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Previous research has demonstrated highly accurate classification of veterans with posttraumatic stress disorder (PTSD) and controls based on synchronous neural interactions (SNI), highlighting the utility of SNI as a biomarker of PTSD. Here we extend that research to classify additional trauma-related outcomes including subthreshold PTSD, partial recovery, and full recovery according to SNI. A total of 219 U.S. veterans completed diagnostic interviews and underwent a magnetoencephalography (MEG) scan from which SNI was computed. Linear discriminant analysis was used to classify the PTSD and control brains, achieving 100% accuracy. That discriminant function was then used to classify each brain in the subthreshold PTSD, partial recovery, and full recovery diagnostic groups as PTSD or Control. All of the subthreshold PTSD diagnostic group were classified as PTSD, as were three-quarters of the partial recovery group. Findings regarding the full recovery group were mixed, documenting variability in the functional brain status of PTSD recovery. The results of the present study add to the literature supporting the discriminatory power of MEG SNI and demonstrate the utility of SNI as a biomarker of various PTSD-related trajectories.

GWI has affected a substantial number of Gulf War (GW) veterans. The disease involves several organ systems among which the brain is most prominent. Neurological, cognitive and mood-related (NCM) symptoms frequently dominate and are at the root of chronic ill-health and disability in veterans suffering from GWI. In addition, such symptoms frequently co-occur with diagnosable mental health disorders, predominantly posttraumatic stress disorder (PTSD). Here we investigated the possibility that increased GWI severity leads, above a threshold, to a diagnosable mental health disorder (excluding psychosis). For this purpose, we used, in separate analyses, symptom severity scores and resting-state brain functional connectivity patterns, as determined by magnetoencephalography (MEG). Two-hundred-thirty GW-era veterans participated in this study. They completed diagnostic interviews to establish the presence of GWI and assess mental health status. This distinguished 3 groups: healthy controls (N = 41), veterans with GWI and no mental illness (GWI group, N = 91), and veterans with both GWI and mental health disorder (GWI+MH, N = 98). For each veteran, symptom severity scores in the 6 GWI domains (fatigue, pain, NCM, skin, gastrointestinal, respiratory) were available as well as 9 summary measures of the distribution of Synchronous Neural Interactions (SNI) derived from the MEG recordings. We tested the hypothesis that, in the presence of GWI, the appearance of a diagnosable mental health disorder may depend on GWI symptom severity. For that purpose, we performed a logistic regression on the GWI population, where the presence (or absence) of the MH disorder was the dependent variable and the age- and gender-adjusted GWI severity in the 6-symptom domains were the predictors. The outcome was the probability that a participant will have MH disorder or not. Similarly, we tested the hypothesis that the presence of the MH disorder can be predicted by the SNI distribution patterns by performing a second logistic regression as above but with the 9 SNI measures as predictors. We found GWI symptom severity differed significantly across groups (GWI+MH > GWI > Control). SNI distributions of the GWI group also differed significantly from the other groups in a systematic hemispheric pattern, such that the presence of GWI involved predominantly the left hemisphere, and presence of mental health disorders involved, in addition, the right hemisphere. Both logistic regressions yielded highly significant outcomes, demonstrating that both GWI symptom severity and SNI distribution measures can predict the presence of MH disorder in GWI. Remarkably, the prediction probabilities for MH presence derived from the symptom-based and SNI-based logistic regressions were positively and highly statistically significantly correlated. Taken together, both objective (neural) and subjective (symptoms) indices suggest that GWI is distinct from healthy controls and varies in severity in a continuum that leads, at the higher end, to a diagnosable MH disorder. The positive correlation between the GWI symptom-based and brain-based predicted classifications provides a key link between GWI symptom severity and synchronous neural interactions in the context of mental illness.

