A somato-cognitive action network alternates with effector regions in motor cortex.

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

Trajectories of remitted psychotic depression: identification of predictors of worsening by machine learning.

BACKGROUND: Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory. METHOD: One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics. RESULTS: Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model. CONCLUSIONS: Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.

A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies.

This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like "Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties". Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.

Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data.

"Functional connectivity" techniques are commonplace tools for studying brain organization. A critical element of these analyses is to distinguish variance due to neurobiological signals from variance due to nonneurobiological signals. Multiecho fMRI techniques are a promising means for making such distinctions based on signal decay properties. Here, we report that multiecho fMRI techniques enable excellent removal of certain kinds of artifactual variance, namely, spatially focal artifacts due to motion. By removing these artifacts, multiecho techniques reveal frequent, large-amplitude blood oxygen level-dependent (BOLD) signal changes present across all gray matter that are also linked to motion. These whole-brain BOLD signals could reflect widespread neural processes or other processes, such as alterations in blood partial pressure of carbon dioxide (pCO2) due to ventilation changes. By acquiring multiecho data while monitoring breathing, we demonstrate that whole-brain BOLD signals in the resting state are often caused by changes in breathing that co-occur with head motion. These widespread respiratory fMRI signals cannot be isolated from neurobiological signals by multiecho techniques because they occur via the same BOLD mechanism. Respiratory signals must therefore be removed by some other technique to isolate neurobiological covariance in fMRI time series. Several methods for removing global artifacts are demonstrated and compared, and were found to yield fMRI time series essentially free of motion-related influences. These results identify two kinds of motion-associated fMRI variance, with different physical mechanisms and spatial profiles, each of which strongly and differentially influences functional connectivity patterns. Distance-dependent patterns in covariance are nearly entirely attributable to non-BOLD artifacts.

Characteristics of respiratory measures in young adults scanned at rest, including systematic changes and "missed" deep breaths.

Breathing rate and depth influence the concentration of carbon dioxide in the blood, altering cerebral blood flow and thus functional magnetic resonance imaging (fMRI) signals. Such respiratory fluctuations can have substantial influence in studies of fMRI signal covariance in subjects at rest, the so-called "resting state functional connectivity" technique. If respiration is monitored during fMRI scanning, it is typically done using a belt about the subject's abdomen to record abdominal circumference. Several measures have been derived from these belt records, including the windowed envelope of the waveform (ENV), the windowed variance in the waveform (respiration variation, RV), and a measure of the amplitude of each breath divided by the cycle time of the breath (respiration volume per time, RVT). Any attempt to gauge respiratory contributions to fMRI signals requires a respiratory measure, but little is known about how these measures compare to each other, or how they perform beyond the small studies in which they were initially proposed. Here, we examine the properties of these measures in hundreds of healthy young adults scanned for an hour each at rest, a subset of the Human Connectome Project chosen for having high-quality physiological records. We find: 1) ENV, RV, and RVT are all correlated, and ENV and RV are more highly correlated to each other than to RVT; 2) respiratory events like deep breaths exhibit characteristic heart rate elevations, fMRI signal changes, head motions, and image quality abnormalities time-locked to large deflections in the belt traces; 3) all measures can "miss" deep breaths; 4) RVT "misses" deep breaths more than ENV or RV; 5) all respiratory measures change systematically over the course of a 14.4-min scan. We discuss the implications of these findings for the literature and ways to move forward in modeling respiratory influences on fMRI scans.

Distinctions among real and apparent respiratory motions in human fMRI data.

Head motion estimates in functional magnetic resonance imaging (fMRI) scans appear qualitatively different with sub-second image sampling rates compared to the multi-second sampling rates common in the past. Whereas formerly the head appeared still for much of a scan with brief excursions from baseline, the head now appears to be in constant motion, and motion estimates often seem to divulge little information about what is happening in a scan. This constant motion has been attributed to respiratory oscillations that do not alias at faster sampling rates, and investigators are divided on the extent to which such motion is "real" motion or only "apparent" pseudomotion. Some investigators have abandoned the use of motion estimates entirely due to these considerations. Here we investigate the properties of motion in several fMRI datasets sampled at rates between 720 and 1160 ms, and describe 5 distinct kinds of respiratory motion: 1) constant real respiratory motion in the form of head nodding most evident in vertical position and pitch, which can be very large; 2) constant pseudomotion at the same respiratory rate as real motion, occurring only in the phase encode direction; 3) punctate real motions occurring at times of very deep breaths; 4) a low-frequency pseudomotion in only the phase encode direction at and after very deep breaths; 5) slow modulation of vertical and anterior-posterior head position by the respiratory envelope. We reformulate motion estimates in light of these considerations and obtain good concordance between motion estimates, physiologic records, image quality measures, and events evident in the fMRI signals. We demonstrate how variables describing respiration or body habitus separately scale with distinct kinds of head motion. We also note heritable aspects of respiration and motion.

