A filtered approach to neural tractography using the Watson directional function

Abnormalities in thalamo-cortical connections in patients with first-episode schizophrenia: a two-tensor tractography study

The "cognitive dysmetria" hypothesis suggests that impairments in cognition and behavior in patients with schizophrenia can be explained by disruptions in the cortico-cerebellar-thalamic-cortical circuit. In this study we examine thalamo-cortical connections in patients with first-episode schizophrenia (FESZ). White matter pathways are investigated that connect the thalamus with three frontal cortex regions including the anterior cingulate cortex (ACC), ventrolateral prefrontal cortex (VLPFC), and lateral oribitofrontal cortex (LOFC). We use a novel method of two-tensor tractography in 26 patients with FESZ compared to 31 healthy controls (HC), who did not differ on age, sex, or education. Dependent measures were fractional anisotropy (FA), Axial Diffusivity (AD), and Radial Diffusivity (RD). Subjects were also assessed using clinical functioning measures including the Global Assessment of Functioning (GAF) Scale, the Global Social Functioning Scale (GF: Social), and the Global Role Functioning Scale (GF: Role). FESZ patients showed decreased FA in the right thalamus-right ACC and right-thalamus-right LOFC pathways compared to healthy controls (HCs). In the right thalamus-right VLPFC tract, we found decreased FA and increased RD in the FESZ group compared to HCs. After correcting for multiple comparisons, reductions in FA in the right thalamus- right ACC and the right thalamus- right VLPC tracts remained significant. Moreover, reductions in FA were significantly associated with lower global functioning scores as well as lower social and role functioning scores. We report the first diffusion tensor imaging study of white matter pathways connecting the thalamus to three frontal regions. Findings of white matter alterations and clinical associations in the thalamic-cortical component of the cortico-cerebellar-thalamic-cortical circuit in patients with FESZ support the cognitive dysmetria hypothesis and further suggest the possible involvement of myelin sheath pathology and axonal membrane disruption in the pathogenesis of the disorder.

Diffusion abnormalities in the corpus callosum in first episode schizophrenia: Associated with enlarged lateral ventricles and symptomatology

INTRODUCTION

Abnormalities in the corpus callosum (CC) and the lateral ventricles (LV) are hallmark features of schizophrenia. These abnormalities have been reported in chronic and in first episode schizophrenia (FESZ). Here we explore further associations between CC and LV in FESZ using diffusion tensor imaging (DTI).

METHODS

. Sixteen FESZ patients and 16 healthy controls (HC), matched on age, gender, and handedness participated in the study. Diffusion and structural imaging scans were acquired on a 3T GE Signa magnet. Volumetric measures for LV and DTI measures for five CC subdivisions were completed in both groups. In addition, two-tensor tractography, the latter corrected for free-water (FA_t), was completed for CC. Correlations between LV and DTI measures of the CC were examined in both groups, while correlations between DTI and clinical measures were examined in only FESZ.

RESULTS

Results from two-tensor tractography demonstrated decreased FA_t and increased trace and radial diffusivity (RD_t) in the five CC subdivisions in FESZ compared to HC. Central CC diffusion measures in FESZ were significantly correlated with volume of the LV, i.e., decreased FA_t values were associated with larger LV volume, while increased RD_t and trace values were associated with larger LV volume. In controls, correlations were also significant, but they were in the opposite direction from FESZ. In addition, decreased FA_t in FESZ was associated with more positive symptoms.

DISCUSSION

Partial volume corrected FA_t, RD_t, and trace abnormalities in the CC in FESZ suggest possible de- or dys-myelination, or changes in axonal diameters, all compatible with neurodevelopmental theories of schizophrenia. Correlational findings between the volume of LV and diffusion measures in FESZ reinforce the concept of a link between abnormalities in the LV and CC in early stages of schizophrenia and are also compatible with neurodevelopmental abnormalities in this population.

