Riluzole, a glutamate modulator, slows cerebral glucose metabolism decline in patients with Alzheimer's disease.

Dysregulation of glutamatergic neural circuits has been implicated in a cycle of toxicity, believed among the neurobiological underpinning of Alzheimer's disease. Previously, we reported preclinical evidence that the glutamate modulator riluzole, which is FDA approved for the treatment of amyotrophic lateral sclerosis, has potential benefits on cognition, structural and molecular markers of ageing and Alzheimer's disease. The objective of this study was to evaluate in a pilot clinical trial, using neuroimaging biomarkers, the potential efficacy and safety of riluzole in patients with Alzheimer's disease as compared to placebo. A 6-month phase 2 double-blind, randomized, placebo-controlled study was conducted at two sites. Participants consisted of males and females, 50 to 95 years of age, with a clinical diagnosis of probable Alzheimer's disease, and Mini-Mental State Examination between 19 and 27. Ninety-four participants were screened, 50 participants who met inclusion criteria were randomly assigned to receive 50 mg riluzole (n = 26) or placebo (n = 24) twice a day. Twenty-two riluzole-treated and 20 placebo participants completed the study. Primary end points were baseline to 6 months changes in (i) cerebral glucose metabolism as measured with fluorodeoxyglucose-PET in prespecified regions of interest (hippocampus, posterior cingulate, precuneus, lateral temporal, inferior parietal, frontal); and (ii) changes in posterior cingulate levels of the neuronal viability marker N-acetylaspartate as measured with in vivo proton magnetic resonance spectroscopy. Secondary outcome measures were neuropsychological testing for correlation with neuroimaging biomarkers and in vivo measures of glutamate in posterior cingulate measured with magnetic resonance spectroscopy as a potential marker of target engagement. Measures of cerebral glucose metabolism, a well-established Alzheimer's disease biomarker and predictor of disease progression, declined significantly less in several prespecified regions of interest with the most robust effect in posterior cingulate, and effects in precuneus, lateral temporal, right hippocampus and frontal cortex in riluzole-treated participants in comparison to the placebo group. No group effect was found in measures of N-acetylaspartate levels. A positive correlation was observed between cognitive measures and regional cerebral glucose metabolism. A group × visit interaction was observed in glutamate levels in posterior cingulate, potentially suggesting engagement of glutamatergic system by riluzole. In vivo glutamate levels positively correlated with cognitive performance. These findings support our main primary hypothesis that cerebral glucose metabolism would be better preserved in the riluzole-treated group than in the placebo group and provide a rationale for more powered, longer duration studies of riluzole as a potential intervention for Alzheimer's disease.

Relationships between plasma biomarkers, tau PET, FDG PET, and volumetric MRI in mild to moderate Alzheimer's disease patients.

INTRODUCTION: The "A/T/N" (amyloid/tau/neurodegeneration) framework provides a biological basis for Alzheimer's disease (AD) diagnosis and can encompass additional changes such as inflammation ("I"). A spectrum of T/N/I imaging and plasma biomarkers was acquired in a phase 2 clinical trial of rasagiline in mild to moderate AD patients. We evaluated these to understand biomarker distributions and relationships within this population. METHODS: Plasma biomarkers of pTau-181, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), other inflammation-related proteins, imaging measures including fluorodeoxyglucose (FDG) positron emission tomography (PET), flortaucipir PET, and volumetric magnetic resonance imaging (MRI), and cognitive endpoints were analyzed to assess characteristics and relationships for the overall population (N = 47 at baseline and N = 21 for longitudinal cognitive comparisons) and within age-decade subgroups (57-69, 70-79, 80-90 years). RESULTS: Data demonstrate wide clinical and biomarker heterogeneity in this population influenced by age and sex. Plasma pTau-181 and GFAP correlate with tau PET, most strongly in left inferior temporal cortex (p = 0.0002, p = 0.0006, respectively). In regions beyond temporal cortex, tau PET uptake decreased with age for the same pTau-181 or GFAP concentrations. FDG PET and brain volumes correlate with tau PET in numerous regions (such as inferior temporal: p = 0.0007, p = 0.00001, respectively). NfL, GFAP, and all imaging modalities correlate with baseline MMSE; subsequent MMSE decline is predicted by baseline parahippocampal and lateral temporal tau PET (p = 0.0007) and volume (p = 0.0006). Lateral temporal FDG PET (p = 0.006) and volume (p = 0.0001) are most strongly associated with subsequent ADAS-cog decline. NfL correlates with FDG PET and baseline MMSE but not tau PET. Inflammation biomarkers are intercorrelated but correlated with other biomarkers in only the youngest group. DISCUSSION: Associations between plasma biomarkers, imaging biomarkers, and cognitive status observed in this study provide insight into relationships among biological processes in mild to moderate AD. Findings show the potential to characterize AD patients regarding likely tau pathology, neurodegeneration, prospective clinical decline, and the importance of covariates such as age. Highlights: Plasma pTau-181 and GFAP correlated with regional and global tau PET in mild to moderate AD.NfL correlated with FDG PET and cognitive endpoints but not plasma pTau-181 or tau PET.Volume and FDG PET showed strong relationships to tau PET, one another, and cognitive status.Temporal volumes most strongly predicted decline in both MMSE and ADAS-cog.Volume and plasma biomarkers can enrich for elevated tau PET with age a significant covariate.

