Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics

Concatenated Spatially-localized Random Forests for Hippocampus Labeling in Adult and Infant MR Brain Images

Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. First, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. Second, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. Furthermore, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.

Diverging white matter trajectories in children after traumatic brain injury: The RAPBI study

OBJECTIVE

To examine longitudinal trajectories of white matter organization in pediatric moderate/severe traumatic brain injury (msTBI) over a 12-month period.

METHODS

We studied 21 children (16 M/5 F) with msTBI, assessed 2-5 months postinjury and again 13-19 months postinjury, as well as 20 well-matched healthy control children. We assessed corpus callosum function through interhemispheric transfer time (IHTT), measured using event-related potentials, and related this to diffusion-weighted MRI measures of white matter (WM) microstructure. At the first time point, half of the patients with TBI had significantly slower IHTT (TBI-slow-IHTT, n = 11) and half were in the normal range (TBI-normal-IHTT, n = 10).

RESULTS

The TBI-normal-IHTT group did not differ significantly from healthy controls, either in WM organization in the chronic phase or in the longitudinal trajectory of WM organization between the 2 evaluations. In contrast, the WM organization of the TBI-slow-IHTT group was significantly lower than in healthy controls across a large portion of the WM. Longitudinal analyses showed that the TBI-slow-IHTT group experienced a progressive decline between the 2 evaluations in WM organization throughout the brain.

CONCLUSIONS

We present preliminary evidence suggesting a potential biomarker that identifies a subset of patients with impaired callosal organization in the first months postinjury who subsequently experience widespread continuing and progressive degeneration in the first year postinjury.

Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation

Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.

Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography

Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we automatically created a multi-subject atlas of SWM diffusion-based bundles of the whole brain. For each subject, the complete cortico-cortical tractogram is first split into sub-tractograms connecting pairs of gyri. Then intra-subject shape-based fiber clustering performs compression of each sub-tractogram into a set of bundles. Proceeding further with shape-based clustering provides a match of the bundles across subjects. Bundles found in most of the subjects are instantiated in the atlas. To increase robustness, this procedure was performed with two independent groups of subjects, in order to discard bundles without match across the two independent atlases. Finally, the resulting intersection atlas was projected on a third independent group of subjects in order to filter out bundles without reproducible and reliable projection. The final multi-subject diffusion-based U-fiber atlas is composed of 100 bundles in total, 50 per hemisphere, from which 35 are common to both hemispheres.

Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm

OBJECTIVE

Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions.

METHODS AND MATERIAL

Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data.

RESULTS

The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71).

CONCLUSIONS

NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity.

Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains

Brain atlases are an essential component in understanding the dynamic cerebral development, especially for the early postnatal period. However, longitudinal atlases are rare for infants, and the existing ones are generally limited by their fuzzy appearance. Moreover, since longitudinal atlas construction is typically performed independently over time, the constructed atlases often fail to preserve temporal consistency. This problem is further aggravated for infant images since they typically have low spatial resolution and insufficient tissue contrast. In this paper, we propose a novel framework for consistent spatial-temporal construction of longitudinal atlases for developing infant brain MR images. Specifically, for preserving structural details, the atlas construction is performed in spatial-temporal wavelet domain simultaneously. This is achieved by a patch-based combination of results from each frequency subband. Compared with the existing infant longitudinal atlases, our experimental results indicate that our approach is able to produce longitudinal atlases with richer structural details and also better longitudinal consistency, thus leading to higher performance when used for spatial normalization of a group of infant brain images.

Development, validation and application of a new fornix template for studies of aging and preclinical Alzheimer's disease

We developed a merged younger-older adult template of the fornix and demonstrated its utility for studies of aging and preclinical Alzheimer's disease (AD). In Experiment 1, probabilistic tractography was used to reconstruct the fornix in younger and older adults and successful streamlines were then averaged to create a merged template in standard space. The new template includes the majority of the fornix from the hippocampal formation to the subcallosal region and the thalamus/hypothalamus. In Experiment 2, the merged template was validated as an appropriate measure for studies of aging, with comparisons against manual tracing measures indicating identical spatial coverage in younger and older adult groups. In Experiment 3, the merged template was found to outperform age-specific templates in measures of sensitivity and specificity computed on diffusion tensor imaging data of an independent participant cohort. In Experiment 4, relevance to preclinical AD was demonstrated via associations between fractional anisotropy within the new fornix template and cerebrospinal fluid markers of AD pathology (Aβ₄₂ and the t-tau/Aβ₄₂ ratio) in a third independent cohort of cognitively normal older adults. Our new template provides an appropriate measure for use in future studies seeking to characterize microstructural alterations in the fornix associated with aging and preclinical AD.

