Diffusion MRI in the brain - Theory and concepts

Denoising complex-valued diffusion MR images using a two-step, nonlocal principal component analysis approach

PURPOSE

To propose a two-step, nonlocal principal component analysis (PCA) method and demonstrate its utility for denoising complex diffusion MR images with a few diffusion directions.

METHODS

A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data preprocessing for g-factor normalization and phase stabilization before data denoising with a nonlocal PCA algorithm. At the heart of our proposed pipeline was the use of a data-driven optimal shrinkage algorithm to manipulate the singular values in a way that would optimally estimate the noise-free signal. Our approach's denoising performances were evaluated using simulation and in vivo human data experiments. The results were compared with those obtained with existing local PCA-based methods.

RESULTS

In both simulation and human data experiments, our approach substantially enhanced image quality relative to the noisy counterpart, yielding improved performances for estimation of relevant diffusion tensor imaging metrics. It also outperformed existing local PCA-based methods in reducing noise while preserving anatomic details. It also led to improved whole-brain tractography relative to the noisy counterpart.

CONCLUSION

The proposed denoising method has the utility for improving image quality for diffusion MRI with a few diffusion directions and is believed to benefit many applications, especially those aiming to achieve high-quality parametric mapping using only a few image volumes.

The effect of Alzheimer's disease genetic factors on limbic white matter microstructure

INTRODUCTION

White matter (WM) microstructure is essential for brain function but deteriorates with age and in neurodegenerative conditions such as Alzheimer's disease (AD). Diffusion MRI, enhanced by advanced bi-tensor models accounting for free water (FW), enables in vivo quantification of WM microstructural differences.

METHODS

To evaluate how AD genetic risk factors affect limbic WM microstructure - crucial for memory and early impacted in disease - we conducted linear regression analyses in a cohort of 2,614 non-Hispanic White aging adults (aged 50.12 to 100.85 years). The study evaluated 36 AD risk variants across 26 genes, the association between AD polygenic scores (PGSs) and WM metrics, and interactions with cognitive status.

RESULTS

AD PGSs, variants in TMEM106B, PTK2B, WNT3, and apolipoprotein E (APOE), and interactions involving MS4A6A were significantly linked to WM microstructure.

DISCUSSION

These findings implicate AD-related genetic factors related to neurodevelopment (WNT3), lipid metabolism (APOE), and inflammation (TMEM106B, PTK2B, MS4A6A) that contribute to alternations in WM microstructure in older adults.

HIGHLIGHTS

AD risk variants in TMEM106B, PTK2B, WNT3, and APOE genes showed distinct associations with limbic FW-corrected WM microstructure metrics. Interaction effects were observed between MS4A6A variants and cognitive status. PGS for AD was associated with higher FW content in the limbic system.

What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review

ABSTRACT

Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.

Evidence Suggesting Prolonged Neuroinflammation in a Subset of Children after Moderate/Severe TBI: A UCLA RAPBI Study

Traumatic brain injury (TBI) presents a public health concern as a leading cause of death and disability in children. Pediatric populations are particularly vulnerable to adverse outcomes following TBI due to periods of rapid growth, synaptic pruning, and myelination. Pediatric patients with moderate-severe TBI (msTBI) and healthy controls were evaluated from the post-acute (2-5 months) to chronic phase (13-19 months) of recovery using diffusion magnetic resonance imaging (dMRI) and interhemispheric transfer time (IHTT), which is an event-related potential measure the speed of information transfer across the corpus callosum. We previously identified two subgroups of patients based on IHTT, with one group showing a significantly slower IHTT (TBI-slow), poorer cognitive performance, and progressive structural damage. In contrast, the other group (TBI-normal) did not differ from controls on IHTT or cognitive performance and showed relative structural recovery over time. Here, we examined group differences in restricted diffusion imaging (RDI), which is a dMRI metric sensitive to inflammation. Comparing TBI-slow, TBI-normal, and controls on RDI cross-sectionally, dMRI connectometry analysis revealed higher RDI across the white matter in the TBI-slow group compared to both the control and TBI-normal groups. Longitudinal analyses indicated that while both TBI groups exhibited a decrease in RDI over time, suggesting resolution of neuroinflammation and recovery, the decreases in the TBI-slow group were smaller. The differences in RDI between TBI-slow and TBI-normal suggest that inflammation may play a key role in the prolonged recovery, including brain structure, cognitive performance, and symptom reports, of pediatric patients with msTBI.

Frontal White Matter Changes and Craving Recovery in Inpatients With Heroin Use Disorder

Importance

Amidst an unprecedented opioid epidemic, identifying neurobiological correlates of change with medication-assisted treatment of heroin use disorder is imperative. White matter impairments in individuals with heroin use disorder (HUD) have been associated with drug craving, a reliable predictor of treatment outcomes; however, little is known about structural connectivity changes with inpatient treatment and abstinence in individuals with HUD.

Objective

To assess white matter microstructure and associations with drug craving changes with inpatient treatment in individuals with HUD (effects of time and rescan compared with controls).

Design, Setting, and Participants

This cohort study conducted from December 2020 to September 2022 included individuals recruited from urban inpatient treatment facilities treating HUD and surrounding communities in New York City. Participants with HUD were receiving medication-assisted treatment. Data were analyzed from October 2022 to March 2023.

Intervention

Between scans, inpatient individuals with HUD continued treatment and related clinical interventions. Control participants were scanned at similar time intervals.

Main Outcomes and Measures

Changes in white matter diffusion metrics (fractional anisotropy and mean, axial, and radial diffusivities) assessed voxelwise with general linear models in addition to baseline and cue-induced drug craving, and other clinical outcome variables (mood, sleep, affect, perceived stress, and therapy attendance).

Results

Thirty-four individuals with HUD (mean [SD] age, 40.5 [11.0] years; 9 women [36%]; 3 Black [9%], 17 White [50%], 14 other race or ethnicity [41%]) and 25 control (mean [SD] age, 42.1 [9.0]; 7 women [21%]; 8 Black [32%], 10 White [40%], 7 other race or ethnicity [28%]) were included. Main voxelwise findings showed HUD-specific white matter microstructure changes (1 - P > .949), including increased fractional anisotropy and decreased mean and radial diffusivities, encompassing mostly frontal major callosal, projection, and association tracts. The increased fractional anisotropy (r = -0.72, P < .001, slope SE = 9.0 × 10-4) and decreased mean diffusivity (r = 0.69, P < .001, slope SE = 1.25 × 10-6) and/or radial diffusivity (r = 0.67, P < .001, slope SE = 1.16 × 10-6) in the genu and body of the corpus callosum and left anterior corona radiata in individuals with HUD correlated with a reduction in baseline craving (voxelwise 1 - P > .949). No other white matter correlations with outcome variables reached significance.

