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.

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.

Subclinical epileptiform activity in the Alzheimer continuum: association with disease, cognition and detection method.

BACKGROUND: Epileptic seizures are an established comorbidity of Alzheimer's disease (AD). Subclinical epileptiform activity (SEA) as detected by 24-h electroencephalography (EEG) or magneto-encephalography (MEG) has been reported in temporal regions of clinically diagnosed AD patients. Although epileptic activity in AD probably arises in the mesial temporal lobe, electrical activity within this region might not propagate to EEG scalp electrodes and could remain undetected by standard EEG. However, SEA might lead to faster cognitive decline in AD. AIMS: 1. To estimate the prevalence of SEA and interictal epileptic discharges (IEDs) in a well-defined cohort of participants belonging to the AD continuum, including preclinical AD subjects, as compared with cognitively healthy controls. 2. To evaluate whether long-term-EEG (LTM-EEG), high-density-EEG (hd-EEG) or MEG is superior to detect SEA in AD. 3. To characterise AD patients with SEA based on clinical, neuropsychological and neuroimaging parameters. METHODS: Subjects (n = 49) belonging to the AD continuum were diagnosed according to the 2011 NIA-AA research criteria, with a high likelihood of underlying AD pathophysiology. Healthy volunteers (n = 24) scored normal on neuropsychological testing and were amyloid negative. None of the participants experienced a seizure before. Subjects underwent LTM-EEG and/or 50-min MEG and/or 50-min hd-EEG to detect IEDs. RESULTS: We found an increased prevalence of SEA in AD subjects (31%) as compared to controls (8%) (p = 0.041; Fisher's exact test), with increasing prevalence over the disease course (50% in dementia, 27% in MCI and 25% in preclinical AD). Although MEG (25%) did not withhold a higher prevalence of SEA in AD as compared to LTM-EEG (19%) and hd-EEG (19%), MEG was significantly superior to detect spikes per 50 min (p = 0.002; Kruskall-Wallis test). AD patients with SEA scored worse on the RBANS visuospatial and attention subset (p = 0.009 and p = 0.05, respectively; Mann-Whitney U test) and had higher left frontal, (left) temporal and (left and right) entorhinal cortex volumes than those without. CONCLUSION: We confirmed that SEA is increased in the AD continuum as compared to controls, with increasing prevalence with AD disease stage. In AD patients, SEA is associated with more severe visuospatial and attention deficits and with increased left frontal, (left) temporal and entorhinal cortex volumes. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04131491. 12/02/2020.

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.

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.

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.

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.

Diffusion tensor imaging of the anterior cruciate ligament following primary repair with internal bracing: A longitudinal study.

Diffusion tensor imaging (DTI) provides information about tissue microstructure and its degree of organization by quantifying water diffusion. We aimed to monitor longitudinal changes in DTI parameters (fractional isotropy, FA; mean diffusivity, MD; axial diffusivity, AD; radial diffusivity, RD) of the anterior cruciate ligament (ACL) following primary repair with internal bracing (IBLA). Fourteen patients undergoing IBLA were enrolled prospectively and scheduled for clinical follow-up, including instrumented laxity testing, and DTI at 3, 6, 12, and 24 months postoperatively. DTI was also performed in seven healthy subjects. Fiber tractography was used for 3D segmentation of the whole ACL volume, from which median DTI parameters were calculated. The posterior cruciate ligament (PCL) served as a control. Longitudinal DTI changes were assessed using a linear mixed model, and repeated measures correlations were calculated between DTI parameters and clinical laxity tests. At follow-up, thirteen patients had a stable knee and one patient sustained an ACL rerupture after 12 months postoperatively. The ACL repair showed a significant decrease of FA within the first 12 months after surgery, followed by stable FA values thereafter, while ACL diffusivities decreased over time returning towards normal values at 24 months postoperatively. For PCL there were no significant DTI changes over time. There was a significant correlation between ACL FA and laxity tests (r = -0.42, P = .017). This study has shown the potential of DTI to longitudinally monitor diffusion changes in the ACL following IBLA. The DTI findings suggest that healing of the ACL repair is incomplete at 24 months postoperatively.

Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges.

