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.

Expansion of a frontostriatal salience network in individuals with depression

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

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.

Precision mapping and transcranial magnetic stimulation of individual-specific functional brain networks in humans

Spatial targeting in transcranial magnetic stimulation protocols does not typically account for the idiosyncratic functional organization of individual human brains. Here, we provide a protocol for implementing targeted functional network stimulation (TANS), which accounts for each individual's unique functional neuroanatomy and cortical folding patterns. Using an example dataset, we describe how to create a head model and estimate the best coil placement and stimulation intensity to minimize off-target effects. For complete details on the use and execution of this protocol, please refer to Lynch et al. (2022).1.

Functional Connectivity Mapping for rTMS Target Selection in Depression

OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) protocols increasingly use subgenual anterior cingulate cortex (sgACC) functional connectivity to individualize treatment targets. However, the efficacy of this approach is unclear, with conflicting findings and varying effect sizes across studies. Here, the authors investigated the effect of the stimulation site's functional connectivity with the sgACC (sgACC-StimFC) on treatment outcome to rTMS in 295 patients with major depression.

METHODS

The reliability and accuracy of estimating sgACC functional connectivity were validated with data from individuals who underwent extensive functional MRI testing. Electric field modeling was used to analyze associations between sgACC-StimFC and clinical improvement using standardized assessments and to evaluate sources of heterogeneity.

RESULTS

An imputation-based method provided reliable and accurate sgACC functional connectivity estimates. Treatment responses weakly but robustly correlated with sgACC-StimFC (r=-0.16), but only when the stimulated cortex was identified using electric field modeling. Surprisingly, this association was driven by patients with strong global signal fluctuations stemming from a specific periodic respiratory pattern (r=-0.49).

CONCLUSIONS

Functional connectivity between the sgACC and the stimulated cortex was correlated with individual differences in treatment outcomes, but the association was weaker than those observed in previous studies and was accentuated in a subgroup of patients with distinct, respiration-related signal patterns in their scans. These findings indicate that in a large representative sample of patients with major depressive disorder, individual differences in sgACC-StimFC explained only ∼3% of the variance in outcomes, which may limit the utility of existing sgACC-based targeting protocols. However, these data also provide strong evidence for a true-albeit small-effect and highlight opportunities for incorporating additional functional connectivity measures to generate models of rTMS response with enhanced predictive power.

Automated optimization of TMS coil placement for personalized functional network engagement

Transcranial magnetic stimulation (TMS) is used to treat multiple psychiatric and neurological conditions by manipulating activity in particular brain networks and circuits, but individual responses are highly variable. In clinical settings, TMS coil placement is typically based on either group average functional maps or scalp heuristics. Here, we found that this approach can inadvertently target different functional networks in depressed patients due to variability in their functional brain organization. More precise TMS targeting should be feasible by accounting for each patient's unique functional neuroanatomy. To this end, we developed a targeting approach, termed targeted functional network stimulation (TANS). The TANS approach improved stimulation specificity in silico in 8 highly sampled patients with depression and 6 healthy individuals and in vivo when targeting somatomotor functional networks representing the upper and lower limbs. Code for implementing TANS and an example dataset are provided as a resource.

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

Resting-state functional magnetic resonance imaging (fMRI) is widely used in cognitive and clinical neuroscience, but long-duration scans are currently needed to reliably characterize individual differences in functional connectivity (FC) and brain network topology. In this report, we demonstrate that multi-echo fMRI can improve the reliability of FC-based measurements. In four densely sampled individual humans, just 10 min of multi-echo data yielded better test-retest reliability than 30 min of single-echo data in independent datasets. This effect is pronounced in clinically important brain regions, including the subgenual cingulate, basal ganglia, and cerebellum, and is linked to three biophysical signal mechanisms (thermal noise, regional variability in the rate of T₂* decay, and S₀-dependent artifacts) with spatially distinct influences. Together, these findings establish the potential utility of multi-echo fMRI for rapid precision mapping using experimentally and clinically tractable scan times and will facilitate longitudinal neuroimaging of clinical populations.

Lynch et al. demonstrate that the test-retest reliability of resting-state connectivity measurements can be improved using multi-echo fMRI. This effect is pronounced in clinically important brain regions and could help facilitate precision mapping of functional brain networks in healthy people and patient populations.

