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Study Objectives: Whereas both insomnia and altered interoception are core symptoms in affective disorders, their neural mechanisms remain insufficiently understood and have not previously been linked. Insomnia Disorder (ID) is characterized by sensory hypersensitivity during wakefulness and sleep. Previous studies on sensory processing in ID addressed external stimuli only, but not interoception. Interoceptive sensitivity can be studied quantitatively by measuring the cerebral cortical response to one's heartbeat (heartbeat-evoked potential, HEP). We here investigated whether insomnia is associated with increased interoceptive sensitivity as indexed by the HEP amplitude., Methods: Sixty-four participants aged 21-70 years were recruited through www.sleepregistry.nl including 32 people suffering from ID and 32 age- and sex-matched controls without sleep complaints. HEPs were obtained from resting-state high-density electroencephalography (HD-EEG) recorded during evening wakeful rest in eyes-open (EO) and eyes-closed (EC) conditions of 5-minute duration each. Significance of group differences in HEP amplitude and their topographical distribution over the scalp were assessed by means of cluster-based permutation tests., Results: In particular during EC, and to a lesser extent during EO, people with ID had a larger amplitude late HEP component than controls at frontal electrodes 376-500 ms after the R-wave peak. Source localization suggested increased neural activity time-locked to heartbeats in people with ID mainly in anterior cingulate/medial frontal cortices., Conclusions: People with insomnia show insufficient adaptation of their brain responses to the ever-present heartbeats. Abnormalities in the neural circuits involved in interoceptive awareness including the salience network may be of key importance to the pathophysiology of insomnia., (© 2016 Associated Professional Sleep Societies, LLC.)

Melanopsin-containing retinal ganglion cells have recently been shown highly relevant to the non-image forming effects of light, through their direct projections on brain circuits that regulate alertness, mood and circadian rhythms. A quantitative assessment of functionality of the melanopsin-signaling pathway could be highly relevant in order to mechanistically understand individual differences in the effects of light on these regulatory systems. We here propose and validate a reliable quantification of the melanopsin-dependent Post-Illumination Pupil Response (PIPR) after blue light, and evaluated its sensitivity to dark adaptation, time of day, body posture, and light exposure history. Pupil diameter of the left eye was continuously measured during a series of light exposures to the right eye, of which the pupil was dilated using tropicamide 0.5%. The light exposure paradigm consisted of the following five consecutive blocks of five minutes: baseline dark; monochromatic red light (peak wavelength: 630 nm, luminance: 375 cd/m(2)) to maximize the effect of subsequent blue light; dark; monochromatic blue light (peak wavelength: 470 nm, luminance: 375 cd/m(2)); and post-blue dark. PIPR was quantified as the difference between baseline dark pupil diameter and post-blue dark pupil diameter (PIPR-mm). In addition, a relative PIPR was calculated by dividing PIPR by baseline pupil diameter (PIPR-%). In total 54 PIPR assessments were obtained in 25 healthy young adults (10 males, mean age ± SD: 26.9 ± 4.0 yr). From repeated measurements on two consecutive days in 15 of the 25 participants (6 males, mean age ± SD: 27.8 ± 4.3 yrs) test-retest reliability of both PIPR outcome parameters was calculated. In the presence of considerable between-subject differences, both outcome parameters had very high test-retest reliability: Cronbach's α > 0.90 and Intraclass Correlation Coefficient > 0.85. In 12 of the 25 participants (6 males, mean age ± SD: 26.5 ± 3.6 yr) we examined the potential confounding effects of dark adaptation, time of the day (morning vs. afternoon), body posture (upright vs. supine position), and 24-h environmental light history on the PIPR assessment. Mixed effect regression models were used to analyze these possible confounders. A supine position caused larger PIPR-mm (β = 0.29 mm, SE = 0.10, p = 0.01) and PIPR-% (β = 4.34%, SE = 1.69, p = 0.02), which was due to an increase in baseline dark pupil diameter; this finding is of relevance for studies requiring a supine posture, as in functional Magnetic Resonance Imaging, constant routine protocols, and bed-ridden patients. There were no effects of dark adaptation, time of day, and light history. In conclusion, the presented method provides a reliable and robust assessment of the PIPR to allow for studies on individual differences in melanopsin-based phototransduction and effects of interventions., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Even under thermoneutral conditions, skin temperature fluctuates spontaneously, most prominently at distal parts of the body. These fluctuations were shown to be associated with fluctuations in vigilance: mild manipulation of skin temperature during nocturnal sleep affects sleep depth and the power spectral density of the electroencephalogram (EEG), and fluctuations in skin temperature during daytime wakefulness are related to sleep propensity and task performance. The association of daytime skin temperature fluctuations with EEG markers of vigilance has not previously been investigated. Therefore, the present study aimed to evaluate the association between daytime fluctuations in skin temperature with those in two quantitative EEG measures: the power spectral density of background EEG, and the event related potential (ERP) elicited by visual stimuli. High-density EEG and skin temperature were obtained in eight healthy adults five times a day while they performed a visual sustained-attention task. Assessments were made after a night of normal sleep and after the challenge of a night of total sleep deprivation. Fluctuations in the distal-to-proximal skin temperature gradient measured from the earlobe and mastoid were associated with fluctuations in parieto-occipital high beta band (20-40 Hz) power of the pre-stimulus background EEG, but only after sleep deprivation. The temperature fluctuations were moreover associated with fluctuations in the latency of the P300 elicited by the stimulus. The findings demonstrate close association between fluctuations in an autonomic correlate of the vigilance state (i.e. the distal-to-proximal skin temperature gradient), and fluctuations in central nervous system correlates of the vigilance state (i.e. background EEG and ERP). The findings are of theoretical and practical relevance for the assessment and manipulation of vigilance., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Directional connectivity in the brain has been typically computed between scalp electroencephalographic (EEG) signals, neglecting the fact that correlations between scalp measurements are partly caused by electrical conduction through the head volume. Although recently proposed techniques are able to identify causality relationships between EEG sources rather than between recording sites, most of them need a priori assumptions about the cerebral regions involved in the EEG generation. We present a novel methodology based on multivariate autoregressive (MVAR) modeling and Independent Component Analysis (ICA) able to determine the temporal activation of the intracerebral EEG sources as well as their approximate locations. The direction of synaptic flow between these EEG sources is then estimated using the directed transfer function (DTF), and the significance of directional coupling strength evaluated with surrogated data. The reliability of this approach was assessed with simulations manipulating the number of data samples, the depth and orientation of the equivalent source dipoles, the presence of different noise sources, and the violation of the non-Gaussianity assumption inherent to the proposed technique. The simulations showed the superior accuracy of the proposed approach over other traditional techniques in most tested scenarios. Its validity was also evaluated analyzing the generation mechanisms of the EEG-alpha rhythm recorded from 20 volunteers under resting conditions. Results suggested that the major generation mechanism underlying EEG-alpha oscillations consists of a strong bidirectional feedback between thalamus and cuneus. The precuneus also seemed to actively participate in the generation of the alpha rhythm although it did not exert a significant causal influence neither on the thalamus nor on the cuneus. All together, these results suggest that the proposed methodology is a promising non-invasive approach for studying directional coupling between mutually interconnected neural populations.

Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.

Objective: The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles., Methods and Materials: All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored bilateral spindles occurring in frontopolar or central brain location. In the present work a new sigma index for spindle detection was developed, based on the fast Fourier transform (FFT) spectrum, aiming at approximating our previous fuzzy spindle detector. The sigma index was complemented with spindle amplitude analysis, based on finite impulse response (FIR) filtering, to form of a combination detector of bilateral spindles. In this combination detector, the spindle amplitude distribution of each recording was estimated and used to tune two different amplitude thresholds. This combination detector was compared to bilaterally extracted sigma indexes and fuzzy detections, which aim to be independent of absolute spindle amplitudes. As a fourth method a fixed spindle amplitude detector was included., Results: The combination detector provided the best overall performance; in S2 sleep a 70% true positive rate was reached with a specificity of 98.6%, and a false-positive rate of 32%. The bilateral sigma indexes provided the second best results, followed by fuzzy detector, while the fixed amplitude detector provided the poorest results so that in S2 sleep a 70% true positive rate was reached with a specificity of 97.7% and false-positive rate of 46%. The spindle amplitude distributions automatically determined for each recording by the combination detector were compared to amplitudes of visually scored spindles and they proved to correspond well. Inter-hemispheric amplitude variation of visually scored bilateral spindles is also presented., Conclusion: Flexibility is beneficial in the detection of bilateral spindles. The present work advances automated spindle detection and increases the knowledge of bilateral sleep spindle characteristics.

The prompt and adequate detection of abnormal cardiac conditions by computer-assisted long-term monitoring systems depends greatly on the reliability of the implemented ECG automatic analysis technique, which has to discriminate between different types of heartbeats. In this paper, we present a comparative study of the heartbeat classification abilities of two techniques for extraction of characteristic heartbeat features from the ECG: (i) QRS pattern recognition method for computation of a large collection of morphological QRS descriptors; (ii) Matching Pursuits algorithm for calculation of expansion coefficients, which represent the time-frequency correlation of the heartbeats with extracted learning basic waveforms. The Kth nearest neighbour classification rule has been applied for assessment of the performances of the two ECG feature sets with the MIT-BIH arrhythmia database for QRS classification in five heartbeat types (normal beats, left and right bundle branch blocks, premature ventricular contractions and paced beats), as well as with five learning datasets-one general learning set (GLS, containing 424 heartbeats) and four local sets (GLS+about 0.5, 3, 6, 12 min from the beginning of the ECG recording). The achieved accuracies by the two methods are sufficiently high and do not show significant differences. Although the GLS was selected to comprise almost all types of appearing heartbeat waveforms in each file, the guaranteed accuracy (sensitivity between 90.7% and 99%, specificity between 95.5% and 99.9%) was reasonably improved when including patient-specific local learning set (sensitivity between 94.8% and 99.9%, specificity between 98.6% and 99.9%), with optimal size found to be about 3 min. The repeating waveforms, like normal beats, blocks, paced beats are better classified by the Matching Pursuits time-frequency descriptors, while the wide variety of bizarre premature ventricular contractions are better recognized by the morphological descriptors.

Accurate analysis of EEG sleep spindle frequency is challenging. The frequency content of true sleep spindles is not known. Therefore, simulated spindle activity was studied in the present work. Five types of simulated test signals were designed, all containing a dominant spindle represented by a 13-Hz sine wave as such or with a waxing and waning pattern accompanied by a secondary spindle activity in three test signals. Background EEG was included in four test signals, modeled either as small additional sinusoids across the spindle frequency range or as filtered Gaussian noise segments. The purpose of this study was to investigate how accurately the dominant spindle frequency of 13 Hz could be resolved with different methods in the presence of the interfering waveforms. A matching pursuit (MP) based approach, discrete Fourier transform (DFT) with Hanning windowing with and without zero padding, Hankel total least squares (HTLS) and wavelet methods were compared in the analyses. MP method provided best overall performance, followed closely by DFT with zero padding. Comparative studies like this are important to decide the method of choice in clinical sleep EEG analysis.