Conclusions and Relevance

This cohort study of inpatients with HUD on medication-assisted treatment found whole-brain normalization of structural connectivity in frontal white matter pathways implicated in emotional regulation and top-down executive control. Observed associations with decreases in baseline craving further support the possibility of recovery, highlighting the relevance of these white matter markers to a major symptom of addiction, with implications for clinical outcome monitoring.

Neuroimaging in thyroid eye disease: A systematic review

Thyroid eye disease (TED) is an organ-specific autoimmune disease secondary largely to hyperthyroid Graves' disease, which profoundly affects patients' visual function, appearance, and physical and mental well-being. Emerging neuroimaging studies have reported alterations in the brains of patients with TED, suggesting that the impact of this autoimmune disease may extend beyond the orbit. This systematic review aims to consolidate the neuroimaging evidence that describes the brain alterations of TED. We analyzed information from thirty-one related studies involving 1349 TED patients and 710 healthy controls, employing multimodal neuroimaging techniques such as structural magnetic resonance imaging (MRI), functional MRI, diffusion MRI, and metabolic MRI. These studies define the brain alterations in regions associated with vision, cognition, and emotion regulation, such as gray matter volume changes, altered functional connectivity and activity, and microstructural modifications, revealing the neurological impact of TED beyond the orbit. Notably, there was convergence across these studies indicating predominant abnormalities within the occipital and parietal lobes. This review underscores the critical role of advanced neuroimaging techniques in unraveling the complex neuropathological mechanism of TED, laying a foundation for future research and potential therapeutic targets.

Diffusion MRI with Machine Learning

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

Retinal Damage and Visual Network Reconfiguration Defines Visual Function Recovery in Optic Neuritis

BACKGROUND AND OBJECTIVES

Recovery of vision after acute optic neuritis (AON) is critical to improving the quality of life of people with demyelinating diseases. The objective of the study was to prospectively assess the changes in visual acuity, retinal layer thickness, and cortical visual network in patients with AON to identify the predictors of permanent visual disability.

METHODS

We studied a prospective cohort of 88 consecutive patients with AON with 6-month follow-up using high and low-contrast (2.5%) visual acuity, color vision, retinal thickness from optical coherence tomography, latencies and amplitudes of multifocal visual evoked potentials, mean deviation of visual fields, and diffusion-based structural (n = 53) and functional (n = 19) brain MRI to analyze the cortical visual network. The primary outcome was 2.5% low-contrast vision, and data were analyzed with mixed-effects and multivariate regression models.

RESULTS

We found that after 6 months, low-contrast vision and quality of vision remained moderately impaired. The thickness of the ganglion cell layer at baseline was a predictor of low-contrast vision 6 months later (ß = 0.49 [CI 0.11-0.88], p = 0.012). The structural cortical visual network at baseline predicted low-contrast vision, the best predictors being the betweenness of the right parahippocampal cortex (ß = -036 [CI -0.66 to 0.06], p = 0.021), the node strength of the right V3 (ß = 1.72 [CI 0.29-3.15], p = 0.02), and the clustering coefficient of the left intraparietal sulcus (ß = 57.8 [CI 12.3-103.4], p = 0.015). The functional cortical visual network at baseline also predicted low-contrast vision, the best predictors being the betweenness of the left ventral occipital cortex (ß = 8.6 [CI: 4.03-13.3], p = 0.009), the node strength of the right intraparietal sulcus (ß = -2.79 [CI: -5.1-0.4], p = 0.03), and the clustering coefficient of the left superior parietal lobule (ß = 501.5 [CI 50.8-952.2], p = 0.03).

DISCUSSION

The assessment of the visual pathway at baseline predicts permanent vision disability after AON, indicating that damage is produced early after disease onset and that it can be used for defining vision impairment and guiding therapy.

Denoising complex-valued diffusion MR images using a two-step non-local principal component analysis approach

Purpose

to propose a two-step non-local principal component analysis (PCA) method and demonstrate its utility for denoising diffusion tensor MRI (DTI) with a few diffusion directions.

Methods

A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data preprocessing for g-factor normalization and phase stabilization before data denoising with a non-local PCA algorithm. At the heart of our proposed pipeline was the use of a data-driven optimal shrinkage algorithm to manipulate the singular values in a way that would optimally estimate the noise-free signal. Our approach's denoising performances were evaluated using simulation and in-vivo human data experiments. The results were compared to those obtained with existing local-PCA-based methods.

Results

In both simulation and human data experiments, our approach substantially enhanced image quality relative to the noisy counterpart, yielding improved performances for estimation of relevant DTI metrics. It also outperformed existing local-PCA-based methods in reducing noise while preserving anatomic details. It also led to improved whole-brain tractography relative to the noisy counterpart.

Conclusion

The proposed denoising method has the utility for improving image quality for DTI with reduced diffusion directions and is believed to benefit many applications especially those aiming to achieve quality parametric mapping using only a few image volumes.

Robust, fast and accurate mapping of diffusional mean kurtosis

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Altered white matter connectivity of ventral language networks in autism spectrum disorder: An automated fiber quantification analysis with multi-site datasets

Comprehension and pragmatic deficits are prevalent in autism spectrum disorder (ASD) and are potentially linked to altered connectivity in the ventral language networks. However, previous magnetic resonance imaging studies have not sufficiently explored the microstructural abnormalities in the ventral fiber tracts underlying comprehension dysfunction in ASD. Additionally, the precise locations of white matter (WM) changes in the long tracts of patients with ASD remain poorly understood. In the current study, we applied the automated fiber-tract quantification (AFQ) method to investigate the fine-grained WM properties of the ventral language pathway and their relationships with comprehension and symptom manifestation in ASD. The analysis included diffusion/T1 weighted imaging data of 83 individuals with ASD and 83 age-matched typically developing (TD) controls. Case-control comparisons were performed on the diffusion metrics of the ventral tracts at both the global and point-wise levels. We also explored correlations between diffusion metrics, comprehension performance, and ASD traits, and conducted subgroup analyses based on age range to examine developmental moderating effects. Individuals with ASD exhibited remarkable hypoconnectivity in the ventral tracts, particularly in the temporal portions of the left inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). These WM abnormalities were associated with poor comprehension and more severe ASD symptoms. Furthermore, WM alterations in the ventral tract and their correlation with comprehension dysfunction were more prominent in younger children with ASD than in adolescents. These findings indicate that WM disruptions in the temporal portions of the left ILF/IFOF are most notable in ASD, potentially constituting the core neurological underpinnings of comprehension and communication deficits in autism. Moreover, impaired WM connectivity and comprehension ability in patients with ASD appear to improve with age.

