The Finger Dexterity Test: Validation study of a smartphone-based manual dexterity assessment.

BACKGROUND: The Nine-Hole Peg Test (9HPT) is the golden standard to measure manual dexterity in people with multiple sclerosis (MS). However, administration requires trained personnel and dedicated time during a clinical visit. OBJECTIVES: The objective of this study is to validate a smartphone-based test for remote manual dexterity assessment, the icompanion Finger Dexterity Test (FDT), to be included into the icompanion application. METHODS: A total of 65 MS and 81 healthy subjects were tested, and 20 healthy subjects were retested 2 weeks later. RESULTS: The FDT significantly correlated with the 9HPT (dominant: ρ = 0.62, p < 0.001; non-dominant: ρ = 0.52, p < 0.001). MS subjects had significantly higher FDT scores than healthy subjects (dominant: p = 0.015; non-dominant: p = 0.013), which was not the case for the 9HPT. A significant correlation with age (dominant: ρ = 0.46, p < 0.001; non-dominant: ρ = 0.40, p = 0.002), Expanded Disability Status Scale (EDSS, dominant: ρ = 0.36, p = 0.005; non-dominant: ρ = 0.31, p = 0.024), and disease duration for the non-dominant hand (ρ = 0.31, p = 0.016) was observed. There was a good test-retest reliability in healthy subjects (dominant: r = 0.69, p = 0.001; non-dominant: r = 0.87, p < 0.001). CONCLUSIONS: The icompanion FDT shows a moderate-to-good concurrent validity and test-retest reliability, differentiates between the MS subjects and healthy controls, and correlates with clinical parameters. This test can be implemented into routine MS care for remote follow-up of manual dexterity.

Treating to target in multiple sclerosis: Do we know how to measure whether we hit it?

BACKGROUND AND PURPOSE: The rapidly evolving landscape of effective treatment options in multiple sclerosis has led to a shift of treatment objectives towards a treat-to-target approach aiming to suppress disease activity below the level of detectability early during the disease. To enable treat-to-target, a thorough reappraisal of available outcome measures with respect to their ability in this regard is required. METHODS: To that end, we conducted a comprehensive systematic literature review of more than 1000 studies using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 methodology focusing on underlying evidence as well as utility and implementability in clinical practice. RESULTS: From there, we propose a set of measurable outcomes for everyday routine clinical practice as well as advanced/aspirational measurables requiring additional resources. We also outline remaining knowledge/technology gaps that need to be overcome to enable a treat-to-target approach. CONCLUSIONS: This work provides the basis for an evidence-based definition of outcome targets for relevant stakeholders and regulatory authorities.

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.

Structural brain dynamics across reading development: A longitudinal MRI study from kindergarten to grade 5.

Primary education is the incubator for learning academic skills that help children to become a literate, communicative, and independent person. Over this learning period, nonlinear and regional changes in the brain occur, but how these changes relate to academic performance, such as reading ability, is still unclear. In the current study, we analyzed longitudinal T1 MRI data of 41 children in order to investigate typical cortical development during the early reading stage (end of kindergarten-end of grade 2) and advanced reading stage (end of grade 2-middle of grade 5), and to detect putative deviant trajectories in children with dyslexia. The structural brain change was quantified with a reliable measure that directly calculates the local morphological differences between brain images of two time points, while considering the global head growth. When applying this measure to investigate typical cortical development, we observed that left temporal and temporoparietal regions belonging to the reading network exhibited an increase during the early reading stage and stabilized during the advanced reading stage. This suggests that the natural plasticity window for reading is within the first years of primary school, hence earlier than the typical period for reading intervention. Concerning neurotrajectories in children with dyslexia compared to typical readers, we observed no differences in gray matter development of the left reading network, but we found different neurotrajectories in right IFG opercularis (during the early reading stage) and in right isthmus cingulate (during the advanced reading stage), which could reflect compensatory neural mechanisms.

High-resolution metabolic mapping of gliomas via patch-based super-resolution magnetic resonance spectroscopic imaging at 7T.

