Improved procedure of complex demodulation and an application to frequency analysis of sleep spindles in EEG

Time course of effective connectivity associated with perspective taking in utterance comprehension

This study discusses the effective connectivity in the brain and its time course in realizing perspective taking in verbal communication through electroencephalogram (EEG) associated with the understanding of Japanese utterances. We manipulated perspective taking in a sentence with the Japanese subsidiary verbs -ageru and -kureru, which mean "to give". We measured the EEG during the auditory presentation of the sentences with a multichannel electroencephalograph, and the partial directed coherence and its temporal variations were analyzed using the source localization method to examine causal interactions between nineteen regions of interest in the brain. Three different processing stages were recognized on the basis of the connectivity hubs, direction of information flow, increase or decrease in flow, and temporal variation. We suggest that perspective taking in speech comprehension is realized by interactions between the mentalizing network, mirror neuron network, and executive control network. Furthermore, we found that individual differences in the sociality of typically developing adult speakers were systematically related to effective connectivity. In particular, attention switching was deeply concerned with perspective taking in real time, and the precuneus played a crucial role in implementing individual differences.

Pain-free default mode network connectivity contributes to tonic experimental pain intensity beyond the role of negative mood and other pain-related factors

BACKGROUND

Alterations in the default mode network (DMN) connectivity across pain stages suggest a possible DMN involvement in the transition to persistent pain.

AIM

This study examined whether pain-free DMN connectivity at lower alpha oscillations (8-10 Hz) accounts for a unique variation in experimental peak pain intensity beyond the contribution of factors known to influence pain intensity.

METHODS

Pain-free DMN connectivity was measured with electroencephalography prior to 1 h of capsaicin-evoked pain using a topical capsaicin patch on the right forearm. Pain intensity was assessed on a (0-10) numerical rating scale and the association between peak pain intensity and baseline measurements was examined using hierarchical multiple regression in 52 healthy volunteers (26 women). The baseline measurements consisted of catastrophizing (helplessness, rumination, magnification), vigilance, depression, negative and positive affect, sex, age, sleep, fatigue, thermal and mechanical pain thresholds and DMN connectivity (medial prefrontal cortex [mPFC]-posterior cingulate cortex [PCC], mPFC-right angular gyrus [rAG], mPFC-left Angular gyrus [lAG], rAG-mPFC and rAG-PCC).

RESULTS

Pain-free DMN connectivity increased the explained variance in peak pain intensity beyond the contribution of other factors (ΔR2  = 0.10, p = 0.003), with the final model explaining 66% of the variation (R2  = 0.66, ANOVA: p < 0.001). In this model, negative affect (β = 0.51, p < 0.001), helplessness (β = 0.49, p = 0.007), pain-free mPFC-lAG connectivity (β = 0.36, p = 0.003) and depression (β = -0.39, p = 0.009) correlated significantly with peak pain intensity. Interestingly, negative affect and depression, albeit both being negative mood indices, showed opposing relationships with peak pain intensity.

CONCLUSIONS

This work suggests that pain-free mPFC-lAG connectivity (at lower alpha) may contribute to individual variations in pain-related vulnerability.

SIGNIFICANCE

These findings could potentially lead the way for investigations in which DMN connectivity is used in identifying individuals more likely to develop chronic pain.

Decreased Default Mode Network Connectivity Following 24 Hours of Capsaicin-induced Pain Persists During Immediate Pain Relief and Facilitation