White Matter Fiber Tractography Using Nonuniform Rational B-Splines Curve Fitting

The study of neural connectivity has grown rapidly in the past decade. Revealing brain anatomical connection improves not only clinical measures but also cognition understanding. In order to achieve this goal, we have to track neural fiber pathways first. Aiming to estimate 3D fiber pathways more accurately from orientation distribution function (ODF) fields, we presented a novel tracking method based on nonuniform rational B-splines (NURBS) curve fitting. First, we constructed ODF fields from high angular resolution diffusion imaging (HARDI) datasets using diffusion orientation transform (DOT) method. Second, under the angular and length constraints, the consecutive diffusion directions were extracted along each fiber pathway starting from a seed voxel. Finally, after the coordinates of the control points and their corresponding weights were determined, NURBS curve fitting was employed to track fiber pathways. The performance of the proposal has been evaluated on the tractometer phantom and real brain datasets. Based on several measure metrics, the resulting fiber pathways show promising anatomic consistency.

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.

Tracking and validation techniques for topographically organized tractography

Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.

Impaired white matter connectivity between regions containing mirror neurons, and relationship to negative symptoms and social cognition, in patients with first-episode schizophrenia

In schizophrenia, abnormalities in structural connectivity between brain regions known to contain mirror neurons and their relationship to negative symptoms related to a domain of social cognition are not well understood. Diffusion tensor imaging (DTI) scans were acquired in 16 patients with first episode schizophrenia and 16 matched healthy controls. FA and Trace of the tracts interconnecting regions known to be rich in mirror neurons, i.e., anterior cingulate cortex (ACC), inferior parietal lobe (IPL) and premotor cortex (PMC) were evaluated. A significant group effect for Trace was observed in IPL-PMC white matter fiber tract (F (1, 28) = 7.13, p = .012), as well as in the PMC-ACC white matter fiber tract (F (1, 28) = 4.64, p = .040). There were no group differences in FA. In addition, patients with schizophrenia showed a significant positive correlation between the Trace of the left IPL-PMC white matter fiber tract, and the Ability to Feel Intimacy and Closeness score (rho = .57, p = 0.034), and a negative correlation between the Trace of the left PMC-ACC and the Relationships with Friends and Peers score (rho = remove -.54, p = 0.049). We have demonstrated disrupted white mater microstructure within the white matter tracts subserving brain regions containing mirror neurons. We further showed that such structural disruptions might impact negative symptoms and, more specifically, contribute to the inability to feel intimacy (a measure conceptually related to theory of mind) in first episode schizophrenia. Further studies are needed to understand the potential of our results for diagnosis, prognosis and therapeutic interventions.

Machine-learning classification of 22q11.2 deletion syndrome: A diffusion tensor imaging study

Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a genetic neurodevelopmental syndrome that has been studied intensively in order to understand relationships between the genetic microdeletion, brain development, cognitive function, and the emergence of psychiatric symptoms. White matter microstructural abnormalities identified using diffusion tensor imaging methods have been reported to affect a variety of neuroanatomical tracts in 22q11.2DS. In the present study, we sought to combine two discovery-based approaches: (1) white matter query language was used to parcellate the brain's white matter into tracts connecting pairs of 34, bilateral cortical regions and (2) the diffusion imaging characteristics of the resulting tracts were analyzed using a machine-learning method called support vector machine in order to optimize the selection of a set of imaging features that maximally discriminated 22q11.2DS and comparison subjects. With this unique approach, we both confirmed previously-recognized 22q11.2DS-related abnormalities in the inferior longitudinal fasciculus (ILF), and identified, for the first time, 22q11.2DS-related anomalies in the middle longitudinal fascicle and the extreme capsule, which may have been overlooked in previous, hypothesis-guided studies. We further observed that, in participants with 22q11.2DS, ILF metrics were significantly associated with positive prodromal symptoms of psychosis.

The social brain network in 22q11.2 deletion syndrome: a diffusion tensor imaging study

BACKGROUND

Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a neurogenetic disorder that is associated with a 25-fold increase in schizophrenia. Both individuals with 22q11.2DS and those with schizophrenia present with social cognitive deficits, which are putatively subserved by a network of brain regions that are involved in the processing of social cognitive information. This study used two-tensor tractography to examine the white matter tracts believed to underlie the social brain network in a group of 57 young adults with 22q11.2DS compared to 30 unaffected controls.