FRONTAL WHITE MATTER CHANGES INDICATE RECOVERY WITH INPATIENT TREATMENT IN HEROIN ADDICTION

Importance

Amidst an unprecedented opioid epidemic, identifying neurobiological correlates of change with medication-assisted treatment of heroin use disorder is imperative. Distributed white matter (WM) impairments in individuals with heroin use disorder (iHUD) have been associated with increased drug craving, a reliable predictor of treatment outcomes. However, little is known about the extent of whole-brain structural connectivity changes with inpatient treatment and abstinence in iHUD.

Objective

To assess WM microstructure and associations with drug craving changes with inpatient treatment in iHUD (effects of time/re-scan compared to controls; CTL).

Design

Longitudinal cohort study (12/2020-09/2022) where iHUD and CTL underwent baseline magnetic resonance imaging (MRI#1) and follow-up (MRI#2) scans, (mean interval of 13.9 weeks in all participants combined).

Setting

The iHUD and CTL were recruited from urban inpatient treatment facilities and surrounding communities, respectively.

Participants

Thirty-four iHUD (42.1yo; 7 women), 25 age-/sex-matched CTL (40.5yo; 9 women).

Intervention

Between scans, inpatient iHUD continued their medically-assisted treatment and related clinical interventions. CTL participants were scanned at similar time intervals.

Main Outcomes and Measures

Changes in white matter diffusion metrics [fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivities (RD)] in addition to baseline and cue-induced drug craving, and other clinical outcome variables (mood, sleep, affect, perceived stress, and therapy attendance).

Results

Main findings showed HUD-specific WM microstructure changes encompassing mostly frontal major callosal, projection, and association tracts, characterized by increased FA (.949<1-p<.986) and decreased MD (.949<1-p<.997) and RD (.949<1-p<.999). The increased FA (r=-0.72, p<.00001) and decreased MD (r=0.69, p<.00001) and RD (r=0.67, p<.0001) in the genu and body of the corpus callosum and the left anterior corona radiata in iHUD were correlated with a reduction in baseline craving (.949<1-p<.999). No other WM correlations with outcome variables reached significance.

Conclusions and Relevance

Our findings suggest whole-brain normalization of structural connectivity with inpatient medically-assisted treatment in iHUD encompassing recovery in frontal WM pathways implicated in emotional regulation and top-down executive control. The association with decreases in baseline craving further supports the relevance of these WM markers to a major symptom in drug addiction, with implications for monitoring clinical outcomes.

Microstructural changes of the white matter in systemic lupus erythematosus patients without neuropsychiatric symptoms: a multi-shell diffusion imaging study

BACKGROUND

Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) provide more comprehensive and informative perspective on microstructural alterations of cerebral white matter (WM) than single-shell diffusion tensor imaging (DTI), especially in the detection of crossing fiber. However, studies on systemic lupus erythematosus patients without neuropsychiatric symptoms (non-NPSLE patients) using multi-shell diffusion imaging remain scarce.

METHODS

Totally 49 non-NPSLE patients and 41 age-, sex-, and education-matched healthy controls underwent multi-shell diffusion magnetic resonance imaging. Totally 10 diffusion metrics based on DKI (fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis and radial kurtosis) and NODDI (neurite density index, orientation dispersion index and volume fraction of the isotropic diffusion compartment) were evaluated. Tract-based spatial statistics (TBSS) and atlas-based region-of-interest (ROI) analyses were performed to determine group differences in brain WM microstructure. The associations of multi-shell diffusion metrics with clinical indicators were determined for further investigation.

RESULTS

TBSS analysis revealed reduced FA, AD and RK and increased ODI in the WM of non-NPSLE patients (P < 0.05, family-wise error corrected), and ODI showed the best discriminative ability. Atlas-based ROI analysis found increased ODI values in anterior thalamic radiation (ATR), inferior frontal-occipital fasciculus (IFOF), forceps major (F_major), forceps minor (F_minor) and uncinate fasciculus (UF) in non-NPSLE patients, and the right ATR showed the best discriminative ability. ODI in the F_major was positively correlated to C3.

CONCLUSION

This study suggested that DKI and NODDI metrics can complementarily detect WM abnormalities in non-NPSLE patients and revealed ODI as a more sensitive and specific biomarker than DKI, guiding further understanding of the pathophysiological mechanism of normal-appearing WM injury in SLE.

Study protocol - elucidating the neural correlates of functional remediation for older adults with bipolar disorder

Introduction

Beyond mood abnormalities, bipolar disorder (BD) includes cognitive impairments that worsen psychosocial functioning and quality of life. These deficits are especially severe in older adults with BD (OABD), a condition expected to represent most individuals with BD in the upcoming years. Restoring the psychosocial functioning of this population will thus soon represent a public health priority. To help tackle the problem, the Bipolar and Depressive Disorders Unit at the Hospital Clínic of Barcelona has recently adapted its Functional Remediation (FR) program to that population, calling it FROA-BD. However, while scarce previous studies localize the neural mechanisms of cognitive remediation interventions in the dorsal prefrontal cortex, the specific mechanisms are seldom unknown. In the present project, we will investigate the neural correlates of FR-OABD to understand its mechanisms better and inform for potential optimization. The aim is to investigate the brain features and changes associated with FROA-BD efficacy.

Methods

Thirty-two individuals with OABD in full or partial remission will undergo a magnetic resonance imaging (MRI) session before receiving FR-OABD. After completing the FR-OABD intervention, they will undergo another MRI session. The MRI sessions will include structural, diffusion-weighted imaging (DWI), functional MRI (fMRI) with working memory (n-back) and verbal learning tasks, and frontal spectroscopy. We will correlate the pre-post change in dorsolateral and dorsomedial prefrontal cortices activation during the n-back task with the change in psychosocial functioning [measured with the Functioning Assessment Short Test (FAST)]. We will also conduct exploratory whole-brain correlation analyses between baseline or pre-post changes in MRI data and other clinical and cognitive outcomes to provide more insights into the mechanisms and explore potential brain markers that may predict a better treatment response. We will also conduct separate analyses by sex.

Discussion

The results of this study may provide insights into how FROA-BD and other cognitive remediations modulate brain function and thus could optimize these interventions.

