Better characterization of acute concussion symptomatology is needed in order to advance clinical and scientific understanding of persistent concussion symptoms. This paper aims to illustrate a novel framework for conceptualizing, collecting, and analyzing concussion symptom data. To that end, we describe the temporal and structural dynamics of acute concussion symptoms at the individualpatient level. Ten recently concussion adolescents and young adults completed 20 days of ecological momentary assessment (EMA) of post-concussion symptoms. Follow-up assessments were completed at 3 months post-injury. Network modeling revealed marked heterogeneity across participants. In the overall sample, temporal patterns explained the most variance in light sensitivity (48%) and the least variance in vomiting (5%). About half of the participants had symptom networks that were sparse after controlling for temporal variation. The other individualized symptom networks were densely interconnected clusters of symptoms. Networks were highly idiosyncratic in nature, yet emotional symptoms (nervousness, emotional, sadness), cognitive symptoms (mental fogginess, slowness), and symptoms of hyperacusis (sensitivity to light, sensitivity to noise) tended to cluster together across participants. Person-specific analytic techniques revealed a number of idiosyncratic features of post-concussion symptomatology. We propose applying this framework to future research to better understand individual differences in concussion recovery.www.nature.com/scientificreports www.nature.com/scientificreports/ Patient 4. Temporal variables accounted for an average of 22% of the variance in symptom variation for Patient 4. Of note, Patient 4 was the only patient who endorsed all symptom variables. Regression models returned temporal predictors for all symptoms except for headaches. Temporal variables accounted for 41%, 48%, and 50% of the variance in nervousness, vision problems, and sadness, respectively. All three of these variables exhibited linear, quadratic, and cubic trends, as well as 24-hr and 12-hr cycles.Nodes for 24-hr cyclic variation and a linear trend were the most central and third-most central nodes respectively. Symptom nodes for drowsiness, sadness, feeling slow, and feeling foggy were the second, fourth, fifth, and sixth most influential. The detrended network exhibited consistent results, with feeling foggy, feeling slow, drowsiness, fatigue, emotional, and sadness the six most central nodes in the network.Patient 5. Patient 5 only endorsed two symptoms during the measurement period, headaches and fatigue. Temporal variation explained 47% and 28% of the variance in these variables, respectively; and both exhibited linear, quadratic, and cubic trends, as well as 24-hr and 12-hr cycles.Regarding the raw data network model, temporal variables were the most central nodes, with the two symptom nodes exhibiting the least and second-least centrality. Twenty-four-hour cyclicity was the most influential of the temporal nodes. Given that this patie...