RESULTS

Results indicated that relative to controls, participants with 22q11.2DS showed significant differences in several DTI metrics within the inferior fronto-occipital fasciculus, cingulum bundle, thalamo-frontal tract, and inferior longitudinal fasciculus. In addition, participants with 22q11.2DS showed significant differences in scores on measures of social cognition, including the Social Responsiveness Scale and Trait Emotional Intelligence Questionnaire. Further analyses among individuals with 22q11.2DS demonstrated an association between DTI metrics and positive and negative symptoms of psychosis, as well as differentiation between individuals with 22q11.2DS and overt psychosis, relative to those with positive prodromal symptoms or no psychosis.

CONCLUSIONS

Findings suggest that white matter disruption, specifically disrupted axonal coherence in the right inferior fronto-occipital fasciculus, may be a biomarker for social cognitive difficulties and psychosis in individuals with 22q11.2DS.

A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation

Diffusion magnetic resonance imaging (dMRI) provides information about the microstructure of white matter in the human brain. From dMRI, streamlining tractography is often used to reconstruct computational representations of white matter tracts from which differences in structural connectivity can be explored. In the fiber tracking process, anatomical information can help reduce tracking errors caused by crossing fibers and image noise. In this paper, we propose a Bayesian method for estimating fiber orientations (FOs) guided by anatomical tract information using diffusion tensor imaging (DTI), which is a standard clinical and research dMRI protocol. The proposed method is named Fiber Orientation Reconstruction guided by Tract Segmentation (FORTS). A first step segments and labels the white matter tracts volumetrically, including explicit representations of crossing regions. A second step estimates the FOs using the diffusion information and the anatomical knowledge from segmented white matter tracts. A single FO is estimated in the noncrossing regions while two FOs are estimated in the crossing regions. A third step carries out streamlining tractography that uses information from both the segmented tracts and the estimated FOs. Experiments performed on a digital crossing phantom, a physical phantom, and brain DTI of 18 healthy subjects show that FORTS is able to use the anatomical information to produce FOs with better accuracy and to reduce anatomically incorrect streamlines. In particular, on the brain DTI data, we studied the connectivity of anatomically defined tracts to cortical areas, which is not straightforwardly achievable using only volumetric tract segmentation. These connectivity results demonstrate the potential application of FORTS to scientific studies.

The white matter query language: a novel approach for describing human white matter anatomy

We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI volumes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist's expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for ten association and 15 projection tracts per hemisphere, along with seven commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia.

Improved fidelity of brain microstructure mapping from single-shell diffusion MRI

Diffusion weighted imaging (DWI) is sensitive to alterations in the diffusion of water molecules caused by microstructural barriers. Different microstructural compartments are characterized by differences in DWI signal. Diffusion tensor imaging conflates the signal from these compartments into a single tensor, which poorly represents multiple white matter fascicles and extra-axonal space. Diffusion compartment imaging (DCI) models overcome this limitation by providing parametric representations for the signal contribution of each compartment, thereby improving the fidelity of brain microstructure mapping. However, current approaches fail to identify DCI model parameters from conventional single-shell DWI with the desired accuracy. It has been demonstrated that part of this inaccuracy is due to the ill-posedness of the estimation of DCI model parameters from conventional single-shell acquisitions. In this paper, we propose to regularize the estimation problem for single-shell DWI by learning a prior distribution of DCI model parameters from DWI acquired at multiple b-values in an external population of subjects. We demonstrate that this population-informed prior enables, for the first time, accurate estimation of DCI models from single-shell DWI typically acquired in clinical practice. We validated our approach on synthetic and in vivo data of healthy subjects and patients with autism spectrum disorder. We applied the approach to population studies of brain microstructure in autism and found that introducing a population-informed prior leads to reliable detection of group differences. Our algorithm enables novel investigation from large existing DWI datasets in normal development and in disease and injury.

Segmentation of the Cerebellar Peduncles Using a Random Forest Classifier and a Multi-object Geometric Deformable Model: Application to Spinocerebellar Ataxia Type 6

