Physiological Noise Correction Research Articles

Central Autonomic Network Alterations in Anorexia Nervosa Following Peripheral Adrenergic Stimulation

BackgroundAnorexia nervosa (AN) is characterized by low body weight, disturbed eating, body image disturbance, anxiety, and interoceptive dysfunction. However, the neural processes underlying these dysfunctions in AN are unclear. This investigation combined an interoceptive pharmacological probe, the peripheral β-adrenergic agonist isoproterenol, with resting-state functional magnetic resonance imaging to examine whether individuals with AN relative to healthy comparison participants show dysregulated neural coupling in central autonomic network brain regions. MethodsResting-state functional magnetic resonance imaging was performed in 23 weight-restored female participants with AN and 23 age- and body mass index–matched healthy comparison participants before and after receiving isoproterenol infusions. Whole-brain functional connectivity (FC) changes were examined using central autonomic network seeds in the amygdala, anterior insular cortex, posterior cingulate cortex, and ventromedial prefrontal cortex after performing physiological noise correction procedures. ResultsRelative to healthy comparison participants, adrenergic stimulation caused widespread FC reductions in the AN group between central autonomic network regions and motor, premotor, frontal, parietal, and visual brain regions. Across both groups, these FC changes were inversely associated with trait anxiety (State-Trait Anxiety Inventory-Trait), trait depression (9-item Patient Health Questionnaire), and negative body image perception (Body Shape Questionnaire) measures, but not with changes in resting heart rate. These results were not accounted for by baseline group FC differences. ConclusionsWeight-restored females with AN show a widespread state-dependent disruption of signaling between central autonomic, frontoparietal, and sensorimotor brain networks that facilitate interoceptive representation and visceromotor regulation. Additionally, trait associations between central autonomic network regions and these other brain networks suggest that dysfunctional processing of interoceptive signaling may contribute to affective and body image disturbance in AN.

Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics.

Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

Unsupervised physiological noise correction of functional magnetic resonance imaging data using phase and magnitude information (PREPAIR).

Of the sources of noise affecting blood oxygen level-dependent functional magnetic resonance imaging (fMRI), respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultrahigh field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing, and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo-planar imaging data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR agree well with those from external devices and retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most used approach based on external recordings, RETROICOR. In comparison with widely used recording-free physiological noise correction tools-PESTICA and FIX, both performed in unsupervised mode-PREPAIR removed significantly more respiratory and cardiac noise than PESTICA, and achieved a larger increase in temporal signal-to-noise-ratio at both 3 and 7T.

Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI

Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus – alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease.

Dynamic states of resting‐state connectivity in patients with mild cognitive impairment

AbstractBackgroundResting‐state functional connectivity (rsFC) has demonstrated promise as a biomarker for Alzheimer’s disease. Traditional rsFC assumes that brain connectivity is unvarying during the entire acquisition window. However, recent studies show that brain connection patterns or states are dynamic and change within a few seconds. We modified existing analyses of dynamic functional connectivity to identify differences between patients with Mild Cognitive Impairment (MCI) and controls.Method50 MCI patients (22 males, 74±8 years old) and 55 controls (35 males, age=75±8 y.o.) from ADNI3 with T1 and fast BOLD fMRI (TR=600ms) images were analyzed. Preprocessing consisted of motion correction, despiking, physiological noise correction, and registration to MNI space. Sliding windows had a stride length of 1 TR and contained 50 TRs. Correlation matrices were generated between brain regions of the default mode (DMN), salience (Sal), dorsal attention (DAN), and frontoparietal (FPN) networks. The data was then harmonized using ComBat to remove scanner‐related differences. Distinct brain connectivity patterns or states were extracted with Davies‐Bouldin optimized k‐means clustering. Dwell time (i.e., the number of TRs for which a state is sustained) and transition probability (i.e., the probability of changing state) were calculated. Significant differences were assessed using a t‐test without correcting for multiple comparisons.ResultWe identified eight states (Figure 1). No significant differences were discovered in dwell time (Figure 2). Six transition probabilities had p<0.05 (Figure 3). These included the transition from 2 to 5 (p=0.03), 2 to 6 (p=0.04), 2 to 7 (p=0.04), 4 to 3 (p=0.005), 6 to 3 (p=0.01), 8 to 4 (p=0.05), and 8 to 7 (p=0.05).ConclusionOur study showed MCI patients were less likely to transition from state 4 to 3 compared to controls. State 4 is characterized by positive rsFC within the DMN and FPN as well as between the networks. Compared to state 4, state 3 had decreased rsFC within the DMN and FPN and between the networks. State 3 also shows increased positive rsFC within and between Sal and DAN. Interaction between the 4 networks has been implicated in AD. The inability to perform this transition could explain cognitive deficits observed in MCI.

