Inter-subject Analysis Research Articles

Heart Rate Variability Metrics during Sleep in Children with Asperger’s Syndrome

Purpose: To describe the characteristics of Heart Rate Variability (HRV) metrics during sleep in children with Asperger Syndrome (AS) and children with Typical Development (TD). Methods: Children aged 6 to 10 years diagnosed with AS (n = 10) and TD (n = 10) were included in the study. Polysomnographic recordings were performed on two consecutive nights, the first night being the adaptation night and the second being used for the analysis of the characteristics of sleep and HRV metrics. Results: Inter-subject analysis showed that children with AS had a shorter heart period in all sleep stages, as well as higher REM sleep latency and fewer sleep cycles compared with children with TD. Likewise, measures related to parasympathetic activity were similar between both groups. Intra-subject analysis showed that children with AS had minimal differences between all sleep stages with most measures of the three domain methods of HRV analysis, while the children with TD presented an HRV according to the characteristics of each stage of sleep. Conclusion: Children with AS showed little autonomic flexibility when moving from one sleep stage to another, evaluated with the three-domain methods of HRV analysis. These results could indicate some degree of immaturity in sleep-related circuits in children with AS, which in turn affects HRV.

Intersubject Dynamic Conditional Correlation: A Novel Method to Track the Framewise Network Implication during Naturalistic Stimuli.

Background: Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These stimuli have high ecological validity due to their rich and multilayered features. However, their complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called intersubject dynamic conditional correlation (ISDCC). Method: ISDCC uses intersubject analysis to remove intrinsic and non-neuronal signals, retaining only intersubject-consistent stimuli-induced signals. It then applies dynamic conditional correlation (DCC) based on the generalized autoregressive conditional heteroskedasticity to calculate the framewise functional connectivity. To validate ISDCC, we analyzed simulation data with known network reconfiguration patterns and two publicly available narrative functional Magnetic Resonance Imaging (fMRI) datasets. Results: (1) ISDCC accurately unveiled the underlying network reconfiguration patterns in simulation data, demonstrating greater sensitivity than DCC; (2) ISDCC identified synchronized network reconfiguration patterns across listeners; (3) ISDCC effectively differentiated between stimulus types with varying temporal coherence; and (4) network reconfigurations unveiled by ISDCC were significantly correlated with listener engagement during narrative comprehension. Conclusion: ISDCC is a precise and dynamic method for tracking network implications in response to naturalistic stimuli.

Attention-Driven Modulation of Auditory Cortex Activity during Selective Listening in a Multispeaker Setting.

Real-world listening settings often consist of multiple concurrent sound streams. To limit perceptual interference during selective listening, the auditory system segregates and filters the relevant sensory input. Previous work provided evidence that the auditory cortex is critically involved in this process and selectively gates attended input toward subsequent processing stages. We studied at which level of auditory cortex processing this filtering of attended information occurs using functional magnetic resonance imaging (fMRI) and a naturalistic selective listening task. Forty-five human listeners (of either sex) attended to one of two continuous speech streams, presented either concurrently or in isolation. Functional data were analyzed using an inter-subject analysis to assess stimulus-specific components of ongoing auditory cortex activity. Our results suggest that stimulus-related activity in the primary auditory cortex and the adjacent planum temporale are hardly affected by attention, whereas brain responses at higher stages of the auditory cortex processing hierarchy become progressively more selective for the attended input. Consistent with these findings, a complementary analysis of stimulus-driven functional connectivity further demonstrated that information on the to-be-ignored speech stream is shared between the primary auditory cortex and the planum temporale but largely fails to reach higher processing stages. Our findings suggest that the neural processing of ignored speech cannot be effectively suppressed at the level of early cortical processing of acoustic features but is gradually attenuated once the competing speech streams are fully segregated.

ThermICA: Novel Approach for a Multivariate Analysis of Facial Thermal Responses.

