Current Clinical Applications of Diffusion-Tensor Imaging in Neurological Disorders

Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging

PURPOSE

The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted images (DWIs). This model addresses the challenge of prolonged data acquisition times in diffusion MRI while preserving metric accuracy.

METHODS

DiffDL was trained using data from the Human Connectome Project, including 300 training/validation subjects and 50 testing subjects. High-quality DTI and DKI metrics were generated using many DWIs and combined with subsets of DWIs to form training pairs. A UNet architecture was used for denoising, trained over 500 epochs with a linear noise schedule. Performance was evaluated against conventional DTI/DKI modeling and a reference UNet model using normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), and Pearson correlation coefficient (PCC).

RESULTS

DiffDL showed significant improvements in the quality and accuracy of fractional anisotropy (FA) and mean diffusivity (MD) maps compared to conventional methods and the baseline UNet model. For DKI metrics, DiffDL outperformed conventional DKI modeling and the UNet model across various acceleration scenarios. Quantitative analysis demonstrated superior NMAE, PSNR, and PCC values for DiffDL, capturing the full dynamic range of DTI and DKI metrics. The generative nature of DiffDL allowed for multiple predictions, enabling uncertainty quantification and enhancing performance.

CONCLUSION

The DiffDL framework demonstrated the potential to significantly reduce data acquisition times in diffusion MRI while maintaining high metric quality. Future research should focus on optimizing computational demands and validating the model with clinical cohorts and standard MRI scanners.

White matter changes in patients with narcolepsy type 2: Peak width of skeletonized mean diffusivity study

OBJECTIVES

This study aimed to investigate white matter (WM) microstructural alterations in patients with narcolepsy type 2 (NT2) using Peak Width of Skeletonized Mean Diffusivity (PSMD), a novel imaging marker associated with small vessel disease (SVD). The study compared PSMD metrics between patients with NT2 and healthy controls to investigate structural disruptions and their implications for NT2 pathophysiology.

METHODS

A total of 42 participants were enrolled, including 20 patients with newly diagnosed NT2 and 22 healthy controls. Diffusion tensor imaging (DTI) was performed using a 3 T MRI scanner. PSMD was calculated using a multi-step process involving preprocessing, skeletonization, application of a custom mask, and histogram analysis with the FSL program. PSMD values were compared between patients with NT2 and healthy controls, and correlation analyses were conducted to examine associations between PSMD and clinical variables.

RESULTS

Patients with NT2 exhibited significantly higher PSMD compared to healthy controls (2.172 × 10-4 mm2/s vs. 2.031 × 10-4 mm2/s, p = 0.011). PSMD also positively correlated with age in both patients with NT2 (r = 0.608, p = 0.004) and healthy controls (r = 0.696, p < 0.001).

CONCLUSION

Patients with NT2 demonstrate increased PSMD, indicating WM microstructural changes potentially linked to SVD. These findings highlight the utility of PSMD as a sensitive neuroimaging marker for detecting WM alterations in sleep disorders. Further studies are needed to validate these results and investigate the underlying mechanisms of WM changes in NT2.

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.

Association between diffusion tensor imaging measurements and cognitive performances in people with multiple sclerosis: A systematic review and meta-analysis

BACKGROUND

Alterations in structural connectivity of brain networks have been linked to complex cognitive functions in people with multiple sclerosis (PwMS). However, a definitive consensus on the optimal diffusion tensor imaging (DTI) markers as indicators of cognitive performance remains incomplete and inconclusive. This systematic review and meta-analysis aimed to explore the evidence on the correlation between DTI metrics and cognitive functions in PwMS.

METHODS

A comprehensive literature search was conducted across PubMed/MEDLINE, Embase, Scopus, and the Web of Science up to March 2024 to identify studies reporting the correlation between DTI metrics and cognitive functions. Cognitive function was assessed using the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT), and Brief Visuospatial Memory Test-Revised (BVMT-R). The pooled correlation coefficients were estimated using R software version 4.4.0 with the random effect model.

RESULTS

Out of 1952 studies, 38 studies on 2055 PwMS fulfilled the inclusion criteria. The meta-analysis indicated that the SDMT exhibited the greatest correlation with corpus callosum fractional anisotropy (FA) (r = 0.54, 95 % CI: 0.4 to 0.66, p-value < 0.001, I2 = 34.1 %, p-heterogeneity = 0.19) and mean diffusivity (MD) (r = -0.48, 95 % CI: 0.61 to -0.33, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.77), white matter FA (r = 0.39, 95 % CI: 0.24 to 0.52, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.1), and fornix FA (r = 0.35, 95 % CI: 0.12 to 0.54, p-value = 0.003, I2 = 50.7 %, p-heterogeneity = 0.18) and MD (r = -0.35, 95 % CI: 0.49 to -0.19, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.5).

CONCLUSION

DTI measurements, including corpus callosum FA and MD, white matter FA, and fornix FA and MD, represent the indicators of cognitive performance in PwMS. Nonetheless, these findings warrant cautious interpretation due to the restricted kinds of cognitive tests and methodological variability across studies.

Obstructive sleep apnea and structural and functional brain alterations: a brain-wide investigation from clinical association to genetic causality

BACKGROUND

Obstructive sleep apnea (OSA) is linked to brain alterations, but the specific regions affected and the causal associations between these changes remain unclear.

METHODS

We studied 20 pairs of age-, sex-, BMI-, and education- matched OSA patients and healthy controls using multimodal magnetic resonance imaging (MRI) from August 2019 to February 2020. Additionally, large-scale Mendelian randomization analyses were performed using genome-wide association study (GWAS) data on OSA and 3935 brain imaging-derived phenotypes (IDPs), assessed in up to 33,224 individuals between December 2023 and March 2024, to explore potential genetic causality between OSA and alterations in whole brain structure and function.

RESULTS

In the cohort study, OSA patients exhibited significantly lower fractional amplitude of low-frequency fluctuation and regional homogeneity in the right posterior cerebellar lobe and bilateral superior and middle frontal gyrus, while showing higher levels in the left occipital lobe and left posterior central gyrus. Decreased fractional anisotropy (FA) but increased apparent diffusion coefficient (ADC) was shown in the bilateral superior longitudinal fasciculus. According to the results of Affiliation file 2: table s6, it is the ADC value of right superior longitudinal fasciculus was shown a positive correlation with the lowest oxygen saturation. In the Mendelian randomization analyses, the area of left inferior temporal sulcus (OR: 0.89; 95% CI: 0.82-0.96), rfMRI connectivity ICA100 edge 893 (OR: 0.88; 95% CI: 0.82-0.96), ICA100 edge 951 (OR: 0.89; 95% CI: 0.82-0.97), and ICA100 edge 1213 (OR: 0.89; 95% CI: 0.82-0.96) were significantly decreased in OSA. Conversely, mean thickness of G-front-inf-Triangul in right hemisphere (OR: 1.14; 95% CI: 1.05-1.23), mean orientation dispersion index in right tapetum (OR: 1.13; 95% CI: 1.04-1.23), and rfMRI connectivity ICA100 edge 258 (OR: 1.13; 95% CI: 1.04-1.22) showed opposite results.

CONCLUSIONS

Nerve fiber damage and imbalances in neuronal activity across multiple brain regions caused by hypoxia, particularly the frontal lobe, underlie the structural and the functional connectivity impairments in OSA.

