The relationship between BOLD signal and autonomic nervous system functions: implications for processing of "physiological noise"

Coactivation of Autonomic and Central Nervous Systems During Processing of Socially Relevant Information in Autism Spectrum Disorder: A Systematic Review

Body-brain interaction provides a novel approach to understand neurodevelopmental conditions such as autism spectrum disorder (ASD). In this systematic review, we analyse the empirical evidence regarding coexisting differences in autonomic (ANS) and central nervous system (CNS) responses to social stimuli between individuals with ASD and typically developing individuals. Moreover, we review evidence of deviations in body-brain interaction during processing of socially relevant information in ASD. We conducted systematic literature searches in PubMed, Medline, PsychInfo, PsychArticles, and Cinahl databases (until 12.1.2022). Studies were included if individuals with ASD were compared with typically developing individuals, study design included processing of social information, and ANS and CNS activity were measured simultaneously. Out of 1892 studies identified based on the titles and abstracts, only six fulfilled the eligibility criteria to be included in synthesis. The quality of these studies was assessed using a quality assessment checklist. The results indicated that individuals with ASD demonstrate atypicalities in ANS and CNS signalling which, however, are context dependent. There were also indications for altered contribution of ANS-CNS interaction in processing of social information in ASD. However, the findings must be considered in the context of several limitations, such as small sample sizes and high variability in (neuro)physiological measures. Indeed, the methodological choices varied considerably, calling for a need for unified guidelines to improve the interpretability of results. We summarize the current experimentally supported understanding of the role of socially relevant body-brain interaction in ASD. Furthermore, we propose developments for future studies to improve incremental knowledge building across studies of ANS-CNS interaction involving individuals with ASD.

I know why the caged bird sings: Distress tolerant individuals show greater resting state connectivity between ventromedial prefrontal cortex and right amygdala as a function of higher vagal tone

BACKGROUND

Intolerance to psychological distress is associated with various forms of psychopathology, ranging from addiction to mood disturbance. The capacity to withstand aversive affective states is often explained by individual differences in cardiovagal tone as well as resting state connectivity of the ventromedial prefrontal cortex (vmPFC), a region involved in the regulation of emotions and cardio-autonomic tone. However, it is unclear which brain regions involved in distress tolerance show greater resting state functional connectivity (rsFC) as a function of resting heart rate variability (HRV).

METHODS

One-hundred and twenty-six adults, aged 20 to 83.5 years, were selected from a lifespan cohort at the Nathan Kline Institute-Rockland Sample. Participants' distress tolerance levels were assessed based upon performance on the Behavioral Indicator of Resiliency to Distress (BIRD) task. Artifact-free resting-state functional brain scans collected during separate sessions were used. While inside the scanner, a pulse oximeter was used to record beat-to-beat intervals to derive high-frequency heart rate variability (HF-HRV). The relationship between HF-HRV and vmPFC to whole brain functional connectivity was compared between distress tolerant (BIRD completers) and distress intolerant (BIRD non-completers).

RESULTS

Groups did not differ in their history of psychiatric diagnosis. Higher resting HF-HRV was associated with longer total time spent on the BIRD task for the entire sample (r = 0.255, p = 0.004). After controlling for age, gender, body mass index, head motion, and gray matter volume. Distress tolerant individuals showed greater rsFC (p < 0.005 (uncorrected), k = 20) between the vmPFC and default-mode network (DMN) hubs including posterior cingulate cortex/precuneus, medial temporal lobes, and the parahippocampal cortex. As a function of higher resting HF-HRV greater vmPFC connectivity was observed with sub-threshold regions in the right amygdala and left anterior prefrontal cortex, with the former passing small volume correction, in distress tolerant versus distress intolerant individuals.

CONCLUSION

In a lifespan sample of community-dwelling adults, distress tolerant individuals showed greater vmPFC connectivity with anterior and posterior hubs of the DMN compared to distress intolerant individuals. As a function of greater HF-HRV, distress tolerant individuals evidenced greater vmPFC with salience and executive control network hubs. These findings are consistent with deficits in neural resource allocation within a triple network resting amongst persons exhibiting behavioral intolerance to psychological distress.

An orderly sequence of autonomic and neural events at transient arousal changes

Resting-state functional magnetic resonance imaging (rsfMRI) allows the study of functional brain connectivity based on spatially structured variations in neuronal activity. Proper evaluation of connectivity requires removal of non-neural contributions to the fMRI signal, in particular hemodynamic changes associated with autonomic variability. Regression analysis based on autonomic indicator signals has been used for this purpose, but may be inadequate if neuronal and autonomic activities covary. To investigate this potential co-variation, we performed rsfMRI experiments while concurrently acquiring electroencephalography (EEG) and autonomic indicator signals, including heart rate, respiratory depth, and peripheral vascular tone. We identified a recurrent and systematic spatiotemporal pattern of fMRI (named as fMRI cascade), which features brief signal reductions in salience and default-mode networks and the thalamus, followed by a biphasic global change with a sensory-motor dominance. This fMRI cascade, which was mostly observed during eyes-closed condition, was accompanied by large EEG and autonomic changes indicative of arousal modulations. Importantly, the removal of the fMRI cascade dynamics from rsfMRI diminished its correlations with various signals. These results suggest that the rsfMRI correlations with various physiological and neural signals are not independent but arise, at least partly, from the fMRI cascades and associated neural and physiological changes at arousal modulations.

fMRI studies evaluating central respiratory control in humans

A plethora of neural centers in the central nervous system control the fundamental respiratory pattern. This control is ensured by neurons that act as pacemakers, modulating activity through chemical control driven by changes in the O2/CO2 balance. Most of the respiratory neural centers are located in the brainstem, but difficult to localize on magnetic resonance imaging (MRI) due to their small size, lack of visually-detectable borders with neighboring areas, and significant physiological noise hampering detection of its activity with functional MRI (fMRI). Yet, several approaches make it possible to study the normal response to different abnormal stimuli or conditions such as CO2 inhalation, induced hypercapnia, volitional apnea, induced hypoxia etc. This review provides a comprehensive overview of the majority of available studies on central respiratory control in humans.

Threat vigilance and intrinsic amygdala connectivity

A well-documented amygdala-dorsomedial prefrontal circuit is theorized to promote attention to threat ("threat vigilance"). Prior research has implicated a relationship between individual differences in trait anxiety/vigilance, engagement of this circuitry, and anxiogenic features of the environment (e.g., through threat-of-shock and movie-watching). In the present study, we predicted that-for those scoring high in self-reported anxiety and a behavioral measure of threat vigilance-this circuitry is chronically engaged, even in the absence of anxiogenic stimuli. Our analyses of resting-state fMRI data (N = 639) did not, however, provide evidence for such a relationship. Nevertheless, in our planned exploratory analyses, we saw a relationship between threat vigilance behavior (but not self-reported anxiety) and intrinsic amygdala-periaqueductal gray connectivity. Here, we suggest this subcortical circuitry may be chronically engaged in hypervigilant individuals, but that amygdala-prefrontal circuitry may only be engaged in response to anxiogenic stimuli.

