Patients Achieving 90°/45°/0° Intraoperative Stability Do Not Require Hip Precautions Following Posterior Approach Total Hip Arthroplasty: A Prospective Randomized Study.

BACKGROUND: Hip precautions are traditionally employed after posterior total hip arthroplasty (THA). The primary purpose was to investigate the necessity of hip precautions after posterior approach THA. We hypothesized that eliminating precautions in patients that achieved appropriate intraoperative stability would not increase the dislocation rate. METHODS: Randomized controlled trial of 346 consecutive eligible patients undergoing primary THA with a mean follow-up of 2.3 years (range 11 months to 3.7 years). EXCLUSION CRITERIA: lumbar fusion, scoliosis, abductor insufficiency, inability to achieve intraoperative stability with combined 90° flexion and 45° internal rotation in 0° adduction. Fisher's exact test was used to compare dislocation rates between the hip precaution (HP) control group and no hip precaution (NP) study group. In addition, Mann-Whitney U test was used to compare differences in HOOS JR scores at 2, 6, 12 weeks between groups. RESULTS: The dislocation rate was not increased in the NP (0/172: 0%) group compared to the HP group 4/174 (2.29%) (P = .418). All dislocations occurred in the precautions group, two of which required revision. There were no differences in mean HOOS Jr. scores at any 2, 6, or 12 weeks (P > .05 at all timepoints) (secondary outcome). CONCLUSION: Eliminating hip precautions in patients undergoing posterior approach THA that achieve 90°/45°/0° intraoperative stability does not increase the rate of dislocation. In fact, every dislocation occurred in patients receiving hip precautions. Short-term patient-reported outcome measures were not affected by hip precautions. Surgeons may discontinue the use of hip precautions as the standard of care in patients achieving 90°/45°/0° stability.

Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-Influence of default mode network.

The ventromedial prefrontal cortex (vmPFC) is involved in regulation of negative emotion and decision-making, emotional and behavioral control, and active resilient coping. This pilot study examined the feasibility of training healthy subjects (n = 27) to self-regulate the vmPFC activity using a real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf). Participants in the experimental group (EG, n = 18) were provided with an ongoing vmPFC hemodynamic activity (rtfMRI-nf signal represented as variable-height bar). Individuals were instructed to raise the bar by self-relevant value-based thinking. Participants in the control group (CG, n = 9) performed the same task; however, they were provided with computer-generated sham neurofeedback signal. Results demonstrate that (a) both the CG and the EG show a higher vmPFC fMRI signal at the baseline than during neurofeedback training; (b) no significant positive training effect was seen in the vmPFC across neurofeedback runs; however, the medial prefrontal cortex, middle temporal gyri, inferior frontal gyri, and precuneus showed significant decreasing trends across the training runs only for the EG; (c) the vmPFC rtfMRI-nf signal associated with the fMRI signal across the default mode network (DMN). These findings suggest that it may be difficult to modulate a single DMN region without affecting other DMN regions. Observed decreased vmPFC activity during the neurofeedback task could be due to interference from the fMRI signal within other DMN network regions, as well as interaction with task-positive networks. Even though participants in the EG did not show significant positive increase in the vmPFC activity among neurofeedback runs, they were able to learn to accommodate the demand of self-regulation task to maintain the vmPFC activity with the help of a neurofeedback signal.

Real-time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm.