Combination of morphological and multiparametric MR neurography enhances carpal tunnel syndrome diagnosis and evaluation

This study aimed to investigate the diagnostic and evaluative significance of combining median nerve (MN) morphological measurements with diffusion tensor imaging (DTI) and T2 mapping metrics for carpal tunnel syndrome (CTS). Morphological and multiparametric magnetic resonance neurography (MRN), along with clinical evaluation, were conducted on 33 CTS patients and 32 healthy controls. The MRN metrics included fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity (AD), radial diffusivity (RD), T2 value, cross-sectional area (CSA) and MN flattening ratio (MNFR) at both the pisiform bone and hamate bone levels. Differences in MRN metrics between the above two levels (Delta FA, Delta ADC, Delta AD, Delta RD and Delta T2) were calculated. T-tests, multivariable regression, and receiver operating characteristic (ROC) curve analyses were used to compare and classify patients with CTS and controls. The correlations between MRN metrics and clinical characteristics were analyzed. Comparisons were also made between MRN metrics in patients with and without significant symptom improvement after treatment. FA, AD, T2 value, and CSA at the pisiform bone level were identified as independent predictors of CTS. The combination of these metrics improved diagnostic performance (AUC 0.922, sensitivity 84.85% and specificity 90.62%). Delta ADC, Delta AD, and Delta T2 correlated with function Boston scores. The T2 value at hamate bone level, along with Delta AD and FA, correlated with visual analogue score (VAS). CSA and Delta T2 had higher AUCs for classifying patients with and without significant symptom improvement after treatment. These findings suggest that combining MN morphological and multiparametric MRN metrics can enhance the diagnostic performance of CTS and has the potential to provide an objective and quantitative basis for further study of the degree of entrapment and prognosis.

Investigating the potential of diffusion tensor atlases to generate anisotropic clinical tumor volumes in glioblastoma patients

Background and purpose

Diffusion tensor imaging (DTI) has been proposed to guide the anisotropic expansion from gross tumor volume to clinical target volume (CTV), aiming to integrate known tumor spread patterns into the CTV. This study investigate the potential of using a DTI atlas as an alternative to patient-specific DTI for generating anisotropic CTVs.

Materials and Methods

The dataset consisted of twenty-eight newly diagnosed glioblastoma patients from a Danish national DTI protocol with post-operative T1-contrast and DTI imaging. Three different DTI atlases, spatially aligned to the patient images using deformable image registration, were considered as alternatives. Anisotropic CTVs were constructed to match the volume of a 15 mm isotropic expansion by generating 3D distance maps using either patient- or atlas-DTI as input to the shortest path solver. The degree of CTV anisotropy was controlled by the migration ratio, modeling tumor cell migration along the dominant white matter fiber direction extracted from DTI. The similarity between patient- and atlas-DTI CTVs was analyzed using the Dice Similarity Coefficient (DSC), with significance testing according to a Wilcoxon test.

Results

The median (range) DSC between anisotropic CTVs generated using patient-specific and atlas-based DTI was 0.96 (0.93-0.97), 0.96 (0.93-0.97), and 0.95 (0.93-0.97) for the three atlases, respectively (p > 0.01), for a migration ratio of 10. The results remained consistent over the range of studied migration ratios (2 to 100).

Conclusion

The high degree of similarity between all anisotropic CTVs indicates that atlas-DTI is a viable replacement for patient-specific DTI for incorporating fiber direction into the CTV.

Predicting glioblastoma progression using MR diffusion tensor imaging: A systematic review

BACKGROUND AND PURPOSE

Despite multimodal treatment of glioblastoma (GBM), recurrence beyond the initial tumor volume is inevitable. Moreover, conventional MRI has shortcomings that hinder the early detection of occult white matter tract infiltration by tumor, but diffusion tensor imaging (DTI) is a sensitive probe for assessing microstructural changes, facilitating the identification of progression before standard imaging. This sensitivity makes DTI a valuable tool for predicting recurrence. A systematic review was therefore conducted to investigate how DTI, in comparison to conventional MRI, can be used for predicting GBM progression.

METHODS

We queried three databases (PubMed, Web of Science, and Scopus) using the search terms: (diffusion tensor imaging OR DTI) AND (glioblastoma OR GBM) AND (recurrence OR progression). For included studies, data pertaining to the study type, number of GBM recurrence patients, treatment type(s), and DTI-related metrics of recurrence were extracted.

RESULTS

In all, 16 studies were included, from which there were 394 patients in total. Six studies reported decreased fractional anisotropy in recurrence regions, and 2 studies described the utility of connectomics/tractography for predicting tumor migratory pathways to a site of recurrence. Three studies reported evidence of tumor progression using DTI before recurrence was visible on conventional imaging.

CONCLUSIONS

These findings suggest that DTI metrics may be useful for guiding surgical and radiotherapy planning for GBM patients, and for informing long-term surveillance. Understanding the current state of the literature pertaining to these metrics' trends is crucial, particularly as DTI is increasingly used as a treatment-guiding imaging modality.

PET, SPECT, and MRI imaging for evaluation of Parkinson's disease

This review assesses the primary neuroimaging techniques used to evaluate Parkinson's disease (PD) - a neurological condition characterized by gradual dopamine-producing nerve cell degeneration. The neuroimaging techniques explored include positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI). These modalities offer varying degrees of insights into PD pathophysiology, diagnostic accuracy, specificity by way of exclusion of other Parkinsonian syndromes, and monitoring of disease progression. Neuroimaging is thus crucial for diagnosing and managing PD, with integrated multimodal approaches and novel techniques further enhancing early detection and treatment evaluation.

Insulin resistance and white matter microstructural abnormalities in nondiabetic adult: A population-based study

BACKGROUND

Insulin resistance (IR) is of growing concern yet its association with white matter integrity remains controversial. We aimed to investigate the association between IR and white matter integrity in nondiabetic adults.

METHODS

This cross-sectional analysis was conducted based on the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study. A total of 1709 nondiabetic community-dwelling adults with available diffusion-weighted imaging based on brain magnetic resonance imaging and completed oral glucose tolerance test were included. IR was measured noninvasively by insulin sensitivity indices (ISI), including ISIcomposite and ISI0,120, as well as homeostasis model assessment of insulin resistance (HOMA-IR). White matter microstructure abnormalities were identified by diffusion-weighted imaging along with tract-based spatial statistical analysis to compare diffusion metrics between groups. The multivariable linear regression models were applied to measure the association between white matter microstructure abnormalities and IR.

RESULTS

A total of 1709 nondiabetic individuals with a mean age of 60.8 ± 6.4 years and 54.1% female were included. We found that IR was associated with a significant increase in mean diffusivity, axial diffusivity, and radial diffusivity extensively in cerebral white matter in regions such as the anterior corona radiata, superior corona radiata, anterior limb of internal capsule, external capsule, and body of corpus callosum. The pattern of associations was more marked for ISIcomposite and ISI0,120. However, the effect of IR on white matter integrity was attenuated after, in addition, adjustment for history of hypertension and cardiovascular disease and antihypertensive medication use.

CONCLUSION

Our findings indicate a significant association between IR and white matter microstructural abnormalities in nondiabetic middle-aged community residents, while these associations were greatly influenced by the history of hypertension and cardiovascular disease, and antihypertensive medication use. Further investigation is needed to clarify the role of IR in white matter integrity, whereas prophylactic strategies of maintaining a low IR status may ameliorate disturbances in white matter integrity.

DATA ACCESSIBILITY STATEMENT

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Function-guided differences of arcuate fascicle and inferior fronto-occipital fascicle tractography as diagnostic indicators for surgical risk stratification

BACKGROUND

Several patients with language-eloquent gliomas face language deterioration postoperatively. Persistent aphasia is frequently associated with damage to subcortical language pathways. Underlying mechanisms still need to be better understood, complicating preoperative risk assessment. This study compared qualitative and quantitative functionally relevant subcortical differences pre- and directly postoperatively in glioma patients with and without aphasia.

