Comparative analytical performance of multiple plasma Aß42 and Aß40 assays and their ability to predict positron emission tomography amyloid positivity.

INTRODUCTION: This report details the approach taken to providing a dataset allowing for analyses on the performance of recently developed assays of amyloid beta (Aß) peptides in plasma and the extent to which they improve the prediction of amyloid positivity. METHODS: Alzheimer's Disease Neuroimaging Initiative plasma samples with corresponding amyloid positron emission tomography (PET) data were run on six plasma Aß assays. Statistical tests were performed to determine whether the plasma Aß measures significantly improved the area under the receiver operating characteristic curve for predicting amyloid PET status compared to age and apolipoprotein E (APOE) genotype. RESULTS: The age and APOE genotype model predicted amyloid status with an area under the curve (AUC) of 0.75. Three assays improved AUCs to 0.81, 0.81, and 0.84 (P < .05, uncorrected for multiple comparisons). DISCUSSION: Measurement of Aß in plasma contributes to addressing the amyloid component of the ATN (amyloid/tau/neurodegeneration) framework and could be a first step before or in place of a PET or cerebrospinal fluid screening study. HIGHLIGHTS: The Foundation of the National Institutes of Health Biomarkers Consortium evaluated six plasma amyloid beta (Aß) assays using Alzheimer's Disease Neuroimaging Initiative samples. Three assays improved prediction of amyloid status over age and apolipoprotein E (APOE) genotype. Plasma Aß42/40 predicted amyloid positron emission tomography status better than Aß42 or Aß40 alone.

Surface based electrode localization and standardized regions of interest for intracranial EEG.

Intracranial recordings captured from subdural electrodes in patients with drug resistant epilepsy offer clinicians and researchers a powerful tool for examining neural activity in the human brain with high spatial and temporal precision. There are two major challenges, however, to interpreting these signals both within and across individuals. Anatomical distortions following implantation make accurately identifying the electrode locations difficult. In addition, because each implant involves a unique configuration, comparing neural activity across individuals in a standardized manner has been limited to broad anatomical regions such as cortical lobes or gyri. We address these challenges here by introducing a semi-automated method for localizing subdural electrode contacts to the unique surface anatomy of each individual, and by using a surface-based grid of regions of interest (ROIs) to aggregate electrode data from similar anatomical locations across individuals. Our localization algorithm, which uses only a postoperative CT and preoperative MRI, builds upon previous spring-based optimization approaches by introducing manually identified anchor points directly on the brain surface to constrain the final electrode locations. This algorithm yields an accuracy of 2 mm. Our surface-based ROI approach involves choosing a flexible number of ROIs with different spatial resolutions. ROIs are registered across individuals to represent identical anatomical locations while accounting for the unique curvature of each brain surface. This ROI based approach therefore enables group level statistical testing from spatially precise anatomical regions.

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns.

Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group-level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.

DBSproc: An open source process for DBS electrode localization and tractographic analysis.

Deep brain stimulation (DBS) is an effective surgical treatment for movement disorders. Although stimulation sites for movement disorders such as Parkinson's disease are established, the therapeutic mechanisms of DBS remain controversial. Recent research suggests that specific white-matter tract and circuit activation mediates symptom relief. To investigate these questions, we have developed a patient-specific open-source software pipeline called 'DBSproc' for (1) localizing DBS electrodes and contacts from postoperative CT images, (2) processing structural and diffusion MRI data, (3) registering all images to a common space, (4) estimating DBS activation volume from patient-specific voltage and impedance, and (5) understanding the DBS contact-brain connectivity through probabilistic tractography. In this paper, we explain our methodology and provide validation with anatomical and tractographic data. This method can be used to help investigate mechanisms of action of DBS, inform surgical and clinical assessments, and define new therapeutic targets.

Task Dependence, Tissue Specificity, and Spatial Distribution of Widespread Activations in Large Single-Subject Functional MRI Datasets at 7T.

It was recently shown that when large amounts of task-based blood oxygen level-dependent (BOLD) data are combined to increase contrast- and temporal signal-to-noise ratios, the majority of the brain shows significant hemodynamic responses time-locked with the experimental paradigm. Here, we investigate the biological significance of such widespread activations. First, the relationship between activation extent and task demands was investigated by varying cognitive load across participants. Second, the tissue specificity of responses was probed using the better BOLD signal localization capabilities of a 7T scanner. Finally, the spatial distribution of 3 primary response types--namely positively sustained (pSUS), negatively sustained (nSUS), and transient--was evaluated using a newly defined voxel-wise waveshape index that permits separation of responses based on their temporal signature. About 86% of gray matter (GM) became significantly active when all data entered the analysis for the most complex task. Activation extent scaled with task load and largely followed the GM contour. The most common response type was nSUS BOLD, irrespective of the task. Our results suggest that widespread activations associated with extremely large single-subject functional magnetic resonance imaging datasets can provide valuable information about the functional organization of the brain that goes undetected in smaller sample sizes.