Real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG allows volitional modulation of BOLD activity of target brain regions and investigation of related electrophysiological activity. We applied this approach to study correlations between thalamic BOLD activity and alpha EEG rhythm. Healthy volunteers in the experimental group (EG, n = 15) learned to upregulate BOLD activity of the target region consisting of the mediodorsal (MD) and anterior (AN) thalamic nuclei using rtfMRI-nf during retrieval of happy autobiographical memories. Healthy subjects in the control group (CG, n = 14) were provided with a sham feedback. The EG participants were able to significantly increase BOLD activities of the MD and AN. Functional connectivity between the MD and the inferior precuneus was significantly enhanced during the rtfMRI-nf task. Average individual changes in the occipital alpha EEG power significantly correlated with the average MD BOLD activity levels for the EG. Temporal correlations between the occipital alpha EEG power and BOLD activities of the MD and AN were significantly enhanced, during the rtfMRI-nf task, for the EG compared to the CG. Temporal correlations with the alpha power were also significantly enhanced for the posterior nodes of the default mode network, including the precuneus/posterior cingulate, and for the dorsal striatum. Our findings suggest that the temporal correlation between the MD BOLD activity and posterior alpha EEG power is modulated by the interaction between the MD and the inferior precuneus, reflected in their functional connectivity. Our results demonstrate the potential of the rtfMRI-nf with simultaneous EEG for noninvasive neuromodulation studies of human brain function.

Amygdala real-time functional magnetic resonance imaging neurofeedback for major depressive disorder: A review.

Advances in imaging technologies have allowed for the analysis of functional magnetic resonance imaging data in real-time (rtfMRI), leading to the development of neurofeedback (nf) training. This rtfMRI-nf training utilizes functional magnetic resonance imaging (fMRI) tomographic localization capacity to allow a person to see and regulate the localized hemodynamic signal from his or her own brain. In this review, we summarize the results of several studies that have developed and applied neurofeedback training to healthy and depressed individuals with the amygdala as the neurofeedback target and the goal to increase the hemodynamic response during positive autobiographical memory recall. We review these studies and highlight some of the challenges and advances in developing an rtfMRI-nf paradigm for broader use in psychiatric populations. The work described focuses on our line of research aiming to develop the rtfMRI-nf into an intervention, and includes a discussion of the selection of a region of interest for feedback, selecting a control condition, behavioral and cognitive effects of training, and predicting which participants are most likely to respond well to training. While the results of these studies are encouraging and suggest the clinical potential of amygdala rtfMRI-nf in alleviating symptoms of major depressive disorder, larger studies are warranted to confirm its efficacy.

Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR).

Head motions during functional magnetic resonance imaging (fMRI) impair fMRI data quality and introduce systematic artifacts that can affect interpretation of fMRI results. Electroencephalography (EEG) recordings performed simultaneously with fMRI provide high-temporal-resolution information about ongoing brain activity as well as head movements. Recently, an EEG-assisted retrospective motion correction (E-REMCOR) method was introduced. E-REMCOR utilizes EEG motion artifacts to correct the effects of head movements in simultaneously acquired fMRI data on a slice-by-slice basis. While E-REMCOR is an efficient motion correction approach, it involves an independent component analysis (ICA) of the EEG data and identification of motion-related ICs. Here we report an automated implementation of E-REMCOR, referred to as aE-REMCOR, which we developed to facilitate the application of E-REMCOR in large-scale EEG-fMRI studies. The aE-REMCOR algorithm, implemented in MATLAB, enables an automated preprocessing of the EEG data, an ICA decomposition, and, importantly, an automatic identification of motion-related ICs. aE-REMCOR has been used to perform retrospective motion correction for 305 fMRI datasets from 16 subjects, who participated in EEG-fMRI experiments conducted on a 3T MRI scanner. Performance of aE-REMCOR has been evaluated based on improvement in temporal signal-to-noise ratio (TSNR) of the fMRI data, as well as correction efficiency defined in terms of spike reduction in fMRI motion parameters. The results show that aE-REMCOR is capable of substantially reducing head motion artifacts in fMRI data. In particular, when there are significant rapid head movements during the scan, a large TSNR improvement and high correction efficiency can be achieved. Depending on a subject's motion, an average TSNR improvement over the brain upon the application of aE-REMCOR can be as high as 27%, with top ten percent of the TSNR improvement values exceeding 55%. The average correction efficiency over the 305 fMRI scans is 18% and the largest achieved efficiency is 71%. The utility of aE-REMCOR on the resting state fMRI connectivity of the default mode network is also examined. The motion-induced position-dependent error in the DMN connectivity analysis is shown to be reduced when aE-REMCOR is utilized. These results demonstrate that aE-REMCOR can be conveniently and efficiently used to improve fMRI motion correction in large clinical EEG-fMRI studies.

