Consensus clustering approach to group brain connectivity matrices

Rasero, Javier; Pellicoro, M.; Angelini, L.; Cortes, Jesús M.; Marinazzo, Daniele; Stramaglia, Sebastiano

doi:10.1162/netn_a_00017

Cited by 37 publications

(51 citation statements)

References 22 publications

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“…We used a statistic -inter-subject similarity -that measured, on average, how similar all pairs of subjects were to one another according to their whole-brain patterns of FC. This statistic could be made more useful by, instead of averaging over all subjects, clustering the intersubject similarity matrix, revealing groups of subjects that may be more internally similar than other groups [76]. This approach could be used for differentiating behavioral phenotypes or revealing sub-structure within a broader disorder [77,78].…”

Section: Future Directionsmentioning

confidence: 99%

Temporal fluctuations in the brain’s modular architecture during movie-watching

Betzel

Byrge

Esfahlani

et al. 2019

Preprint

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Brain networks are flexible and reconfigure over time to support ongoing cognitive processes. However, tracking statistically meaningful reconfigurations across time has proven difficult. This has to do largely with issues related to sampling variability, making instantaneous estimation of network organization difficult, along with increased reliance on task-free (cognitively unconstrained) experimental paradigms, limiting the ability to interpret the origin of changes in network structure over time. Here, we address these challenges using time-varying network analysis in conjunction with a naturalistic viewing paradigm. Specifically, we developed a measure of inter-subject network similarity and used this measure as a coincidence filter to identify synchronous fluctuations in network organization across individuals. Applied to movie-watching data, we found that periods of high inter-subject similarity coincided with reductions in network modularity and increased connectivity between cognitive systems. In contrast, low inter-subject similarity was associated with increased system segregation and more rest-like architectures. We then used a data-driven approach to uncover clusters of functional connections that follow similar trajectories over time and are more strongly correlated during movie-watching than at rest. Finally, we show that synchronous fluctuations in network architecture over time can be linked to a subset of features in the movie. Our findings link dynamic fluctuations in network integration and segregation to patterns of intersubject similarity, and suggest that moment-to-moment fluctuations in FC reflect shared cognitive processing across individuals.

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Section: Future Directionsmentioning

confidence: 99%

Temporal fluctuations in the brain’s modular architecture during movie-watching

Betzel

Byrge

Esfahlani

et al. 2019

Preprint

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“…We applied DTI preprocessing similar to previous work (Alonso- Montes et al, 2015;Amor et al, 2015;Diez, Bonifazi, et al, 2015;Diez et al, 2017;Kroos et al, 2017;Marinazzo et al, 2014;Rasero, Pellicoro, et al 2017;Rasero, Alonso-Montes, et al 2017;Stramaglia et al, 2017) using FSL (FMRIB Software Library v5.0) and the Diffusion Toolkit. First, an eddy current correction was applied to overcome the artifacts produced by variation in the direction of the gradient fields of the MR scanner, together with the artifacts produced by head motion.…”

Section: Imaging Preprocessing 231 | Diffusion Tensor Imagingmentioning

confidence: 99%

“…We applied resting fMRI preprocessing similar to previous work (Alonso-Montes et al, 2015;Amor et al, 2015;Diez, Erramuzpe, et al, 2015;Diez, Bonifazi, et al, 2015;Diez et al, 2017;Mäki-Marttunen, Diez, Cortes, Chialvo, & Villarreal, 2013;Marinazzo et al, 2014;Rasero, Pellicoro, et al, 2017;Stramaglia et al, 2016Stramaglia et al, , 2017Stramaglia, Angelini, Cortes, & Marinazzo, 2015) using FSL and AFNI (http://afni.nimh.nih.gov/afni/). First, slice-time correction was applied to the fMRI data set.…”

Section: Functional Mrimentioning

confidence: 99%

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Bonifazi

Erramuzpe

Diez

et al. 2018

Human Brain Mapping

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Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age—an age estimator resulting from a multi‐scale methodology applied to the structure–function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto‐striato‐thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural–functional connectivity patterns correlating to other biomarkers than ChA.

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“…We applied resting fMRI preprocessing similar to previous work ( [23,24,25,26,27,28]) using FSL and AFNI (http://afni.nimh.nih.gov/afni/). First, slice-time was applied to the fMRI data set.…”

Section: Imaging Preprocessingmentioning

confidence: 99%

Interaction information along lifespan of the resting brain dynamics reveals a major redundant role of the default mode network

Camino-Pontes

Diez

Jiménez-Marín

et al. 2018

Preprint

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Interaction Information generalizes the univariate Shannon Entropy to triplets of variables, allowing the detection of redundant or synergetic interactions in dynamical networks. Here, we calculated interaction information from functional magnetic resonance imaging and asked whether redundancy or synergy vary across brain regions and along lifespan. We found high overlapping between the pattern of high redundancy and the default mode network, and this occurred along lifespan. The pattern of high values of synergy, more heterogeneous and variable along lifespan, was overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, the amount of redundancy and synergy seem to be balanced along lifespan, suggesting informational compensatory mechanisms in brain networks.

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Consensus clustering approach to group brain connectivity matrices

Cited by 37 publications

References 22 publications

Temporal fluctuations in the brain’s modular architecture during movie-watching

Temporal fluctuations in the brain’s modular architecture during movie-watching

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Interaction information along lifespan of the resting brain dynamics reveals a major redundant role of the default mode network

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