An ICA Investigation into the Effect of Physiological Noise Correction on Dimensionality and Spatial Maps of Intrinsic Connectivity Networks

Physiological processes such as cardiac pulsations and respiration can induce signal modulations in functional magnetic resonance imaging (fMRI) time series, and confound inferences made about neural processing from analyses of the blood oxygenation level-dependent (BOLD) signals. Retrospective image space correction of physiological noise (RETROICOR) is a widely used approach to reduce physiological signals in data. Independent component analysis (ICA) is a valuable blind source separation method for analyzing brain networks, referred to as intrinsic connectivity networks (ICNs). Previously, we showed that temporal properties of the ICA-derived networks such as spectral power and functional network connectivity could be impacted by RETROICOR corrections. The goal of this study is to investigate the effect of retrospective correction of physiological artifacts on the ICA dimensionality (model order) and intensities of ICN spatial maps. To this aim, brain BOLD fMRI, heartbeat, and respiration were measured in 22 healthy subjects during resting state. ICA dimensionality was estimated using minimum description length (MDL) based on i.i.d. data samples and smoothness FWHM kernel, and entropy-rate based order selection by finite memory length model (ER-FM) and autoregressive model (ER-AR). Differences in spatial maps between the raw and denoised data were compared using the paired t-test and false discovery rate (FDR) thresholding was used to correct for multiple comparisons. Results showed that ICA dimensionality was greater in the raw data compared to the denoised data. Significant differences were found in the intensities of spatial maps for three ICNs: basal ganglia, precuneus, and frontal network. These preliminary results indicate that the retrospective physiological noise correction can induce change in the resting state spatial map intensity related to the within-network connectivity. More research is needed to understand this effect.

Spontaneous eye movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity

During wakeful rest, individuals make small eye movements during fixation. We examined how these endogenously driven oculomotor patterns impact topography and topology of functional brain networks. We used a dataset consisting of eyes-open resting-state (RS) fMRI data with simultaneous eye tracking. The eye-tracking data indicated minor movements during rest, which correlated modestly with RS BOLD data. However, eye-tracking data correlated well with echo-planar imaging time series sampled from the area of the eye-orbit (EO-EPI), which is a signal previously used to identify eye movements during exogenous saccades and movie viewing. Further analyses showed that EO-EPI data were correlated with activity in an extensive motor and sensorimotor network, including components of the dorsal attention network and the frontal eye fields. Partialling out variance related to EO-EPI from RS data reduced connectivity, primarily between sensorimotor and visual areas. It also produced networks with higher modularity, lower mean connectivity strength, and lower mean clustering coefficient. Our results highlight new aspects of endogenous eye movement control during wakeful rest. They show that oculomotor-related contributions form an important component of RS network topology, and that those should be considered in interpreting differences in network structure between populations or as a function of different experimental conditions.

We studied how subtle eye movements made during fixation, in absence of any other task, are related to resting-state connectivity measured using fMRI. We used a dataset for which eye tracking and BOLD resting-state were acquired simultaneously. We correlated brain activity with both eye-tracking metrics as well as time series sampled from the area of the eye orbits (EO-EPI). Eye-tracking data correlated well with the EO-EPI data. Furthermore, EO-EPI correlated with BOLD signal in sensorimotor and visual brain systems. Removing variance related to EO-EPI reduced connectivity between sensorimotor and visual areas and resulted in more modular resting-state networks. Our findings show that oculomotor-related contributions are an important component of resting-state network topology, and that they can be studied using EPI data from the eye orbits.

The neuronal associations of respiratory-volume variability in the resting state

The desire to enhance the sensitivity and specificity of resting-state (rs-fMRI) measures has prompted substantial recent research into removing noise components. Chief among contributions to noise in rs-fMRI are physiological processes, and the neuronal implications of respiratory-volume variability (RVT), a main rs-fMRI-relevant physiological process, is incompletely understood. The potential implications of RVT in modulating and being modulated by autonomic nervous regulation, has yet to be fully understood by the rs-fMRI community. In this work, we use high-density electroencephalography (EEG) along with simultaneously acquired RVT recordings to help address this question. We hypothesize that (1) there is a significant relationship between EEG and RVT in multiple EEG bands, and (2) that this relationship varies by brain region. Our results confirm our first hypothesis, although all brain regions are shown to be equally implicated in RVT-related EEG-signal fluctuations. The lag between RVT and EEG is consistent with previously reported values. However, an interesting finding is related to the polarity of the correlation between RVT and EEG. Our results reveal potentially two main regimes of EEG-RVT association, one in which EEG leads RVT with a positive association between the two, and one in which RVT leads EEG but with a negative association between the two. We propose that these two patterns can be interpreted differently in terms of the involvement of higher cognition. These results further suggest that treating RVT simply as noise is likely a questionable practice, and that more work is needed to avoid discarding cognitively relevant information when performing physiological correction rs-fMRI.

Neurovisceral integration in the executive control network: A resting state analysis

Neurovisceral integration models emphasize the role of frontal lobes in cognitive, behavioral, and emotional regulation. Two candidate hubs for the regulation of cardio-autonomic control, anxiety, and executive attention are the dorsolateral prefrontal cortex (DLPFC) and middle frontal gyrus (MFG). Two-hundred and seventy-one adults (62.9 % female) aged 18-85 years were selected from the NKI-Rockland Sample. Resting state functional imaging data was preprocessed, and seeds extracted from bilateral DLPFC and MFG to test 4 regression models predicting connectivity with high frequency HRV (HF-HRV), trait anxiety (TA), and reaction time on an executive attention task. After controlling for age, sex, body mass index and head motion, the right DLPFC-MFG seed pair provided strongest support for neurovisceral integration indexed by HF-HRV, low TA and shorter reaction time on the attention network task. This hemispheric effect may underlie the inhibitory role of right PFC in the regulation of cardio-autonomic function, emotion, and executive attention.

The role of anterior and posterior insula in male genital response and in visual attention: an exploratory multimodal fMRI study

Several studies highlighted the role of insula on several functions and in sexual behavior. This exploratory study examines the relationships among genital responses, brain responses, and eye movements, to disentangle the role played by the anterior and posterior insula during different stages of male sexual response and during visual attention to sexual stimuli. In 19 healthy men, fMRI, eye movement, and penile tumescence data were collected during a visual sexual stimulation task. After a whole-brain analysis comparing neutral and sexual clips and confirming a role for the bilateral insulae, we selected two bilateral seed regions in anterior and posterior insula for functional connectivity analysis. Single-ROI-GLMs were run for the FC target regions. Single-ROI-GLMs were performed based on areas to which participants fixate: “Faces”, “Genitals,” and “Background” with the contrast “Genitals > Faces”. Single-ROI-GLMs with baseline, onset, and sustained PT response for the sexual clips were performed. We found stronger effects for the posterior than the anterior insula. In the target regions of the posterior insula, we found three different pathways: the first involved in visual attention, onset of erection, and sustained erection; the second involved only in the onset of erection, and the third limited to sustained erection.

Deciphering the neural signature of human cardiovascular regulation

Cardiovascular regulation is integral to life. Animal studies have identified both neural and endocrine pathways, by which the central nervous system adjusts cardiac output and peripheral vascular resistance to changing physiological demands. The outflow of these pathways is coordinated by various central nervous regions based on afferent information from baroreceptors, chemoreceptors, nociceptors, and circulating hormones, and is modulated by physiologic and behavioural state. In humans, however, knowledge on central cardiovascular regulation below the cortical level is scarce. Here, we show using functional MRI (fMRI) that at least three hypothalamic subsystems are involved in cardiovascular regulation in humans. The rhythmic behaviour of these systems corresponds to high and low frequency oscillations typically seen in blood pressure and heart rate variability.

