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.

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.

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.

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).

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.

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.

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.

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

Simple Summary

Fatigue is considered a brain-derived emotion that could impact performance during the execution of physical exercises. Infrared thermography is a valuable technique able to measure the psychophysiological state associated with emotions in a contactless manner. The aim of the study is to test the capability of infrared thermography to evaluate the central and peripheral physiological effect of fatigue through facial skin and muscle temperature modulations collected during the execution of a unilateral resistance exercise of the lower limb. Both time- and frequency-domain analyses were performed on the temperature time course of the face and limbs. Particularly, significant correlations between features extracted from the thermal signals and the perceived exertion were found. These findings confirmed the ability of thermal imaging to detect both peripheral and central effects of fatigue in response to physical exercises. These results could foster the employment of infrared thermography to monitor the psychophysiological state of the athletes during training. The possibility to calibrate the training load in accordance with the psychophysiological conditions could improve the performance of the athletes during the training process and competitions.

Abstract

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- (∆T_ROI: 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 ∆T_ROI (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

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 30–15_IFT 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 30–15_IFT 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.

Improving Human-Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression

An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s.

Regions of interest selection and thermal imaging data analysis in sports and exercise science: a narrative review

OBJECTIVE

Infrared thermography (IRT) is a non-invasive, contactless and low-cost technology that allows recording of the radiating energy that is released from a body, providing an estimate of its superficial temperature. Thanks to the improvement of infrared thermal detectors, this technique is widely used in the biomedical field to monitor the skin temperature for different purposes (e.g. assessing circulatory diseases, psychophysiological state, affective computing). Particularly, in sports and exercise science, thermography is extensively used to assess sports performance, to investigate superficial vascular changes induced by physical exercise, and to monitor injuries. However, the methods of analysis employed to treat IRT data are not standardized, and hence introduce variability in the results.

APPROACH

This review focuses on the methods of analysis currently used for thermal imaging in sports and exercise science.

MAIN RESULTS

Firstly, the procedures employed for the selection of regions of interest (ROIs) from anatomical body districts are reviewed, paying attention also to the potentialities of morphing algorithms to increase the reproducibility of thermal results. Secondly, the statistical approaches utilized to characterize the temperature frequency and spatial distributions within ROIs are investigated, showing their strengths and weaknesses. Moreover, the importance of employing tracking methods to analyze the temporal thermal oscillations within ROIs is discussed. Thirdly, the capability of employing procedures of investigation based on machine learning frameworks on thermal imaging in sports science is examined.

SIGNIFICANCE

Finally, some proposals to improve the standardization and the reproducibility of IRT data analysis are provided, in order to facilitate the development of a common database of thermal images and to improve the effectiveness of IRT in sports science.

Evidence of Neurovascular Un-Coupling in Mild Alzheimer's Disease through Multimodal EEG-fNIRS and Multivariate Analysis of Resting-State Data

Alzheimer’s disease (AD) is associated with modifications in cerebral blood perfusion and autoregulation. Hence, neurovascular coupling (NC) alteration could become a biomarker of the disease. NC might be assessed in clinical settings through multimodal electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Multimodal EEG-fNIRS was recorded at rest in an ambulatory setting to assess NC and to evaluate the sensitivity and specificity of the methodology to AD. Global NC was evaluated with a general linear model (GLM) framework by regressing whole-head EEG power envelopes in three frequency bands (theta, alpha and beta) with average fNIRS oxy- and deoxy-hemoglobin concentration changes in the frontal and prefrontal cortices. NC was lower in AD compared to healthy controls (HC) with significant differences in the linkage of theta and alpha bands with oxy- and deoxy-hemoglobin, respectively (p = 0.028 and p = 0.020). Importantly, standalone EEG and fNIRS metrics did not highlight differences between AD and HC. Furthermore, a multivariate data-driven analysis of NC between the three frequency bands and the two hemoglobin species delivered a cross-validated classification performance of AD and HC with an Area Under the Curve, AUC = 0.905 (p = 2.17 × 10⁻⁵). The findings demonstrate that EEG-fNIRS may indeed represent a powerful ecological tool for clinical evaluation of NC and early identification of AD.

