Diffusion tensor tractography imaging in pediatric epilepsy - A systematic review

Brain connectivity in status epilepticus as a predictor of outcome: A diffusion tensor imaging study

BACKGROUND AND PURPOSE

We aimed to explore structural connectivity in status epilepticus.

METHODS

We enrolled participants who underwent diffusion tensor imaging. We applied graph theory to investigate structural connectivity. We compared the structural connectivity measures between patients and healthy controls and between patients with poor (modified Rankin Scale [mRS] >3) and good (mRS ≤3) admission outcomes.

RESULTS

We enrolled 28 patients and 31 healthy controls (age 65.5 vs.62.0 years, p = .438). Of these patients, 16 and 12 showed poor and good admission outcome (age 65.5 vs.62.0 years, p = .438). The assortative coefficient (-0.113 vs. -0.121, p = .021), mean clustering coefficient (0.007 vs.0.006, p = .009), global efficiency (0.023 vs.0.020, p = .009), transitivity (0.007 vs.0.006, p = .009), and small-worldness index (0.006 vs.0.005, p = .021) were higher in patients with status epilepticus than in healthy controls. The assortative coefficient (-0.108 vs. -0.119, p = .042), mean clustering coefficient (0.007 vs.0.006, p = .042), and transitivity (0.008 vs.0.007, p = .042) were higher in patients with poor admission outcome than in those with good admission outcome. MRS score was positively correlated with structural connectivity measures, including the assortative coefficient (r = 0.615, p = .003), mean clustering coefficient (r = 0.544, p = .005), global efficiency (r = 0.515, p = .007), transitivity (r = 0.547, p = .007), and small-worldness index (r = 0.435, p = .024).

CONCLUSION

We revealed alterations in structural connectivity, showing increased integration and segregation in status epilepticus, which might be related with neuronal synchronization. This effect was more pronounced in patients with a poor admission outcome, potentially reshaping our understanding for comprehension of status epilepticus mechanisms and the development of more targeted treatments.

The role of preoperative diffusion tensor imaging in predicting and improving functional outcome in pediatric patients undergoing epilepsy surgery: a systematic review

OBJECTIVE

Diffusion tensor imaging (DTI) is a useful neuroimaging technique for surgical planning in adult patients. However, no systematic review has been conducted to determine its utility for pre-operative analysis and planning of Pediatric Epilepsy surgery. We sought to determine the benefit of pre-operative DTI in predicting and improving neurological functional outcome after epilepsy surgery in children with intractable epilepsy.

METHODS

A systematic review of articles in English using PubMed, EMBASE and Scopus databases, from inception to January 10, 2020 was conducted. All studies that used DTI as either predictor or direct influencer of functional neurological outcome (motor, sensory, language and/or visual) in pediatric epilepsy surgical candidates were included. Data extraction was performed by two blinded reviewers. Risk of bias of each study was determined using the QUADAS 2 Scoring System.

RESULTS

13 studies were included (6 case reports/series, 5 retrospective cohorts, and 2 prospective cohorts) with a total of 229 patients. Seven studies reported motor outcome; three reported motor outcome prediction with a sensitivity and specificity ranging from 80 to 85.7 and 69.6 to 100%, respectively; four studies reported visual outcome. In general, the use of DTI was associated with a high degree of favorable neurological outcomes after epilepsy surgery.

CONCLUSION

Multiple studies show that DTI helps to create a tailored plan that results in improved functional outcome. However, more studies are required in order to fully assess its utility in pediatric patients. This is a desirable field of study because DTI offers a non-invasive technique more suitable for children.

ADVANCES IN KNOWLEDGE

This systematic review analyses, exclusively, studies of pediatric patients with drug-resistant epilepsy and provides an update of the evidence regarding the role of DTI, as part of the pre-operative armamentarium, in improving post-surgical neurological sequels and its potential for outcome prediction.

Neuroimaging of Childhood Epilepsy: Focal versus Generalized Epilepsy

Neuroimaging plays an increasingly crucial role in delineating the pathophysiology, and guiding the evaluation, management and monitoring of epilepsy. Imaging contributes to adequately categorizing seizure/epilepsy types in complex clinical situations by demonstrating anatomical and functional changes associated with seizure activity. This article reviews the current status of multimodality neuroimaging in the pediatric population, including focal lesions which may result in focal epileptic findings, focal structural abnormalities that may manifest as generalized epileptiform discharges, and generalized epilepsy without evidence of detectable focal abnormalities.

Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Automated fiber tract reconstruction for surgery planning: Extensive validation in language-related white matter tracts

Diffusion MRI and tractography hold great potential for surgery planning, especially to preserve eloquent white matter during resections. However, fiber tract reconstruction requires an expert with detailed understanding of neuroanatomy. Several automated approaches have been proposed, using different strategies to reconstruct the white matter tracts in a supervised fashion. However, validation is often limited to comparison with manual delineation by overlap-based measures, which is limited in characterizing morphological and topological differences. In this work, we set up a fully automated pipeline based on anatomical criteria that does not require manual intervention, taking advantage of atlas-based criteria and advanced acquisition protocols available on clinical-grade MRI scanners. Then, we extensively validated it on epilepsy patients with specific focus on language-related bundles. The validation procedure encompasses different approaches, including simple overlap with manual segmentations from two experts, feasibility ratings from external multiple clinical raters and relation with task-based functional MRI. Overall, our results demonstrate good quantitative agreement between automated and manual segmentation, in most cases better performances of the proposed method in qualitative terms, and meaningful relationships with task-based fMRI. In addition, we observed significant differences between experts in terms of both manual segmentation and external ratings. These results offer important insights on how different levels of validation complement each other, supporting the idea that overlap-based measures, although quantitative, do not offer a full perspective on the similarities and differences between automated and manual methods.

Utility of MRI, PET, and ictal SPECT in presurgical evaluation of non-lesional pediatric epilepsy

Children with epilepsy and normal structural MRI pose a particular challenge in localization of epileptic foci for surgical resection. Many of these patients have subtle structural lesions such as mild cortical dysplasia that can be missed by conventional MRI but may become detectable by optimized and advanced MRI acquisitions and post-processing. Specificity of objective analytic techniques such as voxel-based morphometry remains an issue. Combination of MRI with functional imaging approaches can improve the accuracy of detecting epileptogenic brain regions. Analysis of glucose positron emission tomography (PET) combined with high-resolution MRI can optimize detection of hypometabolic cortex associated with subtle cortical malformations and can also enhance presurgical evaluation in children with epileptic spasms. Additional PET tracers may detect subtle epileptogenic lesions and cortex with enhanced specificity in carefully selected subgroups with various etiologies; e.g., increased tryptophan uptake can identify epileptogenic cortical dysplasia in the interictal state. Subtraction ictal SPECT can be also useful to delineate ictal foci in those with non-localizing PET or after failed surgical resection. Presurgical delineation of language and motor cortex and the corresponding white matter tracts is increasingly reliable by functional MRI and DTI techniques; with careful preparation, these can be useful even in young and sedated children. While evidence-based pediatric guidelines are still lacking, the data accumulated in the last decade strongly indicate that multimodal imaging with combined analysis of MRI, PET, and/or ictal SPECT data can optimize the detection of subtle epileptogenic lesions and facilitate seizure-free outcome while minimizing the postsurgical functional deficit in children with normal conventional MRI.

Neuroimaging of Early Life Epilepsy

OBJECTIVES

We assessed the adherence to neuroimaging guidelines and the diagnostically relevant yield of neuroimaging in newly presenting early life epilepsy (ELE).

METHODS

There were 775 children with a new diagnosis of epilepsy (<3 years old at onset) who were recruited through the ELE study at 17 US pediatric epilepsy centers (2012-2015) and managed prospectively for 1 year. The data were analyzed to assess the proportion of children who underwent neuroimaging, the type of neuroimaging, and abnormalities.

RESULTS

Of 725 children (93.5%) with neuroimaging, 714 had an MRI (87% with seizure protocols) and 11 had computed tomography or ultrasound only. Etiologically relevant abnormalities were present in 290 individuals (40%) and included: an acquired injury in 97 (13.4%), malformations of cortical development in 56 (7.7%), and other diffuse disorders of brain development in 51 (7.0%). Neuroimaging was abnormal in 160 of 262 (61%) children with abnormal development at diagnosis versus 113 of 463 (24%) children with typical development. Neuroimaging abnormalities were most common in association with focal seizure semiology (40%), spasms (47%), or unclear semiology (42%). In children without spasms or focal semiology with typical development, 29 of 185 (16%) had imaging abnormalities. Pathogenic genetic variants were identified in 53 of 121 (44%) children with abnormal neuroimaging in whom genetic testing was performed.

CONCLUSIONS

Structural abnormalities occur commonly in ELE, and adherence to neuroimaging guidelines is high at US pediatric epilepsy centers. These data support the universal adoption of imaging guidelines because the yield is substantially high, even in the lowest risk group.

White matter tractography for neurosurgical planning: A topography-based review of the current state of the art

We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.