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Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (<0.1Hz) fluctuations in the MRI signal. These correlated fluctuations occur even when the subject is “at rest” (not asked to perform any particular task) and result from spontaneous neuronal activity synchronized within multiple distinct networks of brain regions. This estimate of connectivity, however, can be influenced by physiological noise, such as cardiac and respiratory fluctuations. This brief review looks at the effect of physiological noise on estimates of resting-state functional connectivity, discusses ways to remove physiological noise, and provides a personal recollection of the early developments in these approaches. This review also discusses the importance of physiological noise correction and provides a summary of evidence demonstrating that functional connectivity does have a neuronal underpinning and cannot purely be the result of physiological noise.

There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11min in length. In our present study, we examine the test–retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5min up to 13min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9–12min, while improvements in intrasession reliability plateaued around 12–16min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies. •We examine effects of scan length on the reliability of resting-state connectivity.•Reliability improves for increasing scan lengths, plateauing at 12min or longer.•This increase in reliability is due to the number of time points and scan duration.

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.

Maltreatment during childhood is a major risk factor for anxiety and depression, which are major public health problems. However, the underlying brain mechanism linking maltreatment and internalizing disorders remains poorly understood. Maltreatment may alter the activation of fear circuitry, but little is known about its impact on the connectivity of this circuitry in adolescence and whether such brain changes actually lead to internalizing symptoms. We examined the associations between experiences of maltreatment during childhood, resting-state functional brain connectivity (rs-FC) of the amygdala and hippocampus, and internalizing symptoms in 64 adolescents participating in a longitudinal community study. Childhood experiences of maltreatment were associated with lower hippocampus–subgenual cingulate rs-FC in both adolescent females and males and lower amygdala–subgenual cingulate rs-FC in females only. Furthermore, rs-FC mediated the association of maltreatment during childhood with adolescent internalizing symptoms. Thus, maltreatment in childhood, even at the lower severity levels found in a community sample, may alter the regulatory capacity of the brain’s fear circuit, leading to increased internalizing symptoms by late adolescence. These findings highlight the importance of fronto–hippocampal connectivity for both sexes in internalizing symptoms following maltreatment in childhood. Furthermore, the impact of maltreatment during childhood on both fronto–amygdala and –hippocampal connectivity in females may help explain their higher risk for internalizing disorders such as anxiety and depression.

Changes in the subject’s breathing rate or depth, such as a breath-hold challenge, can cause significant MRI signal changes. However, the response function that best models breath-holding-induced signal changes, as well as those resulting from a wider range of breathing variations including those occurring during rest, has not yet been determined. Respiration related signal changes appear to be slower than neuronally induced BOLD signal changes and are not modeled accurately using the typical hemodynamic response functions used in fMRI. In this study, we derive a new response function to model the average MRI signal changes induced by variations in the respiration volume (breath-to-breath changes in the respiration depth and rate). This was done by averaging the response to a series of single deep breaths performed once every 40 s amongst otherwise constant breathing. The new “respiration response function” consists of an early overshoot followed by a later undershoot (peaking at approximately 16 s), and accurately models the MRI signal changes resulting from breath-holding as well as cued depth and rate changes.

Subtle changes in a subject's breathing rate or depth, which occur naturally during rest at low frequencies (<0.1 Hz), have been shown to be significantly correlated with fMRI signal changes throughout gray matter and near large vessels. The goal of this study was to investigate the impact of these low-frequency respiration variations on both task activation fMRI studies and resting-state functional connectivity analysis. Unlike MR signal changes correlated with the breathing motion (∼0.3 Hz), BOLD signal changes correlated with across-breath variations in respiratory volume (∼0.03 Hz) appear localized to blood vessels and regions with high blood volume, such as gray matter, similar to changes seen in response to a breath-hold challenge. In addition, the respiration-variation-induced signal changes were found to coincide with many of the areas identified as part of the ‘default mode’ network, a set of brain regions hypothesized to be more active at rest. Regions could therefore be classified as being part of a resting network based on their similar respiration-induced changes rather than their synchronized neuronal activity. Monitoring and removing these respiration variations led to a significant improvement in the identification of task-related activation and deactivation and only slight differences in regions correlated with the posterior cingulate at rest. Regressing out global signal changes or cueing the subject to breathe at a constant rate and depth resulted in an improved spatial overlap between deactivations and resting-state correlations among areas that showed deactivation.

