Corticospinal Tract (CST) reconstruction based on fiber orientation distributions(FODs) tractography

The Corticospinal Tract (CST) is a part of pyramidal tract (PT), and it can innervate the voluntary movement of skeletal muscle through spinal interneurons (the 4th layer of the Rexed gray board layers), and anterior horn motorneurons (which control trunk and proximal limb muscles). Spinal cord injury (SCI) is a highly disabling disease often caused by traffic accidents. The recovery of CST and the functional reconstruction of spinal anterior horn motor neurons play an essential role in the treatment of SCI. However, the localization and reconstruction of CST are still challenging issues; the accuracy of the geometric reconstruction can directly affect the results of the surgery. The main contribution of this paper is the reconstruction of the CST based on the fiber orientation distributions (FODs) tractography. Differing from tensor-based tractography in which the primary direction is a determined orientation, the direction of FODs tractography is determined by the probability. The spherical harmonics (SPHARM) can be used to approximate the efficiency of FODs tractography. We manually delineate the three ROIs (the posterior limb of the internal capsule, the cerebral peduncle, and the anterior pontine area) by the ITK-SNAP software, and use the pipeline software to reconstruct both the left and right sides of the CST fibers. Our results demonstrate that FOD-based tractography can show more and correct anatomical CST fiber bundles

Methods for improved mapping of brain lesion connectivity

Recent advances over the past two decades in neuroimaging methods have enabled us to map the connectivity of the brain. In parallel, pathophysiological models of brain disease have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disruptions to interconnected neural networks. Nevertheless, these recent methods for mapping brain connectivity are still under development. Every step of the mapping process becomes a potential source for additional error due to noise or artifacts that could impact final analyses. Segmentation, parcellation, registration, and tractography are some of the steps where this occurs. Moreover, mapping the connectivity in a brain lesion is even more susceptible to errors in these steps. In this body of work, I describe multiple new methods for improving the accuracy of mapping lesion connectivity by reducing errors at the tractography stage which is the most error prone stage. First, we develop an approach for directly normalizing streamlines into a template space that avoids performing tractography in the normalized template space, reducing the error of connectomes constructed in the template space with respect to the ground truth native space connectome. Second, we develop a rapid approach for performing shortest path tractography and constructing shortest path probability weighted connectomes which increases the connection specificity relative to local streamline tracking approaches. We then demonstrate how our shortest path tractography approach can be used construct a disconnectome, a connectivity map of the proportion of connections lost due to intersecting a lesion. We then develop a fast, greedy graph-theoretic algorithm that extracts the maximally disconnected subgraph containing brain regions with the greatest shared loss of connectivity. Finally, we demonstrate how combining methods from diffusion based image inpainting and optimal estimation can be used to restore or inpaint corrupted fiber diffusion models in lesioned white matter tissue, enabling tractography and the study of lesion connectivity and modeling of microstructural measures in the patient’s native space

Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping

BACKGROUND AND PURPOSE: Tractography of the corticospinal tract is paramount to presurgical planning and guidance of intraoperative resection in patients with motor-eloquent gliomas. It is well-known that DTI-based tractography as the most frequently used technique has relevant shortcomings, particularly for resolving complex fiber architecture. The purpose of this study was to evaluate multilevel fiber tractography combined with functional motor cortex mapping in comparison with conventional deterministic tractography algorithms. MATERIALS AND METHODS: Thirty-one patients (mean age, 61.5 [SD, 12.2] years) with motor-eloquent high-grade gliomas underwent MR imaging with DWI (TR/TE ¼ 5000/78 ms, voxel size ¼ 2 × 2 × 2 mm3, 1 volume at b ¼ 0 s/mm2, 32 volumes at b ¼ 1000 s/mm2). DTI, constrained spherical deconvolution, and multilevel fiber tractography–based reconstruction of the corticospinal tract within the tumor-affected hemispheres were performed. The functional motor cortex was enclosed by navigated transcranial magnetic stimulation motor mapping before tumor resection and used for seeding. A range of angular deviation and fractional anisotropy thresholds (for DTI) was tested. RESULTS: For all investigated thresholds, multilevel fiber tractography achieved the highest mean coverage of the motor maps (eg, angular threshold = 60°; multilevel/constrained spherical deconvolution/DTI, 25% anisotropy threshold ¼ 71.8%, 22.6%, and 11.7%) and the most extensive corticospinal tract reconstructions (eg, angular threshold ¼ 60°; multilevel/constrained spherical deconvolution/DTI, 25% anisotropy threshold ¼ 26,485 mm3, 6308 mm3, and 4270 mm3). CONCLUSIONS: Multilevel fiber tractography may improve the coverage of the motor cortex by corticospinal tract fibers compared with conventional deterministic algorithms. Thus, it could provide a more detailed and complete visualization of corticospinal tract architecture, particularly by visualizing fiber trajectories with acute angles that might be of high relevance in patients with gliomas and distorted anatomy.</p

Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation

Tractography traces the peak directions extracted from fiber orientation distribution (FOD) suffering from ambiguous spatial correspondences between diffusion directions and fiber geometry, which is prone to producing erroneous tracks while missing true positive connections. The peaks-based tractography methods 'locally' reconstructed streamlines in 'single to single' manner, thus lacking of global information about the trend of the whole fiber bundle. In this work, we propose a novel tractography method based on a bundle-specific tractogram distribution function by using a higher-order streamline differential equation, which reconstructs the streamline bundles in 'cluster to cluster' manner. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion tensor vector field. At the global level, the tractography process is simplified as the estimation of bundle-specific tractogram distribution (BTD) coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) data for qualitative and quantitative evaluation. The results demonstrate that our approach can reconstruct the complex global fiber bundles directly. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts

Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

Shams et al. report that glioma patients' motor status is predicted accurately by diffusion MRI metrics along the corticospinal tract based on support vector machine method, reaching an overall accuracy of 77%. They show that these metrics are more effective than demographic and clinical variables. Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 +/- 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.Peer reviewe

Effect of different spatial normalization approaches on tractography and structural brain networks

To facilitate the comparison of white matter morphologic connectivity across target populations, it is invaluable to map the data to a standardized neuroanatomical space. Here, we evaluated direct streamline normalization (DSN), where the warping was applied directly to the streamlines, with two publically available approaches that spatially normalize the diffusion data and then reconstruct the streamlines. Prior work has shown that streamlines generated after normalization from reoriented diffusion data do not reliably match the streamlines generated in native space. To test the impact of these different normalization methods on quantitative tractography measures, we compared the reproducibility of the resulting normalized connectivity matrices and network metrics with those originally obtained in native space. The two methods that reconstruct streamlines after normalization led to significant differences in network metrics with large to huge standardized effect sizes, reflecting a dramatic alteration of the same subject’s native connectivity. In contrast, after normalizing with DSN we found no significant difference in network metrics compared with native space with only very small-to-small standardized effect sizes. DSN readily outperformed the other methods at preserving native space connectivity and introduced novel opportunities to define connectome networks without relying on gray matter parcellations. Direct streamline normalization (DSN) directly warps the streamlines into any template space by using the transformations output from Advanced Normalization Tools (ANTs). DSN overcomes the limitations of diffusion weighted images (DWI) spatial normalization. It allows DWIs to be acquired with any desired sampling scheme. Fiber orientation distributions (FODs) or orientation distribution functions (ODFs) can also be reconstructed using any desired method and streamlines generated using any algorithm. Most importantly, it avoids the problem of generating tracts from FODs or ODFs that have become distorted because of spatial normalization. Our results show that DSN has minimal influence on tractography measures such as tract count and structure and does not significantly alter structural networks with only very small to small effect sizes. We have developed a framework in Python that works with most diffusion software platforms. It is available at http://github.com/clintg6/DSN

Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecture

Fibre orientation distribution (FOD) reconstruction using deep learning has the potential to produce accurate FODs from a reduced number of diffusion-weighted images (DWIs), decreasing total imaging time. Diffusion acquisition invariant representations of the DWI signals are typically used as input to these methods to ensure that they can be applied flexibly to data with different b-vectors and b-values; however, this means the network cannot condition its output directly on the DWI signal. In this work, we propose a spherical deconvolution network, a model-driven deep learning FOD reconstruction architecture, that ensures intermediate and output FODs produced by the network are consistent with the input DWI signals. Furthermore, we implement a fixel classification penalty within our loss function, encouraging the network to produce FODs that can subsequently be segmented into the correct number of fixels and improve downstream fixel-based analysis. Our results show that the model-based deep learning architecture achieves competitive performance compared to a state-of-the-art FOD super-resolution network, FOD-Net. Moreover, we show that the fixel classification penalty can be tuned to offer improved performance with respect to metrics that rely on accurately segmented of FODs. Our code is publicly available at https://github.com/Jbartlett6/SDNet .Comment: 10 pages, 7 figures, This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Relationship between large-scale structural and functional brain connectivity in the human lifespan