OBJECTIVES: To demonstrate the feasibility of 7 T magnetic resonance spectroscopic imaging (MRSI), combined with patch-based super-resolution (PBSR) reconstruction, for high-resolution multi-metabolite mapping of gliomas. MATERIALS AND METHODS: Ten patients with WHO grade II, III and IV gliomas (6/4, male/female; 45 ±â€¯9 years old) were prospectively measured between 2014 and 2018 on a 7 T whole-body MR imager after routine 3 T magnetic resonance imaging (MRI) and positron emission tomography (PET). Free induction decay MRSI with a 64 × 64-matrix and a nominal voxel size of 3.4 × 3.4 × 8 mm³ was acquired in six minutes, along with standard T1/T2-weighted MRI. Metabolic maps were obtained via spectral LCmodel processing and reconstructed to 0.9 × 0.9 × 8 mm³ resolutions via PBSR. RESULTS: Metabolite maps obtained from combined 7 T MRSI and PBSR resolved the density of metabolic activity in the gliomas in unprecedented detail. Particularly in the more heterogeneous cases (e.g. post resection), metabolite maps enabled the identification of complex metabolic activities, which were in topographic agreement with PET enhancement. CONCLUSIONS: PBSR-MRSI combines the benefits of ultra-high-field MR systems, cutting-edge MRSI, and advanced postprocessing to allow millimetric resolution molecular imaging of glioma tissue beyond standard methods. An ideal example is the accurate imaging of glutamine, which is a prime target of modern therapeutic approaches, made possible due to the higher spectral resolution of 7 T systems.

Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study.

BACKGROUND: Diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA), allow examining the structural integrity of the brain. However, the true value of these parameters may be confounded by variability in MR hardware, acquisition parameters, and image quality. PURPOSE: To examine the effects of confounding factors on FA and to evaluate the feasibility of statistical methods to model and reduce multicenter variability. STUDY TYPE: Longitudinal multicenter study. PHANTOM: DTI single strand phantom (HQ imaging). FIELD STRENGTH/SEQUENCE: 3T diffusion tensor imaging. ASSESSMENTS: Thirteen European imaging centers participated. DTI scans were acquired every 6 months and whenever maintenance or upgrades to the system were performed. A total of 64 scans were acquired in 2 years, obtained by three scanner vendors, using six individual head coils, and 12 software versions. STATISTICAL TESTS: The variability in FA was assessed by the coefficients of variation (CoV). Several linear mixed effects models (LMEM) were developed and compared by means of the Akaike Information Criterion (AIC). RESULTS: The CoV was 2.22% for mean FA and 18.40% for standard deviation of FA. The variables "site" (P = 9.26 × 10-5 ), "vendor" (P = 2.18 × 10-5 ), "head coil" (P = 9.00 × 10-4 ), "scanner drift," "bandwidth" (P = 0.033), "TE" (P = 8.20 × 10-6 ), "SNR" (P = 0.029) and "mean residuals" (P = 6.50 × 10-4 ) had a significant effect on the variability in mean FA. The variables "site" (P = 4.00 × 10-4 ), "head coil" (P = 2.00 × 10-4 ), "software" (P = 0.014), and "mean voxel outlier intensity count" (P = 1.10 × 10-4 ) had a significant effect on the variability in standard deviation of FA. The mean FA was best predicted by an LMEM that included "vendor" and the interaction term of "SNR" and "head coil" as model factors (AIC -347.98). In contrast, the standard deviation of FA was best predicted by an LMEM that included "vendor," "bandwidth," "TE," and the interaction term between "SNR" and "head coil" (AIC -399.81). DATA CONCLUSION: Our findings suggest that perhaps statistical models seem promising to model the variability in quantitative DTI biomarkers for clinical routine and multicenter studies. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:955-965.

Measurement of Whole-Brain and Gray Matter Atrophy in Multiple Sclerosis: Assessment with MR Imaging.

