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We present an information-theoretical analysis of temporal dependencies in EEG microstate sequences during wakeful rest. We interpret microstate sequences as discrete stochastic processes where each state corresponds to a representative scalp potential topography. Testing low-order Markovianity of these discrete sequences directly, we find that none of the recordings fulfils the Markov property of order 0, 1 or 2. Further analyses show that the microstate transition matrix is non-stationary over time in 80% (window size 10 s), 60% (window size 20 s) and 44% (window size 40 s) of the subjects, and that transition matrices are asymmetric in 14/20 (70%) subjects. To assess temporal dependencies globally, the time-lagged mutual information function (autoinformation function) of each sequence is compared to the first-order Markov model defined by the classical transition matrix approach. The autoinformation function for the Markovian case is derived analytically and numerically. For experimental data, we find non-Markovian behaviour in the range of the main EEG frequency bands where distinct periodicities related to the subject's EEG frequency spectrum appear. In particular, the microstate clustering algorithm induces frequency doubling with respect to the EEG power spectral density while the tail of the autoinformation function asymptotically reaches the first-order Markov confidence interval for time lags above 1000 ms. In summary, our results show that resting state microstate sequences are non-Markovian processes which inherit periodicities from the underlying EEG dynamics. Our results interpolate between two diverging models of microstate dynamics, memoryless Markov models on one side, and long-range correlated models on the other: microstate sequences display more complex temporal dependencies than captured by the transition matrix approach in the range of the main EEG frequency bands, but show finite memory content in the long run. •An information-theoretical analysis of EEG microstates is introduced.•Resting state microstates are non-Markovian and often non-stationary.•Microstate sequences show distinct periodicities related to the EEG spectrum.•Microstate sequences show short-range memory for time lags above 1000 ms.

•Resting-state alpha oscillations form time-periodic spatial patterns in a) continuous space and time, b) in the discrete microstate representation.•The oscillatory properties of microstate sequences are coded by the analytic phase of alpha oscillations and not by the analytic amplitude.•Over the course of 1-2 alpha cycles, the combination of quasi-static amplitude and periodic phase patterns indicate transient standing wave patterns.•Periodic phase patterns emerge from phase rotors, organized around a small number of phase singularities which are dynamic over time.•Pattern formation in resting-state alpha activity can be modelled by near-critical coupled oscillator lattices close to an Andronov-Hopf bifurcation. Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency. To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave. In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis. We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm. These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.

We analyze temporal autocorrelations and the scaling behaviour of EEG microstate sequences during wakeful rest. We use the recently introduced random walk approach and compute its fluctuation function analytically under the null hypothesis of a short-range correlated, first-order Markov process. The empirical fluctuation function and the Hurst parameter H as a surrogate parameter of long-range correlations are computed from 32 resting state EEG recordings and for a set of first-order Markov surrogate data sets with equilibrium distribution and transition matrices identical to the empirical data. In order to distinguish short-range correlations (H ≈ 0.5) from previously reported long-range correlations (H > 0.5) statistically, confidence intervals for H and the fluctuation functions are constructed under the null hypothesis. Comparing three different estimation methods for H, we find that only one data set consistently shows H > 0.5, compatible with long-range correlations, whereas the majority of experimental data sets cannot be consistently distinguished from Markovian scaling behaviour. Our analysis suggests that the scaling behaviour of resting state EEG microstate sequences, though markedly different from uncorrelated, zero-order Markov processes, can often not be distinguished from a short-range correlated, first-order Markov process. Our results do not prove the microstate process to be Markovian, but challenge the approach to parametrize resting state EEG by single parameter models. •To analyze long-range correlations, short-range (exponentially) correlated models should be used as a null hypothesis.•Resting state EEG microstate sequences are often not distinguishable from a first-order Markov null hypothesis.•Point estimates of the Hurst parameter, though H>0.5, are frequently not different from Markov surrogates.•Different partitions of the microstate state space give different answers to the question if H>0.5.•Hurst parameter estimates from different algorithms answer the LRD question for microstate random walks inconsistently.

BackgroundIn inguinal hernia repair, chronic pain must be expected in 10–12% of cases. Around one-quarter of patients (2–4%) experience severe pain requiring treatment. The risk factors for chronic pain reported in the literature include young age, female gender, perioperative pain, postoperative pain, recurrent hernia, open hernia repair, perioperative complications, and penetrating mesh fixation. This present analysis of data from the Herniamed Hernia Registry now investigates the influencing factors for chronic pain in male patients after primary, unilateral inguinal hernia repair in TAPP technique.MethodsIn total, 20,004 patients from the Herniamed Hernia Registry were included in uni- and multivariable analyses. For all patients, 1-year follow-up data were available.ResultsMultivariable analysis revealed that onset of pain at rest, on exertion, and requiring treatment was highly significantly influenced, in each case, by younger age (p < 0.001), preoperative pain (p < 0.001), smaller hernia defect (p < 0.001), and higher BMI (p < 0.001). Other influencing factors were postoperative complications (pain at rest p = 0.004 and pain on exertion p = 0.023) and penetrating compared with glue mesh fixation techniques (pain on exertion p = 0.037).ConclusionsThe indication for inguinal hernia surgery should be very carefully considered in a young patient with a small hernia and preoperative pain.

