Emerging effects of temperature on human cognition, affect, and behaviour.

Human body core temperature is tightly regulated within approximately 37 °C. Global near surface temperature has increased by over 1.2 °C between 1850 and 2020. In light of the challenge this poses to human thermoregulation, the present perspective article sought to provide an overview on the effects of varying ambient and body temperature on cognitive, affective, and behavioural domains of functioning. To this end, an overview of observational and experimental studies in healthy individuals and individuals with mental disorders was provided. Within body core temperature at approximately 37 °C, relatively lower ambient and skin temperatures appear to evoke a need for social connection, whereas comparably higher temperatures appear to facilitate notions of other as closer and more sociable. Above-average ambient temperatures are associated with increased conflicts as well as incident psychotic and depressive symptoms, mental disorders, and suicide. With mild hypo- and hyperthermia, paradoxical effects are observed: whereas the acute states are generally characterised by impairments in cognitive performance, anxiety, and irritability, individuals with depression experience longer-term symptom improvements with treatments deliberately inducing these states for brief amounts of time. When taken together, it has thus become clear that temperature is inexorably associated with human cognition, affect, and (potentially) behaviour. Given the projected increase in global warming, further research into the affective and behavioural sequelae of heat and the mechanisms translating it into mental health outcomes is urgently warranted.

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson's Disease: A Study on Speaker Diarization and Classification Techniques.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King's College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

Local effect of allopregnanolone in rat ovarian steroidogenesis, follicular and corpora lutea development.

Allopregnanolone (ALLO) is a known neurosteroid and a progesterone metabolite synthesized in the ovary, CNS, PNS, adrenals and placenta. Its role in the neuroendocrine control of ovarian physiology has been studied, but its in situ ovarian effects are still largely unknown. The aims of this work were to characterize the effects of intrabursal ALLO administration on different ovarian parameters, and the probable mechanism of action. ALLO administration increased serum progesterone concentration and ovarian 3ß-HSD2 while decreasing 20α-HSD mRNA expression. ALLO increased the number of atretic follicles and the number of positive TUNEL granulosa and theca cells, while decreasing positive PCNA immunostaining. On the other hand, there was an increase in corpora lutea diameter and PCNA immunostaining, whereas the count of TUNEL-positive luteal cells decreased. Ovarian angiogenesis and the immunohistochemical expression of GABAA receptor increased after ALLO treatment. To evaluate if the ovarian GABAA receptor was involved in these effects, we conducted a functional experiment with a specific antagonist, bicuculline. The administration of bicuculline restored the number of atretic follicles and the diameter of corpora lutea to normal values. These results show the actions of ALLO on the ovarian physiology of the female rat during the follicular phase, some of them through the GABAA receptor. Intrabursal ALLO administration alters several processes of the ovarian morpho-physiology of the female rat, related to fertility and oocyte quality.

Assessing Feasibility of Cognitive Impairment Testing Using Social Robotic Technology Augmented with Affective Computing and Emotional State Detection Systems.

Social robots represent a valid opportunity to manage the diagnosis, treatment, care, and support of older people with dementia. The aim of this study is to validate the Mini-Mental State Examination (MMSE) test administered by the Pepper robot equipped with systems to detect psychophysical and emotional states in older patients. Our main result is that the Pepper robot is capable of administering the MMSE and that cognitive status is not a determinant in the effective use of a social robot. People with mild cognitive impairment appreciate the robot, as it interacts with them. Acceptability does not relate strictly to the user experience, but the willingness to interact with the robot is an important variable for engagement. We demonstrate the feasibility of a novel approach that, in the future, could lead to more natural human-machine interaction when delivering cognitive tests with the aid of a social robot and a Computational Psychophysiology Module (CPM).

Assessing the Impact of Electrosuit Therapy on Cerebral Palsy: A Study on the Users' Satisfaction and Potential Efficacy.

