Using Big Data to Comprehensively Delineate the Neurobehavioral Phenotype of Children with Neurofibromatosis Type 1
Abstract
Neurofibromatosis type 1 (NF1) is a rare disease affecting approximately 1 in 3,500 people. It is characterized by a wide range of tumor and non-tumor manifestations. An important non-tumor manifestation of NF1 consists of neurobehavioral problems, including cognitive impairments (e.g., executive function problems), learning difficulties, behavioral problems (e.g., aggression), and socioemotional problems (e.g., depression). In particular, cognitive impairments are present in over 80% of individuals with NF1. However, we currently know little about at what age a given neurobehavioral problem is likely to show up and about how neurobehavioral functions change as a child with NF1 gets older. Additionally, it is well recognized that the manifestation of neurobehavioral problems is heterogeneous across people with NF1. However, there is limited empirical evidence on whether patients can be grouped by distinct neurobehavioral manifestations and on whether the patterns of those manifestations can be predicted based on the patient’s biological and demographic characteristics. Finally, we have limited knowledge of what cognitive factors contribute to learning difficulties, behavioral problems, and socioemotional problems within the NF1 population. Addressing these knowledge gaps is critical for improving future research, patient care, and treatment of neurobehavioral problems in people with NF1. This study will address these knowledge gaps using innovative and advanced methods that have been reliably used in other fields (e.g., integrative data analysis, time-varying effects modeling, and latent profile analysis). This study will build the first big dataset of neurobehavioral outcomes in the NF1 community by combining 13 existing datasets. After establishing the big dataset, this study will investigate (1) the change of neurobehavioral functions across childhood and adolescence (ages 2 to 18); (2) the existence of NF1 subgroups with unique neurobehavioral manifestations; (3) the patterns of neurobehavioral manifestations for people with different biological (e.g., certain tumor manifestations) and demographic characteristics (e.g., socioeconomic status); and (4) the cognitive factors contributing to academic, behavioral, and socioemotional problems in children with NF1. Ultimate Applicability and Contributions of Proposed Research: The findings of this study will be critical for guiding future research and patient management and for maximizing the clinical impact of future intervention studies. First, this study will identify the developmental patterns of neurobehavioral functions for children with NF1. This information can provide clinicians and families with clearer expectations for managing the neurobehavioral problems as a child with NF1 gets older. This information can also inform clinicians on how to provide intervention and support at the most needed time as well as assist clinicians in distinguishing genuine treatment effects from the natural course of development to provide a more accurate evaluation of therapy efficacy. Second, this study will identify subgroups of children with NF1 who have unique patterns of neurobehavioral manifestations. Moreover, this study will also identify biological and demographic predictors for various neurobehavioral outcomes. Both will allow clinicians to tailor intervention programs to appropriately serve children with NF1. For instance, patients with impairments across all neurobehavioral domains (i.e., cognitive, academic, behavioral, socioemotional) can receive particular support and treatment, allowing them to benefit from the most comprehensive neurobehavioral interventions. For patients with predominantly socioemotional problems (e.g., depression) and no impairment in other neurobehavioral domains, the most effective interventions would be directed toward the socioemotional problems, such as Acceptance and Commitment Therapy. Third, this study will identify t
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110504
Entities
People
- Yang Hou
Organizations
- United States Army
- University of Kentucky