Background Age-related brain changes are well-documented and influenced by genetics. Extensive research links apolipoprotein E (apoE) to brain function, with the E4 allele serving as a risk factor for brain disease, including Alzheimer's disease, and the E2 allele conferring protection. Recent evidence also supports protective effects of another gene, human leukocyte antigen (HLA) DRB1*13, on brain disease and age-related brain atrophy in cognitively healthy adults. Here we investigated the effects of apoE and HLA DRB1*13 on brain function by examining changes in neural network properties with age in healthy adults. Methods One hundred seventy-eight cognitively healthy women (28–99 y old) underwent a magnetoencephalography scan and provided a blood sample for genetic analysis. Age-related changes in neural network variability in genetic subgroups of DRB1*13 × apoE genotype combinations were assessed using linear regression of network variability against age. Findings For individuals lacking a DRB1*13 allele and/or carrying an apoE4 allele, network variability increased significantly with age. In contrast, no such increase was observed in the presence of DRB1*13 and/or apoE2. Interpretation These findings extend previous research documenting the protective effect of DRB1*13 on brain structure to include protection against age-related changes in brain function, and demonstrate similar protective effects on neural network variability for either DRB1*13 or apoE2. These protective effects could be due to reduction or elimination of factors known to disrupt brain function, including neuroinflammation and amyloid beta protein. Funding U.S. Department of Veterans Affairs, and University of Minnesota.

Background We recently reported that six alleles from class II genes of the Human Leukocyte Antigen (HLA) confer protection from Gulf War Illness (GWI) (Georgopoulos et al., 2015). The most significant effect is exerted on Neurological-Cognitive-Mood (NCM), Pain, and Fatigue symptoms, such that higher number of copies of the protective alleles are associated with lower symptom severity. Here we tested the hypothesis that this effect is exerted by modulating the strength of neural synchronicity. Methods Eighty-one Gulf War veterans (65 with GWI and 16 healthy controls) underwent a magnetoencephalography (MEG) scan to assess the strength of brain synchronicity by computing zero-lag crosscorrelations (and their Fisher z transforms) between prewhitened MEG time series. A high-resolution HLA genotyping determined the number of copies, k, of the 6 protective alleles above in each participant. We tested the hypothesis above by regressing NCM, Pain and Fatigue symptom severity against the interaction term, k × z (HLA-related effect), while including z (non-HLA-related effect), gender and age as covariates. The k × z and z terms assessed HLA- and non-HLA-related effects, respectively, of neural synchronicity on symptom severity. The distributions of these effects in sensor space were visualized using statistical heatmaps. Findings We found significant, graded HLA- and non-HLA-related effects: (a) NCM > Pain > Fatigue for HLA-related effects, (b) NCM > Fatigue > Pain for non-HLA-related effects, and (c) HLA-related > non-HLA-related effects for all symptoms. These effects had widespread but distinct distributions in sensor space that allowed the orderly separation of the 6 terms (3 symptom domains × 2 HLA factors) in a multidimensional plot, where one dimension separated the symptoms and the other the HLA relation. Interpretation These findings demonstrate the presence of substantial, widespread, distinct and orderly HLA- and non-HLA-related neural influences on NCM, Pain and Fatigue symptom severity in GWI. Funding U.S. Department of Veterans Affairs and University of Minnesota., Highlights • Genetic modulation of neural synchronicity is investigated in Gulf War Illness (GWI). • GWI symptom severity is highly related to specific Human Leukocyte Antigen (HLA) alleles. • The distribution of HLA-related effects on brain regions differed across GWI symptoms. • Non-HLA genetic effects on brain functioning were also observed. Gulf War Illness (GWI) is a chronic and debilitating condition that has affected a large number of veterans who served during the 1990–91 Persian Gulf War. We have recently reported evidence of brain anomalies in GWI and identified a genetic immune protection the lack of which confers susceptibility, and, hence, risk for developing GWI symptoms. In this study, we evaluated the interaction of immunity (Human Leukocyte Antigen, HLA) genes and brain functioning in veterans with and without GWI using magnetoencephalography. The findings indicate genetic modulation of brain functioning on GWI symptoms, particularly with genes involving the HLA immune system. These findings document symptom-related interactions between HLA genes and brain function, thus forging a link between HLA genes, brain function and GWI.

