Diffusion MRI of complex neural architecture

Contrasting association pattern of plasma low-density lipoprotein with white matter integrity in APOE4 carriers versus non-carriers

Apolipoprotein E ε4 (APOE4) is a strong genetic risk factor of Alzheimer's disease and metabolic dysfunction. However, whether APOE4 and markers of metabolic dysfunction synergistically impact the deterioration of white matter (WM) integrity in older adults remains unknown. In the UK Biobank data, we conducted a multivariate analysis to investigate the interactions between APOE4 and 249 plasma metabolites (measured using nuclear magnetic resonance spectroscopy) with whole-brain WM integrity (measured by diffusion-weighted magnetic resonance imaging) in a cohort of 1917 older adults (aged 65.0-81.0 years; 52.4 % female). Although no main association was observed between either APOE4 or metabolites with WM integrity (adjusted P > 0.05), significant interactions between APOE4 and metabolites with WM integrity were identified. Among the examined metabolites, higher concentrations of low-density lipoprotein and very low-density lipoprotein were associated with a lower level of WM integrity (b=-0.12, CI=-0.14,-0.10) among APOE4 carriers. Conversely, among non-carriers, they were associated with a higher level of WM integrity (b=0.05, CI=0.04,0.07), demonstrating a significant moderation role of APOE4 (b =-0.18, CI=-0.20,-0.15, P<0.00001).

The Contributions of the Cerebellar Peduncles and the Frontal Aslant Tract in Mediating Speech Fluency

Fluent speech production is a complex task that spans multiple processes, from conceptual framing and lexical access, through phonological encoding, to articulatory control. For the most part, imaging studies portraying the neural correlates of speech fluency tend to examine clinical populations sustaining speech impairments and focus on either lexical access or articulatory control, but not both. Here, we evaluated the contribution of the cerebellar peduncles to speech fluency by measuring the different components of the process in a sample of 45 neurotypical adults. Participants underwent an unstructured interview to assess their natural speaking rate and articulation rate, and completed timed semantic and phonemic fluency tasks to assess their verbal fluency. Diffusion magnetic resonance imaging with probabilistic tractography was used to segment the bilateral cerebellar peduncles (CPs) and frontal aslant tract (FAT), previously associated with speech production in clinical populations. Our results demonstrate distinct patterns of white matter associations with different fluency components. Specifically, verbal fluency is associated with the right superior CP, whereas speaking rate is associated with the right middle CP and bilateral FAT. No association is found with articulation rate in these pathways, in contrast to previous findings in persons who stutter. Our findings support the contribution of the cerebellum to aspects of speech production that go beyond articulatory control, such as lexical access, pragmatic or syntactic generation. Further, we demonstrate that distinct cerebellar pathways dissociate different components of speech fluency in neurotypical speakers.

From images to insights: a neuroradiologist's practical guide on white matter fiber tract anatomy and DTI patterns for pre-surgical planning

INTRODUCTION

Diffusion tensor imaging (DTI) is a valuable non-invasive imaging modality for mapping white matter tracts and assessing microstructural integrity, and can be used as a "biomarker" in diagnosis, differentiation, and therapeutic monitoring. Although it has gained clinical importance as a marker of neuropathology, limitations in its interpretation underscore the need for caution.

METHODS

This review provides an overview of the principles and clinical applicability of DTI. We focus on major white matter fiber bundles, detailing their normal anatomy and pathological DTI patterns, with emphasis on tracts routinely requested in our neurosurgical department in the preoperative context (uncinate fasciculus, arcuate fasciculus, pyramidal pathway, optic radiation, and dentatorubrothalamic tract).

RESULTS

We guide neuroradiologists and neurosurgeons in defining volumes of interest for mapping individual tracts and demonstrating their 3D reconstructions. The intricate trajectories of white matter tracts pose a challenge for accurate fiber orientation recording, with each bundle exhibiting specific characteristics. Tracts adjacent to brain lesions are categorized as displaced, edematous, infiltrated, or disrupted, illustrated with clinical cases of brain neoplasms. To improve structured reporting, we propose a checklist of topics for inclusion in imaging evaluations and MRI reports.

CONCLUSION

DTI is emerging as a powerful tool for assessing microstructural changes in brain disorders, despite some challenges in standardization and interpretation. This review serves an educational purpose by providing guidance for fiber monitoring and interpretation of pathological patterns observed in clinical cases, highlighting the importance and potential pitfalls of DTI in neuroradiology and surgical planning.

A review of diffusion MRI in mood disorders: mechanisms and predictors of treatment response

By measuring the molecular diffusion of water molecules in brain tissue, diffusion MRI (dMRI) provides unique insight into the microstructure and structural connections of the brain in living subjects. Since its inception, the application of dMRI in clinical research has expanded our understanding of the possible biological bases of psychiatric disorders and successful responses to different therapeutic interventions. Here, we review the past decade of diffusion imaging-based investigations with a specific focus on studies examining the mechanisms and predictors of therapeutic response in people with mood disorders. We present a brief overview of the general application of dMRI and key methodological developments in the field that afford increasingly detailed information concerning the macro- and micro-structural properties and connectivity patterns of white matter (WM) pathways and their perturbation over time in patients followed prospectively while undergoing treatment. This is followed by a more in-depth summary of particular studies using dMRI approaches to examine mechanisms and predictors of clinical outcomes in patients with unipolar or bipolar depression receiving pharmacological, neurostimulation, or behavioral treatments. Limitations associated with dMRI research in general and with treatment studies in mood disorders specifically are discussed, as are directions for future research. Despite limitations and the associated discrepancies in findings across individual studies, evolving research strongly indicates that the field is on the precipice of identifying and validating dMRI biomarkers that could lead to more successful personalized treatment approaches and could serve as targets for evaluating the neural effects of novel treatments.

A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients

PURPOSE

This study aimed to investigate the diagnostic performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in identifying aberrations in the corticospinal tract (CST), whilst elucidating the relationship between abnormalities of CST and patients' motor function.

METHODS

Altogether 21 patients with WHO grade II or grade IV glioma were enrolled and divided into Group 1 and Group 2, according to the presence or absence of preoperative paralysis. DKI and DTI metrics were generated and projected onto the CST. Histograms of the CST along x, y, and z axes were developed based on DKI and DTI metrics, and compared subsequently to determine regions of aberrations on the fibers. The receiver operating characteristic curve was performed to investigate the diagnostic efficacy of DKI and DTI metrics.

RESULTS

In Group 1, a significantly lower fractional anisotropy, radial kurtosis and mean kurtosis, and a higher mean diffusivity were found in the ipsilateral CST as compared to the contralateral CST. Significantly higher relative axial diffusivity, relative radial diffusivity, and relative mean diffusivity (rMD) were found in Group 1, as compared to Group 2. The relative volume of ipsilateral CST abnormalities higher than the maximum value of mean kurtosis combined with rMD exhibited the best diagnostic performance in distinguishing dysfunction of CST with an AUC of 0.93.

CONCLUSION

DKI is sensitive in detecting subtle changes of CST distal from the tumor. The combination of DKI and DTI is feasible for evaluating the impairment of the CST.

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.

The emergence of multiscale connectomics-based approaches in stroke recovery

Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.