Infrared Thermography (IRT) has been used to monitor skin temperature variation in a contactless manner, in both clinical medicine and psychophysiology. Here, we introduce a new methodology to obtain information about autonomic correlates related to perspiration, peripheral vasomotility, and respiration from infrared recordings. Our approach involves a model-based decomposition of facial thermograms using Independent Component Analysis (ICA) and an ad-hoc preprocessing procedure. We tested our approach on 30 healthy volunteers whose psychophysiological state was stimulated as part of an experimental protocol. Within-subject ICA analysis identified three independent components demonstrating correlations with the reference physiological signals. Moreover, a linear combination of independent components effectively predicted each physiological signal, achieving median correlations of 0.9 for electrodermal activity, 0.8 for respiration, and 0.73 for photoplethysmography peaks envelope. In addition, we performed a cross-validated inter-subject analysis, which allows to predict physiological signals from facial thermograms of unseen subjects. Our findings validate the efficacy of features extracted from both original and thermal-derived signals for differentiating experimental conditions. This outcome emphasizes the sensitivity and promise of our approach, advocating for expanded investigations into thermal imaging within biomedical signal analysis. It underscores its potential for enhancing objective assessments of emotional states.

Autonomic synchrony induced by hyperscanning interoception during interpersonal synchronization tasks.

According to previous research, people influence each other's emotional states during social interactions via resonance mechanisms and coordinated autonomic rhythms. However, no previous studies tested if the manipulation of the interoceptive focus (focused attention on the breath for a given time interval) in hyperscanning during synchronized tasks may have an impact on autonomic synchrony. Thus, this study aims to assess the psychophysiological synchrony through autonomic measures recording during dyadic linguistic and motor synchronization tasks performed in two distinct interoceptive conditions: the focus and no focus on the breath condition. 26 participants coupled in 13 dyads were recruited. Individuals' autonomic measures [electrodermal: skin conductance level and response (SCL, SCR); cardiovascular indices: heart rate (HR) and HR variability (HRV)] was continuously monitored during the experiment and correlational coefficients were computed to analyze dyads physiological synchrony. Inter-subject analysis revealed higher synchrony for HR, HRV, SCL, and SCR values in the focus compared to no focus condition during the motor synchronization task and in general more for motor than linguistic task. Higher synchrony was also found for HR, SCL, and SCR values during focus than no focus condition in linguistic task. Overall, evidence suggests that the manipulation of the interoceptive focus has an impact on the autonomic synchrony during distinct synchronization tasks and for different autonomic measures. Such findings encourage the use of hyperscanning paradigms to assess the effect of breath awareness practices on autonomic synchrony in ecological and real-time conditions involving synchronization.

Enhanced hyperalignment via spatial prior information.

Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, even after alignment to a standard anatomical template. Hyperalignment, based on sequential Procrustes orthogonal transformations, has been proposed as a method of aligning shared functional information into a common high-dimensional space and thereby improving inter-subject analysis. Though successful, current hyperalignment algorithms have a number of shortcomings, including difficulties interpreting the transformations, a lack of uniqueness of the procedure, and difficulties performing whole-brain analysis. To resolve these issues, we propose the ProMises (Procrustes von Mises-Fisher) model. We reformulate functional alignment as a statistical model and impose a prior distribution on the orthogonal parameters (the von Mises-Fisher distribution). This allows for the embedding of anatomical information into the estimation procedure by penalizing the contribution of spatially distant voxels when creating the shared functional high-dimensional space. Importantly, the transformations, aligned images, and related results are all unique. In addition, the proposed method allows for efficient whole-brain functional alignment. In simulations and application to data from four fMRI studies we find that ProMises improves inter-subject classification in terms of between-subject accuracy and interpretability compared to standard hyperalignment algorithms.

Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

Anxiety affects approximately 5–10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4–7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8–12 Hz) or beta (13–30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

Bi-LSTM neural network for EEG-based error detection in musicians’ performance

Electroencephalography (EEG) is a tool that allows us to analyze brain activity with high temporal resolution. These measures, combined with deep learning and digital signal processing, are widely used in neurological disorder detection and emotion and mental activity recognition. In this paper, a new method for mental activity recognition is presented: instantaneous frequency, spectral entropy and Mel-frequency cepstral coefficients (MFCC) are used to classify EEG signals using bidirectional LSTM neural networks. It is shown that this method can be used for intra-subject or inter-subject analysis and has been applied to error detection in musician performance reaching compelling accuracy. • Detection of performance error of music players using EEG is considered. • EEG signals are characterized by instantaneous frequency, spectral entropy and MFCCs. • A bidirectional LSTM neural network is used for error detection. • Intra- and inter-subject and instrument scenarios are addressed. • Intra- and inter-subject and instrument performance error detection can be achieved.

Movie Events Detecting Reveals Inter-Subject Synchrony Difference of Functional Brain Activity in Autism Spectrum Disorder

Recently, movie-watching fMRI has been recognized as a novel method to explore brain working patterns. Previous researchers correlated natural stimuli with brain responses to explore brain functional specialization by “reverse correlation” methods, which were based on within-group analysis. However, what external stimuli drove significantly different brain responses in two groups of different subjects were still unknown. To address this, sliding time windows technique combined with inter-Subject functional correlation (ISFC) was proposed to detect movie events with significant group differences between autism spectrum disorder (ASD) and typical development (TD) subjects. Then, using inter-Subject correlation (ISC) and ISFC analysis, we found that in three movie events involving character emotions, the ASD group showed significantly lower ISC in the middle temporal gyrus, temporal pole, cerebellum, caudate, precuneus, and showed decreased functional connectivity between large scale networks than that in TD. Under the movie event focusing on objects and scenes shot, the dorsal and ventral attentional networks of ASD had a strong synchronous response. Meanwhile, ASD also displayed increased functional connectivity between the frontoparietal network (FPN) and dorsal attention network (DAN), FPN, and sensorimotor network (SMN) than TD. ASD has its own unique synchronous response rather than being “unresponsive” in natural movie-watching. Our findings provide a new method and valuable insight for exploring the inconsistency of the brain “tick collectively” to same natural stimuli. This analytic approach has the potential to explore pathological mechanisms and promote training methods of ASD.

The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children.

School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).

Variability of cardiorespiratory interactions under different breathing patterns

The breathing dynamics often change in time and cause different variations in the cardiorespiratory interaction. There exist various breathing patterns, among them one critically important is the variability of the breathing frequency. We investigated the respiratory and the coupled cardiorespiratory system under controlled time-varying breathing patterns. Four breathing scenarios were used for this: spontaneous breathing, one where the subjects changed their breathing frequency according to linear ramp law, another according to a sine law and third according to an aperiodic predefined law. We introduced a framework of variability measures to trace and quantify the effect from the time-varying breathing perturbations. In particular, we studied intra-subject time-average variability, inter-subject subject-average variability and residual variability. These variability measures were estimated from the coupling strength and the similarity of coupling functions, for which we used methods specifically able to follow the time-evolving dynamics – the time–frequency wavelet transform and the adaptive dynamical Bayesian inference. The results demonstrated that the coupling and similarity were significantly greater in controlled, compared to free spontaneous breathing in many cases (p<0.0083). There were differences also among different controlled breathing regimes, and they appear both for intra-subject and inter-subject analysis. However, when the specific breathing perturbation is taken out, the results for the residual variability and the averaged coupling functions showed that the underlying interaction mechanisms remain invariant and not significantly different from spontaneous breathing (p>0.0083). This variability framework carries implications and can be applied more generally to other coupled oscillators and networks.

“My Religion Guides Me, Even as a Teacher”: Examining Religious Identities of English Language Teachers in Kuwait

ABSTRACT This paper investigates the lived experiences of three Kuwaiti women as they construct and negotiate their professional identities as Muslim English language teachers in Kuwait. Building on the paucity of research surrounding religious identity and English language teaching contexts, positioning analysis and tactics of intersubjectivity were used to examine the teachers’ oral narrative accounts, highlighting the ways in which they navigate their professional teaching contexts within the underlying local religious ideologies. Findings show that while the three English language teachers share a common nationality, faith, and gender, they each engaged in different ways with their religious identities within their professional practice. The study concludes with implications for professional development and future research directions.