The cerebellar peduncles, comprising the superior cerebellar peduncles (SCPs), the middle cerebellar peduncle (MCP), and the inferior cerebellar peduncles (ICPs), are white matter tracts that connect the cerebellum to other parts of the central nervous system. Methods for automatic segmentation and quantification of the cerebellar peduncles are needed for objectively and efficiently studying their structure and function. Diffusion tensor imaging (DTI) provides key information to support this goal, but it remains challenging because the tensors change dramatically in the decussation of the SCPs (dSCP), the region where the SCPs cross. This paper presents an automatic method for segmenting the cerebellar peduncles, including the dSCP. The method uses volumetric segmentation concepts based on extracted DTI features. The dSCP and noncrossing portions of the peduncles are modeled as separate objects, and are initially classified using a random forest classifier together with the DTI features. To obtain geometrically correct results, a multi-object geometric deformable model is used to refine the random forest classification. The method was evaluated using a leave-one-out cross-validation on five control subjects and four patients with spinocerebellar ataxia type 6 (SCA6). It was then used to evaluate group differences in the peduncles in a population of 32 controls and 11 SCA6 patients. In the SCA6 group, we have observed significant decreases in the volumes of the dSCP and the ICPs and significant increases in the mean diffusivity in the noncrossing SCPs, the MCP, and the ICPs. These results are consistent with a degeneration of the cerebellar peduncles in SCA6 patients.

A review of neuroimaging findings in repetitive brain trauma

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease confirmed at postmortem. Those at highest risk are professional athletes who participate in contact sports and military personnel who are exposed to repetitive blast events. All neuropathologically confirmed CTE cases, to date, have had a history of repetitive head impacts. This suggests that repetitive head impacts may be necessary for the initiation of the pathogenetic cascade that, in some cases, leads to CTE. Importantly, while all CTE appears to result from repetitive brain trauma, not all repetitive brain trauma results in CTE. Magnetic resonance imaging has great potential for understanding better the underlying mechanisms of repetitive brain trauma. In this review, we provide an overview of advanced imaging techniques currently used to investigate brain anomalies. We also provide an overview of neuroimaging findings in those exposed to repetitive head impacts in the acute/subacute and chronic phase of injury and in more neurodegenerative phases of injury, as well as in military personnel exposed to repetitive head impacts. Finally, we discuss future directions for research that will likely lead to a better understanding of the underlying mechanisms separating those who recover from repetitive brain trauma vs. those who go on to develop CTE.

Tractography from HARDI using an intrinsic unscented Kalman filter

A novel adaptation of the unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multitensor estimation and fiber tractography from diffusion MRI. This technique has the advantage over other tractography methods in terms of computational efficiency, due to the fact that the UKF simultaneously estimates the diffusion tensors and propagates the most consistent direction to track along. This UKF and its variants reported later in literature however are not intrinsic to the space of diffusion tensors. Lack of this key property can possibly lead to inaccuracies in the multitensor estimation as well as in the tractography. In this paper, we propose a novel intrinsic unscented Kalman filter (IUKF) in the space of diffusion tensors which are symmetric positive definite matrices, that can be used for simultaneous recursive estimation of multitensors and propagation of directional information for use in fiber tractography from diffusion weighted MR data. In addition to being more accurate, IUKF retains all the advantages of UKF mentioned above. We demonstrate the accuracy and effectiveness of the proposed method via experiments publicly available phantom data from the fiber cup-challenge (MICCAI 2009) and diffusion weighted MR scans acquired from human brains and rat spinal cords.

Estimating constrained multi-fiber diffusion MR volumes by orientation clustering

Diffusion MRI is a valuable tool for mapping tissue microstructure; however, multi-fiber models present challenges to image analysis operations. In this paper, we present a method for estimating models for such operations by clustering fiber orientations. Our approach is applied to ball-and-stick diffusion models, which include an isotropic tensor and multiple sticks encoding fiber volume and orientation. We consider operations which can be generalized to a weighted combination of fibers and present a method for representing such combinations with a mixture-of-Watsons model, learning its parameters by Expectation Maximization. We evaluate this approach with two experiments. First, we show it is effective for filtering in the presence of synthetic noise. Second, we demonstrate interpolation and averaging by construction of a tractography atlas, showing improved reconstruction of white matter pathways. These experiments indicate that our method is useful in estimating multi-fiber ball-and-stick diffusion volumes resulting from a range of image analysis operations.

White matter tract abnormalities between rostral middle frontal gyrus, inferior frontal gyrus and striatum in first-episode schizophrenia

BACKGROUND

Previous studies have shown that frontostriatal networks, especially those involving dorsolateral prefrontal cortex (DLPFC) and ventrolateral prefrontal cortex (VLPFC) mediate cognitive functions some of which are abnormal in schizophrenia. This study examines white matter integrity of the tracts connecting DLPFC/VLPFC and striatum in patients with first-episode schizophrenia (FESZ), and their associations with cognitive and clinical correlates.