Recent data-sharing initiatives of clinical and preclinical Alzheimer's disease (AD) have led to a growing number of non-clinical researchers analyzing these datasets using modern data-driven computational methods. Cognitive tests are key components of such datasets, representing the principal clinical tool to establish phenotypes and monitor symptomatic progression. Despite the potential of computational analyses in complementing the clinical understanding of AD, the characteristics and multifactorial nature of cognitive tests are often unfamiliar to computational researchers and other non-specialist audiences. This perspective paper outlines core features, idiosyncrasies, and applications of cognitive test data. We report tests commonly featured in data-sharing initiatives, highlight key considerations in their selection and analysis, and provide suggestions to avoid risks of misinterpretation. Ultimately, the greater transparency of cognitive measures will maximize insights offered in AD, particularly regarding understanding the extent and basis of AD phenotypic heterogeneity.

Long-term effectiveness of natalizumab on MRI outcomes and no evidence of disease activity in relapsing-remitting multiple sclerosis patients treated in a Czech Republic real-world setting: A longitudinal, retrospective study.

BACKGROUND: Magnetic resonance imaging (MRI) data from multiple sclerosis (MS) patients treated in real-world settings are important for understanding disease-modifying therapy effects, including no evidence of disease activity (NEDA) assessment. This longitudinal, retrospective, single-cohort analysis assessed MRI and clinical disease outcomes in patients with relapsing-remitting MS treated with natalizumab for up to 5 years in Prague, the Czech Republic. METHODS: The primary study endpoint was the proportion of patients free of new or enlarging fluid-attenuated inversion recovery (FLAIR) lesions after at least 2 years of natalizumab treatment. Secondary endpoints included percentage brain volume change over time, the number of new T1-hypointense lesions that persisted for ≥6 months, FLAIR and T1-hypointense lesion volume change over time, and the proportion of patients with NEDA-3 (defined as no relapses, no confirmed disability worsening, and no new or enlarging FLAIR lesions). RESULTS: A total of 193 patients were included in the study. During year 1 of natalizumab treatment, 78.9% of patients had no new or enlarging FLAIR lesions and 79.5% had no new T1 lesions. These proportions increased in years 2-5, with ≥98.0% of patients free of new or enlarging FLAIR lesions and ≥98.8% free of new T1 lesions. During year 1 on natalizumab, 52.2% of patients achieved NEDA-3; this proportion increased to ≥69.2% in years 2-5. CONCLUSION: This study provides additional evidence that long-term MS disease activity, as measured by both MRI activity and NEDA-3, is well-controlled in patients treated with natalizumab in real-world settings.

Harmonization of Brain Diffusion MRI: Concepts and Methods.

MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.

Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm.

Brain volumes computed from magnetic resonance images have potential for assisting with the diagnosis of individual dementia patients, provided that they have low measurement error and high reliability. In this paper we describe and validate icobrain dm, an automatic tool that segments brain structures that are relevant for differential diagnosis of dementia, such as the hippocampi and cerebral lobes. Experiments were conducted in comparison to the widely used FreeSurfer software. The hippocampus segmentations were compared against manual segmentations, with significantly higher Dice coefficients obtained with icobrain dm (25-75th quantiles: 0.86-0.88) than with FreeSurfer (25-75th quantiles: 0.80-0.83). Other brain structures were also compared against manual delineations, with icobrain dm showing lower volumetric errors overall. Test-retest experiments show that the precision of all measurements is higher for icobrain dm than for FreeSurfer except for the parietal cortex volume. Finally, when comparing volumes obtained from Alzheimer's disease patients against age-matched healthy controls, all measures achieved high diagnostic performance levels when discriminating patients from cognitively healthy controls, with the temporal cortex volume measured by icobrain dm reaching the highest diagnostic performance level (area under the receiver operating characteristic curve = 0.99) in this dataset.

Human Leukocyte Antigen Genotype as a Marker of Multiple Sclerosis Prognosis.