The aim of this study is to evaluate the effectiveness of electrosuit therapy in the clinical treatment of children with Cerebral Palsy, focusing on the effect of the therapy on spasticity and trunk control. Moreover, the compliance of caregivers with respect to the use of the tool was investigated. During the period ranging from 2019 to 2022, a total of 26 children (18 M and 8 F), clinically stable and affected by CP and attending the Neurorehabilitation Unit of the "Padre Pio Foundation and Rehabilitation Centers", were enrolled in this study. A subset of 12 patients bought or rented the device; thus, they received the administration of the EMS-based therapy for one month, whereas the others received only one-hour training to evaluate the feasibility (by the caregivers) and short-term effects. The Gross Motor Function Classification System was utilized to evaluate gross motor functions and to classify the study sample, while the MAS and the LSS were employed to assess the outcomes of the EMS-based therapy. Moreover, between 80% and 90% of the study sample were satisfied with the safety, ease of use, comfort, adjustment, and after-sales service. Following a single session of electrical stimulation with EMS, patients exhibited a statistically significant enhancement in trunk control. For those who continued this study, the subscale of the QUEST with the best score was adaptability (0.74 ± 0.85), followed by competence (0.67 ± 0.70) and self-esteem (0.59 ± 0.60). This study investigates the impact of the employment of the EMS on CP children's ability to maintain trunk control. Specifically, after undergoing a single EMS session, LSS showed a discernible improvement in children's trunk control. In addition, the QUEST and the PIADS questionnaires demonstrated a good acceptability and satisfaction of the garment by the patients and the caregivers.

Facial functional networks during resting state revealed by thermal infrared imaging.

In recent decades, an increasing number of studies on psychophysiology and, in general, on clinical medicine has employed the technique of facial thermal infrared imaging (IRI), which allows to obtain information about the emotional and physical states of the subjects in a completely non-invasive and contactless fashion. Several regions of interest (ROIs) have been reported in literature as salient areas for the psychophysiological characterization of a subject (i.e. nose tip and glabella ROIs). There is however a lack of studies focusing on the functional correlation among these ROIs and about the physiological basis of the relation existing between thermal IRI and vital signals, such as the electrodermal activity, i.e. the galvanic skin response (GSR). The present study offers a new methodology able to assess the functional connection between salient seed ROIs of thermal IRI and all the pixel of the face. The same approach was also applied considering as seed signal the GSR and its phasic and tonic components. Seed correlation analysis on 63 healthy volunteers demonstrated the presence of a common pathway regulating the facial thermal functionality and the electrodermal activity. The procedure was also tested on a pathological case study, finding a completely different pattern compared to the healthy cases. The method represents a promising tool in neurology, physiology and applied neurosciences.

Use and Effectiveness of Electrosuit in Neurological Disorders: A Systematic Review with Clinical Implications.

Electrical stimulation through surface electrodes is a non-invasive therapeutic technique used to improve voluntary motor control and reduce pain and spasticity in patients with central nervous system injuries. The Exopulse Mollii Suit (EMS) is a non-invasive full-body suit with integrated electrodes designed for self-administered electrical stimulation to reduce spasticity and promote flexibility. The EMS has been evaluated in several clinical trials with positive findings, indicating its potential in rehabilitation. This review investigates the effectiveness of the EMS for rehabilitation and its acceptability by patients. The literature was collected through several databases following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Positive effects of the garment on improving motor functions and reducing spasticity have been shown to be related to the duration of the administration period and to the dosage of the treatment, which, in turn, depend on the individual's condition and the treatment goals. Moreover, patients reported wellbeing during stimulation and a muscle-relaxing effect on the affected limb. Although additional research is required to determine the efficacy of this device, the reviewed literature highlights the EMS potential to improve the motor capabilities of neurological patients in clinical practice.

Effect of peripheral neural stimulation with allopregnanolone on ovarian physiology.