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

Infrared thermal imaging (IRI) is a contact-less technology able to monitor human skin temperature for biomedical applications and in real-life contexts. Its capacity to detect fever was exploited for mass screening during past epidemic emergencies as well as for the current COVID-19 pandemic. However, the only assessment of fever may not be selective for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Hence, novel approaches for IRI data analysis have been investigated. The present review aims to describe how IRI have been employed during the last epidemics, highlighting the potentialities and the limitations of this technology to contain the contagions. Specifically, the methods employed for automatic face recognition and fever assessment and IRI's performances in mass screening at airports and hospitals are reviewed. Moreover, an overview of novel machine learning methods for IRI data analysis, aimed to identify respiratory diseases, is provided. In addition, IRI-based smart technologies developed to support the healthcare during the COVID-19 pandemic are described. Finally, relevant guidelines to fully exploit IRI for COVID-19 identification are defined, to improve the effectiveness of IRI in the detection of the SARS-CoV-2 infection.

Prediction of state anxiety by machine learning applied to photoplethysmography data

Background

As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated.

Methods

The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test.

Results

A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10⁻⁹). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.

Working Memory Decline in Alzheimer's Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Alzheimer's disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey-Osterrieth complex figure and Raven's progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.

Does Osteopathic Manipulative Treatment Induce Autonomic Changes in Healthy Participants? A Thermal Imaging Study

Osteopathic manipulative treatment (OMT) has been demonstrated to be an effective therapy in several clinical conditions and age groups. Despite the clinical effectiveness, lack of robust data in terms of neurobiological, specifically autonomic, mechanisms of action is observed. Preliminary studies showed a parasympathetic effect leading to a trophotropic effect of OMT. However, these data are limited to heart rate variability (HRV) analysis. In order to study further the role of OMT on the autonomic nervous system, a cross-over randomized controlled trial RCT has been designed to test the effect of osteopathic treatment compared to sham therapy on a range of autonomic parameters. Thermal images, HRV and skin conductance data were collected on a sample of healthy adults. The study design consisted of two sessions (OMT and SHAM), 1 treatment per week, lasting 35 min each, composed of 5 min of baseline, 25 min of treatment, and 5 min of post-touch. During the baseline and the post-treatment, participants received no touch. Thirty-seven participants (aged 27 ± 5 years old, male ratio 40%) completed the study. Multivariate analysis showed a significant parasympathetic effect of group as well as of epoch on thermographic data of the nose (estimate 0.38; 95% CI 0.12–0.63; p < 0.01), left (0.17; 0.06–0.27; <0.001) and right (0.16; 0.07–0.24; <0.001) perioral as well as on the forehead (0.07; 0.01–0.12; <0.01) regions but not for the chin (0.08; −0.02 to 0.18; 0.13). Consistent with a parasympathetic effect, analyses demonstrated a difference between OMT and sham groups on the nuHF (p < 0.001) and DFA-a1 (p < 0.01) as well as on skin conductance (<0.01). The present research supports the hypothesis that a single session of OMT as compared to sham induces autonomic consequences in healthy non-symptomatic adults. Clinicaltrial.gov identifier: NCT03888456, https://clinicaltrials.gov/ct2/show/NCT03888456.

Assessment of the Autonomic Response in Alzheimer's Patients During the Execution of Memory Tasks: A Functional Thermal Imaging Study

BACKGROUND

Alzheimer's disease (AD) is a form of dementia characterized by failure of memory that becomes more severe with the progression of the disease. The Free and Cued Selective Reminding Test (FCSRT) is a clinical test used to evaluate such a deficit. However, since the cognitive performances could depend also on the psychophysiological state of the individual, it is important to monitor that state through the peripheral autonomic activity during the execution of the test. Thermal infrared imaging has been used for this kind of assessment in order to preserve the free and unbiased interaction between doctor and patient, thanks to the contactless features of the technique.

OBJECTIVE

To investigate whether the variation of facial temperature parameters during the FCSRT is indicative of different autonomic states in the early AD with respect to healthy controls (HC).

