Fiber ball white matter modeling in focal epilepsy

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice. An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tractography, diffusion kurtosis imaging-based fiber tractography, fiber ball imaging-based tractography, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, molecular imaging, and functional ultrasound imaging have been extensively used to delineate epileptic networks. In this review, we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy, and extensively analyze the imaging mechanisms, advantages, limitations, and clinical application ranges of each technique. A greater focus on emerging advanced technologies, new data analysis software, a combination of multiple techniques, and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Neurobehavioral and Clinical Comorbidities in Epilepsy: The Role of White Matter Network Disruption

Epilepsy is a common neurological disorder associated with alterations in cortical and subcortical brain networks. Despite a historical focus on gray matter regions involved in seizure generation and propagation, the role of white matter (WM) network disruption in epilepsy and its comorbidities has sparked recent attention. In this review, we describe patterns of WM alterations observed in focal and generalized epilepsy syndromes and highlight studies linking WM disruption to cognitive and psychiatric comorbidities, drug resistance, and poor surgical outcomes. Both tract-based and connectome-based approaches implicate the importance of extratemporal and temporo-limbic WM disconnection across a range of comorbidities, and an evolving literature reveals the utility of WM patterns for predicting outcomes following epilepsy surgery. We encourage new research employing advanced analytic techniques (e.g., machine learning) that will further shape our understanding of epilepsy as a network disorder and guide individualized treatment decisions. We also address the need for research that examines how neuromodulation and other treatments (e.g., laser ablation) affect WM networks, as well as research that leverages larger and more diverse samples, longitudinal designs, and improved magnetic resonance imaging acquisitions. These steps will be critical to ensuring generalizability of current research and determining the extent to which neuroplasticity within WM networks can influence patient outcomes.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

The importance of discriminative power rather than significance when evaluating potential clinical biomarkers in epilepsy research

OBJECTIVE

The quest for epilepsy biomarkers is on the rise. Variables with statistically significant group-level differences are often misinterpreted as biomarkers with sufficient discriminative power. This study aimed to demonstrate the relationship between significant group-level differences and a variable's power to discriminate between individuals.

METHODS

We simulated normal-distributed datasets from hypothetical populations with varying sample sizes (25-800), effect sizes (Cohen's d: .25-2.50), and variability (standard deviation: 10-35) to assess the impact of these parameters on significance and discriminative power. The simulation data were illustrated by assessing the discriminative power of a potential real-case biomarker-the EEG beta band power-to diagnose generalized epilepsy, using data from 66 children with generalized epilepsy and 385 controls. Additionally, we evaluated recently reported epilepsy biomarkers by comparing their effect sizes to our simulation-derived effect size criterion.

RESULTS

Group size affects significance but not discriminative power. Discriminative power is much more related to variability and effect size. Our real data example supported these simulation results by demonstrating that group-level significance does not translate, one to one, into discriminative power. Although we found a significant difference in the beta band power between children with and without epilepsy, the discriminative power was poor due to a small effect size. A Cohen's d of at least 1.25 is required to reach good discriminative power in univariable prediction modeling. Slightly over 60% of the biomarkers in our literature search met this criterion.

SIGNIFICANCE

Rather than statistical significance of group-level differences, effect size should be used as an indicator of a variable's biomarker potential. The minimal required effects size for individual biomarkers-a Cohen's d of 1.25-is large. This calls for multivariable approaches, in which combining multiple variables with smaller effect sizes could increase the overall effect size and discriminative power.

Neurocysticercosis-related Seizures: Imaging Biomarkers

Neurocysticercosis (NCC)-a parasitic CNS infection endemic to developing nations-has been called the leading global cause of acquired epilepsy yet remains understudied. It is currently unknown why a large proportion of patients develop recurrent seizures, often following the presentation of acute seizures. Furthermore, the presentation of NCC is heterogenous and the features that predispose to the development of an epileptogenic state remain uncertain. Perilesional factors (such as oedema and gliosis) have been implicated in NCC-related ictogenesis, but the effects of cystic factors, including lesion load and location, seem not to play a role in the development of habitual epilepsy. In addition, the cytotoxic consequences of the cyst's degenerative stages are varied and the majority of research, relying on retrospective data, lacks the necessary specificity to distinguish between acute symptomatic and unprovoked seizures. Previous research has established that epileptogenesis can be the consequence of abnormal network connectivity, and some imaging studies have suggested that a causative link may exist between NCC and aberrant network organisation. In wider epilepsy research, network approaches have been widely adopted; studies benefiting predominantly from the rich, multimodal data provided by advanced MRI methods are at the forefront of the field. Quantitative MRI approaches have the potential to elucidate the lesser-understood epileptogenic mechanisms of NCC. This review will summarise the current understanding of the relationship between NCC and epilepsy, with a focus on MRI methodologies. In addition, network neuroscience approaches with putative value will be highlighted, drawing from current imaging trends in epilepsy research.