Prevalent and sex-biased breathing patterns modify functional connectivity MRI in young adults

Resting state functional connectivity magnetic resonance imaging (fMRI) is a tool for investigating human brain organization. Here we identify, visually and algorithmically, two prevalent influences on fMRI signals during 440 h of resting state scans in 440 healthy young adults, both caused by deviations from normal breathing which we term deep breaths and bursts. The two respiratory patterns have distinct influences on fMRI signals and signal covariance, distinct timescales, distinct cardiovascular correlates, and distinct tendencies to manifest by sex. Deep breaths are not sex-biased. Bursts, which are serial taperings of respiratory depth typically spanning minutes at a time, are more common in males. Bursts share features of chemoreflex-driven clinical breathing patterns that also occur primarily in males, with notable neurological, psychiatric, medical, and lifespan associations. These results identify common breathing patterns in healthy young adults with distinct influences on functional connectivity and an ability to differentially influence resting state fMRI studies.

Functional connectivity measured from fMRI data is widely used in neuroscience. Here the authors report an association between two types of breathing signature and obtained BOLD data, and associated sex differences.

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.

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.

Neural correlates of preferred activities: development of an interest-specific go/nogo task

The activities we choose to spend our leisure time with are intrinsically motivating and vary across individuals. Yet it is unknown how impulse control or neural activity changes when processing a preferred stimulus related to a hobby or interest. Developing a task that assesses the response to preferred interests is of importance as it would be relevant to a range of psychiatric disorders that have hyper- or hypo-arousal to such cues. During functional Magnetic Resonance Imaging (fMRI), 39 healthy adults completed a novel task to test approach behavior and cognitive control to cues that were personalized to the participants' interests compared to stimuli the participants identified as being of non-interest and colored shapes. fMRI results showed that cues of one's interest elicited activation in the anterior insula compared to colored shapes. Interests did not change inhibition compared to non-interests and colored shapes and all stimuli equally engaged a frontostriatal circuit. Together the results suggest that adults were sensitive to their interests but were effective at regulating their impulses towards these cues, a skill that is critical for navigating the temptations and distractions in our daily environment.

Temporal interpolation alters motion in fMRI scans: Magnitudes and consequences for artifact detection

Head motion can be estimated at any point of fMRI image processing. Processing steps involving temporal interpolation (e.g., slice time correction or outlier replacement) often precede motion estimation in the literature. From first principles it can be anticipated that temporal interpolation will alter head motion in a scan. Here we demonstrate this effect and its consequences in five large fMRI datasets. Estimated head motion was reduced by 10–50% or more following temporal interpolation, and reductions were often visible to the naked eye. Such reductions make the data seem to be of improved quality. Such reductions also degrade the sensitivity of analyses aimed at detecting motion-related artifact and can cause a dataset with artifact to falsely appear artifact-free. These reduced motion estimates will be particularly problematic for studies needing estimates of motion in time, such as studies of dynamics. Based on these findings, it is sensible to obtain motion estimates prior to any image processing (regardless of subsequent processing steps and the actual timing of motion correction procedures, which need not be changed). We also find that outlier replacement procedures change signals almost entirely during times of motion and therefore have notable similarities to motion-targeting censoring strategies (which withhold or replace signals entirely during times of motion).

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.

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.

Neural plasticity across the lifespan

An essential feature of the brain is its capacity to change. Neuroscientists use the term 'plasticity' to describe the malleability of neuronal connectivity and circuitry. How does plasticity work? A review of current data suggests that plasticity encompasses many distinct phenomena, some of which operate across most or all of the lifespan, and others that operate exclusively in early development. This essay surveys some of the key concepts related to neural plasticity, beginning with how current patterns of neural activity (e.g., as you read this essay) come to impact future patterns of activity (e.g., your memory of this essay), and then extending this framework backward into more development-specific mechanisms of plasticity. WIREs Dev Biol 2017, 6:e216. doi: 10.1002/wdev.216 For further resources related to this article, please visit the WIREs website.

Brain Network Theory Can Predict Whether Neuropsychological Outcomes Will Differ from Clinical Expectations

OBJECTIVE

Theories of brain-network organization based on neuroimaging data have burgeoned in recent years, but the predictive power of such theories for cognition and behavior has only rarely been examined. Here, predictions from clinical neuropsychologists about the cognitive profiles of patients with focal brain lesions were used to evaluate a brain-network theory (Warren et al., 2014).

METHOD

Neuropsychologists made predictions regarding the neuropsychological profiles of a neurological patient sample (N = 30) based on lesion location. The neuropsychologists then rated the congruence of their predictions with observed neuropsychological outcomes, in regard to the "severity" of neuropsychological deficits and the "focality" of neuropsychological deficits. Based on the network theory, two types of lesion locations were identified: "target" locations (putative hubs in a brain-wide network) and "control" locations (hypothesized to play limited roles in network function).

