Bradley L. Schlaggar

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.

Methods to detect, characterize, and remove motion artifact in resting state fMRI

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.

The Human Connectome Project: A data acquisition perspective

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

Defining functional areas in individual human brains using resting functional connectivity MRI

The cerebral cortex is anatomically organized at many physical scales starting at the level of single neurons and extending up to functional systems. Current functional magnetic resonance imaging (fMRI) studies often focus at the level of areas, networks, and systems. Except in restricted domains, (e.g., topographically-organized sensory regions), it is difficult to determine area boundaries in the human brain using fMRI. The ability to delineate functional areas non-invasively would enhance the quality of many experimental analyses allowing more accurate across-subject comparisons of independently identified functional areas. Correlations in spontaneous BOLD activity, often referred to as resting state functional connectivity (rs-fcMRI), are especially promising as a way to accurately localize differences in patterns of activity across large expanses of cortex. In the current report, we applied a novel set of image analysis tools to explore the utility of rs-fcMRI for defining wide-ranging functional area boundaries. We find that rs-fcMRI patterns show sharp transitions in correlation patterns and that these putative areal boundaries can be reliably detected in individual subjects as well as in group data. Additionally, combining surface-based analysis techniques with image processing algorithms allows automated mapping of putative areal boundaries across large expanses of cortex without the need for prior information about a regions function or topography. Our approach reliably produces maps of bounded regions appropriate in size and number for putative functional areas. These findings will hopefully stimulate further methodological refinements and validations.

The development of Human Functional Brain Networks

Recent advances in MRI technology have enabled precise measurements of correlated activity throughout the brain, leading to the first comprehensive descriptions of functional brain networks in humans. This article reviews the growing literature on the development of functional networks, from infancy through adolescence, as measured by resting-state functional connectivity MRI. We note several limitations of traditional approaches to describing brain networks and describe a powerful framework for analyzing networks, called graph theory. We argue that characterization of the development of brain systems (e.g., the default mode network) should be comprehensive, considering not only relationships within a given system, but also how these relationships are situated within wider network contexts. We note that, despite substantial reorganization of functional connectivity, several large-scale network properties appear to be preserved across development, suggesting that functional brain networks, even in children, are organized in manners similar to other complex systems.

Recent progress and outstanding issues in motion correction in resting state fMRI

The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI. In 2011, three groups reported that small head movements produced spurious but structured noise in brain scans, causing distance-dependent changes in signal correlations. This finding has prompted both methods development and the re-examination of prior findings with more stringent motion correction. Since 2011, over a dozen papers have been published specifically on motion artifact in resting state fMRI. We will attempt to distill these papers to their most essential content. We will point out some aspects of motion artifact that are easily or often overlooked. Throughout the review, we will highlight gaps in current knowledge and avenues for future research.

The feasibility of a common stereotactic space for children and adults in fMRI studies of development.

The question of whether pediatric and adult neuroimaging data can be analyzed in a common stereotactic space is a critical issue for developmental neuroscience. Two studies were performed to address this question. In Study 1, high-resolution structural MR brain images of 20 children (7-8 years of age) and 20 young adults (18-30 years of age) were transformed to a common space. Overall brain shape was assessed by tracing the outer boundaries of the brains in three orientations, and more local anatomy was assessed by analysis of portions of 10 selected sulci. Small, but consistent, differences in location and variability were observed in specific locations of the sulcal tracings and outer-boundary sections. In Study 2, a computer simulation was used to assess the extent to which the small anatomical differences observed in Study 1 would produce spurious effects in functional imaging data. Results indicate that, assuming a functional resolution of 5 mm in images averaged across subjects, anatomical differences in either variability or location between children and adults of the magnitude obperved in Study 1 would not negatively affect functional image comparisons. We conclude that atlas-transformed brain morphology is relatively consistent between 7- and 8-year-old children and adults at a resolution appropriate to current functional imaging and that the small anatomical differences present do not limit the usefulness of comparing child and adult functional images within a common stereotactic space.

Comparison of functional activation foci in children and adults using a common stereotactic space

The development of methods allowing direct comparisons between child and adult neuroimaging data is an important prerequisite for studying the neural bases of cognitive development. Several issues arise when attempting to make such direct comparisons, including the comparability of anatomical localization of functional responses and the magnitude and time course of the hemodynamic responses themselves. Previous results suggest that, after transformation into a common stereotactic space, anatomical differences between children (ages 7 and 8) and adults are small relative to the resolution of fMRI data. Here, we investigate whether time courses (BOLD responses) and locations of functional activation foci show similarities as well. Event-related fMRI was performed on 16 children (ages 7 and 8) and 16 adults, who pressed buttons in response to a visual stimulus. After transforming images into Talairach space, the coordinates of four consistent activations in each hemisphere were determined for each subject: two foci in the sensorimotor cortex, one focus in the visual cortex, and one focus in the supplementary motor area (eight activations in total). In seven foci, time courses were similar between children and adults, and peak amplitudes of time courses were comparable in all eight foci. There were negligible between-group differences in location of all foci. Variability of activation location was statistically similar in the two groups. In voxelwise group comparison images, minimal differences were found between children and adults in visual and motor cortex regions. The small differences in time courses and locations of activation foci between child and adult brains validate the feasibility of direct statistical comparison of these groups within a common space.

Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography

Functional neuroimaging is a vital element of neuroscience and cognitive research and, increasingly, is an important clinical tool. Diffuse optical imaging is an emerging, noninvasive technique with unique portability and hemodynamic contrast capabilities for mapping brain function in young subjects and subjects in enriched or clinical environments. We have developed a high-performance, high-density diffuse optical tomography (DOT) system that overcomes previous limitations and enables superior image quality. We show herein the utility of the DOT system by presenting functional hemodynamic maps of the adult human visual cortex. The functional brain images have a high contrast-to-noise ratio, allowing visualization of individual activations and highly repeatable mapping within and across subjects. With the improved spatial resolution and localization, we were able to image functional responses of 1.7 cm in extent and shifts of <1 cm. Cortical maps of angle and eccentricity in the visual field are consistent with retinotopic studies using functional MRI and positron-emission tomography. These results demonstrate that high-density DOT is a practical and powerful tool for mapping function in the human cortex.

Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data

In recent years, there has been growing enthusiasm that functional magnetic resonance imaging (MRI) could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement-related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to (1) examine the impact of emerging techniques for controlling for “micro-movements,” and (2) provide novel insights into the neural correlates of ADHD subtypes. Using support vector machine (SVM)-based multivariate pattern analysis (MVPA) we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C) and Inattentive (ADHD-I) subtypes demonstrated some overlapping (particularly sensorimotor systems), but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that resting-state functional connectivity MRI (rs-fcMRI) data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical heterogeneity of ADHD.