OBJECTIVE: In a previous pilot monocentric study, we investigated the relation between human leukocyte antigen (HLA) genotype and multiple sclerosis (MS) disease progression over 2 years. HLA-A*02 allele was correlated with better outcomes, whereas HLA-B*07 and HLA-B*44 were correlated with worse outcomes. The objective of this extension study was to further investigate the possible association of HLA genotype with disease status and progression in MS as measured by sensitive and complex clinical and imaging parameters. METHODS: Hundred and forty-six MS patients underwent HLA typing. Over a 4-year period of follow-up, we performed three clinical and magnetic resonance imaging (MRI) assessments per patient, which respectively included Expanded Disability Status Scale, Multiple Sclerosis Severity Scale, Timed-25-Foot-Walk, 9-Hole Peg Test, Symbol Digit Modalities Test, Brief Visual Memory Test, California Verbal Learning Test-II, and whole-brain atrophy, fluid-attenuated inversion recovery (FLAIR) lesion volume change and number of new FLAIR lesions using icobrain. We then compared the clinical and MRI outcomes between predefined HLA patient groups. RESULTS: Results of this larger study with a longer follow-up are in line with what we have previously shown. HLA-A*02 allele is associated with potentially better MS outcomes, whereas HLA-B*07, HLA-B*44, HLA-B*08, and HLA-DQB1*06 with a potential negative effect. Results for HLA-DRB1*15 are inconclusive. CONCLUSION: In the era of MS treatment abundance, HLA genotype might serve as an early biomarker for MS outcomes to inform individualized treatment decisions.

Tolerogenic dendritic cell-based treatment for multiple sclerosis (MS): a harmonised study protocol for two phase I clinical trials comparing intradermal and intranodal cell administration.

INTRODUCTION: Based on the advances in the treatment of multiple sclerosis (MS), currently available disease-modifying treatments (DMT) have positively influenced the disease course of MS. However, the efficacy of DMT is highly variable and increasing treatment efficacy comes with a more severe risk profile. Hence, the unmet need for safer and more selective treatments remains. Specifically restoring immune tolerance towards myelin antigens may provide an attractive alternative. In this respect, antigen-specific tolerisation with autologous tolerogenic dendritic cells (tolDC) is a promising approach. METHODS AND ANALYSIS: Here, we will evaluate the clinical use of tolDC in a well-defined population of MS patients in two phase I clinical trials. In doing so, we aim to compare two ways of tolDC administration, namely intradermal and intranodal. The cells will be injected at consecutive intervals in three cohorts receiving incremental doses of tolDC, according to a best-of-five design. The primary objective is to assess the safety and feasibility of tolDC administration. For safety, the number of adverse events including MRI and clinical outcomes will be assessed. For feasibility, successful production of tolDC will be determined. Secondary endpoints include clinical and MRI outcome measures. The patients' immune profile will be assessed to find presumptive evidence for a tolerogenic effect in vivo. ETHICS AND DISSEMINATION: Ethics approval was obtained for the two phase I clinical trials. The results of the trials will be disseminated in a peer-reviewed journal, at scientific conferences and to patient associations. TRIAL REGISTRATION NUMBERS: NCT02618902 and NCT02903537; EudraCT numbers: 2015-002975-16 and 2015-003541-26.

Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging.

Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (≥ 2 years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and 18F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-ß (Aß) burden was quantified using the 18F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on 18F-FDG SUVR. CSF measures included Aß1-42, Aß1-40, T-tau, P-tau181, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ε4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., Δstory recall: ß = +0.43 [p < 0.001] and + 0.37 [p = 0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline Aß burden only if SUVR was referenced to the subcortical white matter (e.g., Δstory recall: ß = -0.28 [p = 0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters.

Targeting phosphocreatine metabolism in relapsing-remitting multiple sclerosis: evaluation with brain MRI, 1H and 31P MRS, and clinical and cognitive testing.

