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.

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.

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.

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.

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.

Robust, atlas-free, automatic segmentation of brain MRI in health and disease.

BACKGROUND: Brain- and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlas-free brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center. METHODS: Proton-density, T2-weighted, T1-weighted, brain-free water, 3D FLAIR, 3D T2-weighted, and 3D T2*-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV + subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV + subjects (cohort two), and the results were compared with that of FreeSurfer and Lesion-TOADS. Finally, C-DEF segmentation was evaluated on subjects clinically diagnosed with various other neurological diseases (cohort three). RESULTS: C-DEF algorithm was optimized using leave-one-out cross validation on five healthy subjects (age 36 ± 11 years), and five subjects infected with HIV (age 57 ± 2.6 years) in cohort one. The optimized C-DEF algorithm outperformed FreeSurfer and Lesion-TOADS segmentation in 49 other subjects infected with HIV (cohort two, age 54 ± 6 years) in qualitative and quantitative comparisons. Although trained only on HIV brains, sensitivity to detect lesions using C-DEF increased by 45% in HTLV-I-associated myelopathy/tropical spastic paraparesis (n = 5; age 58 ± 7 years), 33% in multiple sclerosis (n = 5; 42 ± 9 years old), and 4% in subjects with polymorphism of the cytotoxic T-lymphocyte-associated protein 4 gene (n = 5; age 24 ± 12 years) compared to Lesion-TOADS. CONCLUSION: C-DEF outperformed other segmentation algorithms in the various neurological diseases explored herein, especially in lesion segmentation. While the results reported are from routine images acquired at the NIH, the algorithm can be easily trained and optimized for any set of contrasts and protocols for wider application. We are currently exploring various technical aspects of optimal implementation of CDEF in a clinical setting and evaluating a larger cohort of patients with other neurological diseases. Improving the accuracy of brain segmentation methodology will help better understand the relationship of imaging abnormalities to clinical and neuropsychological markers in disease.

ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids.

BACKGROUND: Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subject's neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT. NEW METHOD: ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individual's cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab®, thus providing an easy, automated and standard tool for ECoG electrode localization. RESULTS: ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method. COMPARISON WITH EXISTING METHODS: A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects. CONCLUSIONS: The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart.

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.

Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke.

INTRODUCTION: Interpretation of the extent of perfusion deficits in stroke MRI is highly dependent on the method used for analyzing the perfusion-weighted signal intensity time-series after gadolinium injection. In this study, we introduce a new model-free standardized method of temporal similarity perfusion (TSP) mapping for perfusion deficit detection and test its ability and reliability in acute ischemia. MATERIALS AND METHODS: Forty patients with an ischemic stroke or transient ischemic attack were included. Two blinded readers compared real-time generated interactive maps and automatically generated TSP maps to traditional TTP/MTT maps for presence of perfusion deficits. Lesion volumes were compared for volumetric inter-rater reliability, spatial concordance between perfusion deficits and healthy tissue and contrast-to-noise ratio (CNR). RESULTS: Perfusion deficits were correctly detected in all patients with acute ischemia. Inter-rater reliability was higher for TSP when compared to TTP/MTT maps and there was a high similarity between the lesion volumes depicted on TSP and TTP/MTT (r(18) = 0.73). The Pearson's correlation between lesions calculated on TSP and traditional maps was high (r(18) = 0.73, p<0.0003), however the effective CNR was greater for TSP compared to TTP (352.3 vs 283.5, t(19) = 2.6, p<0.03.) and MTT (228.3, t(19) = 2.8, p<0.03). DISCUSSION: TSP maps provide a reliable and robust model-free method for accurate perfusion deficit detection and improve lesion delineation compared to traditional methods. This simple method is also computationally faster and more easily automated than model-based methods. This method can potentially improve the speed and accuracy in perfusion deficit detection for acute stroke treatment and clinical trial inclusion decision-making.

Neanderthal-Derived Genetic Variation Shapes Modern Human Cranium and Brain.

Before their disappearance from the fossil record approximately 40,000 years ago, Neanderthals, the ancient hominin lineage most closely related to modern humans, interbred with ancestors of present-day humans. The legacy of this gene flow persists through Neanderthal-derived variants that survive in modern human DNA; however, the neural implications of this inheritance are uncertain. Here, using MRI in a large cohort of healthy individuals of European-descent, we show that the amount of Neanderthal-originating polymorphism carried in living humans is related to cranial and brain morphology. First, as a validation of our approach, we demonstrate that a greater load of Neanderthal-derived genetic variants (higher "NeanderScore") is associated with skull shapes resembling those of known Neanderthal cranial remains, particularly in occipital and parietal bones. Next, we demonstrate convergent NeanderScore-related findings in the brain (measured by gray- and white-matter volume, sulcal depth, and gyrification index) that localize to the visual cortex and intraparietal sulcus. This work provides insights into ancestral human neurobiology and suggests that Neanderthal-derived genetic variation is neurologically functional in the contemporary population.

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.

Open Environment for Multimodal Interactive Connectivity Visualization and Analysis.