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A new merged, younger-older DTI template of the fornix was developed.

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Template anatomical validity, sensitivity and specificity were demonstrated.

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Template metrics correlate with Alzheimer's pathology.

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The new fornix template is an appropriate tool for aging and Alzheimer's research.

3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.

Parametrization of white matter manifold-like structures using principal surfaces

In this manuscript, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as the corpus callosum, using principal surfaces. The problem is approached by finding a geometrically motivated surface-based representation of the corpus callosum and visualized fractional anisotropy (FA) values projected onto the surface. The method also applies to any other diffusion summary. An algorithm is proposed that 1) constructs the principal surface of a corpus callosum; 2) flattens the surface into a parametric 2D map; 3) projects associated FA values on the map. The algorithm is applied to a longitudinal study containing 466 diffusion tensor images of 176 multiple sclerosis (MS) patients observed at multiple visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation studies demonstrate fast convergence and robust performance of the algorithm under a variety of challenging scenarios.

Creation of a whole brain short association bundle atlas using a hybrid approach

The Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we expanded a previously developed method for the automatic creation of a whole brain SWM bundle atlas. The method is based on a hybrid approach. First a cortical parcellation is used to extract fibers connecting two regions. Then an intra-and inter-subject hierarchical clustering are applied to find well-defined SWM bundles reproducible across subjects. In addition to the fronto-parietal and insula regions of the left hemisphere, the analysis was extended to the temporal and occipital lobes, including all their internal regions, for both hemispheres. Validation steps are performed in order to test the robustness of the method and the reproducibility of the obtained bundles. First the method was applied to two independent groups of subjects, in order to discard bundles without match across the two independent atlases. Then, the resulting intersection atlas was projected on a third independent group of subjects in order to filter out bundles without reproducible and reliable projection. The final multi-subject U-fiber atlas is composed of 100 bundles in total, 50 per hemisphere, from which 35 are common to both hemispheres. The atlas can be used in clinical studies for segmentation of the SWM bundles in new subjects, and measure DW values or complement functional data.

An automated string-based approach to extracting and characterizing White Matter fiber-bundles

In this paper, we propose an automated approach to extracting White Matter (WM) fiber-bundles through clustering and model characterization. The key novelties of our approach are: a new string-based formalism, allowing an alternative representation of WM fibers, a new string dissimilarity metric, a WM fiber clustering technique, and a new model-based characterization algorithm. Thanks to these novelties, the complex problem of WM fiber-bundle extraction and characterization reduces to a much simpler and well-known string extraction and analysis problem. Interestingly, while several past approaches extract fiber-bundles by grouping available fibers on the basis of provided atlases (and, therefore, cannot capture possibly existing fiber-bundles nor represented in the atlases), our approach first clusters available fibers once and for all, and then tries to associate obtained clusters with models provided directly and dynamically by users. This more dynamic and interactive way of proceeding can help the detection of fiber-bundles autonomously proposed by our approach and not present in the initial models provided by experts.

Embarrassingly Parallel Acceleration of Global Tractography via Dynamic Domain Partitioning

Global tractography estimates brain connectivity by organizing signal-generating fiber segments in an optimal configuration that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multi-core CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. In other words, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its two ends and also by the diffusion-weighted signal in its proximity. This observation makes it possible to parallelize the Markov chain Monte Carlo (MCMC) algorithm used in the global tractography algorithm so that concurrent updating of independent fiber segments can be carried out. Experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or even improve tractography performance.

Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Subject Information Transfer

Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired, but unfortunately, are not always practically available. In this paper, we propose to improve ODF estimation by using inter-subject correlation. Specifically, diffusion-weighted images acquired from different subjects, when transformed to the space of a target subject, can not only provide signal denoising with additional information, but also drastically increase the number of angular samples for better ODF estimation. This is largely because of the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.