Development, initial validation, and application of a visual read method for [18F]MK-6240 tau PET.

Background: The positron emission tomography (PET) radiotracer [18F]MK-6240 exhibits high specificity for neurofibrillary tangles (NFTs) of tau protein in Alzheimer's disease (AD), high sensitivity to medial temporal and neocortical NFTs, and low within-brain background. Objectives were to develop and validate a reproducible, clinically relevant visual read method supporting [18F]MK-6240 use to identify and stage AD subjects versus non-AD and controls. Methods: Five expert readers used their own methods to assess 30 scans of mixed diagnosis (47% cognitively normal, 23% mild cognitive impairment, 20% AD, 10% traumatic brain injury) and provided input regarding regional and global positivity, features influencing assessment, confidence, practicality, and clinical relevance. Inter-reader agreement and concordance with quantitative values were evaluated to confirm that regions could be read reliably. Guided by input regarding clinical applicability and practicality, read classifications were defined. The readers read the scans using the new classifications, establishing by majority agreement a gold standard read for those scans. Two naïve readers were trained and read the 30-scan set, providing initial validation. Inter-rater agreement was further tested by two trained independent readers in 131 scans. One of these readers used the same method to read a full, diverse database of 1842 scans; relationships between read classification, clinical diagnosis, and amyloid status as available were assessed. Results: Four visual read classifications were determined: no uptake, medial temporal lobe (MTL) only, MTL and neocortical uptake, and uptake outside MTL. Inter-rater kappas were 1.0 for the naïve readers gold standard scans read and 0.98 for the independent readers 131-scan read. All scans in the full database could be classified; classification frequencies were concordant with NFT histopathology literature. Discussion: This four-class [18F]MK-6240 visual read method captures the presence of medial temporal signal, neocortical expansion associated with disease progression, and atypical distributions that may reflect different phenotypes. The method demonstrates excellent trainability, reproducibility, and clinical relevance supporting clinical use. Highlights: A visual read method has been developed for [18F]MK-6240 tau positron emission tomography.The method is readily trainable and reproducible, with inter-rater kappas of 0.98.The read method has been applied to a diverse set of 1842 [18F]MK-6240 scans.All scans from a spectrum of disease states and acquisitions could be classified.Read classifications are consistent with histopathological neurofibrillary tangle staging literature.

Measurement of neurodegeneration using a multivariate early frame amyloid PET classifier.

Introduction: Amyloid measurement provides important confirmation of pathology for Alzheimer's disease (AD) clinical trials. However, many amyloid positive (Am+) early-stage subjects do not worsen clinically during a clinical trial, and a neurodegenerative measure predictive of decline could provide critical information. Studies have shown correspondence between perfusion measured by early amyloid frames post-tracer injection and fluorodeoxyglucose (FDG) positron emission tomography (PET), but with limitations in sensitivity. Multivariate machine learning approaches may offer a more sensitive means for detection of disease related changes as we have demonstrated with FDG. Methods: Using summed dynamic florbetapir image frames acquired during the first 6 minutes post-injection for 107 Alzheimer's Disease Neuroimaging Initiative subjects, we applied optimized machine learning to develop and test image classifiers aimed at measuring AD progression. Early frame amyloid (EFA) classification was compared to that of an independently developed FDG PET AD progression classifier by scoring the FDG scans of the same subjects at the same time point. Score distributions and correlation with clinical endpoints were compared to those obtained from FDG. Region of interest measures were compared between EFA and FDG to further understand discrimination performance. Results: The EFA classifier produced a primary pattern similar to that of the FDG classifier whose expression correlated highly with the FDG pattern (R-squared 0.71), discriminated cognitively normal (NL) amyloid negative (Am-) subjects from all Am+ groups, and that correlated in Am+ subjects with Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale (R = 0.59, 0.63, 0.73) and with subsequent 24-month changes in these measures (R = 0.67, 0.73, 0.50). Discussion: Our results support the ability to use EFA with a multivariate machine learning-derived classifier to obtain a sensitive measure of AD-related loss in neuronal function that correlates with FDG PET in preclinical and early prodromal stages as well as in late mild cognitive impairment and dementia. Highlights: The summed initial post-injection minutes of florbetapir positron emission tomography  correlate with fluorodeoxyglucose.A machine learning classifier enabled sensitive detection of early prodromal Alzheimer's disease.Early frame amyloid (EFA) classifier scores correlate with subsequent change in Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale.EFA classifier effect sizes and clinical prediction outperformed region of interest standardized uptake value ratio.EFA classification may aid in stratifying patients to assess treatment effect.