Elossaily G, Vellapandian C, Prajapati B. Investigating the Potential Impact of Air Pollution on Alzheimer's Disease and the Utility of Multidimensional Imaging for Early Detection

Pollution is ubiquitous, and much of it is anthropogenic in nature, which is a severe risk factor not only for respiratory infections or asthma sufferers but also for Alzheimer's disease, which has received a lot of attention recently. This Review aims to investigate the primary environmental risk factors and their profound impact on Alzheimer's disease. It underscores the pivotal role of multidimensional imaging in early disease identification and prevention. Conducting a comprehensive review, we delved into a plethora of literature sources available through esteemed databases, including Science Direct, Google Scholar, Scopus, Cochrane, and PubMed. Our search strategy incorporated keywords such as "Alzheimer Disease", "Alzheimer's", "Dementia", "Oxidative Stress", and "Phytotherapy" in conjunction with "Criteria Pollutants", "Imaging", "Pathology", and "Particulate Matter". Alzheimer's disease is not only a result of complex biological factors but is exacerbated by the infiltration of airborne particles and gases that surreptitiously breach the nasal defenses to traverse the brain, akin to a Trojan horse. Various imaging modalities and noninvasive techniques have been harnessed to identify disease progression in its incipient stages. However, each imaging approach possesses inherent limitations, prompting exploration of a unified technique under a single umbrella. Multidimensional imaging stands as the linchpin for detecting and forestalling the relentless march of Alzheimer's disease. Given the intricate etiology of the condition, identifying a prospective candidate for Alzheimer's disease may take decades, rendering the development of a multimodal imaging technique an imperative. This research underscores the pressing need to recognize the chronic ramifications of invisible particulate matter and to advance our understanding of the insidious environmental factors that contribute to Alzheimer's disease.

Q-ball high-resolution fiber tractography of language associated tracts: quantitative evaluation of applicability for glioma resections

BACKGROUND

To date, fiber tractography (FT) is predominantly based on diffusion tensor imaging (DTI). High angular resolution diffusion imaging (HARDI)-based reconstructions have become a focus of interest, enabling the resolution of intravoxel fiber crossing. However, experience with high resolution tractography (HRFT) for neurosurgical applications is still limited to a few reports. This prospectively designed feasibility study shares our initial experience using an analytical q-ball approach (QBI) for FT of language-associated pathways in comparison with DTI-FT, focussing on a quantitative analysis and evaluation of its applicability in clinical routine.

METHODS

Probabilistic QBI-, and DTI-FT were performed for the major components of the language-associated fiber bundles (superior longitudinal fasciculus, inferior fronto-occipital fasciculus, medial/inferior longitudinal faciculus) in 11 patients with eloquent gliomas. The data was derived from a routine DWI sequence (b=1000s/mm2, 64 gradient directions). Quantitative analysis evaluated tract volume (TV), tract length (TL) and tract density (TD). Results were correlated to tumor and edema size.

RESULTS

Quantitative analysis showed larger TV and TL of the overall fiber object using QBI-FT compared with DTI-FT (TV: 16.45±1.85 vs. 10.07±1.15cm3; P<0.0001; TL: 81.95±6.14 vs. 72.06±6.92 mm; P=0.0011). Regarding overall TD, DTI delivered significantly higher values (40.57±6.59 vs. 60.98±15.94 points/voxel; P=0.0118). Bland-Altman analysis illustrated a systematic advantage to yield lager TV and TL via QBI compared with DTI for all reconstructed pathways. The results were independent of tumor or edema volume.

CONCLUSIONS

QBI proved to be suitable for an application in the neurosurgical setting without additional expense for the patient. Quantitative analysis of FT reveals larger overall TV, longer TL with lower TD using QBI compared with DTI, suggesting the better depiction of marginal and terminal fibers according to neuroanatomical knowledge. This emphasizes the known limitation of DTI to underestimate the dimensions of a pathway. Rather than relying on DTI, sophisticated HRFT techniques should be considered for preoperative planning and intraoperative guidance in selected cases of eloquent glioma surgery.

Imaging Biomarkers of Peripheral Nerves: Focus on Magnetic Resonance Neurography and Ultrasonography

Peripheral neuropathy is a prevalent and debilitating condition affecting millions of individuals globally. Magnetic resonance neurography (MRN) and ultrasonography (US) are noninvasive methods offering comprehensive visualization of peripheral nerves, using anatomical and functional imaging biomarkers to ensure accurate evaluation. For optimized MRN, superior and high-resolution two-dimensional and three-dimensional imaging protocols are essential. The anatomical MRN and US imaging markers include quantitative measures of nerve and fascicular size and signal, and qualitative markers of course and morphology. Among them, quantitative markers of T2-signal intensity ratio are sensitive to nerve edema-like signal changes, and the T1-mapping technique reveals nerve and muscle tissue fatty and fibrous compositional alterations.The functional markers are derived from physiologic properties of nerves, such as diffusion characteristics or blood flow. They include apparent diffusion coefficient from diffusion-weighted imaging and fractional anisotropy and tractography from diffusion tensor imaging to delve into peripheral nerve microstructure and integrity. Peripheral nerve perfusion using dynamic contrast-enhanced magnetic resonance imaging estimates perfusion parameters, offering insights into nerve health and neuropathies involving edema, inflammation, demyelination, and microvascular alterations in conditions like type 2 diabetes, linking nerve conduction pathophysiology to vascular permeability alterations.Imaging biomarkers thus play a pivotal role in the diagnosis, prognosis, and monitoring of nerve pathologies, thereby ensuring comprehensive assessment and elevating patient care. These biomarkers provide valuable insights into nerve structure, function, and pathophysiology, contributing to the accurate diagnosis and management planning for peripheral neuropathy.

Multi-resolution X-ray phase-contrast and dark-field tomography of human cerebellum with near-field speckles

In this study, we use synchrotron-based multi-modal X-ray tomography to examine human cerebellar tissue in three dimensions at two levels of spatial resolution (2.3 µm and 11.9 µm). We show that speckle-based imaging (SBI) produces results that are comparable to propagation-based imaging (PBI), a well-established phase-sensitive imaging method. The different SBI signals provide complementary information, which improves tissue differentiation. In particular, the dark-field signal aids in distinguishing tissues with similar average electron density but different microstructural variations. The setup's high resolution and the imaging technique's excellent phase sensitivity enabled the identification of different cellular layers and additionally, different cell types within these layers. We also correlated this high-resolution phase-contrast information with measured dark-field signal levels. These findings demonstrate the viability of SBI and the potential benefit of the dark-field modality for virtual histology of brain tissue.

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.

Long period changes of hippocampal diffusion kurtosis imaging and its correlation with cognitive dysfunction after incomplete cerebral ischemia-reperfusion in rats

This study aims to summarize the changes of functional diffusion kurtosis imaging (DKI) parameters in the bilateral hippocampal CA1 region of the hemorrhagic shock reperfusion (HSR) model of rats and their correlation with cognitive dysfunction. Adult male Sprague-Dawley rats (9-10 weeks of age, weighing 350-400 g) were randomized into the HSR group (n = 30) and the sham-operated group (Sham) (n = 30). Rats in the HSR group and the Sham group were subdivided into five time points (1, 2, 4, 8, and 12 weeks) for examination. Diffusion kurtosis imaging (DKI) was performed. Cognitive dysfunction was analyzed by the Morris Water Maze. The correlation between the DKI parameters and cognitive dysfunction was analyzed by the Spearman correlation. In the HSR group, the values of axial kurtosis (Ka), radial kurtosis (Kr), and mean kurtosis (MK) in the bilateral hippocampal CA1 of rats at 1, 2, 4, 8 and 12 weeks after the surgery were significantly higher. The rats in the HSR group had significantly longer escape latency than in the Sham group. The rats in the HSR group had significantly shorter time and shorter distance in target quadrant than those in the Sham group. The escape latency had positive correlation with MK, Ka, and Kr. The distance and the time in target quadrant had negative correlation with MK, Ka, and Kr. The parameters get from the DKI could accurately evaluate the abnormal blood perfusion and microstructure changes in hippocampal CA1 area of the incomplete cerebral ischemia reperfusion rats induced by HSR. MK, Ka, and Kr values could reflect the decreased learning and memory ability in HSR rat model.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

