by predepolarizing or prehyperpolarizing the neural membrane after each phase. The results obtained in this condition were similar to the ones obtained with the initial stimulus polarity and will therefore not be shown here.
FIG. 6.Psychophysical masking patterns. A Description of the four experimental conditions. Anodic-first asymmetric masker (thick line) with a phase separation of 6.4 ms followed by a biphasic probe presented 1 ms after the end of the short, high-amplitude phase (1a) or 1 ms after the end of the long, low-amplitude phase (1b) of the masker. Conditions 2a and 2b are identical to 1a and 1b except that the masker is reversed in polarity. B Difference between masked and unmasked threshold for the four conditions (1a, 1b, 2a, and 2b) and the four subjects (S1–S3 and S8) for a probe on electrode 8. The probe dynamic ranges were 6.5, 4.2, 3.9, and 11.1 dB for S1, S2, S3, and S11, respectively. C Same as B with the probe on electrode 10.
Results
The four CI subjects (S1–S3, S8) were first asked to balance in loudness the two opposite-polarity maskers at their MCL following a procedure described in Macherey et al. (2006). On average, the level of the cathodic masker was adjusted 1.6-dB higher than the anodic one, consistent with the anodic-first pulse being more effective than the cathodic-first one. These levels were used in the following part of the experiment where the subjects had to detect the presence of the biphasic probe in each of the four conditions and also in the unmasked case. Figure 6B shows the threshold shifts (difference between masked and unmasked thresholds) obtained with the probe on electrode 8. For an anodic masker, the short, high-amplitude (anodic) phase produced more masking than the long, low-amplitude (cathodic) phase for all subjects (conditions 1a vs. 1b). No clear pattern was, however, obtained in the case of a cathodic masker (2a vs. 2b), showing across-subject variability. More interestingly, although presented at a lower level, the short, high-amplitude anodic phase (1a) was a more effective masker than the short, high-amplitude cathodic phase (2a). Similar results were obtained with the probe on electrode 10 (Fig. 6C). These observations were corroborated by a two-way repeated measures ANOVA performed on the mean data of all subjects. The treatment factors were the probe channel (8 or 10) and the probe condition. First, there was a significant effect of the probe condition (F(3,9) = 5.01, p = 0.026) but no effect of the probe channel (F(1,3) = 0.52, p = 0.52) or of the interaction factor (F(2.91,8.73) = 0.22, p = 0.88). Second, pairwise comparisons revealed that the short, high-amplitude anodic phase was a better masker than the long, low-amplitude cathodic phase (1a > 1b, p = 0.037) and also than the short, high-amplitude cathodic phase (1a > 2a, p = 0.008). No significant difference was, however, found between the short cathodic and the long anodic phases (p > 0.4). These results demonstrate that the larger ECAP response previously measured for anodic stimulation was not solely caused by antidromic propagation and strengthens the finding that auditory nerve fibers are more sensitive to anodic stimuli.
Discussion
Main findings
We have reported the results of three electrophysiological and psychophysical masking experiments designed to compare the responses of the human auditory nerve to anodic and cathodic current flows. In experiment 1, the anodic phase produced the usual N1-P2 ECAP morphology for all subjects, whereas the cathodic phase showed no measurable response except for one subject. This observation led us to conclude that the anodic phase was more effective than the cathodic phase. There are, however, at least three alternative explanations for this trend.
First, the cathodic phase response of experiment 1 may have had a shorter latency than the anodic phase response and may have consequently been mixed within the uncancelled stimulus artifact. Although this hypothesis is not supported by animal data, usually showing longer latencies for cathodic stimuli (Miller et al. 1998, 1999a, 2004), a potentially important confounding variable is the location of the recording electrode. Most animal ECAP data are recorded using an electrode positioned on the nerve trunk. Consequently, the shorter the response latency, the more central the excitation site is. This may, however, not be the case for intracochlear recordings where the recording and the stimulating electrodes are close to each other (Miller et al. 2004). Here, we used a recording electrode 2 mm apical to the stimulating one, similar to what is used clinically, and this choice may have affected the morphology and the latency of the ECAP responses. We note that our second experiment controlled for any possible effects of response latency to the probe by studying the effects of masker polarity for a fixed probe and by showing that, although the leading polarity of a biphasic probe did affect the response latency, it was always the case that responses were larger with an anodic than with a cathodic masker.
Second, as previously stated, the response to the anodic phase obtained in experiment 1 may not only represent the neural response traveling toward the central auditory system. As the recording electrode is located at the periphery of the nerve fibers, the recorded response possibly contains the antidromic response of the fibers. Although this is possible, this would necessarily imply that the nerve fibers still have remaining peripheral processes to allow the propagation of antidromic spikes. In such a case, this phenomenon may not occur in subjects with a long duration of deafness and presumably a poor peripheral processes’ survival. Furthermore, the psychophysical report of experiment 3 demonstrates that the orthodromic response (propagating centrally) is also larger for the anodic than for the cathodic phase. Antidromic propagation cannot therefore be the only account for our finding.
