Along-tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors

Application of neurite orientation dispersion and density imaging in characterizing brain microstructural changes in classical trigeminal neuralgia and a comparison between the left and right sides

ABSTRACT

Diffusion tensor imaging can detect brain white matter changes in classical trigeminal neuralgia (TN). However, it lacks specificity for individual tissue microstructural features, such as neurite density, orientation dispersions, and extracellular edema. Neurite orientation dispersion and density imaging (NODDI), a novel diffusion magnetic resonance imaging (MRI) technique, can provide these distinct indices. We characterized brain microstructural alterations in patients with unilateral TN using NODDI and compared the difference between left- and right-side TN (LTN and RTN, respectively). Diffusion-weighted imaging was performed on 39 patients with LTN, 37 patients with RTN, and 37 healthy controls. Neurite orientation dispersion and density imaging-related indices, including the intracellular volume fraction (Vic), orientation dispersion index (ODI), and isotropic volume fraction (Fiso), were estimated and compared using tract-based spatial statistics and voxel-based region-of-interest analysis. The LTN and RTN groups exhibited microstructural abnormalities in white and gray matter as measured by decreased fractional anisotropy and Vic and elevated Fiso, respectively. These alterations were associated with clinical features and were mainly located in the frontal lobe, corona radiata, internal capsule, and thalamus. The angular variation of neurites, characterized by ODI, exhibited no significant changes between TN and control groups. Patients with classical TN of either side exhibited reduced Vic and increased Fiso, which indicated decreased density of axons and dendrites and neuroinflammatory edema in bilateral hemispheres. Neurite orientation dispersion and density imaging is a useful technique for in vivo diffusion MRI of white and gray matter in the brain, which provides additional metrics and information closely related to the tissue microstructure that merits further study of TN pathogenesis.

A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI

Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as variable dimensionalities (reflecting the unknown number of distinct white matter fiber populations in a voxel), low signal-to-noise ratios, and non-linear forward models. These challenges have led many existing methods to use biologically implausible simplified models to stabilize estimation, for instance, assuming shared microstructure across all fiber populations within a voxel. In this work, we introduce a novel sequential method for multi-fiber parameter inference that decomposes the task into a series of manageable subproblems. These subproblems are solved using deep neural networks tailored to problem-specific structure and symmetry, and trained via simulation. The resulting inference procedure is largely amortized, enabling scalable parameter estimation and uncertainty quantification across all model parameters. Simulation studies and real imaging data analysis using the Human Connectome Project (HCP) demonstrate the advantages of our method over standard alternatives. In the case of the standard model of diffusion, our results show that under HCP-like acquisition schemes, estimates for extra-cellular parallel diffusivity are highly uncertain, while those for the intra-cellular volume fraction can be estimated with relatively high precision.

White matter characterization in regions of edema surrounding meningioma brain tumor using diffusion MRI: A comparative study of DTI and NODDI

White matter (WM) tract detection is critical in presurgical planning of tumor resection; however, standard-of-care diffusion tensor imaging (DTI) often fails to characterize white matter tracts through regions of edema. This is because the presence of edema has the effect of increasing the isotropic volume fraction within a voxel and thus marginalizing the anisotropic volume fraction associated with white matter presence and directionality. More recent biophysical models of diffusion, such as neurite orientation dispersion and density imaging (NODDI), account for isotropic and anisotropic volume fractions within voxels by compartmentalizing the diffusion signal based on an assumed tissue microenvironment, e.g., "free water" (cerebrospinal fluid (CSF), interstitial fluid (ISF), edema), "intra-neurite", and "extra-neurite" tissue, as a sphere, stick, and tensor, respectively. We hypothesize that a low fractional anisotropy (FA), low orientation dispersion index (ODI) value and high fractional isotropic volume (FISO) would be observed in white matter regions containing edema but a high FA, low ODI value and low FISO would be observed in healthy-appearing contralateral white matter. In our study, we test this hypothesis using multi-shell diffusion MRI data collected from patients bearing meningioma brains tumors. Brains bearing meningioma tumors are selected in this study as meningiomas rarely invade the brain parenchyma and we can thus assume that our analyses of edematous regions are not confounded by infiltrating tumor cells. Here, we show that NODDI-based characterization of white matter is more sensitive than that of standard-of-care DTI through regions of edema. Future studies will focus on implementation of biophysical model-based tractography in cases of glioma and translation of biophysical model-based tractography to the operating room.