A novel framework for in-vivo diffusion tensor distribution MRI of the human brain

Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe subvoxel heterogeneity by describing water diffusion within a voxel using an ensemble of non-exchanging compartments characterized by a probability density function of diffusion tensors. In this study, we provide a new framework for acquiring multiple diffusion encoding (MDE) images and estimating DTD from them in the human brain in vivo. We interfused pulsed field gradients (iPFG) in a single spin echo to generate arbitrary b-tensors of rank one, two, or three without introducing concomitant gradient artifacts. Employing well-defined diffusion encoding parameters we show that iPFG retains salient features of a traditional multiple-PFG (mPFG/MDE) sequence while reducing the echo time and coherence pathway artifacts thereby extending its applications beyond DTD MRI. Our DTD is a maximum entropy tensor-variate normal distribution whose tensor random variables are constrained to be positive definite to ensure their physicality. In each voxel, the second-order mean and fourth-order covariance tensors of the DTD are estimated using a Monte Carlo method that synthesizes micro-diffusion tensors with corresponding size, shape, and orientation distributions to best fit the measured MDE images. From these tensors we obtain the spectrum of diffusion tensor ellipsoid sizes and shapes, and the microscopic orientation distribution function (μODF) and microscopic fractional anisotropy (μFA) that disentangle the underlying heterogeneity within a voxel. Using the DTD-derived μODF, we introduce a new method to perform fiber tractography capable of resolving complex fiber configurations. The results revealed microscopic anisotropy in various gray and white matter regions and skewed MD distributions in cerebellar gray matter not observed previously. DTD MRI tractography captured complex white matter fiber organization consistent with known anatomy. DTD MRI also resolved some degeneracies associated with diffusion tensor imaging (DTI) and elucidated the source of diffusion heterogeneity which may help improve the diagnosis of various neurological diseases and disorders.

An iterative algorithm for computing gradient directions for white matter fascicles detection in brain MRI

This paper proposes a new iterative algorithm for computing gradient directions (GD) to reconstruct the brain's white matter fascicles. In particular, the proposed algorithm extensively overcomes the limitations of existing approaches like Uniform Gradient Directions and Adaptive Gradient Directions (AGD) for this task. The proposed algorithm uses the AGD approach to have a coarse estimation of the fibers in the initial step, and then a refinement is done using an iterative strategy. We begin with GD distributed uniformly inside a grid of bigger size and larger spacing between the points. Both (grid size and spacing between the points) reduce iteratively. The proposed algorithm has higher chance of capturing the fibers' actual position within the grid at each iteration. Hence, the solution tends to the actual position of fiber in each iteration, leading to a better estimation of fiber orientations. Multiple artificial simulations and real dataset experiments on the human brain and optic chiasm of a rat's brain are performed. The excellent performance of the proposed algorithm at different noises ensures stability and robustness. Hence, after processing the MRI data, the proposed algorithm can accurately reflect the ground truth of white matter fascicles connections in reconstructed images. The proposed algorithm helps resolve the structural complexities of the brain caused due to presence of crossing fascicles to a great extent.

Evaluating the renal mild tubulointerstitial damage and renal function in IgAN patients: a comparative study based on diffusion kurtosis imaging and diffusion tensor imaging

OBJECTIVE

To compare the performance of 3.0 T magnetic resonance diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in evaluation of the degree of tubulointerstitial damage and renal function in Immunoglobulin A Nephropathy (IgAN) patients.

METHODS

Both DKI and DTI were performed in 40 IgAN patients and 17 healthy volunteers. IgAN patients were divided into two groups according to tubulointerstitial lesion score: Mild injury group, n = 24; Moderate-severe injury group, n = 16. DKI characteristic parameters [mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr)] and DTI parameters [fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr)] of renal cortex and medulla were measured and compared among different groups. Correlations between DKI, DTI parameters and clinicopathological characteristics were assessed. Diagnostic performance of DKI and DTI to evaluate tubulointerstitial damage of IgAN was compared.

RESULTS

Cortical MK, Kr, Da and parenchymal Ka significantly differed among three groups (P < 0.05). Cortical MK, Kr, Ka were negatively correlated with estimated glomerular filtration rate (eGFR) (MK: r = - 0.613; Kr: r = - 0.539; Ka: r = - 0.664) and positively correlated with tubulointerstitial lesion score (MK: r = 0.655; Kr: r = 0.577; Ka: r = 0.661) (all P < 0.001). Lower correlation coefficient was found among cortical FA, MD, Dr and eGFR, tubulointerstitial lesion score (all|r|< 0.350). The AUCs of DKI and DTI parameters for differentiating Mild injury group from control group were (cortical MK 0.822, cortical Ka 0.816; cortical FA 0.515, cortical MD 0.714) and for differentiating Mild injury group from Moderate-severe injury group were (cortical MK 0.813, cortical Ka 0.831; medulla FA 0.784, medulla MD 0.586).

CONCLUSION

Compared with DTI, DKI was more sensitive and accurate to probe the renal function and the tubulointerstitial damage of IgAN, especially the mild tubulointerstitial damage.

On Modelling Electrical Conductivity of the Cerebral White Matter

The conductivity, in general, of the brain tissues is a characteristic key of functional cerebral changes. White matter electric conductivity appears to be extremely anisotropic, so a tensor (matrix) is needed to describe it. Traditional methods of imaging brain electrical properties fail to capture it and required the interpolation of the diffusion matrix. The electrochemical model is suitable for analysis, while, on the other hand, the volume fraction model is suitable for studying the effect of white matter structural changes in relation to electrical conductivity. It adopts a relevant algorithm, based upon a linear conductivity-to-diffusivity relationship and a volume constraint, respectively. It incorporates the effects of the partial volume of the cerebrospinal fluid and the structure of the neuronal fiber crossing, which was not achieved by the existing algorithms, accomplishing a more accurate estimation of the anisotropic conductivity of the white matter. Diffusion matrix imaging is a powerful noninvasive method for characterizing neuronal tissue in the human brain. The ultimate goal is to study and draw appropriate conclusions, regarding the molecule diffusion in the brain under normal physiological conditions and the changes that occur in development, diseases, and aging. The ability to measure the electrical conductivity of brain tissues in a noninvasive way also helps in characterizing endogenous currents by measuring the associated electromagnetic fields.