METHODS

Language-relevant cortical sites were defined using navigated transcranial magnetic stimulation (nTMS) language mapping in 74 patients between 07/2016 and 07/2019. Post-hoc nTMS-based diffusion tensor imaging tractography was used to compare a tract's pre- and postoperative visualization, volume and fractional anisotropy (FA), and the preoperative distance between tract and lesion and postoperative overlap with the resection cavity between the following groups: no aphasia (NoA), tumor- or previous resection induced aphasia persistent pre- and postoperatively (TIA_P), and surgery-induced transient or permanent aphasia (SIA_T or SIA_P).

RESULTS

Patients with NoA, TIA_P, SIA_T, and SIA_P showed distinct fasciculus arcuatus (AF) and inferior-fronto-occipital fasciculus (IFOF) properties. The AF was more frequently reconstructable, and the FA of IFOF was higher in NoA than TIA_P cases (all p ≤ 0.03). Simultaneously, SIA_T cases showed higher IFOF fractional anisotropy than TIA_P cases (p < 0.001) and the most considerable AF volume loss overall. While not statistically significant, the four SIA_P cases showed complete loss of ventral language streams postoperatively, the highest resection-cavity-AF-overlap, and the shortest AF to tumor distance.

CONCLUSION

Functionally relevant qualitative and quantitative differences in AF and IFOF provide a pre- and postoperative pathophysiological and clinically relevant diagnostic indicator that supports surgical risk stratification.

Transdiagnostic alterations in white matter microstructure associated with suicidal thoughts and behaviours in the ENIGMA Suicidal Thoughts and Behaviours consortium

Previous studies have suggested that alterations in white matter (WM) microstructure are implicated in suicidal thoughts and behaviours (STBs). However, findings of diffusion tensor imaging (DTI) studies have been inconsistent. In this large-scale mega-analysis conducted by the ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium, we examined WM alterations associated with STBs. Data processing was standardised across sites, and resulting WM microstructure measures (fractional anisotropy, axial diffusivity, mean diffusivity and radial diffusivity) for 25 WM tracts were pooled across 40 cohorts. We compared these measures among individuals with a psychiatric diagnosis and lifetime history of suicide attempt (n=652; mean age=35.4±14.7; female=71.8%), individuals with a psychiatric diagnosis but no STB (i.e., clinical controls; n=1871; mean age=34±14.8; female=59.8%), and individuals with no mental disorder diagnosis and no STB (i.e., healthy controls; n=642; mean age=29.6±13.1; female=62.9%). We also compared these measures among individuals with recent suicidal ideation (n=714; mean age=36.3±15.3; female=66.1%), clinical controls (n=1184; mean age=36.8±15.6; female=63.1%), and healthy controls (n=1240; mean age= 31.6±15.5; female=61.0%). We found subtle but statistically significant effects, such as lower fractional anisotropy associated with a history of suicide attempt, over and above the effect of psychiatric diagnoses. These effects were strongest in the corona radiata, thalamic radiation, fornix/stria terminalis, corpus callosum and superior longitudinal fasciculus. Effect sizes were small (Cohen's d < 0.25). Recent suicidal ideation was not associated with alterations in WM microstructure. This large-scale coordinated mega-analysis revealed subtle regional and global alterations in WM microstructure in individuals with a history of suicide attempt. Longitudinal studies are needed to confirm whether these alterations are a risk factor for suicidal behaviour.

Clinical and Pathophysiologic Correlates of Basilar Artery Measurements in Fabry Disease

BACKGROUND AND PURPOSE

Alterations of the basilar artery (BA) anatomy have been suggested as a possible MRA feature of Fabry disease (FD). Nonetheless, no information about their clinical or pathophysiologic correlates is available, limiting our comprehension of the real impact of vessel remodeling in FD.

MATERIALS AND METHODS

Brain MRIs of 53 subjects with FD (mean age, 40.7 [SD, 12.4] years; male/female ratio = 23:30) were collected in this single-center study. Mean BA diameter and its tortuosity index were calculated on MRA. Possible correlations between these metrics and clinical, laboratory, and advanced imaging variables of the posterior circulation were tested. In a subgroup of 20 subjects, a 2-year clinical and imaging follow-up was available, and possible longitudinal changes of these metrics and their ability to predict clinical scores were also probed.

RESULTS

No significant association was found between MRA metrics and any clinical, laboratory, or advanced imaging variable (P values ranging from -0.006 to 0.32). At the follow-up examination, no changes were observed with time for the mean BA diameter (P = .84) and the tortuosity index (P = .70). Finally, baseline MRA variables failed to predict the clinical status of patients with FD at follow-up (P = .42 and 0.66, respectively).

CONCLUSIONS

Alterations of the BA in FD lack of any meaningful association with clinical, laboratory, or advanced imaging findings collected in this study. Furthermore, this lack of correlation seems constant across time, suggesting stability over time. Taken together, these results suggest that the role of BA dolichoectasia in FD should be reconsidered.

The hemostatic system in chronic brain diseases: A new challenging frontier?

Neurological diseases (ND), including neurodegenerative diseases (NDD) and psychiatric disorders (PD), present a significant public health challenge, ranking third in Europe for disability and premature death, following cardiovascular diseases and cancers. In 2017, approximately 540 million cases of ND were reported among Europe's 925 million people, with strokes, dementia, and headaches being most prevalent. Nowadays, more and more evidence highlight the hemostasis critical role in cerebral homeostasis and vascular events. Indeed, hemostasis, thrombosis, and brain abnormalities contributing to ND form a complex and poorly understood equilibrium. Alterations in vascular biology, particularly involving the blood-brain barrier, are implicated in ND, especially dementia, and PD. While the roles of key coagulation players such as thrombin and fibrinogen are established, the roles of other hemostasis components are less clear. Moreover, the involvement of these elements in psychiatric disease pathogenesis is virtually unstudied, except in specific pathological models such as antiphospholipid syndrome. Advanced imaging techniques, primarily functional magnetic resonance imaging and its derivatives like diffusion tensor imaging, have been developed to study brain areas affected by ND and to improve our understanding of the pathophysiology of these diseases. This literature review aims to clarify the current understanding of the connections between hemostasis, thrombosis, and neurological diseases, as well as explore potential future diagnostic and therapeutic strategies.

Ineffectiveness of 6,2',4'-trimethoxyflavone in mitigating cerebral ischemia/reperfusion injury after post-reperfusion administration in rats

BACKGROUND

Pharmacological inhibition of aryl hydrocarbon receptor (AhR) activation after ischemia alleviates cerebral ischemia/reperfusion (IR) injury.

PURPOSE

To investigate whether AhR antagonist administration after reperfusion was also effective in attenuating cerebral IR injury.

MATERIAL AND METHODS

A total of 24 Sprague-Dawley rats were divided into the sham-operated group (no IR), control group (IR), and 6,2',4'-trimethoxyflavone (TMF) group (IR + TMF administration), with 10 rats assigned to each group. Cerebral IR injury was induced by 60 min of middle cerebral artery occlusion followed by reperfusion. TMF (5 mg/kg) was used as the AhR antagonist and was administered intraperitoneally immediately after reperfusion. Cerebral IR injury was observed using magnetic resonance imaging (MRI) and neurobehavioral assessments at baseline, immediately after ischemia, and at 3 days after ischemia.

RESULTS

On MRI, the TMF group showed no significant differences in relative apparent diffusion coefficient (ADC), T2, and fractional anisotropy (FA) values; midline shift value; and infarct volume. In terms of neurobehavioral function, factors such as grip strength, contralateral forelimb use, time to touch, and time to remove adhesive tape from the forepaw, were also not significantly different between the control and TMF groups.

CONCLUSION

This study demonstrated that AhR treatment after reperfusion had no noticeable effect on reducing cerebral IR injury in rats.