RESULTS

We found that patients with lesions of target locations (N = 19) had deficits of greater than expected severity that were more widespread than expected, whereas patients with lesions of control locations (N = 11) showed milder, circumscribed deficits that were more congruent with expectations.

CONCLUSIONS

The findings for the target brain locations suggest that prevailing views of brain-behavior relationships may be sharpened and refined by integrating recently proposed network-oriented perspectives.

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.

Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project

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.

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

•

SVMs classified 6 versus 12 month-old infants above chance based on fcMRI data alone.

•

We carefully accounted for the effects of fcMRI motion artifact.

•

These results coincide with a period of dramatic change in infant development.

•

Two interpretations about connections supporting this age categorization are given.

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.

Studying brain organization via spontaneous fMRI signal

In recent years, some substantial advances in understanding human (and nonhuman) brain organization have emerged from a relatively unusual approach: the observation of spontaneous activity, and correlated patterns in spontaneous activity, in the "resting" brain. Most commonly, spontaneous neural activity is measured indirectly via fMRI signal in subjects who are lying quietly in the scanner, the so-called "resting state." This Primer introduces the fMRI-based study of spontaneous brain activity, some of the methodological issues active in the field, and some ways in which resting-state fMRI has been used to delineate aspects of area-level and supra-areal brain organization.

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.

Intrinsic and task-evoked network architectures of the human brain

Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an "intrinsic," standard architecture of functional brain organization. Furthermore, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain's functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity-areas of neuroscientific inquiry typically considered separately.

Resting state functional connectivity of the ventral attention network in children with a history of depression or anxiety

OBJECTIVE

We examined whether depression and anxiety disorders in early childhood were associated with changes in resting state functional connectivity (RSFC) of the ventral attention network (VAN), and whether RSFC in the VAN was associated with alterations in attention specific to these disorders. Important clinical features of these illnesses, including changes in attention toward novel stimuli and changes in attention to stimuli of negative valence (threat/sad bias), indirectly implicate the VAN.

METHOD

We collected resting state functional magnetic resonance imaging data in children aged 8 to 12 years. Data were volume censored to reduce artifact from submillimeter movement, resulting in analyzable data from 30 children with a history of depression and/or anxiety and 42 children with no psychiatric history. We compared pairwise RSFC among the following VAN regions: right ventro-lateral prefrontal cortex (VLPFC), right posterior superior temporal gyrus (pSTG), and right ventral supramarginal gyrus (vSMG). We also collected measures of threat bias and current clinical symptoms.

RESULTS

Children with a history of depression and/or anxiety had reduced RSFC among the regions of the VAN compared to children with no psychiatric history. The magnitude of VAN RSFC was correlated with measures of attention bias toward threat but not with current depressive, internalizing, or externalizing symptoms. No RSFC changes were detected between groups among homotopic left hemisphere regions.

CONCLUSIONS

Disruption in the VAN may be an early feature of depression and anxiety disorders. VAN changes were associated with attention bias and clinical history but not with current symptoms of depression and anxiety.

Evidence for hubs in human functional brain networks

Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting-state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: (1) finding network nodes that participate in multiple subnetworks of the brain, and (2) finding spatial locations in which several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity.

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.

Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points

Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement.

Functional network architecture of reading-related regions across development

Reading requires coordinated neural processing across a large number of brain regions. Studying relationships between reading-related regions informs the specificity of information processing performed in each region. Here, regions of interest were defined from a meta-analysis of reading studies, including a developmental study. Relationships between regions were defined as temporal correlations in spontaneous fMRI signal; i.e., resting state functional connectivity MRI (RSFC). Graph theory based network analysis defined the community structure of the "reading-related" regions. Regions sorted into previously defined communities, such as the fronto-parietal and cingulo-opercular control networks, and the default mode network. This structure was similar in children, and no apparent "reading" community was defined in any age group. These results argue against regions, or sets of regions, being specific or preferential for reading, instead indicating that regions used in reading are also used in a number of other tasks.

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.