Diffusion kurtosis imaging in patients with tissue-negative transient ischemic attack

Approximately 50–60% of patients with a clinical transient ischemic attack (TIA) do not have diffusion-weighted imaging (DWI) evidence of cerebral ischemia. The purpose of this study was to assess the added diagnostic value of diffusion kurtosis imaging (DKI) in the evaluation of patients with TIA who have normal DWI findings. From September 2014 to May 2017, a total of 179 consecutive patients with suspected TIA were eligible for enrollment in our study. The inclusion criteria were a confirmed diagnosis of TIA confirmed by a stroke neurologist, MRI (including DWI and DKI) within 24 h after symptom onset, no stroke history, and no DWI lesion. A follow-up DWI was performed to establish stroke recurrence within a period of 90 days. A total of 98 patients who had no lesions on the baseline DWI were included for data analysis. Of these 98 patients, 31 (31.6%) had positive findings on the initial DKI. In 29 of the 31 (93.5%) patients, the location of the abnormality observed on DKI was consistent with the clinical symptoms. During the 90-day follow-up period, 14 (14.3%) patients developed recurrent stroke. The prevalence of recurrent stroke was higher in the DKI-positive group than in the DKI-negative group (29.0% vs. 7.5%, p = 0.01). A comparison between the patients with and without recurrent stroke showed that an abnormality on the baseline DKI was associated with stroke recurrence. Furthermore, 8 of the 9 stroke patients in the DKI-positive group developed a new ischemic lesion in the artery territory corresponding to the initial DKI abnormality. The new findings suggest the predictive value of DKI on the recurrence of stroke in the patients with TIA who have negative findings on conventional DWI.

microRNA Biology on Brain Development and Neuroimaging Approach

Proper brain development requires the precise coordination and orchestration of various molecular and cellular processes and dysregulation of these processes can lead to neurological diseases. In the past decades, post-transcriptional regulation of gene expression has been shown to contribute to various aspects of brain development and function in the central nervous system. MicroRNAs (miRNAs), short non-coding RNAs, are emerging as crucial players in post-transcriptional gene regulation in a variety of tissues, such as the nervous system. In recent years, miRNAs have been implicated in multiple aspects of brain development, including neurogenesis, migration, axon and dendrite formation, and synaptogenesis. Moreover, altered expression and dysregulation of miRNAs have been linked to neurodevelopmental and psychiatric disorders. Magnetic resonance imaging (MRI) is a powerful imaging technology to obtain high-quality, detailed structural and functional information from the brains of human and animal models in a non-invasive manner. Because the spatial expression patterns of miRNAs in the brain, unlike those of DNA and RNA, remain largely unknown, a whole-brain imaging approach using MRI may be useful in revealing biological and pathological information about the brain affected by miRNAs. In this review, we highlight recent advancements in the research of miRNA-mediated modulation of neuronal processes that are important for brain development and their involvement in disease pathogenesis. Also, we overview each MRI technique, and its technological considerations, and discuss the applications of MRI techniques in miRNA research. This review aims to link miRNA biological study with MRI analytical technology and deepen our understanding of how miRNAs impact brain development and pathology of neurological diseases.

Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding

In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.

Cumulant expansion with localization: A new representation of the diffusion MRI signal

Diffusion MR is sensitive to the microstructural features of a sample. Fine-scale characteristics can be probed by employing strong diffusion gradients while the low b-value regime is determined by the cumulants of the distribution of particle displacements. A signal representation based on the cumulants, however, suffers from a finite convergence radius and cannot represent the 'localization regime' characterized by a stretched exponential decay that emerges at large gradient strengths. Here, we propose a new representation for the diffusion MR signal. Our method provides not only a robust estimate of the first three cumulants but also a meaningful extrapolation of the entire signal decay.

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Performance of orientation distribution function-fingerprinting with a biophysical multicompartment diffusion model

PURPOSE

Orientation Distribution Function (ODF) peak finding methods typically fail to reconstruct fibers crossing at shallow angles below 40°, leading to errors in tractography. ODF-Fingerprinting (ODF-FP) with the biophysical multicompartment diffusion model allows for breaking this barrier.

METHODS

A randomized mechanism to generate a multidimensional ODF-dictionary that covers biologically plausible ranges of intra- and extra-axonal diffusivities and fraction volumes is introduced. This enables ODF-FP to address the high variability of brain tissue. The performance of the proposed approach is evaluated on both numerical simulations and a reconstruction of major fascicles from high- and low-resolution in vivo diffusion images.

RESULTS

ODF-FP with the suggested modifications correctly identifies fibers crossing at angles as shallow as 10 degrees in the simulated data. In vivo, our approach reaches 56% of true positives in determining fiber directions, resulting in visibly more accurate reconstruction of pyramidal tracts, arcuate fasciculus, and optic radiations than the state-of-the-art techniques. Moreover, the estimated diffusivity values and fraction volumes in corpus callosum conform with the values reported in the literature.

CONCLUSION

The modified ODF-FP outperforms commonly used fiber reconstruction methods at shallow angles, which improves deterministic tractography outcomes of major fascicles. In addition, the proposed approach allows for linearization of the microstructure parameters fitting problem.

A Novel Method to Inspect 3D Ball Joint Socket Products Using 2D Convolutional Neural Network with Spatial and Channel Attention

Product defect inspections are extremely important for industrial manufacturing processes. It is necessary to develop a special inspection system for each industrial product due to their complexity and diversity. Even though high-precision 3D cameras are usually used to acquire data to inspect 3D objects, it is hard to use them in real-time defect inspection systems due to their high price and long processing time. To address these problems, we propose a product inspection system that uses five 2D cameras to capture all inspection parts of the product and a deep learning-based 2D convolutional neural network (CNN) with spatial and channel attention (SCA) mechanisms to efficiently inspect 3D ball joint socket products. Channel attention (CA) in our model detects the most relevant feature maps while spatial attention (SA) finds the most important regions in the extracted feature map of the target. To build the final SCA feature vector, we concatenated the learned feature vectors of CA and SA because they complement each other. Thus, our proposed CNN with SCA provides high inspection accuracy as well as it having the potential to detect small defects of the product. Our proposed model achieved 98% classification accuracy in the experiments and proved its efficiency on product inspection in real-time.

Differential tractography as a dynamic imaging biomarker: A methodological pilot study for Huntington's disease

Huntington's disease (HD) is a neurodegenerative disorder characterized by motor, psychiatric, and cognitive symptoms. Due to its diverse manifestations, the scientific community has long recognized the need for sensitive, objective, individualized, and dynamic disease assessment tools. We examined the feasibility of Differential Tractography as a biomarker to evaluate correlation of symptom severity and of HD progression at the individual level. Differential tractography is a novel tractography modality that maps pathways with axonal injury characterized by a decrease of anisotropic diffusion pattern. We recruited sixteen patients scanned at 0-, 6-, and 12-month intervals by diffusion MRI scans for differential tractography assessment and correlated its volumetric findings with the Unified Huntington's Disease Rating Scale (UHDRS). Deterministic fiber tracking algorithm was applied. Longitudinal data was modeled using the generalized estimating equation (GEE) model and correlated with UHDRS scores, in addition to Spearman correlation for cross-sectional data. Our results show that volumes of affected pathways revealed by differential tractography significantly correlated with UHDRS scores in longitudinal data (p-value < 0.001), and chronological changes in differential tractography also correlated with the changes in UHDRS (p-value < 0.001). This technique opens new clinical avenues as a clinical translational tool to evaluate presymptomatic and symptomatic gene positive individuals. Our results provide support that differential tractography has the potential to be used as a dynamic imaging biomarker to assess at the individual level in a non-invasive manner, disease progression in HD. Critically important, differential tractography proves to be a quantitative tool for following degeneration in presymptomatic patients, with potential applications in clinical trials.

Patterns of white and gray structural abnormality associated with paediatric demyelinating disorders

•

A multi-modal approach was used to evaluate the visual pathway from anterior (retina) to posterior (visual cortex) in both paediatric MOGAD and MS patients.

•

MS patients exhibited more widespread white matter abnormalities; MOGAD patients exhibited white matter changes primarily within the optic radiation.

•

The pattern of cortical thinning differed in MS and MOGAD patients.

•

Reduced RNFLT was associated with lower axonal density in MOGAD and tortuosity in MS.

