The impact of edema and fiber crossing on diffusion MRI metrics assessed in an ex vivo nerve phantom: Multi-tensor model vs. diffusion orientation distribution function

Abnormally glymphatic system functional in patients with migraine: a diffusion kurtosis imaging study

BACKGROUND

The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) method has been used to evaluate glymphatic system function in patients with migraine. However, since the diffusion tensor model cannot accurately describe the diffusion coefficient of the nerve fibre crossing region, we proposed a diffusion kurtosis imaging ALPS (DKI-ALPS) method to evaluate glymphatic system function in patients with migraine.

METHODS

The study included 29 healthy controls and 37 patients with migraine. We used diffusion imaging data from a 3T MRI scanner to calculate DTI-ALPS and DKI-ALPS indices of the two groups. We compared the DTI-ALPS and DKI-ALPS indices between the two groups using a two-sample t-test and performed correlation analyses with clinical variables.

RESULTS

There was no significant difference in DTI-ALPS index between the two groups. Patients with migraine showed a significantly increased right DKI-ALPS index compared to healthy controls (1.6858 vs. 1.5729; p = 0.0301). There was no significant correlation between ALPS indices and clinical variables.

CONCLUSIONS

DKI-ALPS is a potential method to assess glymphatic system function and patients with migraine do not have impaired glymphatic system function.

Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease

Introduction

Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes.

Materials and methods

Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics.

Results

Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs.

Discussion

We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.

Free-Water Diffusion Magnetic Resonance Imaging Differentiates Suicidal Ideation From Suicide Attempt in Treatment-Resistant Depression

BACKGROUND

Suicide attempt is highly prevalent in treatment-resistant depression (TRD); however, the neurobiological profile of suicidal ideation versus suicide attempt is unclear. Neuroimaging methods including diffusion magnetic resonance imaging-based free-water imaging may identify neural correlates underlying suicidal ideation and attempts in individuals with TRD.

METHODS

Diffusion magnetic resonance imaging data were obtained from 64 male and female participants (mean age 44.5 ± 14.2 years), including 39 patients with TRD (n = 21 and lifetime history of suicidal ideation but no attempts [SI group]; n = 18 with lifetime history of suicide attempt [SA group]), and 25 age- and sex-matched healthy control participants. Depression and suicidal ideation severity were examined using clinician-rated and self-report measures. Whole-brain neuroimaging analysis was conducted using tract-based spatial statistics via FSL to identify differences in white matter microstructure in the SI versus SA groups and in patients versus control participants.

RESULTS

Free-water imaging revealed elevated axial diffusivity and extracellular free water in fronto-thalamo-limbic white matter tracts of the SA group compared with the SI group. In a separate comparison, patients with TRD had widespread reductions in fractional anisotropy and axial diffusivity, as well as elevated radial diffusivity compared with control participants (thresholded p < .05, familywise error corrected).

CONCLUSIONS

A unique neural signature consisting of elevated axial diffusivity and free water was identified in patients with TRD and suicide attempt history. Findings of reduced fractional anisotropy, axial diffusivity, and elevated radial diffusivity in patients versus control participants are consistent with previously published studies. Multimodal and prospective investigations are recommended to better understand biological correlates of suicide attempt in TRD.

Diffusion basis spectrum imaging as an adjunct to conventional MRI leads to earlier diagnosis of high-grade glioma tumor progression versus treatment effect

Background

Following chemoradiotherapy for high-grade glioma (HGG), it is often challenging to distinguish treatment changes from true tumor progression using conventional MRI. The diffusion basis spectrum imaging (DBSI) hindered fraction is associated with tissue edema or necrosis, which are common treatment-related changes. We hypothesized that DBSI hindered fraction may augment conventional imaging for earlier diagnosis of progression versus treatment effect.

Methods

Adult patients were prospectively recruited if they had a known histologic diagnosis of HGG and completed standard-of-care chemoradiotherapy. DBSI and conventional MRI data were acquired longitudinally beginning 4 weeks post-radiation. Conventional MRI and DBSI metrics were compared with respect to their ability to diagnose progression versus treatment effect.

Results

Twelve HGG patients were enrolled between August 2019 and February 2020, and 9 were ultimately analyzed (5 progression, 4 treatment effect). Within new or enlarging contrast-enhancing regions, DBSI hindered fraction was significantly higher in the treatment effect group compared to progression group (P = .0004). Compared to serial conventional MRI alone, inclusion of DBSI would have led to earlier diagnosis of either progression or treatment effect in 6 (66.7%) patients by a median of 7.7 (interquartile range = 0-20.1) weeks.

Conclusions

In the first longitudinal prospective study of DBSI in adult HGG patients, we found that in new or enlarging contrast-enhancing regions following therapy, DBSI hindered fraction is elevated in cases of treatment effect compared to those with progression. Hindered fraction map may be a valuable adjunct to conventional MRI to distinguish tumor progression from treatment effect.

Twelve Weeks of Intermittent Caloric Restriction Diet Mitigates Neuroinflammation in Midlife Individuals with Multiple Sclerosis: A Pilot Study with Implications for Prevention of Alzheimer's Disease

BACKGROUND

Multiple sclerosis (MS) is a prototype neuroinflammatory disorder with increasingly recognized role for neurodegeneration. Most first-line treatments cannot prevent the progression of neurodegeneration and the resultant disability. Interventions can improve symptoms of MS and might provide insights into the underlying pathology.