Contrast enhancement by combining T1- and T2-weighted structural brain MR Images.

PURPOSE: In order to more precisely differentiate cerebral structures in neuroimaging studies, a novel technique for enhancing the tissue contrast based on a combination of T1-weighted (T1w) and T2-weighted (T2w) MRI images was developed. METHODS: The combined image (CI) was calculated as CI = (T1w - sT2w)/(T1w + sT2w), where sT2w is the scaled T2-weighted image. The scaling factor was calculated to adjust the gray- matter (GM) voxel intensities in the T2w image so that their median value equaled that of the GM voxel intensities in the T1w image. The image intensity homogeneity within a tissue and the discriminability between tissues in the CI versus the separate T1w and T2w images were evaluated using the segmentation by the FMRIB Software Library (FSL) and FreeSurfer (Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital, Boston, MA) software. RESULTS: The combined image significantly improved homogeneity in the white matter (WM) and GM compared to the T1w images alone. The discriminability between WM and GM also improved significantly by applying the CI approach. Significant enhancements to the homogeneity and discriminability also were achieved in most subcortical nuclei tested, with the exception of the amygdala and the thalamus. CONCLUSION: The tissue discriminability enhancement offered by the CI potentially enables more accurate neuromorphometric analyses of brain structures.

Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback.

Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG-fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI-EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI-EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation in the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI-EEG-nf. Our proof-of-concept results demonstrate the feasibility of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activities of the human brain. They suggest potential applications of rtfMRI-EEG-nf in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression.

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.

EEG-assisted retrospective motion correction for fMRI: E-REMCOR.

We propose a method for retrospective motion correction of fMRI data in simultaneous EEG-fMRI that employs the EEG array as a sensitive motion detector. EEG motion artifacts are used to generate motion regressors describing rotational head movements with millisecond temporal resolution. These regressors are utilized for slice-specific motion correction of unprocessed fMRI data. Performance of the method is demonstrated by correction of fMRI data from five patients with major depressive disorder, who exhibited head movements by 1-3mm during a resting EEG-fMRI run. The fMRI datasets, corrected using eight to ten EEG-based motion regressors, show significant improvements in temporal SNR (TSNR) of fMRI time series, particularly in the frontal brain regions and near the surface of the brain. The TSNR improvements are as high as 50% for large brain areas in single-subject analysis and as high as 25% when the results are averaged across the subjects. Simultaneous application of the EEG-based motion correction and physiological noise correction by means of RETROICOR leads to average TSNR enhancements as high as 35% for extended brain regions. These TSNR improvements are largely preserved after the subsequent fMRI volume registration and regression of fMRI motion parameters. The proposed EEG-assisted method of retrospective fMRI motion correction (referred to as E-REMCOR) can be applied to improve quality of fMRI data with severe motion artifacts and to reduce spurious correlations between the EEG and fMRI data caused by head movements. It does not require any specialized equipment beyond the standard EEG-fMRI instrumentation and can be applied retrospectively to any existing EEG-fMRI data set.

Spatiotemporal dynamics of the brain at rest--exploring EEG microstates as electrophysiological signatures of BOLD resting state networks.

Neuroimaging research suggests that the resting cerebral physiology is characterized by complex patterns of neuronal activity in widely distributed functional networks. As studied using functional magnetic resonance imaging (fMRI) of the blood-oxygenation-level dependent (BOLD) signal, the resting brain activity is associated with slowly fluctuating hemodynamic signals (~10s). More recently, multimodal functional imaging studies involving simultaneous acquisition of BOLD-fMRI and electroencephalography (EEG) data have suggested that the relatively slow hemodynamic fluctuations of some resting state networks (RSNs) evinced in the BOLD data are related to much faster (~100 ms) transient brain states reflected in EEG signals, that are referred to as "microstates". To further elucidate the relationship between microstates and RSNs, we developed a fully data-driven approach that combines information from simultaneously recorded, high-density EEG and BOLD-fMRI data. Using independent component analysis (ICA) of the combined EEG and fMRI data, we identified thirteen microstates and ten RSNs that are organized independently in their temporal and spatial characteristics, respectively. We hypothesized that the intrinsic brain networks that are active at rest would be reflected in both the EEG data and the fMRI data. To test this hypothesis, the rapid fluctuations associated with each microstate were correlated with the BOLD-fMRI signal associated with each RSN. We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These results extend our understanding of the electrophysiological signature of BOLD RSNs and demonstrate the intrinsic connection between the fast neuronal activity and slow hemodynamic fluctuations.