White matter microstructure alterations in idiopathic restless legs syndrome: a study combining crossing fiber-based and tensor-based approaches

Introduction

Restless legs syndrome (RLS) is a common sensorimotor disorder characterized by an irrepressible urge to move the legs and frequently accompanied by unpleasant sensations in the legs. The pathophysiological mechanisms underlying RLS remain unclear, and RLS is hypothesized to be associated with alterations in white matter tracts.

Methods

Diffusion MRI is a unique noninvasive method widely used to study white matter tracts in the human brain. Thus, diffusion-weighted images were acquired from 18 idiopathic RLS patients and 31 age- and sex-matched healthy controls (HCs). Whole brain tract-based spatial statistics (TBSS) and atlas-based analyzes combining crossing fiber-based metrics and tensor-based metrics were performed to investigate the white matter patterns in individuals with RLS.

Results

TBSS analysis revealed significantly higher fractional anisotropy (FA) and partial volume fraction of primary (F1) fiber populations in multiple tracts associated with the sensorimotor network in patients with RLS than in HCs. In the atlas based analysis, the bilateral anterior thalamus radiation, bilateral corticospinal tract, bilateral inferior fronto-occipital fasciculus, left hippocampal cingulum, left inferior longitudinal fasciculus, and left uncinate fasciculus showed significantl increased F1, but only the left hippocampal cingulum showed significantly higher FA.

Discussion

The results demonstrated that F1 identified extensive alterations in white matter tracts compared with FA and confirmed the hypothesis that crossing fiber-based metrics are more sensitive than tensor-based metrics in detecting white matter abnormalities in RLS. The present findings provide evidence that the increased F1 metric observed in sensorimotor tracts may be a critical neural substrate of RLS, enhancing our understanding of the underlying pathological changes.

Direct jet coaxial electrospinning of axon-mimicking fibers for diffusion tensor imaging

Hollow polymer microfibers with variable microstructural and hydrophilic properties were proposed as building elements to create axon-mimicking phantoms for validation of diffusion tensor imaging (DTI). The axon-mimicking microfibers were fabricated in a mm-thick 3D anisotropic fiber strip, by direct jet coaxial electrospinning of PCL/polysiloxane-based surfactant (PSi) mixture as shell and polyethylene oxide (PEO) as core. Hydrophilic PCL-PSi fiber strips were first obtained by carefully selecting appropriate solvents for the core and appropriate fiber collector rotating and transverse speeds. The porous cross-section and anisotropic orientation of axon-mimicking fibers were then quantitatively evaluated using two ImageJ plugins-nearest distance (ND) and directionality based on their scanning electron microscopy (SEM) images. Third, axon-mimicking phantom was constructed from PCL-PSi fiber strips with variable porous-section and fiber orientation and tested on a 3T clinical MR scanner. The relationship between DTI measurements (mean diffusivity [MD] and fractional anisotropy [FA]) of phantom samples and their pore size and fiber orientation was investigated. Two key microstructural parameters of axon-mimicking phantoms including normalized pore distance and dispersion of fiber orientation could well interpret the variations in DTI measurements. Two PCL-PSi phantom samples made from different regions of the same fiber strips were found to have similar MD and FA values, indicating that the direct jet coaxial electrospun fiber strips had consistent microstructure. More importantly, the MD and FA values of the developed axon-mimicking phantoms were mostly in the biologically relevant range.

Improved prediction of glioma-related aphasia by diffusion MRI metrics, machine learning, and automated fiber bundle segmentation

White matter impairments caused by gliomas can lead to functional disorders. In this study, we predicted aphasia in patients with gliomas infiltrating the language network using machine learning methods. We included 78 patients with left-hemispheric perisylvian gliomas. Aphasia was graded preoperatively using the Aachen aphasia test (AAT). Subsequently, we created bundle segmentations based on automatically generated tract orientation mappings using TractSeg. To prepare the input for the support vector machine (SVM), we first preselected aphasia-related fiber bundles based on the associations between relative tract volumes and AAT subtests. In addition, diffusion magnetic resonance imaging (dMRI)-based metrics [axial diffusivity (AD), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and radial diffusivity (RD)] were extracted within the fiber bundles' masks with their mean, standard deviation, kurtosis, and skewness values. Our model consisted of random forest-based feature selection followed by an SVM. The best model performance achieved 81% accuracy (specificity = 85%, sensitivity = 73%, and AUC = 85%) using dMRI-based features, demographics, tumor WHO grade, tumor location, and relative tract volumes. The most effective features resulted from the arcuate fasciculus (AF), middle longitudinal fasciculus (MLF), and inferior fronto-occipital fasciculus (IFOF). The most effective dMRI-based metrics were FA, ADC, and AD. We achieved a prediction of aphasia using dMRI-based features and demonstrated that AF, IFOF, and MLF were the most important fiber bundles for predicting aphasia in this cohort.

Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.

Fiber enhancement and 3D orientation analysis in label-free two-photon fluorescence microscopy

Fluorescence microscopy can be exploited for evaluating the brain's fiber architecture with unsurpassed spatial resolution in combination with different tissue preparation and staining protocols. Differently from state-of-the-art polarimetry-based neuroimaging modalities, the quantification of fiber tract orientations from fluorescence microscopy volume images entails the application of specific image processing techniques, such as Fourier or structure tensor analysis. These, however, may lead to unreliable outcomes as they do not isolate myelinated fibers from the surrounding tissue. In this work, we describe a novel image processing pipeline that enables the computation of accurate 3D fiber orientation maps from both grey and white matter regions, exploiting the selective multiscale enhancement of tubular structures of varying diameters provided by a 3D implementation of the Frangi filter. The developed software tool can efficiently generate orientation distribution function maps at arbitrary spatial scales which may support the histological validation of modern diffusion-weighted magnetic resonance imaging tractography. Despite being tested here on two-photon scanning fluorescence microscopy images, acquired from tissue samples treated with a label-free technique enhancing the autofluorescence of myelinated fibers, the presented pipeline was developed to be employed on all types of 3D fluorescence images and fiber staining.