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.

Mapping dependencies of BOLD signal change to end-tidal CO2: Linear and nonlinear modeling, and effect of physiological noise correction

BackgroundDisentangling physiological noise and signal of interest is a major issue when evaluating BOLD-signal changes in response to breath holding. Currently-adopted approaches for retrospective noise correction are general-purpose, and have non-negligible effects in studies on hypercapnic challenges. New methodWe provide a novel approach to the analysis of specific and non-specific BOLD-signal changes related to end-tidal CO2 (PETCO2) in breath-hold fMRI studies. Multiple-order nonlinear predictors for PETCO2 model a region-dependent nonlinear input-output relationship hypothesized in literature and possibly playing a crucial role in disentangling noise. We explore Retrospective Image-based Correction (RETROICOR) effects on the estimated BOLD response, applying our analysis both with and without RETROICOR and analyzing the linear and non-linear correlation between PETCO2 and RETROICOR regressors. ResultsThe RETROICOR model of noise related to respiratory activity correlated with PETCO2 both linearly and non-linearly. The correction affected the shape of the estimated BOLD response to hypercapnia but allowed to discard spurious activity in ventricles and white matter. Activation clusters were best detected using non-linear components in the BOLD response model. Comparison with existing methodWe evaluated the side-effects of standard physiological noise correction procedure, tailoring our analysis on challenging understudied brainstem and subcortical regions. Our novel approach allowed to characterize delays and non-linearities in BOLD response. ConclusionsRETROICOR successfully avoided false positives, still broadly affecting the estimated non-linear BOLD responses. Non-linearities in the model better explained CO2-related BOLD signal fluctuations. The necessity to modify the standard procedure for physiological-noise correction in breath-hold studies was addressed, stating its crucial importance.

A Hilbert-based method for processing respiratory timeseries

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline.Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).

Functional imaging of the brainstem during visually-guided motor control reveals visuomotor regions in the pons and midbrain

Integrating visual information for motor output is an essential process of visually-guided motor control. The brainstem is known to be a major center involved in the integration of sensory information for motor output, however, limitations of functional imaging in humans have impaired our knowledge about the individual roles of sub-nuclei within the brainstem. Thus, the bulk of our knowledge surrounding the function of the brainstem is based on anatomical and behavioral studies in non-human primates, cats, and rodents, despite studies demonstrating differences in the organization of visuomotor processing between mammals. fMRI studies in humans have examined activity related to visually-guided motor tasks, however, few have done so while controlling for both force without visual feedback activity and visual stimuli without force activity. Of the studies that have controlled for both conditions, none have reported brainstem activity. Here, we employed a novel fMRI paradigm focused on the brainstem and cerebellum to systematically investigate the hypothesis that the pons and midbrain are critical for the integration of visual information for motor control. Visuomotor activity during visually-guided pinch-grip force was measured while controlling for force without visual feedback activity and visual stimuli without force activity in healthy adults. Using physiological noise correction and multiple task repetitions, we demonstrated that visuomotor activity occurs in the inferior portion of the basilar pons and the midbrain. These findings provide direct evidence in humans that the pons and midbrain support the integration of visual information for motor control. We also determined the effect of physiological noise and task repetitions on the visuomotor signal that will be useful in future studies of neurodegenerative diseases affecting the brainstem.