Integration of Simultaneous Resting-State Electroencephalography, Functional Magnetic Resonance Imaging, and Eye-Tracker Methods to Determine and Verify Electroencephalography Vigilance Measure.

Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects. Impact statement We revealed an association between electroencephalography frontal and occipital beta power (FOBP) and pupil size changes during an eyes-open resting state, which supports the conclusion that FOBP could serve as an objective measure of vigilance in healthy human subjects. The results were validated by using simultaneously recorded heart rate and functional magnetic resonance imaging (fMRI). Interestingly, independently verified heart rate changes can also provide an easy-to-determine measure of vigilance during resting-state fMRI. These findings have important implications for an analysis and interpretation of dynamic resting-state fMRI connectivity studies in health and disease.

Brain activity mediators of PTSD symptom reduction during real-time fMRI amygdala neurofeedback emotional training.

Self-regulation of brain activation with real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) is emerging as a promising treatment for psychiatric disorders. The association between the regulation and symptom reduction, however, has not been consistent, and the mechanisms underlying the symptom reduction remain poorly understood. The present study investigated brain activity mediators of the amygdala rtfMRI-nf training effect on combat veterans' PTSD symptom reduction. The training was designed to increase a neurofeedback signal either from the left amygdala (experimental group; EG) or from a control region not implicated in emotion regulation (control group; CG) during positive autobiographical memory recall. We employed a structural equation model mapping analysis to identify brain regions that mediated the effects of the rtfMRI-nf training on PTSD symptoms. Symptom reduction was mediated by low activation in the dorsomedial prefrontal cortex (DMPFC) and the middle cingulate cortex. There was a trend toward less activation in these regions for the EG compared to the CG. Low activation in the precuneus, the right superior parietal, the right insula, and the right cerebellum also mediated symptom reduction while their effects were moderated by the neurofeedback signal; a higher signal was linked to less effect on symptom reduction. This moderation was not specific to the EG. MDD comorbidity was associated with high DMPFC activation, which resulted in less effective regulation of the feedback signal. These results indicated that symptom reduction due to the neurofeedback training was not specifically mediated by the neurofeedback target activity, but broad regions were involved in the process.

Real-time fMRI amygdala neurofeedback positive emotional training normalized resting-state functional connectivity in combat veterans with and without PTSD: a connectome-wide investigation.

Self-regulation of brain activation using real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) is an emerging approach for treating mood and anxiety disorders. The effect of neurofeedback training on resting-state functional connectivity warrants investigation as changes in spontaneous brain activation could reflect the association between sustained symptom relief and brain alteration. We investigated the effect of amygdala-focused rtfMRI-nf training on resting-state functional connectivity in combat veterans with and without posttraumatic stress disorder (PTSD) who were trained to increase a feedback signal reflecting left amygdala activity while recalling positive autobiographical memories (Zotev et al., 2018). The analysis was performed in three stages: i) first, we investigated the connectivity in the left amygdala region; ii) next, we focused on the abnormal resting-state functional connectivity identified in our previous analysis of this data (Misaki et al., 2018); and iii) finally, we performed a novel data-driven longitudinal connectome-wide analysis. We introduced a longitudinal multivariate distance matrix regression (MDMR) analysis to comprehensively examine neurofeedback training effects beyond those associated with abnormal baseline connectivity. These comprehensive exploratory analyses suggested that abnormal resting-state connectivity for combat veterans with PTSD was partly normalized after the training. This included hypoconnectivities between the left amygdala and the left ventrolateral prefrontal cortex (vlPFC) and between the supplementary motor area (SMA) and the dorsal anterior cingulate cortex (dACC). The increase of SMA-dACC connectivity was associated with PTSD symptom reduction. Longitudinal MDMR analysis found a connectivity change between the precuneus and the left superior frontal cortex. The connectivity increase was associated with a decrease in hyperarousal symptoms. The abnormal connectivity for combat veterans without PTSD - such as hypoconnectivity in the precuneus with a superior frontal region and hyperconnectivity in the posterior insula with several regions - could also be normalized after the training. These results suggested that the rtfMRI-nf training effect was not limited to a feedback target region and symptom relief could be mediated by brain modulation in several regions other than in a feedback target area. While further confirmatory research is needed, the results may provide valuable insight into treatment effects on the whole brain resting-state connectivity.

