Navigating the link between processing speed and network communication in the human brain

Imms, Phoebe; D, Juan F. Domínguez; Burmester, Alex; Seguin, Caio; Clemente, Adam; Dhollander, Thijs; Wilson, Peter H.; Poudel, Govinda; Caeyenberghs, Karen

doi:10.1007/s00429-021-02241-8

Cited by 33 publications

(24 citation statements)

References 106 publications

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“…Our connectome processing pipeline is showcased in Figure 1, Supplementary Material 1, and in our previous publication 23 . Our personalised connectomics implementation performs state-of-the-art, single-subject analyses of structural MRI scans.…”

Section: Methodsmentioning

confidence: 99%

“…Despite multiple expert raters and use of an established procedure 22 , DAI grading remains subjective and requires independent confirmation of reliability. Hubs were defined according to previous work citing the use of betweenness centrality – however, other work has shown that finding ‘consensus’ between multiple centrality metrics may be more stable for hub definition 70 .Finally, cognitive outcomes associated with graph measures are still being evaluated – this progress will be essential for providing clinically informative personalised connectomes 23 and is included as part of the STREAMLINES platform.…”

Section: Limitationsmentioning

confidence: 99%

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Personalised structural connectomics for moderate-to-severe traumatic brain injury

Imms

Clemente

Deutscher

et al. 2022

Preprint

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Graph theoretical analysis of the structural connectome has been employed successfully to characterise brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalised connectomics approach that examines structural brain alterations in six chronic patients with moderate-to-severe TBI who underwent anatomical and diffusion magnetic resonance imaging (MRI). We generated individualised profiles of lesion characteristics and network measures (including personalised graph metric ‘GraphMe’ plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N=12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed clinically significant alterations of brain networks with high variability between patients. Our profiling can be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalised rehabilitation protocols based on their unique lesion load and connectome.

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Section: Methodsmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Personalised structural connectomics for moderate-to-severe traumatic brain injury

Imms

Clemente

Deutscher

et al. 2022

Preprint

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“…Most connectome studies focus on macro-level brain modeling employing observable dynamics on functional connectivity [61][62][63][64] followed by the search of select genes that show variation inside their macroconnectome [21,22,25,65], suggesting that only a fraction of genes drives the brain's identity and its functionality. Such top-down modeling drives to a narrow gene screening, constraining the brain endophenotype spectrum which makes it unlikely to find any brain's transcriptional network responsible for the brain's wiring and functions.…”

Section: Gene-wise Analysismentioning

confidence: 99%

A Transcriptome Community-and-Module Approach of the Human Mesoconnectome

Paredes

López

Covantes-Osuna

et al. 2021

Entropy

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Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network

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“…Each of these methods proposes a different conceptualization of how signals traverse the brain's structural connectivity, and determining which approaches most accurately reflect underlying biological processes remains a crucial open question [391]. Nonetheless, recent work has shown that advanced communication measures can elucidate how the organization of structural connectivity shapes neural information processing and functional dynamics [170,291,481,210]. For instance, despite the inherent limitations of dMRI tractography in resolving axonal fiber directionality, navigation efficiency and search information computed on the undirected human connectome can reveal patterns of asymmetric interregional signaling, as evidenced by significant associations to the directionality of effective connectivity computed using dynamic causal modeling [390].…”

Section: Graph Theoretical Measures and Advanced Structural Con-mentioning

confidence: 99%

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review

Zhang¹,

Daducci²,

He³

et al. 2021

Preprint

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Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections at macro scale. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

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Navigating the link between processing speed and network communication in the human brain

Cited by 33 publications

References 106 publications

Personalised structural connectomics for moderate-to-severe traumatic brain injury

Personalised structural connectomics for moderate-to-severe traumatic brain injury

A Transcriptome Community-and-Module Approach of the Human Mesoconnectome

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review

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