Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

Marriage is associated with decreased lesion volumes and less brain atrophy in people with multiple sclerosis

BACKGROUND

Married or long-term partnered patients with chronic diseases generally have better outcomes than unmarried patients, likely due to the potential for multifaceted support. However, the impact of marital status on multiple sclerosis (MS) radiographic disease burden is currently unknown.

OBJECTIVE

To compare total white matter hyperintensity lesion volumes, periventricular lesion volumes, and whole brain and grey matter volumes in married and unmarried people with MS (PwMS).

METHODS

We utilized multivariable linear regression to assess for differences in brain atrophy and lesion volumes between these two groups controlling for sex, MS disease duration in years, hypertension, history of smoking, alcohol consumption, history of depression and/or anxiety, and medication possession ratio (MPR).

RESULTS

Married PwMS had significantly lower total lesion volumes (β =  - 6.3, 95% CI - 12.1 to - 0.5, p = 0.033), lower PV lesion volumes (β =  - 6.1, 95% CI - 11.7 to - 0.6, p = 0.030), higher normalized whole brain volumes (β = 38.3, 95% CI 6.0 to 70.7, p = 0.021), and higher normalized grey matter volumes (β = 20.9, 95% CI - 0.7 to 42.6, p = 0.058) than unmarried PwMS.

CONCLUSION

Being married may be associated with improved MS outcomes as evidenced by decreased radiographic MS disease burden.

Association of Social Determinants of Health With Brain MRI Outcomes in Individuals With Pediatric Onset Multiple Sclerosis

BACKGROUND AND OBJECTIVES

Accumulating evidence points to worse clinical outcomes among adults with multiple sclerosis (MS) belonging to minority or poverty-affected groups. By contrast, little is known about the outcomes of these populations with pediatric-onset MS (POMS). Individuals with POMS represent 5% of the MS population and are more racially diverse yet have been understudied regarding socioeconomic environment or characteristics. In this study, we investigated the association between childhood social determinants of health (SDOH) and brain MRI outcomes in patients with POMS.

METHODS

This is a retrospective single-site cohort study of patients with POMS with brain MRI quantitatively analyzed using icobrain software to yield total white matter lesion, black hole, whole brain, white matter, and gray matter volumes. All patients with POMS evaluated at New York University Langone MS Center and who underwent high-quality volumetric MRI scans were included in this study. SDOH indicators of race, ethnicity, health insurance type, parental education, and childhood neighborhood social vulnerability index (SVI) were examined for association with MRI outcomes using linear least absolute shrinkage selection operator penalized regression modeling. Disease-modifying therapy (DMT) timing and DMT efficacy were compared for each SDOH category.

RESULTS

A total of 138 patients with POMS (70% female) were included with a mean age of 19.86 years and median disease duration of 4 years at time of scan. Public health insurance, Black race, Hispanic ethnicity, low parental education, and high SVI (greater neighborhood disadvantage) were each associated with white matter lesion and black hole volume. SVI was the strongest individual predictor of total white matter lesion (β = 4.63, p = 0.002) and black hole volume (β = 2.91, p = 0.003). In models incorporating all SDOH variables, public health insurance was the strongest predictor of total lesion (β = 2.48, p = 0.01) and black hole volume (β = 1.50, p = 0.02), attenuating the effect of SVI (β = 1.66, p = 0.33 and β = 1.00, p = 0.39). There were no differences in DMT timing or efficacy between categories of social disadvantage.

DISCUSSION

Individual-level and neighborhood-level indicators of social disadvantage are associated with worse brain MRI outcomes in POMS. Further investigation of race, ethnicity, and childhood disadvantage as risk factors of MS susceptibility and severity is needed to reduce MS health disparities.

Utility of icobrain for brain volumetry in multiple sclerosis clinical practice

BACKGROUND

Few studies on multiple sclerosis (MS) have explored the variability of percentage brain volume change (PBVC) measurements obtained from different clinical MRIs. In a retrospective multicentre cohort study, we quantified the variability of annualised PBVC in clinical MRIs.

METHODS

Clinical MRIs of relapse-onset MS patients were assessed by icobrain. Volumetric data were analysed on same-scanner and different-scanner MRI pairs if they passed quality control criteria. Alignment similarity between two images had to be comparable to same-scanner scan-rescan images.

RESULTS

Of 6826 MRIs, 85 % had appropriate volumetric sequences and 4446 serial MRI pairs were analysed. 3334 (75 %) MRI pairs from 1207 patients met the inclusions. The PBVC of included MRI pairs showed variance of 0.78 % for same-scanner pairs and 0.80 % for different-scanner pairs. Further selection of included MRI pairs with the best variance resulted in 1885 (42 %) MRI pairs with PBVC variance of 0.34 %. Excluded MRI pairs with poor alignment similarity had variances of 2.97 % for same-scanner pairs and 20.79 % for different-scanner pairs.

CONCLUSION

Icobrain should be utilised for PBVC determination only on selected MRIs with the best alignment similarity. Applying strict selection criteria for the included MRI pairs and longitudinal imaging on the same scanner remain mandatory to reduce PBVC variability.

Treatment Monitoring in Multiple Sclerosis - Efficacy and Safety

Magnetic resonance imaging is the most sensitive method for detecting inflammatory activity in multiple sclerosis, particularly in the brain where it reveals subclinical inflammation. Established MRI markers include contrast-enhancing lesions and active T2 lesions. Recent promising markers like slowly expanding lesions and phase rim lesions are being explored for monitoring chronic inflammation, but require further validation for clinical use. Volumetric and quantitative MRI techniques are currently limited to clinical trials and are not yet recommended for routine clinical use. Additionally, MRI is crucial for detecting complications from disease-modifying treatments and for implementing MRI-based pharmacovigilance strategies, such as in patients treated with natalizumab.

A deep learning model for brain segmentation across pediatric and adult populations

Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across various life stages. However, existing solutions are specifically designed for certain age ranges, limiting their applicability in monitoring brain development from infancy to late adulthood. This retrospective study aims to develop and validate a brain segmentation model across pediatric and adult populations. First, we trained a deep learning model to segment tissues and brain structures using T1-weighted MR images from 390 patients (age range: 2-81 years) across four different datasets. Subsequently, the model was validated on a cohort of 280 patients from six distinct test datasets (age range: 4-90 years). In the initial experiment, the proposed deep learning-based pipeline, icobrain-dl, demonstrated segmentation accuracy comparable to both pediatric and adult-specific models across diverse age groups. Subsequently, we evaluated intra- and inter-scanner variability in measurements of various tissues and structures in both pediatric and adult populations computed by icobrain-dl. Results demonstrated significantly higher reproducibility compared to similar brain quantification tools, including childmetrix, FastSurfer, and the medical device icobrain v5.9 (p-value< 0.01). Finally, we explored the potential clinical applications of icobrain-dl measurements in diagnosing pediatric patients with Cerebral Visual Impairment and adult patients with Alzheimer's Disease.

The association of menopausal hormone levels with progression-related biomarkers in multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) progression coincides temporally with menopause. However, it remains unclear whether the changes in disease course are related to the changes in reproductive hormone concentrations. We assessed the association of menopausal hormonal levels with progression-related biomarkers of MS and evaluated the changes in serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) levels during menopausal hormone therapy (MHT) in a prospective baseline-controlled design.

METHODS

The baseline serum estradiol, follicle stimulating hormone, and luteinizing hormone levels were measured from menopausal women with MS (n = 16) and healthy controls (HC, n = 15). SNfL and sGFAP were measured by single-molecule array. The associations of hormone levels with sNfL and sGFAP, and with Expanded Disability Status Scale (EDSS) and lesion load and whole brain volumes (WBV) in MRI were analyzed with Spearman's rank correlation and age-adjusted linear regression model. Changes in sNfL and sGFAP during one-year treatment with estradiol hemihydrate combined with cyclic dydrogesterone were assessed with Wilcoxon Signed Ranks Test.

RESULTS

In MS group, baseline estradiol had a positive correlation with WBV in MRI and an inverse correlation with lesion load, sNfL and sGFAP, but no correlation with EDSS. The associations of low estradiol with high sGFAP and low WBV were independent of age. During MHT, there was no significant change in sNfL and sGFAP levels in MS group while in HC, sGFAP slightly decreased at three months but returned to baseline at 12 months.

CONCLUSION

Our preliminary findings suggest that low estradiol in menopausal women with MS has an age-independent association with more pronounced brain atrophy and higher sGFAP and thus advanced astrogliosis which could partially explain the more rapid progression of MS after menopause. One year of MHT did not alter the sGFAP or sNfL levels in women with MS.

Towards validation in clinical routine: a comparative analysis of visual MTA ratings versus the automated ratio between inferior lateral ventricle and hippocampal volumes in Alzheimer's disease diagnosis

PURPOSE

To assess the performance of the inferior lateral ventricle (ILV) to hippocampal (Hip) volume ratio on brain MRI, for Alzheimer's disease (AD) diagnostics, comparing it to individual automated ILV and hippocampal volumes, and visual medial temporal lobe atrophy (MTA) consensus ratings.