The impact of multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein (MOG) - associated disorders (MOGAD) on brain structure in youth remains poorly understood. Reductions in cortical mantle thickness on structural MRI and abnormal diffusion-based white matter metrics (e.g., diffusion tensor parameters) have been well documented in MS but not in MOGAD. Characterizing structural abnormalities found in children with these disorders can help clarify the differences and similarities in their impact on neuroanatomy. Importantly, while MS and MOGAD affect the entire CNS, the visual pathway is of particular interest in both groups, as most patients have evidence for clinical or subclinical involvement of the anterior visual pathway. Thus, the visual pathway is of key interest in analyses of structural abnormalities in these disorders and may distinguish MOGAD from MS patients.

In this study we collected MRI data on 18 MS patients, 14 MOGAD patients and 26 age- and sex-matched typically developing children (TDC). Full-brain group differences in fixel diffusion measures (fibre-bundle populations) and cortical thickness measures were tested using age and sex as covariates. Visual pathway analysis was performed by extracting mean diffusion measures within lesion free optic radiations, cortical thickness within the visual cortex, and retinal nerve fibre layer (RNFL) and ganglion cell layer thickness measures from optical coherence tomography (OCT). Fixel based analysis (FBA) revealed MS patients have widespread abnormal white matter within the corticospinal tract, inferior longitudinal fasciculus, and optic radiations, while within MOGAD patients, non-lesional impact on white matter was found primarily in the right optic radiation. Cortical thickness measures were reduced predominately in the temporal and parietal lobes in MS patients and in frontal, cingulate and visual cortices in MOGAD patients. Additionally, our findings of associations between reduced RNFLT and axonal density in MOGAD and TORT in MS patients in the optic radiations imply widespread axonal and myelin damage in the visual pathway, respectively. Overall, our approach of combining FBA, cortical thickness and OCT measures has helped evaluate similarities and differences in brain structure in MS and MOGAD patients in comparison to TDC.

Accurate Preoperative Identification of Motor Speech Area as Termination of Arcuate Fasciculus Depicted by Q-ball Imaging Tractography

BACKGROUND

Tractography is one way to predict the distribution of cortical functional domains preoperatively. Diffusion tensor tractography (DTT) is commonly used in clinical practice but is known to have limitations in delineating crossed fibers, which can be overcome by q-ball imaging tractography (QBT). In this study, we aimed to compare the reliability of these two methods, based on the spatial correlation between the arcuate fasciculus depicted by tractography and direct cortical stimulation (DCS) during awake surgery.

METHODS

Fifteen patients with glioma underwent awake surgery with DCS. Tractography was depicted in a 3D computer graphic model preoperatively, which was integrated with the photograph of the actual brain cortex using our novel mixed-reality technology. The termination of the arcuate fasciculus depicted by either DTT or QBT and the results of DCS were compared, and sensitivity and specificity were calculated in speech-associated brain gyri: pars triangularis, pars opercularis, ventral precentral gyrus, and middle frontal gyrus.

RESULTS

QBT had significantly better sensitivity and lower false positive rate than DTT in the pars orpercularis. The same trend was noted for the other gyri.

CONCLUSIONS

QBT is more reliable than DTT in identification of the motor speech area and may be clinically useful in brain tumor surgery.

General orientation transform for the estimation of fiber orientations in white matter tissues

PURPOSE

The orientation distribution function (ODF), which is obtained from the radial integral of the probability density function weighted by r n $$ {r}^n $$ ( r $$ r $$ is the radial length), has been used to estimate fiber orientations of white matter tissues. Currently, there is no general expression of the ODF that is suitable for any n value in the HARDI methods.

THEORY AND METHODS

A novel methodology is proposed to calculate the ODF for any n > - 1 $$ n>-1 $$ through the Taylor series expansion and a generalized expression for - 1 < n < 4 $$ -1<n<4 $$ is provided. Then a series of single-shell HARDI methods, termed the general orientation transform (GOT), is developed based on the obtained expression. By combining complementary GOTs, a composite estimator is obtained and further optimized via constrained optimization to take full advantage of individual merits. The final optimized HARDI method is termed the combined GOT with constrained optimization (coGOT). The proposed method is compared with other commonly used HARDI methods on the simulated data, the physical phantom data, the ISMRM 2015 Tractography challenge data, and in vivo HCP datasets.

RESULTS

coGOT can resolve crossing fibers with higher resolution, performs better robustness, generates fewer spurious lobes in glyphs, and thus provides distinct improvement in the tractography. The evaluations show coGOT's superior capability in reconstructing the fiber orientations from dMRI signals.

CONCLUSIONS

Generalization of the ODF allows us to obtain a wide range of HARDI estimators to select suitable candidates for composite formulation. The optimized estimator coGOT has great potential for studying neural architecture and serving as fiber tracking tools.

Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue

The method 3D polarised light imaging (3D-PLI) measures the birefringence of histological brain sections to determine the spatial course of nerve fibres (myelinated axons). While the in-plane fibre directions can be determined with high accuracy, the computation of the out-of-plane fibre inclinations is more challenging because they are derived from the amplitude of the birefringence signals, which depends e.g. on the amount of nerve fibres. One possibility to improve the accuracy is to consider the average transmitted light intensity (transmittance weighting). The current procedure requires effortful manual adjustment of parameters and anatomical knowledge. Here, we introduce an automated, optimised computation of the fibre inclinations, allowing for a much faster, reproducible determination of fibre orientations in 3D-PLI. Depending on the degree of myelination, the algorithm uses different models (transmittance-weighted, unweighted, or a linear combination), allowing to account for regionally specific behaviour. As the algorithm is parallelised and GPU optimised, it can be applied to large data sets. Moreover, it only uses images from standard 3D-PLI measurements without tilting, and can therefore be applied to existing data sets from previous measurements. The functionality is demonstrated on unstained coronal and sagittal histological sections of vervet monkey and rat brains.

Differential Relationships Between Brain Structure and Dual Task Walking in Young and Older Adults

Almost 25% of all older adults experience difficulty walking. Mobility difficulties for older adults are more pronounced when they perform a simultaneous cognitive task while walking (i.e., dual task walking). Although it is known that aging results in widespread brain atrophy, few studies have integrated across more than one neuroimaging modality to comprehensively examine the structural neural correlates that may underlie dual task walking in older age. We collected spatiotemporal gait data during single and dual task walking for 37 young (18-34 years) and 23 older adults (66-86 years). We also collected T 1-weighted and diffusion-weighted MRI scans to determine how brain structure differs in older age and relates to dual task walking. We addressed two aims: (1) to characterize age differences in brain structure across a range of metrics including volumetric, surface, and white matter microstructure; and (2) to test for age group differences in the relationship between brain structure and the dual task cost (DTcost) of gait speed and variability. Key findings included widespread brain atrophy for the older adults, with the most pronounced age differences in brain regions related to sensorimotor processing. We also found multiple associations between regional brain atrophy and greater DTcost of gait speed and variability for the older adults. The older adults showed a relationship of both thinner temporal cortex and shallower sulcal depth in the frontal, sensorimotor, and parietal cortices with greater DTcost of gait. Additionally, the older adults showed a relationship of ventricular volume and superior longitudinal fasciculus free-water corrected axial and radial diffusivity with greater DTcost of gait. These relationships were not present for the young adults. Stepwise multiple regression found sulcal depth in the left precentral gyrus, axial diffusivity in the superior longitudinal fasciculus, and sex to best predict DTcost of gait speed, and cortical thickness in the superior temporal gyrus to best predict DTcost of gait variability for older adults. These results contribute to scientific understanding of how individual variations in brain structure are associated with mobility function in aging. This has implications for uncovering mechanisms of brain aging and for identifying target regions for mobility interventions for aging populations.

