The interacting partner as the immediate environment: Personality, interpersonal dynamics, and bodily synchronization

OBJECTIVE

In social interactions, humans tend to naturally synchronize their body movements. We investigated interpersonal synchronization in conversations and examined its relationship with personality differences and post-interaction appraisals.

METHOD

In a 15-minute semi-structured conversation, 56 previously-unfamiliar dyads introduced themselves, followed by self-disclosing and argumentative conversations. Their bodily movements were video-recorded in a standardized room (112 young adults, aged 18-33, mean = 20.54, SD = 2.74; 58% Dutch, 31% German, 11% other). Interpersonal bodily synchronization was estimated as (a) synchronization strength using Windowed Lagged Cross-Correlations and (b) Dynamic Organization (Determinism/Entropy/Laminarity/Mean Line) using Cross-Recurrence Quantification Analysis. Bodily synchronization was associated with differences in Agreeableness and Extraversion (IPIP-NEO-120) and post-conversational appraisals (affect/closeness/enjoyment) in mixed-effect models.

RESULTS

Agreeable participants exhibited higher complexity in bodily synchronization dynamics (higher Entropy) than disagreeable individuals, who also reported more negative affect afterward. Interpersonal synchronization was stronger among extroverts than among introverts and extroverts appraised conversations as more positive and enjoyable. Bodily synchronization strength and dynamic organization were related to the type of conversation (self-disclosing/argumentative).

CONCLUSIONS

Interpersonal dynamics were intimately connected to differences in Agreeableness and Extraversion, varied across situations, and these parameters affected how pleasant, close, and enjoyable each conversation felt.

Dimensionality reduction and recurrence analysis reveal hidden structures of striatal pathological states

A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: "fingerprints" of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.

Spike Spectra for Recurrences

In recurrence analysis, the τ-recurrence rate encodes the periods of the cycles of the underlying high-dimensional time series. It, thus, plays a similar role to the autocorrelation for scalar time-series in encoding temporal correlations. However, its Fourier decomposition does not have a clean interpretation. Thus, there is no satisfactory analogue to the power spectrum in recurrence analysis. We introduce a novel method to decompose the τ-recurrence rate using an over-complete basis of Dirac combs together with sparsity regularization. We show that this decomposition, the inter-spike spectrum, naturally provides an analogue to the power spectrum for recurrence analysis in the sense that it reveals the dominant periodicities of the underlying time series. We show that the inter-spike spectrum correctly identifies patterns and transitions in the underlying system in a wide variety of examples and is robust to measurement noise.

Experimental Verification of the Impact of Radial Internal Clearance on a Bearing's Dynamics

This paper focuses on the influence of radial internal clearance on the dynamics of a rolling-element bearing. In the beginning, the 2-Degree of Freedom (DOF) model was studied, in which the clearance was treated as a bifurcation parameter. The derived nonlinear mathematical model is based on Hertzian contact theory and takes into consideration shape errors of rolling surfaces and eccentricity reflecting real operating conditions. The analysis showed characteristic dynamical behavior by specific clearance range, which reflects others in a low or high amplitude and can refer to the optimal clearance. The experimental validation was conducted with the use of a double row self-aligning ball bearing (SABB) NTN 2309SK in which the acceleration response was measured by various rotational velocities. The time series obtained from the mathematical model and the experiment were analyzed with the recurrence quantification analysis.

Primary radiation therapy in stage I/II indolent orbital lymphoma - a comprehensive retrospective recurrence and toxicity analysis

PURPOSE OR OBJECTIVE

To provide a comprehensive recurrence and toxicity analysis of patients treated with radiotherapy alone for stage I/II (Ann-Arbor classification) indolent orbital lymphoma.

MATERIAL AND METHODS

We retrospectively reviewed the medical charts of 46 patients (and 51 orbits) treated at our centre with radiotherapy between 1995 and 2012 for biopsy-proven stage I/IIE primary orbital lymphomas. We evaluated treatment response and performed a comprehensive toxicity analysis with correlation to delivered radiation dose.