Connectome-wide investigation of altered resting-state functional connectivity in war veterans with and without posttraumatic stress disorder.

Altered resting-state functional connectivity in posttraumatic stress disorder (PTSD) suggests neuropathology of the disorder. While seed-based fMRI connectivity analysis is often used for the studies, such analysis requires defining a seed location a priori, which restricts search scope and could bias findings toward presupposed areas. Recently, a comprehensive exploratory voxel-wise connectivity analysis, the connectome-wide association approach, has been introduced using multivariate distance matrix regression (MDMR) for resting-state functional connectivity analysis. The current study performed a connectome-wide investigation of resting-state functional connectivity for war veterans with and without PTSD compared to non-trauma-exposed healthy controls using MDMR. Thirty-five male combat veterans with PTSD (unmedicated), 18 male combat veterans without PTSD (veterans control, VC), and 28 age-matched non-trauma-exposed healthy males (NC) participated in a resting-state fMRI scan. MDMR analysis was used to identify between-groups differences in regions with altered connectivity. The identified regions were used as a seed for post-hoc functional connectivity analysis. The analysis revealed that PTSD patients had hypoconnectivity between the left lateral prefrontal regions and the salience network regions as well as hypoconnectivity between the parahippocampal gyrus and the visual cortex areas. Connectivity between the ventromedial prefrontal cortex and the middle frontal gyrus and between the parahippocampal gyrus and the anterior insula were negatively correlated with PTSD symptom severity. VC subjects also had altered functional connectivity compared to NC, including increased connectivity between the posterior insula and several brain regions and decreased connectivity between the precuneus region and several other brain areas. The decreased connectivity between the lateral prefrontal regions and the salience network regions in PTSD was consistent with previous reports that indicated lowered emotion-regulation function in these regions. The decreased connectivity between the parahippocampal gyrus and visual cortex supported the dual representation theory of PTSD, which suggests dissociation between sensory and contextual memory representations in PTSD. The theory also supposes that the precuneus is a region that triggers retrieval of sensory memory of traumatic events. The decreased connectivity at the precuneus for VC might be associated with suppressing such a process.

Altered task-based and resting-state amygdala functional connectivity following real-time fMRI amygdala neurofeedback training in major depressive disorder.