Stand up too fast and you know what happens next. You will feel faint as the blood rushes away from your head. Gravity pulls the blood into your legs, and your blood pressure drops. To correct this imbalance, the brain sends nerve impulses telling the heart to beat faster and the outer blood vessels to tighten. This is the autonomic nervous system at work. It is how the brain adjusts cardiac output, and quietly controls other internal organs in the body. It involves two key regions of the brain, the hypothalamus and the brainstem, and stimulates smooth muscles and glands around the body.

The cardiovascular system also responds to the demands of exercise, with the heart supplying fresh blood laden with oxygen and the blood clearing out waste materials as it flows around the body. Perhaps surprisingly, blood pressure and heart rate fluctuate even at rest. The heart beats faster when breathing in and slower when breathing out. People’s blood pressure, the force that keeps blood moving through arteries, also oscillates in so-called Mayer waves that last about 10 seconds.

Much of the current understanding of the inner workings of the cardiovascular system – and how it is regulated by the brain – stems from animal experiments. This is because few attempts have been made to simultaneously measure how a person’s brain and cardiovascular system work with enough detail to see how brain waves and cardiac oscillations might interact.

To achieve this, Manuel et al. have now measured the brain activity, pulse and blood pressure of twenty-two healthy people while they were lying down in an MRI machine. This revealed that three distinct parts of the hypothalamus regulate cardiovascular output in humans. These ‘subsystems’ communicate with each other and with the lower brainstem, which sits beneath the hypothalamus. Manuel et al. also observed that the rhythmic activity of these subsystems runs in sync with oscillations typically seen in heart rate and blood pressure.

With this work, Manuel et al. have shown that it is feasible to measure different systems of cardiovascular control in humans. In time, with further experiments using this new approach, the understanding of chronic high blood pressure and heart failure may improve.

Distinguishing pain from nociception, salience, and arousal: How autonomic nervous system activity can improve neuroimaging tests of specificity

Pain is a subjective, multidimensional experience that is distinct from nociception. A large body of work has focused on whether pain processing is supported by specific, dedicated brain circuits. Despite advances in human neuroscience and neuroimaging analysis, dissociating acute pain from other sensations has been challenging since both pain and non-pain stimuli evoke salience and arousal responses throughout the body and in overlapping brain circuits. In this review, we discuss these challenges and propose that brain-body interactions in pain can be leveraged in order to improve tests for pain specificity. We review brain and bodily responses to pain and nociception and extant efforts toward identifying pain-specific brain networks. We propose that autonomic nervous system activity should be used as a surrogate measure of salience and arousal to improve these efforts and enable researchers to parse out pain-specific responses in the brain, and demonstrate the feasibility of this approach using example fMRI data from a thermal pain paradigm. This new approach will improve the accuracy and specificity of functional neuroimaging analyses and help to overcome current difficulties in assessing pain specific responses in the human brain.

Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam

The pharmacological modulation of functional connectivity in the brain may underlie therapeutic efficacy for several neurological and psychiatric disorders. Functional magnetic resonance imaging (fMRI) provides a noninvasive method of assessing this modulation, however, the indirect nature of the blood‐oxygen level dependent signal restricts the discrimination of neural from physiological contributions. Here we followed two approaches to assess the validity of fMRI functional connectivity in developing drug biomarkers, using simultaneous electroencephalography (EEG)/fMRI in a placebo‐controlled, three‐way crossover design with ketamine and midazolam. First, we compared seven different preprocessing pipelines to determine their impact on the connectivity of common resting‐state networks. Independent components analysis (ICA)‐denoising resulted in stronger reductions in connectivity after ketamine, and weaker increases after midazolam, than pipelines employing physiological noise modelling or averaged signals from cerebrospinal fluid or white matter. This suggests that pipeline decisions should reflect a drug's unique noise structure, and if this is unknown then accepting possible signal loss when choosing extensive ICA denoising pipelines could engender more confidence in the remaining results. We then compared the temporal correlation structure of fMRI to that derived from two connectivity metrics of EEG, which provides a direct measure of neural activity. While electrophysiological estimates based on the power envelope were more closely aligned to BOLD signal connectivity than those based on phase consistency, no significant relationship between the change in electrophysiological and hemodynamic correlation structures was found, implying caution should be used when making cross‐modal comparisons of pharmacologically‐modulated functional connectivity.

Heart rate variability as a biomarker in health and affective disorders: A perspective on neuroimaging studies

The dynamic embodiment of psychological processes is evident in the association of health outcomes, behavioural traits and psychological functioning with Heart Rate Variability (HRV). The dominant high-frequency component of HRV is an index of the central neural control of heart rhythm, mediated via the parasympathetic vagus nerve. HRV provides a potential objective measure of action policies for the adaptive and predictive allostatic regulation of homeostasis within the cardiovascular system. In its support, a network of brain regions (referred to as the 'central autonomic network') maps internal state, and controls autonomic responses. This network includes regions of prefrontal cortex, anterior cingulate cortex, insula, amygdala, periaqueductal grey, pons and medulla. Human neuroimaging studies of neural activation and functional connectivity broadly endorse this architecture, and its link with cardiac regulation at rest and dysregulation in clinical states that include affective disorders. In this review, we appraise neuroimaging research and related evidence for HRV as an informative marker of autonomic integration with affect and cognition, taking a perspective on function and organisation. We consider evidence for the utility of HRV as a metric to inform targeted interventions to improve autonomic and affective dysregulation, and suggest research questions for further investigation.

A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data

It has been known for decades that head motion/other artifacts affect the blood oxygen level-dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind-source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of event-related functional magnetic resonance imaging (erfMRI) and block-design (bdfMRI) datasets collected with multiband 32- (repetition time [TR] = 460 ms) or older 12-channel (TR = 2,000 ms) head coils. The amount of motion varied across coil designs and task types. Quality control plots indicated small to moderate relationships between head motion estimates and percent signal change in both signal and noise regions. Blind-source denoising strategies eliminated signal as well as noise relative to MP24 regression; however, the undesired effects on signal depended both on algorithm (FIX > AROMA) and design (bdfMRI > erfMRI). Moreover, in contrast to previous results, there were minimal differences between MP12/24 and MP0 pipelines in both erfMRI and bdfMRI designs. MP12/24 pipelines were detrimental for a task with both longer block length (30 ± 5 s) and higher correlations between head MPs and design matrix. In summary, current results suggest that there does not appear to be a single denoising approach that is appropriate for all fMRI designs. However, even nonaggressive blind-source denoising approaches appear to remove signal as well as noise from task-related data at individual subject and group levels.