Exploratory imaging outcomes of a phase 1b/2a clinical trial of allopregnanolone as a regenerative therapeutic for Alzheimer's disease: Structural effects and functional connectivity outcomes.

Introduction: Allopregnanolone (ALLO), an endogenous neurosteroid, promoted neurogenesis and oligogenesis and restored cognitive function in animal models of Alzheimer's disease (AD). Based on these discovery research findings, we conducted a randomized-controlled phase 1b/2a multiple ascending dose trial of ALLO in persons with early AD (NCT02221622) to assess safety, tolerability, and pharmacokinetics. Exploratory imaging outcomes to determine whether ALLO impacted hippocampal structure, white matter integrity, and functional connectivity are reported. Methods: Twenty-four individuals participated in the trial (n = 6 placebo; n = 18 ALLO) and underwent brain magnetic resonance imaging (MRI) before and after 12 weeks of treatment. Hippocampal atrophy rate was determined from volumetric MRI, computed as rate of change, and qualitatively assessed between ALLO and placebo sex, apolipoprotein E (APOE) ε4 allele, and ALLO dose subgroups. White matter microstructural integrity was compared between placebo and ALLO using fractional and quantitative anisotropy (QA). Changes in local, inter-regional, and network-level functional connectivity were also compared between groups using resting-state functional MRI. Results: Rate of decline in hippocampal volume was slowed, and in some cases reversed, in the ALLO group compared to placebo. Gain of hippocampal volume was evident in APOE ε4 carriers (range: 0.6% to 7.8% increased hippocampal volume). Multiple measures of white matter integrity indicated evidence of preserved or improved integrity. ALLO significantly increased fractional anisotropy (FA) in 690 of 690 and QA in 1416 of 1888 fiber tracts, located primarily in the corpus callosum, bilateral thalamic radiations, and bilateral corticospinal tracts. Consistent with structural changes, ALLO strengthened local, inter-regional, and network level functional connectivity in AD-vulnerable regions, including the precuneus and posterior cingulate, and network connections between the default mode network and limbic system. Discussion: Indicators of regeneration from previous preclinical studies and these exploratory MRI-based outcomes from this phase 1b/2a clinical cohort support advancement to a phase 2 proof-of-concept efficacy clinical trial of ALLO as a regenerative therapeutic for mild AD (REGEN-BRAIN study; NCT04838301).

Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data.

Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of activation are organized into a spatial network and the variance of the networked, task-related signals is high enough for the signal to be easily detected in the data. We show that reproducibility of activation maps is a metric that captures this switch in intrinsic dimensionality. Except for reproducibility, all of the methods of dimensionality estimation we considered failed to capture this transition: optimization of Bayesian evidence, minimum description length, supervised and unsupervised LDA prediction, and Stein's unbiased risk estimator. This failure results in sub-optimal ROC performance of LDA in the presence of a spatially distributed network, and may have caused LDA to underperform in many of the reported comparisons in the literature. Using real fMRI data sets, including multi-subject group and within-subject longitudinal analysis we demonstrate the existence of these dimensionality transitions in real data.

Rasagiline effects on glucose metabolism, cognition, and tau in Alzheimer's dementia.