Multiple sclerosis (MS) is an autoimmune inflammatory demyelinating and neurodegenerative disorder of the central nervous system characterized by multifocal white matter brain lesions leading to alterations in connectivity at the subcortical and cortical level. Graph theory, in combination with neuroimaging techniques, has been recently developed into a powerful tool to assess the large-scale structure of brain functional connectivity. Considering the structural damage present in the brain of MS patients, we hypothesized that the topological properties of resting-state functional networks of early MS patients would be re-arranged in order to limit the impact of disease expression. A standardized dual task (Paced Auditory Serial Addition Task simultaneously performed with a paper and pencil task) was administered to study the interactions between behavioral performance and functional network re-organization. We studied a group of 16 early MS patients (35.3±8.3years, 11 females) and 20 healthy controls (29.9±7.0years, 10 females) and found that brain resting-state networks of the MS patients displayed increased network modularity, i.e. diminished functional integration between separate functional modules. Modularity correlated negatively with dual task performance in the MS patients. Our results shed light on how localized anatomical connectivity damage can globally impact brain functional connectivity and how these alterations can impair behavioral performance. Finally, given the early stage of the MS patients included in this study, network modularity could be considered a promising biomarker for detection of earliest-stage brain network reorganization, and possibly of disease progression. •Network modularity (Q) increased in early stage Multiple Sclerosis patients•Impaired dual task performance in patients may predict early cognitive decline.•Modularity is a promising biomarker for detection of early stage brain reorganization.•Modularity is a promising biomarker for possibly detection of disease progression.

ABSTRACTBackgroundAdvanced Parkinson’s disease (PD) may place a high burden on patients and their caregivers. Understanding the determinants of caregiver burden is of critical importance. This understanding requires the availability of adequate assessment tools. Recently, the Parkinson’s disease caregiver burden questionnaire (PDCB) has been developed as a PD-specific measure of caregiver burden. However, the PDCB has only been evaluated in a sample of Australian caregivers of patients at a less advanced stage of the disease. ObjectiveWe tested whether a German translation of the PDCB qualifies as an adequate measure of caregiver burden in a German sample of caregivers of advanced patients with PD. MethodsWe collected PDCB data from 65 caregivers of advanced patients with PD. Reliability of the scale was assessed and compared against the original version. To validate the German version of the PDCB, we examined the correlations with the caregiver burden inventory (CBI), the short form 36 health survey (SF-36), the Parkinson’s disease quality of life questionnaire 39 (PDQ-39), disease duration, and the amount of caregiving time. ResultsThe total PDCB score proved to be reliable and to be significantly related to CBI and SF-36 scores. PDCB scores also increased with increasing amounts of caregiving time. ConclusionsThe German version of the PDCB appears to be an adequate measure of caregiver burden in caregivers of advanced PD patients.

Parkinson’s disease (PD) is a chronic progressive movement disorder with severe reduction in patients’ health-related quality of life (HR-QoL). Motor and cognitive symptoms are especially linked with decreased PD patients’ HR-QoL. However, the relationship of these symptoms to caregiver burden is relatively unclear. Influence of the Montreal Cognitive Assessment scale (MoCA) as a cognitive screening tool and Movement Disorders Society Unified Parkinson’s disease Rating Scale MDS-UPDRS symptoms in relation to patients’ HR-QoL and caregivers` burden was analyzed. PD patients (n = 124) completed MDS-UPDRS, MoCA, and the PD questionnaire 8 (PDQ-8) as a measure of quality of life. Caregivers (n = 78) were assessed by the PD caregiver burden inventory (PDCB). PDQ-8 and PDCB scores were regressed on MDS-UPDRS subscales and MoCA subscores. PDQ-8 correlated with attention (R2 0.1282; p<0.001) and executive (R2 0.0882; p 0.001) MoCA subscores and all parts of the MDS-UPDRS. PDCB correlated most strongly with MDS-UPDRS part III motor symptoms (R2 0.2070; p<0.001) and the MoCA attention subscore (R2 0.1815; p<0.001). While all facets of PD symptoms assessed by the MDS-UPDRS relate to PD patients’ quality of life, motor symptoms are the most relevant factor for the prediction of caregiver burden. In addition, patients’ attentional symptoms seem to affect not only them, but also their caregivers. These findings show the potential of a detailed analysis of MDS-UPDRS and MoCA performance in PD patients.