The relationship between heart rate and functional connectivity of brain regions involved in autonomic control

The peripheral autonomic nervous system (ANS) adjusts the heart rate (HR) to intrinsic and extrinsic demands. It is controlled by a group of functionally connected brain regions assembling the so-called central autonomic network (CAN). More specifically, forebrain cortical regions, limbic and brainstem structures within the CAN have been identified as important components of circuits involved in HR regulation. The present study aimed to investigate whether functional connectivity (FC) between these regions varies in subjects with different heart rates. Thus, 84 healthy subjects were separated according to their HR in slow, medium and fast. We observed a direct association between HR and FC in CAN regions, where stronger FC was related to slower HR. This relationship, however, is non-linear, follows an exponential course and is not restricted to CAN areas only. The network-based analysis (NBS) using time series from 262 independent anatomical ROIs revealed significantly increased functional connectivity in subjects with slow HR compared to subjects with fast HR mainly in regions being part of the salience network, but also of the default-mode network. We additionally simulated the effect of aliasing on the functional connectivity using several TRs and heart rates to exclude the possibility that FC differences might be due to different aliasing effects in the data. The result of the simulation indicated that aliasing cannot explain our findings. Thus, present results imply a functionally meaningful coupling between FC and HR that need to be accounted for in future studies. Moreover, given the established link between HR and emotional, cognitive and social processes, present findings may also be considered to explain individual differences in brain activation or connectivity when using corresponding paradigms in the MR scanner to investigate such processes.

Cause or effect: Altered brain and network activity in cervical dystonia is partially normalized by botulinum toxin treatment

Background

Idiopathic cervical dystonia (CD) is a chronic movement disorder characterized by impressive clinical symptoms and the lack of clear pathological findings in clinical diagnostics and imaging. At present, the injection of botulinum toxin (BNT) in dystonic muscles is an effective therapy to control motor symptoms and pain in CD.

Objectives

We hypothesized that, although it is locally injected to dystonic muscles, BNT application leads to changes in brain and network activity towards normal brain function.

Methods

Using 3 T functional MR imaging along with advanced analysis techniques (functional connectivity, Granger causality, and regional homogeneity), we aimed to characterize brain activity in CD (17 CD patients vs. 17 controls) and to uncover the effects of BNT treatment (at 6 months).

Results

In CD, we observed an increased information flow within the basal ganglia, the thalamus, and the sensorimotor cortex. In parallel, some of these structures became less responsive to regulating inputs. Furthermore, our results suggested an altered somatosensory integration. Following BNT administration, we noted a shift towards normal brain function in the CD patients, especially within the motor cortex, the somatosensory cortex, and the basal ganglia.

Conclusion

The changes in brain function and network activity in CD can be interpreted as related to the underlying cause, the effort to compensate or a mixture of both. Although BNT is applied in the last stage of the cortico-neuromuscular pathway, brain patterns are shifted towards those of healthy controls.

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we characterized brain activity in CD and the effects of BNT using 3T fMR imaging and network analysis techniques

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following treatment with botulinum toxin (BNT), abnormal brain activity patterns in primary dystonia are attenuated

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critical key regions for both the pathophysiology and BNT-induced improvement in cervical dystonia are the BG

Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI

In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between seed-specific sliding window correlation-based DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors. Strong correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not necessarily eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed between the DFC estimates and nuisance norms even after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.

Experimental and methodological factors affecting test-retest reliability of amygdala BOLD responses

Previous studies reported poor to fair test-retest reliability of amygdala BOLD responses to emotional stimuli. However, these findings are very heterogeneous across and within studies. The present study sought to systematically examine experimental and methodological factors that contribute to this heterogeneity. Forty-six young subjects were scanned twice with a mean test-retest interval of 7 weeks. We compared amygdala reliability across three tasks: A face-matching task, passive viewing of emotional faces, and passive viewing of emotional scenes. We also explored whether extraction of physiological noise can affect the stability of amygdala responses. We assessed test-retest reliability of amygdala mean amplitudes at the individual level and spatial repeatability (i.e., stability of the spatial distribution of activation) of the amygdala BOLD signal at the group and individual level. All three tasks evoked robust amygdala activation at the group level. At the individual level, amygdala spatial repeatability was poor during passive viewing of scenes and faces and fair or close to fair in the face-matching task. On the other hand, reliability of amygdala mean responses was very poor in the face-matching task while it was significantly higher during passive viewing of faces and scenes. Physiological noise correction changed reliability rates but not uniformly across the three tasks. The current work suggests that the presence of a concurrent task during emotion processing affects amygdala reliability. The dissociation between spatial repeatability and reliability of mean amplitudes highlights the importance of taking into account both measures for a multidimensional assessment of the reliability of BOLD responses.

Reprint of 'Noise contributions to the fMRI signal: An Overview'

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.

Task-induced deactivation in diverse brain systems correlates with interindividual differences in distinct autonomic indices

Neuroimaging research has shown that different cognitive tasks induce relatively specific activation patterns, as well as less task-specific deactivation patterns. Here we examined whether individual differences in Autonomic Nervous System (ANS) activity during task performance correlate with the magnitude of task-induced deactivation. In an fMRI study, participants performed a continuous mental arithmetic task in a task/rest block design, while undergoing combined fMRI and heart/respiration rate acquisitions using photoplethysmograph and respiration belt. As expected, task performance increased heart-rate and reduced the RMSSD, a cardiac index related to vagal tone. Across participants, higher heart rate during task was linked to increased activation in fronto-parietal regions, as well as to stronger deactivation in ventromedial prefrontal regions. Respiration frequency during task was associated with similar patterns, but in different regions than those identified for heart-rate. Finally, in a large set of regions, almost exclusively limited to the Default Mode Network, lower RMSSD was associated with greater deactivation, and furthermore, the vast majority of these regions were task-deactivated at the group level. Together, our findings show that inter-individual differences in ANS activity are strongly linked to task-induced deactivation. Importantly, our findings suggest that deactivation is a multifaceted construct potentially linked to ANS control, because distinct ANS measures correlate with deactivation in different regions. We discuss the implications for current theories of cortical control of the ANS and for accounts of deactivation, with particular reference to studies documenting a "failure to deactivate" in multiple clinical states.

The effect of the GLP-1 analogue Exenatide on functional connectivity within an NTS-based network in women with and without obesity

Objective

The differential effect of GLP-1 agonist Exenatide on functional connectivity of the nucleus tractus solitaries (NTS), a key region associated with homeostasis, and on appetite-related behaviours was investigated in women with normal weight compared with women with obesity.

Methods

Following an 8-h fast, 19 female subjects (11 lean, 8 obese) participated in a 2-d double blind crossover study. Subjects underwent functional magnetic resonance imaging at fast and 30-min post subcutaneous injection of 5 μg of Exenatide or placebo. Functional connectivity was examined with the NTS. Drug-induced functional connectivity changes within and between groups and correlations with appetite measures were examined in a region of interest approach focusing on the thalamus and hypothalamus.

Results

Women with obesity reported less hunger after drug injection. Exenatide administration increased functional connectivity of the left NTS with the left thalamus and hypothalamus in the obese group only and increased the correlation between NTS functional connectivity and hunger scores in all subjects, but more so in the obese.

Conclusions

Obesity can impact the effects of Exenatide on brain connectivity, specifically in the NTS and is linked to changes in appetite control. This has implications for the use of GLP-1 analogues in therapeutic interventions.