METHODS

Diffusion tensor and structural magnetic resonance images were acquired on a 3T GE Echospeed system from 16 FESZ and 18 demographically comparable healthy controls. FreeSurfer software was used to parcellate regions of interest. Two-tensor tractography was applied to extract fibers connecting striatum with rostral middle frontal gyrus (rMFG) and inferior frontal gyrus (IFG), representing DLPFC and VLPFC respectively. DTI indices, including fractional anisotropy (FA), trace, axial diffusivity (AD) and radial diffusivity (RD), were used for group comparisons. Additionally, correlations were evaluated between these diffusion indices and the Wisconsin Card Sorting Task (WCST) and the Brief Psychiatric Rating Scale (BPRS).

RESULTS

FA was significantly reduced in the left IFG-striatum tract, whereas trace and RD were significantly increased in rMFG-striatum and IFG-striatum tracts, bilaterally. The number of WCST categories completed correlated positively with FA of the right rMFG-striatum tract, and negatively with trace and RD of right rMFG-striatum and right IFG-striatum tracts in FESZ. The BPRS scores did not correlate with these indices.

CONCLUSIONS

These data suggest that white matter tract abnormalities between rMFG/IFG and striatum are present in FESZ and appear to be significantly associated with executive dysfunction but not with symptom severity.

Fiber feature map based landmark initialization for highly deformable DTI registration

This paper presents a novel pipeline for the registration of diffusion tensor images (DTI) with large pathological variations to normal controls based on the use of a novel feature map derived from white matter (WM) fiber tracts. The research presented aims towards an atlas based DTI analysis of subjects with considerable brain pathologies such as tumors or hydrocephalus. In this paper, we propose a novel feature map that is robust against variations in WM fiber tract integrity and use these feature maps to determine a landmark correspondence using a 3D point correspondence algorithm. This correspondence drives a deformation field computed using Gaussian radial basis functions(RBF). This field is employed as an initialization to a standard deformable registration method like demons. We present early preliminary results on the registration of a normal control dataset to a dataset with abnormally enlarged lateral ventricles affected by fatal demyelinating Krabbe disease. The results are analyzed based on a regional tensor matching criterion and a visual assessment of overlap of major WM fiber tracts. While further evaluation and improvements are necessary, the results presented in this paper highlight the potential of our method in handling registration of subjects with severe WM pathology.

Software-based Diffusion MR Human Brain Phantom for Evaluating Fiber-tracking Algorithms

Fiber tracking provides insights into the brain white matter network and has become more and more popular in diffusion MR imaging. Hardware or software phantom provides an essential platform to investigate, validate and compare various tractography algorithms towards a "gold standard". Software phantoms excel due to their flexibility in varying imaging parameters, such as tissue composition, SNR, as well as potential to model various anatomies and pathologies. This paper describes a novel method in generating diffusion MR images with various imaging parameters from realistically appearing, individually varying brain anatomy based on predefined fiber tracts within a high-resolution human brain atlas. Specifically, joint, high resolution DWI and structural MRI brain atlases were constructed with images acquired from 6 healthy subjects (age 22-26) for the DWI data and 56 healthy subject (age 18-59) for the structural MRI data. Full brain fiber tracking was performed with filtered, two-tensor tractography in atlas space. A deformation field based principal component model from the structural MRI as well as unbiased atlas building was then employed to generate synthetic structural brain MR images that are individually varying. Atlas fiber tracts were accordingly warped into each synthetic brain anatomy. Diffusion MR images were finally computed from these warped tracts via a composite hindered and restricted model of diffusion with various imaging parameters for gradient directions, image resolution and SNR. Furthermore, an open-source program was developed to evaluate the fiber tracking results both qualitatively and quantitatively based on various similarity measures.