RESULTS

At a group level, a greater sympathetic activity for the HC with respect to AD during the execution of the test was found, indicative of a suppression of anxiety associated with the performances of the FCSRT in AD patients.

CONCLUSION

These results indicate that AD and HC may present different autonomic activity associated with the execution of a cognitive task, thus suggesting a different modulation of high-cognition and emotion network.

Differential pathlength factor in continuous wave functional near-infrared spectroscopy: reducing hemoglobin's cross talk in high-density recordings

Functional near-infrared spectroscopy (fNIRS) estimates the functional oscillations of oxyhemoglobin and deoxyhemoglobin in the cortex through scalp-located multiwavelength recordings. Hemoglobin oscillations are inferred through temporal changes in continuous-wave (CW) light attenuation. However, because of the diffusive multilayered head tissue structures, the photon path is longer than the source-detector separation, complicating hemoglobin evaluation. This aspect is incorporated in the modified Beer-Lambert law where the source-detector distance is multiplied by the differential pathlength factor (DPF). Since DPF estimation requires photons' time-of-flight information, DPF is assumed a priori in CW-fNIRS. Importantly, errors in the DPF spectrum induce hemoglobin cross talk, which is detrimental for fNIRS. We propose to estimate subject-specific DPF spectral dependence relying on multidistance high-density measurements. The procedure estimates the effective attenuation coefficient (EAC), which is proportional to the geometric mean of absorption and reduced scattering. Since DPF depends on the scattering-to-absorption ratio, EAC limits the spectral dependence assumption to scattering. This approach was compared to a standard frequency-domain multidistance procedure. A good association between the two methods ( r 2 = 0.69 ) was obtained. This approach could estimate low-resolution maps of the DPF spectral dependence through large field of view, high-density systems, reducing hemoglobin cross talk, and increasing fNIRS sensitivity and specificity to brain activity without instrumentation modification.

Autonomic impairment in Alzheimer's disease is revealed by complexity analysis of functional thermal imaging signals during cognitive tasks

OBJECTIVE

Alzheimer's disease (AD) is characterized by progressive memory failures and visuospatial impairment. Moreover, AD can be accompanied by autonomic system alterations, which, among other impacts, affect thermoregulatory activity. We here investigate differences in autonomic activity between AD patients and healthy controls (HC), employing a complexity analysis of functional infrared imaging (fIRI) data acquired at rest and during the execution of clinical cognitive and mnemonic tests.

APPROACH

fIRI allows for contactless monitoring of autonomic activity and its thermoregulatory expression without interfering with the psychophysiological state of the subject, preserving free interaction with the doctor. The signal complexity analysis, based on the sample entropy, was compared to a standard frequency-based analysis of autonomic-related signals.

MAIN RESULTS

AD patients exhibited lower complexity of fIRI signals during the tests, which could be indicative of a stronger sympathetic activity with respect to HC. No significant effects were found at rest. No differences were found on employing frequency-based analysis.

SIGNIFICANCE

This study confirms that AD patients may exhibit peculiar autonomic responses associated with the execution of cognitive tasks that can be measured through fIRI. Moreover, these responses could be highlighted by a nonlinear metric of signal predictability such as the sample entropy establishing autonomic impairment of AD patients.

Modelling Impulse Response Function of Functional Infrared Imaging for General Linear Model Analysis of Autonomic Activity

Functional infrared imaging (fIRI) is a validated procedure to infer autonomic arousal. Currently, fIRI signals are analysed through descriptive metrics, such as average temperature changes in a region of interest (ROI). However, the employment of mathematical models could provide a powerful tool for the accurate identification of autonomic activity and investigation of the mechanisms underlying autonomic arousal. A linear temporal statistical model such as the general linear model (GLM) is particularly suited for its simplicity and direct interpretation. In order to apply the GLM, the thermal response linearity and time-invariance of fIRI have to be demonstrated, and the thermal impulse response (TIR) needs to be characterized. In this study, the linearity and time-invariance of the thermal response to sympathetic activating stimulation were demonstrated, and the TIR for employment of the GLM was characterized. The performance of the GLM-fIRI was evaluated by comparison with the GLM applied on synchronous measurements of the skin conductance response (SCR). In fact, the GLM-SCR is a validated procedure to estimate autonomic arousal. Assuming the GLM-SCR as the gold standard approach, a GLM-fIRI sensitivity and specificity of 86.4% and 75.9% were obtained. The GLM-fIRI may allow increased performances in the evaluation of autonomic activity and a broader range of application of fIRI in both research and clinical settings for the assessment of psychophysiological and psychopathological states.

