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

Radiomic white matter parameters of functional integrity of the corticospinal tract in high-grade glioma

Tractography has become a widely available tool for the planning of neurosurgical operations as well as for neuroscientific research. The absence of patient interaction makes it easily applicable. However, it leaves uncertainty about the functional relevance of the identified bundles. We retrospectively analyzed the correlation of white matter markers with their clinical function in 24 right-handed patients who underwent first surgery for high-grade glioma. Morphological affection of the corticospinal tract (CST) and grade of paresis were assessed before surgery. Tractography was performed manually with MRTrix3 and automatically with TractSeg. Median and mean fractional anisotropy (FA) from manual tractography showed a significant correlation with CST affection (p = 0.008) and paresis (p = 0.015, p = 0.026). CST affection correlated further most with energy, and surface-volume ratio (p = 0.014) from radiomic analysis. Paresis correlated most with maximum 2D column diameter (p = 0.005), minor axis length (p = 0.006), and kurtosis (p = 0.008) from radiomic analysis. Streamline count yielded no significant correlations. In conclusion, mean or median FA can be used for the assessment of CST integrity in high-grade glioma. Also, several radiomic parameters are suited to describe tract integrity and may be used to quantitatively analyze white matter in the future.

Differential tractography and whole brain connectometry in primary motor area gliomas resection: A feasibility study

OBJECTIVE

Establish the evolution of the connectome before and after resection of motor area glioma using a comparison of connectome maps and high-definition differential tractography (DifT).

METHODS

DifT was done using normalized quantitative anisotropy (NQA) with DSI Studio. The quantitative analysis involved obtaining mean NQA and fractional anisotropy (FA) values for the disrupted pathways tracing the corticospinal tract (CST), and white fiber network changes over time.

RESULTS

We described the baseline tractography, DifT, and white matter network changes from two patients who underwent resection of an oligodendroglioma (Case 1) and an IDH mutant astrocytoma, grade 4 (Case 2).

CASE 1

There was a slight decrease in the diffusion signal of the compromised CST in the immediate postop. The NQA and FA values increased at the 1-year follow-up (0.18 vs. 0.32 and 0.35 vs. 0.44, respectively).

CASE 2

There was an important decrease in the immediate postop, followed by an increase in the follow-up. In the 1-year follow-up, the patient presented with radiation necrosis and tumor recurrence, increasing NQA from 0.18 in the preop to 0.29. Fiber network analysis: whole-brain connectome comparison demonstrated no significant changes in the immediate postop. However, in the 1-year follow up there was a notorious reorganization of the fibers in both cases, showing the decreased density of connections.

CONCLUSIONS

Connectome studies and DifT constitute new potential tools to predict early reorganization changes in a patient's networks, showing the brain plasticity capacity, and helping to establish timelines for the progression of the tumor and treatment-induced changes.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

BACKGROUND

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

NEW METHOD

Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

RESULTS

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures.

COMPARISON WITH EXISTING METHOD

Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences.

CONCLUSIONS

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

A detailed spatio-temporal atlas of the white matter tracts for the fetal brain

This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.

Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation

OBJECTIVES

Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This cross-sectional study investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth.

METHODS

Five different tractography methods were compared (three DTI-based and two CSD-based) in 22 preoperative neurosurgical patients undergoing surgery with DES mapping. The corticospinal tract (CST, N = 20) and arcuate fasciculus (AF, N = 7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes.

RESULTS

Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE < .05).

CONCLUSIONS

CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice.

A systematic review of automated methods to perform white matter tract segmentation

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

Opportunities and challenges for the use of deep brain stimulation in the treatment of refractory major depression

Major Depressive Disorder continues to remain one of the most prevalent psychiatric diseases globally. Despite multiple trials of conventional therapies, a subset of patients fail to have adequate benefit to treatment. Deep brain stimulation (DBS) is a promising treatment in this difficult to treat population and has shown strong antidepressant effects across multiple cohorts. Nearly two decades of work have provided insights into the potential for chronic focal stimulation in precise brain targets to modulate pathological brain circuits that are implicated in the pathogenesis of depression. In this paper we review the rationale that prompted the selection of various brain targets for DBS, their subsequent clinical outcomes and common adverse events reported. We additionally discuss some of the pitfalls and challenges that have prevented more widespread adoption of this technology as well as future directions that have shown promise in improving therapeutic efficacy of DBS in the treatment of depression.

Fibre orientation atlas guided rapid segmentation of white matter tracts

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

Current perspectives on tractography-guided deep brain stimulation for the treatment of mood disorders

INTRODUCTION

Deep brain stimulation (DBS) is an emerging therapy for mood disorders, particularly treatment-resistant depression (TRD). Different brain areas implicated in depression-related brain networks have been investigated as DBS targets and variable clinical outcomes highlight the importance of target identification. Tractography has provided insight into how DBS modulates disorder-related brain networks and is being increasingly used to guide DBS for psychiatric disorders.

AREAS COVERED

In this perspective, an overview of the current state of DBS for TRD and the principles of tractography is provided. Next, a comprehensive review of DBS targets is presented with a focus on tractography. Finally, the challenges and future directions of tractography-guided DBS are discussed.

EXPERT OPINION

Tractography-guided DBS is a promising tool for improving DBS outcomes for mood disorders. Tractography is particularly useful for targeting patient-specific white matter tracts that are not visible using conventional structural MRI. Developments in tractography methods will help refine DBS targeting for TRD and may facilitate symptom-specific precision neuromodulation. Ultimately, the standardization of tractography methods will be essential to transforming DBS into an established therapy for mood disorders.

Supervised tractogram filtering using Geometric Deep Learning

A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute.