Loss of white matter integrity mediates the association between cortical cerebral microinfarcts and cognitive dysfunction: A longitudinal study

Cortical cerebral microinfarcts (CMIs) are associated with loss of white matter (WM) integrity and cognitive impairment in cross-sectional studies, while further investigation using longitudinal datasets is required. This study aims to establish the association between cortical CMIs and WM integrity assessed by diffusion-tensor imaging (DTI) measures and to investigate whether DTI measures mediate the relationship between cortical CMIs and cognitive decline. Cortical CMIs were graded on 3T MRI. DTI measures were derived from histogram analysis of mean diffusivity (MD) and fractional anisotropy (FA). Cognitive function was assessed using a neuropsychological test battery. Linear mixed-effect models were employed to examine associations of cortical CMIs with longitudinal changes in DTI measures and cognitive function. Final analysis included 231 patients (71.14 ± 7.60 years). Presence of cortical CMIs at baseline was associated with longitudinal changes in MD median and peak height and FA median and peak height, as well as global cognition (β = -0.50, 95%CI: -0.91, -0.09) and executive function (β = -0.77, 95%CI: -1.25, -0.28). MD median mediated the cross-sectional association between cortical CMIs and global cognition. Further studies are required to investigate whether cortical CMIs and loss of WM integrity are causally related or if they are parallel mechanisms that contribute to cognitive decline.

Utilizing diffusion tensor imaging as an image biomarker in exploring the therapeutic efficacy of forniceal deep brain stimulation in a mice model of Alzheimer's disease

Objective.With prolonged life expectancy, the incidence of memory deficits, especially in Alzheimer's disease (AD), has increased. Although multiple treatments have been evaluated, no promising treatment has been found to date. Deep brain stimulation (DBS) of the fornix area was explored as a possible treatment because the fornix is intimately connected to memory-related areas that are vulnerable in AD; however, a proper imaging biomarker for assessing the therapeutic efficiency of forniceal DBS in AD has not been established.Approach.This study assessed the efficacy and safety of DBS by estimating the optimal intersection volume between the volume of tissue activated and the fornix. Utilizing a gold-electroplating process, the microelectrode's surface area on the neural probe was increased, enhancing charge transfer performance within potential water window limits. Bilateral fornix implantation was conducted in triple-transgenic AD mice (3 × Tg-AD) and wild-type mice (strain: B6129SF1/J), with forniceal DBS administered exclusively to 3 × Tg-AD mice in the DBS-on group. Behavioral tasks, diffusion tensor imaging (DTI), and immunohistochemistry (IHC) were performed in all mice to assess the therapeutic efficacy of forniceal DBS.Main results.The results illustrated that memory deficits and increased anxiety-like behavior in 3 × Tg-AD mice were rescued by forniceal DBS. Furthermore, forniceal DBS positively altered DTI indices, such as increasing fractional anisotropy (FA) and decreasing mean diffusivity (MD), together with reducing microglial cell and astrocyte counts, suggesting a potential causal relationship between revised FA/MD and reduced cell counts in the anterior cingulate cortex, hippocampus, fornix, amygdala, and entorhinal cortex of 3 × Tg-AD mice following forniceal DBS.Significance.The efficacy of forniceal DBS in AD can be indicated by alterations in DTI-based biomarkers reflecting the decreased activation of glial cells, suggesting reduced neural inflammation as evidenced by improvements in memory and anxiety-like behavior.

Diffusion Tensor Imaging: Techniques and Applications for Peripheral Nerve Injury

ABSTRACT

Peripheral nerve injuries (PNIs) represent a complex clinical challenge, necessitating precise diagnostic approaches for optimal management. Traditional diagnostic methods often fall short in accurately assessing nerve recovery as these methods rely on the completion of nerve reinnervation, which can prolong a patient's treatment. Diffusion tensor imaging (DTI), a noninvasive magnetic resonance imaging (MRI) technique, has emerged as a promising tool in this context. DTI offers unique advantages including the ability to quantify nerve recovery and provide in vivo visualizations of neuronal architecture. Therefore, this review aims to examine and outline DTI techniques and its utility in detecting distal nerve regeneration in both preclinical and clinical settings for peripheral nerve injury.

The role of the right hemispheric homologous language pathways in recovery from post-stroke aphasia: A systematic review

The involvement of the right hemisphere, mainly the activation of the right cerebral regions, in recovery from post-stroke aphasia has been widely recognized. In contrast, the role of the right white matter pathways in the recovery from post-stroke aphasia is rarely understood. In this study, we aimed to provide a primary overview of the correlation between the structural integrity of the right hemispheric neural tracts based on the dual-stream model of language organization and recovery from post-stroke aphasia by systematically reviewing prior longitudinal interventional studies. By searching electronic databases for relevant studies according to a standard protocol, a total of 10 records (seven group studies and three case studies) including 79 participants were finally included. After comprehensively analyzing these studies and reviewing the literature, although no definite correlation was found between the right hemispheric neural tracts and recovery from post-stroke aphasia, our review provideds a new perspective for investigating the linguistic role of the right hemispheric neural tracts. This suggests that the involvement of the right hemispheric neural tracts in recovery from post-stroke aphasia may be mediated by multiple factors; thus, this topic should be comprehensively investigated in the future.

Neuroimage analysis using artificial intelligence approaches: a systematic review

In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.

Advanced Diffusion Tensor Imaging in White Matter Injury After Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is recognized as an especially severe stroke variant, notorious for its high mortality and long-term disability rates, in addition to a range of both immediate and enduring neurologic impacts. Over half of the SAH survivors experience varying degrees of neurologic disorders, with many enduring chronic neuropsychiatric conditions. Due to the limitations of traditional imaging techniques in depicting subtle changes within brain tissues posthemorrhage, the accurate detection and diagnosis of white matter (WM) injuries are complicated. Against this backdrop, diffusion tensor imaging (DTI) has emerged as a promising biomarker for structural imaging, renowned for its enhanced sensitivity in identifying axonal damage. This capability positions DTI as an invaluable tool for forming precise and expedient prognoses for SAH survivors. This study synthesizes an assessment of DTI for the diagnosis and prognosis of neurologic dysfunctions in patients with SAH, emphasizing the notable changes observed in DTI metrics and their association with potential pathophysiological processes. Despite challenges associated with scanning technology differences and data processing, DTI demonstrates significant clinical potential for early diagnosis of cognitive impairments following SAH and monitoring therapeutic effects. Future research requires the development of highly standardized imaging paradigms to enhance diagnostic accuracy and devise targeted therapeutic strategies for SAH patients. In sum, DTI technology not only augments our understanding of the impact of SAH but also may offer new avenues for improving patient prognoses.

Exploring white matter microstructural alterations in mild cognitive impairment: a multimodal diffusion MRI investigation utilizing diffusion kurtosis and free-water imaging

Background

Mild Cognitive Impairment (MCI) is a transitional stage from normal aging to dementia, characterized by noticeable changes in cognitive function that do not significantly impact daily life. Diffusion MRI (dMRI) plays a crucial role in understanding MCI by assessing white matter integrity and revealing early signs of axonal degeneration and myelin breakdown before cognitive symptoms appear.

Methods

This study utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to compare white matter microstructure in individuals with MCI to cognitively normal (CN) individuals, employing advanced dMRI techniques such as diffusion kurtosis imaging (DKI), mean signal diffusion kurtosis imaging (MSDKI), and free water imaging (FWI).

Results

Analyzing data from 55 CN subjects and 46 individuals with MCI, this study found significant differences in white matter integrity, particularly in free water levels and kurtosis values, suggesting neuroinflammatory responses and microstructural integrity disruption in MCI. Moreover, negative correlations between Mini-Mental State Examination (MMSE) scores and free water levels in the brain within the MCI group point to the potential of these measures as early biomarkers for cognitive impairment.

Conclusion

In conclusion, this study demonstrates how a multimodal advanced diffusion imaging approach can uncover early microstructural changes in MCI, offering insights into the neurobiological mechanisms behind cognitive decline.

Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas

OBJECTIVE

High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment.