Background: We have previously shown that in participants with major depressive disorder (MDD) trained to upregulate their amygdala hemodynamic response during positive autobiographical memory (AM) recall with real-time fMRI neurofeedback (rtfMRI-nf) training, depressive symptoms diminish. Here, we assessed the effect of rtfMRI-nf on amygdala functional connectivity during both positive AM recall and rest. Method: The current manuscript consists of a secondary analysis on data from our published clinical trial of neurofeedback. Patients with MDD completed two rtfMRI-nf sessions (18 received amygdala rtfMRI-nf, 16 received control parietal rtfMRI-nf). One-week prior-to and following training participants also completed a resting-state fMRI scan. A GLM-based functional connectivity analysis was applied using a seed ROI in the left amygdala. We compared amygdala functional connectivity changes while recalling positive AMs from the baseline run to the final transfer run during rtfMRI-nf training, as well during rest from the baseline to the one-week follow-up visit. Finally, we assessed the correlation between change in depression scores and change in amygdala connectivity, as well as correlations between amygdala regulation success and connectivity changes. Results: Following training, amygdala connectivity during positive AM recall increased with widespread regions in the frontal and limbic network. During rest, amygdala connectivity increased following training within the fronto-temporal-limbic network. During both task and resting-state analyses, amygdala-temporal pole connectivity decreased. We identified increased amygdala-precuneus and amygdala-inferior frontal gyrus connectivity during positive memory recall and increased amygdala-precuneus and amygdala-thalamus connectivity during rest as functional connectivity changes that explained significant variance in symptom improvement. Amygdala-precuneus connectivity changes also explain a significant amount of variance in neurofeedback regulation success. Conclusions: Neurofeedback training to increase amygdala hemodynamic activity during positive AM recall increased amygdala connectivity with regions involved in self-referential, salience, and reward processing. Results suggest future targets for neurofeedback interventions, particularly interventions involving the precuneus.

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

BACKGROUND: In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. NEW METHOD: Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. RESULTS: The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. COMPARISON WITH EXISTING METHOD: The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. CONCLUSIONS: The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal.

Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD.

Posttraumatic stress disorder (PTSD) is a chronic and disabling neuropsychiatric disorder characterized by insufficient top-down modulation of the amygdala activity by the prefrontal cortex. Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging method with potential for modifying the amygdala-prefrontal interactions. We report the first controlled emotion self-regulation study in veterans with combat-related PTSD utilizing rtfMRI-nf of the amygdala activity. PTSD patients in the experimental group (EG, n = 20) learned to upregulate blood­oxygenation-level-dependent (BOLD) activity of the left amygdala (LA) using the rtfMRI-nf during a happy emotion induction task. PTSD patients in the control group (CG, n = 11) were provided with a sham rtfMRI-nf. The study included three rtfMRI-nf training sessions, and EEG recordings were performed simultaneously with fMRI. PTSD severity was assessed before and after the training using the Clinician-Administered PTSD Scale (CAPS). The EG participants who completed the study showed a significant reduction in total CAPS ratings, including significant reductions in avoidance and hyperarousal symptoms. They also exhibited a significant reduction in comorbid depression severity. Overall, 80% of the EG participants demonstrated clinically meaningful reductions in CAPS ratings, compared to 38% in the CG. No significant difference in the CAPS rating changes was observed between the groups. During the first rtfMRI-nf session, functional connectivity of the LA with the orbitofrontal cortex (OFC) and the dorsolateral prefrontal cortex (DLPFC) was progressively enhanced, and this enhancement significantly and positively correlated with the initial CAPS ratings. Left-lateralized enhancement in upper alpha EEG coherence also exhibited a significant positive correlation with the initial CAPS. Reduction in PTSD severity between the first and last rtfMRI-nf sessions significantly correlated with enhancement in functional connectivity between the LA and the left DLPFC. Our results demonstrate that the rtfMRI-nf of the amygdala activity has the potential to correct the amygdala-prefrontal functional connectivity deficiencies specific to PTSD.

Tracking resting state connectivity dynamics in veterans with PTSD.

Posttraumatic stress disorder (PTSD) is a trauma- and stressor-related disorder that may emerge following a traumatic event. Neuroimaging studies have shown evidence of functional abnormality in many brain regions and systems affected by PTSD. Exaggerated threat detection associated with abnormalities in the salience network, as well as abnormalities in executive functions involved in emotions regulations, self-referencing and context evaluation processing are broadly reported in PTSD. Here we aimed to investigate the behavior and dynamic properties of fMRI resting state networks in combat-related PTSD, using a novel, multimodal imaging approach. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) was employed to measure neurobiological brain activity among 36 veterans with combat-related PTSD and 20 combat-exposed veterans without PTSD. Based on the recently established method of measuring temporal-independent EEG microstates, we developed a novel strategy to integrate EEG and fMRI by quantifying the fast temporal dynamics associated with the resting state networks. We found distinctive occurrence rates of microstates associated with the dorsal default mode network and salience networks in the PTSD group as compared with control. Furthermore, the occurrence rate of the microstate for the dorsal default mode network was positively correlated with PTSD severity, whereas the occurrence rate of the microstate for the anterior salience network was negatively correlated with hedonic tone reported by participants with PTSD. Our findings reveal a novel aspect of abnormal network dynamics in combat-related PTSD and contribute to a better understanding of the pathophysiology of the disorder. Simultaneous EEG and fMRI will be a valuable tool in continuing to study the neurobiology underlying PTSD.