Disruptions in Structural and Functional Connectivity Relate to Poststroke Fatigue

Introduction: Poststroke fatigue (PSF) is a disabling condition with unclear etiology. The brain lesion is thought to be an important causal factor in PSF, although focal lesion characteristics such as size and location have not proven to be predictive. Given that the stroke lesion results not only in focal tissue death but also in widespread changes in brain networks that are structurally and functionally connected to damaged tissue, we hypothesized that PSF relates to disruptions in structural and functional connectivity. Materials and Methods: Twelve patients who incurred an ischemic stroke in the middle cerebral artery (MCA) territory 1-3 years prior, and currently experiencing a range of fatigue severity, were enrolled. The patients underwent structural and resting-state functional magnetic resonance imaging (MRI). The structural MRI data were used to measure structural disconnection of gray matter resulting from lesion to white matter pathways. The functional MRI data were used to measure network functional connectivity. Results: The patients showed structural disconnection in varying cortical and subcortical regions. Fatigue severity correlated significantly with structural disconnection of several frontal cortex regions in the ipsilesional (IL) and contralesional hemispheres. Fatigue-related structural disconnection was most severe in the IL rostral middle frontal cortex. Greater structural disconnection of a subset of fatigue-related frontal cortex regions, including the IL rostral middle frontal cortex, trended toward correlating significantly with greater loss in functional connectivity. Among identified fatigue-related frontal cortex regions, only the IL rostral middle frontal cortex showed loss in functional connectivity correlating significantly with fatigue severity. Conclusion: Our results provide evidence that loss in structural and functional connectivity of bihemispheric frontal cortex regions plays a role in PSF after MCA stroke, with connectivity disruptions of the IL rostral middle frontal cortex having a central role. Impact statement Poststroke fatigue (PSF) is a common disabling condition with unclear etiology. We hypothesized that PSF relates to disruptions in structural and functional connectivity secondary to the focal lesion. Using structural and resting-state functional connectivity magnetic resonance imaging (MRI) in patients with chronic middle cerebral artery (MCA) stroke, we found frontal cortex regions in the ipsilesional (IL) and contralesional hemispheres with greater structural disconnection correlating with greater fatigue. Among these fatigue-related cortices, the IL rostral middle frontal cortex showed loss in functional connectivity correlating with fatigue. These findings suggest that disruptions in structural and functional connectivity play a role in PSF after MCA stroke.

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

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

Dose-dependent early white matter alterations in patients with brain metastases after radiotherapy

PURPOSE

Previous diffusion tensor imaging (DTI) studies have mainly focused on dose-dependent white matter (WM) alterations 1 month to 1 year after radiation therapy (RT) with a tract-average method. However, WM alterations immediately after RT are subtle, resulting in early WM alterations that cannot be detected by tract-average methods. Therefore, we performed a study with an along-tract method in patients with brain metastases to explore the early dose-response pattern of WM alterations after RT.

METHODS

Sixteen patients with brain metastases underwent DTI before and 1-3 days after brain RT. DTI metrics, such as fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD), were calculated. Along-tract statistics were then used to resample WM fibre streamlines and generate a WM skeleton fibre tract. DTI metric alterations (post_RT-pre_RT DTI metrics) and the planned doses (max or mean doses) were mapped to 18 WM tracts. A linear fixed model was performed to analyse the main effect of dose on DTI metric alterations.

RESULTS

AD alterations in the left hemispheric uncinated fasciculus (UNC_L) were associated with max doses, in which decreased AD alterations were associated with higher doses.

CONCLUSION

Our findings may provide pathological insight into early dose-dependent WM alterations and may contribute to the development of max dose-constrained RT techniques to protect brain microstructure in the UNC_L.

Karawun: a software package for assisting evaluation of advances in multimodal imaging for neurosurgical planning and intraoperative neuronavigation

PURPOSE

The neuroimaging research community-which includes a broad range of scientific, medical, statistical, and engineering disciplines-has developed many tools to advance our knowledge of brain structure, function, development, aging, and disease. Past research efforts have clearly shaped clinical practice. However, translation of new methodologies into clinical practice is challenging. Anything that can reduce these barriers has the potential to improve the rate at which research outcomes can contribute to clinical practice. In this article, we introduce Karawun, a file format conversion tool, that has become a key part of our work in translating advances in diffusion imaging acquisition and analysis into neurosurgical practice at our institution.

METHODS

Karawun links analysis workflows created using open-source neuroimaging software, to Brainlab (Brainlab AG, Munich, Germany), a commercially available surgical planning and navigation suite. Karawun achieves this using DICOM standards supporting representation of 3D structures, including tractography streamlines, and thus offers far more than traditional screenshot or color overlay approaches.

RESULTS

We show that neurosurgical planning data, created from multimodal imaging data using analysis methods implemented in open-source research software, can be imported into Brainlab. The datasets can be manipulated as if they were created by Brainlab, including 3D visualizations of white matter tracts and other objects.

CONCLUSION

Clinicians can explore and interact with the results of research neuroimaging pipelines using familiar tools within their standard clinical workflow, understand the impact of the new methods on their practice and provide feedback to methods developers. This capability has been important to the translation of advanced analysis techniques into practice at our institution.

Tractography-based navigated TMS language mapping protocol

Introduction

This study explores the feasibility of implementing a tractography-based navigated transcranial magnetic stimulation (nTMS) language mapping protocol targeting cortical terminations of the arcuate fasciculus (AF). We compared the results and distribution of errors from the new protocol to an established perisylvian nTMS protocol that stimulated without any specific targeting over the entire perisylvian cortex.

Methods

Sixty right-handed patients with language-eloquent brain tumors were examined in this study with one half of the cohort receiving the tractographybased protocol and the other half receiving the perisylvian protocol. Probabilistic tractography using MRtrix3 was performed for patients in the tractography-based group to identify the AF's cortical endpoints. nTMS mappings were performed and resulting language errors were classified into five psycholinguistic groups.

Results

Tractography and nTMS were successfully performed in all patients. The tractogram-based group showed a significantly higher median overall ER than the perisylvian group (3.8% vs. 2.9% p <.05). The median ER without hesitation errors in the tractogram-based group was also significantly higher than the perisylvian group (2.0% vs. 1.4%, p <.05). The ERs by error type showed no significant differences between protocols except in the no response ER, with a higher median ER in the tractogram-based group (0.4% vs. 0%, p <.05). Analysis of ERs based on the Corina cortical parcellation system showed especially high nTMS ERs over the posterior middle temporal gyrus (pMTG) in the perisylvian protocol and high ERs over the middle and ventral postcentral gyrus (vPoG), the opercular inferior frontal gyrus (opIFG) and the ventral precentral gyrus (vPrG) in the tractography-based protocol.

Discussion

By considering the white matter anatomy and performing nTMS on the cortical endpoints of the AF, the efficacy of nTMS in disrupting patients' object naming abilities was increased. The newly introduced method showed proof of concept and resulted in AF-specific ERs and noninvasive cortical language maps, which could be applied to additional fiber bundles related to the language network in future nTMS studies.