METHODS

We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes.

RESULTS

The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes.

INTERPRETATION

The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

A Case Report and Review of Literature on Hirayama Disease

Hirayama disease (HD) is a rare, benign, self-limiting condition that typically affects individuals in their 20s. Although the disease is self-limiting, it can result in functional impairment in those affected. The most common presentation is an asymmetrical, unilateral, or bilateral upper limb weakness with wasting. With an interesting pathogenesis and lack of definitive treatment, HD is an interesting neurological conundrum. Mild symptoms in patients often lead to underreporting of the disease, as individuals may not seek medical attention or may not recognize their symptoms. Most case reports in the literature are from Asia and the Middle East. We report a case of HD in a male patient in his 20s with gradual bilateral upper limb weakness and wasting, confirmed by imaging and nerve conduction studies.

Diffusion Magnetic Resonance Imaging and Human Reward System Research: A Bibliometric Analysis and Visualisation of Current Research Trends

Background

The human reward system has been extensively studied using neuroimaging. This bibliometric analysis aimed to determine the global trend in diffusion magnetic resonance imaging (dMRI) and human reward research in terms of the number of documents, the most active countries and their collaborating countries, the top journals and institutions, the most prominent authors and most cited articles, and research hotspots.

Methods

The research datasets were acquired from the Scopus database. The search terms used were 'reward' AND 'human' AND 'diffusion imaging' OR 'diffusion tensor imaging' OR 'diffusion MRI' OR 'diffusion-weighted imaging' OR 'tractography' in the abstract, article title and keywords. A total of 336 publications were analysed using Harzing's Publish or Perish and VOSviewer software.

Results

The results revealed an upward trend in the number of publications with the highest number of articles in 2020 and 2022. Most publications were limited to countries, authors, and institutions in the USA, China and Europe. Bracht, Coenen, Wiest, Federspiel and Feng were among the top authors from Switzerland, Germany and the UK. Neuroimage, Neuroimage Clinical, Frontiers in Human Neuroscience, Human Brain Mapping, and the Journal of Neuroscience were the top journals. Among the top articles, six were reviews and four were original articles, while the top keywords in human reward research were 'diffusion MRI', 'adolescence', 'depression' and 'reward-related brain areas'.

Conclusion

These findings may serve as researchers' references to find collaborative authors, relevant journals, cooperative countries/institutions, and hot topics related to dMRI and reward research.

An MRI evaluation of white matter involvement in paradigmatic forms of spastic ataxia: results from the multi-center PROSPAX study

BACKGROUND

Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS) and Spastic Paraplegia Type 7 (SPG7) are paradigmatic spastic ataxias (SPAX) with suggested white matter (WM) involvement. Aim of this work was to thoroughly disentangle the degree of WM involvement in these conditions, evaluating both macrostructure and microstructure via the analysis of diffusion MRI (dMRI) data.

MATERIAL AND METHODS

In this multi-center prospective study, ARSACS and SPG7 patients and Healthy Controls (HC) were enrolled, all undergoing a standardized dMRI protocol and a clinimetrics evaluation including the Scale for the Assessment and Rating of Ataxia (SARA). Differences in terms of WM volume or global microstructural WM metrics were probed, as well as the possible occurrence of a spatially defined microstructural WM involvement via voxel-wise analyses, and its correlation with patients' clinical status.

RESULTS

Data of 37 ARSACS (M/F = 21/16; 33.4 ± 12.4 years), 37 SPG7 (M/F = 24/13; 55.7 ± 10.7 years), and 29 HC (M/F = 13/16; 42.1 ± 17.2 years) were analyzed. While in SPG7, only a mild mean microstructural damage was found compared to HC, ARSACS patients present a severe WM involvement, with a reduced global volume (p < 0.001), an alteration of all microstructural metrics (all with p < 0.001), without a spatially defined pattern of damage but with a prominent involvement of commissural fibers. Finally, in ARSACS, a correlation between microstructural damage and SARA scores was found (p = 0.004).

CONCLUSION

In ARSACS, but not SPG7 patients, we observed a complex and multi-faced involvement of brain WM, with a clinically meaningful widespread loss of axonal and dendritic integrity, secondary demyelination and, overall, a reduction in cellularity and volume.

Environmental enrichment reduces restricted repetitive behavior by altering gray matter microstructure

Restricted, repetitive behaviors are common symptoms in neurodevelopmental disorders including autism spectrum disorder. Despite being associated with poor developmental outcomes, repetitive behaviors remain poorly understood and have limited treatment options. Environmental enrichment attenuates the development of repetitive behaviors, but the exact mechanisms remain obscure. Using the C58 mouse model of repetitive behavior, we performed diffusion tensor imaging to examine microstructural alterations associated with the development of repetitive behavior and its attenuation by environmental enrichment. The C57BL/6 mouse strain, which displays little or no repetitive behavior, was used as a control group. We observed widespread differences in diffusion metrics between C58 mice and C57BL/6 mice. In juvenile C58 mice, repetitive motor behavior displayed strong negative correlations with fractional anisotropy in multiple gray matter regions, whereas in young adult C58 mice, high repetitive motor behavior was most strongly associated with lower fractional anisotropy and higher radial diffusivity in the striatum. Environmental enrichment increased fractional anisotropy and axial diffusivity throughout gray matter regions in the brains of juvenile C58 mice and overlapped predominantly with cerebellar and sensory regions associated with repetitive behavior. Our results suggest environmental enrichment reduces repetitive behavior development by altering gray matter microstructure in the cerebellum, medial entorhinal cortex, and sensory processing regions in juvenile C58 mice. Under standard laboratory conditions, early pathology in these regions appears to contribute to later striatal and white matter dysfunction in adult C58 mice. Future studies should examine the role these regions play in the development of repetitive behavior and the relationship between sensory processing and cerebellar deficits and repetitive behavior.

Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis

OBJECT

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).

MATERIALS AND METHODS

The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.

RESULTS

The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.

DISCUSSION

In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

The integrity of the corticospinal tract and corpus callosum, and the risk of ALS: univariable and multivariable Mendelian randomization

Studies suggest that amyotrophic lateral sclerosis (ALS) compromises the integrity of white matter fiber tracts, primarily affecting motor fibers. However, it remains uncertain whether the integrity of these fibers influences the risk of ALS. We performed bidirectional two-sample Mendelian randomization (MR) and multivariable MR analyses to evaluate the associative relationships between the integrity of fiber tracts [including the corticospinal tract (CST) and corpus callosum (CC)] and the risk of ALS. Genetic instrumental variables for specific fiber tracts were obtained from published genome-wide association studies (GWASs), including 33,292 European individuals from five diffusion magnetic resonance imaging (dMRI) datasets. Summary-level GWAS data for ALS were derived from 27,205 ALS patients and 110,881 controls. The MR results suggested that an increase in the first principal component (PC1) of fractional anisotropy (FA) in the genu of the CC (GCC) was correlated with an increased risk of ALS (PFDR = 0.001, odds ratio = 1.363, 95% confidence interval 1.178-1.577). Although other neuroimaging phenotypes [mean diffusivity in the CST, radial diffusivity (RD) in the CST, FA in the GCC, PC1 in the body of the CC (BCC), PC1 in the CST, and RD in the GCC] did not pass correction, they were also considered to have suggestive associations with the risk of ALS. No evidence revealed that ALS caused changes in the integrity of fiber tracts. In summary, the results of this study provide genetic support for the potential association between the integrity of specific fiber tracts and the risk of ALS. Greater fiber integrity in the GCC and BCC may be a risk factor for ALS, while greater fiber integrity in the CST may have a protective effect on ALS. This study provides insights into ALS development.

Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging

OBJECTIVE

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disorder with insidious onset. Accurate prediction of the disease progression has received increasing attention. Cognitive scores that reflect patients' cognitive status have become important criteria for predicting AD. Most existing methods consider the relationship between neuroimages and cognitive scores to improve prediction results. However, the inherent structure information in interrelated cognitive scores is rarely considered.

METHOD

In this article, we propose a relation-aware tensor completion multitask learning method (RATC-MTL), in which the cognitive scores are represented as a third-order tensor to preserve the global structure information in clinical scores. We combine both tensor completion and linear regression into a unified framework, which allows us to capture both inter and intra modes correlations in cognitive tensor with a low-rank constraint, as well as incorporate the relationship between biological features and cognitive status by imposing a regression model on multiple cognitive scores.

RESULT

Compared to the single-task and state-of-the-art multi-task algorithms, our proposed method obtains the best results for predicting cognitive scores in terms of four commonly used metrics. Furthermore, the overall performance of our method in classifying AD progress is also the best.

CONCLUSION

Our results demonstrate the effectiveness of the proposed framework in fully exploring the global structure information in cognitive scores.

SIGNIFICANCE

This study introduces a novel concept of leveraging tensor completion to assist in disease diagnoses, potentially offering a solution to the issue of data scarcity encountered in prolonged monitoring scenarios.

Tumefactive demyelinating lesions: A literature review of recent findings

Tumefactive demyelinating lesion is a variant of multiple sclerosis that is a diagnostic challenge. Tumefactive demyelinating lesion requires extensive work-up as its clinical and radiological features are often indistinguishable from other central nervous system lesions, such as tumors. Diagnosis is further complicated by the increasing recognition that tumefactive demyelinating lesions can occur alongside, evolve into, or develop from numerous conditions other than multiple sclerosis, pointing to a possible overlapping etiology. We review herein relevant studies from 2017 onwards to provide a current view on the pathogenesis, clinical and imaging findings, novel diagnostic techniques for differential diagnoses, and management of tumefactive demyelinating lesions.

Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI

PURPOSE

To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform.

METHODS

We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD).

RESULTS

Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences.

CONCLUSION

The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach

BACKGROUND

The prediction of post-stroke language function is essential for the development of individualized treatment plans based on the personal recovery potential of aphasic stroke patients.

OBJECTIVE

To establish a framework for integrating information on connectivity disruption of the language network based on routinely collected clinical magnetic resonance (MR) images into Random Forest modeling to predict post-stroke language function.

METHODS

Language function was assessed in 76 stroke patients from the Non-Invasive Repeated Therapeutic Stimulation for Aphasia Recovery trial, using the Token Test (TT), Boston Naming Test (BNT), and Semantic Verbal Fluency (sVF) Test as primary outcome measures. Individual infarct masks were superimposed onto a diffusion tensor imaging tractogram reference set to calculate Change in Connectivity scores of language-relevant gray matter regions as estimates of structural connectivity disruption. Multivariable Random Forest models were derived to predict language function.

RESULTS

Random Forest models explained moderate to high amount of variance at baseline and follow-up for the TT (62.7% and 76.2%), BNT (47.0% and 84.3%), and sVF (52.2% and 61.1%). Initial language function and non-verbal cognitive ability were the most important variables to predict language function. Connectivity disruption explained additional variance, resulting in a prediction error increase of up to 12.8% with variable omission. Left middle temporal gyrus (12.8%) and supramarginal gyrus (9.8%) were identified as among the most important network nodes.

CONCLUSION

Connectivity disruption of the language network adds predictive value beyond lesion volume, initial language function, and non-verbal cognitive ability. Obtaining information on connectivity disruption based on routine clinical MR images constitutes a significant advancement toward practical clinical application.

The mechanism of Sangdantongluo granule in treating post-stroke spasticity based on multimodal fMRI combined with TMS: Study protocol

Introduction

Post-stroke spasticity (PSS) is among the prevalent complications of stroke, greatly affecting motor function recovery and reducing patients' quality of life without timely treatment. Sangdantongluo granule, a modern traditional Chinese patent medicine, has significant clinical efficacy in treating PSS. However, the mechanism of Sangdantongluo granule in treating PSS is still unknown. We designed this study to explore the mechanism of Sangdantongluo granule in treating PSS through multimodal functional magnetic resonance imaging (fMRI) combined with transcranial magnetic stimulation (TMS).

Methods and analysis

In a single-center, randomized, double-blind, parallel placebo-controlled study, 60 PSS patients will be recruited in China and randomly assigned to either the experimental or control groups at a ratio of 1:1. For eight weeks, Sangdantongluo granule or placebo will be utilized for intervention. The main outcome is the Modified Ashworth Scale (MAS), the secondary outcome includes the Fugl-Meyer Assessment Scale-upper Extremity (FMA-UE), National Institute of Health Stroke Scale (NIHSS), and Modified Rankin Scale (mRS), the mechanism measure is the changes in cortical excitability and multimodal fMRI at baseline and after eight weeks.

Ethics and dissemination

This study was approved by the Ethics Committee of the Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine (approval number: [202364]).

Clinical trial registration

Chinese Clinical Trial Registry, identifier: ChiCTR2300074793. Registered on 16 August 2023.

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning

Objective. Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme.Approach. FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared.Main results. Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error by about 50% on fractional anisotropy and 15% on mean diffusivity, compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme.Significance. FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network.

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

BACKGROUND

This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion-diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability.

METHODS

Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland-Altman analysis. Feature importance was determined for explainability.

RESULTS

In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6-157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1-144.9 mL). These PVs had a volume similarity of 0.88 (0.79-0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland-Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature.

CONCLUSIONS

Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings.

RELEVANCE STATEMENT

The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting.

KEY POINTS

• Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction.

Stepwise reduction of bony density in patients induces a higher risk of annular tears by deteriorating the local biomechanical environment

BACKGROUND CONTEXT

The relationship between osteoporosis and intervertebral disc degeneration (IDD) remains unclear. Considering that annular tear is the primary phenotype of IDD in the lumbar spine, the deteriorating local biomechanical environment may be the main trigger for annular tears.

PURPOSE

To investigate whether poor bone mineral density (BMD) in the vertebral bodies may increase the risk of annular tears via the degradation of the local biomechanical environment.

STUDY DESIGN

This study was a retrospective investigation with relevant numerical mechanical simulations.

PATIENT SAMPLE

A total of 64 patients with low back pain (LBP) and the most severe IDD in the L4-L5 motion segment were enrolled.

OUTCOME MEASURES

Annulus integration status was assessed using diffusion tensor fibre tractography (DTT). Hounsfield unit (HU) values of adjacent vertebral bodies were employed to determine BMD. Numerical simulations were conducted to compute stress values in the annulus of models with different BMDs and body positions.

METHODS

The clinical data of the 64 patients with low back pain were collected retrospectively. The BMD of the vertebral bodies was measured using the HU values, and the annulus integration status was determined according to DTT. The data of the patients with and without annular tears were compared, and regression analysis was used to identify the independent risk factors for annular tears. Furthermore, finite element models of the L4-L5 motion segment were constructed and validated, followed by estimating the maximum stress on the post and postlateral interfaces between the superior and inferior bony endplates (BEPs) and the annulus.

RESULTS

Patients with lower HU values in their vertebral bodies had significantly higher incidence rates of annular tears, with decreased HU values being an independent risk factor for annular tears. Moreover, increased stress on the BEP-annulus interfaces was associated with a stepwise reduction of bony density (ie, elastic modulus) in the numerical models.

CONCLUSIONS

The stepwise reduction of bony density in patients results in a higher risk of annular tears by deteriorating the local biomechanical environment. Thus, osteoporosis should be considered to be a potential risk factor for IDD biomechanically.