Real-Time Functional Magnetic Resonance Imaging Amygdala Neurofeedback Changes Positive Information Processing in Major Depressive Disorder.

BACKGROUND: In participants with major depressive disorder who are trained to upregulate their amygdalar hemodynamic responses during positive autobiographical memory recall with real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) training, depressive symptoms diminish. This study tested whether amygdalar rtfMRI-nf also changes emotional processing of positive and negative stimuli in a variety of behavioral and imaging tasks. METHODS: Patients with major depressive disorder completed two rtfMRI-nf sessions (18 received amygdalar rtfMRI-nf, 16 received control parietal rtfMRI-nf). One week before and following rtfMRI-nf training, participants performed tasks measuring responses to emotionally valenced stimuli including a backward-masking task, which measures the amygdalar hemodynamic response to emotional faces presented for traditionally subliminal duration and followed by a mask, and the Emotional Test Battery in which reaction times and performance accuracy are measured during tasks involving emotional faces and words. RESULTS: During the backward-masking task, amygdalar responses increased while viewing masked happy faces but decreased to masked sad faces in the experimental versus control group following rtfMRI-nf. During the Emotional Test Battery, reaction times decreased to identification of positive faces and during self-identification with positive words and vigilance scores increased to positive faces and decreased to negative faces during the faces dot-probe task in the experimental versus control group following rtfMRI-nf. CONCLUSIONS: rtfMRI-nf training to increase the amygdalar hemodynamic response to positive memories was associated with changes in amygdalar responses to happy and sad faces and improved processing of positive stimuli during performance of the Emotional Test Battery. These results may suggest that amygdalar rtfMRI-nf training alters responses to emotional stimuli in a manner similar to antidepressant pharmacotherapy.

Randomized Clinical Trial of Real-Time fMRI Amygdala Neurofeedback for Major Depressive Disorder: Effects on Symptoms and Autobiographical Memory Recall.

OBJECTIVE: Patients with depression show blunted amygdala hemodynamic activity to positive stimuli, including autobiographical memories. The authors examined the therapeutic efficacy of real-time functional MRI neurofeedback (rtfMRI-nf) training aimed at increasing the amygdala's hemodynamic response to positive memories in patients with depression. METHOD: In a double-blind, placebo-controlled, randomized clinical trial, unmedicated adults with depression (N=36) were randomly assigned to receive two sessions of rtfMRI-nf either from the amygdala (N=19) or from a parietal control region not involved in emotional processing (N=17). Clinical scores and autobiographical memory performance were assessed at baseline and 1 week after the final rtfMRI-nf session. The primary outcome measure was change in score on the Montgomery-Åsberg Depression Rating Scale (MADRS), and the main analytic approach consisted of a linear mixed-model analysis. RESULTS: In participants in the experimental group, the hemodynamic response in the amygdala increased relative to their own baseline and to the control group. Twelve participants in the amygdala rtfMRI-nf group, compared with only two in the control group, had a >50% decrease in MADRS score. Six participants in the experimental group, compared with one in the control group, met conventional criteria for remission at study end, resulting in a number needed to treat of 4. In participants receiving amygdala rtfMRI-nf, the percent of positive specific memories recalled increased relative to baseline and to the control group. CONCLUSIONS: rtfMRI-nf training to increase the amygdala hemodynamic response to positive memories significantly decreased depressive symptoms and increased the percent of specific memories recalled on an autobiographical memory test. These data support a role of the amygdala in recovery from depression.