Prolonged experimental pain models can help assess cortical mechanisms underlying the transition from acute to chronic pain such as resting-state functional connectivity (rsFC), especially in early stages. This crossover study determined the effects of 24-hour-capsaicin-induced pain on the default mode network rsFC, a major network in the dynamic pain connectome. Electroencephalographic rsFC measured by Granger causality was acquired from 24 healthy volunteers (12 women) at baseline, 1hour, and 24hours following the application of a control or capsaicin patch on the right forearm. The control patch was received maximum 1 week before the capsaicin patch. Following 24hours, the patch was cooled and later heated to assess rsFC changes in response to pain relief and facilitation, respectively. Compared to baseline, decreased rsFC at alpha oscillations (8-10Hz) was found following 1hour and 24hours of capsaicin application for connections projecting from medial prefrontal cortex (mPFC) and right angular gyrus (rAG) but not left angular gyrus (lAG) or posterior cingulate cortex (PCC): mPFC-PCC (1hour:P < .001, 24hours:P = .002), mPFC-rAG (1hour:P < .001, 24hours:P = .001), rAG-mPFC (1hour:P < .001, 24hours:P = .001), rAG-PCC (1hour:P < .001, 24hours:P = .004). Comparable decreased rsFC following 1hour and 24hours (P≤0.008) was found at beta oscillations, however, decreased projections from PCC were also found: PCC-rAG (P≤0.005) and PCC-lAG (P≤0.006). Pain NRS scores following 24hours (3.7±0.4) was reduced by cooling (0.3±0.1, P = .004) and increased by heating (4.8±0.6, P = .016). However, neither cooling nor heating altered rsFC. This study shows that 24hours of experimental pain induces a robust decrease in DMN connectivity that persists during pain relief or facilitation suggesting a possible shift to attentional and emotional processing in persistent pain. PERSPECTIVE: This article shows decreased DMN connectivity that might reflect possible attentional and emotional changes during acute and prolonged pain. Understanding these changes could potentially help clinicians in developing therapeutic methods that can better target these attentional and emotional processes before developing into more persistent states.

Angular gyrus connectivity at alpha and beta oscillations is reduced during tonic pain - Differential effect of eye state

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Tonic pain differentially altered AG connectivity during eyes closed and eyes open.

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Negative mood and/or sleep quality can influence pain-related connectivity.

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Eyes closed baseline may allow for a reliable detection of pain-related changes.

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Eyes-closed-eyes-open sequence is crucial for assessing pain-related connectivity.

The angular gyrus (AG) is a common hub in the pain networks. The role of the AG during pain perception, however, is still unclear. This crossover study examined the effect of tonic pain on resting state functional connectivity (rsFC) of the AG under eyes closed (EC) and eyes open (EO). It included two sessions (placebo/pain) separated by 24 hours. Pain was induced using topical capsaicin (or placebo as control) on the right forearm. Electroencephalographic rsFC assessed by Granger causality was acquired from 28 healthy participants (14 women) before (baseline) and 1-hour following the application of placebo/capsaicin. Subjects were randomly assigned and balanced to groups of recording sequence (EC-EO, EO-EC). Decreased rsFC at alpha-1 and beta, but not alpha-2, oscillations was found during pain compared to baseline during EC only. For alpha-1, EC-EO group showed a pain-induced decrease only among connections between the right AG and each of the posterior cingulate cortex (PCC, P = 0.002), medial prefrontal cortex (mPFC, P = 0.005), and the left AG (P = 0.023). For beta rsFC, the EC-EO group showed a bilateral decrease in rsFC spanning the connections between the right AG and mPFC (P = 0.015) and between the left AG and each of PCC (P = 0.004) and mPFC (P = 0.026). In contrast, the EO-EC group showed an increase in beta rsFC only among connections between the left AG and each of PCC (P = 0.012) and mPFC (P = 0.036). No significant change in the AG rsFC was found during EO. These results provide insight into the involvement of the AG in pain perception and reveal methodological considerations when assessing rsFC during EO and EC.

Epidemiology and seasonality of human parainfluenza serotypes 1-3 in Australian children

Background

Parainfluenza viruses are significant contributors to childhood respiratory illness worldwide, although detailed epidemiological studies are lacking. Few recent Australian studies have investigated serotype‐specific PIV epidemiology, and there is a paucity of southern hemisphere PIV reports. We report age‐stratified PIV hospitalisation rates and a mathematical model of PIV seasonality and dynamics in Western Australia (WA).

