The effects of gender and COMT Val158Met polymorphism on fearful facial affect recognition:a fMRI study

The functional catechol-O-methyltransferase (COMT Val108/158Met) polymorphism has been shown to have an impact on tasks of executive function, memory and attention and recently, tasks with an affective component. As oestrogen reduces COMT activity, we focused on the interaction between gender and COMT genotype on brain activations during an affective processing task. We used functional MRI (fMRI) to record brain activations from 74 healthy subjects who engaged in a facial affect recognition task; subjects viewed and identified fearful compared to neutral faces. There was no main effect of the COMT polymorphism, gender or genotypegender interaction on task performance. We found a significant effect of gender on brain activations in the left amygdala and right temporal pole, where females demonstrated increased activations over males. Within these regions, Val/Val carriers showed greater signal magnitude compared to Met/Met carriers, particularly in females. The COMT Val108/158Met polymorphism impacts on gender-related patterns of activation in limbic and paralimbic regions but the functional significance of any oestrogen-related COMT inhibition appears modest. Copyright © 2008 CINP.

Pilot investigation of the changes in cortical activation during facial affect recognition with lamotrigine monotherapy in bipolar disorder

Background: Bipolar disorder is associated with dysfunction in prefrontal and limbic areas implicated in emotional processing. Aims: To explore whether lamotrigine monotherapy may exert its action by improving the function of the neural network involved in emotional processing. Method: We used functional magnetic resonance imaging to examine changes in brain activation during a sad facial affect recognition task in 12 stable patients with bipolar disorder when medication-free compared with healthy controls and after 12 weeks of lamotrigine monotherapy. Results: At baseline, compared with controls, patients with bipolar disorder showed overactivity in temporal regions and underactivity in the dorsal medial and right ventrolateral prefrontal cortex, and the dorsal cingulate gyrus. Following lamotrigine monotherapy, patients demonstrated reduced temporal and increased prefrontal activation. Conclusions: This preliminary evidence suggests that lamotrigine may enhance the function of the neural circuitry involved in affect recognition.

Recognition accuracy and response bias to happy and sad facial expressions in patients with major depression

Impaired facial expression recognition has been associated with features of major depression, which could underlie some of the difficulties in social interactions in these patients. Patients with major depressive disorder and age- and gender-matched healthy volunteers judged the emotion of 100 facial stimuli displaying different intensities of sadness and happiness and neutral expressions presented for short (100 ms) and long (2,000 ms) durations. Compared with healthy volunteers, depressed patients demonstrated subtle impairments in discrimination accuracy and a predominant bias away from the identification as happy of mildly happy expressions. The authors suggest that, in depressed patients, the inability to accurately identify subtle changes in facial expression displayed by others in social situations may underlie the impaired interpersonal functioning.

Producing facial composite sketches in remote cognitive interviews:a preliminary investigation

Justice systems around the world are increasingly turning to videoconferencing as a means to reduce delays and reduce costs in legal processes. This preliminary research examined whether interviewing a witness remotely - without physical co-presence of the witness and interviewer - could facilitate the production of quality facial composite sketches of suspects. In Study 1, 42 adults briefly viewed a photograph of a face. The next day they participated in Cognitive Interviews with a forensic artist, conducted either face-to-face or remotely via videoconference. In Study 2, 20 adults participated in videoconferenced interviews, and we manipulated the method by which they viewed the developing sketch. In both studies, independent groups of volunteers rated the likeness of the composites to the original photographs. The data suggest that remote interviews elicited effective composites; however, in Study 1 these composites were considered poorer matches to the photographs than were those produced in face-to-face interviews. The differences were small, but significant. Participants perceived several disadvantages to remote interviewing, but also several advantages including less pressure and better concentration. The results of Study 2 suggested that different sketch presentation methods offered different benefits. We propose that remote interviewing could be a useful tool for investigators in certain circumstances. © 2013 Taylor & Francis.

Look who's talking! Facial appearance can bias source monitoring

When we see a stranger's face we quickly form impressions of his or her personality, and expectations of how the stranger might behave. Might these intuitive character judgements bias source monitoring? Participants read headlines "reported" by a trustworthy- and an untrustworthy-looking reporter. Subsequently, participants recalled which reporter provided each headline. Source memory for likely-sounding headlines was most accurate when a trustworthy-looking reporter had provided the headlines. Conversely, source memory for unlikely-sounding headlines was most accurate when an untrustworthy-looking reporter had provided the headlines. This bias appeared to be driven by the use of decision criteria during retrieval rather than differences in memory encoding. Nevertheless, the bias was apparently unrelated to variations in subjective confidence. These results show for the first time that intuitive, stereotyped judgements of others' appearance can bias memory attributions analogously to the biases that occur when people receive explicit information to distinguish sources. We suggest possible real-life consequences of these stereotype-driven source-monitoring biases. © 2010 Psychology Press.

