Validation of structural brain connectivity networks: The impact of scanning parameters

Ambrosen, Karen S; Eskildsen, Simon Fristed; Hinne, Max; Krug, Kristine; Lundell, Henrik; Schmidt, Mikkel N.; Gerven, Marcel A. J. van; Mørup, Morten; Dyrby, Tim B.

doi:10.1016/j.neuroimage.2019.116207

Cited by 33 publications

(30 citation statements)

References 86 publications

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“…Despite the variety in dMRI acquisition and analysis methods, as well as the tracer databases used by these studies (see Table 1), there is considerable agreement in receiver operating characteristic (ROC) analyses. For the most part, for a specificity around 60%, tractography achieves sensitivity in the 60%-70% range in most studies (Hagmann et al, 2008; Azadbakht et al, 2015; Donahue et al, 2016; Ambrosen et al, 2020). A study that performed this analysis with a variety of more modern tractography algorithms, found that the sensitivity at the same level of specificity edged somewhat higher, in the 60%-80% range (Girard et al, 2020).…”

Section: Validation Of Tractographymentioning

confidence: 97%

“…Several studies have generated area-to-area connectivity matrices using tractography in dMRI scans of NHP brains and compared them to existing collections of NHP tracer data (Hagmann et al, 2008; van den Heuvel et al, 2015; Azadbakht et al, 2015; Donahue et al, 2016; Ambrosen et al, 2020). Despite the variety in dMRI acquisition and analysis methods, as well as the tracer databases used by these studies (see Table 1), there is considerable agreement in receiver operating characteristic (ROC) analyses.…”

Section: Validation Of Tractographymentioning

confidence: 99%

“…A study that performed this analysis with a variety of more modern tractography algorithms, found that the sensitivity at the same level of specificity edged somewhat higher, in the 60%-80% range (Girard et al, 2020). One of the previous studies examined the effect of dMRI acquisition parameters on the accuracy of the connectivity matrix (Ambrosen et al, 2020). A notable finding was that increasing the b -value from 1477 to 8040 led to indistinguishable ROC performance.…”

Section: Validation Of Tractographymentioning

confidence: 99%

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Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Yendiki

Axer

Howard

et al. 2021

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Despite the impressive advances in diffusion MRI (dMRI) acquisition and analysis that have taken place during the Human Connectome era, dMRI tractography is still an imperfect source of information on the circuitry of the brain. In this review, we discuss methods for post mortem validation of dMRI tractography, fiber orientations, and other microstructural properties of axon bundles that are typically extracted from dMRI data. These methods include anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.

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Section: Validation Of Tractographymentioning

confidence: 97%

Section: Validation Of Tractographymentioning

confidence: 99%

Section: Validation Of Tractographymentioning

confidence: 99%

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Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Yendiki

Axer

Howard

et al. 2021

Preprint

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“…For Gaussian noise, however, the higher number of sampling points given by the higher angular resolution may increase the e↵ective SNR and provide access to smaller diameters. In practice, it has been shown that it is better to increase the number of directions than to perform many repeats of the same shell [59]. Furthermore, an increased angular resolution increases the robustness of the AD estimate to di↵erent underlying fiber configurations and OD, as seen in Fig.…”

Section: Sequence Parameter Considerationsmentioning

confidence: 99%

Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?

Andersson

Pizzolato

Kjer

et al. 2021

Preprint

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Noninvasive estimation of axon diameter with diffusion MRI holds potential to investigate the dynamic properties of the brain network and pathology of neurodegenerative diseases. Recent methods use powder averaging to account for complex white matter architectures, such as fibre crossing regions, but these have not been validated for real axonal geometries. Here, we present 120-313 μm long segmented axons from X-ray nano-holotomography volumes of a splenium and crossing fibre region of a vervet monkey brain. We show that the axons in the complex crossing fibre region, which contains callosal, association, and corticospinal connections, are larger and exhibit a wider distribution than those of the splenium region. To accurately estimate the axon diameter in these regions, therefore, sensitivity to a wide range of diameters is required. We demonstrate how the q-value, b-value, signal-to-noise ratio and the assumed intra-axonal parallel diffusivity influence the range of measurable diameters with powder average approaches. Furthermore, we show how Gaussian distributed noise results in a wider range of measurable diameter at high b-values than with Rician distributed noise, even at high signal-to-noise ratios of 100. The number of gradient directions is also shown to impose a lower bound on measurable diameter. Our results indicate that axon diameter estimation can be performed with only few b-shells, and that additional shells do not improve the accuracy of the estimate. Through Monte Carlo simulations of diffusion, we show that powder averaging techniques succeed in providing accurate estimates of axon diameter across a range of sequence parameters and diffusion times, even in complex white matter architectures. At sufficiently low b-values, the acquisition becomes sensitive to axonal microdispersion and the intra-axonal parallel diffusivity shows time dependency at both in vivo and ex vivo intrinsic diffusivities.

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“…The communication between brain regions is often analyzed through structural and functional connectivity [1]. The former refers to anatomical connections between brain regions generally quantified using tracer injections or diffusion magnetic resonance imaging [2]. The map of these connections is called “connectome” [3], whose analysis includes methods to evaluate the networks defined by nodes (brain regions) and edges (synapses) [4, 5].…”

Section: Introductionmentioning

confidence: 99%

Directed Functional and Structural Connectivity in a Large-Scale Model for the Mouse Cortex

Nunes

Reyes

Mejías

et al. 2021

Preprint

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Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the Generalized Partial Directed Correlation (GPDC), provide estimates of the causal influence between areas. However, such methods have a limitation because their estimates depend on the number of brain regions simultaneously recorded. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale mouse cortical network. The model contains 19 cortical areas modeled using spiking neural populations, and directed weights for long-range projections were obtained from a tract-tracing cortical connectome. We show that the GPDC estimates correlate positively with structural connectivity. Moreover, the correlation between structural and directed functional connectivity is comparable even when using only a few cortical areas for GPDC estimation, a typical scenario for electro-physiological recordings. Finally, GPDC measures also provided a measure of the flow of information among cortical areas.

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Validation of structural brain connectivity networks: The impact of scanning parameters

Cited by 33 publications

References 86 publications

Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?

Directed Functional and Structural Connectivity in a Large-Scale Model for the Mouse Cortex

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