Using connectomics for predictive assessment of brain parcellations

Albers, Kristoffer Jon; Ambrosen, Karen S; Liptrot, Matthew G.; Dyrby, Tim B.; Schmidt, Mikkel N.; Mørup, Morten

doi:10.1016/j.neuroimage.2021.118170

Cited by 13 publications

(21 citation statements)

References 97 publications

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“…the SBM is that it provides a statistically grounded generative model of networks. Thereby, the quantification of these unified parcellations by the SBM naturally follows the predictive framework outlined in Figure 4 that assesses the performance of predicting independent held-out test graphs as a substitute in the absence of ground truth (Albers et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…It was created in a combined data-driven and manual approach to obtain a single parcellation of cortical regions, based on multiple neurobiological properties including both functional information and brain anatomy obtained from 210 healthy subjects. We have previously found this atlas to perform relatively well when predicting single subject structural and functional connectivity networks and therefore include it presently as a baseline (Albers et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…subjects. We have previously found this atlas to perform relatively well when predicting single subject structural and functional connectivity networks and therefore include it presently as a baseline (Albers et al, 2021).…”

Section: The Hcp_mmp10 Atlasmentioning

confidence: 99%

“…An SBM type of framework has previously been used for functional (Mørup et al, 2010 ; Andersen et al, 2014 ; Baldassano et al, 2015 ) and structural parcellation (Ambrosen et al, 2014 ; Baldassano et al, 2015 ) as well as joint modeling of functional and structural connectivity in low resolution (116 network nodes; Andersen et al, 2012a ). We provide statistical evaluation of the predictive performance of the inferred parcellations following a similar framework to the one proposed in Albers et al ( 2021 ) (see Figure 4 ). We compare the results of joint modeling with the comprehensive HCP_MMP1.0 atlas which is constructed using multiple modalities, including neuroanatomy (Glasser et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Uncovering Cortical Units of Processing From Multi-Layered Connectomes

et al. 2022

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Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: The Hcp_mmp10 Atlasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncovering Cortical Units of Processing From Multi-Layered Connectomes

et al. 2022

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“…This level of granularity varies also depending on the modality under investigation. By now, the connectivity-based identification of structures and sub-structures has been based mainly on the analysis of structural or functional properties, or a combination of the two (Albers et al 2021;Zalesky et al 2010). However, the results of a recent work by Nani et al (2021) suggest that a further approach could come from the analysis of structural co-alteration.…”

Section: Introductionmentioning

confidence: 99%

A co-alteration parceling of the cingulate cortex

et al. 2022

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The cingulate cortex is known to be a complex structure, involved in several cognitive and emotional functions, as well as being altered by a variety of brain disorders. This heterogeneity is reflected in the multiple parceling models proposed in the literature. At the present, sub-regions of the cingulate cortex had been identified taking into account functional and structural connectivity, as well as cytological and electrochemical properties. In the present work, we propose an innovative node-wise parceling approach based on meta-analytic Bayesian co-alteration. To this aim, 193 case–control voxel-based morphometry experiments were analyzed, and the Patel’s κ index was used to assess probability of morphometric co-alteration between nodes placed in the cingulate cortex and in the rest of the brain. Hierarchical clustering was then applied to identify nodes in the cingulate cortex exhibiting a similar pattern of whole-brain co-alteration. The obtained dendrogram highlighted a robust fronto-parietal cluster compatible with the default mode network, and being supported by the interplay between the retrosplenial cortex and the anterior and posterior cingulate cortex, rarely described in the literature. This ensemble was further confirmed by the analysis of functional patterns. Leveraging on co-alteration to investigate cortical organization could, therefore, allow to combine multimodal information, resolving conflicting results sometimes coming from the separate use of singular modalities. Crucially, this provides a valuable way to understand the pathological brain using data driven, whole-brain informed and context-specific evidence in a way not yet explored in the field.

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