Sports-related concussion differentially impacts functional brain networks in college athletes

Zach Headley Author

04/05/2021 Added

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Sports-Related concussion differentially impacts functional brain networks in college athletes

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[00:00:00.780]Hello,
[00:00:01.230]my name is Zach Headley and I will be going over my UCARE project today,
[00:00:04.890]titled "Sports related concussions, differentially impact
[00:00:07.980]functional brain networks in college athletes." Athletes receive
[00:00:13.530]around 2 to 4 million sports related concussions each year in the
[00:00:17.310]United States. Sports related
[00:00:19.290]concussions are complex brain injuries causing multiple changes in the
[00:00:23.340]brain, including the disruption of functional brain networks. Overall,
[00:00:27.450]this project uses magnetic resonance imaging to identify changes in individual
[00:00:31.950]network organization due to sports related concussion.
[00:00:35.900]In order to complete this study.
[00:00:37.880]baseline MRI data was collected for all incoming UNL football players
[00:00:42.140]since 2018.
[00:00:43.700]The present results will focus on data from 26 football players diagnosed with
[00:00:48.050]sports related concussion. 24 to 48 hours
[00:00:51.620]after receiving a sports related concussion,
[00:00:54.170]a player receives a post scan and additional scan is taken right before the
[00:00:58.910]player returned to play.
[00:01:00.980]These scans showed the BOLD signals for very small areas of the brain called
[00:01:04.820]voxels.
[00:01:06.350]These voxels can be grouped into 264 larger regions called nodes,
[00:01:10.910]which can then be grouped into networks. Overall, we see,
[00:01:15.110]we wanted to see how the network assignments of nodes varied from baseline
[00:01:20.000]to post-injury to return to play. And in order to accomplish this,
[00:01:24.260]we first had to assign networks to each node.
[00:01:27.200]The BOLD time series data of one node was compared to the meantime series
[00:01:32.090]of, for each network in the power Atlas.
[00:01:36.140]And it was assigned to the network that it was most closely correlated with.
[00:01:40.550]All of the nodes were assigned to the networks in this manner. After each node
[00:01:45.260]had been assigned to a network,
[00:01:46.910]we repeated this process after recalculating the mean network time series,
[00:01:51.890]based on these new network assignments.
[00:01:54.470]This process was iterated until 95% of the nodes did not change their network
[00:01:59.000]assignments from one iteration to the next.
[00:02:01.910]Once we found the stable network assignments for all individuals,
[00:02:05.750]for each of the three scans,
[00:02:07.520]we examined the changes in network assignments related to sports related
[00:02:11.480]concussions.
[00:02:13.160]A key point to our research is the collection of baseline data
[00:02:18.530]that is needed to compare separate individual posts, injury,
[00:02:21.650]and return scared scanned accurately due to the high variability of network
[00:02:26.150]assignments to various nodes across individuals.
[00:02:29.450]The high variability of network organization at baseline is shown in this upper
[00:02:34.010]middle matrix. This matrix shows the functional connectivity of the,
[00:02:38.220]of the 264 nodes across participants.
[00:02:42.050]That color scale from black to yellow represents the standard deviation
[00:02:47.000]from the mean of the functional connectivity with the yellow showing the highest.
[00:02:51.680]variability. As you can
[00:02:54.380]see in this matrix, the connections across individuals differ considerably,
[00:02:59.020]which is why it's important to collect baseline data for each person. Next,
[00:03:03.880]I'm going to show you the network assignments we came up for with at
[00:03:08.590]baseline.
[00:03:09.760]You can see these results on the two brains depicted on the left. In
[00:03:14.230]comparison to the power Atlas organization of networks,
[00:03:20.140]our network assignments are more random and do not follow the general network
[00:03:24.280]topography we'd expect to see.
[00:03:26.890]There are some networks usually located in specific locations,
[00:03:31.690]such as the visual network in the occipital lobe and the default mode
[00:03:36.520]located in the posterior cingulate and the medial prefrontal cortex. However,
[00:03:41.260]with our original approach,
[00:03:42.310]we saw a little correspondence with these established networks,
[00:03:45.760]instead observing that our networks were randomly spread out through the brain.