Third, the cathodic phase may excite a different subpopulation of nerve fibers than a biphasic pulse with no phase separation. In such a case, the masker-probe paradigm that we used in Experiments 1 and 2 would not be effective in the measure of the cathodic phase response, i.e., if the masker does not effectively mask the probe, the subtraction method would not allow the neural response to appear. Although the present data cannot rule out this possibility, it is worth noting that the masker-probe paradigm was successfully used in the measure of the anodic phase response. This would mean that a biphasic pulse (whether anodic- or cathodic-first) with no phase separation excites the same subpopulation as a single anodic phase but a different subpopulation than a single cathodic phase. Consequently, this would not change the conclusion that the anodic phase is the most “effective” phase of a biphasic pulse and that the ECAPs recorded clinically most likely represent the response to the anodic phase of biphasic pulses.
Although the observation of a higher sensitivity to anodic current flows is surprising, some animal data have already shown such a result. Miller et al. (1998) recorded ECAPs to monophasic anodic and cathodic pulses from the cat and the guinea pig. They observed lower cathodic thresholds for cats but lower anodic thresholds for guinea pigs. The reason for this species’ discrepancy was, however, unclear, and the authors proposed that it may relate to differences in anatomy or in electrode size and position. Other stimulus parameters have shown to affect human and animal subjects in different ways (Shepherd and Javel 1999; Carlyon et al. 2005), and several factors may underlie those species’ differences, including differences in anatomy, neural properties, neural survival, and electrode geometry (reviewed by Miller et al. 1999b).
Factors affecting polarity sensitivity
The simplest model of extracellular stimulation that can account for a lower cathodic threshold is the activating function of Rattay (1989). This model describes the spatial pattern of polarization of an infinite, homogeneous fiber subjected to an extracellular electrical current. A cathodic stimulus produces a large depolarization of the fiber proximal to the electrode and a smaller hyperpolarization at both sides of the depolarized region, whereas an anodic stimulus produces the exact opposite pattern of polarization. This model is, however, too restrictive in the case of auditory nerve fibers, which are made of several excitable elements (peripheral process, unmyelinated cell body, and central axon) that presumably have different biophysical properties and, hence, different activation thresholds. Moreover, auditory nerve fibers are curved, and the spatial pattern of polarization produced by an electrical pulse may show multiple regions of depolarization and hyperpolarization (Rattay 1999; Rattay et al. 2001a). There are at least three factors that may influence polarity sensitivity: the electrode position, the nerve parameters, and the stimulus current level. They are discussed in the following paragraphs.
Ranck (1975) reviewed the different mechanisms of electrical excitation of the central nervous system and showed that the electrode position relative to the fiber had a crucial effect on its sensitivity to polarity. He reported that myelinated fibers were more easily excited by cathodic stimuli when the electrode was placed over the central axon. However, for an electrode located near the cell body, on the opposite side from the central axon, the lowest threshold was often obtained with an anodic stimulus. Rattay (1999) and McIntyre and Grill (1999) found similar place-dependent polarity effects on their modeled central neurons. In another modeling study, Rubinstein (1993) focused on the excitation process near the axon termination. He also demonstrated the extreme dependence of polarity sensitivity on the exact electrode position. As a CI electrode presumably excites a large number of nerve fibers with different orientations and locations, it is possible that each fiber is excited differently by anodic and cathodic phases (Rattay et al. 2001b). The present study was restricted to electrodes mid-way along the electrode array. Subsequent studies should explore other electrode positions and other electrode designs.
