Influences of RASopathies on Neuroanatomical Variation in Children.

BACKGROUND: RASopathies are a group of disorders characterized by pathogenic mutations in the Ras/mitogen-activated protein kinase (Ras/MAPK) signaling pathway. Distinct pathogenic variants in genes encoding proteins in the Ras/MAPK pathway cause Noonan syndrome (NS) and neurofibromatosis type 1 (NF1), which are associated with increased risk for autism spectrum disorder and attention-deficit/hyperactivity disorder. METHODS: This study examined the effect of RASopathies (NS and NF1) on human neuroanatomy, specifically on surface area (SA), cortical thickness (CT), and subcortical volumes. Using vertex-based analysis for cortical measures and Desikan region of interest parcellation for subcortical volumes, we compared structural T1-weighted images of children with RASopathies (n = 91, mean age = 8.81 years, SD = 2.12) to those of sex- and age-matched typically developing children (n = 74, mean age = 9.07 years, SD = 1.77). RESULTS: Compared with typically developing children, RASopathies had convergent effects on SA and CT, exhibiting increased SA in the precentral gyrus, decreased SA in occipital regions, and thinner CT in the precentral gyrus. RASopathies exhibited divergent effects on subcortical volumes, with syndrome-specific influences from NS and NF1. Overall, children with NS showed decreased volumes in striatal and thalamic structures, and children with NF1 displayed increased volumes in the hippocampus, amygdala, and thalamus. CONCLUSIONS: Our study reveals the converging and diverging neuroanatomical effects of RASopathies on human neurodevelopment. The convergence of cortical effects on SA and CT indicates a shared influence of Ras/MAPK hyperactivation on the human brain. Therefore, considering these measures as objective outcome indicators for targeted treatments is imperative.

Mode decomposition-based time-varying phase synchronization for fMRI.

Recently, there has been significant interest in measuring time-varying functional connectivity (TVC) between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. However, this requires the a priori choice of type and cut-off frequencies for the bandpass filter needed to perform the analysis. Here we explore alternative approaches based on the use of various mode decomposition (MD) techniques that provide a more data driven solution to this issue. These techniques allow for the data driven decomposition of signals jointly into narrow-band components at different frequencies, thus fulfilling the requirements needed to measure PS. We explore several variants of MD, including empirical mode decomposition (EMD), bivariate EMD (BEMD), noise-assisted multivariate EMD (na-MEMD), and introduce the use of multivariate variational mode decomposition (MVMD) in the context of estimating time-varying PS. We contrast the approaches using a series of simulations and application to rs-fMRI data. Our results show that MVMD outperforms other evaluated MD approaches, and further suggests that this approach can be used as a tool to reliably investigate time-varying PS in rs-fMRI data.

Dynamic Functional Brain Connectivity Underlying Temporal Summation of Pain in Fibromyalgia.

OBJECTIVE: Abnormal central pain processing is a leading cause of pain in fibromyalgia (FM) and is perceptually characterized with the psychophysical measure of temporal summation of pain (TSP). TSP is the perception of increasingly greater pain in response to repetitive or tonic noxious stimuli. Previous neuroimaging studies have used static (i.e., summary) measures to examine the functional magnetic resonance imaging (fMRI) correlates of TSP in FM. However, functional brain activity rapidly and dynamically reorganizes over time, and, similarly, TSP is a temporally evolving process. This study was undertaken to demonstrate how a complete understanding of the neural circuitry supporting TSP in FM thus requires a dynamic measure that evolves over time. METHODS: We utilized novel methods for analyzing dynamic functional brain connectivity in patients with FM in order to examine how TSP-associated fluctuations are linked to the dynamic functional reconfiguration of the brain. In 84 FM patients and age- and sex-matched healthy controls, we collected high-temporal-resolution fMRI data during a resting state and during a state in which sustained cuff pressure pain was applied to the leg. RESULTS: FM patients experienced greater TSP than healthy controls (mean ± SD TSP score 17.93 ± 19.24 in FM patients versus 9.47 ± 14.06 in healthy controls; P = 0.028), but TSP scores varied substantially between patients. In the brain, the presence versus absence of TSP in patients with FM was marked by more sustained enmeshment between sensorimotor and salience networks during the pain period. Furthermore, dynamic enmeshment was noted solely in FM patients with high TSP, as interactions with all other brain networks were dampened during the pain period. CONCLUSION: This study elucidates the dynamic brain processes underlying facilitated central pain processing in FM. Our findings will enable future investigation of dynamic symptoms in FM.

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm.

A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its "pacemaker", continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of "mismatched" data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.

Evaluating phase synchronization methods in fMRI: A comparison study and new approaches.

In recent years there has been growing interest in measuring time-varying functional connectivity between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. There are several ways to perform such analyses, and we compare methods that utilize a PS metric together with a sliding window, referred to here as windowed phase synchronization (WPS), with those that directly measure the instantaneous phase synchronization (IPS). In particular, IPS has recently gained popularity as it offers single time-point resolution of time-resolved fMRI connectivity. In this paper, we discuss the underlying assumptions required for performing PS analyses and emphasize the importance of band-pass filtering the data to obtain valid results. Further, we contrast this approach with the use of Empirical Mode Decomposition (EMD) to achieve similar goals. We review various methods for evaluating PS and introduce a new approach within the IPS framework denoted the cosine of the relative phase (CRP). We contrast methods through a series of simulations and application to rs-fMRI data. Our results indicate that CRP outperforms other tested methods and overcomes issues related to undetected temporal transitions from positive to negative associations common in IPS analysis. Further, in contrast to phase coherence, CRP unfolds the distribution of PS measures, which benefits subsequent clustering of PS matrices into recurring brain states.

Investigating the impact of autocorrelation on time-varying connectivity.

In recent years, a number of studies have reported on the existence of time-varying functional connectivity (TVC) in resting-state functional magnetic resonance imaging (rs-fMRI) data. The sliding-window technique is currently one of the most commonly used methods to estimate TVC. Although previous studies have shown that autocorrelation can negatively impact estimates of static functional connectivity, its impact on TVC estimates is not well known at this time. In this paper, we show both theoretically and empirically that the existence of autocorrelation within a time series can inflate the sampling variability of TVC estimated using the sliding-window technique. This can in turn increase the risk of misinterpreting noise as true TVC and negatively impact subsequent estimation of whole-brain time-varying FC profiles, or "brain states". The latter holds as more variable input measures lead to more variable output measures in the state estimation procedure. Finally, we demonstrate that prewhitening the data prior to analysis can lower the variance of the estimated TVC and improve brain state estimation. These results suggest that careful consideration is required when making inferences on TVC.