Two distinct forms of functional lateralization in the human brain.

The hemispheric lateralization of certain faculties in the human brain has long been held to be beneficial for functioning. However, quantitative relationships between the degree of lateralization in particular brain regions and the level of functioning have yet to be established. Here we demonstrate that two distinct forms of functional lateralization are present in the left vs. the right cerebral hemisphere, with the left hemisphere showing a preference to interact more exclusively with itself, particularly for cortical regions involved in language and fine motor coordination. In contrast, right-hemisphere cortical regions involved in visuospatial and attentional processing interact in a more integrative fashion with both hemispheres. The degree of lateralization present in these distinct systems selectively predicted behavioral measures of verbal and visuospatial ability, providing direct evidence that lateralization is associated with enhanced cognitive ability.

Integrated strategy for improving functional connectivity mapping using multiecho fMRI.

Functional connectivity analysis of resting state blood oxygen level-dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (≤1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals-a characteristic property of BOLD T*2 signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.

Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis.

The brain is the body's largest energy consumer, even in the absence of demanding tasks. Electrophysiologists report on-going neuronal firing during stimulation or task in regions beyond those of primary relationship to the perturbation. Although the biological origin of consciousness remains elusive, it is argued that it emerges from complex, continuous whole-brain neuronal collaboration. Despite converging evidence suggesting the whole brain is continuously working and adapting to anticipate and actuate in response to the environment, over the last 20 y, task-based functional MRI (fMRI) have emphasized a localizationist view of brain function, with fMRI showing only a handful of activated regions in response to task/stimulation. Here, we challenge that view with evidence that under optimal noise conditions, fMRI activations extend well beyond areas of primary relationship to the task; and blood-oxygen level-dependent signal changes correlated with task-timing appear in over 95% of the brain for a simple visual stimulation plus attention control task. Moreover, we show that response shape varies substantially across regions, and that whole-brain parcellations based on those differences produce distributed clusters that are anatomically and functionally meaningful, symmetrical across hemispheres, and reproducible across subjects. These findings highlight the exquisite detail lying in fMRI signals beyond what is normally examined, and emphasize both the pervasiveness of false negatives, and how the sparseness of fMRI maps is not a result of localized brain function, but a consequence of high noise and overly strict predictive response models.

Applications of multivariate modeling to neuroimaging group analysis: a comprehensive alternative to univariate general linear model.

All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance-covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within-subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT) with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse-Geisser and Huynh-Feldt) with MVT-WS. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in the field, and the following two issues were raised: a) the improper formulation of test statistics in some univariate GLM implementations when a within-subject factor is involved in a data structure with two or more factors, and b) the unjustified presumption of uniform sphericity violation and the practice of estimating the variance-covariance structure through pooling across brain regions.

Head-to-head comparison of leading blood tests for Alzheimer's disease pathology.

INTRODUCTION: Blood tests have the potential to improve the accuracy of Alzheimer disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes. METHODS: Plasma samples from the Alzheimers Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes. RESULTS: Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes. DISCUSSION: This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes.

Linear mixed-effects modeling approach to FMRI group analysis.

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details.

Effects of image contrast on functional MRI image registration.

Lack of tissue contrast and existing inhomogeneous bias fields from multi-channel coils have the potential to degrade the output of registration algorithms; and consequently degrade group analysis and any attempt to accurately localize brain function. Non-invasive ways to improve tissue contrast in fMRI images include the use of low flip angles (FAs) well below the Ernst angle and longer repetition times (TR). Techniques to correct intensity inhomogeneity are also available in most mainstream fMRI data analysis packages; but are not used as part of the pre-processing pipeline in many studies. In this work, we use a combination of real data and simulations to show that simple-to-implement acquisition/pre-processing techniques can significantly improve the outcome of both functional-to-functional and anatomical-to-functional image registrations. We also emphasize the need of tissue contrast on EPI images to be able to appropriately evaluate the quality of the alignment. In particular, we show that the use of low FAs (e.g., θ≤40°), when physiological noise considerations permit such an approach, significantly improves accuracy, consistency and stability of registration for data acquired at relatively short TRs (TR≤2s). Moreover, we also show that the application of bias correction techniques significantly improves alignment both for array-coil data (known to contain high intensity inhomogeneity) as well as birdcage-coil data. Finally, improvements in alignment derived from the use of the first infinite-TR volumes (ITVs) as targets for registration are also demonstrated. For the purpose of quantitatively evaluating the different scenarios, two novel metrics were developed: Mean Voxel Distance (MVD) to evaluate registration consistency, and Deviation of Mean Voxel Distance (dMVD) to evaluate registration stability across successive alignment attempts.