Computational models have provided a simple way to investigate the influence of neural parameters. In two different modeling studies, Rattay et al. (1999, 2001a) suggested that intact human auditory nerve fibers would be more sensitive (showing lower excitation thresholds) to cathodic stimuli, whereas fibers with degenerated or demyelinated peripheral processes would be more sensitive to anodic stimuli. First, they predicted that, for intact fibers, the depolarization magnitude would be maximal at the level of the peripheral process and would be larger for a cathodic than for an anodic pulse. Second, for fibers without peripheral processes, their model showed that the only site that could be depolarized by a cathodic pulse was the cell body, whereas an anodic pulse could depolarize the central axon. Given that the cell body was modeled using a channel density ten times smaller than for the central axon, it had a much higher threshold than the central axon, thereby leading to a lower anodic threshold. A similar effect occurred in the case of demyelination of the peripheral process because unmyelinated fibers need much stronger currents to be excited than myelinated ones. Given that peripheral processes in deafened ears are believed to be somewhat degenerated (Fayad and Linthicum 2006) and that this degeneration is often accompanied by demyelination, the predictions of Rattay et al. (1999, 2001a) give a plausible explanation for the higher sensitivity to anodic stimulus currents observed in implanted subjects. This would therefore imply that the excitation site is at the level of the central axon, which is consistent with a report of van den Honert and Stypulkowski (1984) on the responses of cats’ fibers with and without peripheral processes and cell bodies. They found the responses of the degenerated fibers to closely resemble those of the normal fibers stimulated at high current levels. Although the exact location of the excitation site is still a matter of debate and probably depends on the electrode configuration and on the stimulus current level, Miller et al. (1999a) demonstrated that cats’ fibers were almost always excited at the level of their central axon with monopolar stimulation. The response latencies obtained in experiment 1 for anodic stimulation (290 μs on average) also argue for a stimulation at the level of the central axon, given that they are similar to the latencies commonly observed in human CI subjects for symmetric biphasic waveforms but also comparable to those measured intracochlearly in cats for anodic pulses (Brown et al. 1990; Miller et al. 2004). Given that the main morphological discrepancies between humans and cats are the length of the peripheral processes (longer in humans) and the size and degree of myelination of the spiral ganglion cells (larger and unmyelinated in humans; Liberman and Oliver 1984; Spoendlin and Schrott 1989; Rattay et al. 2001a), we would expect significant ECAP latency differences if the auditory nerve fibers were excited at their peripheral process or cell body sites but not if they were excited at the level of their central axon. To test whether neural degeneration and demyelination are the causes of our finding, additional psychophysical measures may be performed. Specifically, in ears with substantial degeneration, the dominance of the anodic phase should be enhanced, the effect of phase separation on threshold should be reduced (Prado-Guitierrez et al. 2006), and the slopes of the strength-duration functions should be shallower (Miller et al. 1995).
Finally, we reported in a previous study (Macherey et al. 2006) a comparison of thresholds and MCLs for anodic- and cathodic-first asymmetric pulses. Although we did find lower MCLs for anodic-first pulses—consistent with the present results—we did not observe any difference at threshold. It is possible that the higher sensitivity to anodic stimuli is specific to suprathreshold levels. One hypothesis proposed by Macherey et al. (2006) was that cathodic-first asymmetric pulses may produce a cathodal block at high levels. This seems, however, unlikely given that cathodal blocking should occur at very high levels above threshold (approximately eight times higher; Ranck 1975). This would correspond to a current level 18-dB higher than threshold, therefore exceeding the common dynamic range values of CI users (for example, the probe dynamic ranges of experiment 3 ranged from about 4 to 11 dB). Another possibility is that the cluster of fibers recruited at threshold is equally sensitive to anodic and cathodic phases or that, alternatively, approximately the same number of fibers is excited by an anodic and by a cathodic phase. At higher levels, more spatially remote fibers may be recruited, and it is possible that a majority of these fibers are more sensitive to anodic stimulation. Interestingly, in their spatial modeling study of the human cochlea, Rattay et al. (2001b) predicted that the nerve fibers close to the stimulating electrode would demonstrate lower cathodic than anodic thresholds but that fibers more spatially remote would in some cases exhibit the opposite trend. However, we note that in experiment 3, the greater masking effectiveness of the anodic phase was observed for probes presented to electrodes adjacent to that used for the masker.
Applications to CIs
Our results have implications not only for theories of electrical stimulation of the human auditory nerve but also for the development of future CIs. Asymmetric pulses may be used in a CI speech processing strategy for two reasons: to reduce power consumption or to increase spatial selectivity.
First, we previously suggested that asymmetric pulses presented in monopolar mode would achieve some worthwhile power savings (Macherey et al. 2006). If such pulses were implemented in a speech processing strategy, then, the results of experiment 3 suggest that the short, high-amplitude phase of the pulses should be anodic relative to the active electrode. In this case, the effect of the long, low-amplitude phase on neural excitation should be kept minimal.
Second, for so-called longitudinal bipolar stimulation, where each intracochlear electrode is stimulated with reference to another nearby intracochlear electrode, the pattern of excitation along the cochlea can sometimes show a bimodal shape with peaks of excitation near the two stimulating electrodes (Kral et al. 1998; Chatterjee et al. 2006). Frijns et al. (1996) suggested that this bimodality may be reduced by the use of asymmetric pulses. Their computational model showed that the nerve fibers proximal to the electrode for which the short, high-amplitude phase is cathodic would be more effectively excited than the fibers proximal to the other electrode. Our results suggest that a similar reduced bimodality and enhanced spatial selectivity may be obtained in CIs, but, crucially, with more excitation near the electrode presenting the short, anodic phase. Note that, in the present work, we used asymmetric pulses with relatively long phase separations, which are unlikely to be useful in multichannel CIs because they would require an extremely low repetition rate. However, we would expect this enhanced spatial selectivity to also hold for bipolar asymmetric stimuli with a shorter or no phase separation, which may be more appropriate candidates for implementation in a CI speech processing strategy.