White matter alterations in MR-negative temporal and frontal lobe epilepsy using fixel-based analysis

This study focuses on white matter alterations in pharmacoresistant epilepsy patients with no visible lesions in the temporal and frontal lobes on clinical MRI (i.e. MR-negative) with lesions confirmed by resective surgery. The aim of the study was to extend the knowledge about group-specific neuropathology in MR-negative epilepsy. We used the fixel-based analysis (FBA) that overcomes the limitations of traditional diffusion tensor image analysis, mainly within-voxel averaging of multiple crossing fibres. Group-wise comparisons of fixel parameters between healthy controls (N = 100) and: (1) frontal lobe epilepsy (FLE) patients (N = 9); (2) temporal lobe epilepsy (TLE) patients (N = 13) were performed. A significant decrease of the cross-section area of the fixels in the superior longitudinal fasciculus was observed in the FLE. Results in TLE reflected widespread atrophy of limbic, thalamic, and cortico-striatal connections and tracts directly connected to the temporal lobe (such as the anterior commissure, inferior fronto-occipital fasciculus, uncinate fasciculus, splenium of corpus callosum, and cingulum bundle). Alterations were also observed in extratemporal connections (brainstem connection, commissural fibres, and parts of the superior longitudinal fasciculus). To our knowledge, this is the first study to use an advanced FBA method not only on the datasets of MR-negative TLE patients, but also MR-negative FLE patients, uncovering new common tract-specific alterations on the group level.

Impact of intra-axonal kurtosis on fiber orientation density functions estimated with fiber ball imaging

PURPOSE

To determine the impact of an intra-axonal kurtosis on estimates of the fiber orientation density function (fODF) obtained with fiber ball imaging (FBI).

THEORY AND METHODS

Standard FBI assumes Gaussian diffusion within individual axons and estimates the fODF by applying an inverse generalized Funk transform to diffusion MRI data for b-values of 4000 s/mm2 or higher. However, recent work based on numeric simulations shows that diffusion inside axons is non-Gaussian with an intra-axonal kurtosis of ∼ 0.4. Here, the theory underlying FBI is extended to incorporate an intra-axonal kurtosis. This is done to first order in the intra-axonal kurtosis without making assumptions about the details of the diffusion dynamics and to all orders for a specific model based on a gamma distribution of diffusivities. The first order approximation is used to assess the effect of an intra-axonal kurtosis on FBI estimates for the fODF and axonal water fraction. The gamma distribution model is used to test the validity of the approximation.

RESULTS

The first order approximation indicates the estimated fODF is altered by a few percent for an intra-axonal kurtosis of 0.4 in comparison to predictions of standard FBI. If one neglects the intra-axonal kurtosis, the angular resolution of the point spread function for the fODF is changed by <1°, whereas the axonal water fraction is overestimated by ∼ 5%. The gamma distribution model shows that the first order approximation is accurate to within a few percent.

CONCLUSION

The intra-axonal kurtosis has a small impact on fODFs estimated with FBI.

High b-value diffusion tractography: Abnormal axonal network organization associated with medication-refractory epilepsy

Diffusion magnetic resonance imaging (dMRI) tractography has played a critical role in characterizing patterns of aberrant brain network reorganization among patients with epilepsy. However, the accuracy of dMRI tractography is hampered by the complex biophysical properties of white matter tissue. High b-value diffusion imaging overcomes this limitation by better isolating axonal pathways. In this study, we introduce tractography derived from fiber ball imaging (FBI), a high b-value approach which excludes non-axonal signals, to identify atypical neuronal networks in patients with epilepsy. Specifically, we compared network properties obtained from multiple diffusion tractography approaches (diffusion tensor imaging, diffusion kurtosis imaging, FBI) in order to assess the pathophysiological relevance of network rearrangement in medication-responsive vs. medication-refractory adults with focal epilepsy. We show that drug-resistant epilepsy is associated with increased global network segregation detected by FBI-based tractography. We propose exploring FBI as a clinically feasible alternative to quantify topological changes that could be used to track disease progression and inform on clinical outcomes.