Complexity of Frontal Cortex fNIRS Can Support Alzheimer Disease Diagnosis in Memory and Visuo-Spatial Tests

Decline in visuo-spatial skills and memory failures are considered symptoms of Alzheimer's Disease (AD) and they can be assessed at early stages employing clinical tests. However, performance in a single test is generally not indicative of AD. Functional neuroimaging, such as functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests in an ecological setting to support diagnosis. Indeed, neuroimaging should not alter clinical practice allowing free doctor-patient interaction. However, block-designed paradigms, necessary for standard functional neuroimaging analysis, require tests adaptation. Novel signal analysis procedures (e.g., signal complexity evaluation) may be useful to establish brain signals differences without altering experimental conditions. In this study, we estimated fNIRS complexity (through Sample Entropy metric) in frontal cortex of early AD and controls during three tests that assess visuo-spatial and short-term-memory abilities (Clock Drawing Test, Digit Span Test, Corsi Block Tapping Test). A channel-based analysis of fNIRS complexity during the tests revealed AD-induced changes. Importantly, a multivariate analysis of fNIRS complexity provided good specificity and sensitivity to AD. This outcome was compared to cognitive tests performances that were predictive of AD in only one test. Our results demonstrated the capabilities of fNIRS and complexity metric to support early AD diagnosis.

Effects of knee extension with different speeds of movement on muscle and cerebral oxygenation

Background

One of the mechanisms responsible for enhancing muscular hypertrophy is the high metabolic stress associated with a reduced muscular oxygenation occurring during exercise, which can be achieved by reducing the speed of movement. Studies have tested that lowered muscle oxygenation artificially induced by an inflatable cuff, could provoke changes in prefrontal cortex oxygenation, hence, to central fatigue. It was hypothesized that (1) exercising with a slow speed of movement would result in greater increase in cerebral and greater decrease in muscle oxygenation compared with exercises of faster speed and (2) the amount of oxygenation increase in the ipsilateral prefrontal cortex would be lower than the contralateral one.

Methods

An ISS Imagent frequency domain near infrared spectroscopy (NIRS) system was used to quantify oxygenation changes in the vastus lateralis muscle and prefrontal cortex (contra- and ipsilateral) during unilateral resistance exercises with different speeds of movement to voluntary fatigue. After one maximal repetition (1RM) test, eight subjects performed three sets of unilateral knee extensions (∼50% of 1RM), separated by 2 min rest periods, following the pace of 1 s, 3 s and 5 s for both concentric and eccentric phases, in a random order, during separate sessions. The amount of change for NIRS parameters for muscle (ΔHb: deoxyhemoglobin, ΔHbO: oxyhemoglobin, ΔHbT: total hemoglobin, ΔStO₂: oxygen saturation) were quantified and compared between conditions and sets by two-way ANOVA RM. Differences in NIRS parameters between contra- and ipsilateral (lobe) prefrontal cortex and conditions were tested.

Results

Exercising with slow speed of movement was associated to larger muscle deoxygenation than normal speed of movement, as revealed by significant interaction (set × condition) for ΔHb (p = 0.01), and by significant main effects of condition for ΔHbO (p = 0.007) and ΔStO₂ (p = 0.016). With regards to the prefrontal cortex, contralateral lobe showed larger oxygenation increase than the ipsilateral one for ΔHb, ΔHbO, ΔHbT, ΔStO₂ in each set (main effect of lobe: p < 0.05). Main effects of condition were significant only in set1 for all the parameters, and significant interaction lobe × condition was found only for ΔHb in set1 (p < 0.05).

