Diffusion tensor image registration using hybrid connectivity and tensor features

Injury of the Vestibulocerebellar Tract and Signs of Ataxia in Patients with Cerebellar Stroke

BACKGROUND

The vestibulocerebellar tract (VCT) is responsible for maintaining balance, spatial orientation, and coordination. Damage to the vestibular system is accompanied by symptoms of balance disorder or ataxia. This study aimed to compare cerebellar dysfunction according to VCT damage in patients with cerebellar stroke.

METHODS

Six patients with cerebellum injury were recruited. This study measured ataxia and hand function related to visuomotor integration and manual dexterity using the Purdue pegboard test. The primary and bilateral secondary VCTs were reconstructed to investigate the integrity of pathways using diffusion tensor imaging (DTI).

RESULTS

The ataxia sign was positive in five patients (83%) at onset. In the result of the pegboard test, all patients had hand dysfunction in the dominant hand (100%). Likewise, all patients also had non-dominant hand dysfunction (100%). On the DTI tractography, the left and right primary VCTs of the patients demonstrated a 25% injury rate. Furthermore, the injury rates of ipsilateral and contralateral secondary VCTs were 50% and 58%.

CONCLUSIONS

Ataxia is related to secondary VCTs, and hand dysfunction is also related to VCTs. Therefore, we believe that the current study will be helpful in evaluating and providing a clinical intervention strategy for patients with ataxia and hand dysfunction following cerebellar injury.

Associations Between Injury of the Parieto-Insular Vestibular Cortex and Changes in Motor Function According to the Recovery Process: Use of Diffusion Tensor Imaging

Background and Purpose: Parieto-insular vestibular cortex (PIVC) injury can cause symptoms such as abnormal gait and affects the integration and processing of sensory inputs contributing to self-motion perception. Therefore, this study investigated the association of the vestibular pathway in the gait and motor function recovery process in patients with PIVC injury using diffusion tensor imaging (DTI).

Methods: We recruited 28 patients with stroke with only PIVC injury and reconstructed the PIVC using a 1.5-T scanner for DTI. Fractional anisotropy (FA), mean diffusivity (MD), and tract volume were measured. The functional ambulatory category (FAC) test was conducted, and motricity index (MI) score was determined. These were conducted and determined at the start (phase 1), end of rehabilitation (phase 2), and during the follow-up 6 months after onset.

Results: Although the tract volume of PIVC showed a decrease in subgroup A, all of DTI parameters were not different between two subgroups in affected side (p > 0.05). The results of MI and FAC were significantly different according to the recovery process (p < 0.05). In addition, FA of the PIVC showed a positive correlation with FAC in phase 2 of the recovery process on the affected side. On the unaffected side, FA of the PIVC showed a significant negative correlation with MI in all processes (p < 0.05).

Conclusion: The degree of projection pathways to PIVC injury at onset time seems to be related to early restoration of gait function. Moreover, we believe that early detection of the projection pathway for PIVC injury using DTI would be helpful in the clinical evaluation and prediction of the prognosis of patients with PIVC injury.

Anatomical Location of the Vestibulocerebellar Tract in the Healthy Human Brain: A Diffusion Tensor Imaging Study

The vestibulocerebellar tract (VCT) is regarded as an important pathway of the central vestibular system. We identified the anatomical characteristics of the primary and secondary VCTs in a normal human brain using diffusion tensor imaging (DTI) tractography. Thirty-one healthy adults were recruited. A 1.5 T scanner was used for DTI tractography. A seed region of interest (ROI) was placed on the superior and medial vestibular nuclei at the pons level and a target ROI was placed on the uvula–nodulus of the cerebellum for reconstructing the primary VCT. In the secondary VCTs, the seed ROI was placed on the inferior and medial vestibular nuclei at the medulla oblongata level, and target ROIs were placed on the bilateral uvula–nodulus of the cerebellum. The primary VCT originated from the superior and medial vestibular nuclei at the pons level and terminated at the ipsilateral uvula–nodulus of the cerebellum. The component of the secondary VCTs originated from the inferior and medial vestibular nuclei at the level of the medulla oblongata and terminated at the bilateral uvula–nodulus of the cerebellum. Among them, 70.97% in the contralateral secondary VCT crossed at the vermis of the cerebellum. In addition, the fractional anisotropies (FAs) and mean diffusivity (MD) values of the primary VCT were significantly higher and lower, respectively, compared to those of the secondary VCTs (p < 0.05). The contralateral secondary VCT was significantly higher and lower in the MD and tract volume, respectively (p < 0.05), compared to the ipsilateral VCT. Therefore, we believe that the results will be useful for future studies of the vestibular projection pathway in the human brain injury aspect of central vestibular syndrome.

