2. Connecting the Concussion Symptom Dots: A Network Analysis of the Concussion Clinical Profiles Screening Tool

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ID: 324482
2026
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Ranked #64 of 79 articles by views in archives of clinical neuropsychology : the official journal of the national academy of neuropsychologists

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Abstract
Abstract Purpose Concussion symptoms are heterogeneous, interrelated, and overlap with pre-existing conditions (e.g., anxiety), which complicates effective management. Researchers developed the Concussion Clinical Profiles Screening (CP Screen) tool, which identifies symptoms associated with concussion clinical subtypes (e.g., anxiety/mood, cognitive, migraine, ocular, vestibular). However, research has yet to examine relationships among these symptoms, which may provide valuable foci for targeted treatment. Therefore, the purpose of this study is to examine clustering of post-concussion symptoms on the CP Screen and identify central/bridging nodes using a network analysis. Method This retrospective study included 913 adolescents (age=15.1±1.8; 43.3% female) presenting to a specialty concussion clinic. Participants completed the 29-item CP Screen during their initial visit (7.1±18.5 days post-injury). CP Screen items were used to estimate networks. Centrality (strength [STR], betweenness, expected influence [EI]) and bridge strength were computed. Network stability (correlation stability coefficient [CS-coefficient]) was assessed via bootstrapped case-dropping procedures (1,000 iterations). Results Symptoms largely clustered within the CP Screen’s a priori clinical profiles (profiles: anxiety/mood, cognitive/fatigue, migraine, ocular, vestibular; modifiers: sleep, neck). The most central symptoms were slow/wavy dizziness (vestibular; STR/EI=1.19), headache with cognitive exertion (cognitive/fatigue; STR/EI=1.14), eye strain (ocular; STR/EI=1.13), headache with sensitivity to light/noise (migraine; STR=1.10, EI=1.07), hyposomnia (sleep; STR=1.08, EI=0.57), and feeling more stressed than usual (anxiety/mood; STR/EI=1.06). Bridge analysis identified headache with cognitive exertion, difficulty in busy environments, post-traumatic migraine-related symptoms, and ocular-motor symptoms as key connectors across clusters. Network stability was excellent (edge-weight CS-coefficient=0.75; strength CS-coefficient=0.67). Conclusions Network analysis supported the a priori clinical profile structure of the CP Screen. Networks also identified which symptoms may be associated with elevated symptom burdens post-concussion and which symptoms may be valuable targets for multidomain treatments.
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Authors G A Thomas, A J Zynda, A M Trbovich, L Phan, M W Collins, A P Kontos, E Reynolds
Journal archives of clinical neuropsychology : the official journal of the national academy of neuropsychologists
Year 2026
DOI
10.1093/arclin/acag055.002
URL
https://doi.org/10.1093/arclin/acag055.002
Keywords Keywords not found

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