Structural Relationship between Cerebral Gray and White Matter Alterations in Degenerative Cervical Myelopathy

Patients with degenerative cervical myelopathy (DCM) undergo adaptive supraspinal changes. However, it remains unknown how subcortical white matter changes reflect the gray matter loss. The current study investigated the interrelationship between gray matter and subcortical white matter alterations in DCM patients. Cortical thickness of gray matter, as well as the intra-cellular volume fraction (ICVF) of subcortical whiter matter, were assessed in a cohort of 44 patients and 17 healthy controls (HCs). The results demonstrated that cortical thinning of sensorimotor and pain related regions is associated with more severe DCM symptoms. ICVF values of subcortical white matter underlying the identified regions were significantly lower in study patients than in HCs. The left precentral gyrus (r = 0.5715, p < 0.0001), the left supramarginal gyrus (r = 0.3847, p = 0.0099), the left postcentral gyrus (r = 0.5195, p = 0.0003), the right superior frontal gyrus (r = 0.3266, p = 0.0305), and the right caudal (r = 0.4749, p = 0.0011) and rostral anterior cingulate (r = 0.3927, p = 0.0084) demonstrated positive correlations between ICVF and cortical thickness in study patients, but no significant correlations between ICVF and cortical thickness were observed in HCs. Results from the current study suggest that DCM may cause widespread gray matter alterations and underlying subcortical neurite loss, which may serve as potential imaging biomarkers reflecting the pathology of DCM.

Three-dimensional printing and 3D slicer powerful tools in understanding and treating neurosurgical diseases

With the development of the 3D printing industry, clinicians can research 3D printing in preoperative planning, individualized implantable materials manufacturing, and biomedical tissue modeling. Although the increased applications of 3D printing in many surgical disciplines, numerous doctors do not have the specialized range of abilities to utilize this exciting and valuable innovation. Additionally, as the applications of 3D printing technology have increased within the medical field, so have the number of printable materials and 3D printers. Therefore, clinicians need to stay up-to-date on this emerging technology for benefit. However, 3D printing technology relies heavily on 3D design. 3D Slicer can transform medical images into digital models to prepare for 3D printing. Due to most doctors lacking the technical skills to use 3D design and modeling software, we introduced the 3D Slicer to solve this problem. Our goal is to review the history of 3D printing and medical applications in this review. In addition, we summarized 3D Slicer technologies in neurosurgery. We hope this article will enable many clinicians to leverage the power of 3D printing and 3D Slicer.

Histogram Analysis Based on Neurite Orientation Dispersion and Density MR Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements

BACKGROUND

Preoperative differentiation of glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) contributes to guide neurosurgical decision-making.

PURPOSE

To explore the value of histogram analysis based on neurite orientation dispersion and density imaging (NODDI) in differentiating between GBM and SBM and comparison of the diagnostic performance of two region of interest (ROI) placements.

STUDY TYPE

Retrospective.

POPULATION

In all, 109 patients with GBM (n = 57) or SBM (n = 52) were enrolled.

FIELD STRENGTH/SEQUENCE

A 3.0 T scanners. T₂ -dark-fluid sequence, contrast-enhanced T₁ magnetization-prepared rapid gradient echo sequence, and NODDI.

ASSESSMENT

ROIs were placed on the peritumoral edema area (ROI1) and whole tumor area (ROI2, included the cystic, necrotic, and hemorrhagic areas). Histogram parameters of each isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and orientation dispersion index (ODI) from NODDI images for two ROIs were calculated, respectively.

STATISTICAL TESTS

Mann-Whitney U test, independent t-test, chi-square test, multivariate logistic regression analysis, DeLong's test.

RESULTS

For the ROI1 and ROI2, the ICVF_min and ODI_mean obtained the highest area under curve (AUC, AUC = 0.741 and 0.750, respectively) compared to other single parameters, and the AUC of the multivariate logistic regression model was 0.851 and 0.942, respectively. DeLong's test revealed significant difference in diagnostic performance between optimal single parameter and multivariate logistic regression model within the same ROI, and the multivariate logistic regression models between two different ROIs.