White matter structural network alterations in congenital bilateral profound sensorineural hearing loss children: A graph theory analysis

Functional magnetic resonance imaging (fMRI) studies have revealed a functional reorganization in patients with sensorineural hearing loss (SNHL). The structural basement of functional changes has also been investigated recently. Graph theory analysis brings a new understanding of the structural connectome and topological features in central neural system diseases. However, little is known about the structural network connectome changes in SNHL patients, especially in children. We explored the differences in topologic organization, rich-club organization, and structural connection between children with congenital bilateral profound SNHL and normal hearing under the age of three using graph theory analysis and probabilistic tractography. Compared with the normal-hearing (NH) group, the SNHL group showed no difference in global and nodal topological parameters. Increased structural connection strength were found in the right cortico-striatal-thalamus-cortical circuity. Decreased cross-hemisphere connections were found between the right precuneus and the left auditory cortex as well as the left subcortical regions. Rich-club organization analysis found increased local connection in the SNHL group. These results revealed structural organizations after hearing deprivation in congenital bilateral profound SNHL children.

Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies

Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta‐analysis aimed to comprehensively elucidate the abnormality in language‐related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta‐regression analysis. Thirty‐three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta‐analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language‐associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under‐connectivity hypothesis and demonstrate the widespread abnormal microstructure of language‐related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere.

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network

Background

Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.

Methods

Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.

Results

(1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.

Conclusion

Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

Systematic validation of structural brain networks in cerebral small vessel disease

Cerebral small vessel disease (SVD) is considered a disconnection syndrome, which can be quantified using structural brain network analysis obtained from diffusion MRI. Network analysis is a demanding analysis approach and the added benefit over simpler diffusion MRI analysis is largely unexplored in SVD. In this pre-registered study, we assessed the clinical and technical validity of network analysis in two non-overlapping samples of SVD patients from the RUN DMC study (n = 52 for exploration and longitudinal analysis and n = 105 for validation). We compared two connectome pipelines utilizing single-shell or multi-shell diffusion MRI, while also systematically comparing different node and edge definitions. For clinical validation, we assessed the added benefit of network analysis in explaining processing speed and in detecting short-term disease progression. For technical validation, we determined test-retest repeatability.Our findings in clinical validation show that structural brain networks provide only a small added benefit over simpler global white matter diffusion metrics and do not capture short-term disease progression. Test-retest reliability was excellent for most brain networks. Our findings question the added value of brain network analysis in clinical applications in SVD and highlight the utility of simpler diffusion MRI based markers.

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

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

Diffusion Kurtosis Imaging of Neonatal Spinal Cord in Clinical Routine

Diffusion kurtosis imaging (DKI) has undisputed advantages over the more classical diffusion magnetic resonance imaging (dMRI) as witnessed by the fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized, in particular in spinal cord (SC) imaging, because of its inherently demanding technological requirements. Due to its extreme sensitivity to non-Gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, which are not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between the SC region and the brain above, managing to apply such a method to the neonatal cohort becomes of utmost importance. This study will (i) mention current methodological challenges associated with the application of advanced dMRI methods, like DKI, in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox for handling the DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present results of its application in a pilot clinical case study. With the proposed pipeline, we preliminarily show that DKI is more sensitive than DTI-related measures to alterations caused by brain white matter injuries in the underlying cervical SC.

Network analysis shows decreased ipsilesional structural connectivity in glioma patients

Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.

Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients.

High fidelity fiber orientation density functions from fiber ball imaging

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

Hippocampus- and Thalamus-Related Fiber-Specific White Matter Reductions in Mild Cognitive Impairment

Early diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer's disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber's DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson's correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.

Predicting the Extent of Resection of Motor-Eloquent Gliomas Based on TMS-Guided Fiber Tracking

Background: Surgical planning with nTMS-based tractography is proven to increase safety during surgery. A preoperative risk stratification model has been published based on the M1 infiltration, RMT ratio, and tumor to corticospinal tract distance (TTD). The correlation of TTD with corticospinal tract to resection cavity distance (TRD) and outcome is needed to further evaluate the validity of the model. Aim of the study: To use the postop MRI-derived resection cavity to measure how closely the resection cavity approximated the preoperatively calculated corticospinal tract (CST) and how this correlates with the risk model and the outcome. Methods: We included 183 patients who underwent nTMS-based DTI and surgical resection for presumed motor-eloquent gliomas. TTD, TRD, and motor outcome were recorded and tested for correlations. The intraoperative monitoring documentation was available for a subgroup of 48 patients, whose responses were correlated to TTD and TRD. Results: As expected, TTD and TRD showed a good correlation (Spearman’s ρ = 0.67, p < 0.001). Both the TTD and the TRD correlated significantly with the motor outcome at three months (Kendall’s Tau-b 0.24 for TTD, 0.31 for TRD, p < 0.001). Interestingly, the TTD and TRD correlated only slightly with residual tumor volume, and only after correction for outliers related to termination of resection due to intraoperative monitoring events or the proximity of other eloquent structures (TTD ρ = 0.32, p < 0.001; TRD ρ = 0.19, p = 0.01). This reflects the fact that intraoperative monitoring (IOM) phenomena do not always correlate with preoperative structural analysis, and that additional factors influence the intraoperative decision to abort resection, such as the adjacency of other vulnerable structures. The TTD was also significantly correlated with variations in motor evoked potential (MEP) responses (no/reversible decrease vs. irreversible decrease; p = 0.03). Conclusions: The TTD approximates the TRD well, confirming the best predictive parameter and giving strength to the nTMS-based risk stratification model. Our analysis of TRD supports the use of the nTMS-based TTD measurement to estimate the resection preoperatively, also confirming the 8 mm cutoff. Nevertheless, the TRD proved to have a slightly stronger correlation with the outcome as the surgeon’s experience, anatomofunctional knowledge, and MEP observations influence the expected EOR.