Validation of semi-automated anatomically labeled SEEG contacts in a brain atlas for mapping connectivity in focal epilepsy.

ObjectiveStereotactic electroencephalography (SEEG) has been widely used to explore the epileptic network and localize the epileptic zone in patients with medically intractable epilepsy. Accurate anatomical labeling of SEEG electrode contacts is critically important for correctly interpreting epileptic activity. We present a method for automatically assigning anatomical labels to SEEG electrode contacts using a 3D‐segmented cortex and coregistered postoperative CT images.MethodStereotactic electroencephalography electrode contacts were spatially localized relative to the brain volume using a standard clinical procedure. Each contact was then assigned an anatomical label by clinical epilepsy fellows. Separately, each contact was automatically labeled by coregistering the subject's MRI to the USCBrain atlas using the BrainSuite software and assigning labels from the atlas based on contact locations. The results of both labeling methods were then compared, and a subsequent vetting of the anatomical labels was performed by expert review.ResultsAnatomical labeling agreement between the two methods for over 17 000 SEEG contacts was 82%. This agreement was consistent in patients with and without previous surgery (P = .852). Expert review of contacts in disagreement between the two methods resulted in agreement with the atlas based over manual labels in 48% of cases, agreement with manual over atlas‐based labels in 36% of cases, and disagreement with both methods in 16% of cases. Labels deemed incorrect by the expert review were then categorized as either in a region directly adjacent to the correct label or as a gross error, revealing a lower likelihood of gross error from the automated method.SignificanceThe method for semi‐automated atlas‐based anatomical labeling we describe here demonstrates potential to assist clinical workflow by reducing both analysis time and the likelihood of gross anatomical error. Additionally, it provides a convenient means of intersubject analysis by standardizing the anatomical labels applied to SEEG contact locations across subjects.

Low b-value diffusion tensor imaging for measuring pseudorandom flow of cerebrospinal fluid.

Cerebrospinal fluid (CSF) plays an important role in the clearance system of the brain. Recently, low b-value diffusion tensor imaging (low-b DTI) has been reported to be useful in the observation of CSF flow; however, the precise flow property observed by low-b DTI has not been fully investigated. Accordingly, a mathematical framework of low-b DTI is proposed for investigating CSF and clarifying its pseudorandom flow. The framework will show that the limit of the diffusion tensor as b-value decreases to zero approximately represents the covariance of the velocity distribution of the CSF's pseudorandom flow. The low b-value diffusion tensor (DTL ) of whole-brain CSF was obtained using diffusion-weighted echo-planar imaging. Seven healthy volunteers were scanned for intersubject analysis; three of the volunteers was consecutively scanned for repeatability analysis. Obtained DTL was visually assessed by ellipsoid-representation map and was statistically evaluated by calculating mean diffusivity (MD) and fractional anisotropy (FA) in regions of interest (ROIs) representing intensive pseudorandom flow. Obtained DTL consistently shows large and anisotropic diffusivity in some segments of CSF, typically the ROIs around the foramen of Monro, the aqueduct, the prepontine cistern, the middle cerebral artery, and the Sylvian fissure throughout the study. The statistical analysis shows high repeatability and consistently high MD and FA in all the ROIs for all the volunteers. From the viewpoint of the proposed framework, the high and anisotropic DTL in the ROIs indicates large covariance of velocity distribution, which represents intensive pseudorandom flows of CSF.

Executive Functions and Domain-Specific Cognitive Skills in Climbers.

