- 2026-02
- Journal of the International Neuropsychological Society : JINS 32(2)
- Shuyuan Shi
- Aaron J Fisher
- Amanda R Rabinowitz
- Noah D Silverberg
Study Design
- Type
- Meta-Analysis
- Sample size
- n = 776
- Population
- 5,776 participants with mild traumatic brain injury
- Methods
- Meta-analytic Gaussian Network Aggregation (MAGNA) of 6 samples
Objective
Network modeling of post-concussion symptoms following mild traumatic brain injury (mTBI) has emerged as a promising tool for understanding how cognitive, emotional, and somatic symptoms co-occur and interact. However, the generalizability of networks developed in individual studies remains unclear. This study aimed to develop the first-ever meta-analytic pooled between-persons network structure of post-concussion symptoms and systematically examine the between-study heterogeneity of these symptom networks.Methods
Using the Meta-Analytic Gaussian Network Aggregation (MAGNA) framework, a single pooled network model was developed by aggregating data from 6 distinct samples, comprising a total of 5,776 participants. Additionally, this study quantitatively assessed the degree of heterogeneity across these studies.Results
Strong symptom clusters between cognitive, emotional, and somatic symptoms were identified. Concentration difficulty and slowed thinking were the most central symptoms in the pooled MAGNA network. Large between-study heterogeneity was observed.Conclusions
Findings from this meta-analysis highlight cognitive symptoms as most important for defining the network structure after mTBI at a group level, potentially perpetuating and/or being perpetuated by symptoms in other domains. The large heterogeneity observed between studies underscores the need for an idiographic (person-specific) approach to studying post-concussion symptom networks to inform precision rehabilitation.