A review of the applications of generative adversarial networks to structural and functional MRI based diagnostic classification of brain disorders

Structural and functional MRI (magnetic resonance imaging) based diagnostic classification using machine learning has long held promise, but there are many roadblocks to achieving their potential. While traditional machine learning models suffered from their inability to capture the complex non-linear mapping, deep learning models tend to overfit the model. This is because there is data scarcity and imbalanced classes in neuroimaging; it is expensive to acquire data from human subjects and even more so in clinical populations. Due to their ability to augment data by learning underlying distributions, generative adversarial networks (GAN) provide a potential solution to this problem. Here, we provide a methodological primer on GANs and review the applications of GANs to classification of mental health disorders from neuroimaging data such as functional MRI and showcase the progress made thus far. We also highlight gaps in methodology as well as interpretability that are yet to be addressed. This provides directions about how the field can move forward. We suggest that since there are a range of methodological choices available to users, it is critical for users to interact with method developers so that the latter can tailor their development according to the users' needs. The field can be enriched by such synthesis between method developers and users in neuroimaging.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Diffusion tensor imaging in anisotropic tissues: application of reduced gradient vector schemes in peripheral nerves

BACKGROUND

In contrast to the brain, fibers within peripheral nerves have distinct monodirectional structure questioning the necessity of complex multidirectional gradient vector schemes for DTI. This proof-of-concept study investigated the diagnostic utility of reduced gradient vector schemes in peripheral nerve DTI.

METHODS

Three-Tesla magnetic resonance neurography of the tibial nerve using 20-vector DTI (DTI20) was performed in 10 healthy volunteers, 12 patients with type 2 diabetes, and 12 age-matched healthy controls. From the full DTI20 dataset, three reduced datasets including only two or three vectors along the x- and/or y- and z-axes were built to calculate major parameters. The influence of nerve angulation and intraneural connective tissue was assessed. The area under the receiver operating characteristics curve (ROC-AUC) was used for analysis.

RESULTS

Simplified datasets achieved excellent diagnostic accuracy equal to DTI20 (ROC-AUC 0.847-0.868, p ≤ 0.005), but compared to DTI20, the reduced models yielded mostly lower absolute values of DTI scalars: median fractional anisotropy (FA) ≤ 0.12; apparent diffusion coefficient (ADC) ≤ 0.25; axial diffusivity ≤ 0.96, radial diffusivity ≤ 0.07). The precision of FA and ADC with the three-vector model was closest to DTI20. Intraneural connective tissue was negatively correlated with FA and ADC (r ≥ -0.49, p < 0.001). Small deviations of nerve angulation had little effect on FA accuracy.

CONCLUSIONS

In peripheral nerves, bulk tissue DTI metrics can be approximated with only three predefined gradient vectors along the scanner's main axes, yielding similar diagnostic accuracy as a 20-vector DTI, resulting in substantial scan time reduction.

RELEVANCE STATEMENT

DTI bulk tissue parameters of peripheral nerves can be calculated with only three predefined gradient vectors at similar diagnostic performance as a standard DTI but providing a substantial scan time reduction.

KEY POINTS

• In peripheral nerves, DTI parameters can be approximated using only three gradient vectors. • The simplified model achieves a similar diagnostic performance as a standard DTI. • The simplified model allows for a significant acceleration of image acquisition. • This can help to introduce multi-b-value DTI techniques into clinical practice.

Fornix fractional anisotropy mediates the association between Mediterranean diet adherence and memory four years later in older adults without dementia

Here, we investigated whether fractional anisotropy (FA) of hippocampus-relevant white-matter tracts mediates the association between baseline Mediterranean diet adherence (MeDiAd) and verbal episodic memory over four years. Participants were healthy older adults with and without subjective cognitive decline and patients with amnestic mild cognitive impairment from the DELCODE cohort study (n = 376; age: 71.47 ± 6.09 years; 48.7 % female). MeDiAd and diffusion data were obtained at baseline. Verbal episodic memory was assessed at baseline and four yearly follow-ups. The associations between baseline MeDiAd and white matter, and verbal episodic memory's mean and rate of change over four years were tested with latent growth curve modeling. Baseline MeDiAd was associated with verbal episodic memory four years later (95 % confidence interval, CI [0.01, 0.32]) but not with its rate of change over this period. Baseline Fornix FA mediated - and, thus, explained - that association (95 % CI [0.002, 0.09]). Fornix FA may be an appropriate response biomarker of Mediterranean diet interventions on verbal memory in older adults.

Based on white matter microstructure to early identify bipolar disorder from patients with depressive episode

OBJECTIVE

Because of similar clinical manifestations, bipolar disorder (BD) patients are often misdiagnosed as major depressive disorder (MDD). This study aimed to compare the difference between depressed patients later converting to BD and unipolar depression (UD) according to diffusion tensor imaging (DTI).

METHOD

Patients with MDD (562 participants) in depressive episode states and healthy controls (HCs) (145 participants) were recruited over 10 years. Demographic and magnetic resonance imaging (MRI) data were collected at the time of recruitment. All patients with MDD were followed up for 5 years and classified into the transfer to BD (tBD) group (83 participants) and UD group (160 participants) according to the follow-up results. DTI and functional magnetic resonance imaging at baseline were compared.

RESULTS

Common abnormalities were found in both tBD and UD groups, including left superior cerebellar peduncle (SCP.L), right anterior limb of the internal capsule (ALIC.R), right superior fronto-occipital fasciculus (SFOF.R), and right inferior fronto-occipital fasciculus (IFOF.R). The tBD showed more extensive abnormalities than the UD in the body of corpus callosum, fornix, left superior corona radiata, left posterior corona radiata, left superior longitudinal fasciculus, and left superior fronto-occipital fasciculus.

CONCLUSION

The study demonstrated the common and distinct abnormalities of tBD and UD when compared to HC. The tBD group showed more extensive disruptions of white matter integrity, which could be a potential biomarker for the early identification of BD.

IVD fibrosis and disc collapse comprehensively aggravate vertebral body disuse osteoporosis and zygapophyseal joint osteoarthritis by posteriorly shifting the load transmission pattern

BACKGROUND

Disuse is a typical phenotype of osteoporosis, but the underlying mechanism has yet to be identified in elderly patients. Disc collapse and intervertebral disc (IVD) fibrosis are two main pathological changes in IVD degeneration (IDD) progression, given that these changes affect load transmission patterns, which may lead to disuse osteoporosis of vertebral bodies and zygapophyseal joint (ZJ) osteoarthritis (ZJOA) biomechanically.

METHODS

Clinical data from 59 patients were collected retrospectively. Patient vertebral bony density, ZJOA grade, and disc collapse status were judged via CT. The IVD fibrosis grade was determined based on the FA measurements. Regression analyses identified potential independent risk factors for osteoporosis and ZJOA. L4-L5 numerical models with and without disc collapse and IVD fibrosis were constructed; stress distributions on the bony endplate (BEP) and zygapophyseal joint (ZJ) cartilages were computed in models with and without disc collapse and IVD fibrosis.

RESULTS

A significantly lower disc height ratio and significantly greater FA were recorded in patients with ZJOA. A significant correlation was observed between lower HU values and two parameters related to IDD progression. These factors were also proven to be independent risk factors for both osteoporosis and ZJOA. Correspondingly, compared to the intact model without IDD. Lower stress on vertebral bodies and greater stress on ZJOA can be simultaneously recorded in models of disc collapse and IVD fibrosis.

CONCLUSIONS

IVD fibrosis and disc collapse simultaneously aggravate vertebral body disuse osteoporosis and ZJOA by posteriorly shifting the load transmission pattern.