Continuous and sequential movements are controlled by widely distributed brain regions. A series of studies have contributed to understanding the functional role of these regions in a variety of visuomotor tasks. However, little is known about the neural interactions underpinning continuous movements. In the current study, we examine the spatiotemporal neural interactions underlying continuous drawing movements and the association of them with behavioral components. We conducted an experiment in which subjects copied a pentagon continuously for ~45 s using an XY joystick, while neuromagnetic fluxes were recorded from their head using a 248-sensor whole-head magnetoencephalography (MEG) device. Each sensor time series was rendered stationary and non-autocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We used the directional variability of the movement as a behavioral measure of the controls generated. The main objective of this study was to assess the relation between neural interactions and the variability of movement direction. That is, we divided the continuous recordings into consecutive periods (i.e., time-bins) of 51 steps duration and computed the pairwise cross-correlations between the prewhitened time series in each time-bin. The circular standard deviation of the movement direction within each time-bin provides an estimate of the directional variability of the 51-ms trajectory segment. We looked at the association between neural interactions and variability of movement direction, separately for each pair of sensors, by running a cross-correlation analysis between the strength of the MEG pairwise cross-correlations and the circular standard deviations. We identified two types of neuronal networks: in one, the neural interactions are correlated with the directional variability of the movement at negative time-lags (feedforward), and in the other, the neural interactions are correlated with the directional variability of the movement at positive time-lags (feedback). Sensors associated mostly with feedforward processes are distributed in the left hemisphere and the right occipital-temporal junction, whereas sensors related to feedback processes are distributed in the right hemisphere and the left cerebellar hemisphere. These results are in line with findings from a series of previous studies showing that specific brain regions are involved in feedforward and feedback control processes to plan, perform, and correct movements. Additionally, we looked at whether changes in movement direction modulate the neural interactions. Interestingly, we found a preponderance of sensors associated with changes in movement direction over the right hemisphere-ipsilateral to the moving hand. These sensors exhibit stronger coupling with the rest of the sensors for trajectory segments with high rather than low directional movement variability. We interpret these results as evidence that ipsilateral cortical regions are recruited for continuous movements when the curvature of the trajectory increases. To the best of our knowledge, this is the first study that shows how neural interactions are associated with a behavioral control parameter in continuous and sequential movements.

Pooling of magnetoencephalography (MEG) data across laboratories is non-trivial because of differences in hardware, software, and environmental noise levels. To investigate these issues, we conducted a study with the same five subjects at three sites with different MEG arrays: Elekta-Neuromag Vectorview (Boston), VSM MedTech Omega 275 (Albuquerque), and 4D Neuroimaging Magnes 3600 WH (Minneapolis). Subjects were run in a simple somatosensory paradigm. Phantom data were also taken at each site. We developed software to allow comparisons of test/retest reliability across subjects, machines, and analysis methods. Preliminary analyses showed excellent test/retest results within subjects across instruments. Analysis of phantom data showed a localization error of less than 2 mm across multiple software packages. Our results show that (1) instruments from different manufacturers yield similar results for somatosensory data, and that (2) multiple software packages produce very accurate results for simple source configurations.

We visualized synchronous dynamic brain networks by using prewhitened (stationary) magnetoencephalography signals. Data were acquired from 248 axial gradiometers while 10 subjects fixated on a spot of light for 45 s. After fitting an autoregressive integrative moving average model and taking the residuals, all pairwise, zero-lag, partial cross-correlations ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*}PCC_{ij}^{0}\end{equation*}\end{document} ) between the i and j sensors were calculated, providing estimates of the strength and sign (positive and negative) of direct synchronous coupling between neuronal populations at a 1-ms temporal resolution. Overall, 51.4% of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*}PCC_{ij}^{0}\end{equation*}\end{document} were positive, and 48.6% were negative. Positive \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*}PCC_{ij}^{0}\end{equation*}\end{document} occurred more frequently at shorter intersensor distances and were 72% stronger than negative ones, on the average. On the basis of the estimated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*}PCC_{ij}^{0}\end{equation*}\end{document} , dynamic neural networks were constructed (one per subject) that showed distinct features, including several local interactions. These features were robust across subjects and could serve as a blueprint for evaluating dynamic brain function.

High density, whole head magnetoencephalography (MEG) was used to study ten healthy human subjects (five females and five males) participating in a continuous shape-copying task. The task was performed with eyes open and fixated. The three-part task began with 45 s of fixation on a blue dot, after which the dot turned red, and a pentagon was presented around it. Subjects continued to fixate on the red dot for 45 s, after which it turned green. The green dot instructed subjects to begin copying the shape continuously for 45 s, without visual feedback, using a joystick mounted at arm's length. Data were collected at 1,017.25 Hz with a 248 sensor axial-gradiometer system. After cardiac artifact subtraction (Leuthold 2003), each corner was identified, and 1 s epochs (centered on each corner) were averaged and filtered from 1 to 44 Hz. Grand average flux maps demonstrated dipolar distributions identifying the most relevant sensors. With these sensors, which were located over flux extrema (Valaki et al. 2004), dipole models were used for source localization within subjects. Consistent dipole locations included the left motor cortex, bilateral parietal, frontal and temporal regions, and the occipital cortex. These results indicate that MEG source-localization may be derived from a limited number of trials of continuous data, and that visual cortex activity may be consistently present during continuous motor activity despite the absence of novel visual stimulation and eye-movements.