Associations between Age-Related Changes in the Core Vestibular Projection Pathway and Balance Ability: A Diffusion Tensor Imaging Study

Objective

We investigated the changes of the vestibulospinal tract (VST) and parietoinsular vestibular cortex (PIVC) using diffusion tensor imaging (DTI) and relation to balance between old and young healthy adults.

Methods

This study recruited eleven old adults (6 males, 5 females; mean age 63.36 ± 4.25 years) and 12 young adults (7 males, 5 females; mean age 28.42 ± 4.40 years). The lateral and medial VST and PIVC were reconstructed using DTI. Fractional anisotropy (FA), mean diffusivity (MD), and tract volume were measured. The six-minute walk test (6-MWT), the timed up and go test (TUG), and the Berg balance scale (BBS) were conducted. Spatiotemporal parameters during tandem gait and values of sway during one-leg standing using the wearable sensors were measured. All parameters between two groups were analyzed by the Mann-Whitney U test and independent t-test.

Results

Statistically significant decrease in old adults was detected in the tract volume of lateral (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (p = 0.005) and medial VST (.

Conclusion

The results suggested that there was a relationship between DTI parameters in the vestibular neural pathway and balance according to aging.

Sparse registration of diffusion weighted images

BACKGROUND AND OBJECTIVE

Registration is a critical step in group analysis of diffusion weighted images (DWI). Image registration is also necessary for construction of white matter atlases that can be used to identify white matter changes. A challenge in the registration of DWI is that the orientation of the fiber bundles should be considered in the process, making their registration more challenging than that of the scalar images. Most of the current registration methods use a model of diffusion profile, limiting the method to the used model.

METHODS

We propose a model-independent method for DWI registration. The proposed method uses a multi-level free-form deformation (FFD), a sparse similarity measure, and a dictionary. We also propose a synthesis K-SVD algorithm for sparse interpolation of images during the registration process. We utilize two dictionaries: analysis dictionary is learned based on diffusion signals while synthesis dictionary is generated based on image patches. The proposed multi-level approach registers anatomical structures at different scales. T-test is used to determine the significance of the differences between different methods.

RESULTS

We have shown the efficiency of the proposed approach using real data. The method results in smaller generalized fractional anisotropy (GFA) root mean square (RMS) error (0.05 improvements, p = 0.0237) and angular error (0.37 ° improvement, p = 0.0330) compared to the large deformation diffeomorphic metric mapping (LDDMM) method and advanced normalization tools (ANTs).

CONCLUSION

Sparse registration of diffusion signals enables registration of diffusion weighted images without using a diffusion model.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

Group-wise consistent cortical parcellation based on connectional profiles

For decades, seeking common, consistent and corresponding anatomical/functional regions across individual brains via cortical parcellation has been a longstanding challenging problem. In our opinion, two major barriers to solve this problem are determining meaningful cortical boundaries that segregate homogeneous regions and establishing correspondences among parcellated regions of multiple brains. To establish a corresponding system across subjects, we recently developed the Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system which possesses group-wise consistent white matter fiber connection patterns across individuals and thus provides a dense map of corresponding cortical landmarks. Despite this useful property, however, the DICCCOL landmarks are still far from covering the whole cerebral cortex and do not provide clear structural/functional cortical boundaries. To address the above limitation while leveraging the advantage of DICCCOL, in this paper, we present a novel approach for group-wise consistent parcellation of the cerebral cortex via a hierarchical scheme. In each hierarchical level, DICCCOLs are used as corresponding samples to automatically determine the cluster number so that other cortical surface vertices are iteratively classified into corresponding clusters across subjects within a group-wise classification framework. Experimental results showed that this approach can achieve consistent fine-granularity cortical parcellation with intrinsically-established structural correspondences across individual brains. Besides, comparisons with resting-state and task-based fMRI datasets demonstrated that the group-wise parcellation boundaries segregate functionally homogeneous areas.

DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures

In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM.

Uncertainty estimation in diffusion MRI using the nonlocal bootstrap

In this paper, we propose a new bootstrap scheme, called the nonlocal bootstrap (NLB) for uncertainty estimation. In contrast to the residual bootstrap, which relies on a data model, or the repetition bootstrap, which requires repeated signal measurements, NLB is not restricted by the data structure imposed by a data model and obviates the need for time-consuming multiple acquisitions. NLB hinges on the observation that local imaging information recurs in an image. This self-similarity implies that imaging information coming from spatially distant (nonlocal) regions can be exploited for more effective estimation of statistics of interest. Evaluations using in silico data indicate that NLB produces distribution estimates that are in closer agreement with those generated using Monte Carlo simulations, compared with the conventional residual bootstrap. Evaluations using in vivo data demonstrate that NLB produces results that are in agreement with our knowledge on white matter architecture.