[00:03:52.020]After this observation, we reevaluated our approach.
[00:03:57.060]There are three
[00:03:57.640]networks from the Power Atlas that could be adding high variability to our
[00:04:01.230]results due to their undefined nature:
[00:04:03.930]the Uncertain, Sub-cortical and Cerebellum networks.
[00:04:07.620]Also the high threshold could be causing additional variability,
[00:04:10.980]forcing the nodes to be reassigned more than necessary. Based on this evidence,
[00:04:15.600]we modified our approach to focus on 11 networks,
[00:04:19.950]excluding those previously stated, and use a lower threshold of 50%.
[00:04:25.230]This approach created the network organization shown on the right in
[00:04:30.000]comparison to the Power Atlas.
[00:04:32.730]These two right
[00:04:33.810]brains are more consistent with the known network organization.
[00:04:37.980]For example, the visual network,
[00:04:40.380]shown in blue, is located
[00:04:44.340]in the occipital lobe is more apparent with this approach compared to the
[00:04:48.720]original.
[00:04:50.220]The default mode network is more consistent with the well-established literature
[00:04:54.420]on this network than originally. Overall,
[00:04:57.180]the new approach provided network assignments that were more consistent with the
[00:05:01.740]known network architecture of the brain. Next,
[00:05:05.130]we compared the number of nodes per network at baseline compared to post these
[00:05:09.900]results are shown in the upper right
[00:05:12.540]hand corner. We did not find
[00:05:15.630]any significant differences in the number of nodes assigned to each network
[00:05:19.440]following concussion. Some networks had more nodes at baseline,
[00:05:23.490]while other networks had more nodes after concussion.
[00:05:28.380]We are currently
[00:05:29.220]analyzing the possible recovery of nodes to their baseline network assignments
[00:05:33.840]before the football player begins to play again.
[00:05:37.140]We specifically focused on hubs because they are crucial to the functional
[00:05:40.860]connectivity
[00:05:42.270]in the brain. Hubs are critical
[00:05:44.310]nodes that interact with many more nodes in the network and across networks
[00:05:48.480]compared to other nodes. Participation.
[00:05:51.240]coefficient is a specific statistic that measures a node's hub characteristic.
[00:05:56.250]We can also identify hubs in the air traffic patterns across the United States.
[00:06:01.310]An airport like Atlanta would be a hub with a large participation coefficient
[00:06:05.840]because is connected to many different airports.
[00:06:09.080]While an airport like Lincoln would not be a hub and have a small participation
[00:06:12.860]coefficient. We'd looked at the proportion of nodes after concussion,
[00:06:17.390]and that returned to play that were assigned to the same network they were
[00:06:22.070]at baseline. We focused
[00:06:24.590]on the networks with the largest participation coefficient,
[00:06:27.650]which were Default Mode, Memory and Dorsal Attention networks.
[00:06:31.340]These averages can be seen in the right middle square.
[00:06:36.410]At return
[00:06:36.920]to play,
[00:06:37.580]there was a greater proportion of nodes that recovered to their baseline
[00:06:41.300]networks. However,
[00:06:42.320]this was not statistically significant. Future analyses will continue to examine
[00:06:47.120]how hubs may be impacted by sports related concussions
[00:06:52.880]To conclude, baseline data is crucial
[00:06:55.810]To conclude,
[00:07:00.910]baseline data is crucial to the high variability and functional connectivity
[00:07:05.200]across participants Through a parameter search,
[00:07:08.380]we found that a less stringent stability threshold did the best job of capturing
[00:07:12.100]individual network organization while preserving the well-established general
[00:07:16.120]network topography.
[00:07:18.070]We did not find any significant changes in the number of nodes assigned to each
[00:07:22.090]functional network from baseline to post-injury. Future analyses
[00:07:25.930]will focus on the stability of networks
[00:07:27.880]hubs compared to nodes that are not classified as hubs.
[00:07:32.620]Finally,
[00:07:33.010]I'd like to acknowledge my co-authors for their assistance and contributions as
[00:07:37.150]well as the NIH Great Plain IDeA CTR Network Pilot Grant,
[00:07:42.100]and UCARE for allowing me to complete this project. Thank you.

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Sports-related concussion differentially impacts functional brain networks in college athletes

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