A retrospective physiological noise correction method for oscillating steady-state imaging.

Oscillating steady-state imaging (OSSI) is an SNR-efficient steady-state sequence with sensitivity suitable for FMRI. Due to the frequency sensitivity of the signal, respiration- and drift-induced field changes can create unwanted signal fluctuations. This study aims to address this issue by developing retrospective signal correction methods that utilize OSSI signal properties to denoise task-based OSSI FMRI experiments. A retrospective denoising approach was developed that leverages the unique signal properties of OSSI to perform denoising without a manually specified noise region of interest and works with both voxel timecourses (oscillating steady-state correction [OSSCOR]) or FID timecourses (F-OSSCOR). Simulations were performed to estimate the number of principal components optimal for denoising. In vivo experiments at 3 T field strength were conducted to compare the performance of proposed methods against a standard principal component analysis-based method, measured using mean t score within an region of interest, number of activations, and mean temporal SNR. Correction using OSSCOR was significantly better than the standard method in all metrics. Correction using F-OSSCOR was not significantly different from the standard method using an equal number of principal components. Increasing the number of OSSCOR principal components decreased activation strength and increased the number of suspected false positives. However, increasing the number of principal components in F-OSSCOR increased activation strength with little to no increase in false activation. Both OSSCOR and F-OSSCOR substantially reduce physiological noise components and increase temporal SNR, improving the functional results of task-based OSSI functional experiments. F-OSSCOR demonstrates a proof of concept utilization of coil-localized FID signal information for physiological noise correction.

Heart Rate and Respiration Affect the Functional Connectivity of Default Mode Network in Resting-State Functional Magnetic Resonance Imaging.

A growing number of brain imaging studies show functional connectivity (FC) between regions during emotional and cognitive tasks in humans. However, emotions are accompanied by changes in physiological parameters such as heart rate and respiration. These changes may affect blood oxygen level-dependent signals, as well as connectivity between brain areas. This study aimed to clarify the effects of physiological noise on the connectivity between areas related to the default mode network using resting-state functional magnetic resonance imaging (rs-fMRI). Healthy adult volunteers (age range: 19–51 years, mean age: 26.9 ± 9.1 years, 8 males and 8 females) underwent rs-fMRI for 10 min using a clinical 3T scanner (MAGNETOM Trio A Tim System, Siemens) with simultaneously recorded respiration and cardiac output. Physiological noise signals were subsequently removed from the acquired fMRI data using the DRIFTER toolbox. Image processing and analysis of the FC between areas related to the default mode network were performed using DPARSF. Network-Based Statistic (NBS) analysis of the functional connectome of the DMN and DMN-related area was used to perform three groups of comparison: without physiological noise correction, with cardiac noise correction, and with cardiac and respiratory noise correction. NBS analysis identified 36 networks with significant differences in three conditions in FC matrices. Post hoc comparison showed no differences between the three conditions, indicating that all three had the same networks. Among the 36 networks, strength of FC of 8 networks was modified under physiological noise correction. Connectivity between left and right anterior medial frontal regions increased strength of connectivity. These areas are located on the medial cerebral hemisphere, close to the sagittal sinus and arteries in the cerebral hemispheres, suggesting that medial frontal areas may be sensitive to cardiac rhythm close to arteries. The other networks observed temporal regions and showed a decrease in their connectivity strength by removing physiological noise, indicating that physiological noise, especially respiration, may be sensitive to BOLD signal in the temporal regions during resting state. Temporal lobe was highly correlated with anxiety-related respiration changes (Masaoka and Homma, 2000), speech processing, and respiratory sensation. These factors may affect the rs-fMRI signaling sensitivity.