APOEε4 potentiates amyloid ß effects on longitudinal tau pathology.

The mechanisms by which the apolipoprotein E ε4 (APOEε4) allele influences the pathophysiological progression of Alzheimer's disease (AD) are poorly understood. Here we tested the association of APOEε4 carriership and amyloid-ß (Aß) burden with longitudinal tau pathology. We longitudinally assessed 94 individuals across the aging and AD spectrum who underwent clinical assessments, APOE genotyping, magnetic resonance imaging, positron emission tomography (PET) for Aß ([18F]AZD4694) and tau ([18F]MK-6240) at baseline, as well as a 2-year follow-up tau-PET scan. We found that APOEε4 carriership potentiates Aß effects on longitudinal tau accumulation over 2 years. The APOEε4-potentiated Aß effects on tau-PET burden were mediated by longitudinal plasma phosphorylated tau at threonine 217 (p-tau217+) increase. This longitudinal tau accumulation as measured by PET was accompanied by brain atrophy and clinical decline. Our results suggest that the APOEε4 allele plays a key role in Aß downstream effects on the aggregation of phosphorylated tau in the living human brain.

SUMA.

Surface-based brain imaging analysis offers the advantages of preserving the topology of cortical activation, increasing statistical power of group-level statistics, estimating cortical thickness, and visualizing with ease the pattern of activation across the whole cortex. SUMA is an open-source suite of programs for performing surface-based analysis and visualization. It was designed since its inception to allow for a fine control over the mapping between volume and surface domains, and for very fast and simultaneous display of multiple surface models and corresponding multitudes of datasets, all while maintaining a direct two-way link to volumetric data from which surface models and data originated. SUMA provides tools for performing spatial operations such as controlled smoothing, clustering, and interactive ROI drawing on folded surfaces in 3D, in addition to the various level-1 and level-2 FMRI statistics including FDR and FWE correction for multiple comparisons. In our contribution to this commemorative issue of Neuroimage we touch on the importance of surface-based analysis and provide a historic backdrop that motivated the creation of SUMA. We also highlight features that are particular to SUMA, notably the standardization procedure of meshes to greatly facilitate group-level analyses, and the ability to control SUMA's graphical interface from external programs making it possible to handle large collections of data with relative ease.

FMRI group analysis combining effect estimates and their variances.

Conventional functional magnetic resonance imaging (FMRI) group analysis makes two key assumptions that are not always justified. First, the data from each subject is condensed into a single number per voxel, under the assumption that within-subject variance for the effect of interest is the same across all subjects or is negligible relative to the cross-subject variance. Second, it is assumed that all data values are drawn from the same Gaussian distribution with no outliers. We propose an approach that does not make such strong assumptions, and present a computationally efficient frequentist approach to FMRI group analysis, which we term mixed-effects multilevel analysis (MEMA), that incorporates both the variability across subjects and the precision estimate of each effect of interest from individual subject analyses. On average, the more accurate tests result in higher statistical power, especially when conventional variance assumptions do not hold, or in the presence of outliers. In addition, various heterogeneity measures are available with MEMA that may assist the investigator in further improving the modeling. Our method allows group effect t-tests and comparisons among conditions and among groups. In addition, it has the capability to incorporate subject-specific covariates such as age, IQ, or behavioral data. Simulations were performed to illustrate power comparisons and the capability of controlling type I errors among various significance testing methods, and the results indicated that the testing statistic we adopted struck a good balance between power gain and type I error control. Our approach is instantiated in an open-source, freely distributed program that may be used on any dataset stored in the universal neuroimaging file transfer (NIfTI) format. To date, the main impediment for more accurate testing that incorporates both within- and cross-subject variability has been the high computational cost. Our efficient implementation makes this approach practical. We recommend its use in lieu of the less accurate approach in the conventional group analysis.