Neurobiological underpinnings of rapid white matter plasticity during intensive reading instruction

Diffusion MRI is a powerful tool for imaging brain structure, but it is challenging to discern the biological underpinnings of plasticity inferred from these and other non-invasive MR measurements. Biophysical modeling of the diffusion signal aims to render a more biologically rich image of tissue microstructure, but the application of these models comes with important caveats. A separate approach for gaining biological specificity has been to seek converging evidence from multi-modal datasets. Here we use metrics derived from diffusion kurtosis imaging (DKI) and the white matter tract integrity (WMTI) model along with quantitative MRI measurements of T1 relaxation to characterize changes throughout the white matter during an 8-week, intensive reading intervention (160 total hours of instruction). Behavioral measures, multi-shell diffusion MRI data, and quantitative T1 data were collected at regular intervals during the intervention in a group of 33 children with reading difficulties (7-12 years old), and over the same period in an age-matched non-intervention control group. Throughout the white matter, mean 'extra-axonal' diffusivity was inversely related to intervention time. In contrast, model estimated axonal water fraction (AWF), overall diffusion kurtosis, and T1 relaxation time showed no significant change over the intervention period. Both diffusion and quantitative T1 based metrics were correlated with pre-intervention reading performance, albeit with distinct anatomical distributions. These results are consistent with the view that rapid changes in diffusion properties reflect phenomena other than widespread changes in myelin density. We discuss this result in light of recent work highlighting non-axonal factors in experience-dependent plasticity and learning.

Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges

Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.

The benefit of the diffusion kurtosis imaging in presurgical evaluation in patients with focal MR-negative epilepsy

The effectivity of diffusion-weighted MRI methods in detecting the epileptogenic zone (EZ) was tested. Patients with refractory epilepsy (N=25) who subsequently underwent resective surgery were recruited. First, the extent of white matter (WM) asymmetry from mean kurtosis (MK) was calculated in order to detect the lobe with the strongest impairment. Second, a newly developed metric was used, reflecting a selection of brain areas with concurrently increased mean Diffusivity, reduced fractional Anisotropy, and reduced mean Kurtosis (iDrArK). A two-step EZ detection was performed as (1) lobe-specific detection, (2) iDrArK voxel-wise detection (with a possible lobe-specific restriction if the result of the first step was significant in a given subject). The method results were compared with the surgery resection zones. From the whole cohort (N=25), the numbers of patients with significant results were: 10 patients in lobe detection and 9 patients in EZ detection. From these subsets of patients with significant results, the impaired lobe was successfully detected with 100% accuracy; the EZ was successfully detected with 89% accuracy. The detection of the EZ using iDrArK was substantially more successful when compared with solo diffusional parameters (or their pairwise combinations). For a subgroup with significant results from step one (N=10), iDrArK without lobe restriction achieved 37.5% accuracy; lobe-restricted iDrArK achieved 100% accuracy. The study shows the plausibility of MK for detecting widespread WM changes and the benefit of combining different diffusional voxel-wise parameters.

Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

Cognitive impairment (CI) occurs in 43 to 70% of multiple sclerosis (MS) patients at both early and later disease stages. Cognitive domains typically involved in MS include attention, information processing speed, memory, and executive control. The growing use of advanced magnetic resonance imaging (MRI) techniques is furthering our understanding on the altered structural connectivity (SC) and functional connectivity (FC) substrates of CI in MS. Regarding SC, different diffusion tensor imaging (DTI) measures (e.g., fractional anisotropy, diffusivities) along tractography-derived white matter (WM) tracts showed relevance toward CI. Novel diffusion MRI techniques, including diffusion kurtosis imaging, diffusion spectrum imaging, high angular resolution diffusion imaging, and neurite orientation dispersion and density imaging, showed more pathological specificity compared to the traditional DTI but require longer scan time and mathematical complexities for their interpretation. As for FC, task-based functional MRI (fMRI) has been traditionally used in MS to brain mapping the neural activity during various cognitive tasks. Analysis methods of resting fMRI (seed-based, independent component analysis, graph analysis) have been applied to uncover the functional substrates of CI in MS by revealing adaptive or maladaptive mechanisms of functional reorganization. The relevance for CI in MS of SC–FC relationships, reflecting common pathogenic mechanisms in WM and gray matter, has been recently explored by novel MRI analysis methods. This review summarizes recent advances on MRI techniques of SC and FC and their potential to provide a deeper understanding of the pathological substrates of CI in MS.

Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis

Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75%^ile) and low (below 25%^ile) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.

Advances in functional and diffusion neuroimaging research into the long-term consequences of very preterm birth

Very preterm birth (<32 weeks of gestation) has been associated with lifelong difficulties in a variety of neurocognitive functions. Magnetic resonance imaging (MRI) combined with advanced analytical approaches have been employed in order to increase our understanding of the neurodevelopmental problems that many very preterm born individuals face as they grow up. In this review, we will focus on two novel imaging techniques that have explored relationships between specific brain mechanisms and behavioural outcomes. These are functional MRI, which maps regional, time-varying changes in brain metabolism and diffusion-weighted MRI, which measures the displacement of water molecules in tissue and provides quantitative information about tissue microstructure. Identifying the neurobiological underpinning of the long-term sequelae associated with very preterm birth could inform the development and implementation of preventative interventions (before any cognitive problem emerges) and could facilitate the identification of behavioural targets for improving the life course outcomes of very preterm individuals.

Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.

Detecting Corticospinal Tract Impairment in Tumor Patients With Fiber Density and Tensor-Based Metrics

Tumors infiltrating the motor system lead to significant disability, often caused by corticospinal tract injury. The delineation of the healthy-pathological white matter (WM) interface area, for which diffusion magnetic resonance imaging (dMRI) has shown promising potential, may improve treatment outcome. However, up to 90% of white matter (WM) voxels include multiple fiber populations, which cannot be correctly described with traditional metrics such as fractional anisotropy (FA) or apparent diffusion coefficient (ADC). Here, we used a novel fixel-based along-tract analysis consisting of constrained spherical deconvolution (CSD)-based probabilistic tractography and fixel-based apparent fiber density (FD), capable of identifying fiber orientation specific microstructural metrics. We addressed this novel methodology's capability to detect corticospinal tract impairment. We measured and compared tractogram-related FD and traditional microstructural metrics bihemispherically in 65 patients with WHO grade III and IV gliomas infiltrating the motor system. The cortical tractogram seeds were based on motor maps derived by transcranial magnetic stimulation. We extracted 100 equally distributed cross-sections along each streamline of corticospinal tract (CST) for along-tract statistical analysis. Cross-sections were then analyzed to detect differences between healthy and pathological hemispheres. All metrics showed significant differences between healthy and pathologic hemispheres over the entire tract and between peritumoral segments. Peritumoral values were lower for FA and FD, but higher for ADC within the entire cohort. FD was more specific to tumor-induced changes in CST than ADC or FA, whereas ADC and FA showed higher sensitivity. The bihemispheric along-tract analysis provides an approach to detect subject-specific structural changes in healthy and pathological WM. In the current clinical dataset, the more complex FD metrics did not outperform FA and ADC in terms of describing corticospinal tract impairment.