Preserved White Matter Integrity and Recovery After Brain Tumor Surgery: A Prospective Pilot Study on the Frontal Aslant Tract

Introduction: Damage to white matter tracts can cause severe neurological deficits, which are often hardly predictable before brain tumor surgery. To explore the possibility of assessing white matter integrity and its preservation, we chose the frontal aslant tract (FAT) due to its involvement in multiple neurological functions such as speech and movement initiation. Methods: Right-handed patients with left hemispheric intracerebral tumors underwent FAT tractography within 7 days before and 3 days after surgery. Neurological performance score and aphasia score were assessed within 7 days before and after surgery, as well as at follow-up 3 months postoperatively. Results: Fifteen patients were prospectively analyzed. After multivariate analysis and receiver operating characteristic analysis, we found that preoperative fractional anisotropy (FA) of the left FAT indicated the preoperative aphasia score (cutoff 0.40, p = 0.015). Aphasia scores 3 months postoperatively were predicted by both postoperative FA of the left FAT (cutoff 0.35, p = 0.005) and postoperatively preserved FA of the left FAT (cutoff 95.8%, p = 0.017). Postoperatively preserved right FAT FA inversely predicted postoperative aphasia score (cutoff 95.1%, p = 0.016). Discussion: Assessment of white matter integrity preservation is possible and correlates with outcome after brain tumor surgery. It may be useful for patient counseling and assessment of rehabilitation potential, as well as to investigate relevant brain networks in the future. Clinical Trial Registration: The trial was prospectively registered at ClinicalTrials.gov (NCT04302857).

Subject-Specific Automatic Reconstruction of White Matter Tracts

MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.

Advances in diffuse glial tumors diagnosis

In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.

Connectome-based approaches in pediatric epilepsy surgery: "State-of-the art" and future perspectives

Modern epilepsy science has overcome the traditional interpretation of a strict region-specific origin of epilepsy, highlighting the involvement of wider patterns of altered neuronal circuits. In selected cases, surgery may constitute a valuable option to achieve both seizure freedom and neurocognitive improvement. Although epilepsy is now considered as a brain network disease, the most relevant literature concerning the "connectome-based" epilepsy surgery mainly refers to adults, with a limited number of studies dedicated to the pediatric population. In this review, the Authors summarized the main current available knowledge on the relevance of WM surgical anatomy in epilepsy surgery, the post-surgical modifications of brain structural connectivity and the related clinical impact of such modifications within the pediatric context. In the last part, possible implications and future perspectives of this approach have been discussed, especially concerning the optimization of surgical strategies and the predictive value of the epilepsy network analysis for planning tailored approaches, with the final aim of improving case selection, presurgical planning, intraoperative management, and postoperative results.

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.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

Background

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

New Method

Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

Results

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures.

Comparison with existing methods

Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences.

Conclusions

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

A preliminary study on corticospinal tract morphology in incidental and symptomatic insular low-grade glioma: implications for post-surgical motor outcomes

OBJECTIVE

Our study aimed to investigate the shape and diffusion properties of the corticospinal tract (CST) in patients with insular incidental and symptomatic low-grade gliomas (LGGs), especially those in the incidental group, and evaluate their association with post-surgical motor function.

METHODS

We performed automatic fiber tracking on 41 LGG patients, comparing macroscopic shape and microscopic diffusion properties of CST between ipsilateral and contralateral tracts in both incidental and symptomatic groups. A correlation analysis was conducted between properties of CST and post-operative motor strength grades.

RESULTS

In the incidental group, no significant differences in mean diffusion properties were found between bilateral CST. While decreased anisotropy of the CST around the superior limiting sulcus and increased axial diffusivity of the CST near the midbrain level were noted, there was no significant correlation between pre-operative diffusion metrics and post-operative motor strength. In comparison, we found significant correlations between the elongation of the affected CST in the preoperative scans and post-operative motor strength in short-term and long-term follow ups (p = 1.810 × 10-4 and p = 9.560 × 10-4, respectively).

CONCLUSIONS

We found a significant correlation between CST shape measures and post-operative motor function outcomes in patients with incidental insular LGGs. CST morphology shows promise as a potential prognostic factor for identifying functional deficits in this patient population.

Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes

BACKGROUND

While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes.

PURPOSE

The purpose was to define the relationship between streamline count and graph-measure value, reproducibility, and repeatability.

STUDY TYPE

Retrospective analysis of previously prospective study.

POPULATION

Ten healthy subjects, 70% female, aged 25.3 ± 5.9 years.

FIELD STRENGTH/SEQUENCE

A 3-T, T1-weighted sequences and diffusion-weighted imaging (DWI) with two gradient strengths (b-values = 1200 and 3000 sec/mm2 , echo time [TE] = 68 msec, repetition time [TR] = 5.4 seconds, 120 slices, field of view = 188 mm2 ).

ASSESSMENT

A total of 13 graph-theory measures were derived for each subject by generating probabilistic whole-brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived.

STATISTICAL TESTS

Paired t-test with P value <0.05 was used to compare graph-measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility.

RESULTS

Modularity and global efficiency converged to their reference mean with ICC > 0.90 and CCC > 0.99. Edge count, small-worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC > 0.90 and CCC > 0.95. Assortativity and average participation coefficient converged with ICC > 0.75 and CCC > 0.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC > 0.90 and CCC > 0.99. For these measures, alternate definitions that converge a reference mean are provided.

DATA CONCLUSION

Modularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 1.

Development of an educational method to rethink and learn oncological brain surgery in an "a la carte" connectome-based perspective

BACKGROUND

Understanding the structural connectivity of white matter tracts (WMT) and their related functions is a prerequisite to implementing an "a la carte" "connectomic approach" to glioma surgery. However, accessible resources facilitating such an approach are lacking. Here we present an educational method that is readily accessible, simple, and reproducible that enables the visualization of WMTs on individual patient images via an atlas-based approach.

METHODS

Our method uses the patient's own magnetic resonance imaging (MRI) images and consists of three main steps: data conversion, normalization, and visualization; these are accomplished using accessible software packages and WMT atlases. We implement our method on three common cases encountered in glioma surgery: a right supplementary motor area tumor, a left insular tumor, and a left temporal tumor.