Brain-machine interface (BMI) efforts have been focused on using either invasive implanted electrodes or training-extensive conscious manipulation of brain rhythms to control prosthetic devices. Here we demonstrate an excellent prediction of movement trajectory by real-time magnetoencephalography (MEG). Ten human subjects copied a pentagon for 45 s using an X-Y joystick while MEG signals were being recorded from 248 sensors. A linear summation of weighted contributions of the MEG signals yielded a predicted movement trajectory of high congruence to the actual trajectory (median correlation coefficient: r = 0.91 and 0.97 for unsmoothed and smoothed predictions, respectively). This congruence was robust since it remained high in cross-validation analyses (based on the first half of data to predict the second half; median correlation coefficient: r = 0.76 and 0.85 for unsmoothed and smoothed predictions, respectively).

The electrical activity of the human stomach, which normally shows a frequency of about 0.05 Hz, may be studied non-invasively by either cutaneous electrogastrography (EGG) or surface magnetogastrography (MGG). Detection of changes in frequency with time may be useful to characterize gastric disorders. The fast Fourier transform (FFT) has been the most commonly used method for the automated spectral analysis of the signals obtained from the EGG or the MGG. We have used an autoregressive (AR) parametric spectrum estimator to analyse simulated signals of gastric electrical activity, and to evaluate the results of human studies using EGG and MGG. In comparison with the FFT, our results showed that the AR spectrum estimator provided more detailed qualitative information about frequency variations of short duration simulated signals than the FFT. In the human studies, the AR estimator was as good as the conventional FFT methods in detecting physiological changes in frequency and in identifying abnormal recordings. We conclude that the AR spectral estimator may provide a better qualitative analysis of frequency variations in small portions of the signal, and is as useful as the FFT to analyse human EGG or MGG studies.

The neural mechanisms underlying spatial cognition in the context of exploring realistic city maps are unknown. We conducted a novel brain imaging study to address the question of whether and how features of special importance for map exploration are encoded in the brain to make a spatial decision. Subjects explored by eyes small city maps exemplifying five different street network types in order to locate a hypothetical City Hall, while neural activity was recorded continuously by 248 magnetoencephalography (MEG) sensors at high temporal resolution. Monitoring subjects’ eye positions, we locally characterized the maps by computing three spatial parameters of the areas that were explored. We computed the number of street intersections, the total street length, and the regularity index in the circular areas of 6 degrees of visual angle radius centered on instantaneous eye positions. We tested the hypothesis that neural activity during exploration is associated with the spatial parameters and modulated by street network type. All time series were rendered stationary and nonautocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We then assessed the associations between the prewhitened time-varying MEG time series from 248 sensors and the prewhitened spatial parameters time series, for each street network type, using multiple linear regression analyses. In accord with our hypothesis, ongoing neural activity was strongly associated with the spatial parameters through localized and distributed networks, and neural processing of these parameters depended on the type of street network. Overall, processing of the spatial parameters seems to predominantly involve right frontal and prefrontal areas, but not for all street network layouts. These results are in line with findings from a series of previous studies showing that frontal and prefrontal areas are involved in the processing of spatial information and decision making. Modulation of neural processing of the spatial parameters by street network type suggests that some street network layouts may contain other types of spatial information that subjects use to explore maps and make spatial decisions.

Foetal electrocardiograms (fECGs) and foetal magnetocardiograms (fMCGs) were recorded in the 26th, 29th and 31st weeks of gestation from a foetus with ectopia cordis-a rare condition in which the heart lies outside the chest wall. This provided an opportunity to study foetal cardiograms uninfluenced by the insulating effects of the foetal skin and vernix caseosa. The fECG of the ectopia cordis foetus was striking. Unlike recordings from age-matched normal foetuses, recordings from this subject had very high signal-to-noise ratio and showed no anomalous signal transmission properties. In contrast, fMCGs recorded from the ectopia cordis foetus and normal foetuses were largely similar. Both showed high signal-to-noise ratio and signal transmission properties consistent with volume conduction. The findings corroborate the hypothesis that high foetal skin resistance due primarily to the vernix caseosa is responsible for the low amplitude and anomalous transmission properties of the normal fECG, and demonstrate that the fMCG is relatively insensitive to conductivity inhomogeneities.