auditory nerve
cochlear implants
psychophysical masking
asymmetric pulses
stimulus polarity
electrical stimulation


Correlation of quantitative MRI and neuropathology in epilepsy surgical resection specimens—T2 correlates with neuronal tissue in gray matter

Newer MRI methods can detect cerebral abnormalities not identified on routine imaging in patients with focal epilepsy. Correlation of MRI with histopathology is necessary to understand the basis of MRI abnormalities and subsequently predict histopathology from in vivo MRI. The aim of this study was to determine if particular quantitative MR parameters were associated with particular histological features.
Introduction
The structural basis of focal epilepsy has a major effect on the natural history of the condition in an individual patient (Semah et al., 1998). In 30% of patients, epilepsy is refractory to current drug treatments. In patients with drug-resistant focal epilepsy, surgical resection may be curative, and the nature of the underlying lesion influences post-operative outcome (McIntosh et al., 2001; Luders and Schuele, 2006).
MRI has greatly facilitated assessment for epilepsy surgery by revealing underlying structural cerebral abnormalities. When optimal MRI and post-operative neuropathological examination of the resection specimen do not identify an underlying structural abnormality, seizure outcome from surgery is less likely to be good (Blume et al., 2004; Jeha et al., 2006), reflecting the absence of a discrete, removed abnormality or one that is currently undetectable by imaging or pathology. This is to some extent a self-fulfilling strategy, such that patients in whom no structural abnormality is identified may not be considered for surgery (Spencer et al., 2005). Even optimal MRI, however, may not reveal known underlying pathological substrates such as focal cortical dysplasia and mild malformations of cortical development (Palmini et al., 2004), which are evident on histopathological examination of the resected tissue (Lee et al., 2005; Widdess-Walsh et al., 2006).
Thus, there is a pressing need for further developments in MRI, such as higher field strengths to improve signal-to-noise ratio, and newer sequences that may better pick out culpable neuropathological lesions in the neocortex.
The correlations between hippocampal volumes and T2 measures with glial and neuronal densities were established more than a decade ago (Cascino et al., 1991; Van Paesschen et al., 1995). For the more recently developed MR contrasts, the correlation between MR parameters and the underlying neocortical structure is not well understood and it is important for this to be clarified if these contrasts are to contribute to presurgical evaluation.
Quantitative histopathology has developed in parallel with MRI and led to more accurate and sophisticated interpretation of microscopic tissue structure (Thom et al., 2005). We have previously shown that temporal lobe resection specimens and their resultant histological slides can be aligned with pre-operative volumetric MRI data, allowing comparison of advanced MRI data sets in regions of interest to the equivalent areas on histopathological slides (Eriksson et al., 2005). Thus, quantitative MRI data and quantitative histopathological data can be directly compared. In this study we compared the data obtained using two quantitative MRI contrasts, T2 relaxation time maps (T2); and magnetization transfer ratio (MTR), with quantitative neuropathological data from the resected material. The aim was to explore the clinical utility of T2 and MTR and determine if particular quantitative MR parameters were associated with particular histological features.
Methods
Subjects
We studied nine consecutive patients for temporal lobe epilepsy surgery at the National Hospital for Neurology and Neurosurgery who had had pre-operative advanced MRI and in whom resection specimens were processed according to our protocol (Eriksson et al., 2005). The study was approved by the Joint Research Ethics Committee of the National Hospital for Neurology and Neurosurgery and Institute of Neurology, UCL. All patient and control subjects gave written informed consent to participate in the study.
The age range of subjects was 31–46 years (median 36 years) and 4 were male. Seven had a right anterior temporal lobe resection, and two had surgery on the left. The surgical procedure was selected after our standard presurgical work-up, including EEG-video telemetry, conventional MRI (T1- and T2-weighted, proton density and FLAIR), psychological and psychiatric assessments and multi-disciplinary case conference.
Control groups for the advanced MRI consisted of healthy volunteers with no history of neurological disease and normal conventional MRI and were the same subjects as reported previously (Salmenpera et al., 2007). Twenty-nine volunteers served as a control population for the analysis of T2 maps and 26 volunteers for MTR.