BACKGROUND: A Phase II proof of concept (POC) randomized clinical trial was conducted to evaluate the effects of rasagiline, a monoamine oxidase B (MAO-B) inhibitor approved for Parkinson disease, in mild to moderate Alzheimer's disease (AD). The primary objective was to determine if 1 mg of rasagiline daily for 24 weeks is associated with improved regional brain metabolism (fluorodeoxyglucose-positron emission tomography [FDG-PET]) compared to placebo. Secondary objectives included measurement of effects on tau PET and evaluation of directional consistency of clinical end points. METHODS: This was a double-blind, parallel group, placebo-controlled, community-based, three-site trial of 50 participants randomized 1:1 to receive oral rasagiline or placebo (NCT02359552). FDG-PET was analyzed for the presence of an AD-like pattern as an inclusion criterion and as a longitudinal outcome using prespecified regions of interest and voxel-based analyses. Tau PET was evaluated at baseline and longitudinally. Clinical outcomes were analyzed using an intention-to-treat (ITT) model. RESULTS: Fifty patients were randomized and 43 completed treatment. The study met its primary end point, demonstrating favorable change in FDG-PET differences in rasagiline versus placebo in middle frontal (P < 0.025), anterior cingulate (P < 0.041), and striatal (P < 0.023) regions. Clinical measures showed benefit in quality of life (P < 0.04). Digit Span, verbal fluency, and Neuropsychiatric Inventory (NPI) showed non-significant directional favoring of rasagiline; no effects were observed in Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog) or activities of daily living. Rasagiline was generally well tolerated with low rates of adverse events and notably fewer neuropsychiatric symptoms in the active treatment group. DISCUSSION: These outcomes illustrate the potential benefits of rasagiline on clinical and neuroimaging measures in patients with mild to moderate AD. Rasagiline appears to affect neuronal activity in frontostriatal pathways, with associated clinical benefit potential warranting a more fully powered trial. This study illustrated the potential benefit of therapeutic repurposing and an experimental medicine proof-of-concept design with biomarkers to characterize patient and detect treatment response.

Novel observations and potential applications using digital infrared iris imaging.

Digital infrared iris photography using a modified digital camera system was performed on approximately 300 subjects seen during routine clinical care and research at one facility. Because this image database offered an opportunity to gain new insight into the potential utility of infrared iris imaging, it was surveyed for unique image patterns. Then, a selection of photographs was compiled that would illustrate the spectrum of this imaging experience. Potentially informative image patterns were observed in subjects with cataracts, diabetic retinopathy, Posner-Schlossman syndrome, iridociliary cysts, long anterior lens zonules, nevi, oculocutaneous albinism, pigment dispersion syndrome, pseudophakia, suspected vascular anomaly, and trauma. Image patterns were often unanticipated regardless of preexisting information and suggest that infrared iris imaging may have numerous potential clinical and research applications, some of which may still not be recognized. These observations suggest further development and study of this technology.

Effect of spatial alignment transformations in PCA and ICA of functional neuroimages.

It has been previously observed that independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper, we seek to determine analytically the conditions under which this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case, we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (fMRI) data. Our analysis is applicable to both intersubject and intrasubject spatial normalization.

Bayesian kernel methods for analysis of functional neuroimages.

We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and 2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. In an on-off design, we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude, and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a Reversible-Jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches.

PET Imaging of Tau Pathology and Relationship to Amyloid, Longitudinal MRI, and Cognitive Change in Down Syndrome: Results from the Down Syndrome Biomarker Initiative (DSBI).

BACKGROUND: Adults with Down syndrome (DS) represent an enriched population for the development of Alzheimer's disease (AD), which could aid the study of therapeutic interventions, and in turn, could benefit from discoveries made in other AD populations. OBJECTIVES: 1) Understand the relationship between tau pathology and age, amyloid deposition, neurodegeneration (MRI and FDG PET), and cognitive and functional performance; 2) detect and differentiate AD-specific changes from DS-specific brain changes in longitudinal MRI. METHODS: Twelve non-demented adults, ages 30 to 60, with DS were enrolled in the Down Syndrome Biomarker Initiative (DSBI), a 3-year, observational, cohort study to demonstrate the feasibility of conducting AD intervention/prevention trials in adults with DS. We collected imaging data with 18F-AV-1451 tau PET, AV-45 amyloid PET, FDG PET, and volumetric MRI, as well as cognitive and functional measures and additional laboratory measures. RESULTS: All amyloid negative subjects imaged were tau-negative. Among the amyloid positive subjects, three had tau in regions associated with Braak stage VI, two at stage V, and one at stage II. Amyloid and tau burden correlated with age. The MRI analysis produced two distinct volumetric patterns. The first differentiated DS from normal (NL) and AD, did not correlate with age or amyloid, and was longitudinally stable. The second pattern reflected AD-like atrophy and differentiated NL from AD. Tau PET and MRI atrophy correlated with several cognitive and functional measures. CONCLUSIONS: Tau accumulation is associated with amyloid positivity and age, as well as with progressive neurodegeneration measurable using FDG and MRI. Tau correlates with cognitive decline, as do AD-specific hypometabolism and atrophy.

