Unsupervised identification of white matter tracts in a mouse brain using a directional correlation-based region growing (DCRG) algorithm

Lin, Chien‐Yuan; Hong, Chung-Yi; Chang, Chen

doi:10.1016/j.neuroimage.2005.06.009

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…Any significant differences that are detected can be ascribed to the ROI, thus offering a possible correlation between structure and function. Potential pitfalls include bias in ROI selection, which can partially be addressed by automation (87)(88)(89)(90). In addition, ROIs drawn on DT images may suffer from artifact and decreased resolution, whereas those drawn on higher resolution images must be accurately registered to the DT images.…”

Section: Discussionmentioning

confidence: 99%

Diffusion-Tensor MR Imaging and Tractography: Exploring Brain Microstructure and Connectivity

Nucifora

Verma²,

Lee³

et al. 2007

Radiology

291

191

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Diffusion magnetic resonance (MR) imaging is evolving into a potent tool in the examination of the central nervous system. Although it is often used for the detection of acute ischemia, evaluation of directionality in a diffusion measurement can be useful in white matter, which demonstrates strong diffusion anisotropy. Techniques such as diffusion-tensor imaging offer a glimpse into brain microstructure at a scale that is not easily accessible with other modalities, in some cases improving the detection and characterization of white matter abnormalities. Diffusion MR tractography offers an overall view of brain anatomy, including the degree of connectivity between different regions of the brain. However, optimal utilization of the wide range of data provided with directional diffusion MR measurements requires careful attention to acquisition and postprocessing. This article will review the principles of diffusion contrast and anisotropy, as well as clinical applications in psychiatric, developmental, neurodegenerative, neoplastic, demyelinating, and other types of disease.

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Section: Discussionmentioning

confidence: 99%

Diffusion-Tensor MR Imaging and Tractography: Exploring Brain Microstructure and Connectivity

Nucifora

Verma²,

Lee³

et al. 2007

Radiology

291

191

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show abstract

“…Most importantly, the achieved quality of the DTI maps and the respective directional information turned out to be sufficient for fiber tractography of the main axonal projections in the mouse brain. While DTI-derived connectivity information has already been obtained for mouse brain ex vivo (Mori et al, 2001;Zhang et al, 2002) and in a single case also in vivo (Lin et al, 2005), this study presents the first in vivo fiber tracts from the corpus callosum and anterior commissure of a living mouse. Further technical improvements in terms of SNR and/or spatial resolution are to be expected by using adapted surface or array coils rather than a transmit/receive birdcage coil or even higher magnetic field strengths.…”

Section: Discussionmentioning

confidence: 99%

High-field diffusion tensor imaging of mouse brain in vivo using single-shot STEAM MRI

Boretius¹,

Wuerfel²,

Zipp³

et al. 2007

Journal of Neuroscience Methods

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Information about the microstructural organization of cerebral white matter that is accessible by magnetic resonance diffusion tensor imaging (DTI) gains increasing importance for studies of animal brain. Particular challenges occur for in vivo conditions as well as at high magnetic fields. Here, we have employed a diffusion-weighted (DW) single-shot STEAM MRI sequence for DTI of mouse brain in vivo at 7 T. The approach exploits the increased longitudinal magnetization and prolonged T1 relaxation times of water protons at higher magnetic field strengths without suffering from susceptibility-induced artifacts. When compared to studies at 2.35 T, half Fourier DW STEAM MRI at 7 T yielded a substantial gain in signal-to-noise ratio (SNR) that could be invested either in a reduction of the measurement time or an increase of the spatial resolution. Thus, for a measurement time of 3h, DTI with a voxel size of 117 microm x 117 microm x 720 microm not only resulted in high-quality maps of the fractional anisotropy and main diffusion direction (MDD), but also allowed for fiber tracking of major mouse brain structures in vivo.

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“…Long acquisition times could be very constraining when performing studies with affected animals that might not be able to survive repeated longitudinal investigation under anesthesia conditions. Previous DT-MRI studies in living mice employed acquisition schemes based on the use of the modified Stejskal and Tanner spin-echo diffusion weighted sequence (15,17,19,20,42) with reduced number of diffusion encoding directions and acquisition times of at least 2 h. The only study performing tractography from this type of in vivo acquired data of a single mouse applied a directional correlation-based region growing algorithm (DCRG) to reconstruct only major WM projections (20). Boretius et al proposed in 2007 (16) and2009 (22) the use of a half-Fourier diffusion-weighted single shot STEAM MRI for imaging the mouse brain and for deterministic fiber tracking.…”

Section: Fiber Tracking and Probability Mapsmentioning

confidence: 99%

“…In three studies in vivo mouse brain axonal connectivity in WM regions with high density of fibers has been demonstrated by using deterministic fiber tracking approaches. Lin et al used a directional correlation-based region growing (DCRG) algorithm to identify visual pathways as well as the corpus callosum from a single mouse image data set acquired using diffusion gradients applied in 6 non collinear directions (20). Boretius et al (16,22) visualized the main brain fiber tracts by using an MR acquisition scheme that employed 12 gradient diffusion directions (16) for an acquisition time of 176 minutes at 7T.…”

Section: Introductionmentioning

confidence: 99%

In vivo diffusion tensor magnetic resonance imaging and fiber tracking of the mouse brain

et al. 2010

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Until very recently, the study of neural architecture using fixed tissue has been a major scientific focus of neurologists and neuroanatomists. A non-invasive detailed insight into the brain's axonal connectivity in vivo has only become possible since the development of diffusion tensor magnetic resonance imaging (DT-MRI). This unique approach of analyzing axonal projections in the living brain was used in the present study to describe major white matter fiber tracts of the mouse brain and also to identify for the first time non-invasively the rich connectivity between the amygdala and different target regions. To overcome the difficulties associated with high spatially and temporally resolved DT-MRI measurements a 4-shot diffusion weighted spin echo (SE) echo planar imaging (EPI) protocol was adapted to mouse brain imaging at 9.4T. Diffusion tensor was calculated from data sets acquired by using 30 diffusion gradient directions while keeping the acquisition time at 91 min. Two fiber tracking algorithms were employed. A deterministic approach (fiber assignment by continuous tracking - FACT algorithm) allowed us to identify and generate the 3D representations of various neural pathways. A probabilistic approach was further used for the generation of probability maps of connectivity with which it was possible to investigate - in a statistical sense - all possible connecting pathways between selected seed points. We show here applications to determine the connection probability between regions belonging to the visual or limbic systems. This method does not require a priori knowledge about the projections' trajectories and is shown to be efficient even if the investigated pathway is long or three-dimensionally complex. Additionally, high resolution images of rotational invariant parameters of the diffusion tensor, such as fractional anisotropy, volume ratio or main eigenvalues allowed quantitative comparisons in-between regions of interest (ROIs) and showed significant differences between various white matter regions.

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Unsupervised identification of white matter tracts in a mouse brain using a directional correlation-based region growing (DCRG) algorithm

Cited by 12 publications

References 19 publications

Diffusion-Tensor MR Imaging and Tractography: Exploring Brain Microstructure and Connectivity

Diffusion-Tensor MR Imaging and Tractography: Exploring Brain Microstructure and Connectivity

High-field diffusion tensor imaging of mouse brain in vivo using single-shot STEAM MRI

In vivo diffusion tensor magnetic resonance imaging and fiber tracking of the mouse brain

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