Craig A. Moodie

The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of macroscopic brain regions, but it is unclear how this mechanism manifests over time. Here we characterized patterns of time-resolved functional connectivity using resting state and task fMRI data from a large cohort of unrelated subjects. Our results demonstrate that dynamic fluctuations in network structure during the resting state reflect transitions between states of integrated and segregated network topology. These patterns were altered during task performance, demonstrating a higher level of network integration that tracked with the complexity of the task and correlated with effective behavioral performance. Replication analysis demonstrated that these results were reproducible across sessions, sample populations and datasets. Together these results provide insight into the brains coordination between integration and segregation and highlight key principles underlying the reorganization of the network architecture of the brain during both rest and behavior.

Characteristics of canonical intrinsic connectivity networks across tasks and monozygotic twin pairs

Intrinsic connectivity networks (ICNs) are becoming more prominent in the analyses of in vivo brain activity as the field of neurometrics has revealed their importance for augmenting traditional cognitive neuroscience approaches. Consequently, tools that assess the coherence, or connectivity, and morphology of ICNs are being developed to support inferences and assumptions about the dynamics of the brain. Recently, we reported trait‐like profiles of ICNs showing reliability over time and reproducibility across different contexts. This study further examined the trait‐like and familial nature of ICNs by utilizing two divergent task paradigms in twins. The study aimed to identify stable network phenotypes that exhibited sensitivity to individual differences and external perturbations in task demands. Analogous ICNs were detected in each task and these ICNs showed consistency in morphology and intranetwork coherence across tasks, whereas the ICN timecourse dynamics showed sensitivity to task demands. Specifically, the timecourse of an arm/hand sensorimotor network showed the strongest correlation with the timeline of a hand imitation task, and the timecourse of a language‐processing network showed the strongest temporal association with a verb generation task. The area V1/simple visual stimuli network exhibited the most consistency in morphology, coherence, and timecourse dynamics within and across tasks. Similarly, this network exhibited familiality in all three domains as well. Hence, this experiment is a proof of principle that the morphology and coherence of ICNs can be consistent both within and across tasks, that ICN timecourses can be differentially and meaningfully modulated by a task, and that these domains can exhibit familiality. Hum Brain Mapp 35:5532–5549, 2014.

FMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed with rapid advances in MR data acquisition and image processing techniques. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for task-based and resting fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection comprising participants from 54 different studies in the OpenfMRI repository. We review the distinctive features of fMRIPrep in a qualitative comparison to other preprocessing workflows. We demonstrate that fMRIPrep achieves higher spatial accuracy as it introduces less uncontrolled spatial smoothness than one commonly used preprocessing tool. FMRIPrep has the potential to transform fMRI research by equipping neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow which can help ensure the validity of inference and the interpretability of their results.

MRIQC: Predicting quality in manual MRI assessment protocols using no-reference image quality measures.

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N=1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%±13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N=265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.Quality control of MR images is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. However, the visual inspection of individual images is time-consuming and limited by both intra- and inter-rater variance. The difficulty of visual inspection scales with study size and with the heterogeneity of multi-site data. Here, we describe a tool for the automated assessment of Tl-weighted MR images of the brain – MRIQC. MRIQC calculates a set of quality measures from each image and uses them as features in a binary (include/exclude) classifier. The classifier was designed to ensure generalization to new samples acquired in different centers and using different scanning parameters from our training dataset. To achieve that goal, the classifier was trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (N=1102), acquired at 17 locations with heterogeneous scanning parameters. We selected random forests from a set of models and pre-processing options using nested cross-validation on the ABIDE dataset. We report a performance of ~89% accuracy of the best model evaluated with nested cross-validation. The best performing classifier was then evaluated on a held-out (unseen) dataset, unrelated to ABIDE and labeled by a different expert, yielding ~73% accuracy. The MRIQC software package and the trained classifier are released as an open source project, so that individual researchers and large consortia can readily curate their data regardless the size of their databases. Robust QC is crucial to identify early structured imaging artifacts in ongoing acquisition efforts, and helps detect individual substandard images that may bias downstream analyses.

An investigation into the drivers of avolition in schizophrenia

Over a century of research has documented that avolition is a core symptom in schizophrenia. However, the drivers of avolition remain unclear. Conceptually, there are at least two potential mutually compatible drivers that could cause avolition in schizophrenia. First, people with schizophrenia might have differences in preferences that result in less goal-directed behavior than non-clinical populations (preference-differences). Second, people with schizophrenia might have difficulty translating their preferences into manifest behavior at rates similar to non-clinical populations (psychological-inertia). In the present work, we modified and validated a well-validated paradigm from the motivation/decision making literature to compare levels of preference-differences and psychological-inertia. To measure preference-differences, people with and without schizophrenia choose between a lower-valenced and higher-valenced image. We measured the rate at which the normatively lower-valenced image was preferred. To measure psychological-inertia, both groups were given the opportunity to volitionally switch from a lower-valenced image and view a higher-valenced image. Contrary to expectations, people with schizophrenia did not differ on either preference-differences or psychological-inertia. Statistical analysis revealed that the possibility of a Type II error for even a weak effect was small. The present data suggest new avenues for research investigating mechanisms underlying avolition and clinical interventions targeting avolition in schizophrenia.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Function

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of macroscopic brain regions, but it is unclear how this mechanism manifests over time. Here we characterized patterns of time-resolved functional connectivity using resting state and task fMRI data from a large cohort of unrelated subjects. Our results demonstrate that dynamic fluctuations in network structure during the resting state reflect transitions between states of integrated and segregated network topology. These patterns were altered during task performance, demonstrating a higher level of network integration that tracked with the complexity of the task and correlated with effective behavioral performance. Replication analysis demonstrated that these results were reproducible across sessions, sample populations and datasets. Together these results provide insight into the brains coordination between integration and segregation and highlight key principles underlying the reorganization of the network architecture of the brain during both rest and behavior.

ArticleThe Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of macroscopic brain regions, but it is unclear how this mechanism manifests over time. Here we characterized patterns of time-resolved functional connectivity using resting state and task fMRI data from a large cohort of unrelated subjects. Our results demonstrate that dynamic fluctuations in network structure during the resting state reflect transitions between states of integrated and segregated network topology. These patterns were altered during task performance, demonstrating a higher level of network integration that tracked with the complexity of the task and correlated with effective behavioral performance. Replication analysis demonstrated that these results were reproducible across sessions, sample populations and datasets. Together these results provide insight into the brains coordination between integration and segregation and highlight key principles underlying the reorganization of the network architecture of the brain during both rest and behavior.