Dissociation of Down syndrome and Alzheimer's disease effects with imaging.

INTRODUCTION: Down Syndrome (DS) adults experience accumulation of Alzheimer's disease (AD)-like amyloid plaques and tangles and a high incidence of dementia and could provide an enriched population to study AD-targeted treatments. However, to evaluate effects of therapeutic intervention, it is necessary to dissociate the contributions of DS and AD from overall phenotype. Imaging biomarkers offer the potential to characterize and stratify patients who will worsen clinically but have yielded mixed findings in DS subjects. METHODS: We evaluated 18F fluorodeoxyglucose positron emission tomography (PET), florbetapir PET, and structural magnetic resonance (sMR) image data from 12 nondemented DS adults using advanced multivariate machine learning methods. RESULTS: Our results showed distinctive patterns of glucose metabolism and brain volume enabling dissociation of DS and AD effects. AD-like pattern expression corresponded to amyloid burden and clinical measures. DISCUSSION: These findings lay groundwork to enable AD clinical trials with characterization and disease-specific tracking of DS adults.

An evaluation of methods for detecting brain activations from functional neuroimages.

Brain activation studies based on PET or fMRI seek to explore neuroscience questions by statistically analyzing the acquired images to produce statistical parametric images (SPIs). An increasingly wide range of univariate and multivariate analysis techniques are used to generate SPIs in order to detect mean-signal activations and/or long-range spatial interactions. However, little is known about the comparative detection performance of even simple techniques in finite data sets. Our aims are (1) to empirically compare the detection performance of a range of techniques using simulations of a simple image phantom and receiver operating characteristics (ROC) analysis, and (2) to construct two near-optimal detectors, both generalized likelihood ratio tests as upper performance bounds. We found that for finite samples of (10-100) images, even when the t-test with single-voxel variance estimates (single-voxel t-test) is the "correct" (i.e. unbiased) model for simple local additive signals, better detection performance is obtained using pooled variance estimates or adaptive, multivariate covariance-based detectors. Normalization by voxel-based variance estimates causes significantly decreased detection performance using either single-voxel t-tests or correlation-coefficient thresholding compared to pooled-variance t-tests or covariance thresholding, respectively. Moreover, we found that SVD by itself, or followed by an adaptive Fisher linear discriminant, provides a detector that is (1) more sensitive to mean differences than a single-voxel t-test, (2) insensitive to the large local signal variances detected by covariance thresholding, and (3) much more sensitive to signal correlations than correlation-coefficient thresholding. Adaptive, multivariate covariance-based approaches and pooled-variance t-tests represent promising directions for obtaining optimal signal detection in functional neuroimaging studies.

Quantification of pupil parameters in diseased and normal eyes with near infrared iris transillumination imaging.

BACKGROUND AND OBJECTIVE: To investigate near infrared iris transillumination (NIRit) imaging as a new method to quantify pupil shape, size, and position because the imaging modality can uniquely provide simultaneous information regarding iris structural details that influence pupil characteristics and because exploration of related techniques could promote discovery helpful to clinical research and care. PATIENTS AND METHODS: Digital NIRit images of normal and diseased eyes were used along with computer-assisted techniques to quantify four primary pupil parameters, including pupil roundness (PR), pupil ovalness (PO), pupil size (PS), and pupil eccentricity (PE). A combined measure of PR and PO was also developed (the pupil circularity index [PCI]). Repeatability of the measures was studied and example analyses were performed. RESULTS: Pupil measures could be calculated for right eyes of 307 subjects (164 normal, 143 other), with fewer than 0.5% exclusions due to image quality. Repeatability study did not show significant bias (P < .05) for any of the four primary measures. Example analyses could show age-associated differences in pupil shape (≥ 50 year olds had less regular pupils than < 50 year olds: median PCI = 0.009 vs 0.006; P < .01) and that a group of pigment dispersion syndrome subjects (n = 27) had less regular pupils than a group of matched controls (PO = 0.9966 vs 0.9990; P < .05). CONCLUSION: Digital NIRit imaging can provide novel, reliable, and informative methods to quantify pupil characteristics while providing simultaneous information about iris structure that may influence these parameters.