RESULTS

Using patient-specific perioperative MRIs with open-sourced and co-registered atlas-derived WMTs, we highlight the critical subnetworks requiring specific surgical monitoring identified intraoperatively using direct electrostimulation mapping with cognitive monitoring. The aim of this didactic method is to provide the neurosurgical oncology community with an accessible and ready-to-use educational tool, enabling neurosurgeons to improve their knowledge of WMTs and to better learn their oncologic cases, especially in glioma surgery using awake mapping.

CONCLUSIONS

Taking no more than 3-5 min per patient and irrespective of their resource settings, we believe that this method will enable junior surgeons to develop an intuition, and a robust 3-dimensional imagery of WMT by regularly applying it to their cases both before and after surgery to develop an "a la carte" connectome-based perspective to glioma surgery.

Diagnostic Approaches to Adult-Type Diffuse Glial Tumors: Comparative Literature and Clinical Practice Study

Gliomas are the most frequent intrinsic central nervous system tumors. The new 2021 WHO Classification of Central Nervous System Tumors brought significant changes into the classification of gliomas, that underline the role of molecular diagnostics, with the adult-type diffuse glial tumors now identified primarily by their biomarkers rather than histology. The status of the isocitrate dehydrogenase (IDH) 1 or 2 describes tumors at their molecular level and together with the presence or absence of 1p/19q codeletion are the most important biomarkers used for the classification of adult-type diffuse glial tumors. In recent years terminology has also changed. IDH-mutant, as previously known, is diagnostically used as astrocytoma and IDH-wildtype is used as glioblastoma. A comprehensive understanding of these tumors not only gives patients a more proper treatment and better prognosis but also highlights new difficulties. MR imaging is of the utmost importance for diagnosing and supervising the response to treatment. By monitoring the tumor on followup exams better results can be achieved. Correlations are seen between tumor diagnostic and clinical manifestation and surgical administration, followup care, oncologic treatment, and outcomes. Minimal resection site use of functional imaging (fMRI) and diffusion tensor imaging (DTI) have become indispensable tools in invasive treatment. Perfusion imaging provides insightful information about the vascularity of the tumor, spectroscopy shows metabolic activity, and nuclear medicine imaging displays tumor metabolism. To accommodate better treatment the differentiation of pseudoprogression, pseudoresponse, or radiation necrosis is needed. In this report, we present a literature review of diagnostics of gliomas, the differences in their imaging features, and our radiology's departments accumulated experience concerning gliomas.

Assessing informative tract segmentation and nTMS for pre-operative planning

BACKGROUND

Deep learning-based (DL) methods are the best-performing methods for white matter tract segmentation in anatomically healthy subjects. However, tract annotations are variable or absent in clinical data and manual annotations are especially difficult in patients with tumors where normal anatomy may be distorted. Direct cortical and subcortical stimulation is the gold standard ground truth to determine the cortical and sub-cortical lo- cation of motor-eloquent areas intra-operatively. Nonetheless, this technique is invasive, prolongs the surgical procedure, and may cause patient fatigue. Navigated Transcranial Magnetic Stimulation (nTMS) has a well-established correlation to direct cortical stimulation for motor mapping and the added advantage of being able to be acquired pre-operatively.

NEW METHOD

In this work, we evaluate the feasibility of using nTMS motor responses as a method to assess corticospinal tract (CST) binary masks and estimated uncertainty generated by a DL-based tract segmentation in patients with diffuse gliomas.

RESULTS

Our results show CST binary masks have a high overlap coefficient (OC) with nTMS response masks. A strong negative correlation is found between estimated uncertainty and nTMS response mask distance to the CST binary mask.

COMPARISON WITH EXISTING METHODS

We compare our approach (UncSeg) with the state-of-the-art TractSeg in terms of OC between the CST binary masks and nTMS response masks.

CONCLUSIONS

In this study, we demonstrate that estimated uncertainty from UncSeg is a good measure of the agreement between the CST binary masks and nTMS response masks distance to the CST binary mask boundary.

Characterization and Classification of Spatial White Matter Tract Alteration Patterns in Glioma Patients Using Magnetic Resonance Tractography: A Systematic Review and Meta-Analysis

INTRODUCTION

Magnetic resonance (MR) tractography can be used to study the spatial relations between gliomas and white matter (WM) tracts. Various spatial patterns of WM tract alterations have been described in the literature. We reviewed classification systems of these patterns, and investigated whether low-grade gliomas (LGGs) and high-grade gliomas (HGGs) demonstrate distinct spatial WM tract alteration patterns.

METHODS

We conducted a systematic review and meta-analysis to summarize the evidence regarding MR tractography studies that investigated spatial WM tract alteration patterns in glioma patients.

RESULTS

Eleven studies were included. Overall, four spatial WM tract alteration patterns were reported in the current literature: displacement, infiltration, disruption/destruction and edematous. There was a considerable heterogeneity in the operational definitions of these terms. In a subset of studies, sufficient homogeneity in the classification systems was found to analyze pooled results for the displacement and infiltration patterns. Our meta-analyses suggested that LGGs displaced WM tracts significantly more often than HGGs (n = 259 patients, RR: 1.79, 95% CI [1.14, 2.79], I² = 51%). No significant differences between LGGs and HGGs were found for WM tract infiltration (n = 196 patients, RR: 1.19, 95% CI [0.95, 1.50], I² = 4%).

CONCLUSIONS

The low number of included studies and their considerable methodological heterogeneity emphasize the need for a more uniform classification system to study spatial WM tract alteration patterns using MR tractography. This review provides a first step towards such a classification system, by showing that the current literature is inconclusive and that the ability of fractional anisotropy (FA) to define spatial WM tract alteration patterns should be critically evaluated. We found variations in spatial WM tract alteration patterns between LGGs and HGGs, when specifically examining displacement and infiltration in a subset of the included studies.

Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective

Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this process is mainly done through time-consuming manual identification performed by neuro-anatomical experts. However, there is a broad interest in automating the pipeline such that it is fast, accurate, and easy to apply in clinical settings and also eliminates the intra-reader variabilities. Following the advancements in medical image analysis using deep learning techniques, there has been a growing interest in using these techniques for the task of tract identification as well. Recent reports on this application show that deep learning-based tract identification approaches outperform existing state-of-the-art methods. This paper presents a review of current tract identification approaches based on deep neural networks. First, we review the recent deep learning methods for tract identification. Next, we compare them with respect to their performance, training process, and network properties. Finally, we end with a critical discussion of open challenges and possible directions for future works.

The Assessment of White Matter Integrity Alteration Pattern in Patients with Brain Tumor Utilizing Diffusion Tensor Imaging: A Systematic Review

Alteration in the surrounding brain tissue may occur in the presence of a brain tumor. The present study aims to assess the characteristics and criteria of the pattern of white matter tract microstructure integrity alteration in brain tumor patients. The Scopus, PubMed/Medline, and Web of Science electronic databases were searched for related articles based on the guidelines established by PRISMA. Twenty-five studies were selected on the morphological changes of white matter tract integrity based on the differential classification of white matter tract (WMT) patterns in brain tumor patients through diffusion tensor imaging (DTI). The characterization was based on two criteria: the visualization of the tract-its orientation and position-and the DTI parameters, which were the fractional anisotropy and apparent diffusion coefficient. Individual evaluations revealed no absolute, mutually exclusive type of tumor in relation to morphological WMT microstructure integrity changes. In most cases, different types and grades of tumors have shown displacement or infiltration. Characterizing morphological changes in the integrity of the white matter tract microstructures is vital in the diagnostic and prognostic evaluation of the tumor's progression and could be a potential assessment for the early detection of possible neurological defects that may affect the patient, as well as aiding in surgery decision-making.

The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis

BACKGROUND

Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography.

METHODS

Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively.

RESULTS

We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions.

CONCLUSIONS

In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.

Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation

White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.

Directionally encoded color track density imaging in brain tumor patients: A potential application to neuro-oncology surgical planning

BACKGROUND

Diffusion magnetic resonance imaging white matter tractography, an increasingly popular preoperative planning modality used for pre-surgical planning in brain tumor patients, is employed with the goal of maximizing tumor resection while sparing postoperative neurological function. Clinical translation of white matter tractography has been limited by several shortcomings of standard diffusion tensor imaging (DTI), including poor modeling of fibers crossing through regions of peritumoral edema and low spatial resolution for typical clinical diffusion MRI (dMRI) sequences. Track density imaging (TDI) is a post-tractography technique that uses the number of tractography streamlines and their long-range continuity to map the white matter connections of the brain with enhanced image resolution relative to the acquired dMRI data, potentially offering improved white matter visualization in patients with brain tumors. The aim of this study was to assess the utility of TDI-based white matter maps in a neurosurgical planning context compared to the current clinical standard of DTI-based white matter maps.

METHODS

Fourteen consecutive brain tumor patients from a single institution were retrospectively selected for the study. Each patient underwent 3-Tesla dMRI scanning with 30 gradient directions and a b-value of 1000 s/mm2. For each patient, two directionally encoded color (DEC) maps were produced as follows. DTI-based DEC-fractional anisotropy maps (DEC-FA) were generated on the scanner, while DEC-track density images (DEC-TDI) were generated using constrained spherical deconvolution based tractography. The potential clinical utility of each map was assessed by five practicing neurosurgeons, who rated the maps according to four clinical utility statements regarding different clinical aspects of pre-surgical planning. The neurosurgeons rated each map according to their agreement with four clinical utility statements regarding if the map 1 identified clinically relevant tracts, (2) helped establish a goal resection margin, (3) influenced a planned surgical route, and (4) was useful overall. Cumulative link mixed effect modeling and analysis of variance were performed to test the primary effect of map type (DEC-TDI vs. DEC-FA) on rater score. Pairwise comparisons using estimated marginal means were then calculated to determine the magnitude and directionality of differences in rater scores by map type.

RESULTS

A majority of rater responses agreed with the four clinical utility statements, indicating that neurosurgeons found both DEC maps to be useful. Across all four investigated clinical utility statements, the DEC map type significantly influenced rater score. Rater scores were significantly higher for DEC-TDI maps compared to DEC-FA maps. The largest effect size in rater scores in favor of DEC-TDI maps was observed for clinical utility statement 2, which assessed establishing a goal resection margin.

CONCLUSION

We observed a significant neurosurgeon preference for DEC-TDI maps, indicating their potential utility for neurosurgical planning.

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.

Highlighted Advances in Therapies for Difficult-To-Treat Brain Tumours Such as Glioblastoma

Glioblastoma multiforme (GBM) remains a challenging disease, as it is the most common and deadly brain tumour in adults and has no curative solution and an overall short survival time. This incurability and short survival time means that, despite its rarity (average incidence of 3.2 per 100,000 persons), there has been an increased effort to try to treat this disease. Standard of care in newly diagnosed glioblastoma is maximal tumour resection followed by initial concomitant radiotherapy and temozolomide (TMZ) and then further chemotherapy with TMZ. Imaging techniques are key not only to diagnose the extent of the affected tissue but also for surgery planning and even for intraoperative use. Eligible patients may combine TMZ with tumour treating fields (TTF) therapy, which delivers low-intensity and intermediate-frequency electric fields to arrest tumour growth. Nonetheless, the blood–brain barrier (BBB) and systemic side effects are obstacles to successful chemotherapy in GBM; thus, more targeted, custom therapies such as immunotherapy and nanotechnological drug delivery systems have been undergoing research with varying degrees of success. This review proposes an overview of the pathophysiology, possible treatments, and the most (not all) representative examples of the latest advancements.