DATA CONCLUSION

The performance of multivariate logistic regression model is superior to optimal single parameter in both ROIs based on NODDI histogram analysis to distinguish SBM from GBM, and the ROI placed on the whole tumor area exhibited better diagnostic performance.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Surgery of Motor Eloquent Glioblastoma Guided by TMS-Informed Tractography: Driving Resection Completeness Towards Prolonged Survival

Background

Surgical treatment of patients with glioblastoma affecting motor eloquent brain regions remains critically discussed given the risk–benefit dilemma of prolonging survival at the cost of motor-functional damage. Tractography informed by navigated transcranial magnetic stimulation (nTMS-informed tractography, TIT) provides a rather robust estimate of the individual location of the corticospinal tract (CST), a highly vulnerable structure with poor functional reorganisation potential. We hypothesised that by a more comprehensive, individualised surgical decision-making using TIT, tumours in close relationship to the CST can be resected with at least equal probability of gross total resection (GTR) than less eloquently located tumours without causing significantly more gross motor function harm. Moreover, we explored whether the completeness of TIT-aided resection translates to longer survival.

Methods

A total of 61 patients (median age 63 years, m = 34) with primary glioblastoma neighbouring or involving the CST were operated on between 2010 and 2015. TIT was performed to inform surgical planning in 35 of the patients (group T; vs. 26 control patients). To achieve largely unconfounded group comparisons for each co-primary outcome (i.e., gross-motor functional worsening, GTR, survival), (i) uni- and multivariate regression analyses were performed to identify features of optimal outcome prediction; (ii), optimal propensity score matching (PSM) was applied to balance those features pairwise across groups, followed by (iii) pairwise group comparison.

Results

Patients in group T featured a significantly higher lesion-CST overlap compared to controls (8.7 ± 10.7% vs. 3.8 ± 5.7%; p = 0.022). The frequency of gross motor worsening was higher in group T, albeit non-significant (n = 5/35 vs. n = 0/26; p = 0.108). PSM-based paired-sample comparison, controlling for the confounders of preoperative tumour volume and vicinity to the delicate vasculature of the insula, showed higher GTR rates in group T (77% vs. 69%; p = 0.025), particularly in patients with a priori intended GTR (87% vs. 78%; p = 0.003). This translates into a prolonged PFS in the same PSM subgroup (8.9 vs. 5.8 months; p = 0.03), with GTR representing the strongest predictor of PFS (p = 0.001) and OS (p = 0.0003) overall.

Conclusion

The benefit of TIT-aided GTR appears to overcome the drawbacks of potentially elevated motor functional risk in motor eloquent tumour localisation, leading to prolonged survival of patients with primary glioblastoma close to the CST.

Aftereffects to Prism Exposure without Adaptation: A Single Case Study

Visuo-motor adaptation to optical prisms (Prism Adaptation, PA), displacing the visual scene laterally, is a behavioral method used for the experimental investigation of visuomotor plasticity, and, in clinical settings, for temporarily ameliorating and rehabilitating unilateral spatial neglect. This study investigated the building up of PA, and the presence of the typically occurring subsequent Aftereffects (AEs) in a brain-damaged patient (TMA), suffering from apperceptive agnosia and a right visual half-field defect, with bilateral atrophy of the parieto-occipital cortices, regions involved in PA and AEs. Base-Right prisms and control neutral lenses were used. PA was achieved by repeated pointing movements toward three types of stimuli: visual, auditory, and bimodal audio-visual. The presence and the magnitude of AEs were assessed by proprioceptive, visual, visuo-proprioceptive, and auditory-proprioceptive straight-ahead pointing tasks. The patient's brain connectivity was investigated by Diffusion Tensor Imaging (DTI). Unlike control participants, TMA did not show any adaptation to prism exposure, but her AEs were largely preserved. These findings indicate that AEs may occur even in the absence of PA, as indexed by the reduction of the pointing error, showing a dissociation between the classical measures of PA and AEs. In the PA process, error reduction, and its feedback, may be less central to the building up of AEs, than the sensorimotor pointing activity per se.

Multimodal magnetic resonance imaging quantification of gray matter alterations in relapsing-remitting multiple sclerosis and neuromyelitis optica spectrum disorder

Herein, we combined neurite orientation dispersion and density imaging (NODDI) and synthetic magnetic resonance imaging (SyMRI) to evaluate the spatial distribution and extent of gray matter (GM) microstructural alterations in patients with relapsing-remitting multiple sclerosis (RRMS) and neuromyelitis optica spectrum disorder (NMOSD). The NODDI (neurite density index [NDI], orientation dispersion index [ODI], and isotropic volume fraction [ISOVF]) and SyMRI (myelin volume fraction [MVF]) measures were compared between age- and sex-matched groups of 30 patients with RRMS (6 males and 24 females; mean age, 51.43 ± 8.02 years), 18 patients with anti-aquaporin-4 antibody-positive NMOSD (2 males and 16 females; mean age, 52.67 ± 16.07 years), and 19 healthy controls (6 males and 13 females; mean age, 51.47 ± 9.25 years) using GM-based spatial statistical analysis. Patients with RRMS showed reduced NDI and MVF and increased ODI and ISOVF, predominantly in the limbic and paralimbic regions, when compared with healthy controls, while only increases in ODI and ISOVF were observed when compared with NMOSD. Compared to NDI and MVF, the changes in ODI and ISOVF were observed more widely, including in the cerebellar cortex. These abnormalities were associated with disease progression and disability. In contrast, patients with NMOSD only showed reduced NDI mainly in the cerebellar, limbic, and paralimbic cortices when compared with healthy controls and patients with RRMS. Taken together, our study supports the notion that GM pathologies in RRMS are distinct from those of NMOSD. However, owing to the limitations of the study, the results should be cautiously interpreted.