BACKGROUND/OBJECTIVES: Fluoxetine and prucalopride might change phosphocreatine (PCr) levels via the cAMP-PKA pathway, an interesting target in the neurodegenerative mechanisms of MS. METHODS: We conducted a two-center double-blind, placebo-controlled, randomized trial including 48 relapsing-remitting MS patients. Patients were randomized to receive placebo (n = 13), fluoxetine (n = 15), or prucalopride (n = 14) for 6 weeks. Proton (1H) and phosphorus (31P) magnetic resonance spectroscopy (MRS) as well as volumetric and perfusion MR imaging were performed at weeks 0, 2, and 6. Clinical and cognitive testing were evaluated at weeks 0 and 6. RESULTS: No significant changes were observed for both 31P and 1H MRS indices. We found a significant effect on white matter volume and a trend towards an increase in grey matter and whole brain volume in the fluoxetine group at week 2; however, these effects were not sustained at week 6 for white matter and whole brain volume. Fluoxetine and prucalopride showed a positive effect on 9-HPT, depression, and fatigue scores. CONCLUSION: Both fluoxetine and prucalopride had a symptomatic effect on upper limb function, fatigue, and depression, but this should be interpreted with caution. No effect of treatment was found on 31P and 1H MRS parameters, suggesting that these molecules do not influence the PCr metabolism.

A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER).

BACKGROUND: Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer's disease (AD). OBJECTIVE: We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression. METHODS: The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (n = 887) and follow-up (FU, n = 95) T1-weighted brain MRIs and time-linked neuropsychological data were available. RESULTS: The cohort consisted of cognitively healthy controls (HC, n = 93), subjective cognitive decline (n = 102), mild cognitive impairment (MCI, n = 379), and AD dementia (n = 313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (n = 95) compared to HC (FU>24months, p = 0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment. CONCLUSION: WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.

HLA genotype as a marker of multiple sclerosis prognosis: A pilot study.

OBJECTIVE: The identification of a biomarker with prognostic value is an unmet need in multiple sclerosis (MS). The objective of this study was to investigate a possible association of HLA genotype with disease status and progression in MS, based on comprehensive and sensitive clinical and magnetic resonance imaging (MRI) parameters to measure disease effects. METHOD: A total of 118 MS patients (79 females, 39 males) underwent HLA typing. Patient MS status was assessed at two time points in a 2-year interval, based on clinical scores (including EDSS, MSSS, T25FW, 9-HPT, SDMT, BVMT, CVLT-II) and MRI evaluations. Quantitative brain MRI values were obtained for whole brain atrophy, FLAIR lesion volume change and number of new lesions using MSmetrix. Predefined HLA patient groups were compared as of disease status and progression. Global assessment was achieved by an overall t-statistic and assessment per measurement by a Welch test and/or Mann Whitney U test. The effects of multiple covariates, including age, gender and disease duration as well as scan parameters, were also evaluated using a regression analysis. RESULTS: The HLA-A*02 allele was associated with better outcomes in terms of MSSS, EDSS and new lesion count (Welch test p-value<0.05). The HLA-B*07 and HLA-B*44 alleles showed a global negative effect on disease status, although none of the measurements reached significance (p-value<0.05). Results for the HLA-DRB1*15, HLA-DQB1*06 and HLA-B*08 alleles were inconclusive. The influence of the confounding variables on the statistical analysis was limited.

Reliable measurements of brain atrophy in individual patients with multiple sclerosis.

INTRODUCTION: As neurodegeneration is recognized as a major contributor to disability in multiple sclerosis (MS), brain atrophy quantification could have a high added value in clinical practice to assess treatment efficacy and disease progression, provided that it has a sufficiently low measurement error to draw meaningful conclusions for an individual patient. METHOD: In this paper, we present an automated longitudinal method based on Jacobian integration for measuring whole-brain and gray matter atrophy based on anatomical magnetic resonance images (MRI), named MSmetrix. MSmetrix is specifically designed to measure atrophy in patients with MS, by including iterative lesion segmentation and lesion filling based on FLAIR and T1-weighted MRI scans. RESULTS: MS metrix is compared with SIENA with respect to test-retest error and consistency, resulting in an average test-retest error on an MS data set of 0.13% (MS metrix) and 0.17% (SIENA) and a consistency error of 0.07% (MS metrix) and 0.05% (SIENA). On a healthy subject data set including physiological variability the test-retest is 0.19% (MS metrix) and 0.31% (SIENA). CONCLUSION: Therefore, we can conclude that MSmetrix could be of added value in clinical practice for the follow-up of treatment and disease progression in MS patients.