The Application of Diffusion Kurtosis Imaging on the Heterogeneous White Matter in Relapsing-Remitting Multiple Sclerosis

Objectives

To evaluate the microstructural damage in the heterogeneity of different white matter areas in relapsing-remitting multiple sclerosis (RRMS) patients by using diffusion kurtosis imaging (DKI) and its correlation with clinical and cognitive status.

Materials and Methods

Kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), and mean diffusivity (MD) in T1-hypointense lesions (T1Ls), pure T2-hyperintense lesions (pure-T2Ls), normal-appearing white matter (NAWM), and white matter in healthy controls (WM in HCs) were measured in 48 RRMS patients and 26 sex- and age-matched HCs. All the participants were assessed with the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Symbol Digit Modalities Test (SDMT) scores as the cognitive status. The Kurtzke Expanded Disability Status Scale (EDSS) scores were used to evaluate the clinical status in RRMS patients.

Results

The lowest KFA, FA, and MK values and the highest MD values were found in T1Ls, followed by pure-T2Ls, NAWM, and WM in HCs. The T1Ls and pure-T2Ls were significantly different in FA (p = 0.002) and MK (p = 0.013), while the NAWM and WM in HCs were significantly different in KFA, FA, and MK (p < 0.001; p < 0.001; p = 0.001). The KFA, FA, MK, and MD values in NAWM (r = 0.360, p = 0.014; r = 0.415, p = 0.004; r = 0.369, p = 0.012; r = −0.531, p < 0.001) were correlated with the MMSE scores and the FA, MK, and MD values in NAWM (r = 0.423, p = 0.003; r = 0.427, p = 0.003; r = −0.359, p = 0.014) were correlated with the SDMT scores.

Conclusion

Applying DKI to the imaging-based white matter classification has the potential to reflect the white matter damage and is correlated with cognitive impairment.

Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography

Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so-called "crossing fiber problem" has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub-cortical terminations. These so-called "bottleneck" regions contribute to the ill-posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50-70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered "single fiber voxels" often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.

Comparative Evaluation of Diffusion Kurtosis Imaging and Diffusion Tensor Imaging in Detecting Cerebral Microstructural Changes in Alzheimer Disease

OBJECTIVE

Comparative evaluation of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) using a whole-brain atlas to comprehensively evaluate microstructural changes in the brain of Alzheimer disease (AzD) patients.

METHODS

Twenty-seven AzD patients and 25 age-matched controls were included. MRI data was analyzed using a whole-brain atlas with inclusion of 98 region of interests. White matter (WM) microstructural changes were assessed by Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), Kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK). Gray matter (GM) integrity was evaluated using KFA, MK, RK, AK and MD. Comparison of the DKI and DTI metrics were done using student t-test (p ≤ 0.001).

RESULTS

In AzD patients widespread increase in MD, AD and RD were found in various WM and GM region of interests. The extent of abnormality for DKI parameters was more limited in both GM and WM regions and revealed reduced kurtosis values except in lentiform nuclei. Both DKI and DTI parameters were sensitive to detect abnormality in WM areas with coherent and complex fiber arrangement. Receiver operating characteristic curve analysis for hippocampal values revealed the highest specificity of 88% for AK <0.6965 and highest sensitivity of 95.2% for MD >1.2659.

CONCLUSION

AzD patients have microstructural changes in both WM and GM and are well-depicted by both DKI and DTI. The alterations in kurtosis parameters, however, are more limited and correlate with areas in the brain primarily involved in cognition.

Brain-age estimation accuracy is significantly increased using multishell free-water reconstruction

Although free-water diffusion reconstruction for diffusion-weighted imaging (DWI) data can be applied to both single-shell and multishell data, recent finding in synthetic data suggests that the free-water indices from single-shell acquisition should be interpreted with care, as they are heavily influenced by initialization parameters and cannot discriminate between free-water and mean diffusivity modifications. However, whether using a longer multishell acquisition protocol significantly improve reconstruction for real human MRI data is still an open question. In this study, we compare canonical diffusion tensor imaging (DTI), single-shell and multishell free-water imaging (FW) indices derived from a short, clinical compatible diffusion protocol (b = 500 s/mm² , b = 1,000 s/mm² , 32 directions each) on their power to predict brain age. Age was chosen as it is well-known to be related to widespread modification of the white matter and because brain-age estimation has recently been found to be relevant to several neurodegenerative diseases. We used a previously developed and validated data-driven whole-brain machine learning pipeline to directly compare the precision of brain-age estimates in a sample of 89 healthy males between 20 and 85 years old. We found that multishell FW outperform DTI indices in estimating brain age and that multishell FW, even when using low (500 ms² ) b-values secondary shell, outperform single-shell FW. Single-shell FW led to lower brain-age estimation accuracy even of canonical DTI indices, suggesting that single-shell FW indices should be used with caution. For all considered reconstruction algorithms, the most discriminant indices were those measuring free diffusion of water in the white matter.

ManifoldNet: A Deep Neural Network for Manifold-Valued Data With Applications

Geometric deep learning is a relatively nascent field that has attracted significant attention in the past few years. This is partly due to the availability of data acquired from non-euclidean domains or features extracted from euclidean-space data that reside on smooth manifolds. For instance, pose data commonly encountered in computer vision reside in Lie groups, while covariance matrices that are ubiquitous in many fields and diffusion tensors encountered in medical imaging domain reside on the manifold of symmetric positive definite matrices. Much of this data is naturally represented as a grid of manifold-valued data. In this paper we present a novel theoretical framework for developing deep neural networks to cope with these grids of manifold-valued data inputs. We also present a novel architecture to realize this theory and call it the ManifoldNet. Analogous to vector spaces where convolutions are equivalent to computing weighted sums, manifold-valued data 'convolutions' can be defined using the weighted Fréchet Mean ([Formula: see text]). (This requires endowing the manifold with a Riemannian structure if it did not already come with one.) The hidden layers of ManifoldNet compute [Formula: see text]s of their inputs, where the weights are to be learnt. This means the data remain manifold-valued as they propagate through the hidden layers. To reduce computational complexity, we present a provably convergent recursive algorithm for computing the [Formula: see text]. Further, we prove that on non-constant sectional curvature manifolds, each [Formula: see text] layer is a contraction mapping and provide constructive evidence for its non-collapsibility when stacked in layers. This captures the two fundamental properties of deep network layers. Analogous to the equivariance of convolution in euclidean space to translations, we prove that the [Formula: see text] is equivariant to the action of the group of isometries admitted by the Riemannian manifold on which the data reside. To showcase the performance of ManifoldNet, we present several experiments using both computer vision and medical imaging data sets.

Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure

Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.

A Preliminary DTI Tractography Study of Developmental Neuroplasticity 5-15 Years After Early Childhood Traumatic Brain Injury

Plasticity is often implicated as a reparative mechanism when addressing structural and functional brain development in young children following traumatic brain injury (TBI); however, conventional imaging methods may not capture the complexities of post-trauma development. The present study examined the cingulum bundles and perforant pathways using diffusion tensor imaging (DTI) in 21 children and adolescents (ages 10–18 years) 5–15 years after sustaining early childhood TBI in comparison with 19 demographically-matched typically-developing children. Verbal memory and executive functioning were also evaluated and analyzed in relation to DTI metrics. Beyond the expected direction of quantitative DTI metrics in the TBI group, we also found qualitative differences in the streamline density of both pathways generated from DTI tractography in over half of those with early TBI. These children exhibited hypertrophic cingulum bundles relative to the comparison group, and the number of tract streamlines negatively correlated with age at injury, particularly in the late-developing anterior regions of the cingulum; however, streamline density did not relate to executive functioning. Although streamline density of the perforant pathway was not related to age at injury, streamline density of the left perforant pathway was significantly and positively related to verbal memory scores in those with TBI, and a moderate effect size was found in the right hemisphere. DTI tractography may provide insight into developmental plasticity in children post-injury. While traditional DTI metrics demonstrate expected relations to cognitive performance in group-based analyses, altered growth is reflected in the white matter structures themselves in some children several years post-injury. Whether this plasticity is adaptive or maladaptive, and whether the alterations are structure-specific, warrants further investigation.

