Nonparametric Bayesian clustering of structural whole brain connectivity in full image resolution

Ambrosen, Karen Marie Sandø; Albers, Kristoffer Jon; Dyrby, Tim B.; Schmidt, Mikkel N.; Mørup, Morten

doi:10.1109/prni.2014.6858507

Cited by 4 publications

(3 citation statements)

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“…The stochastic block model (SBM) (Nowicki and Snijders, 2001 ) is a data-driven Bayesian clustering approach, which, coupled with Markov Chain Monte Carlo (MCMC) sampling, has proven a valid tool for clustering and investigating structure in complex networks (Zhu et al, 2008 ; Schmidt and Mørup, 2013 ). Notably, a non-parametric SBM modeling framework [denoted the infinite relational model (IRM)] (Kemp et al, 2006 ; Xu et al, 2006 ) has previously been used for the separate modeling of functional (Mørup et al, 2010 ; Andersen et al, 2012b , 2014 ) and structural connectivity (Ambrosen et al, 2013 , 2014 ) whereas joint modeling of structural and functional connectivity has been considered in Andersen et al ( 2012a ). Notably, the approach of Andersen et al ( 2012a ) was based on low resolution networks of 116 nodes defined by the AAL atlas (Tzourio-Mazoyer et al, 2002 ) with the ability to impose shared and individual segregated units of the two modalities.…”

Section: Methodsmentioning

confidence: 99%

“…Apart from time-scale differences, the lack of inter-hemispheric structural connections can be attributed to limitations of current tractography methods (Maier-Hein et al, 2017 ). As such, the average area under curve (AUC) of the receiver operator characteristic directly predicting the connectivity of one group of subjects from another group of subjects (i.e., considering the total number of 0–0 matches, 0–1 matches, 1–0 matches, and 1–1 matches; Ambrosen et al, 2014 ; Røge et al, 2017 ) across the FC graphs is 0.901 whereas it is 0.935 for the SC graphs and 0.618 predicting FC from SC for the same group of subjects.…”

Section: Introductionmentioning

confidence: 99%

“…We use a stochastic block model (SBM) (Nowicki and Snijders, 2001 ) which allows us to infer a single parcellation based upon multiple networks (see Figure 3 ). 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 ).…”

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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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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The Missing Link: Predicting Connectomes from Noisy and Partially Observed Tract Tracing Data

Hinne¹,

Meijers²,

Bakker

et al. 2016

Preprint

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Abstract. Our understanding of the wiring map of the brain, known as the connectome, has increased greatly in the last decade, mostly due to technological advancements in neuroimaging techniques and improvements in computational tools to interpret the vast amount of available data. Despite this, with the exception of the C. elegans roundworm, no definitive connectome has been established for any species. In order to obtain this, tracer studies are particularly appealing, as these have proven highly reliable. The downside of tract tracing is that it is costly to perform, and can only be applied ex vivo. In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a 'latent space model' that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two connectivity data sets of the macaque and we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two alternative baselines in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal observations (i.e. anterograde and retrograde tracers) for the mouse neocortex. Finally, our probabilistic approach enables us to make explicit which connections are easy to predict and which prove difficult, allowing for informed follow-up studies.

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Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model

Iskov,

Olsen,

Madsen

et al. 2024

Network Neuroscience

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Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the Multinomial Stochastic Block Model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution dMRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects. The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM’s reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3, 4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher-resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.

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Nonparametric Bayesian clustering of structural whole brain connectivity in full image resolution

Cited by 4 publications

References 20 publications

Uncovering Cortical Units of Processing From Multi-Layered Connectomes

Uncovering Cortical Units of Processing From Multi-Layered Connectomes

The Missing Link: Predicting Connectomes from Noisy and Partially Observed Tract Tracing Data

Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model

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