Longitudinal neurobehavioral profiles in children and young adults with PTEN hamartoma tumor syndrome and reliable methods for assessing neurobehavioral change

This longitudinal neurobehavioral study demonstrates that baseline group differences in cognitive and behavioral function observed in our prior research [5] persist at the same magnitude over a 2-year time period with no group differences in longitudinal neurobehavioral profiles. Specifically, both PTEN groups (PTEN-ASD and PTEN-no ASD) show cognitive patterns suggesting primary involvement of frontal lobe systems, but those with ASD demonstrate more severe deficits in frontal lobe functions along with language, adaptive behavior, and sensory deficits not observed in the PTEN-No ASD group. While both ASD groups (PTEN-ASD and Macro-ASD) showed reduced performance across a broad range of cognitive measures, those with PTEN-ASD showed slower reaction times and more sensory abnormalities than those with Macro-ASD [5]. Our longitudinal data show that these patterns of group differences remain remarkably stable over serial assessments (i.e., 2–3 evaluations over a 2-year time frame) on all cognitive and behavioral measures examined. It should be noted that, while the present data suggest that the neurocognitive functions remain fairly stable in patients with PHTS (with and without ASD), this differs from studies of IQ in an idiopathic ASD population where IQ tended to increase from early childhood to adulthood and remained more variable than expected [10]. It is possible that this difference is simply due to the short observation period of the present study. Clearly, additional longitudinal follow-up of this cohort will be useful for better understanding the stability of IQ and other neurocognitive functions in the PHTS population.

Oftentimes, children with ASD will fail to show the same developmental gains as their same-aged neurotypical peers, resulting in either a drop in normative adaptive scores over time or barely keeping pace even in the context of intensive behavioral intervention [11,12,13,14]. Thus, it is somewhat encouraging that there do not appear to be any longitudinal changes in cognitive performance in any of the three study groups over time. In fact, this suggests that, at the group level, even the reductions in neurobehavioral skills seen in PTEN-ASD cases do not further decline with age. This suggests that common intervention strategies provided to PTEN-ASD cases, such as behavioral intervention, may be useful for sustaining growth, similar to what is seen in more impaired sub-groups of idiopathic ASD [12]. Although group trajectories remain stable, even for PTEN-ASD, analyses demonstrated substantial variability in the level of neurobehavioral function. Thus, it remains crucial that individuals with PHTS, particularly those with ASD, who show substantial reductions in cognitive and functional skills and/or behavior problems receive behavioral and educational interventions needed to help them maximize functioning. Furthermore, RCIs and SRBs can be used to identify cases showing deviations in expected development and guide additional intervention strategies to reduce the likelihood of further deviation from normative trajectories.

In addition to assessing longitudinal neurobehavioral changes at the group level, this rich dataset allowed us to develop RCIs and SRBs that clinicians can use to identify meaningful cognitive and behavioral changes at the individual level in children and adolescents with PHTS. The present data showed significant variability across patients in overall neurobehavioral levels but only minimal individual differences in change over time. However, it is important to note that minimal slope variability may be, in part, a function of the small sample size as a few individuals showed meaningful changes across time points. Regardless, there is always the potential that an individual patient will deviate from expected trajectories. RCI and SRB tools provide a mechanism for detecting these deviations and also a common metric that researchers can use in future studies to more accurately characterize cognitive outcomes in children and adolescents with PHTS. Importantly, the methods for assessing cognitive change provided here allow differentiation between changes in neurobehavioral function over time due to PHTS and changes that may result from associated medical events, interventions, and/or treatments, which cannot be accomplished using traditional methods (e.g., change scores).

Here, we provide two different metrics for assessing cognitive change (i.e., RCIs and SRBs) so that clinicians and researchers can select the method that best addresses their needs. RCI methodology determines the degree of individual change associated with test imprecision and practice effects and identifies the amount of test–retest change required in order to conclude that clinical change has occurred independent of measurement error. RCIs provide cutoff scores to identify meaningful change, requiring no additional calculation beyond test–retest difference scores. As such, they are quick and easy to apply to a patient’s or participant’s test results. However, it should be noted that RCIs do not correct for regression to the mean or other potential modifying factors.

SRB methodology, which is a bit more complicated to use, corrects for multiple confounding factors that RCIs do not. Statistically, SRBs correct for practice by using an individual’s baseline score as a predictor of retest performance. This provides a more accurate adjustment of practice effects than RCIs because practice can be estimated differently at different levels of baseline performance. SRBs also allow for the correction of other potential modifiers that may impact cognitive performance over time. Finally, SRBs convert changes in test scores to a common metric (i.e., z-scores) permitting direct comparison of cognitive change across a wide range of neuropsychological measures.

Studies comparing RCI to SRB methodologies suggest that predictive accuracy is similar for both measures [15, 16]. As a result, many clinicians and researchers prefer to utilize the easily employed RCI cutoffs rather than calculating SRBs for individual patients or participants. Regardless of the method preferred, we have created a Microsoft Excel calculator that calculates both RCIs and SRBs to facilitate the interpretation of cognitive change in individual patients across a wide range of cognitive and behavioral methods. This calculator is available from the corresponding author upon request.

It is interesting to note that some of the neurobehavioral measures in this study were associated with negative practice effects in our sample. Rather than showing the typical practice effects demonstrated by healthy children, children in our PHTS sample achieved lower test scores, on average, during repeat testing on some neurobehavioral measures. Similar findings, with a lack of typical practice effects, have been reported in other disorders that affect the central nervous system, like epilepsy [17, 18]. In our case, this may indicate that, despite relatively stable cognitive profiles at the group level, some children with PHTS may not be developing along the expected trajectory. Most of the measures in our battery are age-normed; therefore, if children with PHTS are not gaining skills at a rate comparable to healthy standardization samples, their scores on these measures will decline over time. Alternatively or additionally, the measures with negative practice effects are all sensitive to frontal lobe functioning, which is known to be affected in PHTS, and may indicate that children with PHTS do not show typical gains in frontal lobe functions compared to their same-aged neurotypical peers. Regardless of the reason for the observed negative practice effects, we accounted for this in our RCI and SRB development by centering intervals around typical changes in scores, regardless of whether the practice effects were positive or negative.

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