Mitigating gyral bias in cortical tractography via asymmetric fiber orientation distributions

Wu, Ye; Hong, Yoonmi; Feng, Yuanjing; Shen, Dinggang; Yap, Pew Thian

doi:10.1016/j.media.2019.101543

Cited by 30 publications

(42 citation statements)

References 42 publications

(63 reference statements)

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“…Recently, Teillac et al (2017) proposed an extension on the spin-glass model to reduce the gyral bias, although their proposal alters the target fibre orientations close to the sulcal walls to allow streamlines to smoothly bend into the gyral walls rather than an explicit constraint on the streamline density. Wu et al (2020) also showed a reduction in the gyral bias by encouraging a smooth transition between the radial fibre orientation in the grey matter and the tangential orientation underneath using asymmetric fibre orientation distribution functions ( Bastiani et al 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Modelling white matter in gyral blades as a continuous vector field

et al. 2021

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Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called “gyral biases” limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemi-spheric connections when compared to tracers.

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Section: Discussionmentioning

confidence: 99%

Modelling white matter in gyral blades as a continuous vector field

et al. 2021

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“…There are some researches that extend DKI in microstructural environments with orientation heterogeneity ( Ankele, 2019 , Huynh et al, 2019b , Ankele and Schultz, 2015 ) and show significantly higher consistency in quantifying microstructure than the conventional DKI in the presence of orientation heterogeneity. Recent works are available on modeling the effects of diffusion in curving structures ( Karayumak et al, 2018 , Bastiani et al, 2017 , Wu et al, 2020 , Lee et al, 2020 ). Diffusion measurements are antipodally symmetric which means the probabilities of displacement along x and − x are equal, while the distribution of fiber orientations within a voxel is not symmetric in general ( Karayumak et al, 2018 ).…”

Section: Signal Representationsmentioning

confidence: 99%

“…Different sub-voxel patterns such as crossing, fanning, and bending, cannot be distinguished if a voxel-wise model is fitted to the signal. Therefore, the spatial information from the neighboring voxels should be considered ( Bastiani et al, 2017 , Wu et al, 2020 )…”

Section: Signal Representationsmentioning

confidence: 99%

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Afzali

Pieciak

Newman

et al. 2021

Journal of Neuroscience Methods

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“…51 Gyral bias refers to the difficulty of tracing highly curved axonal trajectories across WM-GM boundaries in gyral blades, which can affect the performance of tractography algorithms. 116 Schilling and colleagues 117 investigated the impact of gyral bias by comparing tractography against myelin histology performed on a Rhesus macaque brain. This effect was evident for single-and multi-fiber implementations (tensor and CSD) of deterministic or probabilistic tractography, though the bias introduced in tensor-based tractography was larger than that in CSD-based tractography.…”

Section: Impact Of Microstructural Propertiesmentioning

confidence: 99%

A critical review of connectome validation studies

2021

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Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles.We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances. | INTRODUCTIONDiffusion-weighted MRI (dMRI) can be used to non-invasively infer microstructural properties of the brain. Tractography is a reconstruction method based on dMRI to compute the trajectories of white matter (WM) fiber bundles. The primary objective of tractography is to reconstruct non-invasively WM fiber bundles from dMRI. More recently, tractography has been used to map the human structural connectome 1-3 i.e. a comprehensive network description of WM fiber bundles connecting pairs of gray matter (GM) regions (also referred to as structural connectivity).Structural connectomes model the physical links between brain regions, whereas functional connectomes represent the network of functionally connected regions. These connectomes are a static representation of the brain that could change over time. 4,5 When referring to connectomes in this review, we specifically mean structural connectomes mapped with tractography.Generally, connectome mapping using tractography is a five-step process involving (1) dMRI acquisition, (2) segmenting the cortex into regions of interest (ROIs)-cortical parcellation, (3) diffusion/fiber orientation estimation, (4) streamline generation using tractography, and

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Mitigating gyral bias in cortical tractography via asymmetric fiber orientation distributions

Cited by 30 publications

References 42 publications

Modelling white matter in gyral blades as a continuous vector field

Modelling white matter in gyral blades as a continuous vector field

The sensitivity of diffusion MRI to microstructural properties and experimental factors

A critical review of connectome validation studies

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