Customized head molds reduce motion during resting state fMRI scans.

Head motion causes artifacts in functional magnetic resonance imaging (fMRI) scans, a problem especially relevant for task-free resting state paradigms and for developmental, aging, and clinical populations. In a cohort spanning 7-28 years old (mean age 15) we produced customized head-anatomy-specific Styrofoam molds for each subject that inserted into an MRI head coil. We scanned these subjects under two conditions: using our standard procedure of packing the head coil with foam padding about the head to reduce head motion, and using the customized molds to reduce head motion. In 12 of 13 subjects, the molds reduced head motion throughout the scan and reduced the fraction of a scan with substantial motion (i.e., volumes with motion notably above baseline levels of motion). Motion was reduced in all 6 head position estimates, especially in rotational, left-right, and superior-inferior directions. Motion was reduced throughout the full age range studied, including children, adolescents, and young adults. In terms of the fMRI data itself, quality indices improved with the head mold on, scrubbing analyses detected less distance-dependent artifact in scans with the head mold on, and distant-dependent artifact was less evident in both the entire scan and also during only low-motion volumes. Subjects found the molds comfortable. Head molds are thus effective tools for reducing head motion, and motion artifacts, during fMRI scans.

Reward-related regions form a preferentially coupled system at rest.

Neuroimaging studies have implicated a set of striatal and orbitofrontal cortex (OFC) regions that are commonly activated during reward processing tasks. Resting-state functional connectivity (RSFC) studies have demonstrated that the human brain is organized into several functional systems that show strong temporal coherence in the absence of goal-directed tasks. Here we use seed-based and graph-theory RSFC approaches to characterize the systems-level organization of putative reward regions of at rest. Peaks of connectivity from seed-based RSFC patterns for the nucleus accumbens (NAcc) and orbitofrontal cortex (OFC) were used to identify candidate reward regions which were merged with a previously used set of regions (Power et al., 2011). Graph-theory was then used to determine system-level membership for all regions. Several regions previously implicated in reward-processing (NAcc, lateral and medial OFC, and ventromedial prefrontal cortex) comprised a distinct, preferentially coupled system. This RSFC system is stable across a range of connectivity thresholds and shares strong overlap with meta-analyses of task-based reward studies. This reward system shares between-system connectivity with systems implicated in cognitive control and self-regulation, including the fronto-parietal, cingulo-opercular, and default systems. Differences may exist in the pathways through which control systems interact with reward system components. Whereas NAcc is functionally connected to cingulo-opercular and default systems, OFC regions show stronger connectivity with the fronto-parietal system. We propose that future work may be able to interrogate group or individual differences in connectivity profiles using the regions delineated in this work to explore potential relationships to appetitive behaviors, self-regulation failure, and addiction.

A simple but useful way to assess fMRI scan qualities.

This short "how to" article describes a plot I find useful for assessing fMRI data quality. I discuss the reasoning behind the plot and how it is constructed. I create the plot in scans from several publicly available datasets to illustrate different kinds of fMRI signal variance, ranging from thermal noise to motion artifacts to respiratory-related signals. I also show how the plot can be used to understand the variance removed during denoising. Code to make the plot is provided with the article, and supplemental movies show plots for hundreds of additional subjects.

Sources and implications of whole-brain fMRI signals in humans.