Topology of the lateral visual system: The fundus of the superior temporal sulcus and parietal area H connect nonvisual cerebrum to the lateral occipital lobe

BACKGROUND AND PURPOSE

Mapping the topology of the visual system is critical for understanding how complex cognitive processes like reading can occur. We aim to describe the connectivity of the visual system to understand how the cerebrum accesses visual information in the lateral occipital lobe.

METHODS

Using meta-analytic software focused on task-based functional MRI studies, an activation likelihood estimation (ALE) of the visual network was created. Regions of interest corresponding to the cortical parcellation scheme previously published under the Human Connectome Project were co-registered onto the ALE to identify the hub-like regions of the visual network. Diffusion Spectrum Imaging-based fiber tractography was performed to determine the structural connectivity of these regions with extraoccipital cortices.

RESULTS

The fundus of the superior temporal sulcus (FST) and parietal area H (PH) were identified as hub-like regions for the visual network. FST and PH demonstrated several areas of coactivation beyond the occipital lobe and visual network. Furthermore, these parcellations were highly interconnected with other cortical regions throughout extraoccipital cortices related to their nonvisual functional roles. A cortical model demonstrating connections to these hub-like areas was created.

CONCLUSIONS

FST and PH are two hub-like areas that demonstrate extensive functional coactivation and structural connections to nonvisual cerebrum. Their structural interconnectedness with language cortices along with the abnormal activation of areas commonly located in the temporo-occipital region in dyslexic individuals suggests possible important roles of FST and PH in the integration of information related to language and reading. Future studies should refine our model by examining the functional roles of these hub areas and their clinical significance.

Prediction of the Topography of the Corticospinal Tract on T1-Weighted MR Images Using Deep-Learning-Based Segmentation

INTRODUCTION

Tractography is an invaluable tool in the planning of tumor surgery in the vicinity of functionally eloquent areas of the brain as well as in the research of normal development or of various diseases. The aim of our study was to compare the performance of a deep-learning-based image segmentation for the prediction of the topography of white matter tracts on T1-weighted MR images to the performance of a manual segmentation.

METHODS

T1-weighted MR images of 190 healthy subjects from 6 different datasets were utilized in this study. Using deterministic diffusion tensor imaging, we first reconstructed the corticospinal tract on both sides. After training a segmentation model on 90 subjects of the PIOP2 dataset using the nnU-Net in a cloud-based environment with graphical processing unit (Google Colab), we evaluated its performance using 100 subjects from 6 different datasets.

RESULTS

Our algorithm created a segmentation model that predicted the topography of the corticospinal pathway on T1-weighted images in healthy subjects. The average dice score was 0.5479 (0.3513-0.7184) on the validation dataset.

CONCLUSIONS

Deep-learning-based segmentation could be applicable in the future to predict the location of white matter pathways in T1-weighted scans.

Magnetic Resonance Tractography and Intraoperative Direct Electrical Stimulation in Eloquent Area Glioma Surgery for 102 Cases: A Tertiary Care Center Experience From Northwest India

OBJECTIVE

Surgery of eloquent area gliomas is challenging and requires monitoring of the nearby white fiber tracts. In the present study, we analyzed 102 patients with eloquent region gliomas and discussed the concept of intraoperative dynamic white fiber tract navigation and monitoring.

METHODS

A total of 102 patients with an eloquent area glioma (52 insular, 29 motor area, 21 temporoparietal) were evaluated. The position of the white fiber tracts (corticospinal tract [or motor fiber; CST], inferior fronto-occipital fasciculus [ventral language fiber; IFOF], superior longitudinal fasciculus [SLF], and arcuate fasciculus [dorsal language fiber; AF) was recorded. Awake mapping of the cortical and subcortical eloquent structures was performed for all 102 patients. The suction stimulator was coregistered and used as a dynamic stimulator navigator.

RESULTS

Of the 102 patients, 60 were men and 42 were women, with an average age of 39.8 years. Most of the white fiber tracts were normal (CST, 31.3%; IFOF, 39.2%; SLF/AF, 40.19%) or displaced (CST, 59.8%; IFOF, 47.05%; AF/SLF, 44.11%). A few were disrupted (CST, 8.8%; IFOF, 13.7%; SLF/AF, 15.7%). The extent of tumor resection was 82.8%, 86.5%, and 94% for those with insular glioma, motor area glioma, and temporoparietal glioma, respectively. Of the 102 patients, 18 had developed transient speech and language disturbances with improvement, and 14 had developed motor deficits, of whom, all except for 2, had shown gradual improvement. When the dynamic suction stimulator navigator was used, the extent of resection was 96.5%, without any added deficits.

CONCLUSIONS

The use of intraoperative neuronavigation and neurophysiological assessment can help achieve maximal tumor resection of eloquent area gliomas. Use of the integrated suction stimulator navigator provided dynamic navigation and mapping of the peritumoral eloquent fibers.

Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography

Nonlinear gradients impact diffusion weighted MRI by introducing spatial variation in estimated diffusion tensors. Recent studies have shown that increasing signal-to-noise ratios and the use of ultra-strong gradients may lead to clinically significant impacts on analyses due to these nonlinear gradients in microstructural measures. These effects can potentially bias tractography results and cause misinterpretation of data. Herein, we characterize the impact of an "approximate" gradient nonlinearity correction technique in tractography using empirically derived gradient nonlinear fields. This technique scales the diffusion signal by the change in magnitude due to the gradient nonlinearities, without concomitant correction of gradient direction errors. The impact of this correction on tractography is assessed through white matter bundle segmentation and connectomics via bundle-wise volume, fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, primary eigenvector, and length; as well as the modularity, global efficiency, and characteristic path length connectomics graph measures. We investigate the differences between (1) these measures directly and (2) the within session variability of these measures before and after approximate correction in 61 subjects from the MASiVar pediatric reproducibility dataset. We find approximate correction results is little to no differences on the population level, but large differences on the subject-specific level for both the measures directly and their within session variability. Thus, this study suggests though approximate correction of gradient nonlinearities may not change tractography findings on the population level, subject-specific interpretations may exhibit large fluctuations. A limitation is the lack of comparison with the empirical voxel-wise gradient table correction.