OBJECTIVE

To investigate the effect of intermittent caloric restriction on neuroimaging markers of MS.

METHODS

We randomized ten participants with relapsing remitting MS to either a 12-week intermittent calorie restriction (iCR) diet (n = 5) or control (n = 5). Cortical thickness and volumes were measured through FreeSurfer, cortical perfusion was measured by arterial spin labeling and neuroinflammation through diffusion basis spectrum imaging.

RESULTS

After 12 weeks of iCR, brain volume increased in the left superior and inferior parietal gyri (p: 0.050 and 0.049, respectively) and the banks of the superior temporal sulcus (p: 0.01). Similarly in the iCR group, cortical thickness improved in the bilateral medial orbitofrontal gyri (p: 0.04 and 0.05 in right and left, respectively), the left superior temporal gyrus (p: 0.03), and the frontal pole (p: 0.008) among others. Cerebral perfusion decreased in the bilateral fusiform gyri (p: 0.047 and 0.02 in right and left, respectively) and increased in the bilateral deep anterior white matter (p: 0.03 and 0.013 in right and left, respectively). Neuroinflammation, demonstrated through hindered and restricted water fractions (HF and RF), decreased in the left optic tract (HF p: 0.02), and the right extreme capsule (RF p: 0.007 and HF p: 0.003).

CONCLUSION

These pilot data suggest therapeutic effects of iCR in improving cortical volume and thickness and mitigating neuroinflammation in midlife adults with MS.

Standard clinical approaches and emerging modalities for glioblastoma imaging

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

Imaging in experimental models of diabetes

Translational medicine, experimental medicine and experimental animal models, in particular mice and rats, represent a multidisciplinary field that has made it possible to achieve, in the last decades, important scientific progress. In this review, we have summarized the most frequently used imaging animal models, such as ultrasound (US), micro-CT, MRI and the optical imaging methods, and their main implications in diagnostic and therapeutic fields, with a particular focus on diabetes mellitus, a multifactorial disease extremely widespread among the general population.

A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches

OBJECTIVE

Brain age, which is predicted using neuroimaging data, has become an important biomarker in aging research. This study applied diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) model to predict age respectively, with the purpose of evaluating which diffusion model is more accurate in estimating age and revealing age-related changes in the brain.

APPROACH

Diffusion MRI data of 125 subjects from two sites were collected. Fractional anisotropy (FA) and quantitative anisotropy (QA) from the two diffusion models were calculated and were used as features of machine learning models. Sequential backward elimination algorithm was used for feature selection. Six machine learning approaches including linear regression, ridge regression, support vector regression (SVR) with linear kernel, quadratic kernel and radial basis function (RBF) kernel and feedforward neural network were used to predict age using FA and QA features respectively.

MAIN RESULTS

Age predictions using FA features were more accurate than predictions using QA features for all the 6 machine learning algorithms. Post-hoc analysis revealed that FA was more sensitive to age-related white matter alterations in the brain. In addition, SVR with RBF kernel based on FA features achieved better performances than the competing algorithms with MAE ranging from 7.74 to 10.54, MSE ranging from 87.79 to 150.86, and nMSE ranging from 0.05 to 0.14 Significance: FA from DTI model was more suitable than QA from GQI model in age prediction. FA metric was more sensitive to age-related white matter changes in the brain and FA of several brain regions could be used as white matter biomarkers in aging.

Exploring Peritumoral Neural Tracts by Using Neurite Orientation Dispersion and Density Imaging

Diffusion Tensor Imaging (DTI) tractography has been widely used in brain tumor surgery to ensure thorough resection and minimize functional damage. However, due to enhanced anisotropic uncertainty in the area with peritumoral edema, diffusion tractography is generally not practicable leading to high false-negative results in neural tracking. In this study, we evaluated the usefulness of the neurite orientation dispersion and density imaging (NODDI) derived tractography for investigating structural heterogeneity of the brain in patients with brain tumor. A total of 24 patients with brain tumors, characterized by peritumoral edema, and 10 healthy counterparts were recruited from 2014 to 2021. All participants underwent magnetic resonance imaging. Moreover, we used the images obtained from the healthy participants for calibrating the orientation dispersion threshold for NODDI-derived corticospinal tract (CST) reconstruction. Compared to DTI, NODDI-derived tractography has a great potential to improve the reconstruction of fiber tracking through regions of vasogenic edema. The regions with edematous CST in NODDI-derived tractography demonstrated a significant decrease in the intracellular volume fraction (VF_ic, p < 0.000) and an increase in the isotropic volume fraction (VF_iso, p < 0.014). Notably, the percentage of the involved volume of the concealed CST and lesion-to-tract distance could reflect the motor function of the patients. After the tumor resection, four patients with 1–5 years follow-up were showed subsidence of the vasogenic edema and normal CST on DTI tractography. NODDI-derived tractography revealed tracts within the edematous area and could assist neurosurgeons to locate the neural tracts that are otherwise not visualized by conventional DTI tractography.

Diffusion histology imaging differentiates distinct pediatric brain tumor histology

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 85.8%. Receiver operating analysis (ROC) analysis suggested DHI’s great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982–0.986), 0.960 (0.956–0.963), 0.991 (0.990–0.993), 0.950 (0.944–0.956), 0.977 (0.973–0.981) and 0.976 (0.972–0.979) for normal WM, densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If these results could be further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.