Visualizing Human Neuroimaging Data

Project questions 1:

What is the data quality at the subject level?

Goal:

To assess data quality in terms of correlation strength and variance within each network.

Higher mean values indicates that the brain regions comprising that network are communicating well with each other.

Higher standard deviation values (longer lines) means that there is more variance within the network. This could be due to misplaced network nodes, preprocessing errors, excess motion by that subject, or that the regions comprising the network are genuinly not communicating well with each other.

Conclusion:

Subjects 105 and 107 had low means (closer to zero) and high standard deviations. Repositioning the nodes did not improve the outcome. Therefore these subjects' data were excluded from Group analysis for both EPI1 and EPI2.

Project questions 3:

Which data-collection method is the best?

Goal:

To assess data quality in terms of correlation strength within each network.

Higher mean values indicates that the brain regions comprising that network are communicating well with each other.

Higher standard deviation values (longer lines) means that there is more variance within the network, and that brain regions comprising the network are not communicating well with each other.

Conclusion:

While excluding Subjects 105 and 107 due to low means (close to zero) with high standard deviations, the resulting group means and standard deviations suggest that higher temporal resolution (EPI1) is better at capturing connectivity within these networks.

Project questions 1:

What is the data quality at the subject level?

Goal:

To assess data quality in terms of correlation strength within and between networks.

One way to do that is to look at the connections between brain regions. By exploring different thresholds of "connectivity" (correlation z-score), we can compare EPI1 and EPI2 methods. We would expect a superior data collection method will detect more connections within and between networks at any given threshold, while also detecting stronger connections overall.

Conclusion:

While excluding Subjects 105 and 107 due to terrible means (close to zero) and high standard deviations, the resulting group network graphs show that higher temporal resolution (EPI1) is better at detecting connections between brain regions within networks and also between networks.

Project question 3:

Which data-collection method is the best?

Goal:

To assess data quality in terms of correlation strength within and between networks.

One way to do that is to look at the connections between brain regions. By exploring different thresholds of "connectivity" (correlation z-score), we can compare EPI1 and EPI2 methods. We would expect a superior data collection method will detect more connections within and between networks at any given threshold, while also detecting stronger connections overall.

Conclusion:

While excluding Subjects 105 and 107 due to terrible means (close to zero) and high standard deviations, the resulting group network graphs show that higher temporal resolution (EPI1) is better at detecting connections between brain regions within networks and also between networks.

Project question 3:

Which data-collection method is the best?

Goal:

To assess data quality in terms of correlation strength within and between networks.

One way to do that is to look at the connections between brain regions. By exploring different thresholds of "connectivity" (correlation z-score), we can compare EPI1 and EPI2 methods. We would expect a superior data collection method will detect more connections within and between networks at any given threshold, while also detecting stronger connections overall.

Conclusion:

While excluding Subjects 105 and 107 due to terrible means (close to zero) and high standard deviations, the resulting group network graphs show that higher temporal resolution (EPI1) is better at detecting connections between brain regions within networks and also between networks.

Project question 2 and 3:

Can we detect transient networks in resting state data?

Which data collection method is the best?

Goal and Results:

To identify transient networks, "clusters", "centroids", or "brain states" in grouped EPI1 and EPI2 data.

Clusters, or brain states, were identified using an unsupervised clustering algorithm (Dirichlet Process Gaussian Mixture Model [DPGMM]). It found that EPI1 was best divided into two clusters, or brain states, where as EPI2 only contained one cluster. Therefore, only EPI1 Cluster 1 and Cluster 2 are shown here (threshold >0.3 z-score).

Conclusion:

EPI1 resting state data comprises two recurring brain states over the course of nine minutes. One brain state (Cluster 1) consists of a highly connected framework where connectivity is higher between brain regions within and between networks. The second brain state (Cluster 2) is much less connected overall. Therefore, we are able to detect transient networks using high temporal resoution (EPI1).

Project question 2 and 3:

Can we detect transient networks in resting state data?

Which data collection method is the best?

Goal and Results:

To identify transient networks, "clusters", "centroids", or "brain states" in grouped EPI1 and EPI2 data.

Clusters, or brain states, were identified using an unsupervised clustering algorithm (Dirichlet Process Gaussian Mixture Model [DPGMM]). It found that EPI1 was best divided into two clusters, or brain states, where as EPI2 only contained one cluster. Therefore, only EPI1 Cluster 1 and Cluster 2 are shown here.

Conclusion:

EPI1 resting state data comprises two recurring brain states over the course of nine minutes. One brain state (Cluster 1) consists of a highly connected framework where connectivity is higher (more pink) between brain regions within and between networks. The second brain state (Cluster 2) is much less connected overall (more green). Therefore, we are able to detect transient networks using high temporal resoution (EPI1).