Improving sensitivity, specificity, and reproducibility of individual brainstem activation

Functional imaging of the brainstem may open new avenues for clinical diagnostics. However, for reliable assessments of brainstem activation, further efforts improving signal quality are needed. Six healthy subjects performed four repeated functional magnetic resonance imaging (fMRI) sessions on different days with jaw clenching as a motor task to elicit activation in the trigeminal motor nucleus. Functional images were acquired with a 7 T MR scanner using an optimized multiband EPI sequence. Activation measures in the trigeminal nucleus and a control region were assessed using different physiological noise correction methods (aCompCor and RETROICOR-based approaches with variable numbers of regressors) combined with cerebrospinal fluid or brainstem masking. Receiver-operating characteristic analyses accounting for sensitivity and specificity, activation overlap analyses to estimate the reproducibility between sessions, and intraclass correlation analyses (ICC) for testing reliability between subjects and sessions were used to systematically compare the physiological noise correction approaches. Masking the brainstem led to increased activation in the target ROI and resulted in higher values for the area under the curve (AUC) as a combined measure for sensitivity and specificity. With the highest values for AUC, activation overlap, and ICC, the most favorable physiological noise correction method was to control for the cerebrospinal fluid time series (aCompCor with one regressor). Brainstem motor nuclei activation can be reliably identified using high-field fMRI with optimized acquisition and processing strategies—even on single-subject level. Applying specific physiological noise correction methods improves reproducibility and reliability of brainstem activation encouraging future clinical applications.

On the Use of Linear-Modelling-based Algorithms for Physiological Noise Correction in fMRI Studies of the Central Breathing Control.

A full characterization of the physiological behavior of human central chemoreceptors through fMRI is crucial to understand the pathophysiology of central abnormal breathing patterns. In this scenario, physiological noise and activity of interest may be naturally correlated. Here, we examined the adequacy of linear-modelling-based retrospective physiological noise correction for studies of the central breathing control. We focused on the relationship between a nonlinear model of BOLD response, hypothesized to describe neuronal specific activity, and noise modelled by correction algorithms. Analyses were performed on fMRI acquisitions from healthy subjects during a breath hold task. A general linear model including static nonlinearities in the response to end-tidal CO2 was applied to data preprocessed both with and without physiological noise correction. Relations between physiological noise and PETCO2 were explored both with linear and nonlinear measures. Lastly, parametric maps of noise spatial distribution were extracted. Our results evidenced that correction algorithms based on linear modelling remove components that are both linearly and nonlinearly related to end-tidal CO2, whereas uncorrected data showed spurious activations in regions outside gray matter. Thus, despite a correction step is fundamental, these algorithms are shown to be over-conservative approaches to noise correction and need to be adapted to the specific purpose.

Temporal dynamics of the pharmacological MRI response to subanaesthetic ketamine in healthy volunteers: A simultaneous EEG/fMRI study

Pharmacological magnetic resonance imaging has been used to investigate the neural effects of subanaesthetic ketamine in healthy volunteers. However, the effect of ketamine has been modelled with a single time course and without consideration of physiological noise. This study aimed to investigate ketamine-induced alterations in resting neural activity using conventional pharmacological magnetic resonance imaging analysis techniques with physiological noise correction, and a novel analysis utilising simultaneously recorded electroencephalography data. Simultaneous electroencephalography/functional magnetic resonance imaging and physiological data were collected from 30 healthy male participants before and during a subanaesthetic intravenous ketamine infusion. Consistent with previous literature, we show widespread cortical blood-oxygen-level dependent signal increases and decreased blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex following ketamine. However, the latter effect was attenuated by the inclusion of motion regressors and physiological correction in the model. In a novel analysis, we modelled the pharmacological magnetic resonance imaging response with the power time series of seven electroencephalography frequency bands. This showed evidence for distinct temporal time courses of neural responses to ketamine. No electroencephalography power time series correlated with decreased blood-oxygen-level dependent signal in the subgenual anterior cingulate cortex. We suggest the decrease in blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex typically seen in the literature is the result of physiological noise, in particular cardiac pulsatility. Furthermore, modelling the pharmacological magnetic resonance imaging response with a single temporal model does not completely capture the full spectrum of neuronal dynamics. The use of electroencephalography regressors to model the response can increase confidence that the pharmacological magnetic resonance imaging is directly related to underlying neural activity.