METHODS

One-hundred-twelve subjects (mean age ± SD, 66.85 ± 13.64 years) with varying degrees of cognitive decline underwent MRI using a Philips Ingenia 3T. The MTA scale by Scheltens, rated on coronal 3D T1-weighted images, was determined by three experienced radiologists, blinded to diagnosis and sex. Automated volumetry was computed by icobrain dm (v. 5.10) for total, left, right hippocampal, and ILV volumes. The ILV/Hip ratio, defined as the percentage ratio between ILV and hippocampal volumes, was calculated and compared against a normative reference population (n = 1903). Inter-rater agreement, association, classification accuracy, and clinical interpretability on patient level were reported.

RESULTS

Visual MTA scores showed excellent inter-rater agreement. Ordinal logistic regression and correlation analyses demonstrated robust associations between automated brain segmentations and visual MTA ratings, with the ILV/Hip ratio consistently outperforming individual hippocampal and ILV volumes. Pairwise classification accuracy showed good performance without statistically significant differences between the ILV/Hip ratio and visual MTA across disease stages, indicating potential interchangeability. Comparison to the normative population and clinical interpretability assessments showed commensurability in classifying MTA "severity" between visual MTA and ILV/Hip ratio measurements.

CONCLUSION

The ILV/Hip ratio shows the highest correlation to visual MTA, in comparison to automated individual ILV and hippocampal volumes, offering standardized measures for diagnostic support in different stages of cognitive decline.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software

PURPOSE

The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.

METHODS

In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.

RESULTS

A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).

CONCLUSION

The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Co-registration with subtraction and color-coding or fusion improves the detection of new and growing lesions on follow-up MRI examination of patients with multiple sclerosis

PURPOSE

The purpose of this study was to compare the performance of three magnetic resonance imaging (MRI) reading methods in the follow-up of patients with multiple sclerosis (MS).

MATERIALS AND METHODS

This retrospective study included patients with MS who underwent two brain follow-up MRI examinations with three-dimensional fluid-attenuated inversion recovery (FLAIR) sequences between September 2016 and December 2019. Two neuroradiology residents independently reviewed FLAIR images using three post-processing methods including conventional reading (CR), co-registration fusion (CF), and co-registration subtraction with color-coding (CS), while being blinded to all data but FLAIR images. The presence and number of new, growing, or shrinking lesions were compared between reading methods. The reading time, reading confidence, and inter- and intra-observer agreements were also assessed. An expert neuroradiologist established the standard of reference. Statistical analyses were corrected for multiple testing.

RESULTS

A total of 198 patients with MS were included. There were 130 women and 68 men, with a mean age of 41 ± 12 (standard deviation) years (age range: 21-79 years). Using CS and CF, more patients were detected with new lesions compared to CR (93/198 [47%] and 79/198 [40%] vs. 54/198 [27%], respectively; P < 0.01). The median number of new hyperintense FLAIR lesions detected was significantly greater using CS and CF compared to CR (2 [Q1, Q3: 0, 6] and 1 [Q1, Q3: 0, 3] vs. 0 [Q1, Q3: 0, 1], respectively; P < 0.001). The mean reading time was significantly shorter using CS and CF compared to CR (P < 0.001), with higher confidence in readings and higher inter- and intra-observer agreements.

CONCLUSION

Post-processing tools such as CS and CF substantially improve the accuracy of follow-up MRI examinations in patients with MS while reducing reading time and increasing readers' confidence and reproducibility.

BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation

In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA-MS to other widely used tools. Second, we tested how BIANCA-MS performs in separate datasets. Finally, we evaluated BIANCA-MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA-MS clearly outperformed other available tools in both high- and low-resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA-MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA-MS is a robust and accurate approach for automated MS lesion segmentation.

Higher dietary quality is prospectively associated with lower MRI FLAIR lesion volume, but not with hazard of relapse, change in disability or black hole volume in people with Multiple Sclerosis

BACKGROUND

The influence of diet quality on multiple sclerosis (MS) progression or inflammatory activity is not well understood.

METHODS

Study participants with MS from the AusLong cohort, were followed annually (10 years, n = 223 post-onset). At baseline, 5 and 10-year reviews, indices of dietary quality - the Australian Recommended Food Score (ARFS) and Diet Quality Tracker (DQT) - were calculated from self-reported dietary intake data of the preceding 12 months (Food Frequency Questionnaire, Dietary Questionnaire for Epidemiological Studies v2). Associations were examined between measures of dietary quality with measures of MS progression and inflammatory activity hazard of relapse, annualised disability progression (Expanded Disability Status Scale, EDSS) and Magnetic Resonance Imaging (MRI) outcomes. MRI outcomes included fluid-attenuated inversion recovery (FLAIR, T2 MRI) lesion volume and black hole volume (T1 MRI) in the juxtacortical, periventricular, and infratentorial regions of the brain, as well as total calculated from the sum of the three regions.

RESULTS

A higher diet quality (at least with the ARFS) was associated with lower FLAIR lesion volume in the periventricular region only (highest vs lowest quartile: β=-1.89,95%CI=-3.64, -0.13, p = 0.04, periventricular FLAIR region median (IQR) for 5-year review: 4.41 (6.06) and 10-year review: 4.68 (7.27)). Associations with black hole lesion volume, hazard of relapse, and annualised EDSS progression, lacked in significance and/or dose-dependency.

CONCLUSION

We found evidence that diet quality may have a role in modulating one aspect of MS inflammatory activity (periventricular MRI FLAIR lesion volume), but not other MRI and clinical outcome measures.

Reduced alpha2 power is associated with slowed information processing speed in multiple sclerosis

OBJECTIVE

Cognitive impairment is common in multiple sclerosis (MS), significantly impacts daily functioning, is time-consuming to assess, and is prone to practice effects. We examined whether the alpha band power measured with magnetoencephalography (MEG) is associated with the different cognitive domains affected by MS.

METHODS

Sixty-eight MS patients and 47 healthy controls underwent MEG, T1- and FLAIR-weighted magnetic resonance imaging (MRI), and neuropsychological testing. Alpha power in the occipital cortex was quantified in the alpha1 (8-10 Hz) and alpha2 (10-12 Hz) bands. Next, we performed best subset regression to assess the added value of neurophysiological measures to commonly available MRI measures.

RESULTS

Alpha2 power significantly correlated with information processing speed (p < 0.001) and was always retained in all multilinear models, whereas thalamic volume was retained in 80% of all models. Alpha1 power was correlated with visual memory (p < 0.001) but only retained in 38% of all models.

CONCLUSIONS

Alpha2 (10-12 Hz) power in rest is associated with IPS, independent of standard MRI parameters. This study stresses that a multimodal assessment, including structural and functional biomarkers, is likely required to characterize cognitive impairment in MS. Resting-state neurophysiology is thus a promising tool to understand and follow up changes in IPS.

Stability of longitudinal DTI metrics in MS with treatment of injectables, fingolimod and dimethyl fumarate

BACKGROUND AND PURPOSE

Diffusion MRI (dMRI) is sensitive to microstructural changes in white matter of people with relapse-remitting multiple sclerosis (pw-RRMS) that lead to progressive disability. The role of diffusion in assessing the efficacy of different therapies requires more investigation. This study aimed to evaluate selected dMRI metrics in normal-appearing white matter and white matter-lesion in pw-RRMS and healthy controls longitudinally and compare the effect of therapies given.

MATERIAL AND METHODS

Structural and dMRI scans were acquired from 78 pw-RRMS (29 injectables, 36 fingolimod, 13 dimethyl fumarate) and 43 HCs at baseline and 2-years follow-up. Changes in dMRI metrics and correlation with clinical parameters were evaluated.

RESULTS

Differences were observed in most clinical parameters between pw-RRMS and HCs at both timepoints (p ≤ 0.01). No significant differences in average changes over time were observed for any dMRI metric between treatment groups in either tissue type. Diffusion metrics in NAWM and WML correlated negatively with most cognitive domains, while FA correlated positively at baseline but only for NAWM at follow-up (p ≤ 0.05). FA correlated negatively with disability in NAWM and WML over time, while MD and RD correlated positively only in NAWM.

CONCLUSIONS

This is the first DTI study comparing the effect of different treatments on dMRI parameters over time in a stable cohort of pw-RRMS. The results suggest that brain microstructural changes in a stable MS cohort are similar to HCs independent of the therapies used.

Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

A pro-inflammatory diet in people with multiple sclerosis is associated with an increased rate of relapse and increased FLAIR lesion volume on MRI in early multiple sclerosis: A prospective cohort study

BACKGROUND

A pro-inflammatory diet has been posited to induce chronic inflammation within the central nervous system (CNS), and multiple sclerosis (MS) is an inflammatory disease of the CNS.

OBJECTIVE

We examined whether Dietary Inflammatory Index (DII^®)) scores are associated with measures of MS progression and inflammatory activity.