Insights into Infusion-Based Targeted Drug Delivery in the Brain: Perspectives, Challenges and Opportunities

Targeted drug delivery in the brain is instrumental in the treatment of lethal brain diseases, such as glioblastoma multiforme, the most aggressive primary central nervous system tumour in adults. Infusion-based drug delivery techniques, which directly administer to the tissue for local treatment, as in convection-enhanced delivery (CED), provide an important opportunity; however, poor understanding of the pressure-driven drug transport mechanisms in the brain has hindered its ultimate success in clinical applications. In this review, we focus on the biomechanical and biochemical aspects of infusion-based targeted drug delivery in the brain and look into the underlying molecular level mechanisms. We discuss recent advances and challenges in the complementary field of medical robotics and its use in targeted drug delivery in the brain. A critical overview of current research in these areas and their clinical implications is provided. This review delivers new ideas and perspectives for further studies of targeted drug delivery in the brain.

Brain correlates of depression, post-traumatic distress, and inflammatory biomarkers in COVID-19 survivors: A multimodal magnetic resonance imaging study

Psychiatric sequelae substantially contribute to the post-acute burden of disease associated with COVID-19, persisting months after clearance of the virus. Brain imaging shows white matter (WM) hypodensities/hyperintensities, and the involvement of grey matter (GM) in prefrontal, anterior cingulate (ACC) and insular cortex after COVID, but little is known about brain correlates of persistent psychopathology. With a multimodal approach, we studied whole brain voxel-based morphometry, diffusion-tensor imaging, and resting-state connectivity, to correlate MRI measures with depression and post-traumatic distress (PTSD) in 42 COVID-19 survivors without brain lesions, at 90.59 ​± ​54.66 days after COVID. Systemic immune-inflammation index (SII) measured in the emergency department, which reflects the immune response and systemic inflammation based on peripheral lymphocyte, neutrophil, and platelet counts, predicted worse self-rated depression and PTSD, widespread lower diffusivity along the main axis of WM tracts, and abnormal functional connectivity (FC) among resting state networks. Self-rated depression and PTSD inversely correlated with GM volumes in ACC and insula, axial diffusivity, and associated with FC. We observed overlapping associations between severity of inflammation during acute COVID-19, brain structure and function, and severity of depression and post-traumatic distress in survivors, thus warranting interest for further study of brain correlates of the post-acute COVID-19 syndrome. Beyond COVID-19, these findings support the hypothesis that regional GM, WM microstructure, and FC could mediate the relationship between a medical illness and its psychopathological sequelae, and are in agreement with current perspectives on the brain structural and functional underpinnings of depressive psychopathology.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Along-tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors

Along‐tract statistics analysis enables the extraction of quantitative diffusion metrics along specific white matter fiber tracts. Besides quantitative metrics derived from classical diffusion tensor imaging (DTI), such as fractional anisotropy and diffusivities, new parameters reflecting the relative contribution of different diffusion compartments in the tissue can be estimated through advanced diffusion MRI methods as neurite orientation dispersion and density imaging (NODDI), leading to a more specific microstructural characterization. In this study, we extracted both DTI‐ and NODDI‐derived quantitative microstructural diffusion metrics along the most eloquent fiber tracts in 15 healthy subjects and in 22 patients with brain tumors. We obtained a robust intraprotocol reference database of normative along‐tract microstructural metrics, and their corresponding plots, from healthy fiber tracts. Each diffusion metric of individual patient's fiber tract was then plotted and statistically compared to the normative profile of the corresponding metric from the healthy fiber tracts. NODDI‐derived metrics appeared to account for the pathological microstructural changes of the peritumoral tissue more accurately than DTI‐derived ones. This approach may be useful for future studies that may compare healthy subjects to patients diagnosed with other pathological conditions.