Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the human connectome roject

Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR

Hippocampal shape and volume changes with antipsychotics in early stage psychotic illness

Progression of hippocampal shape and volume abnormalities has been described in psychotic disorders such as schizophrenia. However it is unclear how specific antipsychotic medications influence the development of hippocampal structure. We conducted a longitudinal, randomized, controlled, multisite, double-blind study involving 14 academic medical centers (United States 11, Canada 1, Netherlands 1, and England 1). 134 first-episode psychosis (receiving either haloperidol or olanzapine) patients and 51 healthy controls were treated and followed up for up to 104 weeks using magnetic resonance imaging and large-deformation high-dimensional brain mapping of the hippocampus. Changes in hippocampal volume and shape metrics (i.e., percentage of negative surface vertex slopes, and surface deformation) were evaluated. Mixed-models analysis did not show a significant group-by-time interaction for hippocampal volume. However, the cumulative distribution function of hippocampal surface vertex slopes showed a notable left shift with haloperidol treatment compared to olanzapine treatment and to controls. Olanzapine treatment was associated with a significantly lower percentage of large magnitude negative surface vertex slopes compared to haloperidol treatment (p=0.004). Surface deformation maps however did not localize any hippocampal regions that differentially contracted over time with olanzapine treatment, after FDR correction. These results indicate that surface analysis provides supplementary information to volumetry in detecting differential treatment effects of the hippocampus. Our results suggest that olanzapine is associated with less longitudinal hippocampal surface deformation than haloperidol, however the hippocampal regions affected appear to be variable across patients

Test-retest reliability of fMRI-measured brain activity during decision making under risk

Neural correlates of decision making under risk are being increasingly utilized as biomarkers of risk for substance abuse and other psychiatric disorders, treatment outcomes, and brain development. This research relies on the basic assumption that fMRI measures of decision making represent stable, trait-like individual differences. However, reliability needs to be established for each individual construct. Here we assessed long-term test-retest reliability (TRR) of regional brain activations related to decision making under risk using the Balloon Analogue Risk Taking task (BART) and identified regions with good TRRs and familial influences, an important prerequisite for the use of fMRI measures in genetic studies. A secondary goal was to examine the factors potentially affecting fMRI TRRs in one particular risk task, including the magnitude of neural activation, data analytical approaches, different methods of defining boundaries of a region, and participant motion. For the average BOLD response, reliabilities ranged across brain regions from poor to good (ICCs of 0 to 0.8, with a mean ICC of 0.17) and highest reliabilities were observed for parietal, occipital, and temporal regions. Among the regions that were of a priori theoretical importance due to their reported associations with decision making, the activation of left anterior insula and right caudate during the decision period showed the highest reliabilities (ICCs of 0.54 and 0.63, respectively). Among the regions with highest reliabilities, the right fusiform, right rostral anterior cingulate and left superior parietal regions also showed high familiality as indicated by intrapair monozygotic twin correlations (ranging from 0.66 to 0.69). Overall, regions identified by modeling the average BOLD response to a specific event type (rather than its modulation by a parametric regressor), regions including significantly activated vertices (compared to a whole parcel), and regions with greater magnitude of task-related activations showed greater reliabilities. Participant motion had a moderate negative effect on TRR. Regions activated during decision period rather than outcome period of risky decisions showed the greatest TRR and familiality. Regions with reliable activations can be utilized as neural markers of individual differences or endophenotypes in future clinical neuroscience and genetic studies of risk-taking

Test-retest reliability of neural correlates of response inhibition and error monitoring: An fMRI study of a stop-signal task

Response inhibition (RI) and error monitoring (EM) are important processes of adaptive goal-directed behavior, and neural correlates of these processes are being increasingly used as transdiagnostic biomarkers of risk for a range of neuropsychiatric disorders. Potential utility of these purported biomarkers relies on the assumption that individual differences in brain activation are reproducible over time; however, available data on test-retest reliability (TRR) of task-fMRI are very mixed. This study examined TRR of RI and EM-related activations using a stop signal task in young adults