Purpose To compare available methods for whole-brain and gray matter (GM) atrophy estimation in multiple sclerosis (MS) in terms of repeatability (same magnetic resonance [MR] imaging unit) and reproducibility (different system/field strength) for their potential clinical applications. Materials and Methods The softwares ANTs-v1.9, CIVET-v2.1, FSL-SIENAX/SIENA-5.0.1, Icometrix-MSmetrix-1.7, and SPM-v12 were compared. This retrospective study, performed between March 2015 and March 2017, collected data from (a) eight simulated MR images and longitudinal data (2 weeks) from 10 healthy control subjects to assess the cross-sectional and longitudinal accuracy of atrophy measures, (b) test-retest MR images in 29 patients with MS acquired within the same day at different imaging unit field strengths/manufacturers to evaluate precision, and (c) longitudinal data (1 year) in 24 patients with MS for the agreement between methods. Tissue segmentation, image registration, and white matter (WM) lesion filling were also evaluated. Multiple paired t tests were used for comparisons. Results High values of accuracy (0.87-0.97) for whole-brain and GM volumes were found, with the lowest values for MSmetrix. ANTs showed the lowest mean error (0.02%) for whole-brain atrophy in healthy control subjects, with a coefficient of variation of 0.5%. SPM showed the smallest mean error (0.07%) and coefficient of variation (0.08%) for GM atrophy. Globally, good repeatability (P > .05) but poor reproducibility (P < .05) were found for all methods. WM lesion filling technique mainly affected ANTs, MSmetrix, and SPM results (P < .05). Conclusion From this comparison, it would be possible to select a software for atrophy measurement, depending on the requirements of the application (research center, clinical trial) and its goal (accuracy and repeatability or reproducibility). An improved reproducibility is required for clinical application. © RSNA, 2018 Online supplemental material is available for this article.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

A future of AI-driven personalized care for people with multiple sclerosis.

Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the 'Clinical Impact through AI-assisted MS Care' (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

Importance: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-ß-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. Objective: To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA. Design, Setting, and Participants: This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023. Exposures: Use of icobrain aria, an AI-based assistive software for ARIA detection and quantification. Main Outcomes and Measures: Coprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC). Results: Among the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types. Conclusions and Relevance: This diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-ß-directed monoclonal antibody therapies.

Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study.

OBJECTIVES: This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. METHODS: The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject. RESULTS: MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. CONCLUSIONS: Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.

Neuroinflammation as potential precursor of leukoencephalopathy in early-stage breast cancer patients: A cross-sectional PET-MRI study.

BACKGROUND: Although chemotherapy-induced leukoencephalopathy has been described in case and cohort studies, literature remains inconclusive about its prevalence and mechanisms. Therefore, we investigated the presence of leukoencephalopathy after multiagent chemotherapy in women treated for breast cancer and potential underlying neuroinflammatory processes. METHODS: In this exploratory study, 15 chemotherapy-treated and 15 age-matched chemotherapy-naïve patients with early-stage breast cancer, as well as 15 healthy controls underwent simultaneous PET-MR neuroimaging, including T1-weighted MPRAGE, T2-weighted FLAIR and dynamic PET with the 18-kDA translocator protein (TSPO) radioligand [18F]DPA-714. Total and regional (juxtacortical, periventricular, deep white matter and infratentorial) lesion burden were compared between the groups with one-way ANOVA. With paired t-tests, [18F]DPA-714 volume of distribution [VT, including partial volume correction (PVC)] in lesioned and normal appearing white matter (NAWM) were compared within subjects, to investigate inflammation. Finally, two general linear models were used to examine the predictive values of neurofilament light-chain (NfL) serum levels on (1) total lesion burden or (2) PVC [18F]DPA-714 VT of lesions showing elevated inflammation. RESULTS: No significant differences were found in total or localized lesion burden. However, significantly higher (20-45%) TSPO uptake was observed in juxtacortical lesions (p ≤ 0.008, t ≥ 3.90) compared to NAWM in both cancer groups, but only persisted for chemotherapy-treated patients after PVC (p = 0.005, t = 4.30). NfL serum levels were not associated with total lesion volume or tracer uptake in juxtacortical lesions. CONCLUSION: This multimodal neuroimaging study suggests that neuroinflammatory processes could be involved in the development of juxtacortical, but not periventricular or deep white matter, leukoencephalopathy shortly after chemotherapy for early-stage breast cancer.

AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs.

Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.