MR acquisition
All scans were acquired on the same 1.5 T GE Signa MR scanner (GE Medical Systems, Milwaukee, WI). The T1-weighted volume sequence from the conventional scan protocol for all subjects was required for the MR:pathology correlation. The sequence parameters were: TE/TR/TI/NEX, 4.2/15/450/1; flip angle 20°; acquisition matrix 256 × 192; field of view 24 × 18 cm; 3/4 phase FOV, 124 contiguous 1.5 mm slices, giving a voxel size of 0.94 × 1.25 × 1.5 mm.
Subjects had two additional scans to generate quantitative MR data:
T2 mapping
Dual-echo CSF suppressed, FFT2 (fast FLAIR T2) data sets were acquired using TR/TI/TE1,2 = 5000/1638/15,120 ms. Twenty-eight contiguous axial slices were obtained with an acquisition matrix of 256 × 256 and a 24 cm in-plane field of view, giving a voxel size of 0.94 × 0.94 × 5 mm. T2 signal intensity was calculated pixel-by-pixel using the dual-echo data sets, one set from the early echo (TE1) and one from the late echo (TE2), according to the equation: T2 signal = (TE2 − TE1) / ln (S1/S2) where S1 and S2 represent the signal intensities for the early and late echoes respectively (Rugg-Gunn et al., 2005). Total acquisition time was 13 min and 50 s.
Magnetization transfer imaging
Magnetization transfer (MT)-weighted and non-MT-weighted three-dimensional data sets were acquired using TR/TE = 22.6/5.4 ms. Volume matrix was 256 × 256 (in-plane) × 124 over a 240 × 240 × 180 mm field of view, giving a voxel size of 0.94 × 0.94 × 1.5 mm. MT-weighted images were collected after the application of a pre-pulse to saturate the broad resonance of immobile macromolecule protons. The MT radiofrequency pulse used was a three-lobe Hamming apodized sinc pulse with a duration of 6.4 ms and of 5.1 μT, applied 2 kHz off-resonance (Rugg-Gunn et al., 2003). Total acquisition time was 13 min and 54 s.
MRI quantitation
Voxel-by-voxel statistical comparisons were performed on each of the two image contrasts. This process used SPM99 (Wellcome Dept. of Imaging Neuroscience, Institute of Neurology, London, UK) to produce intensity probability maps that were normalized to a standard space defined by a template of all the controls (Salmenpera et al., 2007). The normalized T2 maps were smoothed with a 4 mm and MTR maps an 8 mm isotropic Gaussian kernel to improve signal to noise ratio, to allow the images to conform more closely to a Gaussian field model and to increase the validity of statistical inference. A standard univariate t-test was applied using SPM99 to every voxel in the image in order to create a map from which statistical inference was drawn. Significant increases or decreases in MR contrast signal intensity were detected at an individual voxel threshold of p < 0.001. The theory of Gaussian fields was used to calculate a corrected multiple comparison p value of 0.05. We tested for regional differences in intensity between each patient and the control group. Further details of the image processing are given in a previous paper (Salmenpera et al., 2007).
Quantitative MR values were obtained by overlaying a region of interest on the quantitative MR data. The mean intensity value of all voxels within the region of interest was calculated.
MR:pathology visual correlation
All subjects had a standard anterior temporal lobe resection performed by the same surgeon. During and after resection, specimens were processed according to standard histopathological and co-registration protocols and digital images of the tissue blocks were correlated with the pre-operative volumetric MRI data (Eriksson et al., 2005). Briefly, after fixation the resected temporal lobe was sliced in 5 mm blocks perpendicular to the maximum linear extent of the superior temporal sulcus. The MR volume data were rotated and reformatted into an oblique coronal plane that matched the orientation of these blocks. These oblique coronal images were visually inspected in comparison to the pathological blocks. Using standardized criteria, the best fit MRI slice for any one pathological block was identified and matches for adjacent blocks of tissue ensued. Two raters (SF and SE) performed this task separately and reached a consensus in any case with discrepancy.
All subjects had post-operative MRI scans to indicate the extent of the resection.