It has been known for many years that the power of beta-band oscillatory activity in motor-related brain regions decreases during the preparation and execution of voluntary movements. However, it is not clear yet whether the amplitude of this desynchronization is modulated by any parameter of the motor task. Here, we examined whether the degree of uncertainty about the upcoming movement direction modulated beta-band desynchronization during motor preparation. To this end, we recorded whole-head neuromagnetic signals while human subjects performed an instructed-delay reaching task with one, two, or three possible target directions. We found that the reduction of power of beta-band activity (16–28 Hz) during motor preparation was scaled relative to directional uncertainty. Furthermore, we show that the change of beta-band power correlates with the change of latency of response associated with response uncertainty. Finally, we show that the main source of beta-band desynchronization was located in the peri-Rolandic region. The results establish directional uncertainty as an important determinant of beta-band power during motor preparation and indicate that neural activity in the sensorimotor cortex during motor preparation covaries with directional uncertainty.

Despite substantial efforts to connect cognitive-linguistic models with appropriate anatomical correlates, the question of which cognitive model best accounts for the neuropsychological and functional neuroimaging evidence remains open. The two most popular models are grounded in conceptually different bases and thus make quasi-distinct predictions in regard to the patterns of activation that should be observed in imaging investigations of linguistic processing. Dual-mechanism models propose that high-frequency regular and irregular words are processed through a lexicon-based word code, which facilitates their processing and pronunciation latencies relative to pseudowords. In contrast, single-mechanism models suggest the same behavioral effects can be explained through semantic mediation without the existence of a lexicon. In most previous studies, words and pronounceable pseudowords were presented in lexical-decision or word reading paradigms, and hemodynamic techniques were utilized to distinguish involved anatomical areas. The results typically indicated that both word classes activated largely congruent tissues, with a magnitude advantage for pseudowords in most or all activated regions. However, since the dual-mechanism model predicts both word types utilize the entire linguistic network, but that certain operations are merely obligatorily involved, these results do not sharply refute nor clearly support the model's main tenets. In the current study, we approach the dual- versus single-mechanism question differently by focusing on the temporal dynamics of MEG imaged neuronal activity, during performance of an oddball version of continuous lexical-decision, to determine whether the onset latency of any cortical language region shows effects of word class that are indicative of preferential versus obligatory processing pathways. The most remarkable aspect of our results indicated that both words and pseudowords initially activate the left posterior fusiform region, but that the spatiotemporal dynamics clearly distinguish the two word classes thereafter. For words, this left fusiform activation was followed by engagement of the left posterior inferior temporal, and subsequently activation reached the left posterior superior temporal region. For pseudowords, this sequential order of left temporal area activations was reversed, as activity proceeded from the left fusiform to the left superior temporal and then the left inferior temporal region. For both classes, this dynamic sequential spread manifested within the first 300 ms of stimulus processing. We contend these results provide strong support for the existence of dual-mechanisms underlying reading in a deep orthographic language (i.e., English).

Atrial and ventricular fetal heart rate tracings from a patient with isolated congenital complete heart block treated with dexamethasone showed a remarkable degree of correlation and greater reactivity for the ventricular than the atrial fetal heart rate. Ventriculophasic sinus arrhythmia was present continually in the atrial fetal heart rate tracings.

Magnetoencephalography was used to noninvasively record auditory evoked responses from fetuses in utero at 34 to 37 weeks' gestation. The recordings were similar to auditory evoked responses from neonates at age 7 to 14 days.