Methods

We used linked perinatal, hospital admission and laboratory diagnostic data of 469 589 children born in WA between 1996 and 2012. Age‐specific rates of viral testing and PIV detection in hospitalised children were determined using person time‐at‐risk analysis. PIV seasonality was modelled using a compartmental SEIRS model and complex demodulation methods.

Results

From 2000 to 2012, 9% (n = 43 627) of hospitalised children underwent PIV testing, of which 5% (n = 2218) were positive for PIV‐1, 2 or 3. The highest incidence was in children aged 1‐5 months (PIV‐1:62.6 per 100 000 child‐years, PIV‐2:26.3/100 000, PIV‐3:256/100 000), and hospitalisation rates were three times higher for Aboriginal children compared with non‐Aboriginal children overall (IRR: 2.93). PIV‐1 peaked in the autumn of even‐numbered years, and PIV‐3 annually in the spring, whereas PIV‐2 had inconsistent peak timing. Fitting models to the higher incidence serotypes estimated reproduction numbers of 1.24 (PIV‐1) and 1.72 (PIV‐3).

Conclusion

PIV‐1 and 3 are significant contributors towards infant respiratory hospitalisations. Interventions should prioritise children in the first 6 months of life, with respect to the observed autumn PIV‐1 and spring PIV‐3 activity peaks. Continued surveillance of all serotypes and investigation into PIV‐1 and 3 interventions should be prioritised.

Complex Modulation Method for Measuring Cross-Frequency Coupling of Neural Oscillations

There is growing evidence from human intracranial electrocorticography (ECoG) studies that interactions between cortical frequencies are important for sensory perception, cognition and inter-regional neuronal communication. Recent studies have focused mainly on the strength of phase-amplitude coupling in cross-frequency interactions. Here, we introduce a complex modulation method based on measures of coherence to investigate cross-frequency coupling in the neural time series. This novel approach uses complex demodulation transform and coherence measures from the transformed signals. We used this method to quantify power coupling between two cortical frequency bands: theta (47 Hz) and high gamma (70-150 Hz) in ECoG signals recorded during an auditory task. We compared complex modulation results with traditional phase-amplitude coupling measures (PAC) derived from the same ECoG dataset. Our results suggest that cross-frequency coupling may involve changes in both phase-amplitude and power relationships between frequencies, reflecting the complexity of neuronal oscillatory interactions.

Dynamic coupling between slow waves and sleep spindles during slow wave sleep in humans is modulated by functional pre-sleep activation

Co-existent sleep spindles and slow waves have been viewed as a mechanism for offline information processing. Here we explored if the temporal synchronization between slow waves and spindle activity during slow wave sleep (SWS) in humans was modulated by preceding functional activations during pre-sleep learning. We activated differentially the left and right hemisphere before sleep by using a lateralized variant of serial response time task (SRTT) and verified these inter-hemispheric differences by analysing alpha and beta electroencephalographic (EEG) activities during learning. The stability and timing of coupling between positive and negative phases of slow waves and sleep spindle activity during SWS were quantified. Spindle activity was temporally synchronized with both positive (up-state) and negative (down-state) slow half waves. Synchronization of only the fast spindle activity was laterally asymmetric after learning, corresponding to hemisphere-specific activations before sleep. However, the down state was associated with decoupling, whereas the up-state was associated with increased coupling of fast spindle activity over the pre-activated hemisphere. These observations provide original evidence that (1) the temporal grouping of fast spindles by slow waves is a dynamic property of human SWS modulated by functional pre-sleep activation patterns, and (2) fast spindles synchronized by slow waves are functionally distinct.