Plasma p217+tau versus NAV4694 amyloid and MK6240 tau PET across the Alzheimer's continuum.

Introduction: We evaluated a new Simoa plasma assay for phosphorylated tau (P-tau) at aa217 enhanced by additional p-tau sites (p217+tau). Methods: Plasma p217+tau levels were compared to 18F-NAV4694 amyloid beta (Aß) positron emission tomography (PET) and 18F-MK6240 tau PET in 174 cognitively impaired (CI) and 223 cognitively unimpaired (CU) participants. Results: Compared to Aß- CU, the plasma levels of p217+tau increased 2-fold in Aß+ CU and 3.5-fold in Aß+ CI. In Aß- the p217+tau levels did not differ significantly between CU and CI. P217+tau correlated with Aß centiloids P = .67 (CI, P = .64; CU, P = .45) and tau SUVRMT P = .63 (CI, P = .69; CU, P = .34). Area under curve (AUC) for Alzheimer's disease (AD) dementia versus Aß- CU was 0.94, for AD dementia versus other dementia was 0.93, for Aß+ versus Aß- PET was 0.89, and for tau+ versus tau- PET was 0.89. Discussion: Plasma p217+tau levels elevate early in the AD continuum and correlate well with Aß and tau PET.

Segmentation priors from local image properties: without using bias field correction, location-based templates, or registration.

We present a novel approach for generating information about a voxel's tissue class membership based on its signature--a collection of local image textures estimated over a range of neighborhood sizes. The approach produces a form of tissue class priors that can be used to initialize and regularize image segmentation. The signature-based approach is a departure from current location-based methods, which derive tissue class likelihoods based on a voxel's location in standard template space. To use location-based priors, one needs to register the volume in question to the template space, and estimate the image intensity bias field. Two optimizations, over more than a thousand parameters, are needed when high order nonlinear registration is employed. In contrast, the signature-based approach is independent of volume orientation, voxel position, and largely insensitive to bias fields. For these reasons, the approach does not require the use of population derived templates. The prior information is generated from variations in image texture statistics as a function of spatial scale, and an SVM approach is used to associate signatures with tissue types. With the signature-based approach, optimization is needed only during the training phase for the parameter estimation stages of the SVM hyperplanes, and associated PDFs; a training process separate from the segmentation step. We found that signature-based priors were superior to location-based ones aligned under favorable conditions, and that signature-based priors result in improved segmentation when replacing location-based ones in FAST (Zhang et al., 2001), a widely used segmentation program. The software implementation of this work is freely available as part of AFNI http://afni.nimh.nih.gov.

The effects of inhibition of fatty acid amide hydrolase (FAAH) by JNJ-42165279 in social anxiety disorder: a double-blind, randomized, placebo-controlled proof-of-concept study.

JNJ-42165279 is a selective inhibitor of fatty acid amide hydrolase (FAAH), the enzyme responsible for the degradation of fatty acid amides (FAA) including anandamide (AEA), palmitoylethanolamide (PEA), and N-oleoylethanolamide (OEA). We assessed the efficacy, safety, tolerability, pharmacokinetics, and pharmacodynamics of treatment with JNJ-42165279 in subjects with social anxiety disorder (SAD). This was a multicenter, double-blind, placebo-controlled study randomizing subjects to 12 weeks of treatment with either JNJ-42165279 (25 mg daily) or placebo (PBO). The primary endpoint was the change in the Liebowitz Social Anxiety Scale (LSAS) total score from baseline to end of study. Secondary endpoints included the Hamilton Anxiety Scale (HAM-A), Hamilton Depression Rating Scale (HDRS17), and the Clinical Global Impression-Improvement (CGI-I). Samples were collected for plasma concentration of AEA, PEA, OEA, and JNJ-42165279. A total of 149 subjects were enrolled with a mean baseline LSAS total score of 102.6 (SD 16.84). The mean change from baseline (SD) in LSAS total score at week 12 was numerically greater for JNJ-42165279: -29.4 (27.47) compared to PBO: -22.4 (23.57) but not significant. The percentage of subjects with ≥30% improvement from baseline in the LSAS total score was significantly higher for JNJ-42165279 (42.4%) compared to PBO (23.6%) (p value = 0.04). The percentage of subjects with a CGI-I score of much or very much improved was also significantly higher for JNJ-42165279 (44.1%) than for PBO (23.6%) (p value = 0.02). The drug was well tolerated. JNJ-42165279 appears to elicit an anxiolytic effect in subjects with SAD although trough concentrations with 25 mg once daily appeared to be insufficient to completely inhibit FAAH activity which may have led to suboptimal efficacy. ClinicalTrials.gov Identifier: NCT02432703.

