Functional network architecture and dynamics of the somato-cognitive action network
Poster No:
1484
Submission Type:
Late-Breaking Abstract Submission
Authors:
Rick Betzel1
Institutions:
1University of Minnesota, Minneapolis, MN
First Author:
Late Breaking Reviewer(s):
Eduardo Garza-Villarreal, M.D., Ph.D.
Universidad Nacional Autónoma de México campus Juriquilla
Juriquilla, Querétaro
Universidad Nacional Autónoma de México campus Juriquilla
Juriquilla, Querétaro
Introduction:
The human brain can be divided into networks. Historically, networks have been defined at the population level, emphasizing group characteristics at the expense of the individual. Recently precision functional mapping has refocused network detection onto the individual. This approach yields personalized estimates of network boundaries. In addition, it aided in the discovery of a novel network -- the somato-cognitive action network (SCAN). This network is positioned along the motor strip and interdigitates somatomotor networks.
However, owing to its relative newness, its functional connectivity architecture has not been fully described. Further, virtually nothing is known about the "dynamics" of SCAN -- i.e. how activity and connectivity of SCAN nodes to one another and to the rest of the brain fluctuates over short timescales.
In this submission, we leverage two PFM datasets -- the Midnight Scan Club (MSC; 10 brains, 10 sessions) and the casting dataset (3 brains; approximately 45 sessions per brain). We develop semi-automated techniques to detect SCAN and characterize novel functional connectivity features. We also use study time-varying fluctuations in SCAN activity using co-activation patterns (CAPS). Finally, we leverage a recently described "edge-centric" method for parsing the precise contributions of each CAP to the time-invariant (static) functional connectivity of SCAN.
However, owing to its relative newness, its functional connectivity architecture has not been fully described. Further, virtually nothing is known about the "dynamics" of SCAN -- i.e. how activity and connectivity of SCAN nodes to one another and to the rest of the brain fluctuates over short timescales.
In this submission, we leverage two PFM datasets -- the Midnight Scan Club (MSC; 10 brains, 10 sessions) and the casting dataset (3 brains; approximately 45 sessions per brain). We develop semi-automated techniques to detect SCAN and characterize novel functional connectivity features. We also use study time-varying fluctuations in SCAN activity using co-activation patterns (CAPS). Finally, we leverage a recently described "edge-centric" method for parsing the precise contributions of each CAP to the time-invariant (static) functional connectivity of SCAN.
Methods:
Datasets: We analyze two datasets. Both are publicly available (https://openneuro.org/datasets/ds000224/versions/1.0.4 and https://openneuro.org/datasets/ds002766/versions/3.0.0) and processed into CIFTI files. We performed no additional processing.
CAPS: We used time-series template matching to assign each surface vertex (grayordinate) to one of 20 previously-described functional network templates, including SCAN. For each participant, we calculated the root mean square amplitude of SCAN activity (averaged over all vertices assigned to SCAN). We compared the observed amplitude at each frame with a null distribution generated by "spinning" the SCAN network assignment map. We retained whole-brain activity maps (cortical vertices + subcortical voxels + cerebellar voxels) for peak frames of the intact RMS time series whose amplitude was statistically greater than that of the null distribution. We then used a consensus clustering and modularity maximization approach to group these patterns into clusters based on the spatial similarity to one another. This procedure was carried out independently for each casting participant and collectively for MSC participants after aggregating peak activation patterns across participants.
CAPS: We used time-series template matching to assign each surface vertex (grayordinate) to one of 20 previously-described functional network templates, including SCAN. For each participant, we calculated the root mean square amplitude of SCAN activity (averaged over all vertices assigned to SCAN). We compared the observed amplitude at each frame with a null distribution generated by "spinning" the SCAN network assignment map. We retained whole-brain activity maps (cortical vertices + subcortical voxels + cerebellar voxels) for peak frames of the intact RMS time series whose amplitude was statistically greater than that of the null distribution. We then used a consensus clustering and modularity maximization approach to group these patterns into clusters based on the spatial similarity to one another. This procedure was carried out independently for each casting participant and collectively for MSC participants after aggregating peak activation patterns across participants.
Results:
We showed the SCAN exhibits strong FC to a sensory-action complex comprising somatomotor, dorsal attention, and action mode networks (Fig 1b). We showed, however, that this SCAN-averaged FC could be parsed into contributions from three spatially distributed and bilaterally symmetric "nodes" (Fig 1c). Next, we used an overlapping clustering algorithm that allowed vertices to belong to multiple networks simultaneously, and demonstrated that SCAN exhibited the highest level of overlap compared to all other pre-defined networks (Fig 1d). Next, we detected CAPS in both the MSC and casting datasets. We found that high-amplitude SCAN activity could be divided into two CAPS that were shared across all participants and datasets (Fig 1e). However, leveraging the massive amounts of data per brain in the casting dataset, we demonstrated that, when CAPS are estimated on a per-person basis, novel, person-specific patterns appear. Finally, we used edge time series to demonstrate that SCAN seed FC can be explained based on the superposition of high-amplitude connectivity CAPS (and their corresponding instantaneous connectivity; Fig 1f).
Conclusions:
We study dynamics properties of SCAN for the first time.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 2
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Other - functional connectivity; network neuroscience; somato-cognitive action network
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Newbold, D. J., Laumann, T. O., Hoyt, C. R., Hampton, J. M., Montez, D. F., Raut, R. V., ... & Dosenbach, N. U. (2020). Plasticity and spontaneous activity pulses in disused human brain circuits. Neuron, 107(3), 580-589.