Imaging outcome measures for progressive multiple sclerosis trials

Imaging markers that are reliable, reproducible and sensitive to neurodegenerative changes in progressive multiple sclerosis (MS) can enhance the development of new medications with a neuroprotective mode-of-action. Accordingly, in recent years, a considerable number of imaging biomarkers have been included in phase 2 and 3 clinical trials in primary and secondary progressive MS. Brain lesion count and volume are markers of inflammation and demyelination and are important outcomes even in progressive MS trials. Brain and, more recently, spinal cord atrophy are gaining relevance, considering their strong association with disability accrual; ongoing improvements in analysis methods will enhance their applicability in clinical trials, especially for cord atrophy. Advanced magnetic resonance imaging (MRI) techniques (e.g. magnetization transfer ratio (MTR), diffusion tensor imaging (DTI), spectroscopy) have been included in few trials so far and hold promise for the future, as they can reflect specific pathological changes targeted by neuroprotective treatments. Position emission tomography (PET) and optical coherence tomography have yet to be included. Applications, limitations and future perspectives of these techniques in clinical trials in progressive MS are discussed, with emphasis on measurement sensitivity, reliability and sample size calculation

Consistency between Treatment Effects on Clinical and Brain Atrophy Outcomes in Alzheimer's Disease Trials

Background: Longitudinal changes in volumetric MRI outcome measures have been shown to correlate well with longitudinal changes in clinical instruments and have been widely used as biomarker outcomes in clinical trials for Alzheimer’s disease (AD). While instances of discordant findings have been noted in some trials, especially the recent amyloid-removing therapies, the overall relationship between treatment effects on brain atrophy and clinical outcomes, and how it might depend on treatment target or mechanism, clinical instrument or imaging variable is not yet clear. / Objective: To systematically assess the consistency and therapeutic class-dependence of treatment effects on clinical outcomes and on brain atrophy in published reports of clinical trials conducted in mild cognitive impairment (MCI) and/or AD. / Design: Quantitative review of the published literature. The consistency of treatment effects on clinical and brain atrophy outcomes was assessed in terms of statistical agreement with hypothesized equal magnitude effects (e.g., 30% slowing of both) and nominal directional concordance, as a function of therapeutic class. Setting: Interventional randomized clinical trials. / Participants: MCI or AD trial participants. / Intervention: Treatments included were those that involved ingestion or injection of a putatively active substance into the body, encompassing both pharmacological and controlled dietary interventions. / Measurements: Each trial included in the analysis reported at least one of the required clinical outcomes (ADAS-Cog, CDR-SB or MMSE) and at least one of the required imaging outcomes (whole brain, ventricular or hippocampal volume). / Results: Data from 35 trials, comprising 185 pairwise comparisons, were included. Overall, the 95% confidence bounds overlapped with the line of identity for 150/185 (81%) of the imaging-clinical variable pairs. The greatest proportion of outliers was found in trials of anti-amyloid antibodies that have been shown to dramatically reduce the level of PET-detectable amyloid plaques, for which only 13/33 (39%) of observations overlapped the identity line. A Deming regression calculated using all data points yielded a slope of 0.54, whereas if data points from the amyloid remover class were excluded, the Deming regression line had a slope of 0.92. Directional discordance of treatment effects was also most pronounced for the amyloid-removing class, and for comparisons involving ventricular volume. / Conclusion: Our results provide a frame of reference for the interpretation of clinical and brain atrophy results from future clinical trials and highlight the importance of mechanism of action in the interpretation of imaging results

Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning

BACKGROUND: Alzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable. METHODS: We identified AD patients in primary care EHR from the Clinical Practice Research Datalink (CPRD) using a previously validated rule-based phenotyping algorithm. We extracted and included a range of comorbidities, symptoms and demographic features as patient features. We evaluated four different clustering methods (k-means, kernel k-means, affinity propagation and latent class analysis) to cluster Alzheimer's disease patients. We compared clusters on clinically relevant outcomes and evaluated each method using measures of cluster structure, stability, efficiency of outcome prediction and replicability in external data sets. RESULTS: We identified 7,913 AD patients, with a mean age of 82 and 66.2% female. We included 21 features in our analysis. We observed 5, 2, 5 and 6 clusters in k-means, kernel k-means, affinity propagation and latent class analysis respectively. K-means was found to produce the most consistent results based on four evaluative measures. We discovered a consistent cluster found in three of the four methods composed of predominantly female, younger disease onset (43% between ages 42-73) diagnosed with depression and anxiety, with a quicker rate of progression compared to the average across other clusters. CONCLUSION: Each clustering approach produced substantially different clusters and K-Means performed the best out of the four methods based on the four evaluative criteria. However, the consistent appearance of one particular cluster across three of the four methods potentially suggests the presence of a distinct disease subtype that merits further exploration. Our study underlines the variability of the results obtained from different clustering approaches and the importance of systematically evaluating different approaches for identifying disease subtypes in complex EHR

Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients

OBJECTIVE: Integrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network.; METHODS: Resting-state magnetoencephalographic (MEG) data for T1DM patients with (n=42) and without (n=41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated.; RESULTS: Significant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p<0.05). General cognitive performance for both patient groups was worse compared with healthy controls. Lower DMN alpha band functional connectivity correlated with poorer general cognitive ability in patients with microvascular complications.; DISCUSSION: Altered RSN functional connectivity was found in T1DM patients depending on clinical status. Lower DMN functional connectivity was related to poorer cognitive functioning. These results indicate that functional connectivity may play a key role in T1DM-related cognitive dysfunction

Ultra-pure digital sideband separation at sub-millimeter wavelengths

Deep spectral-line surveys in the mm and sub-mm range can detect thousands of lines per band uncovering the rich chemistry of molecular clouds, star forming regions and circumstellar envelopes, among others objects. The ability to study the faintest features of spectroscopic observation is, nevertheless, limited by a number of factors. The most important are the source complexity (line density), limited spectral resolution and insufficient sideband (image) rejection (SRR). Dual Sideband (2SB) millimeter receivers separate upper and lower sideband rejecting the unwanted image by about 15 dB, but they are difficult to build and, until now, only feasible up to about 500 GHz (equivalent to ALMA Band 8). For example ALMA Bands 9 (602-720 GHz) and 10 (787-950 GHz) are currently DSB receivers. Aims: This article reports the implementation of an ALMA Band 9 2SB prototype receiver that makes use of a new technique called calibrated digital sideband separation. The new method promises to ease the manufacturing of 2SB receivers, dramatically increase sideband rejection and allow 2SB instruments at the high frequencies currently covered only by Double Sideband (DSB) or bolometric detectors. Methods: We made use of a Field Programmable Gate Array (FPGA) and fast Analog to Digital Converters (ADCs) to measure and calibrate the receiver's front end phase and amplitude imbalances to achieve sideband separation beyond the possibilities of purely analog receivers. The technique could in principle allow the operation of 2SB receivers even when only imbalanced front ends can be built, particularly at very high frequencies. Results: This digital 2SB receiver shows an average sideband rejection of 45.9 dB while small portions of the band drop below 40 dB. The performance is 27 dB (a factor of 500) better than the average performance of the proof-of-concept Band 9 purely-analog 2SB prototype receiver.Comment: 5 page

In vivo imaging of chronic active lesions in multiple sclerosis

New clinical activity in multiple sclerosis (MS) is often accompanied by acute inflammation which subsides. However, there is growing evidence that a substantial proportion of lesions remain active well beyond the acute phase. Chronic active lesions are most frequently found in progressive MS and are characterised by a border of inflammation associated with iron-enriched cells, leading to ongoing tissue injury. Identifying imaging markers for chronic active lesions in vivo are thus a major research goal. We reviewed the literature on imaging of chronic active lesion in MS, focussing on 'slowly expanding lesions' (SELs), detected by volumetric longitudinal magnetic resonance imaging (MRI) and 'rim-positive' lesions, identified by susceptibility iron-sensitive MRI. Both SELs and rim-positive lesions have been found to be prognostically relevant to future disability. Little is known about the co-occurrence of rims around SELs and their inter-relationship with other emerging techniques such as dynamic contrast enhancement (DCE) and positron emission tomography (PET)

Fetal Familial Cerebral Cavernous Malformation With a Novel Heterozygous KRIT1 Pathogenic Variant

OBJECTIVES: To identify fetal familial cerebral cavernous malformation (CCMs) and a novel mutation. METHODS: A 37-year-old pregnant woman (G4P0) presented right-handed numbness since two weeks at 31 weeks of gestation. Evaluation with brain magnetic resonance imaging (MRI) revealed multiple CCMs. As a result, fetal MRI, fetal Whole Exome Sequencing (WES), and maternal Sanger sequencing were performed. RESULTS: The mother's brain MRI demonstrated numerous CCMs involving the brain stem, cerebral hemispheres, and cerebellum. Fetal MRI showed a CCM located in the left frontal lobe in SWI. The neuroimaging characteristics of the mother and the fetus suggested that their CCMs may be familial. Genetic analysis revealed a novel mutation in KRIT1 (c.1A>G, p.0?), also called CCM1, in the mother and the baby. The mother delivered a daughter at 32 weeks of gestation with an Apgar score of 10 by cesarean section. DISCUSSION: This mutation of the initial codon in the KRIT1 gene leads to a phenotype with an early-onset. To our knowledge, this is the first-ever reported case of fetal familial CCM and this novel mutation. Brain MRI has excellent sensitivity and specificity, providing the best option for detecting CCMs, even in utero, primarily when SWI is used

Wearable technologies to measure clinical outcomes in multiple sclerosis: A scoping review

Wearable technology refers to any sensor worn on the person, making continuous and remote monitoring available to many people with chronic disease, including multiple sclerosis (MS). Daily monitoring seems an ideal solution either as an outcome measure or as an adjunct to support rater-based monitoring in both clinical and research settings. There has been an increase in solutions that are available, yet there is little consensus on the most appropriate solution to use in either MS research or clinical practice. We completed a scoping review (using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines) to summarise the wearable solutions available in MS, to identify those approaches that could potentially be utilised in clinical trials, by evaluating the following: scalability, cost, patient adaptability and accuracy. We identified 35 unique products that measure gait, cognition, upper limb function, activity, mood and fatigue, with most of these solutions being phone applications