Psychiatric Times.
No. 9
Novel Methods to Predict Outcome Using Neuroimaging
By Alexander Gantman, MA, Dana Wittenberg, MA, and Fumiko Hoeft, MD, PhD |
September 1, 2006
Interestingly, a model combining behavioral and neuroimaging measures predicted future reading abilities significantly better than the behavioral or neuroimaging model alone, explaining 78% of the variance (Figure 1D). Various validation analyses--including leave-one-out cross-validation analysis--showed that the combined model of behavioral and neuroimaging measures showed significantly less deviation between the actual outcome scores and predicted scores, compared with the behavioral or neuroimaging models (both, P< .05; Figure 1E). There were no significant differences between the behavioral and neuroimaging models (P> .05). We further replicated the basic findings using a larger sample of 64 children who had variable reading ability ranging from poor to good (F. Hoeft, MD, unpublished data, 2006). These studies showed that neuroimaging measures can predict future reading ability; combined with behavioral measures, neuroimaging can be a powerful way to predict future reading ability.
Findings from a prospective study
Based on these promising results derived from a retrospective prediction model, the next study sought to prospectively predict children's future reading skills.47 Thus, this study went beyond correlating behavioral outcome with initial behavioral and neuroimaging data. The sample consisted of 59 children aged 8 through 12 years at the time of initial assessment (Time 1). The children included both good and poor readers. WA-ss and neuroimaging measures collected at Time 1 and WA-ss collected approximately 6 months later (Time 2) were used to predict reading ability as of approximately 2 years later (ie, WA-ss at Time 3). The neuroimaging model created was similar to that of the previous studies in our laboratory. For some analyses, we also used reading comprehension scores (WRMT Passage Comprehension standard scores [PC-ss]) as the outcome measure.
The results of this study indicated that the neuroimaging model was as successful as Time 1 WA-ss was in predicting future reading ability (r= 0.55 and r= 0.57, respectively; both, P< .001). We further assessed the clinical use of determining reading disability (RD) by examining sensitivity, specificity, and positive predictive values. Various criteria to define RD (WA-ss < 85, 86, 87, 88, 89, or 90) were tested. Results from this analysis indicated that the combination of behavioral and neuroimaging measures was now superior to using Time 1 WA-ss (Figure 2A). It is thought that the sensitivity index, specificity index, and positive predictive values should all reach at least 0.75 in order for a measure to be considered acceptable for practical use and suitable for screening purposes.48 It is promising that the combination of existing behavioral measures and novel neuroimaging measures passed these criteria and outperformed behavioral measures. Finally, when we applied these models to predict reading comprehension skills (PC-ss)--which is the ultimate reading goal--similarly high sensitivity, specificity, and positive predictive value were achieved (Figure 2B).
Limitations and future direction
While we found that combining behavioral and neuroimaging measures can predict future reading ability significantly better than using the behavioral or neuroimaging models alone and that prospective analyses showed promise for practical use, the results thus far have several limitations. First, the patients were followed up for only 1 or 2 school years. (It will be interesting to examine the predictive value in the long term.) Second, the behavioral and neuroimaging predictors used here were selected from multivariate analyses from the total sample and were applied to the validation tests. Finally, the models examined linear relationships, and an increasing number of studies show nonlinear effects of development.49 These possible limitations may have potentially biased the data, which should be interpreted with caution until results are replicated in larger samples. Future studies of preschool children before they learn to read are warranted and should include environmental variables, such as socioeconomic status, and genetic measures, which play a large part in development of reading disabilities.
Conclusion
Our results raise the intriguing possibility of using neuroimaging data as a critical component in the assessment and prediction of children's reading skills. If we are able to overcome the aforementioned limitations, our results may have a significant impact on public health, since it may help identify at-risk children at an early stage and provide them with opportunities to receive optimal interventions earlier. Furthermore, these studies may help bridge the research and actual practice of psychiatrists, neurologists, and educators. Finally, similar models using neuroimaging or a combination of behavioral and neuroimaging measures may be used to predict the development or decline of other cognitive functions as well as therapeutic outcomes.
Mr Gantman and Ms Wittenberg are researchers in the department of psychiatry and behavioral sciences in the Center for Interdisciplinary Brain Sciences Research (CIBSR) at the Stanford University School of Medicine and members of the PGSP (Pacific Graduate School of Psychology)-Stanford PsyD Consortium in Palo Alto, Calif. Dr Hoeft is senior research scientist in the department of psychiatry and behavioral sciences in the CIBSR at the Stanford University School of Medicine. They report that they have no conflicts of interest concerning the subject matter of this article.
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