METHODS

A cohort with a first clinical diagnosis of CNS demyelination was followed annually (10 years, n = 223). At baseline, 5- and 10-year reviews, DII and energy-adjusted DII (E-DII^TM) scores were calculated (food frequency questionnaire) and assessed as predictors of relapses, annualised change in disability (Expanded Disability Status Scale) and two magnetic resonance imaging measures; fluid-attenuated inversion recovery (FLAIR) lesion volume and black hole lesion volume.

RESULTS

A more pro-inflammatory diet was associated with a higher relapse risk (highest vs. lowest E-DII quartile: hazard ratio = 2.24, 95% confidence interval (CI) = -1.16, 4.33, p = 0.02). When we limited analyses to those assessed on the same manufacturer of scanner and those with a first demyelinating event at study entry (to reduce error and disease heterogeneity), an association between E-DII score and FLAIR lesion volume was evident (β = 0.38, 95% CI = 0.04, 0.72, p = 0.03).

CONCLUSION

There is a longitudinal association between a higher DII and a worsening in relapse rate and periventricular FLAIR lesion volume in people with MS.

Automated Registration and Color Labeling of Serial 3D Double Inversion Recovery MR Imaging for Detection of Lesion Progression in Multiple Sclerosis

Automated co-registration and subtraction techniques have been shown to be useful in the assessment of longitudinal changes in multiple sclerosis (MS) lesion burden, but the majority depend on T2-fluid-attenuated inversion recovery sequences. We aimed to investigate the use of a novel automated temporal color complement imaging (CCI) map overlapped on 3D double inversion recovery (DIR), and to assess its diagnostic performance for detecting disease progression in patients with multiple sclerosis (MS) as compared to standard review of serial 3D DIR images. We developed a fully automated system that co-registers and compares baseline to follow-up 3D DIR images and outputs a pseudo-color RGB map in which red pixels indicate increased intensity values in the follow-up image (i.e., progression; new/enlarging lesion), blue-green pixels represent decreased intensity values (i.e., disappearing/shrinking lesion), and gray-scale pixels reflect unchanged intensity values. Three neuroradiologists blinded to clinical information independently reviewed each patient using standard DIR images alone and using CCI maps based on DIR images at two separate exams. Seventy-six follow-up examinations from 60 consecutive MS patients who underwent standard 3 T MR brain MS protocol that included 3D DIR were included. Median cohort age was 38.5 years, with 46 women, 59 relapsing-remitting type MS, and median follow-up interval of 250 days (interquartile range: 196-394 days). Lesion progression was detected in 67.1% of cases using CCI review versus 22.4% using standard review, with a total of 182 new or enlarged lesions using CCI review versus 28 using standard review. There was a statistically significant difference between the two methods in the rate of all progressive lesions (P < 0.001, McNemar's test) as well as cortical progressive lesions (P < 0.001). Automated CCI maps using co-registered serial 3D DIR, compared to standard review of 3D DIR alone, increased detection rate of MS lesion progression in patients undergoing clinical brain MRI exam.

Longitudinal changes in global structural brain connectivity and cognitive performance in former hospitalized COVID-19 survivors: an exploratory study

BACKGROUND

Long-term sequelae of COVID-19 can result in reduced functionality of the central nervous system and substandard quality of life. Gaining insight into the recovery trajectory of admitted COVID-19 patients on their cognitive performance and global structural brain connectivity may allow a better understanding of the diseases' relevance.

OBJECTIVES

To assess whole-brain structural connectivity in former non-intensive-care unit (ICU)- and ICU-admitted COVID-19 survivors over 2 months following hospital discharge and correlate structural connectivity measures to cognitive performance.

METHODS

Participants underwent Magnetic Resonance Imaging brain scans and a cognitive test battery after hospital discharge to evaluate structural connectivity and cognitive performance. Multilevel models were constructed for each graph measure and cognitive test, assessing the groups' influence, time since discharge, and interactions. Linear regression models estimated whether the graph measurements affected cognitive measures and whether they differed between ICU and non-ICU patients.

RESULTS

Six former ICU and six non-ICU patients completed the study. Across the various graph measures, the characteristic path length decreased over time (β = 0.97, p = 0.006). We detected no group-level effects (β = 1.07, p = 0.442) nor interaction effects (β = 1.02, p = 0.220). Cognitive performance improved for both non-ICU and ICU COVID-19 survivors on four out of seven cognitive tests 2 months later (p < 0.05).

CONCLUSION

Adverse effects of COVID-19 on brain functioning and structure abate over time. These results should be supported by future research including larger sample sizes, matched control groups of healthy non-infected individuals, and more extended follow-up periods.

Semisupervised white matter hyperintensities segmentation on MRI

This study proposed a semisupervised loss function named level-set loss (LSLoss) for cerebral white matter hyperintensities (WMHs) segmentation on fluid-attenuated inversion recovery images. The training procedure did not require manually labeled WMH masks. Our image preprocessing steps included biased field correction, skull stripping, and white matter segmentation. With the proposed LSLoss, we trained a V-Net using the MRI images from both local and public databases. Local databases were the small vessel disease cohort (HKU-SVD, n = 360) and the multiple sclerosis cohort (HKU-MS, n = 20) from our institutional imaging center. Public databases were the Medical Image Computing Computer-assisted Intervention (MICCAI) WMH challenge database (MICCAI-WMH, n = 60) and the normal control cohort of the Alzheimer's Disease Neuroimaging Initiative database (ADNI-CN, n = 15). We achieved an overall dice similarity coefficient (DSC) of 0.81 on the HKU-SVD testing set (n = 20), DSC = 0.77 on the HKU-MS testing set (n = 5), and DSC = 0.78 on MICCAI-WMH testing set (n = 30). The segmentation results obtained by our semisupervised V-Net were comparable with the supervised methods and outperformed the unsupervised methods in the literature.

Patient reported outcomes in a secondary progressive MS cohort related to cognition, MRI and physical outcomes

BACKGROUND

Patient-reported outcomes (PROs) are increasingly being used as outcomes in secondary progressive multiple sclerosis (SPMS) trials. We examined how PROs reflect disease burden in SPMS.

METHODS

In this observational prospective study, 65 SPMS patients were examined by five different PROs (Fatigue Scale Motor Cognition (FSMC), Multiple Sclerosis Impact Scale version 2 (MSIS-29v2), 36-Item Short Form Health Survey version 2 (SF-36v2), EQ-5D-5L and Work Productivity and Activity Impairment Questionnaire: Multiple Sclerosis version 2.0 (WPAI:MS)); two different rating scales, Multiple Sclerosis Impairment Scale (MSIS) and Expanded Disability Status Scale (EDSS); functional tests of mobility (Timed-25-Foot Walk (T-25FW), 6-Spot Step Test (6-SST) and (9-Hole Peg Test (9-HPT)); cognitive tests (Symbol Digital Modalities Test (SDMT) and Brief Visuospatial Memory Test-Revised (BVMT-R)); and multimodal Magnetic Resonance Imaging (MRI).

RESULTS

When the PROs were divided into physical and psychological subscores, the PRO physical subscores of FSMC, MSIS-29v2 and SF-36v2 correlated with physical rating scales (EDSS, MSIS) and physical measures of upper (9-HPT) and lower extremity function (T-25FW and 6-SST)) (p = 0.04-0.0001). 9-HPT correlated the least with physical subscores of PROs but showed the strongest correlation with activity impairment (subscore of WPAI:MS). In contrast, psychological PRO subscores of FSMC, MSIS-29v2 and SF-36v2 did not reflect the cognitive outcomes (SDMT and BVMT-R), although the cognitive scores correlated with disease burden indicated by MRI lesion volumes. The psychological PRO subscores did not correlate with fatigue, physical and MRI outcomes either.

CONCLUSION

Correlation between PRO physical subscores and physical outcomes supports PROs as potentially useful clinical endpoints in SPMS. The results of this study indicate that patients with SPMS highly perceive their mobility on function of their lower extremities, while they perceive their daily activities highly dependent on function of the upper extremities. Psychological subscores of MS specific PROs may be less suitable as surrogate markers for the cognitive status and should be considered as a mental quality of life measurement independent of disease burden.

Volumetric brain changes in MOGAD: A cross-sectional and longitudinal comparative analysis

BACKGROUND

Relatively little is known about how global and regional brain volumes changes in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) compare with Multiple Sclerosis (MS), Neuromyelitis optica spectrum disorder (NMOSD), and healthy controls (HC).

OBJECTIVE

To compare global and regional brain volumes in MOGAD, MS, NMOSD, and HC cross-sectionally as well as longitudinally in a subset of patients.

METHODS

We retrospectively reviewed all adult MOGAD and NMOSD patients with brain MRI performed in stable remission and compared them with MS patients and HC. Volumetric parameters were assessed using the FDA-approved icobrain software. adjusted for age and sex.

RESULTS

Twenty-four MOGAD, 47 NMOSD, 40 MS patients, and 37 HC were included in the cross-sectional analyses. Relative to HC, the age-adjusted whole brain (WB) volume was significantly lower in patients with MOGAD (p=0.0002), NMOSD (p=0.042), and MS (p=0.01). Longitudinal analysis of a subset of 8 MOGAD, 22 NMOSD, and 34 MS patients showed a reduction in the WB and cortical gray matter (CGM) volumes over time in all three disease groups, without statistically significant differences between groups. The MOGAD group had a greater loss of thalamic volume compared to MS (p=0.028) and NMOSD (p=0.023) and a greater loss of hippocampal volumes compared to MS (p=0.007).