Beta modulation reflects name retrieval in the human anterior temporal lobe: an intracranial recording study

Naming people, places, and things is a fundamental human ability that is often impaired in patients with language-dominant anterior temporal lobe (ATL) dysfunction or ATL resection as part of epilepsy treatment. Convergent lines of evidence point to the importance of the ATL in name retrieval. The physiologic mechanisms that mediate name retrieval in the ATL, however, are not well understood. The purpose of this study was to characterize the electrophysiologic responses of the human ATL during overt cued naming of famous people and objects. Eight neurosurgical patients with suspected temporal lobe epilepsy who underwent implantation of intracranial electrodes for seizure focus localization were the subjects of this study. Specialized coverage of the ATL was achieved in each subject. The subjects named pictures of U.S. presidents and images of common hand-held tools. Event-related band power was measured for each ATL recording site. Both the left and right ATL demonstrated robust and focal increases in beta-band (14-30 Hz) power during person and tool naming. The onset of this response typically occurred at 400 ms but sometimes as early as 200 ms. Visual naming of famous people and tools is associated with robust and localized modulation of the beta band in both the left and right ATL. Measurement of visual naming responses may provide the groundwork for future mapping modalities to localize eloquent cortex in the ATL.

The demodulated band transform

BACKGROUND

Windowed Fourier decompositions (WFD) are widely used in measuring stationary and non-stationary spectral phenomena and in describing pairwise relationships among multiple signals. Although a variety of WFDs see frequent application in electrophysiological research, including the short-time Fourier transform, continuous wavelets, bandpass filtering and multitaper-based approaches, each carries certain drawbacks related to computational efficiency and spectral leakage. This work surveys the advantages of a WFD not previously applied in electrophysiological settings.

NEW METHODS

A computationally efficient form of complex demodulation, the demodulated band transform (DBT), is described.

RESULTS

DBT is shown to provide an efficient approach to spectral estimation with minimal susceptibility to spectral leakage. In addition, it lends itself well to adaptive filtering of non-stationary narrowband noise.

COMPARISON WITH EXISTING METHODS

A detailed comparison with alternative WFDs is offered, with an emphasis on the relationship between DBT and Thomson's multitaper. DBT is shown to perform favorably in combining computational efficiency with minimal introduction of spectral leakage.

CONCLUSION

DBT is ideally suited to efficient estimation of both stationary and non-stationary spectral and cross-spectral statistics with minimal susceptibility to spectral leakage. These qualities are broadly desirable in many settings.

Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization

A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing "background" sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters. Complex demodulation was used to extract instantaneous power in the spindle band. To account for intra- and inter-individual differences, the signal was z-score transformed using a 60 s sliding window, per channel, over the course of the recording. Spindle events were detected with a z-score threshold corresponding to a low probability (e.g., 99th percentile). Spindle characteristics, such as amplitude, duration and oscillatory frequency, were derived for each individual spindle following detection, which permits spindles to be subsequently and flexibly categorized as slow or fast spindles from a single detection pass. Spindles were automatically detected in 15 young healthy subjects. Two experts manually identified spindles from C3 during Stage 2 sleep, from each recording; one employing conventional guidelines, and the other, identifying spindles with the aid of a sigma (11-16 Hz) filtered channel. These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives. This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles.

A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings

Sleep spindles are a hallmark of NREM sleep. They result from a widespread thalamo-cortical loop and involve synchronous cortical networks that are still poorly understood. We investigated whether brain activity during spindles can be characterized by specific patterns of functional connectivity among cortical generators. For that purpose, we developed a wavelet-based approach aimed at imaging the synchronous oscillatory cortical networks from simultaneous MEG-EEG recordings. First, we detected spindles on the EEG and extracted the corresponding frequency-locked MEG activity under the form of an analytic ridge signal in the time-frequency plane (Zerouali et al., 2013). Secondly, we performed source reconstruction of the ridge signal within the Maximum Entropy on the Mean framework (Amblard et al., 2004), yielding a robust estimate of the cortical sources producing observed oscillations. Lastly, we quantified functional connectivity among cortical sources using phase-locking values. The main innovations of this methodology are (1) to reveal the dynamic behavior of functional networks resolved in the time-frequency plane and (2) to characterize functional connectivity among MEG sources through phase interactions. We showed, for the first time, that the switch from fast to slow oscillatory mode during sleep spindles is required for the emergence of specific patterns of connectivity. Moreover, we show that earlier synchrony during spindles was associated with mainly intra-hemispheric connectivity whereas later synchrony was associated with global long-range connectivity. We propose that our methodology can be a valuable tool for studying the connectivity underlying neural processes involving sleep spindles, such as memory, plasticity or aging.

Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states

Backpropagation artificial neural network (ANN) has been designed to classify sleep-wake stages. Four hours continuous three channel polygraphic signals such as EEG (electroencephalogram), EOG (electrooculogram) and EMG (electromyogram) from conscious subjects were digitally recorded and stored in computer. EOG and EMG signals were used for manual identification of sleep states before training and testing of ANN. The percentages power of the 2 s epochs of the digitized EEG signals from each of three sleep-wake patterns, sleep spindles (SS), rapid eye movement (REM) sleep and awake (AWA) sates, were calculated and analyzed to select the manually confirmed sleep-wake states for each epoch. Further, second order Daubechies mother wavelet has been used to get the wavelet coefficients for the selected EEG epochs. The wavelet coefficients for the EEG epochs (64 data) were selected as inputs for the training the network and to classify SS, REM sleep and AWA stages. The ANN architecture used (64-14-3) in present study shows overall very good agreement with manual sleep stage scoring with an average of 95.35% for all the 1,140 samples tested from SS, REM and AWA stages. This architecture of ANN was also found effectively differentiating the EEG power spectra from different sleep-wake states (96.84% in SS, 93.68% in REM sleep, 95.52% in AWA state). The high performance observed with the system based on wavelet coefficients along with the ANN, highlights the need of this computational tool into the field of sleep research.

Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study

An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.5%, while the false positive rate ranges from 3.8% to 15.5%. Furthermore, due to the operation of the ANN on time-domain EEG data, there is agreement with visual assessment concerning temporal resolution. Specifically, the total inter-spindle interval duration and the total duration of spindles are calculated with 99% and 92% accuracy, respectively. Therefore, the present method may be suitable for investigations of the dynamics among successive inter-spindle intervals, which could provide information on the role of spindles in the sleep process, and for studies of pharmacological effects on sleep structure, as revealed by the modification of total spindle duration.

Low-resolution brain electromagnetic tomography revealed simultaneously active frontal and parietal sleep spindle sources in the human cortex

Analyses of scalp-recorded sleep spindles have demonstrated topographically distinct slow and fast spindle waves. In the present paper, the electrical activity in the brain corresponding to different types of sleep spindles was estimated by means of low-resolution electromagnetic tomography. In its new implementation, this method is based on realistic head geometry and solution space is restricted to the cortical gray matter and hippocampus. In multichannel all-night electroencephalographic recordings, 10-20 artifact-free 1.25-s epochs with frontally, parietally and approximately equally distributed spindles were marked visually in 10 normal healthy subjects aged 20-35years. As a control condition, artifact-free non-spindle epochs 1-3s before or after the corresponding spindle episodes were marked. Low-resolution electromagnetic tomography demonstrated, independent of the scalp distribution, a distributed spindle source in the prefrontal cortex (Brodmann areas 9 and 10), oscillating with a frequency below 13Hz, and in the precuneus (Brodmann area 7), oscillating with a frequency above 13Hz. In extremely rare cases only the prefrontal or the parietal source was active. Brodmann areas 9 and 10 have principal connections to the dorsomedial thalamic nucleus; Brodmann area 7 is connected to the lateroposterior, laterodorsal and rostral intralaminar centrolateral thalamic nuclei. Thus, the localized cortical brain regions are directly connected with adjacent parts of the dorsal thalamus, where sleep spindles are generated. The results demonstrated simultaneously active cortical spindle sources which differed in frequency by approximately 2Hz and were located in brain regions known to be critically involved in the processing of sensory input, which is in line with the assumed functional role of sleep spindles.