Discussion

These findings provided evidence that speed of movement influences the amount of muscle oxygenation. Since the lack of oxygen in muscle is associated to increased metabolic stress, manipulating the speed of movement may be useful in planning resistance-training programs. Moreover, consistent oxygenation increases in both right and left prefrontal lobes were found, suggesting a complementary interaction between the ipsi- and contralateral prefrontal cortex, which also seems related to fatigue.

New Frontiers for Applications of Thermal Infrared Imaging Devices: Computational Psychopshysiology in the Neurosciences

Thermal infrared imaging has been proposed, and is now used, as a tool for the non-contact and non-invasive computational assessment of human autonomic nervous activity and psychophysiological states. Thanks to a new generation of high sensitivity infrared thermal detectors and the development of computational models of the autonomic control of the facial cutaneous temperature, several autonomic variables can be computed through thermal infrared imaging, including localized blood perfusion rate, cardiac pulse rate, breath rate, sudomotor and stress responses. In fact, all of these parameters impact on the control of the cutaneous temperature. The physiological information obtained through this approach, could then be used to infer about a variety of psychophysiological or emotional states, as proved by the increasing number of psychophysiology or neurosciences studies that use thermal infrared imaging. This paper presents a review of the principal achievements of thermal infrared imaging in computational psychophysiology, focusing on the capability of the technique for providing ubiquitous and unwired monitoring of psychophysiological activity and affective states. It also presents a summary on the modern, up-to-date infrared sensors technology.

Aetiology of Preoperative Anaemia in Patients Undergoing Elective Cardiac Surgery—the Challenge of Pillar One of Patient Blood Management

Preoperative anaemia is common in patients undergoing cardiac surgery. Whilst there is a strong association with increased morbidity and mortality, it is currently unclear whether treatment of anaemia leads to patient benefit. This retrospective study aimed to determine the aetiology of preoperative anaemia in a cohort of patients undergoing elective cardiac surgery over two years at a tertiary hospital. Laboratory data obtained at the preoperative assessment clinic visit were assessed to stratify patients into four groups—iron deficiency anaemia (IDA), possible IDA, anaemia of chronic disease (ACD) and non-anaemic patients with low ferritin according to the ‘Preoperative haemoglobin assessment and optimisation template’ of the Australian Patient Blood Management (PBM) Guidelines. Of patients with preoperative anaemia, 23.1% had IDA, 6.6% had possible IDA and 70.3% had possible ACD. Of the patients with possible ACD, 30% had a ferritin <100 μg/l, representing limited iron stores or coexisting absolute iron deficiency in the setting of chronic disease. In addition, 46.2% of those with possible ACD had iron studies indicative of functional iron deficiency. Time between assessment and surgery was as little as one day in a third of patients and in only 7% was it more than seven days. Our findings indicate that about one-third of our patients with preoperative anaemia had evidence of iron deficiency, a potentially reversible cause of anaemia. In addition, a significant number had either limited iron stores that may render them iron deficient by surgery, or a functional iron deficiency.

"The face of ostracism": The impact of the social categorization on the thermal facial responses of the target and the observer

Ostracism has been shown to elicit pain in both the target and the observers. Two experiments investigated the autonomic thermal signature associated with an ostracism experience and assessed whether and how social categorization impacts the autonomic arousal of both the target and the observer. Autonomic response was assessed using thermal infrared imaging, recording facial temperature variation during an online game of ball toss (i.e., Cyberball). Social categorization was manipulated using a minimal group paradigm. The results show a more intense autonomic response during ostracism (vs. inclusion), marked by an increase in facial temperature in the nose and the perioral area. This autonomic response is stronger when individuals are ostracized by ingroup (vs. outgroup) members. Similar pattern of temperature variations emerge when individuals observe an ostracism episode involving ingroup members. Our findings advance the understanding of psycho-physiological mechanisms underlying the ostracism experience and emphasize the impact of social categorization in such mechanisms.

