Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox

Lange, Siemon C. de; Helwegen, Koen; Heuvel, Martijn P. van den

doi:10.1016/j.neuroimage.2023.120108

Cited by 21 publications

(8 citation statements)

References 62 publications

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“…This reconstruction of the DWI‐based structural connectivity matrices was done with the Connectivity Analysis TOolbox (CATO, v3.1.2. ; de Lange et al, 2023 ). To this end, additional (preprocessing) steps had to be performed on the anatomical (T1) and DW images.…”

Section: Methodsmentioning

confidence: 97%

“…To this end, we collected diffusion weighted imaging (DWI) data of participants and used a combined diffusion tensor imaging (DTI) and generalized Q‐sampling imaging (GQI; Yeh et al, 2010 ) approach (de Lange et al, 2023 ) to identify the RC hubs in a group average connectome. During a functional magnetic resonance imaging (fMRI) session, the same group of participants saw nested, hierarchically structured digit sequences.…”

Section: Introductionmentioning

confidence: 99%

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The significance of structural rich club hubs for the processing of hierarchical stimuli

Mecklenbrauck,

Gruber,

Siestrup

et al. 2023

Human Brain Mapping

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The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well‐interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior‐posterior and medial‐lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole‐brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

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Section: Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

The significance of structural rich club hubs for the processing of hierarchical stimuli

Mecklenbrauck,

Gruber,

Siestrup

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…For the functional and gene‐expression analyses, we created our own group version of the Lausanne‐219 parcellation based on the T1‐weighted structural images acquired alongside the functional imaging data. Structural images were run through HCP's freesurfer pipeline (Glasser et al, 2013) and subsequently parcellated according to the Lausanne‐219 atlas with scripts distributed with the CATO toolbox (de Lange & van den Heuvel, 2021). All individual parcellations were subsequently aligned with fsaverage_32LR space and a group atlas was created by assigning each vertex to the most frequent Lausanne‐label across participants.…”

Section: Methodsmentioning

confidence: 99%

Molecular signatures of attention networks

Schindler,

Jawinski,

Arnatkevičiūtė

et al. 2024

Human Brain Mapping

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Attention network theory proposes three distinct types of attention—alerting, orienting, and control—that are supported by separate brain networks and modulated by different neurotransmitters, that is, norepinephrine, acetylcholine, and dopamine. Here, we explore the extent of cortical, genetic, and molecular dissociation of these three attention systems using multimodal neuroimaging. We evaluated the spatial overlap between fMRI activation maps from the attention network test (ANT) and cortex‐wide gene expression data from the Allen Human Brain Atlas. The goal was to identify genes associated with each of the attention networks in order to determine whether specific groups of genes were co‐expressed with the corresponding attention networks. Furthermore, we analyzed publicly available PET‐maps of neurotransmitter receptors and transporters to investigate their spatial overlap with the attention networks. Our analyses revealed a substantial number of genes (3871 for alerting, 6905 for orienting, 2556 for control) whose cortex‐wide expression co‐varied with the activation maps, prioritizing several molecular functions such as the regulation of protein biosynthesis, phosphorylation, and receptor binding. Contrary to the hypothesized associations, the ANT activation maps neither aligned with the distribution of norepinephrine, acetylcholine, and dopamine receptor and transporter molecules, nor with transcriptomic profiles that would suggest clearly separable networks. Independence of the attention networks appeared additionally constrained by a high level of spatial dependency between the network maps. Future work may need to reconceptualize the attention networks in terms of their segregation and reevaluate the presumed independence at the neural and neurochemical level.

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“…Connectivity Analysis TOolbox (39) (CATO, version 3.1.2) and FMRIB Software Library (40) (FSL version 6.0.4) were used to preprocess diffusion weighted data and reconstruct structural connectivity matrices. For network reconstruction we used the fine-grained Cammoun sub-parcellation of the Desikan-Killiany atlas consisting of 114 cortical regions (39,41). To validate our findings and include subcortical regions, we also used the Desikan-Killiany combined cortical and subcortical atlas as present in FreeSurfer (42) consisting of 82 regions.…”

Section: Structural Connectome Reconstructionmentioning

confidence: 98%

Insomnia subtypes have differentiating deviations in brain structural connectivity

Bresser,

Blanken,

de Lange

et al. 2023

Preprint

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ObjectiveInsomnia disorder is the most common sleep disorder. A better understanding of insomnia-related deviations in the brain could inspire better treatment. Insufficiently recognized heterogeneity within the insomnia population could obscure involved brain circuits. The present study investigated whether structural brain connectivity deviations differ between recently discovered and validated insomnia subtypes.MethodsStructural and diffusion weighted 3-Tesla MRI data of four independent studies were harmonized. The sample consisted of 73 controls without sleep complaints and 204 participants with insomnia grouped into five subtypes based on their fingerprint of personality and mood traits assessed with the Insomnia Type Questionnaire. Linear regression correcting for age, sex, and brain volume evaluated group differences in structural connectivity strength, indicated by fractional anisotropy and mean diffusivity, and evaluated within two different atlases.ResultsInsomnia subtypes showed differentiating profiles of deviating structural connectivity which moreover concentrated in different functional networks. Permutation testing against randomly drawn heterogeneous subsamples indicated significant specificity of deviation profiles in four of the five subtypes:highly distressed(p=0.019), moderately distressed reward insensitive(p=0.014), slightly distressed low reactive(p=0.006) andslightly distressed high reactive(p=0.006).ConclusionsOur results provide a first indication that different insomnia subtypes exhibit distinct profiles of deviations in structural brain connectivity. Subtyping of insomnia could be essential for a better understanding of brain mechanisms that contribute to insomnia vulnerability.

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Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox

Cited by 21 publications

References 62 publications

The significance of structural rich club hubs for the processing of hierarchical stimuli

The significance of structural rich club hubs for the processing of hierarchical stimuli

Molecular signatures of attention networks

Insomnia subtypes have differentiating deviations in brain structural connectivity

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