Tractography of Porcine Meniscus Microstructure Using High-Resolution Diffusion Magnetic Resonance Imaging

To noninvasively evaluate the three-dimensional collagen fiber architecture of porcine meniscus using diffusion MRI, meniscal specimens were scanned using a 3D diffusion-weighted spin-echo pulse sequence at 7.0 T. The collagen fiber alignment was revealed in each voxel and the complex 3D collagen network was visualized for the entire meniscus using tractography. The proposed automatic segmentation methods divided the whole meniscus to different zones (Red-Red, Red-White, and White-White) and different parts (anterior, body, and posterior). The diffusion tensor imaging (DTI) metrics were quantified based on the segmentation results. The heatmap was generated to investigate the connections among different regions of meniscus. Strong zonal-dependent diffusion properties were demonstrated by DTI metrics. The fractional anisotropy (FA) value increased from 0.13 (White-White zone) to 0.26 (Red-Red zone) and the radial diffusivity (RD) value changed from 1.0 × 10^-3 mm²/s (White-White zone) to 0.7 × 10^-3 mm²/s (Red-Red zone). Coexistence of both radial and circumferential collagen fibers in the meniscus was evident by diffusion tractography. Weak connections were found between White-White zone and Red-Red zone in each part of the meniscus. The anterior part and posterior part were less connected, while the body part showed high connections to both anterior part and posterior part. The tractography based on diffusion MRI may provide a complementary method to study the integrity of meniscus and nondestructively visualize the 3D collagen fiber architecture.

Widespread effects of dMRI data quality on diffusion measures in children

Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large‐scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5–17 years of age) from six different centers. Six data quality metrics—contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)—and four diffusion measures—fractional anisotropy, mean diffusivity, tract density, and length—were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between‐site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group‐wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.

Comparison of diffusion signal models for fiber tractography in eloquent glioma surgery - determination of accuracy under awake craniotomy conditions

OBJECTIVE

Fiber tractography(FT) has become an important non-invasive tool to ensure maximal safe tumor resection in eloquent glioma surgery. Intraoperatively applied FT is still predominantly based on Diffusion Tensor Imaging(DTI). However, reconstruction schemes of high angular resolution diffusion imaging(HARDI) data for high resolution fiber tractography(HRFT) are gaining increasing attention. The aim of this prospective study was to compare the accuracy of sophisticated HRFT-models compared with DTI-FT.

METHODS

Ten patients with eloquent gliomas underwent surgery under awake craniotomy conditions. The localization of acquisition points(AP), representing deteriorations during intraoperative electrostimulation(IOM) and neuropsychological mapping, were documented. The offsets of AP to the respective fiber bundle were calculated. Probabilistic QBI- and CSD-FT were compared to DTI-FT for the major language-associated fiber bundles (superior longitudinal fasciclus (SLF) II-IV, inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus/medial longitudinal fasciculus (ILF/MLF).

RESULTS

Among 186 offset values, 46% were located closer than 10mm to the estimated fiber bundle (CSD:36%; DTI:40% and QBI:60%). Moreover, only 10 offsets were further away than 30mm (5%). Lowest mean min-offsets (SLF: 7.7±7.9mm; IFOF: 12.7±8.3mm; ILF/MLF: 17.7±6.7mm) were found for QBI, indicating a significant advantage compared with CSD or DTI (p<0.001), respectively. No significant differences were found between CSD-, and DTI-FT offsets (p=0.105), albeit for the compound SLF exclusively (p<0.001).

CONCLUSIONS

Comparing HRFT techniques QBI and CSD with DTI, QBI delivered significantly better results with lowest offsets and good correlation to IOM results. Besides, QBI-FT was feasible for neurosurgical pre- and intraoperative applications. Our findings suggest that a combined approach of QBI-FT and IOM under awake craniotomy is considerable for best preservation of neurological function in the presented setting. Overall, the implementation of selected HRFT models into neuronavigation systems seems to be a promising tool in glioma surgery.

On the down-sampling of diffusion MRI data along the angular dimension

BACKGROUND

It has been established that the diffusion gradient directions in diffusion MRI should be uniformly distributed in 3D spherical space, so that orientation-dependent diffusion properties (e.g., fractional anisotropy or FA) can be properly quantified. Sometimes the acquired data need to be down-sampled along the angular dimension before computing diffusion properties (e.g., to exclude data points corrupted by motion artifact; to harmonize data obtained with different protocols). It is important to quantitatively assess the impact of data down-sampling on measurement of diffusion properties.

MATERIALS AND METHODS

Here we report 1) a numerical procedure for down-sampling diffusion MRI (e.g., for data harmonization), and 2) a spatial uniformity index of diffusion directions, aiming to predict the quality of the chosen down-sampling schemes (e.g., from data harmonization; or rejection of motion corrupted data points). We quantitatively evaluated human diffusion MRI data, which were down-sampled from 64 or 60 diffusion gradient directions to 30 directions, in terms of their 1) FA value accuracy (using fully-sampled data as the ground truth), 2) FA fitting residuals, and 3) spatial uniformity indices.

RESULTS

Our experimental data show that the proposed spatial uniformity index is correlated with errors in FA obtained from down-sampled diffusion MRI data. The FA fitting residuals that are typically used to assess diffusion MRI quality are not correlated with either FA errors or spatial uniformity index.

CONCLUSIONS

These results suggest that the spatial uniformity index could be more valuable in assessing quality of down-sampled diffusion MRI data, as compared with FA fitting residual measures. We expect that our implemented software procedure should prove valuable for 1) guiding data harmonization for multi-site diffusion MRI studies, and 2) assessing the impact of rejecting motion corrupted data points on the accuracy of diffusion measures.

White matter tracts in Bipolar Disorder patients: A comparative study based on diffusion kurtosis and tensor imaging

BACKGROUND

Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging (DTI), is a powerful tool for studying human brain.The purpose is to investigate differences between DKI and DTI by comparing parameters in same analysis methods with bipolar disorder (BD) patients.

METHODS

In this study, we attained in 47 BD patients and 49 age-, sex-, and education-matched healthy controls, complimented DTI and DKI scanning and got Fractional Anisotropy (FA), Mean Diffusion (MD) and Mean Kurtosis (MK). Voxel-wise statistical analysis was performed by the tract-based spatial statistics (TBSS) analysis and atlas-based regional data analysis.

RESULTS

TBSS analysis showed more widespread regions and higher fidelity in DKI parameters than DTI parameters with the same p-value threshold, and DKI parameters showed significant alterations after Family-Wise Error correction. The DKI-FA value in the corpus callosum, bilateral cingulum (cingulate gyrus), bilateral superior corona radiata, left anterior corona radiata and left posterior corona radiata of BD patients was negatively correlated with the duration of illness. In the atlas-based regional data analysis, the effect size of DTI-FA, DTI-MD, DKI-FA and DKI-MD were quantified using Cohen's d value. DKI-FA and DKI-MD demonstrated more between-group different regions and the higher (p < 0.001) absolute Cohen's d value than DTI-FA.

LIMITATIONS

This study did not consider the difference between sub-types of BD.