Neuroactive steroids can rapidly regulate multiple physiological functions in the central and peripheral nervous systems. The aims of the present study were to determine whether allopregnanolone (ALLO), administered in low nanomolar and high micromolar concentrations, can: (i) induce changes in the ovarian progesterone (P4) and estradiol (E2) release; (ii) modify the ovarian mRNA expression of Hsd3b1 (3ß-hydroxysteroid dehydrogenase, 3ß-HSD)3ß-, Akr1c3 (20α-hydroxysteroid dehydrogenase, 20α-HSD), and Akr1c14 (3α-hydroxy steroid oxidoreductase, 3α-HSOR)); and (iii) modulate the ovarian expression of progesterone receptors A and B, α and ß estrogenic receptors, luteinizing hormone receptor (LHR) and follicle-stimulating hormone receptor (FSHR). To further characterize ALLO peripheral actions, the effects were evaluated using a superior mesenteric ganglion-ovarian nervous plexus-ovary (SMG-ONP-O) and a denervated ovary (DO) systems. ALLO SMG administration increased P4 concentration in the incubation liquid by decreasing ovarian 20α-HSD mRNA, and it also increased ovarian 3α-HSOR mRNA expression. In addition, ALLO neural peripheral modulation induced an increase in the expression of ovarian LHR, PRA, PRB, and ERα. Direct ALLO administration to the DO decreased E2 and increased P4 concentration in the incubation liquid. The mRNA expression of 3ß-HSD decreased and 20α-HSD increased. Further, ALLO in the OD significantly changed ovarian FSHR and PRA expression. This is the first evidence of ALLO's direct effect on ovarian steroidogenesis. Our results provide important insights about how this neuroactive steroid interacts both with the PNS and the ovary, and these findings might help devise some of the pleiotropic effects of neuroactive steroids on female reproduction. Moreover, ALLO modulation of ovarian physiology might help uncover novel treatment approaches for reproductive diseases.

Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue?

Surface electromyography (sEMG) is the acquisition, from the skin, of the electrical signal produced by muscle activation. Usually, sEMG is measured through electrodes with electrolytic gel, which often causes skin irritation. Capacitive contactless electrodes have been developed to overcome this limitation. However, contactless EMG devices are still sensitive to motion artifacts and often not comfortable for long monitoring. In this study, a non-invasive contactless method to estimate parameters indicative of muscular activity and fatigue, as they are assessed by EMG, through infrared thermal imaging (IRI) and cross-validated machine learning (ML) approaches is described. Particularly, 10 healthy participants underwent five series of bodyweight squats until exhaustion interspersed by 1 min of rest. During exercising, the vastus medialis activity and its temperature were measured through sEMG and IRI, respectively. The EMG average rectified value (ARV) and the median frequency of the power spectral density (MDF) of each series were estimated through several ML approaches applied to IRI features, obtaining good estimation performances (r = 0.886, p < 0.001 for ARV, and r = 0.661, p < 0.001 for MDF). Although EMG and IRI measure physiological processes of a different nature and are not interchangeable, these results suggest a potential link between skin temperature and muscle activity and fatigue, fostering the employment of contactless methods to deliver metrics of muscular activity in a non-invasive and comfortable manner in sports and clinical applications.

Intraoperative thermal infrared imaging in neurosurgery: machine learning approaches for advanced segmentation of tumors.

Surgical resection is one of the most relevant practices in neurosurgery. Finding the correct surgical extent of the tumor is a key question and so far several techniques have been employed to assist the neurosurgeon in preserving the maximum amount of healthy tissue. Some of these methods are invasive for patients, not always allowing high precision in the detection of the tumor area. The aim of this study is to overcome these limitations, developing machine learning based models, relying on features obtained from a contactless and non-invasive technique, the thermal infrared (IR) imaging. The thermal IR videos of thirteen patients with heterogeneous tumors were recorded in the intraoperative context. Time (TD)- and frequency (FD)-domain features were extracted and fed different machine learning models. Models relying on FD features have proven to be the best solutions for the optimal detection of the tumor area (Average Accuracy = 90.45%; Average Sensitivity = 84.64%; Average Specificity = 93,74%). The obtained results highlight the possibility to accurately detect the tumor lesion boundary with a completely non-invasive, contactless, and portable technology, revealing thermal IR imaging as a very promising tool for the neurosurgeon.

