Spatial analysis of diffusion tensor tractography statistics along the inferior fronto-occipital fasciculus with application in progressive supranuclear palsy

Combining white matter diffusion and geometry for tract-specific alignment and variability analysis

We present a framework for along-tract analysis of white matter (WM) fiber bundles based on diffusion tensor imaging (DTI) and tractography. We introduce the novel concept of fiber-flux density for modeling fiber tracts' geometry, and combine it with diffusion-based measures to define vector descriptors called Fiber-Flux Diffusion Density (FFDD). The proposed model captures informative features of WM tracts at both the microscopic (diffusion-related) and macroscopic (geometry-related) scales, thus enabling improved sensitivity to subtle structural abnormalities that are not reflected by either diffusion or geometrical properties alone. A key step in this framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tracts enable meaningful inter-subject comparisons and group-wise statistical analysis. Moreover, we show that the FMM alignment can be generalized in a straight forward manner to a single-shot co-alignment of multiple fiber bundles. The proposed alignment technique is shown to outperform a well-established, commonly used DTI registration algorithm. We demonstrate the FFDD framework on the Human Connectome Project (HCP) diffusion MRI dataset, as well as on two different datasets of contact sports players. We test our method using longitudinal scans of a basketball player diagnosed with a traumatic brain injury, showing compatibility with structural MRI findings. We further perform a group study comparing mid- and post-season scans of 13 active football players exposed to repetitive head trauma, to 17 non-player control (NPC) subjects. Results reveal statistically significant FFDD differences (p-values<0.05) between the groups, as well as increased abnormalities over time at spatially-consistent locations within several major fiber tracts of football players.

Decreased integrity of the fronto-temporal fibers of the left inferior occipito-frontal fasciculus associated with auditory verbal hallucinations in schizophrenia

Auditory verbal hallucinations (AVH) have been proposed to result from altered connectivity between frontal speech production regions and temporal speech perception regions. Whilst the dorsal language pathway, serviced by the arcuate fasciculus, has been extensively studied in relation to AVH, the ventral language pathway, serviced by the inferior occipito-frontal fasciculus (IOFF) has been rarely studied in relation to AVH. This study examined whether structural changes in anatomically defined subregions of the IOFF were associated with AVH in patients with schizophrenia. Diffusion tensor imaging scans and clinical data were obtained from the Australian Schizophrenia Research Bank for 113 schizophrenia patients, of whom 39 had lifetime experience of AVH (18 had current AVH, 21 had remitted AVH), 74 had no lifetime experience of AVH, and 40 healthy controls. Schizophrenia patients with a lifetime experience of AVH exhibited reduced fractional anisotropy (FA) in the fronto-temporal fibers of the left IOFF compared to both healthy controls and schizophrenia patients without AVH. In contrast, structural abnormalities in the temporal and occipital regions of the IOFF were observed bilaterally in both patient groups, relative to the healthy controls. These results suggest that while changes in the structural integrity of the bilateral IOFF are associated with schizophrenia per se, integrity reductions in the fronto-temporal fibers of the left IOFF may be specifically associated with AVH.

Segmentation of the inferior longitudinal fasciculus in the human brain: A white matter dissection and diffusion tensor tractography study

The inferior longitudinal fascicle (ILF) is one of the major occipital-temporal association pathways. Several studies have mapped its hierarchical segmentation to specific functions. There is, however, no consensus regarding a detailed description of ILF fibre organisation. The aim of this study was to establish whether the ILF has a constant number of subcomponents. A secondary aim was to determine the quantitative diffusion proprieties of each subcomponent and assess their anatomical trajectories and connectivity patterns. A white matter dissection of 14 post-mortem normal human hemispheres was conducted to define the course of the ILF and its subcomponents. These anatomical results were then investigated in 24 right-handed, healthy volunteers using in vivo diffusion tensor imaging (DTI) and streamline tractography. Fractional anisotropy (FA), volume, fibre length and the symmetry coefficient of each fibre group were analysed. In order to show the connectivity pattern of the ILF, we also conducted an analysis of the cortical terminations of each segment. We confirmed that the main structure of the ILF is composed of three constant components reflecting the occipital terminations: the fusiform, the lingual and the dorsolateral-occipital. ILF volume was significantly lateralised to the right. The examined indices of ILF subcomponents did not show any significant difference in lateralisation. The connectivity pattern and the quantitative distribution of ILF subcomponents suggest a pivotal role for this bundle in integrating information from highly specialised modular visual areas with activity in anterior temporal territory, which has been previously shown to be important for memory and emotions.

A genetic algorithm-based model for longitudinal changes detection in white matter fiber-bundles of patient with multiple sclerosis

Analysis of white matter (WM) tissue is essential to understand the mechanisms of neurodegenerative pathologies like multiple sclerosis (MS). Recently longitudinal studies started to show how the temporal component is important to investigate temporal diffuse effects of neurodegenerative pathologies. Diffusion tensor imaging (DTI) constitutes one of the most sensitive techniques for the detection and characterization of brain related pathological processes and allows also the reconstruction of WM fibers. The analysis of spatial and temporal pathological changes along the fibers are thus possible by merging quantitative maps with structural information provided by DTI. In this work, we present a new genetic algorithm (GA) based method to analyze longitudinal changes occurring along WM fiber-bundles. In the first part of this paper, we describe the data processing pipeline, including data registration and fiber tract post-processing. In the second part, we focus our attention to the description of our GA model. In the last part, we show the tests we performed on simulated and real MS longitudinal data. Our method reached a high level of precision, recall and F-Measure in the detection of longitudinal pathological alterations occurring along different WM fiber-bundles.