Control-related systems in the human brain

A fundamental question in cognitive neuroscience is how the human brain self-organizes to perform tasks. Multiple accounts of this self-organization are currently influential and in this article we survey one of these accounts. We begin by introducing a psychological model of task control and several neuroimaging signals it predicts. We then discuss where such signals are found across tasks with emphasis on brain regions where multiple control signals are present. We then present results derived from spontaneous task-free functional connectivity between control-related regions that dovetail with distinctions made by control signals present in these regions, leading to a proposal that there are at least two task control systems in the brain. This prompts consideration of whether and how such control systems distinguish themselves from other brain regions in a whole-brain context. We present evidence from whole-brain networks that such distinctions do occur and that control systems comprise some of the basic system-level organizational elements of the human brain. We close with observations from the whole-brain networks that may suggest parsimony between multiple accounts of cognitive control.

Parcellation in left lateral parietal cortex is similar in adults and children

A key question in developmental neuroscience involves understanding how and when the cerebral cortex is partitioned into distinct functional areas. The present study used functional connectivity MRI mapping and graph theory to identify putative cortical areas and generate a parcellation scheme of left lateral parietal cortex (LLPC) in 7 to 10-year-old children and adults. Results indicated that a majority of putative LLPC areas could be matched across groups (mean distance between matched areas across age: 3.15 mm). Furthermore, the boundaries of children's putative LLPC areas respected the boundaries generated from the adults' parcellation scheme for a majority of children's areas (13/15). Consistent with prior research, matched LLPC areas showed age-related differences in functional connectivity strength with other brain regions. These results suggest that LLPC cortical parcellation and functional connectivity mature along different developmental trajectories, with adult-like boundaries between LLPC areas established in school-age children prior to adult-like functional connectivity.

Modulation of the brain's functional network architecture in the transition from wake to sleep

The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes.

Development of the brain's functional network architecture

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.

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.

Prediction of individual brain maturity using fMRI

Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.

A parcellation scheme for human left lateral parietal cortex

The parietal lobe has long been viewed as a collection of architectonic and functional subdivisions. Though much parietal research has focused on mechanisms of visuospatial attention and control-related processes, more recent functional neuroimaging studies of memory retrieval have reported greater activity in left lateral parietal cortex (LLPC) when items are correctly identified as previously studied ("old") versus unstudied ("new"). These studies have suggested functional divisions within LLPC that may provide distinct contributions toward recognition memory judgments. Here, we define regions within LLPC by developing a parcellation scheme that integrates data from resting-state functional connectivity MRI and functional MRI. This combined approach results in a 6-fold parcellation of LLPC based on the presence (or absence) of memory-retrieval-related activity, dissociations in the profile of task-evoked time courses, and membership in large-scale brain networks. This parcellation should serve as a roadmap for future investigations aimed at understanding LLPC function.

Identifying Basal Ganglia divisions in individuals using resting-state functional connectivity MRI

Studies in non-human primates and humans reveal that discrete regions (henceforth, "divisions") in the basal ganglia are intricately interconnected with regions in the cerebral cortex. However, divisions within basal ganglia nuclei (e.g., within the caudate) are difficult to identify using structural MRI. Resting-state functional connectivity MRI (rs-fcMRI) can be used to identify putative cerebral cortical functional areas in humans (Cohen et al., 2008). Here, we determine whether rs-fcMRI can be used to identify divisions in individual human adult basal ganglia. Putative basal ganglia divisions were generated by assigning basal ganglia voxels to groups based on the similarity of whole-brain functional connectivity correlation maps using modularity optimization, a network analysis tool. We assessed the validity of this approach by examining the spatial contiguity and location of putative divisions and whether divisions' correlation maps were consistent with previously reported patterns of anatomical and functional connectivity. Spatially constrained divisions consistent with the dorsal caudate, ventral striatum, and dorsal caudal putamen could be identified in each subject. Further, correlation maps associated with putative divisions were consistent with their presumed connectivity. These findings suggest that, as in the cerebral cortex, subcortical divisions can be identified in individuals using rs-fcMRI. Developing and validating these methods should improve the study of brain structure and function, both typical and atypical, by allowing for more precise comparison across individuals.

The role of brain-gut peptides in the control of sodium appetite

Ingestion of food and fluid stimulates release of a number of peptides from the gastrointestinal system. These peptides are recognized to act as neurotransmitters/neuromodulators and act at both peripheral and central receptors. Many studies indicate that these peptides are important signals in terminating meals. Recent studies suggest that bombesin, a peptide related to gastrin-releasing peptide, suppresses sodium appetite. We have investigated the role of cholecystokinin (CCK) in the control of sodium appetite. Our studies indicate that CCK is effective at reducing saline intake. We found that exogenous, intraperitoneal CCK octapeptide suppresses saline intake. Moreover, administration of trypsin inhibitor to stimulate endogenous CCK release resulted in suppression of saline intake. Finally, intraperitoneal administration of the CCK receptor antagonist lorglumide resulted in increased saline intake. These observations extend the potential role of gastrointestinal peptides in the modulation of ingestive behavior.