Identification of Functional Cortical Plasticity in Children with Cerebral Palsy Associated to Robotic-Assisted Gait Training: An fNIRS Study.

Cerebral palsy (CP) is a non-progressive neurologic condition that causes gait limitations, spasticity, and impaired balance and coordination. Robotic-assisted gait training (RAGT) has become a common rehabilitation tool employed to improve the gait pattern of people with neurological impairments. However, few studies have demonstrated the effectiveness of RAGT in children with CP and its neurological effects through portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS). The aim of the study is to evaluate the neurophysiological processes elicited by RAGT in children with CP through fNIRS, which was acquired during three sessions in one month. The repeated measure ANOVA was applied to the ß-values delivered by the General Linear Model (GLM) analysis used for fNIRS data analysis, showing significant differences in the activation of both prefrontal cortex (F (1.652, 6.606) = 7.638; p = 0.022), and sensorimotor cortex (F (1.294, 5.175) = 11.92; p = 0.014) during the different RAGT sessions. In addition, a cross-validated Machine Learning (ML) framework was implemented to estimate the gross motor function measure (GMFM-88) from the GLM ß-values, obtaining an estimation with a correlation coefficient r = 0.78. This approach can be used to tailor clinical treatment to each child, improving the effectiveness of rehabilitation for children with CP.

Psychophysiological Assessment of Children with Cerebral Palsy during Robotic-Assisted Gait Training through Infrared Imaging.

Cerebral palsy (CP) is a non-progressive neurologic pathology representing a leading cause of spasticity and concerning gait impairments in children. Robotic-assisted gait training (RAGT) is widely employed to treat this pathology to improve children's gait pattern. Importantly, the effectiveness of the therapy is strictly related to the engagement of the patient in the rehabilitation process, which depends on his/her psychophysiological state. The aim of the study is to evaluate the psychophysiological condition of children with CP during RAGT through infrared thermography (IRT), which was acquired during three sessions in one month. A repeated measure ANOVA was performed (i.e., mean value, standard deviation, and sample entropy) extracted from the temperature time course collected over the nose and corrugator, which are known to be indicative of the psychophysiological state of the individual. Concerning the corrugator, significant differences were found for the sample entropy (F (1.477, 5.907) = 6.888; p = 0.033) and for the mean value (F (1.425, 5.7) = 5.88; p = 0.047). Regarding the nose tip, the sample entropy showed significant differences (F (1.134, 4.536) = 11.5; p = 0.041). The findings from this study suggests that this approach can be used to evaluate in a contactless manner the psychophysiological condition of the children with CP during RAGT, allowing to monitor their engagement to the therapy, increasing the benefits of the treatment.

Classification of Drivers' Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals.

Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test-DST and Ray Auditory Verbal Learning Test-RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers' response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR/unimodal HRV/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers' MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention.

Altered Microcirculation in Alzheimer's Disease Assessed by Machine Learning Applied to Functional Thermal Imaging Data.

Alzheimer's disease (AD) is characterized by progressive memory failures accompanied by microcirculation alterations. Particularly, impaired endothelial microvascular responsiveness and altered flow motion patterns have been observed in AD patients. Of note, the endothelium influences the vascular tone and also the small superficial blood vessels, which can be evaluated through infrared thermography (IRT). The advantage of IRT with respect to other techniques relies on its contactless features and its capability to preserve spatial information of the peripheral microcirculation. The aim of the study is to investigate peripheral microcirculation impairments in AD patients with respect to age-matched healthy controls (HCs) at resting state, through IRT and machine learning (ML) approaches. Particularly, several classifiers were tested, employing as regressors the power of the nose tip temperature time course in different physiological frequency bands. Among the ML classifiers tested, the Decision Tree Classifier (DTC) delivered the best cross-validated accuracy (accuracy = 82%) when discriminating between AD and HCs. The results further demonstrate the alteration of microvascular patterns in AD in the early stages of the pathology, and the capability of IRT to assess vascular impairments. These findings could be exploited in clinical practice, fostering the employment of IRT as a support for the early diagnosis of AD.