Athletes in a particular sport have specific cognitive skills acquired due to regular confrontation with sport-specific requirements. Studies show that the particular type of sport carried out and fostered by general physical activity impacts executive functions (EFs) such as inhibition, working memory, and cognitive flexibility. There are inconsistent results on the connections between domain-specific cognitive skills and executive functions. This study aimed to evaluate the relations between EFs and domain-specific cognitive skills in climbing. Due to that, we examined the executive functions (neuropsychological tests) and domain-specific cognitive skills (climbing-specific test: a preview of the route vs. climbed moves; climbed moves vs. recognition of moves) of 19 climbers (10 novices, 9 experts, grades 5 to 6a vs. 6c+ to 7b). The inter-subject effects analysis shows that novices and experts in sport climbing do not differ in executive functions in this particular case. Concerning domain-specific cognitive skills, there are differences between experts and novices. Experts show a significantly higher level in planning performance or route idea (p < 0.001) as well as in memorizing of climbed moves (p = 0.004). There are no relations between executive functions and domain-specific cognitive skills in climbers.

Gratitude Affects Inter-Subjective Synchronicity for Cognitive Performance and Autonomic Responsiveness.

Recently, social neurosciences have been interested in the investigation of neurophysiological responses related to the experience of positive emotions, such as gratitude, during social interactions. Specifically, the aim of the present research was to investigate whether gratitude related to gift exchange could favor cooperative behavior and bond construction, by improving behavioral and autonomic responsivity. At this regard, the autonomic synchronization and behavioral performance of 16 friends coupled in dyads were recorded during a joint attentional task. Gift exchange could be occurred either at the beginning or in the middle of the task. For the recording of simultaneous autonomic activity [heart rate (HR) and skin conductance level (SCL)], a hyperscanning biofeedback paradigm was used. Intra-subjective analysis showed an increase in behavioral [accuracy (ACC)] and autonomic responses (HR and SCL) when the gift exchange took place at the beginning of the task rather than in the middle. Moreover, inter-subjective analysis revealed an increase in behavioral performance and greater autonomic synchronization of HR index. The present research, therefore, shows how gratitude and trust experienced following gift exchange can modify participants’ reactions by creating a shared cognition and the adoption of joint strategies.

MEG Intersubject Phase Locking of Stimulus-Driven Activity during Naturalistic Speech Listening Correlates with Musical Training.

Musical training is associated with increased structural and functional connectivity between auditory sensory areas and higher-order brain networks involved in speech and motor processing. Whether such changed connectivity patterns facilitate the cortical propagation of speech information in musicians remains poorly understood. We here used magnetoencephalography (MEG) source imaging and a novel seed-based intersubject phase-locking approach to investigate the effects of musical training on the interregional synchronization of stimulus-driven neural responses during listening to naturalistic continuous speech presented in silence. MEG data were obtained from 20 young human subjects (both sexes) with different degrees of musical training. Our data show robust bilateral patterns of stimulus-driven interregional phase synchronization between auditory cortex and frontotemporal brain regions previously associated with speech processing. Stimulus-driven phase locking was maximal in the delta band, but was also observed in the theta and alpha bands. The individual duration of musical training was positively associated with the magnitude of stimulus-driven alpha-band phase locking between auditory cortex and parts of the dorsal and ventral auditory processing streams. These findings provide evidence for a positive relationship between musical training and the propagation of speech-related information between auditory sensory areas and higher-order processing networks, even when speech is presented in silence. We suggest that the increased synchronization of higher-order cortical regions to auditory cortex may contribute to the previously described musician advantage in processing speech in background noise.SIGNIFICANCE STATEMENT Musical training has been associated with widespread structural and functional brain plasticity. It has been suggested that these changes benefit the production and perception of music but can also translate to other domains of auditory processing, such as speech. We developed a new magnetoencephalography intersubject analysis approach to study the cortical synchronization of stimulus-driven neural responses during the perception of continuous natural speech and its relationship to individual musical training. Our results provide evidence that musical training is associated with higher synchronization of stimulus-driven activity between brain regions involved in early auditory sensory and higher-order processing. We suggest that the increased synchronized propagation of speech information may contribute to the previously described musician advantage in processing speech in background noise.