The application of functional imaging in visual field defects: a brief review

Visual field defects (VFDs) represent a prevalent complication stemming from neurological and ophthalmic conditions. A range of factors, including tumors, brain surgery, glaucoma, and other disorders, can induce varying degrees of VFDs, significantly impacting patients' quality of life. Over recent decades, functional imaging has emerged as a pivotal field, employing imaging technology to illustrate functional changes within tissues and organs. As functional imaging continues to advance, its integration into various clinical aspects of VFDs has substantially enhanced the diagnostic, therapeutic, and management capabilities of healthcare professionals. Notably, prominent imaging techniques such as DTI, OCT, and MRI have garnered widespread adoption, yet they possess unique applications and considerations. This comprehensive review aims to meticulously examine the application and evolution of functional imaging in the context of VFDs. Our objective is to furnish neurologists and ophthalmologists with a systematic and comprehensive comprehension of this critical subject matter.

Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) has been regarded as a disconnection syndrome with functional and structural disturbances. However, as the anatomic determinants, the structural disconnections in PD have yet to be fully elucidated.

PURPOSE

To non-invasively construct structural networks based on microstructural complexity and to further investigate their potential topological abnormalities in PD given the technical superiority of diffusion kurtosis imaging (DKI) to the quantification of microstructure.

MATERIAL AND METHODS

The microstructural data of gray matter in both the PD group and the healthy control (HC) group were acquired using DKI. The structural networks were constructed at the group level by a covariation approach, followed by the calculation of topological properties based on graph theory and statistical comparisons between groups.

RESULTS

A total of 51 patients with PD and 50 HCs were enrolled. Individuals were matched between groups with respect to demographic characteristics (P >0.05). The constructed structural networks in both the PD and HC groups featured small-world properties. In comparison with the HC group, the PD group exhibited significantly altered global properties, with higher normalized characteristic path lengths, clustering coefficients, local efficiency values, and characteristic path lengths and lower global efficiency values (P <0.05). In terms of nodal centralities, extensive nodal disruptions were observed in patients with PD (P <0.05); these disruptions were mainly distributed in the sensorimotor network, default mode network, frontal-parietal network, visual network, and subcortical network.

CONCLUSION

These findings contribute to the technical application of DKI and the elucidation of disconnection syndrome in PD.

Diffusion tensor imaging in photosensitive and nonphotosensitive juvenile myoclonic epilepsy

INTRODUCTION/BACKGROUND

Juvenile myoclonic epilepsy (JME) syndrome is known to cause alterations in brain structure and white matter integrity. The study aimed to determine structural white matter changes in patients with JME and to reveal the differences between the photosensitive (PS) and nonphotosensitive (NPS) subgroups by diffusion tensor imaging (DTI) using the tract-based spatial statistics (TBSS) method.

METHODS

This study included data from 16 PS, 15 NPS patients with JME, and 41 healthy participants. The mean fractional anisotropy (FA) values of these groups were calculated, and comparisons were made via the TBSS method over FA values in the whole-brain and 81 regions of interest (ROI) obtained from the John Hopkins University White Matter Atlas.

RESULTS

In the whole-brain TBSS analysis, no significant differences in FA values were observed in pairwise comparisons of JME patient group and subgroups with healthy controls (HCs) and in comparison between JME subgroups. In ROI-based TBSS analysis, an increase in FA values of right anterior corona radiata and left corticospinal pathways was found in JME patient group compared with HC group. When comparing JME-PS patients with HCs, an FA increase was observed in the bilateral anterior corona radiata region, whereas when comparing JME-NPS patients with HCs, an FA increase was observed in bilateral corticospinal pathway. Moreover, in subgroup comparison, an increase in FA values was noted in corpus callosum genu region in JME-PS compared with JME-NPS.

CONCLUSIONS

Our results support the disruption in thalamofrontal white matter integrity in JME, and subgroups and highlight the importance of using different analysis methods to show the underlying microstructural changes.

Diffusion Tensor Imaging in Alzheimer's Studies

In this chapter, we describe the use of quantitative metrics of white matter obtained from the diffusion tensor model based on diffusion-weighted imaging in Alzheimer's disease (AD). Our description synthesizes insights not only from patient populations with AD dementia but also from participants at risk for AD dementia (e.g., amnestic mild cognitive impairment, subjective cognitive decline, or familial AD mutation carriers). A reference to studies examining correlations with behavioral variables is also included. Our main message is to caution against the overinterpretation of diffusion metrics and to favor analyses that focus on regions of interest or major white matter tracts for biomarker studies in AD.

Diffusion tensor imaging changes of the cortico-thalamic-striatal tracts correlate with fatigue and disability in people with relapsing-remitting MS

PURPOSE

To investigate how the microstructural neural integrity of cortico-thalamic-striatal (CTS) tracts correlate with fatigue and disability over time. The primary outcome was diffusion tensor imaging (DTI) metrics change over time, and the secondary outcome was correlations with fatigue and disability in people with RRMS (pw-RRMS).

METHODS

76 clinically stable pw-RRMS and 43 matched healthy controls (HCs). The pw-RRMS cohort consisted of three different treatment subgroups. All participants underwent disability, cognitive, fatigue and mental health assessments. Structural and diffusion scans were performed at baseline (BL) and 2-year follow-up (2-YFU) for all participants. Fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD, AD) of normal-appearing white matter (NAWM) and white matter lesion (WML) in nine tracts-of-interests (TOIs) were estimated using our MRtrix3 in-house pipeline.

RESULTS

We found significant BL and 2-YFU differences in most diffusion metrics in TOIs in pw-RRMS compared to HCs (pFDR ≤ 0.001; false-detection-rate (FDR)-corrected). There was a significant decrease in WML diffusivities and an increase in FA over the follow-up period in most TOIs (pFDR ≤ 0.001). Additionally, there were no differences in DTI parameters across treatment groups. AD and MD were positively correlated with fatigue scores (r ≤ 0.33, p ≤ 0.01) in NAWM-TOIs, while disability (EDSS) was negatively correlated with FA in most NAWM-TOIs (|r|≤0.31, p ≤ 0.01) at both time points. Disability scores correlated with all diffusivity parameters (r ≤ 0.29, p ≤ 0.01) in most WML-TOIs at both time points.

CONCLUSION

Statistically significant changes in diffusion metrics in WML might be indicative of integrity improvement over two years in CTS tracts in clinically stable pw-RRMS. This finding represents structural changes within lesioned tracts. Measuring diffusivity in pw-RRMS affected tracts might be a relevant measure for future remyelination clinical trials.

What clinical parameter strongly associates white matter tract alterations in a Multiple Sclerosis population with voiding dysfunction? A prospective exploratory study

Purpose

To correlate clinical and urodynamics parameters in Multiple Sclerosis patients (MS) presenting Lower Urinary Tract Symptoms (LUTS) with both Expanded Disability Status Scale (EDSS) and changes in white matter integrity as seen on Diffusion Tensor Images (DTI). LUTS worsen throughout MS, as does lesion burden. We investigated which symptoms correlated best with structural changes in white matter structure.

Materials and Methods

Ten adult women >18 years were recruited with stable MS for ≥3 months and voiding dysfunction defined as %PVR/BV > 20%. Patients participated in a clinical Urodynamic Study (UDS) and completed several questionnaires (i.e., HAM, AUASS, NBS-QoL). DTI images were acquired using a 7-Tesla Siemens MAGNETOM Terra MRI scanner. DTI maps were constructed, and individual patients were co-registered with the ICBM-DTI-81 white matter atlas to extract fractional anisotropy (FA) and mean diffusivity (MD). Pearson's correlation test was performed between each WMT and clinical parameters and between clinical parameters and the EDSS score as well. P-values < 0.05 were considered significant.

Results

Of the clinical parameters, %PVR/BV obtained from the average of multiple un-instrumented uroflow assessments had significant correlations to the greatest number of WMTs. Furthermore, we observed that in all recorded clinical parameters, %PVR/BV was the only significant parameter correlated to the EDSS score.

Conclusion

This study demonstrates that %PVR/BV can be used as an objective parameter to gauge WMT changes and disease progression in MS patients. Future studies are needed to refine this model.