Deep Constrained Spherical Deconvolution for Robust Harmonization

Diffusion weighted magnetic resonance imaging (DW-MRI) captures tissue microarchitecture at millimeter scale. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, DW-MRI suffers from measurement variability (e.g., inter- and intra-site variability, hardware performance, and sequence design), which consequently yields inferior performance on multi-site and/or longitudinal diffusion studies. In this study, we propose a novel, deep learning-based method to harmonize DW-MRI signals for a more reproducible and robust estimation of microstructure. Our method introduces a data-driven scanner-invariant regularization scheme to model a more robust fiber orientation distribution function (FODF) estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intra-site scan/rescan data). The 8th order spherical harmonics coefficients are employed as data representation. The results show that the proposed harmonization approach maintains higher angular correlation coefficients (ACC) with the ground truth signals (0.954 versus 0.942), while achieves higher consistency of FODF signals for intra-scanner data (0.891 versus 0.826), as compared with the baseline supervised deep learning scheme. Furthermore, the proposed data-driven framework is flexible and potentially applicable to a wider range of data harmonization problems in neuroimaging.

Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives

MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.

Preoperative language tract integrity is a limiting factor in recovery from aphasia after glioma surgery

Aphasia can occur in a broad range of pathological conditions that affect cortical or subcortical structures. Here we test the hypothesis that white matter integrity of language pathways assessed by preoperative diffusion tensor imaging (DTI) is associated with language performance and its recovery after glioma resection. 27 patients with preoperative DTI were included. Segmentation of the arcuate fascicle (AF), the inferior fronto-occipital fascicle (IFOF), the inferior longitudinal fascicle (ILF), the superior longitudinal fascicle (SLF), and the uncinate fascicle (UF) was performed with a fully-connected neural network (FCNN, TractSeg). Median fractional anisotropy (FA) was extracted from the resulting volumes as surrogate marker for white matter integrity and tested for correlation with clinical parameters. After correction for demographic data and multiple testing, preoperative white matter integrity of the IFOF, the ILF, and the UF in the left hemisphere were independently and significantly associated with aphasia three months after surgery. Comparison between patients with and without aphasia three months after surgery revealed significant differences in preoperative white matter integrity of the left AF (p = 0.021), left IFOF (p = 0.015), left ILF (p = 0.003), left SLF (p = 0.001, p = 0.021, p = 0.043 for respective sub-bundles 1-3), left UF (p = 0.041) and the right AF (p = 0.027). Preoperative assessment of white matter integrity of the language network by time-efficient MRI protocols and FCNN-driven segmentation may assist in the evaluation of postoperative rehabilitation potential in glioma patients.

Visualization of human optic nerve by diffusion tensor mapping and degree of neuropathy

Diffusion-weighted magnetic resonance imaging of the human optic nerve and tract is technically difficult because of its small size, the inherent strong signal generated by the surrounding fat and the cerebrospinal fluid, and due to eddy current-induced distortions and subject movement artifacts. The effects of the bone canal through which the optic nerve passes, and the proximity of blood vessels, muscles and tendons are generally unknown. Also, the limited technical capabilities of the scanners and the minimization of acquisition times result in poor quality diffusion-weighted images. It is challenging for current tractography methods to accurately track optic pathway fibers that correspond to known anatomy. Despite these technical limitations and low image resolution, here we show how to visualize the optic nerve and tract and quantify nerve atrophy. Our visualization method based on the analysis of the diffusion tensor shows marked differences between a healthy male subject and a male subject with progressive optic nerve neuropathy. These differences coincide with diffusion scalar metrics and are not visible on standard morphological images. A quantification of the degree of optic nerve atrophy in a systematic way is provided and it is tested on 9 subjects from the Human Connectome Project.

Endoscopic imaging of white matter fiber tracts using polarization-sensitive optical coherence tomography

Polarization sensitive optical coherence tomography (PSOCT) has been shown to image and delineate white matter fibers in a label-free manner by revealing optical birefringence within the myelin sheath using a microscope setup. In this proof-of-concept study, we adapt recent advancements in endoscopic PSOCT to perform depth-resolved imaging of white matter structures deep inside intact porcine brain tissue ex-vivo, through a small, rotational fiber probe. The probe geometry is comparable to microelectrodes currently used in neurosurgical interventions. The presented imaging system is mobile, robust, and uses biologically safe levels of optical radiation making it well suited for clinical translation. In neurosurgery, where accuracy is imperative, endoscopic PSOCT through a narrow-gauge fiber probe could provide intra-operative feedback on the location of critical white matter structures.

Preoperative and postoperative high angular resolution diffusion imaging tractography of cerebellar pathways in posterior fossa tumors

This study aimed to utilize high angular resolution diffusion magnetic resonance imaging (HARDI) tractography in the mapping of the pathways of the cerebellum associated with posterior fossa tumors (infratentorial neoplasms) and to determine whether it is useful for preoperative and postoperative evaluation. Retrospective data from 30 patients (age 2-16 yr) with posterior fossa tumor (17 low grade, 13 high grade) and 30 age-sex-matched healthy controls were used. Structural and diffusion-weighted images were collected at a 3-tesla scanner. Tractography was performed using Diffusion Toolkit software, Q-ball model, FACT algorithm, and angle threshold of 45 degrees. Manually assessed regions of interest were placed to identify reconstructed fiber pathways passing through the superior, medial, and inferior cerebellar peduncles for the preoperative, postoperative, and healthy control groups. Fractional anisotropy (FA), apparent diffusion coefficient (ADC), and track volume measures were obtained and analyzed. Statistically significant differences were found between the preop/postop, preop/control, and postop/control comparisons for the volume of the tracts in both groups. Displacement and disruption of the pathways seemed to differ in relation to the severity of the tumor. The loss of pathways after the operation was associated with selective resection during surgery due to tumor infiltration. There were no FA differences but significantly higher ADC in low-grade tumors, and no difference in both FA and ADC in high-grade tumors. The effects of posterior fossa tumors on cerebellar peduncles and reconstructed pathways were successfully evaluated by HARDI tractography. The technique appears to be useful not only for preoperative but also for postoperative evaluation.