Accelerating Global Tractography Using Parallel Markov Chain Monte Carlo

Global tractography estimates brain connectivity by determining the optimal configuration of signal-generating fiber segments that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multi-core CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. That is, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its both ends and also by the diffusion-weighted signal in its proximity. This observation makes it possible to parallelize the Markov chain Monte Carlo (MCMC) algorithm used in the global tractography algorithm so that updating of independent fiber segments can be done concurrently. The experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or improve tractography performance.

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.

Development of human white matter fiber pathways: From newborn to adult ages

Major long-range white matter pathways (cingulum, fornix, uncinate fasciculus [UF], inferior fronto-occipital fasciculus [IFOF], inferior longitudinal fasciculus [ILF], thalamocortical [TC], and corpus callosal [CC] pathways) were identified in eighty-three healthy humans ranging from newborn to adult ages. We tracked developmental changes using high-angular resolution diffusion MR tractography. Fractional anisotropy (FA), apparent diffusion coefficient, number, length, and volume were measured in pathways in each subject. Newborns had fewer, and more sparse, pathways than those of the older subjects. FA, number, length, and volume of pathways gradually increased with age and reached a plateau between 3 and 5 years of age. Data were further analyzed by normalizing with mean adult values as well as with each subject's whole brain values. Comparing subjects of 3 years old and under to those over 3 years old, the studied pathways showed differential growth patterns. The CC, bilateral cingulum, bilateral TC, and the left IFOF pathways showed significant growth both in volume and length, while the bilateral fornix, bilateral ILF and bilateral UF showed significant growth only in volume. The TC and CC took similar growth patterns with the whole brain. FA values of the cingulum and IFOF, and the length of ILF showed leftward asymmetry. The fornix, ILF and UF occupied decreased space compared to the whole brain during development with higher FA values, likely corresponding to extensive maturation of the pathways compared to the mean whole brain maturation. We believe that the outcome of this study will provide an important database for future reference.

Denoising Magnetic Resonance Images Using Collaborative Non-Local Means

Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. It is thus often desirable to remove such artifacts beforehand for more robust and effective quantitative analysis. It is important to preserve the integrity of relevant image information while removing noise in MR images. A variety of approaches have been developed for this purpose, and the non-local means (NLM) filter has been shown to be able to achieve state-of-the-art denoising performance. For effective denoising, NLM relies heavily on the existence of repeating structural patterns, which however might not always be present within a single image. This is especially true when one considers the fact that the human brain is complex and contains a lot of unique structures. In this paper we propose to leverage the repeating structures from multiple images to collaboratively denoise an image. The underlying assumption is that it is more likely to find repeating structures from multiple scans than from a single scan. Specifically, to denoise a target image, multiple images, which may be acquired from different subjects, are spatially aligned to the target image, and an NLM-like block matching is performed on these aligned images with the target image as the reference. This will significantly increase the number of matching structures and thus boost the denoising performance. Experiments on both synthetic and real data show that the proposed approach, collaborative non-local means (CNLM), outperforms the classic NLM and yields results with markedly improved structural details.

Multilevel Deficiency of White Matter Connectivity Networks in Alzheimer's Disease: A Diffusion MRI Study with DTI and HARDI Models

Alzheimer's disease (AD) is the most common form of dementia in elderly people. It is an irreversible and progressive brain disease. In this paper, we utilized diffusion-weighted imaging (DWI) to detect abnormal topological organization of white matter (WM) structural networks. We compared the differences between WM connectivity characteristics at global, regional, and local levels in 26 patients with probable AD and 16 normal control (NC) elderly subjects, using connectivity networks constructed with the diffusion tensor imaging (DTI) model and the high angular resolution diffusion imaging (HARDI) model, respectively. At the global level, we found that the WM structural networks of both AD and NC groups had a small-world topology; however, the AD group showed a significant decrease in both global and local efficiency, but an increase in clustering coefficient and the average shortest path length. We further found that the AD patients had significantly decreased nodal efficiency at the regional level, as well as weaker connections in multiple local cortical and subcortical regions, such as precuneus, temporal lobe, hippocampus, and thalamus. The HARDI model was found to be more advantageous than the DTI model, as it was more sensitive to the deficiencies in AD at all of the three levels.