RESULTS

At diagnosis, the median age was 63.5 years (range: 20-92). At initial diagnosis 43 and 3 patients had unilateral, synchronous bilateral involvement while there were 2 cases of contralateral metachronous failure. The predominant histological subtype was extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue in 42 (91.3%), follicular in 1 (2.2%), lymphoplasmacytic lymphoma in 1 (2.2%) and other indolent histology in 2 (4.3%) patients. Most lymphomas were located in the conjunctiva (18/35.3%) or eyelids (18/35.3%). Thirty-eight (82.6%) patients presented with stage I while 8/46 (17.4%) with stage II disease. The median radiation dose was 39.6 Gy (range: 21.6-48.6 Gy) delivered in 1.8-2 Gy single fractions. At a median follow-up of 83 months (range: 7-258 months), the complete remission rate was 98%. A local relapse was observed in 2/51 (3.9%) orbits and 4/46 (8.7%) patients had systemic relapse. The 5- and 10-year PFS rates were 79.2% (95% CI: 73.0%-85.4%) and 67.6% (95% CI: 59.4%-75.8%); 5- and 10-year OS was 83.6% (95% CI: 77.9%-89.3%) and 76.5% (95% CI: 69.4%-83.6%), respectively. In total, 66 acute toxicity events (all-grade) were observed: 5/51 (9.8%) ≥G2 acute conjunctivitis, 2/51 (3.9%) cases of G2 acute keratitis, 1/51 (2%) cases of ≥G2 ophthalmagia and 12/51 (23.5%) cases of ≥G2 xerophthalmia. Furthermore, 45 chronic adverse events were observed in 34/51 (66.7%) irradiated orbits with 30 late adverse events attributed to cataract.

CONCLUSION

Our analysis confirms the role of radiotherapy alone at lower doses in the treatment of indolent orbital lymphomas. Further research is required to assess the efficacy of ultra-low-dose radiotherapy and anti-CD20 monoclonal antibodies to further mitigate long-term sequelae.

NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis

In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of non-linear methods. Available packages mostly specialize in only one aspect of non-linear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for non-linear time series analysis. In comparison to other currently available non-linear packages, NoLiTiA offers (1) an implementation of a broad range of classic and recently developed methods, (2) an implementation of newly proposed spatially and time-resolved recurrence amplitude analysis and (3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make non-linear methods accessible to the broad neuroscientific community engaged in time series processing.

A Waveform-Independent Measure of Recurrent Neural Activity

Rhythmic neural activity, so-called oscillations, plays a key role in neural information transmission, processing, and storage. Neural oscillations in distinct frequency bands are central to physiological brain function, and alterations thereof have been associated with several neurological and psychiatric disorders. The most common methods to analyze neural oscillations, e.g., short-time Fourier transform or wavelet analysis, assume that measured neural activity is composed of a series of symmetric prototypical waveforms, e.g., sinusoids. However, usually, the models generating the signal, including waveform shapes of experimentally measured neural activity are unknown. Decomposing asymmetric waveforms of nonlinear origin using these classic methods may result in spurious harmonics visible in the estimated frequency spectra. Here, we introduce a new method for capturing rhythmic brain activity based on recurrences of similar states in phase-space. This method allows for a time-resolved estimation of amplitude fluctuations of recurrent activity irrespective of or specific to waveform shapes. The algorithm is derived from the well-established field of recurrence analysis, which, in comparison to Fourier-based analysis, is still very uncommon in neuroscience. In this paper, we show its advantages and limitations in comparison to short-time Fourier transform and wavelet convolution using periodic signals of different waveform shapes. Furthermore, we demonstrate its application using experimental data, i.e., intracranial and noninvasive electrophysiological recordings from the human motor cortex of one epilepsy patient and one healthy adult, respectively.

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

Recurrence Quantitative Analysis of Wavelet-Based Surrogate Data for Nonlinearity Testing in Heart Rate Variability

Exploring the presence of nonlinearity through surrogate data testing provides insights into the nature of physical and biological systems like those obtained from heart rate variability (HRV). Short-term HRV time series are of great clinical interest to study autonomic impairments manifested in chronic diseases such as the end stage renal disease (ESRD) and the response of patients to treatment with hemodialysis (HD). In contrast to Iterative Amplitude Adjusted Fourier Transform (IAAFT), the Pinned Wavelet Iterative Amplitude Adjusted Fourier Transform (PWIAAFT) surrogates preserve nonstationary behavior in time series, a common characteristic of HRV. We aimed to test synthetic data and HRV time series for the existence of nonlinearity. Recurrence Quantitative Analysis (RQA) indices were used as discriminative statistics in IAAFT and PWIAAFT surrogates of linear stationary and nonstationary processes. HRV time series of healthy subjects and 29 ESRD patients before and after HD were tested in this setting during an active standing test. Contrary to PWIAAFT, linear nonstationary time series may be erroneously regarded as nonlinear according to the IAAFT surrogates. Here, a lower proportion of HRV time series was classified as nonlinear with PWIAAFT, compared to IAAFT, confirming that the nonstationarity condition influences the testing of nonlinear behavior in HRV. A contribution of nonlinearity was found in the HRV data of healthy individuals. A lower proportion of nonlinear time series was also found in ESRD patients, but statistical significance was not found. Although this proportion tends to be lower in ESRD patients, as much as 60% of time series proved to be nonlinear in healthy subjects. Given the important contribution of nonlinearity in HRV data, a nonlinear point of view is required to achieve a broader understanding of cardiovascular physiology.