On describing human white matter anatomy: the white matter query language

The main contribution of this work is the careful syntactical definition of major white matter tracts in the human brain based on a neuroanatomist's expert knowledge. We present a technique to formally describe white matter tracts and to automatically extract them from diffusion MRI data. The framework is based on a novel query language with a near-to-English textual syntax. This query language allows us to construct a dictionary of anatomical definitions describing white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This enables automated coherent labeling of white matter anatomy across subjects. We use our method to encode anatomical knowledge in human white matter describing 10 association and 8 projection tracts per hemisphere and 7 commissural tracts. The technique is shown to be comparable in accuracy to manual labeling. We present results applying this framework to create a white matter atlas from 77 healthy subjects, and we use this atlas in a proof-of-concept study to detect tract changes specific to schizophrenia.

Probabilistic tractography using Q-ball imaging and particle filtering: application to adult and in-utero fetal brain studies

By assuming that orientation information of brain white matter fibers can be inferred from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) measurements, tractography algorithms provide an estimation of the brain connectivity in vivo. The two key ingredients of tractography are the diffusion model (tensor, high-order tensor, Q-ball, etc.) and the means to deal with uncertainty during the tracking process (deterministic vs probabilistic mathematical framework). In this paper, we investigate the use of an analytical Q-ball model for the diffusion data within a well-formalized particle filtering framework. The proposed method is validated and compared to other tracking algorithms on the MICCAI'09 contest Fiber Cup phantom. Tractographies of in vivo adult and fetal brain Diffusion-Weighted Images (DWIs) are also shown to illustrate the robustness of the algorithm.

Structural human brain networks: hot topics in diffusion tractography

PURPOSE OF REVIEW

After more than 10 years of methodological developments and clinical applications, diffusion imaging tractography has reached a crossroad. Although the method is still in its infancy, the time has come to address some important questions. Can tractography reproduce reliably known anatomy or describe new anatomical pathways? Are interindividual differences, for example in tract lateralization, important to understand heterogeneity of clinical manifestations? Do novel tractography algorithms provide a real advantage over previous methods? Here we focus on some of the most exciting recent advancements in diffusion tractography and critically highlight their advantages and limitations.

RECENT FINDINGS

A flourishing of diffusion methods and models are bringing new solutions to the well known limitations of classical tractography based on the tensor model. However, these methods pose also new challenges and require the convergence and integration of different disciplines before they can replace what is currently widely available.

SUMMARY

Rigorous postmortem validation, clinical optimization and experimental confirmation are obligatory steps before advanced diffusion technologies can translate into clear benefits for neurological patients.

The translational role of diffusion tensor image analysis in animal models of developmental pathologies

Diffusion tensor magnetic resonance imaging (DTI) has proven itself a powerful technique for clinical investigation of the neurobiological targets and mechanisms underlying developmental pathologies. The success of DTI in clinical studies has demonstrated its great potential for understanding translational animal models of clinical disorders, and preclinical animal researchers are beginning to embrace this new technology to study developmental pathologies. In animal models, genetics can be effectively controlled, drugs consistently administered, subject compliance ensured, and image acquisition times dramatically increased to reduce between-subject variability and improve image quality. When pairing these strengths with the many positive attributes of DTI, such as the ability to investigate microstructural brain organization and connectivity, it becomes possible to delve deeper into the study of both normal and abnormal development. The purpose of this review is to provide new preclinical investigators with an introductory source of information about the analysis of data resulting from small animal DTI studies to facilitate the translation of these studies to clinical data. In addition to an in-depth review of translational analysis techniques, we present a number of relevant clinical and animal studies using DTI to investigate developmental insults in order to further illustrate techniques and to highlight where small animal DTI could potentially provide a wealth of translational data to inform clinical researchers.

Statistical analysis of fiber bundles using multi-tensor tractography: application to first-episode schizophrenia

This work proposes a new method to detect abnormalities in fiber bundles of first-episode (FE) schizophrenia patients. Existing methods have either examined a particular region of interest or used voxel-based morphometry or used tracts generated using the single tensor model for locating statistically different fiber bundles. Further, a two-sample t test, which assumes a Gaussian distribution for each population, is the most widely used statistical hypothesis testing algorithm. In this study, we use the unscented Kalman filter based two-tensor tractography algorithm for tracing neural fiber bundles of the brain that connect 105 different cortical and subcortical regions. Next, fiber bundles with significant connectivity across the entire population were determined. Several diffusion measures derived from the two-tensor model were computed and used as features in the subsequent analysis. For each fiber bundle, an affine-invariant descriptor was computed, thus obviating the need for precise registration of patients to an atlas. A kernel-based statistical hypothesis testing algorithm, which makes no assumption regarding the distribution of the underlying population, was then used to determine the abnormal diffusion properties of all fiber bundles for 20 FE patients and 20 age-matched healthy controls. Of the 1254 fiber bundles with significant connectivity, 740 fiber bundles were found to be significantly different in at least one diffusion measure after correcting for multiple comparisons. Thus, the changes affecting first-episode patients seem to be global in nature (spread throughout the brain).