Connectivity-based whole brain dual parcellation by group ICA reveals tract structures and decreased connectivity in schizophrenia

Mapping brain connectivity based on neuroimaging data is a promising new tool for understanding brain structure and function. In this methods paper, we demonstrate that group independent component analysis (GICA) can be used to perform a dual parcellation of the brain based on its connectivity matrix (cmICA). This dual parcellation consists of a set of spatially independent source maps, and a corresponding set of paired dual maps that define the connectivity of each source map to the brain. These dual maps are called the connectivity profiles of the source maps. Traditional analysis of connectivity matrices has been used previously for brain parcellation, but the present method provides additional information on the connectivity of these segmented regions. In this paper, the whole brain structural connectivity matrices were calculated on a 5 mm(3) voxel scale from diffusion imaging data based on the probabilistic tractography method. The effect of the choice of the number of components (30 and 100) and their stability were examined. This method generated a set of spatially independent components that are consistent with the canonical brain tracts provided by previous anatomic descriptions, with the high order model yielding finer segmentations. The corpus-callosum example shows how this method leads to a robust parcellation of a brain structure based on its connectivity properties. We applied cmICA to study structural connectivity differences between a group of schizophrenia subjects and healthy controls. The connectivity profiles at both model orders showed similar regions with reduced connectivity in schizophrenia patients. These regions included forceps major, right inferior fronto-occipital fasciculus, uncinate fasciculus, thalamic radiation, and corticospinal tract. This paper provides a novel unsupervised data-driven framework that summarizes the information in a large global connectivity matrix and tests for brain connectivity differences. It has the potential for capturing important brain changes related to disease in connectivity-based disorders.

Identification of Infants at Risk for Autism Using Multi-parameter Hierarchical White Matter Connectomes

Autism spectrum disorder (ASD) is a variety of developmental disorders that cause life-long communication and social deficits. However, ASD could only be diagnosed at children as early as 2 years of age, while early signs may emerge within the first year. White matter (WM) connectivity abnormalities have been documented in the first year of lives of ASD subjects. We introduce a novel multi-kernel support vector machine (SVM) framework to identify infants at high-risk for ASD at 6 months old, by utilizing the diffusion parameters derived from a hierarchical set of WM connectomes. Experiments show that the proposed method achieves an accuracy of 76%, in comparison to 70% with the best single connectome. The complementary information extracted from hierarchical networks enhances the classification performance, with the top discriminative connections consistent with other studies. Our framework provides essential imaging connectomic markers and contributes to the evaluation of ASD risks as early as 6 months.

Multi-view Classification for Identification of Alzheimer's Disease

In this paper, we propose a multi-view learning method using Magnetic Resonance Imaging (MRI) data for Alzheimer's Disease (AD) diagnosis. Specifically, we extract both Region-Of-Interest (ROI) features and Histograms of Oriented Gradient (HOG) features from each MRI image, and then propose mapping HOG features onto the space of ROI features to make them comparable and to impose high intra-class similarity with low inter-class similarity. Finally, both mapped HOG features and original ROI features are input to the support vector machine for AD diagnosis. The purpose of mapping HOG features onto the space of ROI features is to provide complementary information so that features from different views can not only be comparable (i.e., homogeneous) but also be interpretable. For example, ROI features are robust to noise, but lack of reflecting small or subtle changes, while HOG features are diverse but less robust to noise. The proposed multi-view learning method is designed to learn the transformation between two spaces and to separate the classes under the supervision of class labels. The experimental results on the MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that the proposed multi-view method helps enhance disease status identification performance, outperforming both baseline methods and state-of-the-art methods.