Behavioral and facial thermal variations in 3-to 4-month-old infants during the Still-Face Paradigm

Behavioral and facial thermal responses were recorded in twelve 3- to 4-month-old infants during the Still-Face Paradigm (SFP). As in the usual procedure, infants were observed in a three-step, face-to-face interaction: a normal interaction episode (3 min); the “still-face” episode in which the mother became unresponsive and assumed a neutral expression (1 min); a reunion episode in which the mother resumed the interaction (3 min). A fourth step that consisted of a toy play episode (5 min) was added for our own research interest. We coded the behavioral responses through the Infant and Caregiver Engagement Phases system, and recorded facial skin temperature via thermal infrared (IR) imaging. Comparing still-face episode to play episode, the infants’ communicative engagement decreased, their engagement with the environment increased, and no differences emerged in self-regulatory and protest behaviors. We also found that facial skin temperature increased. For the behavioral results, infants recognized the interruption of the interactional reciprocity caused by the still-face presentation, without showing upset behaviors. According to autonomic results, the parasympathetic system was more active than the sympathetic, as usually happens in aroused but not distressed situations. With respect to the debate about the causal factor of the still-face effect, thermal data were consistent with behavioral data in showing this effect as related to the infants’ expectations of the nature of the social interactions being violated. Moreover, as these are associated to the infants’ subsequent interest in the environment, they indicate the thermal IR imaging as a reliable technique for the detection of physiological variations not only in the emotional system, as indicated by research to date, but also in the attention system. Using this technique for the first time during the SFP allowed us to record autonomic data in a more ecological manner than in previous studies.

Assessing embodied interpersonal emotion regulation in somatic symptom disorders: a case study

The aim of the present study was to examine the intra- and interpersonal emotion regulation of patients with somatic symptom disorders (SSDs) during interactions with significant others (i.e., romantic partners). We presented two case couples for analysis. The first couple consisted of a patient with SSD and his healthy partner, whereas the second couple consisted of two healthy partners. The couples underwent an interpersonal experiment that involved baseline, anger and relaxation tasks. During each task, partners' cutaneous facial temperature, heart rate and skin conductance levels were measured simultaneously. Participants' trait-emotion regulation, state-affect reports for self and other, and attachment styles were also examined. The experimental phases were successful in creating variations in physiological processes and affective experience. As expected, emotion regulation difficulties predicted higher increase in the course of temperature at each phase. Besides, the patient showed restricted awareness and reflection to emotions despite his higher autonomic activity compared to healthy controls. Both partners of the first couple revealed limited ability in understanding the other's emotions, whereas the second couple performed relatively better in that domain. The temperature variations between the patient and his partner were significantly correlated while the correlations of temperature changes between the second couple were negligible except anger task. The study supported the merits of an embodied interpersonal approach in clinical studies. The tentative results of the cases were discussed in the light of findings in emotion regulation and attachment research.

Time-domain analysis of scrotal thermoregulatory impairment in varicocele

Varicocele is a common male disease defined as the pathological dilatation of the pampiniform plexus and scrotal veins with venous blood reflux. Varicocele usually impairs the scrotal thermoregulation via a hemodynamic alteration, thus inducing an increase in cutaneous temperature. The investigation of altered scrotal thermoregulation by means of thermal infrared imaging has been proved to be useful in the study of the functional thermal impairment. In this study, we use the Control System Theory to analyze the time-domain dynamics of the scrotal thermoregulation in response to a mild cold challenge. Four standard time-domain dynamic parameters of a prototype second order control system (Delay Time, Rise Time, closed poles locations, steady state error) and the static basal temperatures were directly estimated from thermal recovery curves. Thermal infrared imaging data from 31 healthy controls (HCS) and 95 varicocele patients were processed. True-positive predictions, by comparison with standard echo color Doppler findings, higher than 87% were achieved into the proper classification of the disease stage. The proposed approach could help to understand at which specific level the presence of the disease impacts the scrotal thermoregulation, which is also involved into normal spermatogenesis process.

