The microstructure-weighted human connectome: Network metrics and relationship with conduction speed
Poster No:
1181
Submission Type:
Abstract Submission
Authors:
Arthur Spencer1, Yasser Alemán-Gómez1, Saina Asadi1, Maciej Jedynak2, Michael Chan3, Alexandre Cionca3, Dimitri Van De Ville3, Olivier David4, Patric Hagmann1, Ileana Jelescu1
Institutions:
1Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland, 2Univ. Aix Marseille, Marseille, France, 3École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland, 4Univ. Aix Marseille and Fondation Lenval, Marseille, France
First Author:
Arthur Spencer
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Co-Author(s):
Yasser Alemán-Gómez
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Saina Asadi
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Patric Hagmann
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Ileana Jelescu
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Introduction:
Traditionally, diffusion MRI-derived structural connectivity analysis has used fractional anisotropy (FA) of the diffusion tensor as a measure of connection strength (Liu, 2017). Although sensitive to white matter microstructure and organisation, FA lacks specificity in characterising fibre properties. Biophysical microstructure models provide quantitative diffusion measures with higher sensitivity and specificity to white matter microstructure (Jelescu, 2020), thus may provide higher fidelity characterisation of the structural connectome and its relation to function.
Methods:
Diffusion-weighted datasets of 66 subjects from the Human Connectome project (b-values of 1000, 2000, & 3000 s mm-2 with 90 directions per shell) were used, from which a multi-scale white matter atlas was previously computed (Alemán-Gómez, 2022). Microstructure parameters were fit using the Standard Model Imaging (SMI) toolbox (Coelho, 2022; Novikov, 2018). Microstructure properties were computed for subject-specific fibre bundles defining connections between 82 grey matter regions, by calculating the mean along each streamline, then the mean across all streamlines connecting a given pair of regions. SMI-weighted connectomes were constructed using either the axonal water fraction f or the inverse extra-axonal radial diffusivity 1/De⏊, and DTI-weighted connectomes using either FA or inverse radial diffusivity 1/RD (Fig 1).
Network metrics (characteristic path length, local and global efficiency, modularity, and small-world propensity) were calculated for each weighted connectome of each subject (Rubinov, 2010; Muldoon, 2016). Metrics were compared between weighting schemes using two-tailed paired t-tests with Bonferroni correction. Conduction delays measured by intracranial EEG from the F-TRACT project (measured with a 50 ms cut-off) were available for a subset of white matter atlas bundles (Lemaréchal, 2022). We measured the Pearson correlation coefficient between the group-average of each microstructure metric, bundle length and conduction delay, as well as the variance explained in multiple linear regression models using: i) f, 1/De⏊ and length; ii) FA, 1/RD and length; and iii) length only.
Network metrics (characteristic path length, local and global efficiency, modularity, and small-world propensity) were calculated for each weighted connectome of each subject (Rubinov, 2010; Muldoon, 2016). Metrics were compared between weighting schemes using two-tailed paired t-tests with Bonferroni correction. Conduction delays measured by intracranial EEG from the F-TRACT project (measured with a 50 ms cut-off) were available for a subset of white matter atlas bundles (Lemaréchal, 2022). We measured the Pearson correlation coefficient between the group-average of each microstructure metric, bundle length and conduction delay, as well as the variance explained in multiple linear regression models using: i) f, 1/De⏊ and length; ii) FA, 1/RD and length; and iii) length only.
·Figure 1
Results:
Notably, 1/RD connectomes had greater network integration (high global efficiency and low characteristic path length), while 1/De⏊ connectomes had greater network segregation (high local efficiency and modularity) (Fig 2A). Exhibiting high levels of both local and global connectivity is reflected by the small-world propensity, which was higher for 1/De⏊ connectomes. This parameter has indeed been shown to be sensitive and specific to white matter myelination and g-ratio (Jelescu, 2016).
Only very weak correlations were found between dMRI features and conduction delays while a strong correlation was found between f and bundle length (Fig 2B). Using multiple linear regression, f, 1/De⏊ and length explained 7.7% of variance in conduction delays, compared to 4.6% with FA, 1/RD and length, and 2.6% with length alone. These weak associations suggest the speed of transmission of neural information along white matter tracts is dependent on more than just the microstructural properties detected by these metrics. The strong association between f and bundle length could reflect either a bias in tractography reconstructions or an intrinsic brain property whereby long distances require high axonal density to minimize delays and enable proper information integration (Seidl, 2014).
Only very weak correlations were found between dMRI features and conduction delays while a strong correlation was found between f and bundle length (Fig 2B). Using multiple linear regression, f, 1/De⏊ and length explained 7.7% of variance in conduction delays, compared to 4.6% with FA, 1/RD and length, and 2.6% with length alone. These weak associations suggest the speed of transmission of neural information along white matter tracts is dependent on more than just the microstructural properties detected by these metrics. The strong association between f and bundle length could reflect either a bias in tractography reconstructions or an intrinsic brain property whereby long distances require high axonal density to minimize delays and enable proper information integration (Seidl, 2014).
·Figure 2
Conclusions:
SMI-weighted networks offer more comprehensive characterisation of the human connectome, possibly allowing more sensitive investigation of brain connectivity alterations in diseases or disorders (Liu, 2017). In future, SMI-weighted and DTI-weighted connectomes will be calculated for the other (finer) scales of parcellation described by the multi-scale atlas and made publicly available to serve as a reference for connectomics analysis.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Keywords:
Electroencephaolography (EEG)
MRI
Myelin
STRUCTURAL MRI
Tractography
White Matter
Other - Connectomics
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Muldoon, S. F. (2016). Small-world propensity and weighted brain networks. Scientific reports, 6(1), 22057.
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Seidl, A. H. (2014). Regulation of conduction time along axons. Neuroscience, 276, 126-134.