Neural Responses to Heartbeats in the Default Network Encode the Self in Spontaneous Thoughts

The default network (DN) has been consistently associated with self-related cognition, but also to bodily state monitoring and autonomic regulation. We hypothesized that these two seemingly disparate functional roles of the DN are functionally coupled, in line with theories proposing that selfhood is grounded in the neural monitoring of internal organs, such as the heart. We measured with magnetoencephalograhy neural responses evoked by heartbeats while human participants freely mind-wandered. When interrupted by a visual stimulus at random intervals, participants scored the self-relatedness of the interrupted thought. They evaluated their involvement as the first-person perspective subject or agent in the thought ("I"), and on another scale to what degree they were thinking about themselves ("Me"). During the interrupted thought, neural responses to heartbeats in two regions of the DN, the ventral precuneus and the ventromedial prefrontal cortex, covaried, respectively, with the "I" and the "Me" dimensions of the self, even at the single-trial level. No covariation between self-relatedness and peripheral autonomic measures (heart rate, heart rate variability, pupil diameter, electrodermal activity, respiration rate, and phase) or alpha power was observed. Our results reveal a direct link between selfhood and neural responses to heartbeats in the DN and thus directly support theories grounding selfhood in the neural monitoring of visceral inputs. More generally, the tight functional coupling between self-related processing and cardiac monitoring observed here implies that, even in the absence of measured changes in peripheral bodily measures, physiological and cognitive functions have to be considered jointly in the DN.

SIGNIFICANCE STATEMENT

The default network (DN) has been consistently associated with self-processing but also with autonomic regulation. We hypothesized that these two functions could be functionally coupled in the DN, inspired by theories according to which selfhood is grounded in the neural monitoring of internal organs. Using magnetoencephalography, we show that heartbeat-evoked responses (HERs) in the DN covary with the self-relatedness of ongoing spontaneous thoughts. HER amplitude in the ventral precuneus covaried with the "I" self-dimension, whereas HER amplitude in the ventromedial prefrontal cortex encoded the "Me" self-dimension. Our experimental results directly support theories rooting selfhood in the neural monitoring of internal organs. We propose a novel functional framework for the DN, where self-processing is coupled with physiological monitoring.

Methods for cleaning the BOLD fMRI signal

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

The global signal in fMRI: Nuisance or Information?

The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.

Optimizing fMRI preprocessing pipelines for block-design tasks as a function of age

Functional Magnetic Resonance Imaging (fMRI) is a powerful neuroimaging tool, which is often hampered by significant noise confounds. There is evidence that our ability to detect activations in task fMRI is highly dependent on the preprocessing steps used to control noise and artifact. However, the vast majority of studies examining preprocessing pipelines in fMRI have focused on young adults. Given the widespread use of fMRI for characterizing the neurobiology of aging, it is critical to examine how the impact of preprocessing choices varies as a function of age. In this study, we employ the NPAIRS cross-validation framework, which optimizes pipelines based on metrics of prediction accuracy (P) and spatial reproducibility (R), to compare the effects of pipeline optimization between young (21-33 years) and older (61-82 years) cohorts, for three different block-design contrasts. Motion is shown to be a greater issue in the older cohort, and we introduce new statistical approaches to control for potential biases due to head motion during pipeline optimization. In comparison, data-driven methods of physiological noise correction show comparable benefits for both young and old cohorts. Using our optimization framework, we demonstrate that the optimal pipelines tend to be highly similar across age cohorts. In addition, there is a comparable, significant benefit of pipeline optimization across age cohorts, for (P, R) metrics and independent validation measures of activation overlap (both between-subject, within-session and within-subject, between-session). The choice of task contrast consistently shows a greater impact than the age cohort, for (P, R) metrics and activation overlap. Finally, adaptive pipeline optimization per task run shows improved sensitivity to age-related changes in brain activity, particularly for weaker, more complex cognitive contrasts. The current study provides the first detailed examination of preprocessing pipelines across age cohorts, demonstrating a significant benefit of adaptive pipeline optimization across age groups.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

Noise contributions to the fMRI signal: An overview

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.

Diminished Auditory Responses during NREM Sleep Correlate with the Hierarchy of Language Processing

Natural sleep provides a powerful model system for studying the neuronal correlates of awareness and state changes in the human brain. To quantitatively map the nature of sleep-induced modulations in sensory responses we presented participants with auditory stimuli possessing different levels of linguistic complexity. Ten participants were scanned using functional magnetic resonance imaging (fMRI) during the waking state and after falling asleep. Sleep staging was based on heart rate measures validated independently on 20 participants using concurrent EEG and heart rate measurements and the results were confirmed using permutation analysis. Participants were exposed to three types of auditory stimuli: scrambled sounds, meaningless word sentences and comprehensible sentences. During non-rapid eye movement (NREM) sleep, we found diminishing brain activation along the hierarchy of language processing, more pronounced in higher processing regions. Specifically, the auditory thalamus showed similar activation levels during sleep and waking states, primary auditory cortex remained activated but showed a significant reduction in auditory responses during sleep, and the high order language-related representation in inferior frontal gyrus (IFG) cortex showed a complete abolishment of responses during NREM sleep. In addition to an overall activation decrease in language processing regions in superior temporal gyrus and IFG, those areas manifested a loss of semantic selectivity during NREM sleep. Our results suggest that the decreased awareness to linguistic auditory stimuli during NREM sleep is linked to diminished activity in high order processing stations.

Brain-heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field

Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and trade-offs associated with UHF imaging, focusing on the application to studying brain-heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain-heart interactions.

Sensitivity of the resting-state haemodynamic response function estimation to autonomic nervous system fluctuations

The haemodynamic response function (HRF) is a key component of the blood oxygen level-dependent (BOLD) signal, providing the mapping between neural activity and the signal measured with functional magnetic resonance imaging (fMRI). Most of the time the HRF is associated with task-based fMRI protocols, in which its onset is explicitly included in the design matrix. On the other hand, the HRF also mediates the relationship between spontaneous neural activity and the BOLD signal in resting-state protocols, in which no explicit stimulus is taken into account. It has been shown that resting-state brain dynamics can be characterized by looking at sparse BOLD 'events', which can be retrieved by point process analysis. These events can be then used to retrieve the HRF at rest. Crucially, cardiac activity can also induce changes in the BOLD signal, thus affecting both the number of these events and the estimation of the haemodynamic response. In this study, we compare the resting-state haemodynamic response retrieved by means of a point process analysis, taking the cardiac fluctuations into account. We find that the resting-state HRF estimation is significantly modulated in the brainstem and surrounding cortical areas. From the analysis of two high-quality datasets with different temporal and spatial resolution, and through the investigation of intersubject correlation, we suggest that spontaneous point process response durations are associated with the mean interbeat interval and low-frequency power of heart rate variability in the brainstem.

Impact of task-related changes in heart rate on estimation of hemodynamic response and model fit

The blood oxygen level dependent (BOLD) signal, as measured using functional magnetic resonance imaging (fMRI), is widely used as a proxy for changes in neural activity in the brain. Physiological variables such as heart rate (HR) and respiratory variation (RV) affect the BOLD signal in a way that may interfere with the estimation and detection of true task-related neural activity. This interference is of particular concern when these variables themselves show task-related modulations. We first establish that a simple movement task reliably induces a change in HR but not RV. In group data, the effect of HR on the BOLD response was larger and more widespread throughout the brain than were the effects of RV or phase regressors. The inclusion of HR regressors, but not RV or phase regressors, had a small but reliable effect on the estimated hemodynamic response function (HRF) in M1 and the cerebellum. We next asked whether the inclusion of a nested set of physiological regressors combining phase, RV, and HR significantly improved the model fit in individual participants' data sets. There was a significant improvement from HR correction in M1 for the greatest number of participants, followed by RV and phase correction. These improvements were more modest in the cerebellum. These results indicate that accounting for task-related modulation of physiological variables can improve the detection and estimation of true neural effects of interest.

