Is Dispositional Self-Compassion Associated With Psychophysiological Flexibility Beyond Mindfulness? An Exploratory Pilot Study.

BACKGROUND: Dispositional mindfulness and self-compassion are shown to associate with less self-reported emotional distress. However, previous studies have indicated that dispositional self-compassion may be an even more important buffer against such distress than dispositional mindfulness. To our knowledge, no study has yet disentangled the relationship between dispositional self-compassion and mindfulness and level of psychophysiological flexibility as measured with vagally mediated heart rate variability (vmHRV). The aim was thus to provide a first exploratory effort to expand previous research relying on self-report measures by including a psychophysiological measure indicative of emotional stress reactivity. METHODS: Fifty-three university students filled out the "Five Facet Mindfulness Questionnaire" (FFMQ) and the "Self-Compassion Scale" (SCS), and their heart rate was measured during a 5 min resting electrocardiogram. Linear hierarchical regression analyses were conducted to examine the common and unique variance explained by the total scores of the FFMQ and the SCS on level of resting vmHRV. RESULTS: Higher SCS total scores associated significantly with higher levels of vmHRV also when controlling for the FFMQ total scores. The SCS uniquely explained 7% of the vmHRV. The FFMQ total scores did not associate with level of vmHRV. CONCLUSION: These results offer preliminary support that dispositional self-compassion associates with better psychophysiological regulation of emotional arousal above and beyond mindfulness.

Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients.

The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer's disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia.

Trait Self-Compassion Reflects Emotional Flexibility Through an Association with High Vagally Mediated Heart Rate Variability.

Converging evidence shows a positive effect of self-compassion on self-reported well-being and mental health. However, few studies have examined the relation between self-compassion and psychophysiological measures. In the present study, we therefore examined the relation between trait self-compassion and vagally mediated heart rate variability (vmHRV) in 53 students (39 female, mean age = 23.63). Trait self-compassion was assessed using the Self-Compassion Scale, and resting vmHRV was measured during a 5-min ECG baseline period. We hypothesized that higher levels of trait self-compassion would predict higher levels of resting vmHRV. Controlling for potential covariates (including age, gender, and BMI), the results confirmed our hypotheses, showing that higher levels of trait self-compassion predicted higher vmHRV. These results were validated with a 24-h measure of vmHRV, acquired from a subsample of the participants (n = 26, 16 female, mean age = 23.85), confirming the positive correlation between high trait self-compassion and higher vmHRV. The relation between trait self-compassion, vmHRV, self-reported trait anxiety (the trait scale of the State-Trait Anxiety Inventory; STAI) and self-reported rumination (the Rumination subscale of the Rumination-Reflection Questionnaire; RRQ-Rum) was also investigated. Higher levels of trait anxiety and rumination were highly correlated with low levels of trait self-compassion. Trait anxiety, but not rumination, correlated marginally significantly with the level of vmHRV. The findings of the present study indicate that trait self-compassion predicts a better ability to physiologically and psychologically adapt emotional responses. Possible implications and limitations of the study are discussed.

EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia.

Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.

Disturbance of automatic auditory change detection in dementia associated with Parkinson's disease: A mismatch negativity study.

OBJECTIVE: To investigate whether automatic auditory change detection, as measured by the mismatch negativity (MMN) event-related potential waveform, differs in dementia associated with Parkinson's disease (PDD) and dementia with Lewy-bodies (DLB) as compared to Alzheimer's disease (AD), Parkinson's disease without dementia (PD) and healthy control subjects (HC). METHOD: Seventeen DLB, 15 PDD, 16 PD, 16 AD patients and 18 HC subjects participated. A passive MMN event-related potential paradigm and an oddball-distractor reaction time paradigm were presented. RESULTS: The PDD patients had reduced MMN area and amplitude compared to the DLB, PD, and the HC groups. The MMN area correlated significantly with number of missed target stimuli in the oddball-distractor task, and the PDD group missed targets significantly more often than the DLB group. CONCLUSION: The results indicate that PDD patients to a larger degree than patients with DLB have a deficit of automatic auditory change detection that contributes to impairment in their ability to selectively attend and respond to deviant auditory stimuli.

Realignment parameter-informed artefact correction for simultaneous EEG-fMRI recordings.

In this work we introduce a new algorithm to correct the imaging artefacts in the EEG signal measured during fMRI acquisition. The correction techniques proposed so far cannot optimally represent transitions, i.e. when abrupt changes of the artefact properties due to head movements occur. The algorithm developed here takes the head movement parameters from the fMRI signal into account to calculate adequate EEG artefact templates and subsequently correct the distorted EEG data. The data reported in this work demonstrate that the realignment parameter-informed algorithm outperforms the commonly used moving average algorithm if head movements occur. The superiority is reflected by comparing the residual variance after artefact correction with either method. The residual variance is lower around head-movements that exceed head deflections of about 1 mm when applying the realignment parameter-informed algorithm. Additionally, the signal to noise ratio of a surrogate event-related potential (ERP) increased by 10-40% for head displacements larger than 1 mm. The algorithm developed here is particularly suited for studies where head movements of the subject cannot be prevented as in studies with patients, children, or during sleep. Furthermore, the enhanced signal to noise ratio of a single trial ERP indicates the power of the presented algorithm for single trial ERP-fMRI studies in which EEG signal quality is a critical factor.

