Aberrant hierarchical functional system following subcortical infarction
Poster No:
174
Submission Type:
Abstract Submission
Authors:
Ke Wu1, Gang liu2, Jinsheng Zeng2, Gaolang Gong1
Institutions:
1Beijing Normal University, Beijing, China, 2Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province
First Author:
Co-Author(s):
Gang liu
Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong Province
Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong Province
Jinsheng Zeng
Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong Province
Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong Province
Introduction:
The brain is comprised of multiple systems that are optimized to process information through both within-system and between-system connections (Keller et al., 2023). Focal stroke damage disrupts these widespread connections, affecting overall brain network integration (Siegel et al., 2016). Subcortical regions (basal ganglia, thalamus, hippocampus) play a crucial role in interconnecting distant parts of the cerebral cortex, underlying many neurological and psychiatric disorders (Alexander et al., 1986). To date, how subcortical damage alter the communication between brain systems is still poorly understood. This study analyzed resting-state fMRI data from patients with subcortical infarction compared to healthy controls, aiming to explore the changes in functional hierarchical structures and functional connectivity profiles.
Methods:
Participants
A total of 55 patients, who experienced first episode of unilateral subcortical infarct involving the anterior circulation without cortical lesions. Patients underwent neurological assessment and acquisition of multi-modal MRI data within the first week after symptom onset. Additionally, 49 healthy controls (HC) underwent similar assessment and MRI scans for comparison.
MRI processing
Resting-state functional MRI data were preprocessed using fMRIPrep (Esteban et al., 2019). To reduce computational demands, the preprocessed rsfMRI surface was downsampled to 10k_LR surface space, resulting in 18,738 vertexes. Subsequent functional connectivity and gradient analyses were performed at vertex level. Cortical network allocations are based on Yeo network classification, which includes seven networks.
Connectome gradient analysis
We applied Pearson correlation to estimate an 18,738 × 18,738 functional connectivity (FC) matrix for each participant. Gradient computation used the BrainSpace MATLAB toolbox (Vos de Wael et al., 2020), with diffusion map embedding algorithm, 90% threshold, α = 0.5, and t = 0. To compare stroke and HC groups, an average connectivity matrix from controls was used to create a gradient component template. We then performed Procrustes rotation to align the gradients of each participant to this template.
Functional connectivity strength
Between-network FCS map was derived by averaging the connection strength between each vertex and vertices from different networks. The within-network functional connectivity map is obtained by averaging the connection strength between each vertex and other vertices within the same network.
Statistical analysis
We compare each value in the gradient maps and FCS maps between the control group and the patient group using t-test analysis.
A total of 55 patients, who experienced first episode of unilateral subcortical infarct involving the anterior circulation without cortical lesions. Patients underwent neurological assessment and acquisition of multi-modal MRI data within the first week after symptom onset. Additionally, 49 healthy controls (HC) underwent similar assessment and MRI scans for comparison.
MRI processing
Resting-state functional MRI data were preprocessed using fMRIPrep (Esteban et al., 2019). To reduce computational demands, the preprocessed rsfMRI surface was downsampled to 10k_LR surface space, resulting in 18,738 vertexes. Subsequent functional connectivity and gradient analyses were performed at vertex level. Cortical network allocations are based on Yeo network classification, which includes seven networks.
Connectome gradient analysis
We applied Pearson correlation to estimate an 18,738 × 18,738 functional connectivity (FC) matrix for each participant. Gradient computation used the BrainSpace MATLAB toolbox (Vos de Wael et al., 2020), with diffusion map embedding algorithm, 90% threshold, α = 0.5, and t = 0. To compare stroke and HC groups, an average connectivity matrix from controls was used to create a gradient component template. We then performed Procrustes rotation to align the gradients of each participant to this template.
Functional connectivity strength
Between-network FCS map was derived by averaging the connection strength between each vertex and vertices from different networks. The within-network functional connectivity map is obtained by averaging the connection strength between each vertex and other vertices within the same network.
Statistical analysis
We compare each value in the gradient maps and FCS maps between the control group and the patient group using t-test analysis.
Results:
As shown in Figure 1, The principal gradient of FC exhibited a similar sensorimotor-to-association (S-A) gradient of cortical organization in stroke and HC. Similarly, the secondary gradient displayed a comparable sensorimotor-to-visual gradient. Of note, there was significant group difference in the principal gradient, but not the secondary gradient. Compared to HC, stroke patients showed increased gradient values in sensorimotor and visual network and decreased gradient scores in association regions.
The group difference in FCS mainly observed in between-network FCS, rather than within-network FCS (Fig.2A). Compared to HC, functional connectivity between sensorimotor/visual networks and association networks was stronger in stroke patients (Fig.2E). Notably, the pattern of S-A axis loadings and the pattern of FCS loadings were highly correlated, indicating that functional group differences along the S-A axis are associated with differences in between-network FC profiles.
The group difference in FCS mainly observed in between-network FCS, rather than within-network FCS (Fig.2A). Compared to HC, functional connectivity between sensorimotor/visual networks and association networks was stronger in stroke patients (Fig.2E). Notably, the pattern of S-A axis loadings and the pattern of FCS loadings were highly correlated, indicating that functional group differences along the S-A axis are associated with differences in between-network FC profiles.
Conclusions:
The present study revealed a diminished separation between sensory and association systems in subcortical infarction patients. Notably, this aberrant cortical hierarchy organization was associated with stronger synchronization between functional networks, especially between sensory and association systems.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Univariate Modeling
Motor Behavior:
Motor Behavior Other
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Sub-Cortical
Other - Stroke
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?
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.
2. Siegel, J. S., Ramsey, L. E., Snyder, A. Z., Metcalf, N. V., Chacko, R. V., Weinberger, K., ... & Corbetta, M. (2016). Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proceedings of the National Academy of Sciences, 113(30), E4367-E4376.
3. Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual review of neuroscience, 9(1), 357-381.
4. Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., ... & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods, 16(1), 111-116.
5. Vos de Wael, R., Benkarim, O., Paquola, C., Lariviere, S., Royer, J., Tavakol, S., ... & Bernhardt, B. C. (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Communications biology, 3(1), 103.