Estimation of Heart Rate Variability Parameters by Machine Learning Approaches Applied to Facial Infrared Thermal Imaging.

Heart rate variability (HRV) is a reliable tool for the evaluation of several physiological factors modulating the heart rate (HR). Importantly, variations of HRV parameters may be indicative of cardiac diseases and altered psychophysiological conditions. Recently, several studies focused on procedures for contactless HR measurements from facial videos. However, the performances of these methods decrease when illumination is poor. Infrared thermography (IRT) could be useful to overcome this limitation. In fact, IRT can measure the infrared radiations emitted by the skin, working properly even in no visible light illumination conditions. This study investigated the capability of facial IRT to estimate HRV parameters through a face tracking algorithm and a cross-validated machine learning approach, employing photoplethysmography (PPG) as the gold standard for the HR evaluation. The results demonstrated a good capability of facial IRT in estimating HRV parameters. Particularly, strong correlations between the estimated and measured HR (r = 0.7), RR intervals (r = 0.67), TINN (r = 0.71), and pNN50 (%) (r = 0.70) were found, whereas moderate correlations for RMSSD (r = 0.58), SDNN (r = 0.44), and LF/HF (r = 0.48) were discovered. The proposed procedure allows for a contactless estimation of the HRV that could be beneficial for evaluating both cardiac and general health status in subjects or conditions where contact probe sensors cannot be used.

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach.

Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect's four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.

Central and Peripheral Thermal Signatures of Brain-Derived Fatigue during Unilateral Resistance Exercise: A Preliminary Study.

Infrared thermography (IRT) allows to evaluate the psychophysiological state associated with emotions from facial temperature modulations. As fatigue is a brain-derived emotion, it is possible to hypothesize that facial temperature could provide information regarding the fatigue related to exercise. The aim of this study was to investigate the capability of IRT to assess the central and peripheral physiological effect of fatigue by measuring facial skin and muscle temperature modulations in response to a unilateral knee extension exercise until exhaustion. Rate of perceived exertion (RPE) was recorded at the end of the exercise. Both time- (∆TROI: pre-post exercise temperature variation) and frequency-domain (∆PSD: pre-post exercise power spectral density variation of specific frequency bands) analyses were performed to extract features from regions of interest (ROIs) positioned on the exercised and nonexercised leg, nose tip, and corrugator. The ANOVA-RM revealed a significant difference between ∆TROI (F(1.41,9.81) = 15.14; p = 0.0018), and between ∆PSD of myogenic (F(1.34,9.39) = 15.20; p = 0.0021) and neurogenic bands (F(1.75,12.26) = 9.96; p = 0.0034) of different ROIs. Moreover, significant correlations between thermal features and RPE were found. These findings suggest that IRT could assess both peripheral and central responses to physical exercise. Its applicability in monitoring the psychophysiological responses to exercise should be further explored.

Superior mesenteric ganglion neural modulation of ovarian angiogenesis, apoptosis and proliferation by the neuroactive steroid allopregnanolone.