Association between resting-state brain functional connectivity and muscle sympathetic burst incidence

The insula (IC) and cingulate are key components of the central autonomic network and central nodes of the salience network (SN), a set of spatially distinct but temporally correlated brain regions identified with resting-state (task free) functional MRI (rsMRI). To examine the SN's involvement in sympathetic outflow, we tested the hypothesis that individual differences in intrinsic connectivity of the SN correlate positively with resting postganglionic muscle sympathetic nerve activity (MSNA) burst incidence (BI) in subjects without and with obstructive sleep apnea (OSA). Overnight polysomnography, 5-min rsMRI, and fibular MSNA recording were performed in 36 subjects (mean age 57 yr; 10 women, 26 men). Independent component analysis (ICA) of the entire cohort identified the SN as including bilateral IC, pregenual anterior cingulate cortex (pgACC), midcingulate cortex (MCC), and the temporoparietal junction (TPJ). There was a positive correlation between BI and the apnea-hypopnea index (AHI) (P < 0.001), but dual-regression analysis identified no differences in SN functional connectivity between subjects with no or mild OSA (n = 17) and moderate or severe (n = 19) OSA. Correlation analysis relating BI to the strength of connectivity within the SN revealed large (i.e., spatial extent) and strong correlations for the left IC (P < 0.001), right pgACC/MCC (P < 0.006), left TPJ (P < 0.004), thalamus (P < 0.035), and cerebellum (P < 0.013). Indexes of sleep apnea were unrelated to BI and the strength of SN connectivity. There were no relationships between BI and default or sensorimotor network connectivity. This study links connectivity within the SN to MSNA, demonstrating several of its nodes to be key sympathoexcitatory regions.

Subjective Effects of Ethanol, Morphine, Δ(9)-Tetrahydrocannabinol, and Ketamine Following a Pharmacological Challenge Are Related to Functional Brain Connectivity

This analysis examines the neuronal foundation of drug-induced psychomimetic symptoms by relating the severity of these symptoms to changes in functional connectivity for a range of different psychoactive compounds with varying degrees of psychomimetic effects. The repeated measures design included 323 resting-state functional magnetic resonance imaging time series and measures of subjective effects in 36 healthy male volunteers. Four different pharmacological challenges with ethanol, morphine, Δ(9)-tetrahydrocannabinol, and ketamine (12 subjects per drug) were applied. A set of 10 "template" resting-state networks was used to determine individual connectivity maps. Linear regression was used for each individual subject to relate these connectivity maps to three clusters of drug-induced subjective psychomimetic effects ("perception," "relaxation," and "dysphoria") as measured with visual analogue scales. Group analysis showed that the subjective effects of perception correlated significantly across drugs with the connectivity of the posterior cingulate cortex and precentral gyrus with the sensorimotor network (p < 0.005, corrected). No significant correlations were found for relaxation or dysphoria. The posterior cingulate cortex has a role in visuospatial evaluation and the precentral gyrus has been associated with auditory hallucinations. Both the posterior cingulate cortex and the precentral gyrus show changes in activation in patients with schizophrenia, which can be related to the severity of positive symptoms (i.e., hallucinations and delusions), and have previously been related to changes induced by psychoactive drugs. The similarity of functional connectivity changes for drug-induced psychomimetic effects and symptoms of psychosis provides further support for the use of pharmacological challenges with psychomimetic drugs as models for psychosis.

Save the global: global signal connectivity as a tool for studying clinical populations with functional magnetic resonance imaging

The global signal is commonly removed from resting-state data, as it was presumed to reflect physiological noise. However, removal of the global signal is now under debate, as this signal may reflect important neuronal components, and its removal may introduce artifacts into the data. Here, we show that the functional connectivity (FC) of the global signal is of functional relevance, as it differentiates between schizophrenia patients and healthy controls during rest. We also demonstrate that other reported findings related to various clinical populations may actually reflect alternations in global signal FC. The evidence of the clinical relevance of the global signal propose its usage as a research tool, and extend previously reported perils of global signal removal in resting-state data of clinical populations.

Esophageal acid stimulation alters insular cortex functional connectivity in gastroesophageal reflux disease

BACKGROUND

The insula plays a significant role in the interoceptive processing of visceral stimuli. We have previously shown that gastroesophageal reflux disease (GERD) patients have increased insular cortex activity during esophageal stimulation, suggesting a sensitized esophago-cortical neuraxis. However, information regarding the functional connectivity (FC) of the insula during visceral stimulation is lacking. The primary aim of this study was to investigate the FC of insular subregions during esophageal acid stimulation.

METHODS

Functional imaging data were obtained from 12 GERD patients and 14 healthy subjects during four steady state conditions: (i) presence of transnasal esophageal catheter (pre-infusion); (ii) neutral solution; (iii) acid infusion; (iv) presence of transnasal esophageal catheter following infusions (post-infusion). The insula was parcellated into six regions of interest. FC maps between each insular ROI and interoceptive regions were created. Differences in FC between GERD patients and healthy subjects were determined across the 4 study conditions.

KEY RESULTS

All GERD patients experienced heartburn during and after esophageal acidification. Significant differences between GERD patients and healthy subjects were seen in: (i) insula-thalamic FC (neutral solution infusion, acid infusion, post-infusion); (ii) insula-amygdala FC (acid infusion, post-infusion); (iii) insula-hippocampus and insula-cingulate FC (post-infusion).

CONCLUSIONS & INFERENCES

Esophageal stimulation in GERD patients revealed significant insular cortex FC differences with regions involved in viscerosensation and interoception. The results of our study provide further evidence that the insula, located at the transition of afferent physiologic information to human feelings, is essential for both visceral homeostasis and the experience of heartburn in GERD patients.

Altered functional connectivity within the central reward network in overweight and obese women

Background/Objectives:

Neuroimaging studies in obese subjects have identified abnormal activation of key regions of central reward circuits, including the nucleus accumbens (NAcc), in response to food-related stimuli. We aimed to examine whether women with elevated body mass index (BMI) show structural and resting state (RS) functional connectivity alterations within regions of the reward network.

Subjects/Methods:

Fifty healthy, premenopausal women, 19 overweight and obese (high BMI=26–38 kg m⁻²) and 31 lean (BMI=19–25 kg m⁻²) were selected from the University of California Los Angeles' Oppenheimer Center for Neurobiology of Stress database. Structural and RS functional scans were collected. Group differences in grey matter volume (GMV) of the NAcc, oscillation dynamics of intrinsic brain activity and functional connectivity of the NAcc to regions within the reward network were examined.

