The network-based underpinnings of persisting symptoms after concussion: a multimodal neuroimaging meta-analysis

Persisting symptoms after concussion (PSaC) represent a complex and poorly understood neuropsychiatric phenomenon with limited treatment options. Neural network dysfunction has been associated with PSaC, and neuromodulation, particularly repetitive transcranial magnetic stimulation, may be a promising intervention. However, neuroimaging findings have been inconsistent, limiting understanding of underlying network dysfunction. We aimed to identify a core neural network associated with PSaC and explore whether this network could yield candidate cortical targets for neuromodulation at the individual level. We hypothesized that differences in network disruption would be evident between individuals with high versus low symptom burden in PSaC. Here we show that a convergent multi-analytic approach combining symptom-activation maps generated from existing fMRI datasets, systematic review of resting-state fMRI studies of PSaC, and network-based meta-analysis of coordinates derived from these studies co-localize to the salience network in high symptom burden PSaC. Using Human Connectome Project data, we mapped this network to cortical regions that could serve as individualized targets for neuromodulation. This aligns with current clinical models of PSaC and may present a new direction for network-based therapy.