CONCLUSIONS

Age-adjusted WB volume loss was evident in all neuroinflammatory conditions relative to HC in cross-sectional comparisons. In longitudinal analyses, MOGAD patients had a higher thalamic atrophy rate relative to MS and NMOSD, and a higher hippocampal atrophy rate relative to MS. Larger studies are needed to validate these findings and to investigate their clinical implications.

Brief international cognitive assessment for MS (BICAMS) and global brain volumes in early stages of MS - A longitudinal correlation study

BACKGROUND

Cognitive impairment is common in patients with multiple sclerosis, even in the early stages of the disease. The Brief International Cognitive Assessment for multiple sclerosis (BICAMS) is a short screening tool developed to assess cognitive function in everyday clinical practice.

OBJECTIVE

To investigate associations between volumetric brain measures derived from a magnetic resonance imaging (MRI) examination and performance on BICAMS subtests in early stages of multiple sclerosis (MS).

METHODS

BICAMS was used to assess cognitive function in 49 MS patients at baseline and after one and two years. The patients were separated into two groups (with or without cognitive impairment) based on their performances on BICAMSs subtests. MRI data were analysed by a software tool (MSMetrix), yielding normalized measures of global brain volumes and lesion volumes. Associations between cognitive tests and brain MRI measures were analysed by running correlation analyses, and differences between subgroups and changes over time with independent and paired samples tests, respectively.

RESULTS

The strongest baseline correlations were found between the BICAMS subtests and normalized whole brain volume (NBV) and grey matter volume (NGV); processing speed r = 0.54/r = 0.48, verbal memory r = 0.49/ r = 0.42, visual memory r = 0.48 /r = 0.39. Only the verbal memory test had significant correlations with T2 and T1 lesion volumes (LV) at both time points; T2LV r = 0.39, T1LV r = 0.38. There were significant loss of grey matter and white matter volume overall (NGV p<0.001, NWV p = 0.003), as well as an increase in T1LV (p = 0.013). The longitudinally defined confirmed cognitively impaired (CCI) and preserved (CCP) patients showed significant group differences on all MRI volume measures at both time points, except for NWV. Only the CCI subgroup showed significant white matter atrophy (p = 0.006) and increase in T2LV (p = 0.029).

CONCLUSIONS

The present study found strong correlations between whole brain and grey matter volumes and performance on the BICAMS subtests as well as significant changes in global volumes from baseline to follow-up with clear differences between patients defined as cognitively impaired and preserved at both baseline and follow-up.

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence

PURPOSE

MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the accuracy and objectivity of MRI-based assessments. Several QReports are commercially available; however, validation can be difficult to establish and does not currently follow a common pathway. To aid evidence-based clinical decision-making, we performed a systematic review of commercial QReports for use in MS including technical details and published reports of validation and in-use evaluation.

METHODS

We categorized studies into three types of testing: technical validation, for example, comparison to manual segmentation, clinical validation by clinicians or interpretation of results alongside clinician-rated variables, and in-use evaluation, such as health economic assessment.

RESULTS

We identified 10 companies, which provide MS lesion and brain segmentation and volume quantification, and 38 relevant publications. Tools received regulatory approval between 2006 and 2020, contextualize results to normative reference populations, ranging from 620 to 8000 subjects, and require T1- and T2-FLAIR-weighted input sequences for longitudinal assessment of whole-brain volume and lesions. In MS, six QReports provided evidence of technical validation, four companies have conducted clinical validation by correlating results with clinical variables, only one has tested their QReport by clinician end-users, and one has performed a simulated in-use socioeconomic evaluation.

CONCLUSION

We conclude that there is limited evidence in the literature regarding clinical validation and in-use evaluation of commercial MS QReports with a particular lack of clinician end-user testing. Our systematic review provides clinicians and institutions with the available evidence when considering adopting a quantitative reporting tool for MS.

Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis

BACKGROUND

A study was undertaken to evaluate remote monitoring via smartphone sensor-based tests in people with multiple sclerosis (PwMS). This analysis aimed to explore regional neural correlates of digital measures derived from these tests.

METHODS

In a 24-week, non-randomized, interventional, feasibility study (NCT02952911), sensor-based tests on the Floodlight Proof-of-Concept app were used to assess cognition (smartphone-based electronic Symbol Digit Modalities Test), upper extremity function (Draw a Shape Test, Pinching Test), and gait and balance (Static Balance Test, Two-Minute Walk Test, U-Turn Test). In this post-hoc analysis, digital measures and standard clinical measures (e.g., Nine-Hole Peg Test [9HPT]) were correlated against regional structural magnetic resonance imaging outcomes. Seventy-six PwMS aged 18-55 years with an Expanded Disability Status Scale score of 0.0-5.5 were enrolled from two different sites (USA and Spain). Sixty-two PwMS were included in this analysis.

RESULTS

Worse performance on digital and clinical measures was associated with smaller regional brain volumes and larger ventricular volumes. Whereas digital and clinical measures had many neural correlates in common (e.g., putamen, globus pallidus, caudate nucleus, lateral occipital cortex), some were observed only for digital measures. For example, Draw a Shape Test and Pinching Test measures, but not 9HPT score, correlated with volume of the hippocampus (r = 0.37 [drawing accuracy over time on the Draw a Shape Test]/ - 0.45 [touching asynchrony on the Pinching Test]), thalamus (r = 0.38/ - 0.41), and pons (r = 0.35/ - 0.35).

CONCLUSIONS

Multiple neural correlates were identified for the digital measures in a cohort of people with early MS. Digital measures showed associations with brain regions that clinical measures were unable to demonstrate, thus providing potential novel information on functional ability compared with standard clinical assessments.

Radial diffusivity reflects general decline rather than specific cognitive deterioration in multiple sclerosis

Advanced structural brain imaging techniques, such as diffusion tensor imaging (DTI), have been used to study the relationship between DTI-parameters and cognitive scores in multiple sclerosis (MS). In this study, we assessed cognitive function in 61 individuals with MS and a control group of 35 healthy individuals with the Symbol Digit Modalities Test, the California Verbal Learning Test-II, the Brief Visuospatial Memory Test-Revised, the Controlled Oral Word Association Test, and Stroop-test. We also acquired diffusion-weighted images (b = 1000; 32 directions), which were processed to obtain the following DTI scalars: fractional anisotropy, mean, axial, and radial diffusivity. The relation between DTI scalars and cognitive parameters was assessed through permutations. Although fractional anisotropy and axial diffusivity did not correlate with any of the cognitive tests, mean and radial diffusivity were negatively correlated with all of these tests. However, this effect was not specific to any specific white matter tract or cognitive test and demonstrated a general effect with only low to moderate individual voxel-based correlations of <0.6. Similarly, lesion and white matter volume show a general effect with medium to high voxel-based correlations of 0.5-0.8. In conclusion, radial diffusivity is strongly related to cognitive impairment in MS. However, the strong associations of radial diffusivity with both cognition and whole brain lesion volume suggest that it is a surrogate marker for general decline in MS, rather than a marker for specific cognitive functions.

Selective vulnerability of brainstem and cervical spinal cord regions in people with non-progressive multiple sclerosis of Black or African American and European ancestry

Background:

We evaluated imaging features suggestive of neurodegeneration within the brainstem and upper cervical spinal cord (UCSC) in non-progressive multiple sclerosis (MS).

Methods:

Standardized 3-Tesla three-dimensional brain magnetic resonance imaging (MRI) studies were prospectively acquired. Rates of change in volume, surface texture, curvature were quantified at the pons and medulla-UCSC. Whole and regional brain volumes and T2-weighted lesion volumes were also quantified. Independent regression models were constructed to evaluate differences between those of Black or African ancestry (B/AA) and European ancestry (EA) with non-progressive MS.

Results:

209 people with MS (pwMS) having at least two MRI studies, 29% possessing 3–6 timepoints, resulted in 487 scans for analysis. Median follow-up time between MRI timepoints was 1.33 (25th–75th percentile: 0.51–1.98) years. Of 183 non-progressive pwMS, 88 and 95 self-reported being B/AA and EA, respectively. Non-progressive pwMS demonstrated greater rates of decline in pontine volume ( p < 0.0001) in B/AA and in medulla-UCSC volume ( p < 0.0001) for EA pwMS. Longitudinal surface texture and curvature changes suggesting reduced tissue integrity were observed at the ventral medulla-UCSC ( p < 0.001), dorsal pons ( p < 0.0001) and dorsal medulla ( p < 0.0001) but not the ventral pons ( p = 0.92) between groups.

Conclusions:

Selectively vulnerable regions within the brainstem-UCSC may allow for more personalized approaches to disease surveillance and management.