Space-Frequency Detail-Preserving Construction of Neonatal Brain Atlases

Brain atlases are an integral component of neuroimaging studies. However, most brain atlases are fuzzy and lack structural details, especially in the cortical regions. In particular, neonatal brain atlases are especially challenging to construct due to the low spatial resolution and low tissue contrast. This is mainly caused by the image averaging process involved in atlas construction, often smoothing out high-frequency contents that indicate fine anatomical details. In this paper, we propose a novel framework for detail-preserving construction of atlases. Our approach combines space and frequency information to better preserve image details. This is achieved by performing reconstruction in the space-frequency domain given by wavelet transform. Sparse patch-based atlas reconstruction is performed in each frequency subband. Combining the results for all these subbands will then result in a refined atlas. Compared with existing atlases, experimental results indicate that our approach has the ability to build an atlas with more structural details, thus leading to better performance when used to normalize a group of testing neonatal images.

Callosal Function in Pediatric Traumatic Brain Injury Linked to Disrupted White Matter Integrity

Traumatic brain injury (TBI) often results in traumatic axonal injury and white matter (WM) damage, particularly to the corpus callosum (CC). Damage to the CC can lead to impaired performance on neurocognitive tasks, but there is a high degree of heterogeneity in impairment following TBI. Here we examined the relation between CC microstructure and function in pediatric TBI. We used high angular resolution diffusion-weighted imaging (DWI) to evaluate the structural integrity of the CC in humans following brain injury in a sample of 32 children (23 males and 9 females) with moderate-to-severe TBI (msTBI) at 1-5 months postinjury, compared with well matched healthy control children. We assessed CC function through interhemispheric transfer time (IHTT) as measured using event-related potentials (ERPs), and related this to DWI measures of WM integrity. Finally, the relation between DWI and IHTT results was supported by additional results of neurocognitive performance assessed using a single composite performance scale. Half of the msTBI participants (16 participants) had significantly slower IHTTs than the control group. This slow IHTT group demonstrated lower CC integrity (lower fractional anisotropy and higher mean diffusivity) and poorer neurocognitive functioning than both the control group and the msTBI group with normal IHTTs. Lower fractional anisotropy-a common sign of impaired WM-and slower IHTTs also predicted poor neurocognitive function. This study reveals that there is a subset of pediatric msTBI patients during the post-acute phase of injury who have markedly impaired CC functioning and structural integrity that is associated with poor neurocognitive functioning.

SIGNIFICANCE STATEMENT

Traumatic brain injury (TBI) is the primary cause of death and disability in children and adolescents. There is considerable heterogeneity in postinjury outcome, which is only partially explained by injury severity. Imaging biomarkers may help explain some of this variance, as diffusion weighted imaging is sensitive to the white matter disruption that is common after injury. The corpus callosum (CC) is one of the most commonly reported areas of disruption. In this multimodal study, we discovered a divergence within our pediatric moderate-to-severe TBI sample 1-5 months postinjury. A subset of the TBI sample showed significant impairment in CC function, which is supported by additional results showing deficits in CC structural integrity. This subset also had poorer neurocognitive functioning. Our research sheds light on postinjury heterogeneity.

Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks

Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best single-parameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis.

Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative-50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the "rich club" - a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline.

An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

The neuroscientific community today is very much interested in analyzing specific white matter bundles like the arcuate fasciculus, the corticospinal tract, or the recently discovered Aslant tract to study sex differences, lateralization and many other connectivity applications. For this reason, experts spend time manually segmenting these fascicles and bundles using streamlines obtained from diffusion MRI tractography. However, to date, there are very few computational tools available to register these fascicles directly so that they can be analyzed and their differences quantified across populations. In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter. We also show how our novel method can be used to create bundle-specific atlases in a straightforward manner and we give an example of a probabilistic atlas construction of the optic radiation. In summary, Streamline-based Linear Registration provides a solid registration framework for creating new methods to study the white matter and perform group-level tractometry analysis.

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease

Alzheimer's disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods - four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one "ball-and-stick" approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.

AUTOMATED MULTI-ATLAS LABELING OF THE FORNIX AND ITS INTEGRITY IN ALZHEIMER'S DISEASE

Alzheimer's disease is the most common form of dementia. Diffusion imaging provides information on white matter integrity not available with standard MRI, revealing additional information on how Alzheimer's disease affects the brain. Here we implemented and tested a multi-atlas labeling algorithm to segment the fornix and a point-correspondence tract matching scheme to assess fiber integrity in the fornix in diffusion MRI from 210 participants scanned as part of the Alzheimer's Disease Neuroimaging Initiative. Various diffusion-derived measures were used to relate fornix degeneration to cognitive decline. On 3D parametric tract models, mean diffusivity (MD) was more sen-sitive to group differences than fractional anisotropy (FA). Compared to previous studies, we mapped diffusion information along the fornix, yielding 3-D maps of degenerative changes along the tract in people with different stages of Alzheimer's disease.