Volumetric Segmentation of White Matter Tracts with Label Embedding

Convolutional neural networks have achieved state-of-the-art performance for white matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI). However, the segmentation can still be difficult for challenging WM tracts with thin bodies or complicated shapes; the segmentation is even more problematic in challenging scenarios with reduced data quality or domain shift between training and test data, which can be easily encountered in clinical settings. In this work, we seek to improve the segmentation of WM tracts, especially for challenging WM tracts in challenging scenarios. In particular, our method is based on volumetric WM tract segmentation, where voxels are directly labeled without performing tractography. To improve the segmentation, we exploit the characteristics of WM tracts that different tracts can cross or overlap and revise the network design accordingly. Specifically, because multiple tracts can co-exist in a voxel, we hypothesize that the different tract labels can be correlated. The tract labels at a single voxel are concatenated as a label vector, the length of which is the number of tract labels. Due to the tract correlation, this label vector can be projected into a lower-dimensional space-referred to as the embedded space-for each voxel, which allows the segmentation network to solve a simpler problem. By predicting the coordinate in the embedded space for the tracts at each voxel and subsequently mapping the coordinate to the label vector with a reconstruction module, the segmentation result can be achieved. To facilitate the learning of the embedded space, an auxiliary label reconstruction loss is integrated with the segmentation accuracy loss during network training, and network training and inference are end-to-end. Our method was validated on two dMRI datasets under various settings. The results show that the proposed method improves the accuracy of WM tract segmentation, and the improvement is more prominent for challenging tracts in challenging scenarios.

High fidelity fiber orientation density functions from fiber ball imaging

The fiber orientation density function (fODF) in white matter is a primary physical quantity that can be estimated with diffusion MRI. It has often been employed for fiber tracking and microstructural modeling. Requirements for the construction of high fidelity fODFs, in the sense of having good angular resolution, adequate data to avoid sampling errors, and minimal noise artifacts, are described for fODFs calculated with fiber ball imaging. A criterion is formulated for the number of diffusion encoding directions needed to achieve a given angular resolution. The advantages of using large b-values (≥6000 s/mm² ) are also discussed. For the direct comparison of different fODFs, a method is developed for defining a local frame of reference tied to each voxel's individual axonal structure. The Matusita anisotropy axonal is proposed as a scalar fODF measure for quantifying angular variability. Experimental results, obtained at 3 T from human volunteers, are used as illustrations.

Fiber Ball White Matter Modeling Reveals Microstructural Alterations in Healthy Brain Aging

Age-related white matter degeneration is characterized by myelin breakdown and neuronal fiber loss that preferentially occur in regions that myelinate later in development. Conventional diffusion MRI (dMRI) has demonstrated age-related increases in diffusivity but provide limited information regarding the tissue-specific changes driving these effects. A recently developed dMRI biophysical modeling technique, Fiber Ball White Matter (FBWM) modeling, offers enhanced biological interpretability by estimating microstructural properties specific to the intra-axonal and extra-axonal spaces. We used FBWM to illustrate the biological mechanisms underlying changes throughout white matter in healthy aging using data from 63 cognitively unimpaired adults ages 45-85 with no radiological evidence of neurodegeneration or incipient Alzheimer's disease. Conventional dMRI and FBWM metrics were computed for two late-myelinating (genu of the corpus callosum and association tracts) and two early-myelinating regions (splenium of the corpus callosum and projection tracts). We examined the associations between age and these metrics in each region and tested whether age was differentially associated with these metrics in late- vs. early-myelinating regions. We found that conventional metrics replicated patterns of age-related increases in diffusivity in late-myelinating regions. FBWM additionally revealed specific intra- and extra-axonal changes suggestive of myelin breakdown and preferential loss of smaller-diameter axons, yielding in vivo corroboration of findings from histopathological studies of aged brains. These results demonstrate that advanced biophysical modeling approaches, such as FBWM, offer novel information about the microstructure-specific alterations contributing to white matter changes in healthy aging. These tools hold promise as sensitive indicators of early pathological changes related to neurodegenerative disease.

Fiber ball white matter modeling in focal epilepsy

Multicompartment diffusion magnetic resonance imaging (MRI) approaches are increasingly being applied to estimate intra‐axonal and extra‐axonal diffusion characteristics in the human brain. Fiber ball imaging (FBI) and its extension fiber ball white matter modeling (FBWM) are such recently described multicompartment approaches. However, these particular approaches have yet to be applied in clinical cohorts. The modeling of several diffusion parameters with interpretable biological meaning may offer the development of new, noninvasive biomarkers of pharmacoresistance in epilepsy. In the present study, we used FBI and FBWM to evaluate intra‐axonal and extra‐axonal diffusion properties of white matter tracts in patients with longstanding focal epilepsy. FBI/FBWM diffusion parameters were calculated along the length of 50 white matter tract bundles and statistically compared between patients with refractory epilepsy, nonrefractory epilepsy and controls. We report that patients with chronic epilepsy had a widespread distribution of extra‐axonal diffusivity relative to controls, particularly in circumscribed regions along white matter tracts projecting to cerebral cortex from thalamic, striatal, brainstem, and peduncular regions. Patients with refractory epilepsy had significantly greater markers of extra‐axonal diffusivity compared to those with nonrefractory epilepsy. The extra‐axonal diffusivity alterations in patients with epilepsy observed in the present study could be markers of neuroinflammatory processes or a reflection of reduced axonal density, both of which have been histologically demonstrated in focal epilepsy. FBI is a clinically feasible MRI approach that provides the basis for more interpretive conclusions about the microstructural environment of the brain and may represent a unique biomarker of pharmacoresistance in epilepsy.