Individual differences in visual evoked potential latency are associated with variance in brain tissue volume in people with multiple sclerosis: An analysis of brain function-structure correlates

Visual evoked potentials (VEP) index visual pathway functioning, and are often used for clinical assessment and as outcome measures in people with multiple sclerosis (PwMS). VEPs may also reflect broader neural disturbances that extend beyond the visual system, but this possibility requires further investigation. In the present study, we examined the hypothesis that delayed latency of the P100 component of the VEP would be associated with broader structural changes in the brain in PwMS. We obtained VEP latency for a standard pattern-reversal checkerboard stimulus paradigm, in addition to Magnetic Resonance Imaging (MRI) measures of whole brain volume (WBV), gray matter volume (GMV), white matter volume (WMV), and T2-weighted fluid attenuated inversion recovery (FLAIR) white matter lesion volume (FLV). Correlation analyses indicated that prolonged VEP latency was significantly associated with lower WBV, GMV, and WMV, and greater FLV. VEP latency remained significantly associated with WBV, GMV, and WMV even after controlling for the variance associated with inter-ocular latency, age, time between VEP and MRI assessments, and other MRI variables. VEP latency delays were most pronounced in PwMS that exhibited low volume in both white and gray matter simultaneously. Furthermore, PwMS that had delayed VEP latency based on a clinically relevant cutoff (VEP latency ≥ 113 ms) in both eyes had lower WBV, GMV, and WMV and greater FLV in comparison to PwMS that had normal VEP latency in one or both eyes. The findings suggest that PwMS that have delayed latency in both eyes may be particularly at risk for exhibiting greater brain atrophy and lesion volume. These analyses also indicate that VEP latency may index combined gray matter and white matter disturbances, and therefore broader network connectivity and efficiency. VEP latency may therefore provide a surrogate marker of broader structural disturbances in the brain in MS.

Neural diffusion tensor imaging metrics correlate with clinical measures in people with relapsing-remitting MS

BACKGROUND AND PURPOSE

Diffusion tensor imaging (DTI) can detect microstructural changes of white matter in multiple sclerosis (MS) and might clarify mechanisms responsible for disability. Thus, we aimed to compare DTI metrics in relapsing-remitting MS patients (RRMS) with healthy controls (HCs), and explore the correlations between DTI metrics, total brain white matter (TBWM) and white matter lesion (WML) with clinical parameters compared to volumetric measures.

MATERIAL AND METHODS

37 RRMS patients and 19 age/sex-matched HCs were included. All participants had clinical assessments, structural and diffusion scans on a 3T MRI. Volumetric and white matter DTI metrics; fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD and AD) were estimated and correlated with clinical parameters. The mean group differences were calculated using t-tests, and univariate correlations with Pearson correlation coefficients.

RESULTS

Compared to HCs, statistically significant increases in MD (+3.6%), RD (+4.8%), AD (+2.7%) and a decrease in FA (-4.3%) for TBWM in RRMS was observed (p < .01). MD and RD in TBWM and AD in WML correlated moderately with disability status. Volumetric segmentation indicated a decrease in the total brain volume, GM and WM(-5%) with a reciprocal increase in CSF(+26%) in RRMS(p < .01). Importantly, DTI parameters showed a medium correlation with cognitive domains in contrast to white matter-related volumetric measurements in RRMS(Pearson correlation, p < .05).

CONCLUSIONS

Our study shows a correlation of DTI metrics with clinical symptoms of MS, in particular cognition. More generally, these findings indicated that DTI is a useful and unique technique for evaluating the clinical features of white matter disease and warrants further investigation into its clinical role.

Menopausal symptoms and hormone therapy in women with multiple sclerosis: A baseline-controlled study

BACKGROUND

Depression, sleep disturbances, and cognitive difficulties impair the quality of life in people with multiple sclerosis (MS). Similar symptoms are also frequent during the menopausal transition. In clinical practice, it is important to consider the multifactorial causes of these overlapping symptoms and the potential benefits of menopausal hormone therapy (MHT). The objective of this study was to evaluate vasomotor symptoms (VMS), mood, sleep, and cognition of menopausal women with and without MS at baseline and during one year of MHT.

METHODS

In this prospective baseline-controlled study, peri- and early postmenopausal participants with (n=14) and without (n=13) MS received MHT containing 1 or 2 mg of estradiol and cyclical 10 mg dydrogesterone for one year. VMS frequency, depressive symptoms (measured by Beck Depression Inventory), insomnia severity (Insomnia Severity Index), and cognitive performance (Paced Auditory Serial Addition Test; PASAT, Symbol Digit Modalities Test; SDMT) were evaluated at baseline and at 3 and 12 months of treatment. Differences in the outcome measures between groups at baseline were assessed using the Mann-Whitney U test. Changes during follow-up compared to baseline within groups were evaluated by Wilcoxon Signed Ranks Test. P < 0.05 was considered for statistical significance. MS activity was monitored by clinical assessment and brain MRI at baseline and at 12 months.

RESULTS

Depressive symptoms were more common in MS group, while vasomotor and insomnia symptoms were equally common. During follow-up with MHT, VMS frequency decreased in both groups. Depressive symptoms decreased at 3 months (p = 0.031 with MS; p = 0.024 without MS) and the reduction was sustained at 12 months (p = 0.017; p = 0.042, respectively). Alleviation in insomnia symptoms was seen in participants without MS at 3 months (p = 0.029) and in those participants with MS suffering insomnia at baseline (p = 0.016 at 3 months; p = 0.047 at 12 months). Both groups improved their performance in PASAT, but no significant change was observed in SDMT. MS activity at baseline was mainly stable, and no increase in activity was detected during MHT.

CONCLUSION

Improvements in vasomotor, depressive, and insomnia symptoms observed during one year of MHT are encouraging and suggest that larger placebo-controlled studies of MHT in women with MS are warranted. Cognitive implications were inconclusive because the findings in PASAT likely result from practice effect. MHT did not show any adverse effect on MS activity and increasing safety data will hopefully facilitate patient recruitment for future studies.

A polynomial regression-based approach to estimate relaxation rate maps suitable for multiparametric segmentation of clinical brain MRI studies in multiple sclerosis

BACKGROUND AND OBJECTIVE

Relaxation parameter maps (RPMs) calculated from spin-echo data have provided a basis for the segmentation of normal brain tissues and white matter lesions in multiple sclerosis (MS) MRI studies. However, Conventional Spin-Echo (CSE) sequences, once the core of clinical MRI studies, have been largely replaced by faster ones, which do not allow the calculation a-posteriori of RPMs from clinical studies. Aim of the study was to develop and validate a method to estimate RPMs (pseudo-RPMs) from routine clinical MRI protocols (including 3D-Gradient Echo T1w, FLAIR and fast-T2w sequences), suitable for fully automatic multiparametric segmentation of normal-appearing and pathological brain tissues in MS.

METHODS

The proposed method processes spatially normalized clinical MRI studies through a multistep pipeline, to collect a set of data points of matched signal intensities (from MRI studies) and relaxation parameters (from a CSE-derived digital template and an MS lesion database), which are then fitted by a multiple and multivariate 4-th degree polynomial regression, providing pseudo-RPMs. The method was applied to a dataset of 59 clinical MRI studies providing pseudo-RPMs that were segmented through a method originally developed for the CSE-derived RPMs. Results of the segmentation in 12 studies were used to iteratively optimize method parameters. Accuracy of segmentation of normal-appearing brain tissues from the pseudo-RPMs was assessed by comparing their age-related changes, as measured in 47 clinical studies, against those measured acquired using CSE sequences in a comparable dataset of 47 patients. Lesion segmentation was validated against manual segmentation carried out by three neuroradiologists.

RESULTS

Age-related changes of normal-appearing brain tissue volumes measured using the pseudo-RPMs substantially overlapped those measured using the RPMs obtained from CSE sequences, and segmentation of MS lesions showed a moderate-high spatial overlap with manual segmentation, comparable to that achieved by the widely used Lesion Segmentation Tool on FLAIR images, with a greater volumetric agreement.

CONCLUSIONS

The proposed approach allows calculation from clinical studies of pseudo-RPMs, which are equivalent to those obtainable from CSE sequences, avoiding the need for the acquisition of additional, dedicated sequences for segmentation purposes.

Brain age as a surrogate marker for cognitive performance in multiple sclerosis

Background and purpose

Data from neuro‐imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as ‘how old the brain looks’ and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS).

Methods

A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain‐predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT).

Results

Brain age was significantly related to SDMT scores in the MS_test dataset (r = −0.46, p < 0.001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r = −0.24, p < 0.001) and a significant weight (−0.25, p = 0.002) in a multivariate regression equation with age.

Conclusions

Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health.

Reproducibility of Lesion Count in Various Subregions on MRI Scans in Multiple Sclerosis

Purpose

Lesion number and burden can predict the long-term outcome of multiple sclerosis, while the localization of the lesions is also a good predictive marker of disease progression. These biomarkers are used in studies and in clinical practice, but the reproducibility of lesion count is not well-known.