Football Match Dynamics Explored by Recurrence Analysis

A widely accepted notion of football matches in performance analysis (PA) is to consider them as dynamic interaction processes with emerging behaviors. The description and analysis of these processes requires specific methods. Recurrence analysis is a technique for analyzing complex systems in many domains like astrophysics, earth sciences, engineering, biology, cardiology, and neuroscience. Its general concept is to examine the recurrence behavior of a system, as in when, how often and how close its trajectory in a phase space returns to a previous state. The aim of the study is to apply recurrence analysis to football matches. Positional data from 21 football matches of a German Bundesliga team were examined. The phase space was made up of the field players' x,y-positions at each second of the match. For each pair of seconds, the average distance of all the players between their positions at these two time points was calculated. Recurrence plots (RPs) were obtained by color-coding these distances. With a recurrence threshold of rt = 9 m and a minimum line length of lmin = 3 s, general recurrence parameters were calculated to characterize the individual recurrence behaviors of each match. Three football-specific recurrence parameters were defined to represent recurrence properties of open play. RPs showed commonalities (typical features indicating set plays and continuous gameplay) as well as unique structures during each match (number, distribution, and sequence of typical features). The recurrence parameters showed several significant correlations with traditional performance indicators like number of goals and passes completed, e.g., the correlation between number of goals and recurrence rate is r = -0.622 (p = 0.003). By extending the sample and design of recurrence studies, there is great potential for recurrence analysis to improve both the practical and theoretical potential of performance analysis.

Bimanual Coordination in a Whole-Body Dynamic Balance Sport, Slacklining: A Comparison of Novice and Expert

As previous studies have suggested that bimanual coordination is important for slacklining, the authors questioned whether this important skill plays a role in the performance of a fundamental task of slacklining. To address this question, the authors compared single-leg standing on the slackline between novices and experts in terms of bimanual coordination dynamics within a dynamical systems framework using relative phase and recurrence quantification analysis measures. Five novices and five experts participated in the experiment. Participants were required to perform single-leg standing on a slackline. To collect motion data while slacklining, the authors used a 3D motion capture system and obtained time series data on the wrist position of both hands. The authors compared bimanual coordination dynamics between novices and experts. Although this preliminary study was limited in its sample size, the results suggest that experts tend to show a more antiphase coordination pattern than novices do and that they can more sustainably coordinate their hands compared with novices in terms of temporal structure in diagonal-related recurrence measures (i.e., maxline, mean line, and percentage determinism).

An Approach to Aligning Categorical and Continuous Time Series for Studying the Dynamics of Complex Human Behavior

An emerging perspective on human cognition and performance sees it as a kind of self-organizing phenomenon involving dynamic coordination across the body, brain and environment. Measuring this coordination faces a major challenge. Time series obtained from such cognitive, behavioral, and physiological coordination are often complicated in terms of non-stationarity and non-linearity, and in terms of continuous vs. categorical scales. Researchers have proposed several analytical tools and frameworks. One method designed to overcome these complexities is recurrence quantification analysis, developed in the study of non-linear dynamics. It has been applied in various domains, including linguistic (categorical) data or motion (continuous) data. However, most previous studies have applied recurrence methods individually to categorical or continuous data. To understand how complex coordination works, an integration of these types of behavior is needed. We aimed to integrate these methods to investigate the relationship between language (categorical) and motion (continuous) directly. To do so, we added temporal information (a time stamp) to categorical data (i.e., language), and applied joint recurrence analysis methods to visualize and quantify speech-motion coordination coupling during a rap performance. We illustrate how new dynamic methods may capture this coordination in a small case-study design on this expert rap performance. We describe a case study suggesting this kind of dynamic analysis holds promise, and end by discussing the theoretical implications of studying complex performances of this kind as a dynamic, coordinated phenomenon.

Periodontal regeneration versus extraction and dental implant or prosthetic replacement of teeth severely compromised by attachment loss to the apex: A randomized controlled clinical trial reporting 10-year outcomes, survival analysis and mean cumulative cost of recurrence

Background

Periodontal regeneration can change tooth prognosis and represents an alternative to extraction in teeth compromised by severe intra‐bony defects. The aim of this study was to compare periodontal regeneration (PR) with tooth extraction and replacement (TER) in a population with attachment loss to or beyond the apex of the root in terms of professional, patient‐reported and economic outcomes.