The autonomic signature of guilt in children: a thermal infrared imaging study

So far inferences on early moral development and higher order self conscious emotions have mostly been based on behavioral data. Emotions though, as far as arguments support, are multidimensional notions. Not only do they involve behavioral actions upon perception of an event, but they also carry autonomic physiological markers. The current study aimed to examine and characterise physiological signs that underlie self-conscious emotions in early childhood, while grounding them on behavioral analyses. For this purpose, the "mishap paradigm" was used as the most reliable method for evoking feelings of "guilt" in children and autonomic facial temperature variation were detected by functional Infrared Imaging (fIRI). Fifteen children (age: 39-42 months) participated in the study. They were asked to play with a toy, falsely informed that it was the experimenter's "favourite", while being unaware that it was pre-planned to break. Mishap of the toy during engagement caused sympathetic arousal as shown by peripheral nasal vasoconstriction leading to a marked temperature drop, compared to baseline. Soothing after the mishap phase induced an increase in nose temperature, associated with parasympathetic activity suggesting that the child's distress was neutralized, or even overcompensated. Behavioral analyses reported signs of distress evoked by the paradigm, backing up the thermal observation. The results suggest that the integration of physiological elements should be crucial in research concerning socio-emotional development. fIRI is a non invasive and non contact method providing a powerful tool for inferring early moral emotional signs based on physiological observations of peripheral vasoconstriction, while preserving an ecological and natural context.

Astigmatism correction with toric IOL: analysis of visual performance, position, and wavefront error

PURPOSE

To evaluate astigmatism correction, visual performance, intraocular lens (IOL) position, and wavefront error after implantation of toric IOLs in patients with cataract.

METHODS

This prospective study comprised 30 eyes of 30 patients with cataract who were candidates for phacoemulsification and implantation of the AcrySof toric IOL (Alcon Laboratories, Inc., Fort Worth, TX). Mean preoperative corneal keratometric and subjective refractive cylinder were 2.10 ± 0.47 and 2.17 ± 0.41 diopters (D), respectively.

RESULTS

The refractive cylinder decreased significantly from 2.17 ± 0.41 to 0.73 ± 0.45 D (P = .001) at 180 days postoperatively. The difference between preoperative corneal astigmatism and postoperative refractive astigmatism was statistically significant (P < .05). At 180 days postoperatively, the uncorrected distance visual acuity was 0.20 logMAR (Snellen 20/32) in 100% of patients and 0.0 logMAR (Snellen 20/20) in 64% of patients. The root mean square of internal coma and trefoil aberrations showed a trend toward reduction; internal spherical aberration significantly decreased, whereas corneal trefoil aberration significantly increased (P < .05). A low amount of IOL decentration and tilt were detected at 30 and 180 days postoperatively, respectively.

CONCLUSIONS

Toric IOL implantation is an effective procedure for correction of preexisting corneal astigmatism, improving visual performance, and inducing a low amount of higher-order aberrations. Moreover, the toric IOLs is well positioned early after surgery and stable over time.

Mom feels what her child feels: thermal signatures of vicarious autonomic response while watching children in a stressful situation

Maternal attunement with an infant's emotional states is thought to represent a distinctive feature of the human primary bond. It implies the mother's ability of empathizing with her child in order to fulfil the child's needs in an immediate and appropriate manner. Thus, it is particularly involved in stressful situations. By assuming that maternal attunement embodies a direct sharing of physiological responses with the child, we compared the autonomic response of mothers observing their own distressed child with those of other women observing an unknown child involved in an ecological distressful condition (mishap paradigm). The hypothesis was that the adult's response was more attuned with the child's response in the former group than in the latter group. The autonomic response was non-invasively evaluated through the recording of the thermal facial imprints by means of thermal infrared (IR) imaging. Nine mother-child dyads and 9 woman-unknown child dyads were studied. We found marked similarities between the facial temperature dynamics of women and children along the experimental procedure, thus providing evidence for a direct emotional sharing within the adult-child dyad. The evidence for common dynamics in the time course of the temperatures was assessed through correlation analysis and, nevertheless, resulted stronger in the mother-child dyads than in the other women-child dyads. In addition, temporal analysis showed a faster response in mothers than in other women, thus confirming our study hypothesis. Besides confirming the extraordinary capability of IR imaging to preserve ecological context in the study of social or non-verbal interactions, these results suggest that maternity appears to potentiate the emotional attunement with the child. Although based on preliminary results, this study opens new perspectives in the study of the factors modulating vicarious socio-emotional processes.