Methods

In total, five raters evaluated T2 hyperintense lesions in 140 patients with multiple sclerosis in six localizations: periventricular, juxtacortical, deep white matter, infratentorial, spinal cord, and optic nerve. Black holes on T1-weighted images and brain atrophy were subjectively measured on a binary scale. Reproducibility was measured using the intraclass correlation coefficient (ICC). ICCs were also calculated for the four most accurate raters to see how one outlier can influence the results.

Results

Overall, moderate reproducibility (ICC 0.5-0.75) was shown, which did not improve considerably when the most divergent rater was excluded. The areas that produced the worst results were the optic nerve region (ICC: 0.118) and atrophy judgment (ICC: 0.364). Comparing high- and low-lesion burdens in each region revealed that the ICC is higher when the lesion count is in the mid-range. In the periventricular and deep white matter area, where lesions are common, higher ICC was found in patients who had a lower lesion count. On the other hand, juxtacortical lesions and black holes that are less common showed higher ICC when the subjects had more lesions. This difference was significant in the juxtacortical region when the most accurate raters compared patients with low (ICC: 0.406 CI: 0.273-0.546) and high (0.702 CI: 0.603-0.785) lesion loads.

Conclusion

Lesion classification showed high variability by location and overall moderate reproducibility. The excellent range was not achieved, owing to the fact that some areas showed poor performance. Hence, putting effort toward the development of artificial intelligence for the evaluation of lesion burden should be considered.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

OBJECTIVES

QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists.

METHODS

Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists.

RESULTS

The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes.

CONCLUSIONS

QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

KEY POINTS

• QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Bacterial neurotoxic metabolites in multiple sclerosis cerebrospinal fluid and plasma

The identification of intestinal dysbiosis in patients with neurological and psychiatric disorders has highlighted the importance of gut-brain communication, and yet the question regarding the identity of the components responsible for this cross-talk remains open. We previously reported that relapsing remitting multiple sclerosis patients treated with dimethyl fumarate have a prominent depletion of the gut microbiota, thereby suggesting that studying the composition of plasma and CSF samples from these patients may help to identify microbially derived metabolites. We used a functional xenogeneic assay consisting of cultured rat neurons exposed to CSF samples collected from multiple sclerosis patients before and after dimethyl fumarate treatment to assess neurotoxicity and then conducted a metabolomic analysis of plasma and CSF samples to identify metabolites with differential abundance. A weighted correlation network analysis allowed us to identify groups of metabolites, present in plasma and CSF samples, whose abundance correlated with the neurotoxic potential of the CSF. This analysis identified the presence of phenol and indole group metabolites of bacterial origin (e.g. p-cresol sulphate, indoxyl sulphate and N-phenylacetylglutamine) as potentially neurotoxic and decreased by treatment. Chronic exposure of cultured neurons to these metabolites impaired their firing rate and induced axonal damage, independent from mitochondrial dysfunction and oxidative stress, thereby identifying a novel pathway of neurotoxicity. Clinical, radiological and cognitive test metrics were also collected in treated patients at follow-up visits. Improved MRI metrics, disability and cognition were only detected in dimethyl fumarate-treated relapsing remitting multiple sclerosis patients. The levels of the identified metabolites of bacterial origin (p-cresol sulphate, indoxyl sulphate and N-phenylacetylglutamine) were inversely correlated to MRI measurements of cortical volume and directly correlated to the levels of neurofilament light chain, an established biomarker of neurodegeneration. Our data suggest that phenol and indole derivatives from the catabolism of tryptophan and phenylalanine are microbially derived metabolites, which may mediate gut-brain communication and induce neurotoxicity in multiple sclerosis.

Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects

•

Automatic segmentation of MS lesions and age-related WMH from 3D T1 and T2-FLAIR.

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Comparison to consensus show improved performance of WHASA-3D compared to WHASA.

•

WHASA-3D outperforms available state-of-the-art methods with their default settings.

•

WHASA-3D could be a useful tool for clinical practice and clinical trials.

Different types of white matter hyperintensities (WMH) can be observed through MRI in the brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from MS and age-related WMH for subjects with cognitive disorders and/or elderly people. To better diagnose and monitor the disease progression, the quantitative evaluation of WMH load has proven to be useful for clinical routine and trials. Since manual delineation for WMH segmentation is highly time-consuming and suffers from intra and inter observer variability, several methods have been proposed to automatically segment either MS lesions or age-related WMH, but none is validated on both WMH types. Here, we aim at proposing the White matter Hyperintensities Automatic Segmentation Algorithm adapted to 3D T2-FLAIR datasets (WHASA-3D), a fast and robust automatic segmentation tool designed to be implemented in clinical practice for the detection of both MS lesions and age-related WMH in the brain, using both 3D T1-weighted and T2-FLAIR images. In order to increase its robustness for MS lesions, WHASA-3D expands the original WHASA method, which relies on the coupling of non-linear diffusion framework and watershed parcellation, where regions considered as WMH are selected based on intensity and location characteristics, and finally refined with geodesic dilation. The previous validation was performed on 2D T2-FLAIR and subjects with cognitive disorders and elderly subjects. 60 subjects from a heterogeneous database of dementia patients, multiple sclerosis patients and elderly subjects with multiple MRI scanners and a wide range of lesion loads were used to evaluate WHASA and WHASA-3D through volume and spatial agreement in comparison with consensus reference segmentations. In addition, a direct comparison on the MS database with six available supervised and unsupervised state-of-the-art WMH segmentation methods (LST-LGA and LPA, Lesion-TOADS, lesionBrain, BIANCA and nicMSlesions) with default and optimised settings (when feasible) was conducted. WHASA-3D confirmed an improved performance with respect to WHASA, achieving a better spatial overlap (Dice) (0.67 vs 0.63), a reduced absolute volume error (AVE) (3.11 vs 6.2 mL) and an increased volume agreement (intraclass correlation coefficient, ICC) (0.96 vs 0.78). Compared to available state-of-the-art algorithms on the MS database, WHASA-3D outperformed both unsupervised and supervised methods when used with their default settings, showing the highest volume agreement (ICC = 0.95) as well as the highest average Dice (0.58). Optimising and/or retraining LST-LGA, BIANCA and nicMSlesions, using a subset of the MS database as training set, resulted in improved performances on the remaining testing set (average Dice: LST-LGA default/optimized = 0.41/0.51, BIANCA default/optimized = 0.22/0.39, nicMSlesions default/optimized = 0.17/0.63, WHASA-3D = 0.58). Evaluation and comparison results suggest that WHASA-3D is a reliable and easy-to-use method for the automated segmentation of white matter hyperintensities, for both MS lesions and age-related WMH. Further validation on larger datasets would be useful to confirm these first findings.

Multiple sclerosis impairment scale and brain MRI in secondary progressive multiple sclerosis

OBJECTIVE

To examine the Multiple Sclerosis Impairment Scale (MSIS) in secondary progressive MS (SPMS) in relation to the Expanded Disability Status Scale (EDSS), magnetic resonance imaging (MRI) outcomes, and mobility.

METHODS

In this observational single-center study, 68 secondary progressive multiple sclerosis (SPMS) patients were examined by MSIS, EDSS, functional mobility tests of upper/lower extremities, and multimodal MRI. Participants had EDSS ≥3.5, a decline in daily activities over the last year unrelated to relapses, and/or 6-month confirmed disability progression.

RESULTS

Mean disease duration was 23.1 ± 8.3 years and mean age 54.4 ± 8.1 years. MSIS, EDSS, and their corresponding motor, cerebellar, and sensory subscores correlated (p < .0001). Motor subscores of MSIS correlated stronger with Timed-25-Foot-Walk (T25FW) than pyramidal functional system score (FSS) (p = .03), but EDSS had a stronger correlation to T25FW than the total MSIS score (p = .01). MSIS cerebellar subscore correlated stronger with 9-Hole Peg Test (9-HPT) than cerebellar FSS (p = .04). The sensory MSIS subscore also showed correlation with 9-HPT in contrast to sensory FSS (p = .006). MSIS subscores had stronger correlations with MRI volumetry measures than FSS scores (lesion volume and putamen, thalamus, corpus callosum volumetry, p = .0001-0.0017).

CONCLUSION

In patients with SPMS, MSIS correlated with functional motor tests. MSIS showed stronger correlations with atrophy of central nervous system areas, and may be more sensitive to scale cerebellar and sensory function than EDSS.

An optimized MRI and PET based clinical protocol for improving the differential diagnosis of geriatric depression and Alzheimer's disease

Amyloid positron emission tomography (PET) and hippocampal volume derived from magnetic resonance imaging may be useful clinical biomarkers for differentiating between geriatric depression and Alzheimer's disease (AD). Here we investigated the incremental value of using hippocampal volume and 18F-flutemetmol amyloid PET measures in tandem and sequentially to improve discrimination in unclassified participants. Two approaches were compared in 41 participants with geriatric depression and 27 participants with probable AD: (1) amyloid and hippocampal volume combined in one model and (2) classification based on hippocampal volume first and then subsequent stratification using standardized uptake value ratio (SUVR)-determined amyloid positivity. Hippocampal volume and amyloid SUVR were significant diagnostic predictors of depression (sensitivity: 95%, specificity: 89%). 51% of participants were correctly classified according to clinical diagnosis based on hippocampal volume alone, increasing to 87% when adding amyloid data (sensitivity: 94%, specificity: 78%). Our results suggest that hippocampal volume may be a useful gatekeeper for identifying depressed individuals at risk for AD who would benefit from additional amyloid biomarkers when available.