Seeking a common ground for the Nautical Archaeology Digital Library (NADL). Reflections on science, method, theory and templates

&lt;p class="VARKeywords"&gt;Data sharing is a fundamental process for the advancement of both natural and social sciences. Starting from the idea that computers and the internet have drastically changed the world in the last decades, this paper advocates for the creation of a space where archaeologists from around the world can share information about maritime history and exchange data with colleagues. Following the principles of open access, we argue that raw data publication is necessary and significant for the development and democratization of the discipline. This study explains the fundamental aspects of the Nautical Archaeology Digital Library (NADL) and its efforts to standardize information collection for shipwrecks and related sites, so that scholars can create a community to disseminate both raw data and complete information in the field of maritime archaeology. To achieve this, our purpose is to facilitate the development of common-ground methodology and terminology that promotes an intelligible dialogue within the global community of nautical archaeologists. This paper addresses some considerations on terminology and systematization in scientific disciplines and discusses the theoretical and methodological issues linked to the process of making a template for recording shipwrecks. Furthermore, this article analyses some of the problems related to the standardization of description processes and the necessity to create a flexible system that accounts for data diversity. The third section discusses how science is greatly enhanced by publishing information in open access platforms.&lt;/p&gt;&lt;p&gt;Highlights:&lt;/p&gt;&lt;p&gt;• Standardization of data allows robust comparative and inter-subjective analysis of coastal and maritime projects, shipwrecks and nautical technology.&lt;br /&gt;• Research is strongly benefited by sharing information underlying publications and raw data generated within a project in open source platforms.&lt;br /&gt;• Digital databases such as NADL enhance cooperative research, as well as teaching and outreach strategies.&lt;/p&gt;

BODILY-AFFECTIVE ATTUNEMENT IN SOCIAL INTERACTION

The paper intends to supplement the studies of emotional affordances of BAA by elaborating on the conception of participatory sense-making as well as developmental studies on joint attention and interattentionality. I address different spheres of expertise from the experience-based phenomenological perspective, which allows exploring the problem both from the first-person and second-person perspectives. This research presents the conception of inter-selfness that carries on M. Merleau-Ponty’s idea of intercorporeality, T. Fuchs’ et al. analysis of intersubjectivity and phenomenologically oriented psychoanalysis by E. Z. Tronick et al., R. Stolorow et al. The mechanism of BAA is presented through the conception of participatory sense-making and the idea of minimal inter-attentionality in developmental studies. The paper presents an emotional affordances scheme that illustrates the emotional regulation of BAA. By examining this process of regulation one could see in what way the self becomes an inter-self in communication. The article also postulates correlation between cultural mediation of emotional affordances and their direct accessibility from the second-person perspective. In the last part of the paper, I examine social interaction from the viewpoint of developmental studies (C. Trevarthen, V. Reddy, M. Carpenter). The developmental perspective supplements the idea of emotional regulation in interaction, by focusing on primary such forms of BAA between a caregiver and a baby, as joint attention and mutual gaze. Herein, I demonstrate how the initial forms of the positive bodily-affective attunement develop into the interattentionality and self-representation practices of the subject. This point could contribute to the theory of personal identity by exploring the process of maturing of the sense of self in its different aspects. The results of the research could be useful for further study of BAA and its pathologies. The results could also be of use for the discussion on non-human or human-like affordance-based technological interaction theory.

Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach

Different from traditional intra-subject analysis, the goal of inter-subject analysis (ISA) is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. ISA has important applications in neuroscience to study the functional connectivity between brain regions under natural stimuli. We propose a modeling framework for ISA that is based on Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of a partial Gaussian graphical model. The main statistical challenge is that we do not impose sparsity constraints on the whole precision matrix and we only assume the inter-subject part is sparse. For estimation, we propose to estimate an alternative parameter to get around the nonsparse issue and it can achieve asymptotic consistency even if the intra-subject dependency is dense. For inference, we propose an “untangle and chord” procedure to de-bias our estimator. It is valid without the sparsity assumption on the inverse Hessian of the log-likelihood function. This inferential method is general and can be applied to many other statistical problems, thus it is of independent theoretical interest. Numerical experiments on both simulated and brain imaging data validate our methods and theory. Supplementary materials for this article are available online.