Mother and child in synchrony: thermal facial imprints of autonomic contagion

Mothers' ability to empathically share offspring's emotional feelings is considered integral to primary affective bonds and a healthy socio-emotional development. What neurobiological mechanism is responsible for this ability in humans? It has been proposed that the psychological and neural components of affective experiences are strictly associated with autonomic-visceral changes. Hence, the vicarious response of empathy may also embody a sharing of changes in body physiology. The present study aimed at investigating whether maternal empathy is accompanied by a synchrony in autonomic responses. We simultaneously recorded, in an ecological context with contact free methodology, the facial thermal imprints of mother and child, while the former observed the latter when involved in a distressing situation. The results showed a situation-specific parallelism between mothers' and children's facial temperature variations, providing preliminary evidence for a direct affective sharing involving autonomic responding. These findings support a multidimensional approach for the comprehension of emotional parent-child relationships.

Propofol infusion syndrome

The clinical features of propofol infusion syndrome (PRIS) are acute refractory bradycardia leading to asystole, in the presence of one or more of the following: metabolic acidosis (base deficit > 10 mmol.l(-1)), rhabdomyolysis, hyperlipidaemia, and enlarged or fatty liver. There is an association between PRIS and propofol infusions at doses higher than 4 mg.kg(-1).h(-1) for greater than 48 h duration. Sixty-one patients with PRIS have been recorded in the literature, with deaths in 20 paediatric and 18 adult patients. Seven of these patients (four paediatric and three adult patients) developed PRIS during anaesthesia. It is proposed that the syndrome may be caused by either a direct mitochondrial respiratory chain inhibition or impaired mitochondrial fatty acid metabolism mediated by propofol. An early sign of cardiac instability associated with the syndrome is the development of right bundle branch block with convex-curved ('coved type') ST elevation in the right praecordial leads (V1 to V3) of the electrocardiogram. Predisposing factors include young age, severe critical illness of central nervous system or respiratory origin, exogenous catecholamine or glucocorticoid administration, inadequate carbohydrate intake and subclinical mitochondrial disease. Treatment options are limited. Haemodialysis or haemoperfusion with cardiorespiratory support has been the most successful treatment.

Grade III acromioclavicular joint injury in Australian Rules Football

We have reviewed fourteen Australian Rules Football players who were seen consecutively by a single surgeon with grade III acromioclavicular joint injuries between January 1993-June 1997. After discussion at initial consultation eight players elected for non-operative management and six for operative management. Two players in the non-operative group subsequently underwent surgical reconstruction after failure of non-operative treatment. The mean return time to non-contact training was 2.4 weeks (range 1-4, S.D. 1.52) in the non-operative group and 6.3 weeks (range 3.5-10, S.D. 2.99) in the operative group. However return to sports specific training (contact training) was at a mean of 20.8 weeks (range 10-32. S.D. 8.56) in the non-operative group and 13.6 weeks (range 6-24. S.D. 7.06) in the operative group. Return to competitive football matches for the non-operative group was at a mean of 26.2 weeks (range 10-34,S.D. 8.84) and 18.8 weeks (9.5-28. S.D. 8.05) for those treated operatively. These results were not statistically significant. At final follow up the subjective scores for the current overall condition of the shoulder when compared to the uninjured side, were 72.5 (range 20-100, S:D.24.9) for the non operative group and 87.3 (range 75-100, S:D.10.61) for the operative group and this difference was also not statistically significant. Conclusions in this study are limited by small numbers and lack of statistically significant results. The results show a trend towards faster return to ARF and a more satisfactory outcome for patients undergoing surgery compared to their non operative cohorts.

Cloning and preliminary characterization of two satellite-like DNA sequences from the brown frog Rana graeca

Two repetitive DNA sequences of about 0.29 Kb and 0.39 Kb have been isolated from Rana graeca genomic DNA by digestion with Asp718I and have been cloned in pTZ18R. Hybridization data obtained with DNA probes derived from these clones indicate that: i) both sequences are highly repetitive and species-specific; ii) the two sequences are partially homologous; iii) the 0.29 Kb sequence is present in the frog genome with the typical tandem organization of satellite DNA; iv) the 0.39 Kb sequence is presumably part of a longer repetitive sequence of satellite DNA.