Magnetic particle imaging (MPI) is an emerging medical imaging modality that is on the verge of clinical use. In recent years, cardiovascular applications have shown huge potential like, e.g., intraprocedural imaging guidance of stent placement through MPI. Due to the lack of signal generation, nano-modifications have been necessary to visualize commercial medical instruments until now. In this work, it is investigated if commercial interventional devices can be tracked with MPI without any nano-modification. Potential MPI signal generation of nine endovascular metal stents was tested in a commercial MPI scanner. Two of the stents revealed sufficient MPI signal. Because one of the two stents showed relevant heating, the imaging experiments were carried out with a single stent model (Boston Scientific/Wallstent-Uni Endoprothesis, diameter: 16 mm, length: 60 mm). The nitinol stent and its delivery system were investigated in seven different scenarios. Therefore, the samples were placed at 49 defined spatial positions by a robot in a meandering pattern during MPI scans. Image reconstruction was performed, and the mean absolute errors (MAE) between the signals' centers of mass (COM) and ground truth positions were calculated. The stent material was investigated by magnetic particle spectroscopy (MPS) and vibrating sample magnetometry (VSM). To detect metallic components within the delivery system, nondestructive testing via computed tomography was performed. The tracking of the stent and its delivery system was possible without any nano-modification. The MAE of the COM were 1.49 mm for the stent mounted on the delivery system, 3.70 mm for the expanded stent and 1.46 mm for the delivery system without the stent. The results of the MPS and VSM measurements indicate that besides material properties eddy currents seem to be responsible for signal generation. It is possible to image medical instruments with dedicated designs without modifications by means of MPI. This enables a variety of applications without compromising the mechanical and biocompatible properties of the instruments.

ObjectivesThe diagnostic reading of follow-up low-dose whole-body computed tomography (WBCT) examinations in patients with multiple myeloma (MM) is a demanding process. This study aimed to evaluate the diagnostic accuracy and benefit of a novel software program providing rapid-subtraction maps for bone lesion change detection.MethodsSixty patients (66 years ± 10 years) receiving 120 WBCT examinations for follow-up evaluation of MM bone disease were identified from our imaging archive. The median follow-up time was 292 days (range 200–641 days). Subtraction maps were calculated from 2-mm CT images using a nonlinear deformation algorithm. Reading time, correctly assessed lesions, and disease classification were compared to a standard reading software program. De novo clinical reading by a senior radiologist served as the reference standard. Statistics included Wilcoxon rank-sum test, Cohen’s kappa coefficient, and calculation of sensitivity, specificity, positive/negative predictive value, and accuracy.ResultsCalculation time for subtraction maps was 84 s ± 24 s. Both readers reported exams faster using subtraction maps (reader A, 438 s ± 133 s; reader B, 1049 s ± 438 s) compared to PACS software (reader A, 534 s ± 156 s; reader B, 1486 s ± 587 s; p < 0.01). The course of disease was correctly classified by both methods in all patients. Sensitivity for lesion detection in subtraction maps/conventional reading was 92%/80% for reader A and 88%/76% for reader B. Specificity was 98%/100% for reader A and 95%/96% for reader B.ConclusionA software program for the rapid-subtraction map calculation of follow-up WBCT scans has been successfully tested and seems suited for application in clinical routine. Subtraction maps significantly facilitated reading of WBCTs by reducing reading time and increasing sensitivity.Key Points• A novel algorithm has been successfully applied to generate motion-corrected bone subtraction maps of whole-body low-dose CT scans in less than 2 min.• Motion-corrected bone subtraction maps significantly facilitate the reading of follow-up whole-body low-dose CT scans in multiple myeloma by reducing reading time and increasing sensitivity.

We developed an algorithm for the automated detection and analysis of elementary Ca 2+ release events (ECRE) based on the two-dimensional nondecimated wavelet transform. The transform is computed with the “à trous” algorithm using the cubic B-spline as the basis function and yields a multiresolution analysis of the image. This transform allows for highly efficient noise reduction while preserving signal amplitudes. ECRE detection is performed at the wavelet levels, thus using the whole spectral information contained in the image. The algorithm was tested on synthetic data at different noise levels as well as on experimental data of ECRE. The noise dependence of the statistical properties of the algorithm (detection sensitivity and reliability) was determined from synthetic data and detection parameters were selected to optimize the detection of experimental ECRE. The wavelet-based method shows considerably higher detection sensitivity and less false-positive counts than previously employed methods. It allows a more efficient detection of elementary Ca 2+ release events than conventional methods, in particular in the presence of elevated background noise levels. The subsequent analysis of the morphological parameters of ECRE is reliably reproduced by the analysis procedure that is applied to the median filtered raw data. Testing the algorithm more rigorously showed that event parameter histograms (amplitude, rise time, full duration at half-maximum, and full width at half-maximum) were faithfully extracted from synthetic, “in-focus” and “out-of-focus” line scan sparks. Most importantly, ECRE obtained with laser scanning confocal microscopy of chemically skinned mammalian skeletal muscle fibers could be analyzed automatically to reproducibly establish event parameter histograms. In summary, our method provides a new valuable tool for highly reliable automated detection of ECRE in muscle but can also be adapted to other preparations.