MRI biomarkers for Alzheimer's disease: the impact of functional connectivity in the default mode network and structural connectivity between lobes on diagnostic accuracy

Background

At present, clinical use of MRI in Alzheimer's disease (AD) is mostly focused on the assessment of brain atrophy, namely in the hippocampal region. Despite this, multiple biomarkers reflecting structural and functional brain connectivity changes have shown promising results in the assessment of AD. To help identify the most relevant ones that may stand a chance of being used in clinical practice, we compared multiple biomarker in terms of their value to discriminate AD from healthy controls and analyzed their age dependency.

Methods

20 AD patients and 20 matched controls underwent MRI-scanning (3T GE), including T1-weighted, diffusion-MRI, and resting-state-fMRI (rsfMRI). Whole brain, white matter, gray matter, cortical gray matter and hippocampi volumes were measured using icobrain. rsfMRI between regions of the default-mode-network (DMN) was assessed using group independent-component-analysis. Median diffusivity and kurtosis were determined in gray and white-matter. DTI data was used to evaluate pairwise structural connectivity between lobar regions and the hippocampi.

Logistic-Regression and Random-Forest models were trained to classify AD-status based on, respectively different isolated features and age, and feature-groups combined with age.

Results

Hippocampal features, features reflecting the functional connectivity between the medial-Pre-Frontal-Cortex (mPFC) and the posterior regions of the DMN, and structural interhemispheric frontal connectivity showed the strongest differences between AD-patients and controls. Structural interhemispheric parietal connectivity, structural connectivity between the parietal lobe and hippocampus in the right hemisphere, and mPFC-DMN-features, showed only an association with AD-status (p < 0.05) but not with age. Hippocampi volumes showed an association both with age and AD-status (p < 0.05).

Smallest-hippocampus-volume was the most discriminative feature. The best performance (accuracy:0.74, sensitivity:0.74, specificity:0.74) was obtained with an RF-model combining the best feature from each feature-group (smallest hippocampus volume, WM volume, median GM MD, lTPJ-mPFC connectivity and structural interhemispheric frontal connectivity) and age.

Conclusions

Brain connectivity changes caused by AD are reflected in multiple MRI-biomarkers. Decline in both the functional DMN-connectivity and the parietal interhemispheric structural connectivity may assist sepparating healthy-aging driven changes from AD, complementing hippocampal volumes which are affected by both aging and AD.

Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patients neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-observer errors. Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. However, their effectiveness is limited by the heterogeneity of both MRI data acquisition techniques and the appearance of MS lesions. By learning complex lesion representations directly from images, deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications. Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.

T1/T2 ratio: A quantitative sensitive marker of brain tissue integrity in multiple sclerosis

BACKGROUND AND PURPOSE

The aim of this study is to determine whether cerebral white matter (WM) microstructural damage, defined by decreased fractional anisotropy (FA) and increased axial (AD) and radial (RD) diffusivities, could be detected as accurately by measuring the T1/T2 ratio, in relapsing-remitting multiple sclerosis (RRMS) patients compared to healthy control (HC) subjects.

METHODS

Twenty-eight RRMS patients and 24 HC subjects were included in this study. Region-based analysis based on the ICBM-81 diffusion tensor imaging (DTI) atlas WM labels was performed to compare T1/T2 ratio to DTI values in normal-appearing WM (NAWM) regions of interest. Lesions segmentation was also performed and compared to the HC global WM.

RESULTS

A significant 19.65% decrease of T1/T2 ratio values was observed in NAWM regions of RRMS patients compared to HC. A significant 6.30% decrease of FA, as well as significant 4.76% and 10.27% increases of AD and RD, respectively, were observed in RRMS compared to the HC group in various NAWM regions. Compared to the global WM HC mask, lesions have significantly decreased T1/T2 ratio and FA and increased AD and RD (p < . 001).

CONCLUSIONS

Results showed significant differences between RRMS and HC in both DTI and T1/T2 ratio measurements. T1/T2 ratio even demonstrated extensive WM abnormalities when compared to DTI, thereby highlighting the ratio's sensitivity to subtle differences in cerebral WM structural integrity using only conventional MRI sequences.

White matter hyperintensities segmentation using an ensemble of neural networks

White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network and ensemble models, combining U‐Net, SE‐Net, and multi‐scale features, to automatically segment WMHs and estimate their volumes and locations. We evaluated our method in two datasets: a clinical routine dataset comprising 60 patients (selected from Chinese National Stroke Registry, CNSR) and a research dataset composed of 60 patients (selected from MICCAI WMH Challenge, MWC). The performance of our pipeline was compared with four freely available methods: LGA, LPA, UBO detector, and U‐Net, in terms of a variety of metrics. Additionally, to access the model generalization ability, another research dataset comprising 40 patients (from Older Australian Twins Study and Sydney Memory and Aging Study, OSM), was selected and tested. The pipeline achieved the best performance in both research dataset and the clinical routine dataset with DSC being significantly higher than other methods (p < .001), reaching .833 and .783, respectively. The results of model generalization ability showed that the model trained on the research dataset (DSC = 0.736) performed higher than that trained on the clinical dataset (DSC = 0.622). Our method outperformed widely used pipelines in WMHs segmentation. This system could generate both image and text outputs for whole brain, lobar and anatomical automatic labeling WMHs. Additionally, software and models of our method are made publicly available at https://www.nitrc.org/projects/what_v1.

Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer's Disease and Controls

Magnetic Resonance Imaging (MRI) has become part of the clinical routine for diagnosing neurodegenerative disorders. Since acquisitions are performed at multiple centers using multiple imaging systems, detailed analysis of brain volumetry differences between MRI systems and scan-rescan acquisitions can provide valuable information to correct for different MRI scanner effects in multi-center longitudinal studies. To this end, five healthy controls and five patients belonging to various stages of the AD continuum underwent brain MRI acquisitions on three different MRI systems (Philips Achieva dStream 1.5T, Philips Ingenia 3T, and GE Discovery MR750w 3T) with harmonized scan parameters. Each participant underwent two subsequent MRI scans per imaging system, repeated on three different MRI systems within 2 h. Brain volumes computed by icobrain dm (v5.0) were analyzed using absolute and percentual volume differences, Dice similarity (DSC) and intraclass correlation coefficients, and coefficients of variation (CV). Harmonized scans obtained with different scanners of the same manufacturer had a measurement error closer to the intra-scanner performance. The gap between intra- and inter-scanner comparisons grew when comparing scans from different manufacturers. This was observed at image level (image contrast, similarity, and geometry) and translated into a higher variability of automated brain volumetry. Mixed effects modeling revealed a significant effect of scanner type on some brain volumes, and of the scanner combination on DSC. The study concluded a good intra- and inter-scanner reproducibility, as illustrated by an average intra-scanner (inter-scanner) CV below 2% (5%) and an excellent overlap of brain structure segmentation (mean DSC > 0.88).

A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI

Most data-driven methods are very susceptible to data variability. This problem is particularly apparent when applying Deep Learning (DL) to brain Magnetic Resonance Imaging (MRI), where intensities and contrasts vary due to acquisition protocol, scanner- and center-specific factors. Most publicly available brain MRI datasets originate from the same center and are homogeneous in terms of scanner and used protocol. As such, devising robust methods that generalize to multi-scanner and multi-center data is crucial for transferring these techniques into clinical practice. We propose a novel data augmentation approach based on Gaussian Mixture Models (GMM-DA) with the goal of increasing the variability of a given dataset in terms of intensities and contrasts. The approach allows to augment the training dataset such that the variability in the training set compares to what is seen in real world clinical data, while preserving anatomical information. We compare the performance of a state-of-the-art U-Net model trained for segmenting brain structures with and without the addition of GMM-DA. The models are trained and evaluated on single- and multi-scanner datasets. Additionally, we verify the consistency of test-retest results on same-patient images (same and different scanners). Finally, we investigate how the presence of bias field influences the performance of a model trained with GMM-DA. We found that the addition of the GMM-DA improves the generalization capability of the DL model to other scanners not present in the training data, even when the train set is already multi-scanner. Besides, the consistency between same-patient segmentation predictions is improved, both for same-scanner and different-scanner repetitions. We conclude that GMM-DA could increase the transferability of DL models into clinical scenarios.

