Brain injury monitoring system
Systems, methods, and apparatus are described that employ a fast concussion model usable to detect occurrence of traumatic events that can contribute to mild traumatic brain injury (mTBI) and to assess the risk that mTBI has occurred or is likely to occur should further traumatic events occur. A method includes receiving motion data related to a head acceleration event from sensors configured to detect head motion of a subject, using the motion data to obtain an estimate of axon signal dysfunction affecting a component of the subject's brain as a result of the head acceleration event, determining a Neurologic Injury Measure (NIM) from the estimate of axon signal dysfunction, and generating a probability that the subject has been concussed by the head acceleration event based on the NIM. The estimate of axon signal dysfunction may be based on strain at nodes of Ranvier in the component of the subject's brain.
1. A method implemented at a portable communication device, comprising:
receiving motion data related to an extraordinary head acceleration event from a plurality of sensors configured to detect head motion of a subject;
using the motion data to obtain an estimate of axon signal dysfunction affecting one or more components of the subject's brain as a result of the head acceleration event;
determining a Neurologic Injury Measure (NIM) from the estimate of axon signal dysfunction; and
generating a probability that the subject has been concussed by the head acceleration event based on the NIM,
wherein the estimate of axon signal dysfunction is obtained by:
determining strain at nodes of Ranvier in the one or more components of the subject's brain; and
estimating axon signal dysfunction by translating the strain at the nodes of Ranvier to functional decrement of axon signaling.
2. The method of claim 1 , wherein determining the strain at the nodes of Ranvier comprises:
determining strain at nodes of Ranvier in the subject's corpus callosum.
3. The method of claim 1 , further comprising:
determining the NIM using a volume-weighted average of axon signal dysfunction over a plurality of axons in the one or more components of the subject's brain.
4. The method of claim 3 , wherein the plurality of axons is located in a corpus callosum of the subject's brain.
5. The method of claim 1 , wherein the head acceleration event results from an impact to the subject's head or exposure of the subject's head to a blast.
6. The method of claim 1 , wherein the head acceleration event results from an application of an accelerative load to the subject's body.
7. The method of claim 1 , further comprising:
transforming the motion data to obtain head kinematics abstracted from placement of the plurality of sensors with respect to the subject's head.
8. The method of claim 7 , wherein at least one sensor in the plurality of sensors is mechanically decoupled from the subject's head.
9. The method of claim 7 , wherein one or more sensors in the plurality of sensors provides a stream of image data that captures movement of the subject's head.
10. The method of claim 7 , further comprising:
determining a probability that the subject has been concussed using dose-response information obtained from a mechanistic concussion model.
11. The method of claim 10 , wherein the dose-response information indicates a cumulative effect of multiple extraordinary head acceleration events affecting the subject's head.
12. The method of claim 7 , further comprising:
correlating the NIM to an injury outcome using dose-response information obtained from a mechanistic concussion model.
13. The method of claim 7 , further comprising:
correlating the NIM to an injury outcome using a fast concussion model comprising a plurality of transforms received from a network server that maintains a mechanistic concussion model.
14. The method of claim 13 , further comprising:
transmitting the motion data related to the extraordinary head acceleration event to the network server that maintains the mechanistic concussion model, wherein information obtained from a plurality of extraordinary head acceleration events affecting one or more subjects is used to update the fast concussion model; and
receiving one or more updated transforms from the network server that maintains the mechanistic concussion model after the fast concussion model is updated.
15. The method of claim 13 , further comprising:
receiving visualization data from the network server that maintains the mechanistic concussion model; and
providing a visualization of the extraordinary head acceleration event on the portable communication device.
16. The method of claim 7 , further comprising:
configuring the plurality of sensors based on format and timing requirements defined for an interface that transforms the motion data.
17. The method of claim 16 , further comprising:
generating visualization data using the motion data related to the extraordinary head acceleration event and based on configuration of the plurality of sensors; and
providing a visualization of the extraordinary head acceleration event on the portable communication device.
18. The method of claim 1 , further comprising:
communicating with the plurality of sensors periodically using a wireless communication protocol, wherein the motion data related to the extraordinary head acceleration event is received during one or more periodic communication events.
19. The method of claim 1 , further comprising:
receiving the motion data related to the extraordinary head acceleration event while communicating with the plurality of sensors responsive to an indication that the extraordinary head acceleration event has occurred.
20. The method of claim 1 , further comprising:
alerting a user of the portable communication device when the probability that the subject has been concussed by the extraordinary head acceleration event exceeds a configured threshold.
21. An apparatus, comprising:
a communication interface adapted to couple the apparatus to a plurality of wearable sensors;
a storage medium configured with transforms generated from a mechanistic model of head kinematics; and
a processing circuit configured to:
receive motion data related to an extraordinary head acceleration event from one or more of the plurality of wearable sensors that are configured to detect head motion of a subject;
use the motion data to obtain an estimate of axon signal dysfunction affecting one or more components of the subject's brain as a result of the head acceleration event;
wherein the estimate of axon signal dysfunction is obtained by:
determining strain at nodes of Ranvier in one or more components of the subject's brain resulting from the head acceleration event; and
estimating axon signal dysfunction affecting the one or more components of the subject's brain by translating the strain at the nodes of Ranvier to functional decrement of axon signaling;
determine a Neurologic Injury Measure (NIM) from estimated axon signal dysfunction; and
generate a probability that the subject has been concussed by the head acceleration event based on the NIM.
22. A non-transitory computer readable storage medium comprising instructions that, when executed by one or more processors, causes the one or more processors to:
receive motion data related to an extraordinary head acceleration event from a plurality of sensors configured to detect head motion of a subject;
use the motion data to obtain an estimate of axon signal dysfunction affecting one or more components of the subject's brain as a result of the head acceleration event by:
determining strain at nodes of Ranvier in one or more components of the subject's brain resulting from the head acceleration event; and
estimating axon signal dysfunction affecting the one or more components of the subject's brain by translating the strain at the nodes of Ranvier to functional decrement of axon signaling;
determine a Neurologic Injury Measure (NIM) from estimated axon signal dysfunction; and
generate a probability that the subject has been concussed by the head acceleration event based on the NIM.