Brain connectivity investigations are becoming increasingly multimodal and they present challenges for quantitatively characterizing and interactively visualizing data. In this study, we present a new set of network-based software tools for combining functional and anatomical connectivity from magnetic resonance imaging (MRI) data. The computational tools are available as part of Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), a toolbox that interfaces with Analysis of Functional NeuroImages (AFNI) and SUrface MApping (SUMA) for interactive queries and visualization. This includes a novel, tractographic mini-probabilistic approach to improve streamline tracking in networks. We show how one obtains more robust tracking results for determining white matter connections by utilizing the uncertainty of the estimated diffusion tensor imaging (DTI) parameters and a few Monte Carlo iterations. This allows for thresholding based on the number of connections between target pairs to reduce the presence of tracts likely due to noise. To assist users in combining data, we describe an interface for navigating and performing queries in two-dimensional and three-dimensional data defined over voxel, surface, tract, and graph domains. These varied types of information can be visualized simultaneously and the queries performed interactively using SUMA and AFNI. The methods have been designed to increase the user's ability to visualize and combine functional MRI and DTI modalities, particularly in the context of single-subject inferences (e.g., in deep brain stimulation studies). Finally, we present a multivariate framework for statistically modeling network-based features in group analysis, which can be implemented for both functional and structural studies.

Shifts in connectivity during procedural learning after motor cortex stimulation: A combined transcranial magnetic stimulation/functional magnetic resonance imaging study.

Inhibitory transcranial magnetic stimulation (TMS), of which continuous theta burst stimulation (cTBS) is a common form, has been used to inhibit cortical areas during investigations of their function. cTBS applied to the primary motor area (M1) depresses motor output excitability via a local effect and impairs procedural motor learning. This could be due to an effect on M1 itself and/or to changes in its connectivity with other nodes in the learning network. To investigate this issue, we used functional magnetic resonance imaging to measure changes in brain activation and connectivity during implicit procedural learning after real and sham cTBS of M1. Compared to sham, real cTBS impaired motor sequence learning, but caused no local or distant changes in brain activation. Rather, it reduced functional connectivity between motor (M1, dorsal premotor & supplementary motor areas) and visual (superior & inferior occipital gyri) areas. It also increased connectivity between frontal associative (superior & inferior frontal gyri), cingulate (dorsal & middle cingulate), and temporal areas. This potentially compensatory shift in coupling, from a motor-based learning network to an associative learning network accounts for the behavioral effects of cTBS of M1. The findings suggest that the inhibitory TMS affects behavior via relatively subtle and distributed effects on connectivity within networks, rather than by taking the stimulated area "offline".

Detecting the subtle shape differences in hemodynamic responses at the group level.

The nature of the hemodynamic response (HDR) is still not fully understood due to the multifaceted processes involved. Aside from the overall amplitude, the response may vary across cognitive states, tasks, brain regions, and subjects with respect to characteristics such as rise and fall speed, peak duration, undershoot shape, and overall duration. Here we demonstrate that the fixed-shape (FSM) or adjusted-shape (ASM) methods may fail to detect some shape subtleties (e.g., speed of rise or recovery, or undershoot). In contrast, the estimated-shape method (ESM) through multiple basis functions can provide the opportunity to identify some subtle shape differences and achieve higher statistical power at both individual and group levels. Previously, some dimension reduction approaches focused on the peak magnitude, or made inferences based on the area under the curve (AUC) or interaction, which can lead to potential misidentifications. By adopting a generic framework of multivariate modeling (MVM), we showcase a hybrid approach that is validated by simulations and real data. With the whole HDR shape integrity maintained as input at the group level, the approach allows the investigator to substantiate these more nuanced effects through the unique HDR shape features. Unlike the few analyses that were limited to main effect, two- or three-way interactions, we extend the modeling approach to an inclusive platform that is more adaptable than the conventional GLM. With multiple effect estimates from ESM for each condition, linear mixed-effects (LME) modeling should be used at the group level when there is only one group of subjects without any other explanatory variables. Under other situations, an approximate approach through dimension reduction within the MVM framework can be adopted to achieve a practical equipoise among representation, false positive control, statistical power, and modeling flexibility. The associated program 3dMVM is publicly available as part of the AFNI suite.

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.

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.

The spatial structure of resting state connectivity stability on the scale of minutes.

Resting state functional MRI (rsfMRI) connectivity patterns are not temporally stable, but fluctuate in time at scales shorter than most common rest scan durations (5-10 min). Consequently, connectivity patterns for two different portions of the same scan can differ drastically. To better characterize this temporal variability and understand how it is spatially distributed across the brain, we scanned subjects continuously for 60 min, at a temporal resolution of 1 s, while they rested inside the scanner. We then computed connectivity matrices between functionally-defined regions of interest for non-overlapping 1 min windows, and classified connections according to their strength, polarity, and variability. We found that the most stable connections correspond primarily to inter-hemispheric connections between left/right homologous ROIs. However, only 32% of all within-network connections were classified as most stable. This shows that resting state networks have some long-term stability, but confirms the flexible configuration of these networks, particularly those related to higher order cognitive functions. The most variable connections correspond primarily to inter-hemispheric, across-network connections between non-homologous regions in occipital and frontal cortex. Finally we found a series of connections with negative average correlation, but further analyses revealed that such average negative correlations may be related to the removal of CSF signals during pre-processing. Using the same dataset, we also evaluated how similarity of within-subject whole-brain connectivity matrices changes as a function of window duration (used here as a proxy for scan duration). Our results suggest scanning for a minimum of 10 min to optimize within-subject reproducibility of connectivity patterns across the entire brain, rather than a few predefined networks.

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.

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.