Unmixing concurrent EEG-fMRI with parallel independent component analysis.

Concurrent event-related EEG-fMRI recordings pick up volume-conducted and hemodynamically convoluted signals from latent neural sources that are spatially and temporally mixed across the brain, i.e. the observed data in both modalities represent multiple, simultaneously active, regionally overlapping neuronal mass responses. This mixing process decreases the sensitivity of voxel-by-voxel prediction of hemodynamic activation by the EEG when multiple sources contribute to either the predictor and/or the response variables. In order to address this problem, we used independent component analysis (ICA) to recover maps from the fMRI and timecourses from the EEG, and matched these components across the modalities by correlating their trial-to-trial modulation. The analysis was implemented as a group-level ICA that extracts a single set of components from the data and directly allows for population inferences about consistently expressed function-relevant spatiotemporal responses. We illustrate the utility of this method by extracting a previously undetected but relevant EEG-fMRI component from a concurrent auditory target detection experiment.

Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation.

An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

Hemispheric asymmetries in the processing of temporal acoustic cues in consonant-vowel syllables.

PURPOSE: In order to examine auditory lateralization of prelexical speech processing, a dichotic listening task was performed with concurrent EEG measurement. METHODS: Subjects were tested with dichotic pairs of six consonant-vowel (CV) syllables that initially started with a voiced (/ba/, /da/, /ga/) or a voiceless stop consonant (/pa/, /ta/, /ka/). Electrophysiological correlates were analyzed by a low resolution electromagnetic tomography (LORETA) approach to estimate the sources of N1 event-related potentials (ERP) in the 3D brain. RESULTS: Behavioral and electrophysiological measures revealed different ear advantages and ERP amplitude measures for voiced and voiceless syllables. Fronto-central N1 amplitudes were larger for syllables with voiced than voiceless initial consonants. LORETA source estimates revealed a lateralization effect, with stronger leftward lateralization for voiced than voiceless CV syllables. CONCLUSIONS: The present study demonstrates that auditory lateralization is affected by temporal cues in CV syllables. The lateralization effect suggests that functional hemispheric differences exist at an early prelexical level of speech processing.

Tracking pattern learning with single-trial event-related potentials.

OBJECTIVE: The main aim was to track the dynamics of pattern-learning using single-trial event-related potentials (ERPs). A new 'learning-oddball' paradigm was employed presenting eight random targets (the 'no-pattern') followed by eight regular targets (the 'pattern'). In total, six repetitions of the 'no-pattern' followed by the 'pattern' were presented. METHODS: We traced the dynamics of learning by measuring responses to 16 (eight random-eight regular) targets. Since this alternation of the 'no-pattern' followed by the 'pattern' was repeated six times, we extracted single-trial responses to all 96 targets to determine if learning occurred more rapidly with each repetition of the 'pattern.' RESULTS: Following random targets, ERPs contained a marked P3-N2 component that decreased to regular targets, whereas a contingent negative variation (CNV) appeared. ERP changes could be best described by sigmoid 'learning' curves. Single-trial analyses showed that learning occurred more rapidly over repetitions and suggested that the CNV developed prior to the decay of the N2-P3 component. CONCLUSIONS: We show a new paradigm-analysis methodology to track learning processes directly from brain signals. SIGNIFICANCE: Single-trial ERPs analyses open a wide range of applications. Tracking the dynamic structure of cognitive functions may prove crucial in the understanding of learning and in the study of different pathologies.

Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI.

The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (odd-ball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se, single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology.

Asymmetry of evoked potential latency to speech sounds predicts the ear advantage in dichotic listening.

The functional organization of the human auditory cortex is still not well understood with respect to speech perception and language lateralization. Especially, there is comparatively little data available in the brain imaging literature focusing on the timing of phonetic processing. We recorded auditory-evoked potentials (AEP) from 27 scalp and additional EOG channels in 12 healthy volunteers performing a free report dichotic listening task with simple speech sounds (CV syllables: [ba], [da], [ga], [pa], [ta], [ka]). ERP analysis employed independent components analysis (ICA) wavelet denoising for artifact reduction and improvement of the SNR. The main finding was a 15-ms shorter average latency of the N1-AEP recorded from the scalp approximately overlying the left supratemporal cortical plane compared to the N1-AEP over the homologous right side. Corresponding N1 amplitudes did not differ between these sites. The individual AEP latency differences significantly correlated with the ear advantage as an index of speech/language lateralization. The behaviorally relevant difference in N1 latency between the hemispheres indicates that an important key to understanding speech perception is to consider the functional implications of neuronal event timing.

Antecedent reactivation by surface and deep anaphora in Norwegian.

Anaphora are expressions in language that depend on other linguistic entities for their full meaning. They can furthermore be divided into two types according to the level of representation where they find their antecedents: Surface anaphora, which resolve their reference at the sentence representation level, and deep anaphora, which resolve their reference at the non-grammatical level of discourse representation. The linguistic theory of these two anaphor types, and recent findings about processing differences at these two levels, combine to predict that surface anaphora should show fast and immediate reactivation of their antecedents, whereas deep anaphora should have a slower time course of antecedent re-access. These predictions were confirmed with two lexical decision task experiments with Norwegian stimuli.