Reliability and stability challenges in ABCD task fMRI data

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and long-term stability of individual differences in task-fMRI measures using fMRI measures of brain activation provided by the adolescent brain cognitive development (ABCD) Study Release v4.0 as an individual\u27s average regional activity, using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability (quantified as the ratio of non-scanner related stable variance to all variances) was poor in virtually all brain regions, with an average value of 0.088 and 0.072 for short term (within-session) reliability and long-term (between-session) stability, respectively, in regions of interest (ROIs) historically-recruited by the tasks. Only one reliability or stability value in ROIs exceeded the \u27poor\u27 cut-off of 0.4, and in fact rarely exceeded 0.2 (only 4.9%). Motion had a pronounced effect on estimated reliability/stability, with the lowest motion quartile of participants having a mean reliability/stability 2.5 times higher (albeit still \u27poor\u27) than the highest motion quartile. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue urgently needs to be addressed through optimization of task design, scanning parameters, data acquisition protocols, preprocessing pipelines, and data denoising methods

M87: A Misaligned BL LAC?

The nuclear region of M87 was observed with the Faint Object Spectrograph (FOS) on the Hubble Space Telescope (HST) at 6 epochs, spanning 18 months, after the HST image quality was improved with the deployment of the corrective optics (COSTAR) in December 1993. From the FOS target acquisition data, we have established that the flux from the optical nucleus of M87 varies by a factor ~2 on time scales of ~2.5 months and by as much as 25% over 3 weeks, and remains unchanged (<= 2.5%) on time scales of ~1 day. The changes occur in an unresolved central region <= 5 pc in diameter, with the physical size of the emitting region limited by the observed time scales to a few hundred gravitational radii. The featureless continuum spectrum becomes bluer as it brightens while emission lines remain unchanged. This variability combined with the observations of the continuum spectral shape, strong relativistic boosting and the detection of significant superluminal motions in the jet, strongly suggest that M87 belongs to the class of BL Lac objects but is viewed at an angle too large to reveal the classical BL Lac properties.Comment: 12 pages, 3 Postscript figure

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the WU-Minn-Ox HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The HCP-style neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium

Kassiopeia: A Modern, Extensible C++ Particle Tracking Package

The Kassiopeia particle tracking framework is an object-oriented software package using modern C++ techniques, written originally to meet the needs of the KATRIN collaboration. Kassiopeia features a new algorithmic paradigm for particle tracking simulations which targets experiments containing complex geometries and electromagnetic fields, with high priority put on calculation efficiency, customizability, extensibility, and ease of use for novice programmers. To solve Kassiopeia's target physics problem the software is capable of simulating particle trajectories governed by arbitrarily complex differential equations of motion, continuous physics processes that may in part be modeled as terms perturbing that equation of motion, stochastic processes that occur in flight such as bulk scattering and decay, and stochastic surface processes occuring at interfaces, including transmission and reflection effects. This entire set of computations takes place against the backdrop of a rich geometry package which serves a variety of roles, including initialization of electromagnetic field simulations and the support of state-dependent algorithm-swapping and behavioral changes as a particle's state evolves. Thanks to the very general approach taken by Kassiopeia it can be used by other experiments facing similar challenges when calculating particle trajectories in electromagnetic fields. It is publicly available at https://github.com/KATRIN-Experiment/Kassiopei

Developmental trajectories of cortical thickness by functional brain network: The roles of pubertal timing and socioeconomic status

The human cerebral cortex undergoes considerable changes during development, with cortical maturation patterns reflecting regional heterogeneity that generally progresses in a posterior-to-anterior fashion. However, the organizing principles that govern cortical development remain unclear. In the current study, we characterized age-related differences in cortical thickness (CT) as a function of sex, pubertal timing, and two dissociable indices of socioeconomic status (i.e., income-to-needs and maternal education) in the context of functional brain network organization, using a cross-sectional sample (n = 789) diverse in race, ethnicity, and socioeconomic status from the Lifespan Human Connectome Project in Development (HCP-D). We found that CT generally followed a linear decline from 5 to 21 years of age, except for three functional networks that displayed nonlinear trajectories. We found no main effect of sex or age by sex interaction for any network. Earlier pubertal timing was associated with reduced mean CT and CT in seven networks. We also found a significant age by maternal education interaction for mean CT across cortex and CT in the dorsal attention network, where higher levels of maternal education were associated with steeper age-related decreases in CT. Taken together, our results suggest that these biological and environmental variations may impact the emerging functional connectome