Gradient waveform design for tensor-valued encoding in diffusion MRI

Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.

Towards a tractography-based risk stratification model for language area associated gliomas

•

Injury to major white matter pathways during language-area associated glioma surgery often results in permanent aphasia.

•

DTI-based tractography of language pathways allows to correlate individual tract injury profiles with functional outcome.

•

Infiltration of the AF is particularly associated with functional deterioration.

•

The temporo-parieto-occipital junction and the temporal stem were confirmed as pivotal functional nodes.

•

Standardized DTI-based tractography can help to determine the individual aphasia risk profile before surgery.

Objectives

Injury to major white matter pathways during language-area associated glioma surgery often leads to permanent loss of neurological function. The aim was to establish standardized tractography of language pathways as a predictor of language outcome in clinical neurosurgery.

Methods

We prospectively analyzed 50 surgical cases of patients with left perisylvian, diffuse gliomas. Standardized preoperative Diffusion-Tensor-Imaging (DTI)-based tractography of the 5 main language tracts (Arcuate Fasciculus [AF], Frontal Aslant Tract [FAT], Inferior Fronto-Occipital Fasciculus [IFOF], Inferior Longitudinal Fasciculus [ILF], Uncinate Fasciculus [UF]) and spatial analysis of tumor and tracts was performed. Postoperative imaging and the resulting resection map were analyzed for potential surgical injury of tracts. The language status was assessed preoperatively, postoperatively and after 3 months using the Aachen Aphasia Test and Berlin Aphasia Score. Correlation analyses, two-step cluster analysis and binary logistic regression were used to analyze associations of tractography results with language outcome after surgery.

Results

In 14 out of 50 patients (28%), new aphasic symptoms were detected 3 months after surgery. The preoperative infiltration of the AF was associated with functional worsening (cc = 0.314; p = 0.019). Cluster analysis of tract injury profiles revealed two areas particularly related to aphasia: the temporo-parieto-occipital junction (TPO; temporo-parietal AF, middle IFOF, middle ILF) and the temporal stem/peri-insular white matter (middle IFOF, anterior ILF, temporal UF, temporal AF). Injury to these areas (TPO: OR: 23.04; CI: 4.11 – 129.06; temporal stem: OR: 21.96; CI: 2.93 – 164.41) was associated with a higher-risk of persisting aphasia.

Conclusions

Tractography of language pathways can help to determine the individual aphasia risk profile pre-surgically. The TPO and temporal stem/peri-insular white matter were confirmed as functional nodes particularly sensitive to surgical injuries.

Harmonization of diffusion MRI data sets with adaptive dictionary learning

Diffusion magnetic resonance imaging can indirectly infer the microstructure of tissues and provide metrics subject to normal variability in a population. Potentially abnormal values may yield essential information to support analysis of controls and patients cohorts, but subtle confounds could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability of the data. Our harmonization algorithm does not require paired training data sets, nor spatial registration or matching spatial resolution. Overcomplete dictionaries are learned iteratively from all data sets at the same time with an adaptive regularization criterion, removing variability attributable to the scanners in the process. The obtained mapping is applied directly in the native space of each subject toward a scanner-space. The method is evaluated with a public database which consists of two different protocols acquired on three different scanners. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner. Experiments with alterations using a free water compartment, which is not simulated in the training data, shows that the modifications applied to the diffusion weighted images are preserved in the diffusion metrics after harmonization, while still reducing global variability at the same time. The algorithm could help multicenter studies pooling their data by removing scanner specific confounds, and increase statistical power in the process.

Analytical and fast Fiber Orientation Distribution reconstruction in 3D-Polarized Light Imaging

Three dimensional Polarized Light Imaging (3D-PLI) is an optical technique which allows mapping the spatial fiber architecture of fibrous postmortem tissues, at sub-millimeter resolutions. Here, we propose an analytical and fast approach to compute the fiber orientation distribution (FOD) from high-resolution vector data provided by 3D-PLI. The FOD is modeled as a sum of K orientations/Diracs on the unit sphere, described on a spherical harmonics basis and analytically computed using the spherical Fourier transform. Experiments are performed on rich synthetic data which simulate the geometry of the neuronal fibers and on human brain data. Results indicate the analytical FOD is computationally efficient and very fast, and has high angular precision and angular resolution. Furthermore, investigations on the right occipital lobe illustrate that our strategy of FOD computation enables the bridging of spatial scales from microscopic 3D-PLI information to macro- or mesoscopic dimensions of diffusion Magnetic Resonance Imaging (MRI), while being a means to evaluate prospective resolution limits for diffusion MRI to reconstruct region-specific white matter tracts. These results demonstrate the interest and great potential of our analytical approach.

Magnetic Resonance Imaging and Molecular Dynamics Characterization of Ionic Liquid in Poly(ethylene oxide)-Based Polymer Electrolytes

Ternary systems consisting of polymers, lithium salts, and ionic liquids (ILs) are promising materials for the development of next-generation lithium batteries. The ternary systems combine the advantages of polymer-salt and IL-salt systems, thus providing media with high ionic conductivity and solid-like mechanical properties. In this work, we apply nuclear magnetic resonance 1H microimaging [magnetic resonance imaging (MRI)] techniques and molecular dynamics (MD) simulations to study the translational and rotational dynamics of the N-butyl-N-methylpyrrolidinium (PYR14) cation in poly(ethylene oxide) (PEO) matrices containing the lithium bis(trifluoromethanesulfonyl) imide salt (LiTFSI) and the PYR14TFSI IL. The analysis of diffusion-weighted images in PEO/LiTFSI/PYR14TFSI samples with varying mole ratios (10:1:x, with x = 1, 2, 3, and 4) shows, in a wide range of temperatures, a spatially heterogeneous distribution of PYR14 diffusion coefficients. Their weight-averaged values increase with IL content but remain well below the values estimated for the neat IL. The analysis of T2 (spin-spin relaxation) parametric images shows that the PEO matrix significantly hinders PYR14 rotational freedom, which is only partially restored by increasing the IL content. The MD simulations, performed on IL-filled cavities within the PEO matrix, reveal that PYR14 diffusion is mainly affected by Li/TFSI coordination within the IL phase. In agreement with MRI experiments, increasing the IL content increases the PYR14 diffusion coefficients. Finally, the analysis of MD trajectories suggests that Li diffusion mostly develops within the IL phase, although a fraction of Li cations is strongly coordinated by PEO oxygen atoms.