The Lamellar Structure of the Brain Fiber Pathways

Galinsky, V. L.; Frank, Lawrence R.

doi:10.1162/neco_a_00896

Cited by 14 publications

(20 citation statements)

References 36 publications

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“…Currently, post-mortem dissection with Klingler's method reveals the macroscopic organization of the human brain white matter (11,(64)(65)(66). In the future, the community will have to gain further insights into the underlying principles of white matter organization and increasingly learn how to leverage such information for tractography (1,67,68).…”

Section: Discussionmentioning

confidence: 99%

Tractography-based connectomes are dominated by false-positive connections

Maier‐Hein

Neher

Houde³

et al. 2016

Preprint

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Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

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Section: Discussionmentioning

confidence: 99%

Tractography-based connectomes are dominated by false-positive connections

Maier‐Hein

Neher

Houde³

et al. 2016

Preprint

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“…We assumed here that anisotropic form of σ (1) i j with ε = 0.001, 0.01, or 0.1 can be used to describe different levels of intercellular conductivity as well as microstructure extracellular conductivity in the vicinity of cell membranes (where the z direction corresponds to the direction of the fiber). Using full brain tractography results [34] we generated the anisotropic part of the conductivity tensor σ(r) in voxel at r location as an average over all fiber orientations assuming N fibers inside the voxel with R k orientation matrix for every fiber k, i.e.,…”

Section: Varying Anisotropy Orientation and Value Estimated From Mmentioning

confidence: 99%

“…Assuming a linear dependence between diffusion and conductivity tensors [35] several combinations of the diffusion tensor eigenvectors were used to describe the conductivity tensor anisotropy. In a more complex approach, the full brain tractography [18] generated fiber distributions [34] were used to infer anisotropy through direct integration of single-fiber anisotropy parameters in each voxel. FIG.…”

Section: Appendix G: Persistent Wave Loop Patternsmentioning

confidence: 99%

Universal theory of brain waves: From linear loops to nonlinear synchronized spiking and collective brain rhythms

Galinsky

Frank

2020

Phys. Rev. Research

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An inhomogeneous anisotropic physical model of the brain cortex is presented that predicts the emergence of nonevanescent (weakly damped) wavelike modes propagating in the thin cortex layers transverse to both the mean neural fiber direction and the cortex spatial gradient. Although the amplitude of these modes stays below the typically observed axon spiking potential, the lifetime of these modes may significantly exceed the spiking potential inverse decay constant. Full brain numerical simulations based on parameters extracted from diffusion and structural MRI confirm the existence and extended duration of these wave modes. Contrary to the standard paradigm that the neural fibers determine the pathways for signal propagation in the brain, the signal propagation due to the cortex wave modes in highly folded areas will exhibit no apparent correlation with the fiber directions. The results are consistent with numerous recent experimental animal and human brain studies demonstrating the existence of electrostatic field activity in the form of traveling waves (including studies where neuronal connections were severed) and with wave-loop-induced peaks observed in EEG spectra. In addition, we demonstrate that the resonant and nonresonant terms of the nonlinear coupling between multiple modes produce both synchronous spiking-like high-frequency wave activity as well as low-frequency wave rhythms as a result of their unique dispersion properties. Numerical simulation of forced multiple mode dynamics shows that as forcing increases there is a transition from a damped to an oscillatory regime that subsequently decays away as over-excitation is reached. The resonant nonlinear coupling results in the emergence of low-frequency rhythms with frequencies that are several orders of magnitude below the linear frequencies of modes taking part in the coupling. The localization and persistence of these cortical wave modes, and this mechanism of their collective input to the generation of synchronized spiking and low-frequency rhythms, have significant implications in particular for neuroimaging methods that detect electromagnetic physiological activity, such as EEG and MEG, and in general for the understanding of brain activity, including mechanisms of memory.

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“…The existence of sheet structure at multiple scales throughout the brain is an ongoing topic of debate (Catani et al, 2012; Galinsky and Frank, 2016; Tax et al, 2016b; Wedeen et al, 2012a; Wedeen et al, 2012b), mainly because obtaining proof with reliable quantification is challenging. Previous work has proposed the SPI as a novel quantitative measure to investigate the extent at which directional data support sheet structure (Tax et al, 2015; Tax et al, 2014a).…”

Section: Discussionmentioning

confidence: 99%

Quantifying the brain's sheet structure with normalized convolution

Tax

Westin

Haije

et al. 2017

Medical Image Analysis

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The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as “pages of a book” has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of “path neighborhoods” reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure.

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The Lamellar Structure of the Brain Fiber Pathways

Cited by 14 publications

References 36 publications

Tractography-based connectomes are dominated by false-positive connections

Tractography-based connectomes are dominated by false-positive connections

Universal theory of brain waves: From linear loops to nonlinear synchronized spiking and collective brain rhythms

Quantifying the brain's sheet structure with normalized convolution

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