Results:

GMV of the left NAcc was significantly greater in the high BMI group than in the lean group (P=0.031). Altered frequency distributions were observed in women with high BMI compared with lean group in the left NAcc (P=0.009) in a medium-frequency (MF) band, and in bilateral anterior cingulate cortex (ACC) (P=0.014, <0.001) and ventro-medial prefrontal cortex (vmPFC) (P=0.034, <0.001) in a high-frequency band. Subjects with high BMI had greater connectivity of the left NAcc with bilateral ACC (P=0.024) and right vmPFC (P=0.032) in a MF band and with the left ACC (P=0.03) in a high frequency band.

Conclusions:

Overweight and obese women in the absence of food-related stimuli show significant structural and functional alterations within regions of reward-related brain networks, which may have a role in altered ingestive behaviors.

Combining sudomotor nerve impulse estimation with fMRI to investigate the central sympathetic response to nausea

The skin conductance (SC) signal is one of the most important non-invasive indirect measures of autonomic outflow. Several mathematical models have been proposed in the literature to characterize specific SC features. In this work, we present a method for the estimation of central control of sudomotor nerve impulse (SMI) function using SC. The method is based on a differential formulation decomposed into two first order differential equations. We validate our estimation framework by applying it on an experimental protocol where eleven motion sickness-prone subjects were exposed to a nauseogenic visual stimulus while SC and fMRI signals were recorded. Our results show an expected significant increase in the mean amplitude of SMI peaks during the highest reported nausea, as well as a decreasing trend during recovery, which was not evident for skin conductance level. Importantly, SMI/fMRI analysis found a negative association between SMI and fMRI signal in orbitofrontal, dorsolateral prefrontal, and posterior insula cortices, consistent with previous studies correlating brain fMRI and microneurographic signals.

Abnormal autonomic and associated brain activities during rest in autism spectrum disorder

Autism spectrum disorders are associated with social and emotional deficits, the aetiology of which are not well understood. A growing consensus is that the autonomic nervous system serves a key role in emotional processes, by providing physiological signals essential to subjective states. We hypothesized that altered autonomic processing is related to the socio-emotional deficits in autism spectrum disorders. Here, we investigated the relationship between non-specific skin conductance response, an objective index of sympathetic neural activity, and brain fluctuations during rest in high-functioning adults with autism spectrum disorder relative to neurotypical controls. Compared with control participants, individuals with autism spectrum disorder showed less skin conductance responses overall. They also showed weaker correlations between skin conductance responses and frontal brain regions, including the anterior cingulate and anterior insular cortices. Additionally, skin conductance responses were found to have less contribution to default mode network connectivity in individuals with autism spectrum disorders relative to controls. These results suggest that autonomic processing is altered in autism spectrum disorders, which may be related to the abnormal socio-emotional behaviours that characterize this condition.

Advances in functional magnetic resonance imaging of the human brainstem

The brainstem is of tremendous importance for our daily survival, and yet the functional relationships between various nuclei, their projection targets, and afferent regulatory areas remain poorly characterized. The main reason for this lies in the sub-optimal performance of standard neuroimaging methods in this area. In particular, fMRI signals are much harder to detect in the brainstem region compared to cortical areas. Here we describe and validate a new approach to measure activation of brainstem nuclei in humans using standard fMRI sequences and widely available tools for statistical image processing. By spatially restricting an independent component analysis to an anatomically defined brainstem mask, we excluded those areas from the analysis that were strongly affected by physiological noise. This allowed us to identify for the first time intrinsic connectivity networks in the human brainstem and to map brainstem-cortical connectivity purely based on functionally defined regions of interest.

PHYCAA+: an optimized, adaptive procedure for measuring and controlling physiological noise in BOLD fMRI

The presence of physiological noise in functional MRI can greatly limit the sensitivity and accuracy of BOLD signal measurements, and produce significant false positives. There are two main types of physiological confounds: (1) high-variance signal in non-neuronal tissues of the brain including vascular tracts, sinuses and ventricles, and (2) physiological noise components which extend into gray matter tissue. These physiological effects may also be partially coupled with stimuli (and thus the BOLD response). To address these issues, we have developed PHYCAA+, a significantly improved version of the PHYCAA algorithm (Churchill et al., 2011) that (1) down-weights the variance of voxels in probable non-neuronal tissue, and (2) identifies the multivariate physiological noise subspace in gray matter that is linked to non-neuronal tissue. This model estimates physiological noise directly from EPI data, without requiring external measures of heartbeat and respiration, or manual selection of physiological components. The PHYCAA+ model significantly improves the prediction accuracy and reproducibility of single-subject analyses, compared to PHYCAA and a number of commonly-used physiological correction algorithms. Individual subject denoising with PHYCAA+ is independently validated by showing that it consistently increased between-subject activation overlap, and minimized false-positive signal in non gray-matter loci. The results are demonstrated for both block and fast single-event task designs, applied to standard univariate and adaptive multivariate analysis models.

Weighing brain activity with the balance: Angelo Mosso's original manuscripts come to light

Neuroimaging techniques, such as positron emission tomography and functional magnetic resonance imaging are essential tools for the analysis of organized neural systems in working and resting states, both in physiological and pathological conditions. They provide evidence of coupled metabolic and cerebral local blood flow changes that strictly depend upon cellular activity. In 1890, Charles Smart Roy and Charles Scott Sherrington suggested a link between brain circulation and metabolism. In the same year William James, in his introduction of the concept of brain blood flow variations during mental activities, briefly reported the studies of the Italian physiologist Angelo Mosso, a multifaceted researcher interested in the human circulatory system. James focused on Mosso's recordings of brain pulsations in patients with skull breaches, and in the process only briefly referred to another invention of Mosso's, the 'human circulation balance', which could non-invasively measure the redistribution of blood during emotional and intellectual activity. However, the details and precise workings of this instrument and the experiments Mosso performed with it have remained largely unknown. Having found Mosso's original manuscripts in the archives, we remind the scientific community of his experiments with the 'human circulation balance' and of his establishment of the conceptual basis of non-invasive functional neuroimaging techniques. Mosso unearthed and investigated several critical variables that are still relevant in modern neuroimaging such as the 'signal-to-noise ratio', the appropriate choice of the experimental paradigm and the need for the simultaneous recording of differing physiological parameters.

Correlated slow fluctuations in respiration, EEG, and BOLD fMRI

Low-frequency temporal fluctuations of physiological signals (<0.1 Hz), such as the respiration and cardiac pulse rate, occur naturally during rest and have been shown to be correlated with blood-oxygenation-level-dependent (BOLD) signal fluctuation. Such physiological signal modulations have been considered as sources of noise and their effects on BOLD signal are commonly removed in functional magnetic resonance imaging (fMRI) studies. However, possible neural correlates of the physiological fluctuations have not been considered nor examined in detail. In the present study we investigated this possibility by simultaneously acquiring electroencephalogram (EEG) with BOLD fMRI data, respiratory and cardiac waveforms in healthy human subjects at eyes-closed and eyes-open resting. We quantified the concurrent changes of the EEG power in the alpha frequency band, the respiration volume, and the cardiac pulse rate, then assessed the temporal correlations between alpha EEG power and physiological signal fluctuations. In addition, time-shifted time courses of alpha EEG power or physiological data were included as regressors to examine their correlations with the whole-brain BOLD fMRI signals. We observed a significant correlation between alpha EEG global field power and respiration, particularly at eyes-closed resting condition. Similar spatial patterns were observed between the correlation maps of BOLD with alpha EEG power and respiration, with negative correlations coinciding in the visual cortex, superior/middle temporal gyrus, inferior frontal gyrus, and inferior parietal lobule and positive correlations in the thalamus and caudate. Regressing out the physiological variations in the BOLD signal resulted in reduced correlation between BOLD and alpha EEG power. These results suggest a mutual link of neuronal origin between alpha EEG power, respiration, and BOLD signals.