Histopathology
Standard laboratory protocols were used to prepare resected tissue in sections stained for Nissl (cresyl violet/LFB), glial fibrillary acidic protein (GFAP; Dako, Cambridge, UK; polyclonal 1:1500) and neuronal nuclear antigen (NeuN; Chemicon, Temecula, CA, USA, monoclonal 1:2000). An experienced epilepsy neuropathologist (MT) made a qualitative tissue assessment for clinical purposes. For quantitative histopathology the following sections were used: the standard 7 μm GFAP sections; additional 20 μm sections stained with NeuN; 20 μm sections stained for microtubule associated protein (MAP2; SIGMA, Saint Louis, MO, USA; 1:2000); 7 μm sections stained with anti-synaptophysin antibody (Dako, Cambridge, UK; 1:100). All sections were processed in the same batch to ensure uniform immunostaining, which is important when assessing the field fraction, which relies on the intensity of staining. A commercial image analysis system (Histometrix, Kinetic Imaging, Liverpool, UK), attached to a Zeiss Axioskop microscope, was used to generate quantitative histopathology measures. Two regions of tissue were assessed in each subject: ROI1 in the deep white matter of the middle temporal gyrus (a region identified from MRI-directed correlation, see below); and ROI2, in the cortex in the gyral crown of the middle temporal gyrus. Quantitative measures in ROI1 were: stereological counts of neurons; field fraction estimates of immunostaining for GFAP, MAP2 and synaptophysin. Quantitative measures in ROI2 were stereological counts of neurons and field fraction estimates of staining for NeuN and GFAP. The stereological neuronal counts were performed as previously described (Eriksson et al., 2006). For field fraction estimates the ROI was outlined on the computer screen of the image analysis system at × 2.5 magnification. The number of fields within the ROI to be analyzed was chosen (70–90 in our study) and the computer software randomly chose a starting point and then a field spacing to cover the whole ROI at the relevant sampling rate. All cortical layers were included in the analysis. Magnification was then increased to × 40 and each field displayed on the computer screen. In the first field for analysis, light intensity parameters were optimized to visualize the immunopositive pixels and the RGB (red-green-blue) parameters representing immunopositive pixels were set. Light intensity and RGB parameters were kept constant for all subsequent fields. The software program then automatically estimated the number of immunopositive pixels as a proportion of the entire field. An average of field fraction for all fields was calculated, thus reporting a single field fraction value for each ROI in a given patient. RGB parameters were defined uniquely for each case before analyses as signal intensity can vary slightly between cases. An example of images used for field fraction analyses in one patient is shown in Fig. 1. Measures in ROI1 were made by an experienced epilepsy neuropathologist (MT) and measures in ROI2 by a neurologist (SE) with extensive experience of quantitative neuropathology.
Histopathology and MRI correlation
MRI-directed
Abnormal regions in the SPM results for the MRI data were observed for several subjects (Table 1). To correlate MRI data with histopathological analyses in the same area in all cases, region of interest analyses were performed. A region that had abnormal T2 signal on voxel based analysis in one subject that was within the deep white matter of the middle temporal gyrus was selected and designated ROI1. This region was of sufficient size to provide robust histopathological data (Eriksson et al., 2006) and was present in tissue which had been resected in all 9 subjects. Since ROI1 and all quantitative MRI data used for the SPM analyses were normalized to SPM space, quantitative MTR and T2 data could be achieved by superimposing ROI1 on each case's normalized MTR and T2 data.
To achieve quantitative histopathological data, the ROI was aligned with the volume MRI of each patient by a sequence of two normalizations as follows (Fig. 2).
Each subject's volume MRI (cropped and tilted as used for the initial MR:pathology correlation) was normalized to the T1 image template in SPM99 using the default normalization protocol in SPM99 (Fig. 2). The T1 image template was in the same image space as ROI1, thus ROI1 could be associated with each subject's normalized MRI. The normalized volume was then back normalized to the volume MRI in each subject's native image space. The parameters used for the back-normalization were also used to transform ROI1 from T1 space back to the native space (Fig. 2). This ROI could then be transferred to the equivalent pathological space by visual inspection of the previously determined MRI:pathology correlation (Fig. 2). Thus ROI1 was located on the pathology slices of each subject. This region, directly drawn onto the pathology slides of interest, was used for subsequent quantitative histopathology. An example of the images of ROI1 for one subject is shown in Fig. 3.
We also defined a region in the pathological data and determined its MR characteristics. To achieve this, the MRI volume data were segmented in subject space using intensity thresholding and manual drawing in Analyze AVW 5.0 (BIR, Mayo Clinic, Minnesota). The entire resection volume and the middle temporal gyrus within the resection volume were extracted by manual drawing guided by visual inspection of the previously determined MR:pathology correlation. Intensity thresholding and visual inspection were used to separate gray and white matter. A final manual segmentation step was used to separate the gray matter of the gyral crown (ROI2), from the gray matter in the rest of the middle temporal gyrus. This step used similar criteria to characterize the limits of the gyral crown as were used by the neuropathologist in the quantitative neuropathology. The data from the initial MR:pathology correlation were used to determine which MR slices within the middle temporal gyrus in each subject corresponded to ROI2. Each subject's MR volume data were registered to the advanced MR image data in the native subject space using SPM99 and the default registration option. Because of the different qualities of the T2 maps and MTR image data, different registration was performed and different templates used that more closely resembled the two data sets.