Allopregnanolone (ALLO), a potent neuroactive steroid, is synthesized and active in the peripheral nervous system. Previous studies have shown that ALLO participates in the central regulation of reproduction with effects on ovarian physiology, although there is little evidence for its ability to modulate peripheral tissues. The present study aimed to determine whether ALLO, administered to an ex vivo system that comprises the superior mesenteric ganglion (SMG), the ovarian nervous plexus (ONP) and the ovary (O), or to the denervated ovary (DO), was able to modify ovarian apoptosis, proliferation and angiogenesis. For this purpose, the SMG-ONP-O system and DO were incubated during 120 min at 37°C, in the presence of two ALLO doses (0.06 µm and 6 µm). The intrinsic and extrinsic pathways of apoptosis were analyzed. Incubation of the SMG-ONP-O system with ALLO 0.06 µm led to an increase in the BAX/BCL-2 ratio and a reduction of FAS-L mRNA levels. ALLO 6 µm induced a decrease of FAS-L levels. Incubation of DO with ALLO 0.06 µm reduced FAS-L, whereas ALLO 6 µm significantly increased it. Cyclin D1 mRNA was measured to evaluate proliferation. Treatment with ALLO 6 µm increased proliferation in both SMG-ONP-O and DO. ALLO 0.06 µm produced an increase of Cyclin D1 in DO only. Administration of either ALLO dose led to a higher ovarian expression of vascular endothelial growth factor in the SMG-ONP-O system, but a lower one in the DO system. ALLO 6 µm induced ovarian sensitization to GABA by increasing GABAA receptor expression. In conclusion, ALLO participates in the peripheral neural modulation of ovarian physiology. It can also interact directly with the ovarian tissue, modulating key mechanisms involved in normal and pathological processes in a dose-dependent manner.

Interpersonal Emotion Dynamics in Couples With Somatic Symptom Disorder: Dyadic Coherence in Facial Temperature During Emotional Interactions.

OBJECTIVE: Disturbances in emotional processes are commonly reported in patients with a somatic symptom disorder (SSD). Although emotions usually occur in social interactions, little is known about interpersonal emotion dynamics of SSD patients during their actual emotional encounters. This study examined physiological coherence (linkage) between SSD patients and their partners, and in healthy couples during their emotional interactions. Secondarily, we explored group-level relationships between participants' and their partners' subjective affect. METHODS: Twenty-nine romantic couples (16 healthy and 13 SSD patient-couples) underwent a dyadic conversation task with neutral and anger-eliciting topics followed by a guided relaxation. Partners' cutaneous facial temperature was recorded simultaneously by functional infrared thermal imaging. Immediately after each condition, participants reported on their pain intensity, self-affect, and perceived partner-affect. RESULTS: Emotional conditions and having a partner with an SSD significantly affected coherence amplitude on the forehead (F(2,54) = 4.95, p = .011) and nose tip temperature (F(2,54) = 3.75, p = .030). From baseline to anger condition, coherence amplitude significantly increased in the patient-couples, whereas it decreased in the healthy couples. Correlation changes between partners' subjective affect comparably accompanied the changes in physiological coherence in healthy and patient-couples. CONCLUSIONS: Inability to reduce emotional interdependence in sympathetic activity and subjective affect during a mutual conflict observed in SSD patient-couples seems to capture emotion co-dysregulation. Interventions should frame patients' emotional experiences as embodied and social. Functional infrared thermal imaging confirms to be an ecological and reliable method for examining autonomic changes in interpersonal contexts.Registration Page: https://osf.io/8eyjr.

The Prediction of Running Velocity during the 30-15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach.

Measuring exercise variables is one of the most important points to consider to maximize physiological adaptations. High-intensity interval training (HIIT) is a useful method to improve both cardiovascular and neuromuscular performance. The 30-15IFT is a field test reflecting the effort elicited by HIIT, and the final velocity reached in the test is used to set the intensity of HIIT during the training session. In order to have a valid measure of the velocity during training, devices such as GPS can be used. However, in several situations (e.g., indoor setting), such devices do not provide reliable measures. The aim of the study was to predict exact running velocity during the 30-15IFT using accelerometry-derived metrics (i.e., Player Load and Average Net Force) and heart rate (HR) through a machine learning (ML) approach (i.e., Support Vector Machine) with a leave-one-subject-out cross-validation. The SVM approach showed the highest performance to predict running velocity (r = 0.91) when compared to univariate approaches using PL (r = 0.62), AvNetForce (r = 0.73) and HR only (r = 0.87). In conclusion, the presented multivariate ML approach is able to predict running velocity better than univariate ones, and the model is generalizable across subjects.