Benzodiazepine use in pregnancy and major malformations or oral cleft: meta-analysis of cohort and case-control studies

OBJECTIVE

To determine if exposure to benzodiazepines during the first trimester of pregnancy increases risk of major malformations or cleft lip or palate.

DESIGN

Meta-analysis.

SETTING

Studies from 1966 to present.

SUBJECTS

Studies were located with Medline, Embase, Reprotox, and from references of textbooks, reviews, and included articles. Included studies were original, concurrently controlled studies in any language.

INTERVENTIONS

Data extraction and quality assessment were done independently and in duplicate.

MAIN OUTCOME MEASURES

Maternal exposure to benzodiazepines in at least the first trimester; incidence of major malformations or oral cleft alone, measured as odds ratios and 95% confidence intervals with a random effects model.

RESULTS

Of over 1400 studies reviewed, 74 were retrieved and 23 included. In the analysis of cohort studies fetal exposure to benzodiazepine was not associated with major malformations (odds ratio 0.90; 95% confidence interval 0.61 to 1. 35) or oral cleft (1.19; 0.34 to 4.15). Analysis of case-control studies showed an association between exposure to benzodiazepines and development of major malformations (3.01; 1.32 to 6.84) or oral cleft alone (1.79; 1.13 to 2.82).

CONCLUSIONS

Pooled data from cohort studies showed no association between fetal exposure to benzodiazepines and the risk of major malformations or oral cleft. On the basis of pooled data from case-control studies, however, there was a significant increased risk for major malformations or oral cleft alone. Until more research is reported, level 2 ultrasonography should be used to rule out visible forms of cleft lip.

Detection of human papillomavirus type 16/18 DNA in cervicovaginal cells by fluorescence based in situ hybridization and automated image cytometry

Automatic fluorescence image cytometry (AFIC) is a fast, sensitive, and reliable approach for screening slide-based clinical specimens. In this study, we applied AFIC to identify cancer-associated human papillomavirus (HPV) genotypes 16 and 18 in individual cells of cervical smears using a sensitive fluorescence based in situ hybridization (FISH) assay. HPV sequences were labeled by FISH and the cells imaged using an epi-fluorescence microscope coupled to a low-light color CCD camera. Before application to clinical specimens, AFIC was assessed using fluorescent calibration beads and cervical cancer cell lines containing known numbers of integrated HPV genomes per nucleus. Assessment showed that our AFIC had a linear response, was quantitatively accurate, and had the sensitivity to detect one HPV genome per nucleus. After acquisition of images, computer algorithms identified every cell nucleus (via a fluorescent DNA counterstain) and quantified the FISH signal per nucleus. AFIC was employed to screen 27 patient specimens for HPV 16/18, of which 12 were positive. The HPV status of the specimens positively correlated with the pathological diagnosis, and since AFIC automatically and correctly located every cell, it was possible to directly compare morphology and HPV status in the same cell. In conclusion, the combination of FISH and AFIC is a sensitive and quantitative method to detect high risk HPV sequences in cervical smears.

Characterization of the surface polypeptides of Strongyloides ratti: a comparison of homogonic and heterogonic strains

Surface iodination, extraction and SDS-PAGE analysis techniques were employed to characterize and compare the surface polypeptides of two strains of Strongyloides ratti. Third stage infective larvae and parasitic adults of homogonic and heterogonic strains were studied using a variety of surface labelling procedures and detergents for the extraction of labelled molecules. Profiles obtained from SDS-PAGE analysis demonstrated that homogonic and heterogonic strains of S. ratti have identical surface antigens.

Attenuation of 8-OH-DPAT induced feeding after lesions of the area postrema/immediately adjacent nucleus of the solitary tract

Rats with lesions of the area postrema/immediately adjacent nucleus of the solitary tract (AP/mNTS-lesions) have an attenuated feeding response after several manipulations that induce food intake in intact control rats. In this study we examined the ingestive response of rats with AP/mNTS-lesions after treatment with 8-OH-DPAT, a 5-HT1A agonist. Rats with AP/mNTS-lesions failed to increase their food intake after treatment with 8-OH-DPAT at doses that stimulated food intake in intact rats. These data suggest that altered serotonergic function may contribute to the attenuation of feeding observed in rats with AP/mNTS-lesions after treatment with some orexigenic stimuli.