For the MTR images, the segmented gray and white matter (summed) were used, while the MR volume data were registered to the proton density image which was acquired simultaneously with the T2 data, thus effectively registering the MR volume to the T2 data. Once optimal registration parameters were identified, these were used to register ROI2 in the MR volume data to the T2 maps and MTR data sets (Fig. 4). Thus, for example, gray matter in the middle temporal gyrus could be identified on the T2 image and T2 values determined for the gray matter in that region. An example of the images of ROI2 for one subject is shown in Fig. 5.
Quantitative MR and histopathology measures were compared using SPSS (version 11.0.0).
Results
Careful correlation of abnormal regions in the SPM data and the resected tissue indicated that regions that were abnormal on SPM analysis were physically present in the resected tissue in four patients (Table 1). In one patient (patient 3) qualitative neuropathology identified a hamartia (a subtle malformation of cortical development) in the white matter approximately 3 mm in diameter (Table 2). This was not detected using the SPM analysis and was not a part of ROI1 or ROI2. The abnormal region in one patient (T2 region in patient 5) was in the white matter of the middle temporal gyrus and was labeled as the region of interest, ROI1. The quantitative histopathology and quantitative MR data for this region for the nine patients are given in Table 3. Spearman Rho correlation did not reveal any significant correlations between any of these pathological and MR measures. This region had an abnormal average T2 value in one other patient (patient 1) but MT values were within the normal range for all subjects. Clustering analysis of the histopathological data could not separate the two patients with abnormal T2 values from those subjects with normal T2 values in ROI1.
Quantitative histopathology was determined for the gray matter in the gyral crown of the middle temporal gyrus. The registration process enabled us to determine quantitative MR data for the same region (ROI2) (Table 4). There was a significant Spearman Rho negative correlation between the field fraction measure of NeuN and the T2 value in ROI2 (correlation coefficient − 0.72, p = 0.028).
Discussion
We have previously shown that in vivo MRI and pathological blocks may be aligned using visual inspection and a standardized protocol (Eriksson et al., 2005). We have now shown that, with additional registration protocols, quantitative parameters from MRI and histopathology can be directly compared. There was a negative correlation between the histopathological field fraction measure of NeuN immunopositivity and the MR measure of T2 relaxation time in the gray matter of the middle temporal gyrus. This is the first correlation between a quantitative MR parameter and histopathological data from neocortex in patients with epilepsy.
We wished to compare the same area in all patients because it is not known how much histopathological measures and MR parameters differ as a function of location within cerebral tissue. Although some of the histopathological features of interest were readily quantified with a semi-automated protocol, stereological measures require significant expert time input. In consequence, we restricted this study to two regions of interest. The first region investigated corresponded to a region of abnormal T2 signal on SPM analysis in one patient and was chosen since it was of sufficient size to provide robust histopathological data and was available in all resections. Our second region was identified in each subject's histopathological sections, the gray matter of the crown of the middle temporal gyrus overlying ROI1, giving us data in adjacent gray matter.
The limitation of the initial MR and pathology matching is that the matching was visual and was guided by anatomy and was to some extent subjective. Our previous study, however, showed that the protocol gave consistent, reliable and accurate registrations within the structural limitations of the histopathological data (Eriksson et al., 2005). The subsequent matching of specific MR or pathology regions with their respective pathology or MR regions was carried out using computational normalization and registration, both of which were entirely repeatable. This may seem a complex procedure when, for example, the middle temporal gyrus is generally readily identified on anatomical MRI. However, the advanced quantitative MR sequences are optimized for the appropriate MR parameter and not for visualization of anatomy and it can therefore be difficult to confidently identify anatomical features using these contrasts. Identifying the structure of interest in a high quality anatomical scan and then using registration and/or normalization protocols reduced the number of subjective steps (only one definition of the tissue structure was required rather than one in each MR scan) and therefore reduced computation time and increased repeatability.
The T2 signal in MRI is dependent upon the relationship between bound and free water in a tissue, which is itself dependent upon the macromolecular environment. Disruption of tissue integrity can result in an increase in the free water in tissue and an increase in T2 relaxation time. Changes in T2 in the cerebrum have been related to, among other processes, gliosis, demyelination, edema, neuronal loss and infarction. In an earlier study from our group, significant increases in T2 signal were observed in 51% of patients with cryptogenic focal epilepsy (Rugg-Gunn et al., 2005). The MTR signal is dependent upon the exchange of magnetization between free and bound protons in tissue and this depends upon the integrity of the macromolecular environment. Changes in MTR have been related to histopathologic changes of axonal loss, gliosis, demyelination and edema (Lexa et al., 1994; Brochet and Dousset, 1999). Our earlier study found reductions of MTR in acquired lesions, malformations of cortical development and in regions that were normal on conventional MRI (Rugg-Gunn et al., 2003). In the present study ROI1 was defined by an abnormal T2 value in one subject. Additional analysis revealed that this region also had an abnormal T2 signal in one other subject. MTR values were normal in this region in all subjects. There were no significant linear correlations between the MR values and the histopathological values in ROI1 in our group. The two subjects with abnormal T2 values did not differ from the other seven subjects in relation to their standard or quantitative histopathological data. The T2 and MTR values were all normal in ROI2.