Brain Volume Loss Can Occur at the Rate of Normal Aging in Patients with Multiple Sclerosis Who Are Free from Disease Activity.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and degenerative disorder of the central nervous system. Accelerated brain volume loss (BVL) has emerged as a promising magnetic resonance imaging marker (MRI) of neurodegeneration, correlating with present and future clinical disability. We have systematically selected MS patients fulfilling 'no evidence of disease activity-3' (NEDA-3) criteria under high-efficacy disease-modifying treatment (DMT) from the database of two Belgian MS centers. BVL between both MRI scans demarcating the NEDA-3 period was assessed and compared with a group of prospectively recruited healthy volunteers who were matched for age and gender. Annualized whole brain volume percentage change was similar between 29 MS patients achieving NEDA-3 and 24 healthy controls (-0.25 ± 0.49 versus -0.24 ± 0.20, p = 0.9992; median follow-up 21 versus 33 months; respectively). In contrast, we found a mean BVL increase of 72%, as compared with the former, in a second control group of MS patients (n = 21) whom had been excluded from the NEDA-3 group due to disease activity (p = 0.1371). Our results suggest that neurodegeneration in MS can slow down to the rate of normal aging once inflammatory disease activity has been extinguished and advocate for an early introduction of high-efficacy DMT to reduce the risk of future clinical disability.

Health Economic Impact of Software-Assisted Brain MRI on Therapeutic Decision-Making and Outcomes of Relapsing-Remitting Multiple Sclerosis Patients-A Microsimulation Study.

AIM: To develop a microsimulation model to assess the potential health economic impact of software-assisted MRI in detecting disease activity or progression in relapsing-remitting multiple sclerosis (RRMS) patients. METHODS: We develop a simulated decision analytical model based on a hypothetical cohort of RRMS patients to compare a baseline decision-making strategy in which only clinical evolution (relapses and disability progression) factors are used for therapy decisions in MS follow-up, with decision-making strategies involving MRI. In this context, we include comparisons with a visual radiologic assessment of lesion evolution, software-assisted lesion detection, and software-assisted brain volume loss estimation. The model simulates clinical (EDSS transitions, number of relapses) and subclinical (new lesions and brain volume loss) disease progression and activity, modulated by the efficacy profiles of different disease-modifying therapies (DMTs). The simulated decision-making process includes the possibility to escalate from a low efficacy DMT to a high efficacy DMT or to switch between high efficacy DMTs when disease activity is detected. We also consider potential error factors that may occur during decision making, such as incomplete detection of new lesions, or inexact computation of brain volume loss. Finally, differences between strategies in terms of the time spent on treatment while having undetected disease progression/activity, the impact on the patient's quality of life, and costs associated with health status from a US perspective, are reported. RESULTS: The average time with undetected disease progression while on low efficacy treatment is shortened significantly when using MRI, from around 3 years based on clinical criteria alone, to 2 when adding visual examination of MRI, and down to only 1 year with assistive software. Hence, faster escalation to a high efficacy DMT can be performed when MRI software is added to the radiological reading, which has positive effects in terms of health outcomes. The incremental utility shows average gains of 0.23 to 0.37 QALYs over 10 and 15 years, respectively, when using software-assisted MRI compared to clinical parameters only. Due to long-term health benefits, the average annual costs associated with health status are lower by $1500-$2200 per patient when employing MRI and assistive software. CONCLUSIONS: The health economic burden of MS is high. Using assistive MRI software to detect and quantify lesions and/or brain atrophy has a significant impact on the detection of disease activity, treatment decisions, health outcomes, utilities, and costs in patients with MS.

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.

Increased brain atrophy and lesion load is associated with stronger lower alpha MEG power in multiple sclerosis patients.

In multiple sclerosis, the interplay of neurodegeneration, demyelination and inflammation leads to changes in neurophysiological functioning. This study aims to characterize the relation between reduced brain volumes and spectral power in multiple sclerosis patients and matched healthy subjects. During resting-state eyes closed, we collected magnetoencephalographic data in 67 multiple sclerosis patients and 47 healthy subjects, matched for age and gender. Additionally, we quantified different brain volumes through magnetic resonance imaging (MRI). First, a principal component analysis of MRI-derived brain volumes demonstrates that atrophy can be largely described by two components: one overall degenerative component that correlates strongly with different cognitive tests, and one component that mainly captures degeneration of the cortical grey matter that strongly correlates with age. A multimodal correlation analysis indicates that increased brain atrophy and lesion load is accompanied by increased spectral power in the lower alpha (8-10 Hz) in the temporoparietal junction (TPJ). Increased lower alpha power in the TPJ was further associated with worse results on verbal and spatial working memory tests, whereas an increased lower/upper alpha power ratio was associated with slower information processing speed. In conclusion, multiple sclerosis patients with increased brain atrophy, lesion and thalamic volumes demonstrated increased lower alpha power in the TPJ and reduced cognitive abilities.

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.

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).

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.