The Use of Physiological Signals in Brainstem/Midbrain fMRI.

Brainstem and midbrain nuclei are closely linked to cognitive performance and autonomic function. To advance the localization in this area, precise functional imaging is fundamental. In this study, we used a sophisticated fMRI technique as well as physiological recordings to investigate the involvement of brainstem/midbrain nuclei in cognitive control during a Stroop task. The temporal signal-to-noise ratio (tSNR) increased due to physiological noise correction (PNC) especially in regions adjacent to arteries and cerebrospinal fluid. Within the brainstem/cerebellum template an average tSNR of 68 ± 16 was achieved after the simultaneous application of a high-resolution fMRI, specialized co-registration, and PNC. The analysis of PNC data revealed an activation of the substantia nigra in the Stroop interference contrast whereas no significant results were obtained in the midbrain or brainstem when analyzing uncorrected data. Additionally, we found that pupil size indicated the level of cognitive effort. The Stroop interference effect on pupillary responses was correlated to the effect on reaction times (R2 = 0.464, p < 0.05). When Stroop stimuli were modulated by pupillary responses, we observed a significant activation of the LC in the Stroop interference contrast. Thus, we demonstrated the beneficial effect of PNC on data quality and statistical results when analyzing neuronal responses to a cognitive task. Parametric modulation of task events with pupillary responses improved the model of LC BOLD activations in the Stroop interference contrast.

Improvement of reproducibility in quantitative susceptibility mapping (QSM) and transverse relaxation rates ( ) after physiological noise correction.

Numerous studies have suggested that quantitative susceptibility mapping (QSM) and transverse relaxation rates ( ) are useful to monitor neurological diseases. For clinical use of QSM and , reproducibility is an important feature. However, respiration-induced local magnetic field variation makes artifacts in gradient echo-based images and reduces the reproducibility of QSM and . To investigate the improvement of reproducibility of QSM and after the correction of respiration-induced field variation, and to assess the effect of varying types of the region of interest (ROI) analysis on reproducibility. Reproducibility study. Ten controls. 3T/multiecho gradient echo sequence. Intrascan reproducibility of QSM and was investigated in ROIs before and after the respiration correction. Reproducibility was obtained by the square of voxel-wise correlation coefficients between scans. A paired t-test was performed for comparison between before and after the respiration correction and between QSM and . Based on the ROI analysis, reproducibility increased after the respiration correction. Reproducibility in the white matter (11.89% increased in QSM and 23.38% in , P = 0.009 and 0.024, respectively) and deep gray matter (5.50% increased in QSM and 13.96% in , P = 0.024 and 0.019, respectively) increased significantly after the respiration correction. Reproducibility of was higher than that of QSM in the whole brain and cortical gray matter, while QSM maps showed higher reproducibility than in the white matter and deep gray matter. Respiration-induced error correction significantly improved reproducibility in QSM and mapping. QSM and mapping showed a different level of reproducibility depending on the types of ROI analysis. 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

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 7weeks. 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.

Retrospective Correction of Physiological Noise: Impact on Sensitivity, Specificity, and Reproducibility of Resting-State Functional Connectivity in a Reading Network Model.

