Macroscale angioarchitectural properties of functional and structural networks
Poster No:
1803
Submission Type:
Abstract Submission
Authors:
Stefano Moia1, Omer Faruk Gulban2, Enrico Amico3, Maria Preti4, Benedikt A. Poser5, Dimo Ivanov5
Institutions:
1Maastricht University, Maastricht, Linburg, 2Maastricht University, Maastricht, Netherlands, 3Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, 4University of Geneva, Geneva, Geneva, 5Maastricht University, Maastricht, Limburg
First Author:
Co-Author(s):
Introduction:
Research with concurrent BOLD fMRI and physiological recordings showed cortical areas correlating with synchronous physiological fluctuations[1,2]; such vascular networks seem to support related functional network activity[2]. Translated to anatomy, capillaries should support neighbouring neurons, with vascular density correlating with neuronal activity[3,4]. We aim at verifying whether angioarchitecture relates to structural connectivity (SC), function-structure coupling (via structural decoupling index, SDI)[5], and total, coupled, and decoupled functional connectivity (FC)[6].
Methods:
Data from the Human Connectome Project's (HCP) 100 independent subjects release were preprocessed as per[5]. Resting State (RS) timeseries and diffusion data were used to compute SDI, and node strength (NS) of SC (SCNS) and total, coupled, and decoupled FC (FCNS)[6] within the Glasser atlas (360 parcels) with nigsp[7]. The VENAT atlas[8] density map was adopted to mimic average venous-dominated vascular density for each parcel. The thresholded (.04) VENAT tissue probability map was used to compute average geodesic distance of each parcel from vessels using LayNii. The nodal spatial correlation of all metrics was computed.
Preprocessed T1w, T2w, and Time of Flight (ToF) images from the Natural Scenes Dataset[9] (NSD) subject 1 were brain-extracted and their intensity folded on a 2D plane onto which manual clustering was carried out to extract vascular positions with Segmentator[10]. This mask, arteries-dominated, was further manually corrected. Preprocessed fibre tracts and RS timeseries were processed matching steps described above.
Preprocessed T1w, T2w, and Time of Flight (ToF) images from the Natural Scenes Dataset[9] (NSD) subject 1 were brain-extracted and their intensity folded on a 2D plane onto which manual clustering was carried out to extract vascular positions with Segmentator[10]. This mask, arteries-dominated, was further manually corrected. Preprocessed fibre tracts and RS timeseries were processed matching steps described above.
Results:
Figure 1 shows the metric distributions for the HCP (top) and NSD data (bottom) in the Galsser atlas.
Figure 2 shows nodal correlation between different measures for HCP (left) and NSD (right). Nodal correlation between SCNS and all FCNSs is comparable in both datasets. While HCP data feature stronger correlation between FCNS and SCNS and weaker correlation between FCNS and decoupled FCNS than NSD data, the most prominent differences lay in the correlation of FCNS and SCNS with vascular metrics.
In HCP data, FCNS weakly but positively correlates with vascular density and negatively with vascular distance and SDI shows the opposite, suggesting a tendency for areas with more prominent FC to maximise on blood supply redundancy by increasing vascular density and maximise efficiency by lowering the distance from major vessels, in line with previous hypotheses[4].
We expected coupled FCNS to feature strong correlations with vascular properties: coupled FC embeds non-idiosyncratic characteristics of functional organisation[6]. Instead, we found negligible correlation with vascular density and low correlation with vascular distance.
In NSD data, correlations of all metrics with vascular properties based on VENAT are negligible, except a weak positive correlation between SDI and vascular distance. Vascular distance based on the subject vascular map weakly correlates negatively with SDI and positively with FCNS and decoupled FCNS.
Such opposite relationship of SC and FCs with vascular distance based on VENAT or on the subject-specific vascular map could be attributed to the different nature of the two sets of vessels, venous in the former case and arterial in the latter, being the BOLD contrast mainly a venous effect. The difference between NSD and HCP results indicate how idiosyncratic vascular properties can be.
Figure 2 shows nodal correlation between different measures for HCP (left) and NSD (right). Nodal correlation between SCNS and all FCNSs is comparable in both datasets. While HCP data feature stronger correlation between FCNS and SCNS and weaker correlation between FCNS and decoupled FCNS than NSD data, the most prominent differences lay in the correlation of FCNS and SCNS with vascular metrics.