We used standard time series modeling to analyze magnetoencephalographic (MEG) data acquired during three tasks. Each task lasted 45 s, for a total data acquisition period of 135 s. Ten healthy human subjects fixated their eyes on a central blue point for 45 s (fixation only, "F" task). Then a pentagon (visual template) appeared surrounding the fixation point which simultaneously became red (fixation + template, "FT" task). After 45 s, the fixation point changed to green, which was the "go" signal for the subjects to begin continuously copying the pentagon for 45 s using a joystick and without visual feedback of their movement trajectory (fixation + template + copying, "FTC" task). MEG data were acquired continuously from 248 axial gradiometers at a sampling rate of 1017.25 Hz. After removal of cardiac artifacts and rejection of records with eyeblink artifacts, a Box-Jenkins autoregressive integrative moving average (ARIMA) analysis was applied to the unsmoothed, unaveraged MEG time series for model identification and estimation within 25 time lags (approximately 25 ms). We found that an ARIMA model of 25th order autoregressive, first order differencing, and first order moving average (p=25, d=1, q=1) adequately modeled the series and yielded residuals practically stationary with respect to their mean, variance, and autocorrelation structure. These "prewhitened" residuals were then used for assessing pairwise associations between series using crosscorrelation analysis with +/-25 time lags (approximately +/-25 ms). The cross-correlograms thus obtained revealed rich and consistent patterns of interactions between series with respect to positive and/or negative correlations. The overall prevalence of these patterns was very similar in the three tasks used, and, for particular sensor pairs, they tended to be preserved across tasks.

A simple thermally-controlled superconducting switch was developed for switching low-level low-frequency signals. The device was fabricated and tested using different materials for superconductor, control line, and insulating layer. Refractory materials yielded the best combination of performance and ruggedness. Switching speeds of 1 MHz and control currents of 0.3 mA were achieved. >

A simple thermally controlled, thin‐film superconducting switch was developed for the purpose of multiplexing superconducting quantum interference device detection coils. Switching speeds of a few microseconds were achieved and multiplexing at low frequencies was demonstrated.

We report high precision assessment of fetal rhythm in utero in a case of isolated congenital complete heart block using fetal magnetocardiography. The recordings reveal a remarkably strong tendency for the atria and ventricles to synchronize, which is manifested by the continual presence of ventriculophasic sinus arrhythmia and frequent episodes of accrochage and isorhythmic AV dissociation.

The first studies of fetal neuromagnetism and fetal cardiomagnetism were published more than ten and twenty years ago, respectively [1,2]. Despite considerable interest generated by these reports, relatively few follow-up studies appeared. In retrospect, this is somewhat surprising given that 1) in several respects, magnetic detection is almost uniquely suited to intrauterine evaluation, and 2) there is a paucity of alternate fetal evaluation techniques that provide functional information. During the last few years, interest in fetal biomagnetism has reemerged, as a number of groups have reported findings at meetings and in the literature. This paper presents fetal MCG and fetal MEG studies performed recently by our group at the University of Wisconsin-Madison.

Fetal cardiac waveform intervals were assessed from fetal magnetocardiogram (FMCG) recordings taken from 59 pregnant women at 17-41 weeks gestation. Beyond 27 weeks' gestation PR and QRS intervals, measured from averaged waveforms, could be obtained from all subjects; however, prior to 21 weeks' gestation the success rate was 50% or less due to low signal-to-noise ratio. QT interval could not be assessed accurately in most subjects. Weak, but statistically significant, correlations with gestational age were found for PR interval (n = 145, R2 = 0.033, P = 0.028) and QRS interval (n = 145, R2 = 0.140, P < 0.0005). Abnormal waveform morphology was documented in several patients with cardiac malformations.

Magnetocardiographic recordings from a fetus at 34 weeks' gestation with a complex heart malformation revealed an unusual fetal arrhythmia, where the rhythm was observed to alternate between extended periods of sinus and junctional rhythms. Fetal heart rate measurement showed that the junctional rhythm was strongly associated with respiratory arrhythmia. We hypothesize that changes in autonomic balance due to the onset and cessation of fetal breathing movements were largely responsible for the rhythm alternations. To our knowledge this is the first report of a fetal junction rhythm and the first observation of fetal respiratory arrhythmia during nonsinus rhythm.

The authors show that oscillations at foetal breathing frequencies observed in foetal heart rate (FHR) tracings obtained from foetal magnetocardiogram (FMCG) recordings are often accompanied by synchronous modulation of FMCG signal amplitude. This implies an association between the FHR oscillations and foetal chest wall movements, corroborating the hypothesis that the oscillations are due to respiratory sinus arrhythmia.

Medical patients are screened prior to magnetic resonance imaging by using a superconducting quantum interference device magnetometer to detect the unknown presence of an implanted ferromagnetic object. The patient is placed on a table which vibrates at approximately five Hertz. The magnetometer is sequentially held stationary at a plurality of positions adjacent to the patient being vibrated. The magnetic field intensity is measured at each position with the magnetometer and the magnetic field measurements are used to determine the presence and location of a ferromagnetic object within the patient.