It is well accepted that physiological noise (PN) obscures the detection of neural fluctuations in resting-state functional connectivity (rsFC) magnetic resonance imaging. However, a clear consensus for an optimal PN correction (PNC) methodology and how it can impact the rsFC signal characteristics is still lacking. In this study, we probe the impact of three PNC methods: RETROICOR: (Glover et al., 2000 ), ANATICOR: (Jo et al., 2010 ), and RVTMBPM: (Bianciardi et al., 2009 ). Using a reading network model, we systematically explore the effects of PNC optimization on sensitivity, specificity, and reproducibility of rsFC signals. In terms of specificity, ANATICOR was found to be effective in removing local white matter (WM) fluctuations and also resulted in aggressive removal of expected cortical-to-subcortical functional connections. The ability of RETROICOR to remove PN was equivalent to removal of simulated random PN such that it artificially inflated the connection strength, thereby decreasing sensitivity. RVTMBPM maintained specificity and sensitivity by balanced removal of vasodilatory PN and local WM nuisance edges. Another aspect of this work was exploring the effects of PNC on identifying reading group differences. Most PNC methods accounted for between-subject PN variability resulting in reduced intersession reproducibility. This effect facilitated the detection of the most consistent group differences. RVTMBPM was most effective in detecting significant group differences due to its inherent sensitivity to removing spatially structured and temporally repeating PN arising from dense vasculature. Finally, results suggest that combining all three PNC resulted in "overcorrection" by removing signal along with noise.

Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T.

Physiological noise often dominates the blood-oxygen level-dependent (BOLD) signal fluctuations in high-field functional MRI (fMRI) data. Therefore, to optimize fMRI protocols, it becomes crucial to investigate how physiological signal fluctuations impact various acquisition and reconstruction schemes at different acquisition speeds. In particular, further differences can arise between 2D and 3D fMRI acquisitions due to different encoding strategies, thereby impacting fMRI sensitivity in potentially significant ways. The amount of physiological noise to be removed from the BOLD fMRI signal acquired at 7 T was quantified for different sampling rates (repetition time from 3300 to 350 ms, acceleration 1 to 8) and techniques dedicated to fast fMRI (simultaneous multislice echo planar imaging [EPI] and 3D EPI). Resting state fMRI (rsfMRI) performances were evaluated using temporal signal-to-noise ratio (tSNR) and network characterization based on seed correlation and independent component analysis. Overall, acceleration enhanced tSNR and rsfMRI metrics. 3D EPI benefited the most from physiological noise removal at long repetition times. Differences between 2D and 3D encoding strategies disappeared at high acceleration factors (6- to 8-fold). After physiological noise correction, 2D- and 3D-accelerated sequences provide similar performances at high fields, both in terms of tSNR and resting state network identification and characterization. Magn Reson Med 78:888-896, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects

Several strategies have been proposed to model and remove physiological noise from resting-state fMRI (rs-fMRI) data, particularly at ultrahigh fields (7 T), including contributions from respiratory volume (RV) and heart rate (HR) signal fluctuations. Recent studies suggest that these contributions are highly variable across subjects and that physiological noise correction may thus benefit from optimization at the subject or even voxel level. Here, we systematically investigated the impact of the degree of spatial specificity (group, subject, newly proposed cluster, and voxel levels) on the optimization of RV and HR models. For each degree of spatial specificity, we measured the fMRI signal variance explained (VE) by each model, as well as the functional connectivity underlying three well-known resting-state networks (RSNs) obtained from the fMRI data after removal of RV+HR contributions. Whole-brain, high-resolution rs-fMRI data were acquired from twelve healthy volunteers at 7 T, while simultaneously recording their cardiac and respiratory signals. Although VE increased with spatial specificity up to the voxel level, the accuracy of functional connectivity measurements improved only up to the cluster level, and subsequently decreased at the voxel level. This suggests that voxelwise modeling over-fits to local fluctuations with no physiological meaning. In conclusion, our results indicate that 7 T rs-fMRI connectivity measurements improve if a cluster-based physiological noise correction approach is employed in order to take into account the individual spatial variability in the HR and RV contributions.