Subject specific BOLD fMRI respiratory and cardiac response functions obtained from global signal

Subtle changes in either breathing pattern or cardiac pulse rate alter blood oxygen level dependent functional magnetic resonance imaging signal (BOLD fMRI). This is problematic because such fluctuations could possibly not be related to underlying neuronal activations of interest but instead the source of physiological noise. Several methods have been proposed to eliminate physiological noise in BOLD fMRI data. One such method is to derive a template based on average multi-subject data for respiratory response function (RRF) and cardiac response function (CRF) by simultaneously utilizing an external recording of cardiac and respiratory waveforms with the fMRI. Standard templates can then be used to model, map, and remove respiration and cardiac fluctuations from fMRI data. Utilizing these does not, however, account for intra-subject variations in physiological response. Thus, performing a more individualized approach for single subject physiological noise correction becomes more desirable, especially for clinical purposes. Here we propose a novel approach that employs subject-specific RRF and CRF response functions obtained from the whole brain or brain tissue-specific global signals (GS). Averaging multiple voxels in global signal computation ensures physiological noise dominance over thermal and system noise in even high-spatial-resolution fMRI data, making the GS suitable for deriving robust estimations of both RRF and CRF for individual subjects. Using these individualized response functions instead of standard templates based on multi-subject averages judiciously removes physiological noise from the data, assuming that there is minimal neuronal contribution in the derived individualized filters. Subject-specific physiological response functions obtained from the GS better maps individuals' physiological characteristics.

Central nervous system involvement in the autonomic responses to psychological distress

Psychological distress can trigger acute coronary syndromes and sudden cardiac death in vulnerable patients. The primary pathophysiological mechanism that plays a role in stress-induced cardiac events involves the autonomic nervous system, particularly disproportional sympathetic activation and parasympathetic withdrawal. This article describes the relation between psychological distress and autonomic nervous system function, with a focus on subsequent adverse cardiovascular outcomes. The role of the central nervous system in these associations is addressed, and a systematic review is presented of studies examining the association between stress-induced central nervous system responses measured by neuroimaging techniques and autonomic nervous system activation. Results of the systematic review indicate that the primary brain areas involved in the autonomic component of the brain-heart association are the insula, medial prefrontal cortex, and cerebellum (based on 121 participants across three studies that fitted the inclusion criteria). Other areas involved in stress-induced autonomic modulation are the (anterior) cingulate cortex, parietal cortex, somatomotor cortex/precentral gyrus, and temporal cortex. The interaction between central and autonomic nervous system responses may have implications for further investigations of the brain-heart associations and mechanisms by which acute and chronic psychological distress increase the risk of myocardial infarction, cardiac arrhythmias, and sudden cardiac death.

Functional and developmental significance of amplitude variance asymmetry in the BOLD resting-state signal

It is known that the brain's resting-state activity (RSA) is organized in low frequency oscillations that drive network connectivity. Recent research has also shown that elements of RSA described by high-frequency and nonoscillatory properties are non-random and functionally relevant. Motivated by this research, we investigated nonoscillatory aspects of the blood-oxygen-level-dependent (BOLD) RSA using a novel method for characterizing subtle fluctuation dynamics. The metric that we develop quantifies the relative variance of the amplitude of local-maxima and local-minima in a BOLD time course (amplitude variance asymmetry; AVA). This metric reveals new properties of RSA activity, without relying on connectivity as a descriptive tool. We applied the AVA analysis to data from 3 different participant groups (2 adults, 1 child) collected from 3 different centers. The analyses show that AVA patterns a) identify 3 types of RSA profiles in adults' sensory systems b) differ in topology and pattern of dynamics in adults and children, and c) are stable across magnetic resonance scanners. Furthermore, children with higher IQ demonstrated more adult-like AVA patterns. These findings indicate that AVA reflects important and novel dimensions of brain development and RSA.

Spontaneous brain activity relates to autonomic arousal

Although possible sources and functions of the resting-state networks (RSNs) of the brain have been proposed, most evidence relies on circular logic and reverse inference. We propose that autonomic arousal provides an objective index of psychophysiological states during rest that may also function as a driving source of the activity and connectivity of RSNs. Recording blood oxygenation level-dependent (BOLD) signal using functional magnetic resonance imaging and skin conductance simultaneously during rest in human subjects, we found that the spontaneous fluctuations of BOLD signals in key nodes of RSNs are associated with changes in nonspecific skin conductance response, a sensitive psychophysiological index of autonomic arousal. Our findings provide evidence of an important role for the autonomic nervous system to the spontaneous activity of the brain during "rest."

The impact of "physiological correction" on functional connectivity analysis of pharmacological resting state fMRI

Growing interest in pharmacological resting state fMRI (RSfMRI) necessitates developing standardized and robust analytical approaches that are insensitive to spurious correlated physiological signals. However, in pharmacological experiments physiological variations constitute an important aspect of the pharmacodynamic/pharmacokinetic profile of drug action; therefore retrospective corrective methods that discard physiological signals as noise may not be suitable. Previously, we have shown that template-based dual regression analysis is a sensitive method for model-free and objective detection of drug-specific effects on functional brain connectivity. In the current study, the robustness of this standard approach to physiological variations in a placebo controlled, repeated measures pharmacological RSfMRI study of morphine and alcohol in 12 healthy young men is tested. The impact of physiology-related variations on statistical inferences has been studied by: 1) modeling average physiological rates in higher level group analysis; 2) Regressing out the instantaneous respiration variation (RV); 3) applying retrospective image correction (RETROICOR) in the preprocessing stage; and 4) performing combined RV and heart rate correction (RVHRCOR) by regressing out physiological pulses convolved with canonical respiratory and cardiac hemodynamic response functions. Results indicate regional sensitivity of the BOLD signal to physiological variations, especially in the vicinity of large vessels, plus certain brain structures that are reported to be involved in physiological regulation, such as posterior cingulate, precuneus, medial prefrontal and insular cortices, as well as the thalamus, cerebellum and the brainstem. The largest impact of "correction" on final statistical test outcomes resulted from including the average respiration frequency and heart rate in the higher-level group analysis. Overall, the template-based dual regression method seems robust against physical noise that is corrected by RV regression or RETROICOR. However, convolving the RV and HR with canonical hemodynamic response functions caused a notable change in the BOLD signal variance, and in resting state connectivity estimates. The impact of RVHRCOR on statistical tests was limited to elimination of both morphine and alcohol effects related to the somatosensory network that consists of insula and cingulate cortex-important structures for autonomic regulation. Although our data do not warrant speculations about neuronal or vascular origins of these effects, these observations raise caution about the implications of physiological 'noise' and the risks of introducing false positives (e.g. increased white matter connectivity) by using generalized physiological correction methods in pharmacological studies. The obvious sensitivity of the posterior part of the default mode network to different correction schemes, underlines the importance of controlling for physiological fluctuations in seed-based functional connectivity analyses.