Preoperative Navigated Transcranial Magnetic Stimulation: New Insight for Brain Tumor-Related Language Mapping

Preoperative brain mapping methods are particularly important in modern neuro-oncology when a tumor affects eloquent language areas since damage to parts of the language circuits can cause significant impairments in daily life. This narrative review examines the literature regarding preoperative and intraoperative language mapping using repetitive navigated transcranial magnetic stimulation (rnTMS) with or without direct electrical stimulation (DES) in adult patients with tumors in eloquent language areas. The literature shows that rnTMS is accurate in detecting preexisting language disorders and positive intraoperative mapping regions. In terms of the region extent and clinical outcomes, rnTMS has been shown to be accurate in identifying positive sites to guide resection, reducing surgery duration and craniotomy size and thus improving clinical outcomes. Before incorporating rnTMS into the neurosurgical workflow, the refinement of protocols and a consensus within the neuro-oncology community are required.

Decoding transcriptional signatures of the association between free water and macroscale organizations in healthy adolescents

We leveraged a novel index of diffusion MRI to investigate the relationships among cortical free water, macro-organizations and gene expression in healthy adults. Few research has been conducted to investigate the role of free water in the healthy adults due to it can easily be affected also by aging diseases. High quality data of 350 subjects from Human Connectome Project were used in our study. Cortical free water was estimated by using a bi-tensor model. The free water was high in the limbic, insular and somatosensory cortex, while being lower in motor and association cortex. The negative correlation between the free water and cortical thickness has been consistently identified in almost all the cortical regions. Negative correlation between the cortical free water and structural covariance (rho=-0.38, p_spin=0.005) revealed the free water was sensitive to cortical heterogeneity. Using human gene expression dataset, we found the gene expression pattern of the relationship between the free water and cortical thickness spatially coupled with primary gradient of structural covariance network (rho=0.40, p_spin=0.004). Our findings indicated the free water was sensitive to the cortical cellular status. The relationship between free water and macroscale organization also reflected hierarchal structures of cerebral cortex.

Assessment of intraoperative diffusion EPI distortion and its impact on estimation of supratentorial white matter tract positions in pediatric epilepsy surgery

The effectiveness of correcting diffusion Echo Planar Imaging (EPI) distortion and its impact on tractography reconstruction have not been adequately investigated in the intraoperative MRI setting, particularly for High Angular Resolution Diffusion Imaging (HARDI) acquisition. In this study, we evaluated the effectiveness of EPI distortion correction using 27 legacy intraoperative HARDI datasets over two consecutive surgical time points, acquired without reverse phase-encoded data, from 17 children who underwent epilepsy surgery at our institution. The data was processed with EPI distortion correction using the Synb0-Disco technique (Schilling et al., 2019) and without distortion correction. The corrected and uncorrected b0 diffusion-weighted images (DWI) were first compared visually. The mutual information indices between the original T1-weighted images and the fractional anisotropy images derived from corrected and uncorrected DWI were used to quantify the effect of distortion correction. Sixty-four white matter tracts were segmented from each dataset, using a deep-learning based automated tractography algorithm for the purpose of a standardized and unbiased evaluation. Displacement was calculated between tracts generated before and after distortion correction. The tracts were grouped based on their principal morphological orientations to investigate whether the effects of EPI distortion vary with tract orientation. Group differences in tract distortion were investigated both globally, and regionally with respect to proximity to the resecting lesion in the operative hemisphere. Qualitatively, we observed notable improvement in the corrected diffusion images, over the typically affected brain regions near skull-base air sinuses, and correction of additional distortion unique to intraoperative open cranium images, particularly over the resection site. This improvement was supported quantitatively, as mutual information indices between the FA and T1-weighted images were significantly greater after the correction, compared to before the correction. Maximum tract displacement between the corrected and uncorrected data, was in the range of 7.5 to 10.0 mm, a magnitude that would challenge the safety resection margin typically tolerated for tractography-informed surgical guidance. This was particularly relevant for tracts oriented partially or fully in-line with the acquired diffusion phase-encoded direction. Portions of these tracts passing close to the resection site demonstrated significantly greater magnitude of displacement, compared to portions of tracts remote from the resection site in the operative hemisphere. Our findings have direct clinical implication on the accuracy of intraoperative tractography-informed image guidance and emphasize the need to develop a distortion correction technique with feasible intraoperative processing time.

Reproducibility of MRI-based white matter tract estimation using multi-fiber probabilistic tractography: effect of user-defined parameters and regions

Objective

There is a pressing need to assess user-dependent reproducibility of multi-fibre probabilistic tractography in order to encourage clinical implementation of these advanced and relevant approaches. The goal of this study was to evaluate both intrinsic and inter-user reproducibility of corticospinal tract estimation.

Materials and methods

Six clinical datasets including motor functional and diffusion MRI were used. Three users performed an independent tractography analysis following identical instructions. Dice indices were calculated to quantify the reproducibility of seed region, fMRI-based end region, and streamline maps.

Results

The inter-user reproducibility ranged 41–93%, 29–94%, and 50–92%, for seed regions, end regions, and streamline maps, respectively. Differences in streamline maps correlated with differences in seed and end regions. Good inter-user agreement in seed and end regions, yielded inter-user reproducibility close to the intrinsic reproducibility (92–97%) and in most cases higher than 80%.