A Novel Digital Care Management Platform to Monitor Clinical and Subclinical Disease Activity in Multiple Sclerosis

In multiple sclerosis (MS), the early detection of disease activity or progression is key to inform treatment changes and could be supported by digital tools. We present a novel CE-marked and FDA-cleared digital care management platform consisting of (1) a patient phone/web application and healthcare professional portal (icompanion) including validated symptom, disability, cognition, and fatigue patient-reported outcomes; and (2) clinical brain magnetic resonance imaging (MRI) quantifications (icobrain ms). We validate both tools using their ability to detect (sub)clinical disease activity (known-groups validity) and real-world data insights. Surveys showed that 95.6% of people with MS (PwMS) were interested in using an MS app, and 98.2% were interested in knowing about MRI changes. The icompanion measures of disability (p < 0.001) and symptoms (p = 0.005) and icobrain ms MRI parameters were sensitive to (sub)clinical differences between MS subtypes. icobrain ms also decreased intra- and inter-rater lesion count variability and increased sensitivity for detecting disease activity/progression from 24% to 76% compared to standard radiological reading. This evidence shows PwMS’ interest, the digital care platform’s potential to improve the detection of (sub)clinical disease activity and care management, and the feasibility of linking different digital tools into one overarching MS care pathway.

Brain atrophy and lesion burden are associated with disability progression in a multiple sclerosis real-world dataset using only T2-FLAIR: The NeuroSTREAM MSBase study

BACKGROUND

Methodological challenges limit the use of brain atrophy and lesion burden measures in the follow-up of multiple sclerosis (MS) patients on clinical routine datasets.

OBJECTIVE

To determine the feasibility of T2-FLAIR-only measures of lateral ventricular volume (LVV) and salient central lesion volume (SCLV), as markers of disability progression (DP) in MS.

METHODS

A total of 3,228 MS patients from 9 MSBase centers in 5 countries were enrolled. Of those, 2,875 (218 with clinically isolated syndrome, 2,231 with relapsing-remitting and 426 with progressive disease subtype) fulfilled inclusion and exclusion criteria. Patients were scanned on either 1.5 T or 3 T MRI scanners, and 5,750 brain scans were collected at index and on average after 42.3 months at post-index. Demographic and clinical data were collected from the MSBase registry. LVV and SCLV were measured on clinical routine T2-FLAIR images.

RESULTS

Longitudinal LVV and SCLV analyses were successful in 96% of the scans. 57% of patients had scanner-related changes over the follow-up. After correcting for age, sex, disease duration, disability, disease-modifying therapy and LVV at index, and follow-up time, MS patients with DP (n = 671) had significantly greater absolute LVV change compared to stable (n = 1,501) or disability improved (DI, n = 248) MS patients (2.0 mL vs. 1.4 mL vs. 1.1 mL, respectively, ANCOVA p < 0.001, post-hoc pair-wise DP vs. Stable p = 0.003; and DP vs. DI, p = 0.002). Similar ANCOVA model was also significant for SCLV (p = 0.03).

CONCLUSIONS

LVV-based atrophy and SCLV-based lesion outcomes are feasible on clinically acquired T2-FLAIR scans in a multicenter fashion and are associated with DP over mid-term.

Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study

Background:

Magnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer’s disease (AD) dementia (ADD) patients in selected research cohorts.

Objective:

This study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis.

Methods:

The study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm’s (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages.

Results:

icobrain dm outperformed FreeSurfer in processing time (15–30 min versus 9–32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%).

Conclusion:

Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting.

Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity

BACKGROUND

Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration processes. It leads to different clinical courses and degrees of disability that need to be anticipated by the neurologist for personalized therapy. Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically non-transparent. Using brain structural connectivity data, a fully automatic ensemble learning model, augmented with an interpretable model, is proposed for the estimation of MS patients' disability, measured by the Expanded Disability Status Scale (EDSS).

METHOD

An ensemble of four boosting-based models (GBM, XGBoost, CatBoost, LightBoost) organized following a stacking generalization scheme, was developed using DTI-based structural connectivity data. In addition, an interpretable model based on conditional logistic regression was developed to explain the best performances in terms of white matter (WM) links for three classes of EDSS (Low, Medium, High).

RESULTS

The ensemble model reached excellent level of performance (RMSE of 0.92 ± 0.28) compared to single-based models and provided a better EDSS estimation using DTI-based structural connectivity data compared to conventional MRI measures associated with patient data (age, gender and disease duration). Used for interpretation of the estimation process, the counterfactual method showed the importance of certain brain networks, corresponding mainly to left hemisphere WM links, connecting the left superior temporal with the left posterior cingulate and the right precuneus gray matter regions, and the inter-hemispheric WM links constituting the corpus callosum. Also, a better accuracy estimation was found for the high disability class.

CONCLUSION

The combination of advanced ML models and sensitive techniques such as DTI-based structural connectivity demonstrated to be useful for the estimation of MS patients' disability and to point out the most important brain WM networks involved in disability.

Evaluation of Ultrafast Wave-Controlled Aliasing in Parallel Imaging 3D-FLAIR in the Visualization and Volumetric Estimation of Cerebral White Matter Lesions

BACKGROUND AND PURPOSE

Our aim was to evaluate an ultrafast 3D-FLAIR sequence using Wave-controlled aliasing in parallel imaging encoding (Wave-FLAIR) compared with standard 3D-FLAIR in the visualization and volumetric estimation of cerebral white matter lesions in a clinical setting.

MATERIALS AND METHODS

Forty-two consecutive patients underwent 3T brain MR imaging, including standard 3D-FLAIR (acceleration factor = 2, scan time = 7 minutes 50 seconds) and resolution-matched ultrafast Wave-FLAIR sequences (acceleration factor  = 6, scan time = 2 minutes 45 seconds for the 20-channel coil; acceleration factor = 9, scan time  = 1 minute 50 seconds for the 32-channel coil) as part of clinical evaluation for demyelinating disease. Automated segmentation of cerebral white matter lesions was performed using the Lesion Segmentation Tool in SPM. Student t tests, intraclass correlation coefficients, relative lesion volume difference, and Dice similarity coefficients were used to compare volumetric measurements among sequences. Two blinded neuroradiologists evaluated the visualization of white matter lesions, artifacts, and overall diagnostic quality using a predefined 5-point scale.

RESULTS

Standard and Wave-FLAIR sequences showed excellent agreement of lesion volumes with an intraclass correlation coefficient of 0.99 and mean Dice similarity coefficient of 0.97 (SD, 0.05) (range, 0.84-0.99). Wave-FLAIR was noninferior to standard FLAIR for visualization of lesions and motion. The diagnostic quality for Wave-FLAIR was slightly greater than for standard FLAIR for infratentorial lesions (P < .001), and there were fewer pulsation artifacts on Wave-FLAIR compared with standard FLAIR (P < .001).

CONCLUSIONS

Ultrafast Wave-FLAIR provides superior visualization of infratentorial lesions while preserving overall diagnostic quality and yields white matter lesion volumes comparable with those estimated using standard FLAIR. The availability of ultrafast Wave-FLAIR may facilitate the greater use of 3D-FLAIR sequences in the evaluation of patients with suspected demyelinating disease.

icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions

Multiple sclerosis (MS) is a chronic autoimmune, inflammatory neurological disease of the central nervous system. Its diagnosis nowadays commonly includes performing an MRI scan, as it is the most sensitive imaging test for MS. MS plaques are commonly identified from fluid-attenuated inversion recovery (FLAIR) images as hyperintense regions that are highly varying in terms of their shapes, sizes and locations, and are routinely classified in accordance to the McDonald criteria. Recent years have seen an increase in works that aimed at development of various semi-automatic and automatic methods for detection, segmentation and classification of MS plaques. In this paper, we present an automatic combined method, based on two pipelines: a traditional unsupervised machine learning technique and a deep-learning attention-gate 3D U-net network. The deep-learning network is specifically trained to address the weaker points of the traditional approach, namely difficulties in segmenting infratentorial and juxtacortical plaques in real-world clinical MRIs. It was trained and validated on a multi-center multi-scanner dataset that contains 159 cases, each with T1 weighted (T1w) and FLAIR images, as well as manual delineations of the MS plaques, segmented and validated by a panel of raters. The detection rate was quantified using lesion-wise Dice score. A simple label fusion is implemented to combine the output segmentations of the two pipelines. This combined method improves the detection of infratentorial and juxtacortical lesions by 14% and 31% respectively, in comparison to the unsupervised machine learning pipeline that was used as a performance assessment baseline.

Multiple sclerosis lesion segmentation from brain MRI using U-Net based on wavelet pooling

PURPOSE

The purpose of this work is to segment multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) images, in which lesions in different sizes are segmented with appropriate accuracy. Automated segmentation as a powerful tool can assist professionals to increase the accuracy of disease diagnosis and its level of progression.

METHODS

We present a deep neural network based on the U-Net architecture in which wavelet transform-based pooling replaces max pooling. In the first part of the network, the wavelet transform is used, and in the second part, it's inverse. In addition to decomposing the input image and reducing its size, the wavelet transform highlights sharp changes in the image and better describes local features. This transform has the multi-resolution characteristic, so its use provides improvement in the detection of lesions of different sizes and segmentation.

RESULTS

The results of this study show that the proposed method has a better Dice similarity coefficient (DSC) value compared to the max pooling and average pooling methods.

CONCLUSION

The proposed method has better results for segmenting MS lesions of different sizes in MRI images than the max and average pooling methods and other methods studied.