Background Gulf War Illness (GWI) has affected many Gulf War veterans. It involves several organs, most notably the brain. Neurological-cognitive-mood-related symptoms frequently dominate and are at the root of chronic ill-health and disability in GWI. Here we investigated the neural mechanisms underlying brain dysfunction in GWI in the absence of mental health disorders. Methods Eighty-six veterans completed diagnostic interviews to establish the presence of GWI and assess mental health status. Participants diagnosed with GWI met both Center for Disease Control and Kansas criteria. We studied 46 healthy controls and 40 veterans with GWI without mental illness. They all underwent a resting-state magnetoencephalographic (MEG) scan to assess brain communication based on synchronous neural interactions (SNI; Georgopoulos et al., 2007). Findings We found substantial differences in SNI between control and GWI groups centered on the cerebellum and frontal cortex. In addition, using the maxima and minima of SNI per sensor as predictors, we successfully classified 94.2% of the 86 participants (95% sensitivity, 93.5% specificity). Interpretation These findings document distinct differences in brain function between control and GWI in the absence of mental health comorbidities, differences that are excellent predictors of GWI. Funding U.S. Department of Veterans Affairs and University of Minnesota., Highlights • This is the first study of brain mechanisms in Gulf War Illness using magnetoencephalography. • Substantial differences in brain synchronicity were found in veterans with Gulf War Illness compared to control Gulf War veterans. • These differences involved mainly the cerebellum and frontal cortex. Gulf War Illness is a debilitating disorder of unknown etiology that has affected a substantial number of veterans who served in the 1990–91 Gulf War. A prominent feature of the disease is the presence of neurological, cognitive and mood disorders. Here we assessed dynamic brain function in veterans suffering from Gulf War Illness (in the absence of mental illness) using magnetoencephalography, a high-fidelity method that measures brain activity. We found neural miscommunication in Gulf War Illness (as compared to control participants) entered mainly on the cerebellum and frontal cortex. These results document dynamic brain anomalies in Gulf War Illness and pave the way for further investigations to elucidate the mechanisms leading to those anomalies.

Modern graph theory provides powerful quantitative tools and measures for the analysis of complex networks. The network measures are critical for creating metrics that allow for the comparison of connectivity patterns and neural interactions across subjects and brain measurements. In addition, these measures provide a framework for the characterization of network structure by identifying local contributions of individual nodes and connections, as well as the network's global capacity to integrate information. Since neural communication is the basis of brain function, assessing the dynamic status of the brain as a network is important. However, a major challenge is how to reliably measure and assess the network, and evaluate its utility. For that purpose, we used resting-state magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) recordings from 65 cognitively healthy women to reliably assess the brain as a dynamic network. MEG time series were prewhitened using ARIMA(50,1,1) and fMRI time series were prewhitened using ARIMA(15,1,1) to yield practically white noise innovations. For MEG, nodes were defined to be the individual sensors (n = 248); for fMRI, a modified functional template [1] was used to define nodes equaling those used for MEG in order to compare networks across the two modalities. Connectivity matrices were then calculated using pairwise Pearson correlations between the processed (1) whole time series, and (2) windowed time series (fMRI = 6 s window, MEG = 50 ms window) of all nodes. Weighted graphs of each of these connectivity matrices were evaluated using the following metrics: (a) local connectivity (strength, diversity), (b) global connectivity (mean correlation coefficient), (c) local network properties (degree, clustering coefficient, local efficiency, betweenness centrality), and (d) global network properties (smallworldness, centrality, global efficiency, path length, assortativity, transivity). For the whole series, this analysis yielded MEG and fMRI "static" networks for each subject, characterized by the set of the values obtained for the parameters above. The effect of age was assessed by performing a multivariate regression where the network parameters were the dependent variables and age was the independent variable. Through an iterative, stepwise procedure those parameters that varied systematically with age were detected and retained. This analysis was performed for both the MEG and fMRI data, and the resulting MEG- and fMRI-based network parameters were compared. Similarly, for the windowed series, the same analyses were performed per time window to yield a "dynamic" network variation and the variation/stability of network structure over time was assessed. This provided an additional temporal network measure for evaluating the effect of age on brain network structure. We believe that this approach is at the heart of assessing brain networks at varying time scales, at different states in health and disease, and during performance of different tasks.