CONCLUSIONS

Compared to DTI parameters, DKI parameters were more sensitive and stable to probe the local microstructure, and particularly powerful to exploit cerebral alterations in BD patients.

Evidence for microscopic kurtosis in neural tissue revealed by correlation tensor MRI

PURPOSE

The impact of microscopic diffusional kurtosis (µK), arising from restricted diffusion and/or structural disorder, remains a controversial issue in contemporary diffusion MRI (dMRI). Recently, correlation tensor imaging (CTI) was introduced to disentangle the sources contributing to diffusional kurtosis, without relying on a-priori multi-gaussian component (MGC) or other microstructural assumptions. Here, we investigated µK in in vivo rat brains and assessed its impact on state-of-the-art methods ignoring µK.

THEORY AND METHODS

CTI harnesses double diffusion encoding (DDE) experiments, which were here improved for speed and minimal bias using four different sets of acquisition parameters. The robustness of the improved CTI protocol was assessed via simulations. In vivo CTI acquisitions were performed in healthy rat brains using a 9.4T pre-clinical scanner equipped with a cryogenic coil, and targeted the estimation of µK, anisotropic kurtosis, and isotropic kurtosis.

RESULTS

The improved CTI acquisition scheme substantially reduces scan time and importantly, also minimizes higher-order-term biases, thus enabling robust µK estimation, alongside K_aniso and K_iso metrics. Our CTI experiments revealed positive µK both in white and gray matter of the rat brain in vivo; µK is the dominant kurtosis source in healthy gray matter tissue. The non-negligible µK substantially were found to bias prior MGC analyses of K_iso and K_aniso .

CONCLUSIONS

Correlation Tensor MRI offers a more accurate and robust characterization of kurtosis sources than its predecessors. µK is non-negligible in vivo in healthy white and gray matter tissues and could be an important biomarker for future studies. Our findings thus have both theoretical and practical implications for future dMRI research.

Interactions Between Aging and Alzheimer's Disease on Structural Brain Networks

Normative aging and Alzheimer's disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks. The 139 participants consist of 45 normal controls (NCs), 37 with early mild cognitive impairment (EMCI), 27 with late mild cognitive impairment (LMCI), and 30 AD patients. All subjects were further divided into three subgroups based on their age (56-65, 66-75, and 71-85 years). After the structural connectivity networks were built using anatomically-constrained deterministic tractography, their global and nodal topological properties were estimated, including network efficiency, characteristic path length, transitivity, modularity coefficient, clustering coefficient, and betweenness. Statistical analyses were then performed on these metrics using linear regression, and one- and two-way ANOVA testing to examine group differences and interactions between aging and AD propagation. No significant interactions were found between aging and AD propagation in the global topological metrics (network efficiency, characteristic path length, transitivity, and modularity coefficient). However, nodal metrics (clustering coefficient and betweenness centrality) of some cortical parcels exhibited significant interactions between aging and AD propagation, with affected parcels including left superior temporal, right pars triangularis, and right precentral. The results collectively confirm the age-related deterioration of structural networks in MCI and AD patients, providing novel insight into the cross effects of aging and AD disorder on brain structural networks. Some early symptoms of AD may also be due to age-associated anatomic vulnerability interacting with early anatomic changes associated with AD.

Recent Advances in Parameter Inference for Diffusion MRI Signal Models

In this paper, fundamentals and recent progress for obtaining biological features quantitatively by using diffusion MRI are reviewed. First, a brief description of diffusion MRI history, application, and development was presented. Then, well-known parametric models including diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), and neurite orientation dispersion diffusion imaging (NODDI) are introduced with several classifications in various viewpoints with other modeling schemes. In addition, this review covers mathematical generalization and examples of methodologies for the model parameter inference from conventional fitting to recent machine learning approaches, which is called Q-space learning (QSL). Finally, future perspectives on diffusion MRI parameter inference are discussed with the aspects of imaging modeling and simulation.

Microstructural white matter alterations in Alzheimer's disease and amnestic mild cognitive impairment and its diagnostic value based on diffusion kurtosis imaging: a tract-based spatial statistics study

This prospective study aimed to explore the white matter microstructural alterations in Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) using the Tract-based Spatial Statistics (TBSS) method of diffusion kurtosis imaging (DKI).Diffusion images were collected from 45 AD patients, 42 aMCI patients, and 35 healthy controls (HC). The differences of DKI-derived parameters, including kurtosis fractional anisotropy (KFA), mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD), were compared across the three groups using the TBSS method. Correlation between the altered DKI-derived parameters and the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores were analyzed. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of different white matter parameters with the strongest correlations. As a result, compared with the HC group, KFA values decreased significantly in the aMCI group. Compared with both the HC and aMCI groups, the FA, KFA, and MK values decreased significantly and the MD value increased significantly in the AD group. FA, MD, KFA, and MK values of many white matter fiber tracts were significantly correlated with MMSE and MoCA scores. The area under the ROC curve (AUC) for the splenium of corpus callosum KFA values were highest for the diagnosis of aMCI and AD patients. In conclusion, the compactness and complexity of white matter microstructures were reduced in AD and aMCI patients. DKI can provide information about the severity of AD progression, and KFA might be more sensitive for the detection of white matter microstructural alterations.

Age-Related Differences in White Matter: Understanding Tensor-Based Results Using Fixel-Based Analysis

Aging is associated with widespread alterations in cerebral white matter (WM). Most prior studies of age differences in WM have used diffusion tensor imaging (DTI), but typical DTI metrics (e.g., fractional anisotropy; FA) can reflect multiple neurobiological features, making interpretation challenging. Here, we used fixel-based analysis (FBA) to investigate age-related WM differences observed using DTI in a sample of 45 older and 25 younger healthy adults. Age-related FA differences were widespread but were strongly associated with differences in multi-fiber complexity (CX), suggesting that they reflected differences in crossing fibers in addition to structural differences in individual fiber segments. FBA also revealed a frontolimbic locus of age-related effects and provided insights into distinct microstructural changes underlying them. Specifically, age differences in fiber density were prominent in fornix, bilateral anterior internal capsule, forceps minor, body of the corpus callosum, and corticospinal tract, while age differences in fiber cross section were largest in cingulum bundle and forceps minor. These results provide novel insights into specific structural differences underlying major WM differences associated with aging.

Cellular and extracellular white matter alterations indicate conversion to psychosis among individuals at clinical high-risk for psychosis

OBJECTIVES

It is important to find biomarkers associated with transition to illness in individuals at clinical high-risk for psychosis (CHR). Here, we use free-water imaging, an advanced diffusion MRI technique, to identify white matter alterations in the brains of CHR subjects who subsequently develop psychosis (CHR-P) compared to those who do not (CHR-NP).

METHODS

Twenty-four healthy controls (HC) and 30 CHR individuals, 8 of whom converted to schizophrenia after a mean follow-up of 15.16 months, received baseline MRI scans. Maps of fractional anisotropy (FA), FA of cellular tissue (FA_T), and extracellular free-water (FW) were extracted using tract-based spatial statistics after which voxel-wise non-parametric group statistics and correlations with symptom severity were performed.

RESULTS

There were no significant differences between HCs and the combined CHR group. However, prior to conversion, CHR-P showed widespread lower FA compared to CHR-NP (p_FWE < 0.05). FA changes in CHR-P were associated with significantly lower FA_T and higher FW, compared to CHR-NP. Positive symptoms correlated significantly with diffusion parameters in similar regions as those discriminating CHR-P from CHR-NP.

CONCLUSIONS

Our study suggests that cellular (FA_T) and extracellular (FW) white matter alterations are associated with positive symptom severity and indicate an elevated illness risk among CHR individuals.