Atypical Guillain-Barré Syndrome Presenting After COVID-19 Infection.

Guillain-Barré syndrome (GBS) is an acute autoimmune disease affecting the peripheral nervous system presenting as a symmetric, ascending polyneuropathy. The syndrome arises after a stimulus, such as infection or vaccination, and provokes an autoimmune response in the body. Common symptoms include rapidly progressive weakness in the extremities and generalized hyporeflexia or areflexia. However, GBS may have various presentations, which can make for a challenging diagnosis. We present a case of a 46-year-old female with asymmetric ascending weakness, paresthesias, and acute onset urinary retention occurring after Coronavirus Disease 2019 (COVID-19) infection. Of note, this patient did not present with albuminocytologic dissociation in cerebrospinal fluid (CSF) studies. The complex presentation of her symptoms prompted a diagnosis of atypical GBS. Her diagnosis was achieved through a series of diagnostic tests ruling out other etiologies, such as meningitis and spinal cord compression syndromes.

Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI.

Objective.Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR).Approach.The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs);n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database.Main results.We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data.Significance.APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.

Improved autoregressive model for correction of noise serial correlation in fast fMRI.

PURPOSE: In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction. METHODS: Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (ARAICc ) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth). RESULTS: The measured false positive characteristics agreed well with the theoretical curve when using the ARAICc , and the activation maps in the smoothed data matched the ground truth. The ARAICc showed improved performance than the fixed AR(p) method. CONCLUSION: The ARAICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the ARAICc correction in fast fMRI.

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.

Correction: Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls.

[This corrects the article DOI: 10.1371/journal.pone.0199144.].

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.

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.

Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls.

We employ a time-dependent Hurst analysis to identify EEG signals that differentiate between healthy controls and combat-related PTSD subjects. The Hurst exponents, calculated using a rescaled range analysis, demonstrate a significant differential response between healthy and PTSD samples which may lead to diagnostic applications. To overcome the non-stationarity of EEG data, we apply an appropriate window length wherein the EEG data displays stationary behavior. We then use the Hurst exponents for each channel as hypothesis test statistics to identify differences between PTSD cases and controls. Our study included a cohort of 12 subjects with half healthy controls. The Hurst exponent of the PTSD subjects is found to be significantly smaller than the healthy controls in channel F3. Our results indicate that F3 may be a useful channel for diagnostic applications of Hurst exponents in distinguishing PTSD and healthy subjects.

Interoception is associated with heartbeat-evoked brain potentials (HEPs) in adolescents.

Heartbeat-evoked brain potentials (HEPs) are an index of the cortical reflection of cardiac interoceptive signals. Studies which have examined interoception in adolescents with the use of HEPs are not known to the authors so far. This study investigated the function of the HEP as a marker of interoception in adolescents. EEG and ECG were recorded in 46 adolescents during a resting condition and during a heartbeat detection task. Participants were asked for confidence in their interoceptive accuracy during heartbeat perception. HEPs appeared during both conditions, showing maximal activity over frontocentral electrodes in the heartbeat condition, and highest activity over occipital locations in the resting condition. Interoceptive accuracy (IAc) was positively associated with the HEP at frontocentral locations only for the heartbeat condition. Interoceptive sensibility was not associated with the HEP. No significant association between IAc and interoceptive sensibility was revealed. Our results highlight the relevance of the HEP as a neural marker of interoception in adolescents. Its use as an indicator of vulnerability for affective, physical and mental dysfunctions during adolescence should be exploited in future studies.

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.

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.

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.

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.

Probing of susceptibility structures through the distant dipolar field effect.

In this paper, the utilization of the distant dipolar field (DDF) signal to extract the properties of susceptibility structures over a subvoxel length scale is investigated. Numerical simulations are performed to study a system of randomly distributed blood vessels with a susceptibility offset inside a voxel. It is shown that the DDF signal of the system as a function of the strength of the correlation gradient field manifests a peak that depends on the volume ratio, size, and susceptibility offset of the blood vessels. In particular, the location of the signal peak is found to vary as powers of these parameters. As a result, by varying the strength of the correlation gradient field, the characteristic properties of the blood vessels can be extracted from the peak position of the DDF signal. It is also found that, for a given volume ratio of the blood vessels, a smaller size of the blood vessels can be probed when the susceptibility offset is increased. Nevertheless, it is demonstrated that, owing to the broad width of the signal peak, the DDF effect generally cannot be used for the preferential selection of the signal arising from the blood vessels on the length scale determined by the correlation length.

Theoretical analysis of the sensitivity of dipolar field signal to local field variations by perturbative expansion of the magnetization.

A perturbation method based on the integral form of the Bloch equation is used to calculate the distant dipolar field (DDF) signal formed by the correlation spectroscopy revamped by asymmetric z-gradient echo detection (CRAZED) sequence in the presence of a susceptibility-induced field. The properties of the DDF signal are analyzed through the series expansion of the magnetization, and the first order DDF result is applied to study the use of the DDF effect to probe sub-voxel field distributions. Numerical calculations are carried out with the sub-voxel field distributions modeled by rectangular tubes of uniform frequency shifts (the block model) and cylinders of a finite susceptibility difference (the blood vessel model) using the parameters for brain at 9.4T. The DDF signal is found to exhibit features arising from the sub-voxel structures.

Theoretical studies of the effect of the dipolar field in multiple spin-echo sequences with refocusing pulses of finite duration.

It has been observed recently that the finite duration of refocusing rf pulses in a multiecho acquisition of the signal formed under the influence of the dipolar field leads to significant signal attenuation [S. Kennedy, Z. Chen, C.K. Wong, E.W.-C. Kwok, J. Zhong, Investigation of multiple-echo spin-echo signal acquisition under distant dipole-dipole interactions, Proc. Int. Soc. Magn. Reson. Med. 13 (2005) 2288]. Hereto, we quantify the phenomenon by evaluating analytically the influences of both the distant dipolar field (DDF) and transverse relaxation T2 on the magnetization in a multiecho pulse sequence based on correlation spectroscopy revamped by asymmetric z-gradient echo detection (CRAZED). Analytic expressions for the magnetization were obtained, which demonstrate explicitly the origin of rephased signal in the presence of the finite pi pulses in the multiecho train. The expressions also explain the effects of the DDF and T2 during the refocusing pulses on the signal strength, and show the substantial signal dependence on the phase of the rf pulses. We show that when the DDF effect during the pulse is canceled, the signal rises primarily during the free evolution time in the acquisition period. This elucidates the signal attenuation when the rf pulses cover a significant proportion of time in the sequence. In addition, we performed an optimization on the number of refocusing pulses that maximizes the total acquired signal using parameters for water, brain white matter, and muscle. We found that maximal signal-to-noise ratio is obtained when the pulse duration approximately equals the free evolution time in the samples with a wide range of T2.