Instantaneous frequency maps, dipole models and potential distributions of pattern reversal-evoked potential fields for correct recognition of stimulated hemiretinae

Lateral hemifield pattern-reversal visual evoked potential (PVEP) field data were evaluated using potential distributions, dipole modelling and distributions of Hilbert transformation-based instantaneous frequency in order to determine the stimulated hemisphere. Twenty channel records were collected from 35 normal volunteers in two laboratories using similar stimulus conditions (11-20.5 degrees target, 60-75 min checks, 2/s reversal, 500 ms analysis epoch). P100 latency was determined in each average by the global field power maximum between 90 and 120 ms. Using the data from O1 and O2 at P100 latency, the stimulated hemisphere was identified by maximal potential or minimal instantaneous frequency on the stimulus-contralateral side, or, using the 20-electrodes data at P100 by the ipsilateral lateralization of the dipole model. Correct classification of the stimulated 70 hemiretinae was achieved by potential distribution in 44 cases, by dipole modelling in 54 cases and by instantaneous frequencies in 68 cases. Errors in the classification by potential distribution and dipole location were twice as frequent for decisions based on expected locations over the left than over the right hemisphere. This finding might be caused by the relatively larger size of the left occipital lobe. We conclude that a single value of instantaneous frequency which implies a massive data reduction can serve as a robust parameter for the characterization of the input conditions of hemifield PVEP (i.e. the stimulated hemiretina). It is more successful than potential distribution or dipole modelling, probably because instantaneous frequency incorporates considerably more information than the other two measures. It is suggested to explore instantaneous frequency as a parameter to recognize small retinal area stimuli in perimetry studies.

The future of computer-assisted investigation of the polysomnogram: sleep microstructure

Previous attempts at automated analysis of sleep were mainly directed towards imitating the Rechtschaffen and Kales rules (RKR) in order to save scoring time and further objectify the procedure. RKR, however, do not take into consideration the sleep microstructure of REM, stage 2, and SWS. While the microstructure of stage 2 has been analyzed in the past decade, the microstructure of REM and SWS are virtually unknown. In stage 2 the amount and distribution of spindles, K complexes, and arousal reactions have been studied. At least two types of spindles (12/s and 14/s) with different dynamics and locations have been identified. Two different shapes for K complexes have been described: one related to external sensory stimuli with similarities to evoked potentials and another one more related to sinusoidal slow wave activity seen in SWS. These two different K complex shapes have different distributions and, obviously, different functions. The authors also suggest that one should differentiate between arousal reactions and true arousals. Recent investigations suggest two types of delta waves in SWS. The more sinusoidal 1-3/s delta waves with a frontal maximum are already seen with lower amplitude in late stage 2 and increase their amplitude and incidence towards stage 3 and Stage 4. The other delta-wave type is slower (< 1/s), polymorphic, and has varying amounts of theta and higher frequency waves superimposed. During REM sleep it seems to be important to separate phases with rapid eye movements from those with none (REM sine REM), and count the amount and distribution of sawtooth activity. Background activity during REM and REM sine REM, as well as intra- and interhemispheric coherence should be analyzed separately. Only if the microstructure of the sleep EEG can be analyzed automatically using newer techniques such as transformation into wavelets and pattern classification with neuronal networks, and only if we learn more about the importance of microstructure elements, can automated sleep analysis go beyond the limited information obtained from scoring according to RKR.

Endogenous opioid peptide level changes under electrostimulation and their assessment by the EEG

Endogenous opioid peptide (EOP) system plays an important role in the interaction of human organism with different stress factors, providing stress-limiting and stress-protective functions. Different kinds of electrostimulation seem to produce anti-stress and pain relief effects due to EOP system activation. The presented paper reviews recent literature concerning EOP system activation under electrostimulation and its reflections in the EEG characteristics. The results and opportunities of high resolution EEG structure analysis utilization for EOP level control, as well as for stress-induced state assessment and correction via resonance activation of brain EEG oscillators by means of frequency-tuned external stimulation are presented.