WHITE MATTER INTEGRITY IN TRAUMATIC BRAIN INJURY: EFFECTS OF PERMISSIBLE FIBER TURNING ANGLE

Traumatic brain injury (TBI) is the leading cause of death and disability in children. Diffusion weighted imaging (DWI) methods have been shown to be especially sensitive to white matter abnormalities in TBI. We used our newly developed autoMATE algorithm (automated multi-atlas tract extraction) to map altered WM integrity in TBI. Even so, tractography methods include a free parameter that limits the maximum permissible turning angles for extracted fibers, with little investigation of how this may affect statistical group comparisons. Here, we examined WM integrity calculated over a range of fiber turning angles to determine to what extent this parameter affects our ability to detect group differences. Fiber turning angle threshold has a subtle, but sometimes significant, effect on the differences we were able to detect between TBI and healthy children.

White matter disruption in moderate/severe pediatric traumatic brain injury: advanced tract-based analyses

Traumatic brain injury (TBI) is the leading cause of death and disability in children and can lead to a wide range of impairments. Brain imaging methods such as DTI (diffusion tensor imaging) are uniquely sensitive to the white matter (WM) damage that is common in TBI. However, higher-level analyses using tractography are complicated by the damage and decreased FA (fractional anisotropy) characteristic of TBI, which can result in premature tract endings. We used the newly developed autoMATE (automated multi-atlas tract extraction) method to identify differences in WM integrity. 63 pediatric patients aged 8–19 years with moderate/severe TBI were examined with cross sectional scanning at one or two time points after injury: a post-acute assessment 1–5 months post-injury and a chronic assessment 13–19 months post-injury. A battery of cognitive function tests was performed in the same time periods. 56 children were examined in the first phase, 28 TBI patients and 28 healthy controls. In the second phase 34 children were studied, 17 TBI patients and 17 controls (27 participants completed both post-acute and chronic phases). We did not find any significant group differences in the post-acute phase. Chronically, we found extensive group differences, mainly for mean and radial diffusivity (MD and RD). In the chronic phase, we found higher MD and RD across a wide range of WM. Additionally, we found correlations between these WM integrity measures and cognitive deficits. This suggests a distributed pattern of WM disruption that continues over the first year following a TBI in children.

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We examined pediatric traumatic brain injury patients at 2 time points post injury.

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Cross sectional analyses were completed at the post-acute and chronic stages.

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We used novel tract-based methods to reveal widespread white matter disruption.

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White matter disruption chronically was related to cognitive deficits.

Sequential Hierarchical Agglomerative Clustering of White Matter Fiber Pathways

OBJECTIVE

We consider the problem of clustering white matter fiber pathways, extracted from diffusion MRI data via tractography, into bundles that are consistent with the neuroanatomy.

METHODS

We cast this problem as clustering streams of data, and use a sequential framework to process one fiber at a time. Our method, named as sequential hierarchical agglomerative clustering (HAC), represents the clusters with parametric models, performs HAC of relatively small number of fibers only when the parameters need to be initialized and/or updated, and assigns the labels to the following streams of data according to the current models.

RESULTS

Experiments on phantom data evaluate the sensitivity of our method to initialization and parameter tuning, and show its advantages over alternative techniques. Experiments on real data demonstrate its efficacy and speed in clustering white matter fiber pathways into anatomically distinct bundles.

CONCLUSION

Sequential HAC is a fast method that benefits from having a predefined number of clusters, and rapidly assigns labels to incoming data with high accuracy. It can be thought of as a mechanism that does clustering, while simultaneously accepting newly computed fibers; thereby, alleviating the burden of computing the distances between every pair of fibers in a tractogram.

SIGNIFICANCE

Sequential HAC is a practical tool that can interactively cluster fiber pathways and can be integrated into fiber tracking, which will be very useful for clinical researchers and neuroanatomists.