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.

A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis

Diffusion tensor imaging (DTI) is a sensitive tool for the assessment of microstructural alterations in brain white matter (WM). We propose a new processing technique to detect, local and global longitudinal changes of diffusivity metrics, in homologous regions along WM fiber-bundles. To this end, a reliable and automatic processing pipeline was developed in three steps: 1) co-registration and diffusion metrics computation, 2) tractography, bundle extraction and processing, and 3) longitudinal fiber-bundle analysis. The last step was based on an original Gaussian mixture model providing a fine analysis of fiber-bundle cross-sections, and allowing a sensitive detection of longitudinal changes along fibers. This method was tested on simulated and clinical data. High levels of F-Measure were obtained on simulated data. Experiments on cortico-spinal tract and inferior fronto-occipital fasciculi of five patients with Multiple Sclerosis (MS) included in a weekly follow-up protocol highlighted the greater sensitivity of this fiber scale approach to detect small longitudinal alterations.

Preoperative Quantitative MR Tractography Compared with Visual Tract Evaluation in Patients with Neuropathologically Confirmed Gliomas Grades II and III: A Prospective Cohort Study

Background and Purpose. Low-grade gliomas show infiltrative growth in white matter tracts. Diffusion tensor tractography can noninvasively assess white matter tracts. The aim was to preoperatively assess tumor growth in white matter tracts using quantitative MR tractography (3T). The hypothesis was that suspected infiltrated tracts would have altered diffusional properties in infiltrated tract segments compared to noninfiltrated tracts. Materials and Methods. Forty-eight patients with suspected low-grade glioma were included after written informed consent and underwent preoperative diffusion tensor imaging in this prospective review-board approved study. Major white matter tracts in both hemispheres were tracked, segmented, and visually assessed for tumor involvement in thirty-four patients with gliomas grade II or III (astrocytomas or oligodendrogliomas) on postoperative neuropathological evaluation. Relative fractional anisotropy (rFA) and mean diffusivity (rMD) in tract segments were calculated and compared with visual evaluation and neuropathological diagnosis. Results. Tract segment infiltration on visual evaluation was associated with a lower rFA and high rMD in a majority of evaluated tract segments (89% and 78%, resp.). Grade II and grade III gliomas had similar infiltrating behavior. Conclusion. Quantitative MR tractography corresponds to visual evaluation of suspected tract infiltration. It may be useful for an objective preoperative evaluation of tract segment involvement.

Diffusion tensor tractography versus volumetric imaging in the diagnosis of behavioral variant frontotemporal dementia

MRI diffusion tensor imaging (DTI) studies of white matter integrity in behavioral variant frontotemporal dementia have consistently shown involvement of frontal and temporal white matter, corresponding to regional loss of cortical volume. Volumetric imaging has a suboptimal sensitivity as a diagnostic tool and thus we wanted to explore if DTI is a better method to discriminate patients and controls than volumetric imaging. We examined the anterior cingulum bundle in 14 patients with behavioral variant frontotemporal dementia and 22 healthy controls using deterministic manual diffusion tensor tractography, and compared DTI parameters with two measures of cortical atrophy, VBM and cortical thickness, of the anterior cingulate cortex (ACC). Statistically significant changes between patients and controls were detected in all DTI parameters, with large effect sizes. ROC-AUC was for the best DTI parameters: 0.92 (fractional anisotropy) to 0.97 (radial diffusivity), 0.82 for the best cortical parameter, VBM of the ACC. Results from the AUC were confirmed with binary logistic regression analysis including demographic variables, but only for fractional anisotropy and mean diffusivity. Ability to classify patient/nonpatient status was significantly better for mean diffusivity vs. VBM (p=0.031), and borderline significant for fractional anisotropy vs. VBM (p=0.062). The results indicate that DTI could offer advantages in comparison with the assessment of cortical volume in differentiating patients with behavioral variant frontotemporal dementia and controls.

Assessment of global and regional diffusion changes along white matter tracts in parkinsonian disorders by MR tractography

Purpose

The aim of the study was to determine the usefulness of diffusion tensor tractography (DTT) in parkinsonian disorders using a recently developed method for normalization of diffusion data and tract size along white matter tracts. Furthermore, the use of DTT in selected white matter tracts for differential diagnosis was assessed.

Methods

We quantified global and regional diffusion parameters in major white matter tracts in patients with multiple system atrophy (MSA), progressive nuclear palsy (PSP), idiopathic Parkinson’s disease (IPD) and healthy controls). Diffusion tensor imaging data sets with whole brain coverage were acquired at 3 T using 48 diffusion encoding directions and a voxel size of 2×2×2 mm³. DTT of the corpus callosum (CC), cingulum (CG), corticospinal tract (CST) and middle cerebellar peduncles (MCP) was performed using multiple regions of interest. Regional evaluation comprised projection of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and the apparent area coefficient (AAC) onto a calculated mean tract and extraction of their values along each structure.

Results

There were significant changes of global DTT parameters in the CST (MSA and PSP), CC (PSP) and CG (PSP). Consistent tract-specific variations in DTT parameters could be seen along each tract in the different patient groups and controls. Regional analysis demonstrated significant changes in the anterior CC (MD, RD and FA), CST (MD) and CG (AAC) of patients with PSP compared to controls. Increased MD in CC and CST, as well as decreased AAC in CG, was correlated with a diagnosis of PSP compared to IPD.

Conclusions

DTT can be used for demonstrating disease-specific regional white matter changes in parkinsonian disorders. The anterior portion of the CC was identified as a promising region for detection of neurodegenerative changes in patients with PSP, as well as for differential diagnosis between PSP and IPD.