In HCP data, FCNS weakly but positively correlates with vascular density and negatively with vascular distance and SDI shows the opposite, suggesting a tendency for areas with more prominent FC to maximise on blood supply redundancy by increasing vascular density and maximise efficiency by lowering the distance from major vessels, in line with previous hypotheses[4].
We expected coupled FCNS to feature strong correlations with vascular properties: coupled FC embeds non-idiosyncratic characteristics of functional organisation[6]. Instead, we found negligible correlation with vascular density and low correlation with vascular distance.
In NSD data, correlations of all metrics with vascular properties based on VENAT are negligible, except a weak positive correlation between SDI and vascular distance. Vascular distance based on the subject vascular map weakly correlates negatively with SDI and positively with FCNS and decoupled FCNS.
Such opposite relationship of SC and FCs with vascular distance based on VENAT or on the subject-specific vascular map could be attributed to the different nature of the two sets of vessels, venous in the former case and arterial in the latter, being the BOLD contrast mainly a venous effect. The difference between NSD and HCP results indicate how idiosyncratic vascular properties can be.
Conclusions:
Our results provide motivation to suggest to better differentiate and further investigate the type of vasculature (arterial vs. venous) that relate with BOLD-fMRI FC, while paying attention to idiosyncrasies of vascular properties.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Neuroanatomy Other
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 2
Physiology, Metabolism and Neurotransmission :
Physiology, Metabolism and Neurotransmission Other
Keywords:
ANGIOGRAPHY
FUNCTIONAL MRI
MR ANGIOGRAPHY
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - connectivity, physiology
1|2Indicates the priority used for review
Provide references using author date format
2. Bright, M. G., Whittaker, J. R., Driver, I. D., & Murphy, K. (2020). Vascular physiology drives functional brain networks. NeuroImage, 217. https://doi.org/10.1101/475491
3. Harel, N., Bolan, P. J., Turner, R., Ugurbil, K., & Yacoub, E. (2010). Recent Advances in High-Resolution MR Application and Its Implications for Neurovascular Coupling Research. Frontiers in Neuroenergetics, 2(September), 1–8. https://doi.org/10.3389/fnene.2010.001
4. Woolsey, T. A., Rovainen, C. M., Cox, S. B., Henegar, M. H., Liang, G. E., Liu, D., … Wei, L. (1996). Neuronal units linked to microvascular modules in cerebral cortex: Response elements for imaging the brain. Cerebral Cortex, 6(5), 647–660. https://doi.org/10.1093/cercor/6.5.647
5. Preti, M. G., & Van De Ville, D. (2019). Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nature Communications, 10(1), 1–7. https://doi.org/10.1038/s41467-019-12765-7
6. Griffa, A., Amico, E., Liégeois, R., Van De Ville, D., & Preti, M. G. (2022). Brain structure-function coupling provides signatures for task decoding and individual fingerprinting. NeuroImage, 250(November 2021), 118970. https://doi.org/10.1016/j.neuroimage.2022.118970
7. Moia, S. (2022). NiGSP: A python library (and toolbox!) to run Graph Signal Processing on multimodal MRI data. Zenodo. https://doi.org/10.5281/ZENODO.6855998
8. Huck, J., Wanner, Y., Fan, A. P., Jäger, A. T., Grahl, S., Schneider, U., … Gauthier, C. J. (2019). High resolution atlas of the venous brain vasculature from 7 T quantitative susceptibility maps. Brain Structure and Function, 224(7), 2467–2485. https://doi.org/10.1007/s00429-019-01919-4
9. Allen, E. J., St-Yves, G., Wu, Y., Breedlove, J. L., Prince, J. S., Dowdle, L. T., … Kay, K. (2022). A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nature Neuroscience, 25(1), 116–126. https://doi.org/10.1038/s41593-021-00962-x
10. Gulban, O. F., Schneider, M., Marquardt, I., Haast, R. A. M., & De Martino, F. (2018). A scalable method to improve gray matter segmentation at ultra high field MRI. PLoS ONE (Vol. 13). https://doi.org/10.1371/journal.pone.0198335