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- W4308682338 abstract "In Brief Mild traumatic brain injury (mTBI), is a leading cause of disability worldwide, with acute pain manifesting as one of its most debilitating symptoms. Understanding acute postinjury pain is important because it is a strong predictor of long-term outcomes. In this study, we imaged the brains of 157 patients with mTBI, following a motorized vehicle collision. We extracted white matter structural connectivity networks and used a machine learning approach to predict acute pain. Stronger white matter tracts within the sensorimotor, thalamiccortical, and default-mode systems predicted 20% of the variance in pain severity within 72 hours of the injury. This result generalized in 2 independent groups: 39 mTBI patients and 13 mTBI patients without whiplash symptoms. White matter measures collected at 6 months after the collision still predicted mTBI pain at that timepoint (n = 36). These white matter connections were associated with 2 nociceptive psychophysical outcomes tested at a remote body site—namely, conditioned pain modulation and magnitude of suprathreshold pain—and with pain sensitivity questionnaire scores. Our findings demonstrate a stable white matter network, the properties of which determine an important amount of pain experienced after acute injury, pinpointing a circuitry engaged in the transformation and amplification of nociceptive inputs to pain perception. Structural brain connectivity within sensorimotor, thalamiccortical, and default-mode systems predict 20% of early acute pain after a mild traumatic brain injury." @default.
- W4308682338 created "2022-11-14" @default.
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- W4308682338 date "2022-11-09" @default.
- W4308682338 modified "2023-09-23" @default.
- W4308682338 title "Structural brain connectivity predicts early acute pain after mild traumatic brain injury" @default.
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- W4308682338 doi "https://doi.org/10.1097/j.pain.0000000000002818" @default.
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- W4308682338 hasPublicationYear "2022" @default.
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