The relationship between the anatomical structure of the brain and its functional organization is not straightforward and has not been elucidated yet, despite the growing interest this topic has received in the last decade. In particular, a new area of research has been defined in these years, called \u2019connectomics\u2019: this is the study of the different kinds of \u2019connections\u2019 existing among micro- and macro-areas of the brain, from structural connectivity \u2014 described by white matter fibre tracts physically linking cortical areas \u2014 to functional connectivity \u2014 defined as temporal correlation between electrical activity of different brain regions \u2014 to effective connectivity\u2014defining causal relationships between functional activity of different brain areas. Cortical areas of the brain physically linked by tracts of white matter fibres are known to exhibit a more coherent functional synchronization than areas which are not anatomically linked, but the absence of physical links between two areas does not imply a similar absence of functional correspondence. Development and ageing, but also structural modifications brought on by malformations or pathology, can modify the relation between structure and function. The aim of my PhD work has been to further investigate the existing relationship between structural and functional connectivity in the human brain at different ages of the human lifespan, in particular in healthy adults and both healthy and pathological neonates and children. These two \u2019categories\u2019 of subjects are very different in terms of the analysis techniques which can be applied for their study, due to the different characteristics of the data obtainable from them: in particular, while healthy adult data can be studied with the most advanced state-of-the-art methods, paediatric and neonatal subjects pose hard constraints to the acquisition methods applicable, and thus to the quality of the data which can be analysed. During this PhD I have studied this relation in healthy adult subjects by comparing structural connectivity from DWI data with functional connectivity from stereo-EEG recordings of epileptic patients implanted with intra-cerebral electrodes. I have then focused on the paediatric age, and in particular on the challenges posed by the paediatric clinical environment to the analysis of structural connectivity. In collaboration with the Neuroradiology Unit of the Giannina Gaslini Hospital in Genova, I have adapted and tested advanced DWI analysis methods for neonatal and paediatric data, which is commonly studied with less effective methods. We applied the same methods to the study of the effects of a specific brain malformation on the structural connectivity in 5 paediatric patients. While diffusion weighted imaging (DWI) is recognised as the best method to compute structural connectivity in the human brain, the most common methods for estimating functional connectivity data \u2014 functional MRI (fMRI) and electroencephalography (EEG) \u2014 suffer from different limitations: fMRI has good spatial resolution but low temporal resolution, while EEG has a better temporal resolution but the localisation of each signal\u2019s originating area is difficult and not always precise. Stereo-EEG (SEEG) combines strong spatial and temporal resolution with a high signal-to-noise ratio and allows to identify the source of each signal with precision, but is not used for studies on healthy subjects because of its invasiveness. Functional connectivity in children can be computed with either fMRI, EEG or SEEG, as in adult subjects. On the other hand, the study of structural connectivity in the paediatric age is met with obstacles posed by the specificity of this data, especially for the application of the advanced DWI analysis techniques commonly used in the adult age. Moreover, the clinical environment introduces even more constraints on the quality of the available data, both in children and adults, further limiting the possibility of applying advanced analysis methods for the investigation of connectivity in the paediatric age. Our results on adult subjects showed a positive correlation between structural and functional connectivity at different granularity levels, from global networks to community structures to single nodes, suggesting a correspondence between structural and functional organization which is maintained at different aggregation levels of brain units. In neonatal and paediatric subjects, we successfully adapted and applied the same advanced DWI analysis method used for the investigation in adults, obtaining white matter reconstructions more precise and anatomically plausible than with methods commonly used in paediatric clinical environments, and we were able to study the effects of a specific type of brain malformation on structural connectivity, explaining the different physical and functional manifestation of this malformation with respect to similar pathologies. This work further elucidates the relationship between structural and functional connectivity in the adult subject, and poses the basis for a corresponding work in the neonatal and paediatric subject in the clinical environment, allowing to study structural connectivity in the healthy and pathological child with clinical data