The authors assessed the contributions of early life stress (ELS) and childhood cortisol levels to adolescent resting-state functional connectivity. In females, ELS predicted increased cortisol levels in childhood, which predicted decreased amygdala-ventromedial prefrontal cortex (vmPFC) functional connectivity. Amygdala-vmPFC connectivity was inversely correlated with anxious sympotms and positively correlated with depressive symptoms. Early life stress (ELS) and function of the hypothalamic-pituitary-adrenal axis predict later psychopathology. Animal studies and cross-sectional human studies suggest that this process might operate through amygdala–ventromedial prefrontal cortex (vmPFC) circuitry implicated in the regulation of emotion. Here we prospectively investigated the roles of ELS and childhood basal cortisol amounts in the development of adolescent resting-state functional connectivity (rs-FC), assessed by functional connectivity magnetic resonance imaging (fcMRI), in the amygdala-PFC circuit. In females only, greater ELS predicted increased childhood cortisol levels, which predicted decreased amygdala-vmPFC rs-FC 14 years later. For females, adolescent amygdala-vmPFC functional connectivity was inversely correlated with concurrent anxiety symptoms but positively associated with depressive symptoms, suggesting differing pathways from childhood cortisol levels function through adolescent amygdala-vmPFC functional connectivity to anxiety and depression. These data highlight that, for females, the effects of ELS and early HPA-axis function may be detected much later in the intrinsic processing of emotion-related brain circuits.

Most of what is known about the reorganization of functional brain networks that accompanies normal aging is based on neuroimaging studies in which participants perform specific tasks. In these studies, reorganization is defined by the differences in task activation between young and old adults. However, task activation differences could be the result of differences in task performance, strategy, or motivation, and not necessarily reflect reorganization. Resting-state fMRI provides a method of investigating functional brain networks without such confounds. Here, a support vector machine (SVM) classifier was used in an attempt to differentiate older adults from younger adults based on their resting-state functional connectivity. In addition, the information used by the SVM was investigated to see what functional connections best differentiated younger adult brains from older adult brains. Three separate resting-state scans from 26 younger adults (18–35yrs) and 26 older adults (55–85) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available in the 1000 Functional Connectomes project www.nitrc.org/projects/fcon_1000. 100 seed-regions from four functional networks with 5mm3 radius were defined based on a recent study using machine learning classifiers on adolescent brains. Time-series for every seed-region were averaged and three matrices of z-transformed correlation coefficients were created for each subject corresponding to each individual's three resting-state scans. SVM was then applied using leave-one-out cross-validation. The SVM classifier was 84% accurate in classifying older and younger adult brains. The majority of the connections used by the classifier to distinguish subjects by age came from seed-regions belonging to the sensorimotor and cingulo-opercular networks. These results suggest that age-related decreases in positive correlations within the cingulo-opercular and default networks, and decreases in negative correlations between the default and sensorimotor networks, are the distinguishing characteristics of age-related reorganization.

The human brain undergoes both morphological and functional modifications across the human lifespan. It is important to understand the aspects of brain reorganization that are critical in normal aging. To address this question, one approach is to investigate age-related topological changes of the brain. In this study, we developed a brain network model using graph theory methods applied to the resting-state functional magnetic resonance imaging data acquired from two groups of normal healthy adults classified by age. We found that brain functional networks demonstrated modular organization in both groups with modularity decreased with aging, suggesting less distinct functional divisions across whole brain networks. Local efficiency was also decreased with aging but not with global efficiency. Besides these brain-wide observations, we also observed consistent alterations of network properties at the regional level in the elderly, particularly in two major functional networks—the default mode network (DMN) and the sensorimotor network. Specifically, we found that measures of regional strength, local and global efficiency of functional connectivity were increased in the sensorimotor network while decreased in the DMN with aging. These results indicate that global reorganization of brain functional networks may reflect overall topological changes with aging and that aging likely alters individual brain networks differently depending on the functional properties. Moreover, these findings highly correspond to the observation of decline in cognitive functions but maintenance of primary information processing in normal healthy aging, implying an underlying compensation mechanism evolving with aging to support higher-level cognitive functioning.

The goal of resting-state functional magnetic resonance imaging (fMRI) is to investigate the brain's functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain “at rest” as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of fMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state fMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO2 concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state fMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state fMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline. •Resting-state fMRI measures temporal similarity between BOLD signals.•Confounds can arise that affect the BOLD signals leading to spurious results.•Motion, cardiac/respiration, arterial CO2 concentration & blood pressure are sources.•Techniques to remove resting-state fMRI confounds are reviewed.•Noise correction is an essential step for resting-state fMRI analyses.