Discussion

Uncertainties related to user-dependent decisions and the probabilistic nature of the analysis should be considered when interpreting probabilistic tractography data. The standardization of the methods used to define seed and end regions is a necessary step to improve the accuracy and robustness of multi-fiber probabilistic tractography in a clinical setting. Clinical users should choose a feasible compromise between reproducibility and analysis duration.

Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 ± 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts’ profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model’s performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.

Standard clinical approaches and emerging modalities for glioblastoma imaging

Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.

Informative and Reliable Tract Segmentation for Preoperative Planning

Identifying white matter (WM) tracts to locate eloquent areas for preoperative surgical planning is a challenging task. Manual WM tract annotations are often used but they are time-consuming, suffer from inter- and intra-rater variability, and noise intrinsic to diffusion MRI may make manual interpretation difficult. As a result, in clinical practice direct electrical stimulation is necessary to precisely locate WM tracts during surgery. A measure of WM tract segmentation unreliability could be important to guide surgical planning and operations. In this study, we use deep learning to perform reliable tract segmentation in combination with uncertainty quantification to measure segmentation unreliability. We use a 3D U-Net to segment white matter tracts. We then estimate model and data uncertainty using test time dropout and test time augmentation, respectively. We use a volume-based calibration approach to compute representative predicted probabilities from the estimated uncertainties. In our findings, we obtain a Dice of ≈0.82 which is comparable to the state-of-the-art for multi-label segmentation and Hausdorff distance <10mm. We demonstrate a high positive correlation between volume variance and segmentation errors, which indicates a good measure of reliability for tract segmentation ad uncertainty estimation. Finally, we show that calibrated predicted volumes are more likely to encompass the ground truth segmentation volume than uncalibrated predicted volumes. This study is a step toward more informed and reliable WM tract segmentation for clinical decision-making.

The Utilization of Diffusion Tensor Imaging as an Image-Guided Tool in Brain Tumor Resection Surgery: A Systematic Review

Simple Summary

Diffusion tensor imaging (DTI) is an image-guided tool, especially in brain tumor resection surgery. Neuroimaging tools are essential for operative planning, particularly for maximizing tumor resection and, at the same time, preserving brain function. In this systematic review, we discuss the utilization of DTI in brain tumor resection, by looking into its ability to assess the perioperative approach, as well as evaluating its benefits for successful surgery. The present study proposes to use DTI as a vital neuroimaging tool for preoperative planning in brain tumor resection surgery.

Abstract

The diffusion tensor imaging technique has been recognized as a neuroimaging tool for in vivo visualization of white matter tracts. However, DTI is not a routine procedure for preoperative planning for brain tumor resection. Our study aimed to systematically evaluate the effectiveness of DTI and the outcomes of surgery. The electronic databases, PubMed/MEDLINE and Scopus, were searched for relevant studies. Studies were systematically reviewed based on the application of DTI in pre-surgical planning, modification of operative planning, re-evaluation of preoperative DTI data intraoperatively, and the outcome of surgery decisions. Seventeen studies were selected based on the inclusion and exclusion criteria. Most studies agreed that preoperative planning using DTI improves postoperative neuro-deficits, giving a greater resection yield and shortening the surgery time. The results also indicate that the re-evaluation of preoperative DTI intraoperatively assists in a better visualization of white matter tract shifts. Seven studies also suggested that DTI modified the surgical decision of the initial surgical approach and the rate of the GTR in tumor resection surgery. The utilization of DTI may give essential information on white matter tract pathways, for a better surgical approach, and eventually reduce the risk of neurologic deficits after surgery.

Fibre tract segmentation for intraoperative diffusion MRI in neurosurgical patients using tract-specific orientation atlas and tumour deformation modelling

Purpose:

Intraoperative diffusion MRI could provide a means of visualising brain fibre tracts near a neurosurgical target after preoperative images have been invalidated by brain shift. We propose an atlas-based intraoperative tract segmentation method, as the standard preoperative method, streamline tractography, is unsuitable for intraoperative implementation.

Methods:

A tract-specific voxel-wise fibre orientation atlas is constructed from healthy training data. After registration with a target image, a radial tumour deformation model is applied to the orientation atlas to account for displacement caused by lesions. The final tract map is obtained from the inner product of the atlas and target image fibre orientation data derived from intraoperative diffusion MRI.

Results:

The simple tumour model takes only seconds to effectively deform the atlas into alignment with the target image. With minimal processing time and operator effort, maps of surgically relevant tracts can be achieved that are visually and qualitatively comparable with results obtained from streamline tractography.

Conclusion:

Preliminary results demonstrate feasibility of intraoperative streamline-free tract segmentation in challenging neurosurgical cases. Demonstrated results in a small number of representative sample subjects are realistic despite the simplicity of the tumour deformation model employed. Following this proof of concept, future studies will focus on achieving robustness in a wide range of tumour types and clinical scenarios, as well as quantitative validation of segmentations.

Multimodal Presurgical Evaluation of Medically Refractory Focal Epilepsy in Adults: An Update for Radiologists

Surgery is a potentially curative treatment option for patients with medically refractory focal epilepsy. Advanced neuroimaging modalities often improve surgical outcomes by contributing key information during the highly individualized surgical planning process and intraoperative localization. Hence, neuroradiologists play an integral role as part of the multidisciplinary management team. In this review, we initially present the conceptual background and practical framework of the presurgical evaluation process, including a description of the surgical treatment approaches in medically refractory focal epilepsy in adults. This background is followed by an overview of the advanced modalities commonly used during the presurgical workup at level IV epilepsy centers including diffusion imaging techniques, blood oxygen level dependent (BOLD) functional MRI (fMRI), PET, SPECT, and subtraction ictal SPECT, as well as by introductions to 7-T MRI and electrophysiologic techniques including electroencephalography (EEG) and magnetoencephalography (MEG). We also provide illustrative case examples of multimodal neuroimaging including PET/MRI, PET/MRI-DTI, subtraction ictal SPECT, and image-guided stereotactic planning with fMRI-DTI.