There was a significant negative correlation between the T2 relaxation time and the field fraction estimate of NeuN staining in the cortex of the middle temporal gyrus. A decrease in NeuN staining could suggest a reduction in the number of mature neurons in the tissue, altered packing density or alterations in cell size or NeuN immunoreactivity. An increase in T2 signal has been correlated with neuronal loss and increased gliosis (Van Paesschen et al., 1997; Rugg-Gunn et al., 2005). Increased gliosis may further reduce the relative concentration of neurons in a region, although sometimes, tissue sclerosis or gliosis causes reduction in tissue volume which could paradoxically increase packing density. We did not observe any relationship between T2 value and GFAP field fraction. Thus, the correlation detected may reflect a consistent biological relationship focused on neuronal loss. However, one subject had neocortical neuronal loss observed during routine qualitative neuropathology (Case 4). This subject had the lowest value of neuronal count in the gray matter of ROI2 (the crown of the middle temporal gyrus) but also the lowest value of T2 in ROI2 which appears contrary to the correlation observed in the whole group. The reason for this is not clear. The cortical NeuN field fraction in this case was 0.11 (range 0.08–0.13).
Gray matter neuronal counts did not correlate with the field fraction estimate of NeuN. The gray matter neuronal counts are a count for each neuron observed in the region of interest expressed as a proportion of the total area assessed. The field fraction estimate of NeuN is an estimate of the area occupied by tissue immunopositive for NeuN as a proportion of the total area assessed. This tissue is predominantly neuronal cell bodies with some axons and dendrites. Thus 5 large neurons will occupy a greater area of immunopositivity than 5 small neurons. The neuronal count would be the same in each case but the field fraction would be different. This may explain the lack of correlation between the field fraction and the neuronal count. The relation of these two measures with the T2 relaxation value suggests that the T2 relaxation is dependent on more than just the number of neurons, also on their size or complexity, which may reflect their function. Further staining protocols and analyses of neuronal size are necessary to elucidate further this difference.
The lack of any other correlations between the MR and the histopathology measures could be due to a number of factors. A real correlation may exist and not have been detected, possibly because of the limited number of subjects in the study. A change of MR scanner curtailed data collection for this series and we are now recruiting more subjects imaged using a new 3 T scanner. We could also have missed a true correlation because the registration between the MR region and the pathological region was inadequate as discussed previously.
Alternatively, there may not be any other true correlations between the MRI sequences and pathological measures studied. The pathological measures characterize real physical features — for example, the actual number of neurons and the proportion of tissue affected by gliosis. However, the underlying physical significance of the MR data is not yet well characterized. We may have chosen pathological measures which do not relate to the features characterized by the MR signals. Myelin content, for example, might influence both T2 and MTR. We did not think it likely that myelin studies would be of relevance in epilepsy in the context of this paper, but additional histopathological analyses, such as LFB for myelin or myelin basic protein, may lead to further useful data in future studies.
There was also limited tissue specificity for absolute T2 and MTR values between white and gray matter (see Tables 3 and 4 for details). Thus the MR measures and the histopathology measures may be measuring different features of the tissue and would not be expected to correlate. A hamartia, seen as a cluster of neurons in the white matter microscopically, was not detected as an abnormal MR region with either MTR or T2 using SPM. There are new histopathological techniques in development and some of these may prove a better match to the MR signal changes. MR acquisition on a 3 T scanner will produce data with better signal-to-noise qualities and improved spatial resolution and may increase the sensitivity of quantitative MR measures. Further correlations of quantitative histopathology and quantitative MRI measures may lead to derivation of MRI measures that are most sensitive to pathological changes.
Our protocol for matching MR and pathology has demonstrated how we can investigate both the histopathology directed by an MR finding and the MR data directed by a histopathological finding. We identified a biologically plausible correlation between an increased T2 relaxation time and a reduction in neuronal labeling in the cortex of the middle temporal gyrus. If newer, quantitative MRI sequences are to be adopted more widely in routine clinical practice, further research exploring their underlying biological and pathological substrates will be necessary and may ultimately lead to a reduction in the proportion of patients with drug-resistant focal epilepsy whose optimal MRI is said to be “normal”.

mri
epilepsy
histology
mtr
fast flair t2