Methods

This was a 10‐year randomized controlled clinical trial. 50 stage III or stage IV periodontitis subjects with a severely compromised tooth with attachment loss to or beyond the apex were randomized to PR or TER with either an implant‐ or a tooth‐supported fixed partial denture. Subjects were kept on a strict periodontal supportive care regimen every 3 months and examined yearly. Survival and recurrence analysis were performed.

Results

88% and 100% survival rates were observed in the PR and TER groups. Complication‐free survival was not significantly different: 6.7–9.1 years for PR and 7.3–9.1 years for TER (p = .788). In PR, the observed 10‐year attachment gain was 7.3 ± 2.3 mm and the residual probing depths were 3.4 ± 0.8 mm. Recurrence analysis showed that the 95% confidence interval of the costs was significantly lower for PR compared with TER throughout the whole 10‐year period. Patient‐reported outcomes and oral health‐related quality‐of‐life measurements improved in both groups.

Conclusions

Periodontal regeneration can change the prognosis of hopeless teeth and is a less costly alternative to tooth extraction and replacement. The complexity of the treatment limits widespread application to the most complex cases but provides powerful proof of principle for the benefits of PR in deep intra‐bony defect.

Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis

A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM. Applying these features to an existing published data set containing acceleration data, we achieve up to 9% average increase in accuracy compared to current state-of-the-art published results. Furthermore, we provide evidence that a single torso sensor can automatically detect multiple types of SMM in ASD, and that our approach allows recognition of SMM with high accuracy in individuals when using a person-independent classifier.

Asymmetric Dynamic Attunement of Speech and Gestures in the Construction of Children's Understanding

As children learn they use their speech to express words and their hands to gesture. This study investigates the interplay between real-time gestures and speech as children construct cognitive understanding during a hands-on science task. 12 children (M = 6, F = 6) from Kindergarten (n = 5) and first grade (n = 7) participated in this study. Each verbal utterance and gesture during the task were coded, on a complexity scale derived from dynamic skill theory. To explore the interplay between speech and gestures, we applied a cross recurrence quantification analysis (CRQA) to the two coupled time series of the skill levels of verbalizations and gestures. The analysis focused on (1) the temporal relation between gestures and speech, (2) the relative strength and direction of the interaction between gestures and speech, (3) the relative strength and direction between gestures and speech for different levels of understanding, and (4) relations between CRQA measures and other child characteristics. The results show that older and younger children differ in the (temporal) asymmetry in the gestures-speech interaction. For younger children, the balance leans more toward gestures leading speech in time, while the balance leans more toward speech leading gestures for older children. Secondly, at the group level, speech attracts gestures in a more dynamically stable fashion than vice versa, and this asymmetry in gestures and speech extends to lower and higher understanding levels. Yet, for older children, the mutual coupling between gestures and speech is more dynamically stable regarding the higher understanding levels. Gestures and speech are more synchronized in time as children are older. A higher score on schools' language tests is related to speech attracting gestures more rigidly and more asymmetry between gestures and speech, only for the less difficult understanding levels. A higher score on math or past science tasks is related to less asymmetry between gestures and speech. The picture that emerges from our analyses suggests that the relation between gestures, speech and cognition is more complex than previously thought. We suggest that temporal differences and asymmetry in influence between gestures and speech arise from simultaneous coordination of synergies.

Detecting event-related recurrences by symbolic analysis: applications to human language processing

Quasi-stationarity is ubiquitous in complex dynamical systems. In brain dynamics, there is ample evidence that event-related potentials (ERPs) reflect such quasi-stationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study, we elaborate a recent approach for detecting quasi-stationary states as recurrence domains by means of recurrence analysis and subsequent symbolization methods. We address two pertinent problems of contemporary recurrence analysis: optimizing the size of recurrence neighbourhoods and identifying symbols from different realizations for sequence alignment. As possible solutions for these problems, we suggest a maximum entropy criterion and a Hausdorff clustering algorithm. The resulting recurrence domains for single-subject ERPs are obtained as partition cells reflecting quasi-stationary brain states.

Recurrence quantification of fractal structures

By definition, fractal structures possess recurrent patterns. At different levels repeating patterns can be visualized at higher magnifications. The purpose of this chapter is threefold. First, general characteristics of dynamical systems are addressed from a theoretical mathematical perspective. Second, qualitative and quantitative recurrence analyses are reviewed in brief, but the reader is directed to other sources for explicit details. Third, example mathematical systems that generate strange attractors are explicitly defined, giving the reader the ability to reproduce the rich dynamics of continuous chaotic flows or discrete chaotic iterations. The challenge is then posited for the reader to study for themselves the recurrent structuring of these different dynamics. With a firm appreciation of the power of recurrence analysis, the reader will be prepared to turn their sights on real-world systems (physiological, psychological, mechanical, etc.).