Findings of Efficacy, Safety, and Biomarker Outcomes of Atabecestat in Preclinical Alzheimer Disease: A Truncated Randomized Phase 2b/3 Clinical Trial.

Importance: Atabecestat, a nonselective oral ß-secretase inhibitor, was evaluated in the EARLY trial for slowing cognitive decline in participants with preclinical Alzheimer disease. Preliminary analyses suggested dose-related cognitive worsening and neuropsychiatric adverse events (AEs). Objective: To report efficacy, safety, and biomarker findings in the EARLY trial, both on and off atabecestat treatment, with focus on potential recovery of effects on cognition and behavior. Design, Setting, and Participants: Randomized, double-blind, placebo-controlled, phase 2b/3 study conducted from November 2015 to December 2018 after being stopped prematurely. The study was conducted at 143 centers across 14 countries. Participants were permitted to be followed off-treatment by the original protocol, collecting safety and efficacy data. From 4464 screened participants, 557 amyloid-positive, cognitively normal (Clinical Dementia Rating of 0; aged 60-85 years) participants (approximately 34% of originally planned 1650) were randomized before the trial sponsor stopped enrollment. Interventions: Participants were randomized (1:1:1) to atabecestat, 5 mg (n = 189), 25 mg (n = 183), or placebo (n = 185). Main Outcomes and Measures: Primary outcome: change from baseline in Preclinical Alzheimer Cognitive Composite score. Secondary outcomes: change from baseline in the Cognitive Function Index and the Repeatable Battery for the Assessment of Neuropsychological Status total scale score. Safety was monitored throughout the study. Results: Of 557 participants, 341 were women (61.2%); mean (SD) age was 70.4 (5.56) years. In May 2018, study medication was stopped early owing to hepatic-related AEs; participants were followed up off-treatment for 6 months. Atabecestat, 25 mg, showed significant cognitive worsening vs placebo for Preclinical Alzheimer Cognitive Composite at month 6 (least-square mean difference, -1.09; 95% CI, -1.66 to -0.53; P < .001) and month 12 (least-square mean, -1.62; 95% CI, -2.49 to -0.76; P < .001), and at month 3 for Repeatable Battery for the Assessment of Neuropsychological Status (least-square mean, -3.70; 95% CI, -5.76 to -1.63; P < .001). Cognitive Function Index participant report showed nonsignificant worsening at month 12. Systemic and neuropsychiatric-related treatment-emergent AEs were greater in atabecestat groups vs placebo. After stopping treatment, follow-up cognitive testing and AE assessment provided evidence of reversibility of drug-induced cognitive worsening and AEs in atabecestat groups. Conclusions and Relevance: Atabecestat treatment was associated with dose-related cognitive worsening as early as 3 months and presence of neuropsychiatric treatment-emergent AEs, with evidence of reversibility after 6 months off treatment. Trial Registration: ClinicalTrials.gov Identifier: NCT02569398.

Mapping sources of correlation in resting state FMRI, with artifact detection and removal.

Many components of resting-state (RS) FMRI show non-random structure that has little to do with neural connectivity but can covary over multiple brain structures. Some of these signals originate in physiology and others are hardware-related. One artifact discussed herein may be caused by defects in the receive coil array or the RF amplifiers powering it. During a scan, this artifact results in small image intensity shifts in parts of the brain imaged by the affected array components. These shifts introduce artifactual correlations in RS time series on the spatial scale of the coil's sensitivity profile, and can markedly bias RS connectivity results. We show that such a transient artifact can be substantially removed from RS time series by using locally formed regressors from white matter tissue. This is particularly important in arrays with larger numbers of coils, which may generate smaller artifact zones. In such a case, brain-wide average noise estimates would fail to capture the artifact. We also examine the anatomical structure of artifactual variance in RS FMRI time series, by identifying sources that contribute to these signals and where in the brain are they manifested. We consider current methods for reducing confounding sources (or noises) and their effects on connectivity maps, and offer an improved approach (ANATICOR) that can also reduce hardware artifacts. The methods described herein are currently available with AFNI, in addition to tools for rapid, interactive generation of seed-based correlation maps at single-subject and group levels.