Whole-brain fMRI signals are a subject of intense interest: variance in the global fMRI signal (the spatial mean of all signals in the brain) indexes subject arousal, and psychiatric conditions such as schizophrenia and autism have been characterized by differences in the global fMRI signal. Further, vigorous debates exist on whether global signals ought to be removed from fMRI data. However, surprisingly little research has focused on the empirical properties of whole-brain fMRI signals. Here we map the spatial and temporal properties of the global signal, individually, in 1000+ fMRI scans. Variance in the global fMRI signal is strongly linked to head motion, to hardware artifacts, and to respiratory patterns and their attendant physiologic changes. Many techniques used to prepare fMRI data for analysis fail to remove these uninteresting kinds of global signal fluctuations. Thus, many studies include, at the time of analysis, prominent global effects of yawns, breathing changes, and head motion, among other signals. Such artifacts will mimic dynamic neural activity and will spuriously alter signal covariance throughout the brain. Methods capable of isolating and removing global artifactual variance while preserving putative "neural" variance are needed; this paper adopts no position on the topic of global signal regression.

Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.

Network measures predict neuropsychological outcome after brain injury.

Hubs are network components that hold positions of high importance for network function. Previous research has identified hubs in human brain networks derived from neuroimaging data; however, there is little consensus on the localization of such hubs. Moreover, direct evidence regarding the role of various proposed hubs in network function (e.g., cognition) is scarce. Regions of the default mode network (DMN) have been frequently identified as "cortical hubs" of brain networks. On theoretical grounds, we have argued against some of the methods used to identify these hubs and have advocated alternative approaches that identify different regions of cortex as hubs. Our framework predicts that our proposed hub locations may play influential roles in multiple aspects of cognition, and, in contrast, that hubs identified via other methods (including salient regions in the DMN) might not exert such broad influence. Here we used a neuropsychological approach to directly test these predictions by studying long-term cognitive and behavioral outcomes in 30 patients, 19 with focal lesions to six "target" hubs identified by our approaches (high system density and participation coefficient) and 11 with focal lesions to two "control" hubs (high degree centrality). In support of our predictions, we found that damage to target locations produced severe and widespread cognitive deficits, whereas damage to control locations produced more circumscribed deficits. These findings support our interpretation of how neuroimaging-derived network measures relate to cognition and augment classic neuroanatomically based predictions about cognitive and behavioral outcomes after focal brain injury.

Developmental changes in the organization of functional connections between the basal ganglia and cerebral cortex.

The basal ganglia (BG) comprise a set of subcortical nuclei with sensorimotor, cognitive, and limbic subdivisions, indicative of functional organization. BG dysfunction in several developmental disorders suggests the importance of the healthy maturation of these structures. However, few studies have investigated the development of BG functional organization. Using resting-state functional connectivity MRI (rs-fcMRI), we compared human child and adult functional connectivity of the BG with rs-fcMRI-defined cortical systems. Because children move more than adults, customized preprocessing, including volume censoring, was used to minimize motion-induced rs-fcMRI artifact. Our results demonstrated functional organization in the adult BG consistent with subdivisions previously identified in anatomical tracing studies. Group comparisons revealed a developmental shift in bilateral posterior putamen/pallidum clusters from preferential connectivity with the somatomotor "face" system in childhood to preferential connectivity with control/attention systems (frontoparietal, ventral attention) in adulthood. This shift was due to a decline in the functional connectivity of these clusters with the somatomotor face system over development, and no change with control/attention systems. Applying multivariate pattern analysis, we were able to reliably classify individuals as children or adults based on BG-cortical system functional connectivity. Interrogation of the features driving this classification revealed, in addition to the somatomotor face system, contributions by the orbitofrontal, auditory, and somatomotor hand systems. These results demonstrate that BG-cortical functional connectivity evolves over development, and may lend insight into developmental disorders that involve BG dysfunction, particularly those involving motor systems (e.g., Tourette syndrome).

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.

Parcellating an individual subject's cortical and subcortical brain structures using snowball sampling of resting-state correlations.

We describe methods for parcellating an individual subject's cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first describe the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify the centers of cortical areas, subdivisions of subcortical nuclei, and the cerebellum. RSFC-Snowballing parcellation is then compared with parcellation derived from identifying locations where RSFC maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain. RSFC parcellation is relatively reliable within a subject scanned across multiple days, and while the locations of many area centers and boundaries appear to exhibit considerable overlap across subjects, there is also cross-subject variability-reinforcing the motivation to parcellate brains at the level of individuals. Finally, examination of a large meta-analysis of task-evoked functional magnetic resonance imaging data reveals that area centers defined by task-evoked activity exhibit correspondence with area centers defined by RSFC-Snowballing. This observation provides important evidence for the ability of RSFC to parcellate broad expanses of an individual's brain into functionally meaningful units.

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.