Evaluating reproducibility and subject-specificity of microstructure-informed connectivity

Koch, Philipp; Girard, Gabriel; Brügger, Julia; Cadic‐Melchior, Andéol; Beanato, Elena; Park, Chang-Hyun; Morishita, Takuya; Wessel, Maximilian J.; Pizzolato, Marco; Canales-Rodríguez, Erick Jorge; Fischi-Gómez, Elda; Schiavi, Simona; Daducci, Alessandro; Piredda, Gian Franco; Hilbert, Tom; Kober, Tobias; Thiran, Jean‐Philippe; Hummel, Friedhelm C.

doi:10.1016/j.neuroimage.2022.119356

Cited by 5 publications

(4 citation statements)

References 56 publications

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“…We were unable to assess repeatability in this work as we did not have scan-rescan data. However, reproducibility has already been assessed for NODDI ( Chung et al, 2016 ; Lehmann et al, 2021 ), MP2RAGE-derived T1 maps ( Marques et al, 2010 ), diffusion-tractography-based structural connectivity ( Bonilha et al, 2015 ), as well as COMMIT and SIFT2 tractogram filtering ( Koch et al, 2022 ). The reproducibility of the tractometry features (R 1 , FA, RD, ICVF) will mainly depend on these previous steps and the accuracy of the spatial alignment of the multimodal data.…”

Section: Discussionmentioning

confidence: 99%

The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts

Nelson

Royer

et al. 2023

Network Neuroscience

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A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.

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Section: Discussionmentioning

confidence: 99%

The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts

Nelson

Royer

et al. 2023

Network Neuroscience

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“…Compared with tractograms reconstructed simply by the number of streamlines, SIFT2 modulates the weight of individual streamlines so that the tractogram is aligned with the underlying image data ( Smith, Calamante, et al, 2020 ; Smith, Raffelt, et al, 2020 ; Smith et al, 2015a , 2015b ). SIFT2 has high reproducibility ( Girard et al, 2020 ; Koch et al, 2022 ) and increases the biological interpretability of the white matter tracts estimated ( Frigo et al, 2020 ; McColgan et al, 2018 ). McColgan et al (2018) also found that compared with unfiltered tractograms, SIFT2 improved the detection of group differences and lead to stronger clinical correlations.…”

Section: Methodsmentioning

confidence: 99%

Exploring personalized structural connectomics for moderate to severe traumatic brain injury

Imms

Clemente

Deutscher

et al. 2023

Network Neuroscience

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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 five 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 alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could 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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“…For each patient, we will compute a total connectome and an unaffected connectome, in order to incorporate the paths that have been disrupted by the lesion, together with respective scales of network science like e.g., global efficiency, a metric reflecting the functional integration within networks ( 53 , 54 ). Principal component analyses will be performed, as it has been shown to reduce dimensionality whilst remaining high subject specificity when being used on whole brain connectomes ( 55 ). Further individual structural and functional connectivity of specific functional networks like sensorimotor and attention will be reconstructed.…”

Section: Methodsmentioning

confidence: 99%

Toward individualized medicine in stroke—The TiMeS project: Protocol of longitudinal, multi-modal, multi-domain study in stroke

et al. 2022

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Despite recent improvements, complete motor recovery occurs in <15% of stroke patients. To improve the therapeutic outcomes, there is a strong need to tailor treatments to each individual patient. However, there is a lack of knowledge concerning the precise neuronal mechanisms underlying the degree and course of motor recovery and its individual differences, especially in the view of brain network properties despite the fact that it became more and more clear that stroke is a network disorder. The TiMeS project is a longitudinal exploratory study aiming at characterizing stroke phenotypes of a large, representative stroke cohort through an extensive, multi-modal and multi-domain evaluation. The ultimate goal of the study is to identify prognostic biomarkers allowing to predict the individual degree and course of motor recovery and its underlying neuronal mechanisms paving the way for novel interventions and treatment stratification for the individual patients. A total of up to 100 patients will be assessed at 4 timepoints over the first year after the stroke: during the first (T1) and third (T2) week, then three (T3) and twelve (T4) months after stroke onset. To assess underlying mechanisms of recovery with a focus on network analyses and brain connectivity, we will apply synergistic state-of-the-art systems neuroscience methods including functional, diffusion, and structural magnetic resonance imaging (MRI), and electrophysiological evaluation based on transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) and electromyography (EMG). In addition, an extensive, multi-domain neuropsychological evaluation will be performed at each timepoint, covering all sensorimotor and cognitive domains. This project will significantly add to the understanding of underlying mechanisms of motor recovery with a strong focus on the interactions between the motor and other cognitive domains and multimodal network analyses. The population-based, multi-dimensional dataset will serve as a basis to develop biomarkers to predict outcome and promote personalized stratification toward individually tailored treatment concepts using neuro-technologies, thus paving the way toward personalized precision medicine approaches in stroke rehabilitation.

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Evaluating reproducibility and subject-specificity of microstructure-informed connectivity

Cited by 5 publications

References 56 publications

The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts

The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts

Exploring personalized structural connectomics for moderate to severe traumatic brain injury

Toward individualized medicine in stroke—The TiMeS project: Protocol of longitudinal, multi-modal, multi-domain study in stroke

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