White matter bundle registration and population analysis based on Gaussian processes

This paper proposes a method for the registration of white matter tract bundles traced from diffusion images and its extension to atlas generation, Our framework is based on a Gaussian process representation of tract density maps. Such a representation avoids the need for point-to-point correspondences, is robust to tract interruptions and reconnections and seamlessly handles the comparison and combination of white matter tract bundles. Moreover, being a parametric model, this approach has the potential to be defined in the Gaussian processes' parameter space, without the need for resampling the fiber bundles during the registration process. We use the similarity measure of our Gaussian process framework, which is in fact an inner product, to drive a diffeomorphic registration algorithm between two sets of homologous bundles which is not biased by point-to-point correspondences or the parametrization of the tracts. We estimate a dense deformation of the underlying white matter using the bundles as anatomical landmarks and obtain a population atlas of those fiber bundles. Finally we test our results in several different bundles obtained from in-vivo data.

Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents

This paper proposes a generic framework for the registration, the template estimation and the variability analysis of white matter fiber bundles extracted from diffusion images. This framework is based on the metric on currents for the comparison of fiber bundles. This metric measures anatomical differences between fiber bundles, seen as global homologous structures across subjects. It avoids the need to establish correspondences between points or between individual fibers of different bundles. It can measure differences both in terms of the geometry of the bundles (like its boundaries) and in terms of the density of fibers within the bundle. It is robust to fiber interruptions and reconnections. In addition, a recently introduced sparse approximation algorithm allows us to give an interpretable representation of the fiber bundles and their variations in the framework of currents. First, we used this metric to drive the registration between two sets of homologous fiber bundles of two different subjects. A dense deformation of the underlying white matter is estimated, which is constrained by the bundles seen as global anatomical landmarks. By contrast, the alignment obtained from image registration is driven only by the local gradient of the image. Second, we propose a generative statistical model for the analysis of a collection of homologous bundles. This model consistently estimates prototype fiber bundles (called template), which capture the anatomical invariants in the population, a set of deformations, which align the geometry of the template to that of each subject and a set of residual perturbations. The statistical analysis of both the deformations and the residuals describe the anatomical variability in terms of geometry (stretching, torque, etc.) and "texture" (fiber density, etc.). Third, this statistical modeling allows us to simulate new synthetic bundles according to the estimated variability. This gives a way to interpret the anatomical features that the model detects consistently across the subjects. This may be used to better understand the bias introduced by the fiber extraction methods and eventually to give anatomical characterization of the normal or pathological variability of fiber bundles.

A geometry-based particle filtering approach to white matter tractography

We introduce a fibre tractography framework based on a particle filter which estimates a local geometrical model of the underlying white matter tract, formulated as a 'streamline flow' using generalized helicoids. The method is not dependent on the diffusion model, and is applicable to diffusion tensor (DT) data as well as to high angular resolution reconstructions. The geometrical model allows for a robust inference of local tract geometry, which, in the context of the causal filter estimation, guides tractography through regions with partial volume effects. We validate the method on synthetic data and present results on two types in vivo data: diffusion tensors and a spherical harmonic reconstruction of the fibre orientation distribution function (fODF).

Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework

In analyzing diffusion magnetic resonance imaging, multi-tensor models address the limitations of the single diffusion tensor in situations of partial voluming and fiber crossings. However, selection of a suitable number of fibers and numerical difficulties in model fitting have limited their practical use. This paper addresses both problems by making spherical deconvolution part of the fitting process: We demonstrate that with an appropriate kernel, the deconvolution provides a reliable approximative fit that is efficiently refined by a subsequent descent-type optimization. Moreover, deciding on the number of fibers based on the orientation distribution function produces favorable results when compared to the traditional F-Test. Our work demonstrates the benefits of unifying previously divergent lines of work in diffusion image analysis.