Serum procalcitonin, interleukin-6, soluble intercellular adhesin molecule-1 and IgG to short-chain exocellular lipoteichoic acid as predictors of infection in total joint prosthesis revision

The diagnosis of prosthetic joint infection and its differentiation from aseptic loosening remains problematic. The definitive laboratory diagnostic test is the recovery of identical infectious agents from multiple intraoperative tissue samples; however, interpretation of positive cultures is often complex as infection is frequently associated with low numbers of commensal microorganisms, in particular the coagulase-negative staphylococci (CNS). In this investigation, the value of serum procalcitonin (PCT), interleukin-6 (IL-6) and soluble intercellular adhesion molecule-1 (sICAM-1) as predictors of infection in revision hip replacement surgery is assessed. Furthermore, the diagnostic value of serum IgG to short-chain exocellular lipoteichoic acid (sce-LTA) is assessed in patients with infection due to CNS. Presurgical levels of conventional serum markers of infection including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and white blood cell count (WBC) is also established. Forty-six patients undergoing revision hip surgery were recruited with a presumptive clinical diagnosis of either septic (16 patients) or aseptic loosening (30 patients). The diagnosis was confirmed microbiologically and levels of serum markers were determined. Serum levels of IL-6 and sICAM-1 were significantly raised in patients with septic loosening (P=0.001 and P=0.0002, respectively). Serum IgG to sce-LTA was elevated in three out of four patients with infection due to CNS. In contrast, PCT was not found to be of value in differentiating septic and aseptic loosening. Furthermore, CRP, ESR and WBC were significantly higher (P=0.0001, P=0.0001 and P=0.003, respectively) in patients with septic loosening. Serum levels of IL-6, sICAM-1 and IgG to sce-LTA may provide additional information to facilitate the diagnosis of prosthetic joint infection.

Dissociation of detection and evaluation of facial expressions in adolescence

Holistic face perception, i.e. the mandatory integration of featural information across the face, hasbeen considered to play a key role when recognizing emotional face expressions (e.g., Tanaka et al.,2002). However, despite their early onset holistic processing skills continue to improvethroughout adolescence (e.g., Schwarzer et al., 2010) and therefore might modulate theevaluation of facial expressions. We tested this hypothesis using an attentional blink (AB)paradigm to compare the impact of happy, fearful and neutral faces in adolescents (10–13 years)and adults on subsequently presented neutral target stimuli (animals, plants and objects) in a rapidserial visual presentation stream. Adolescents and adults were found to be equally reliable whenreporting the emotional expression of the face stimuli. However, the detection of emotional butnot neutral faces imposed a significantly stronger AB effect on the detection of the neutral targetsin adults compared to adolescents. In a control experiment we confirmed that adolescents ratedemotional faces lower in terms of valence and arousal than adults. The results suggest a protracteddevelopment of the ability to evaluate facial expressions that might be attributed to the latematuration of holistic processing skills.

Theoretical and applied evaluations of facial composite systems

Police often use facial composites during their investigations, yet research suggests that facial composites are generally not effective. The present research included two experiments on facial composites. The first experiment was designed to test the usefulness of the encoding specificity principle for determining when facial composites will be effective. Instructions were used to encourage holistic or featural cues at encoding. The method used to construct facial composites was manipulated to encourage holistic or featural cues at retrieval. The encoding specificity principle suggests that an interaction effect should occur. If the same cues are used at encoding and retrieval, better composites should be constructed than when the cues are not the same. However, neither the expected interaction nor the main effects for encoding and retrieval were significant. The second study was conducted to assess the effectiveness of composites generated by two different facial composite construction systems, E-Fit and Mac-A-Mug Pro. These systems differ in that the E-Fit system uses more sophisticated methods of composite construction and may construct better quality facial composites. A comparison of E-Fit and Mac-A-Mug Pro composites demonstrated that E-Fit composites were of better quality than Mac-A-Mug Pro composites. However, neither E-Fit nor Mac-A-Mug Pro composites were useful for identifying the target person from a photograph lineup. Further, lineup performance was at floor level such that both E-Fit and Mac-A-Mug Pro composites were no more useful than a verbal description. Possible limitations of the studies are discussed, as well as suggestions for future research. ^

Thermal imaging as a biometrics approach to facial signature authentication

This dissertation develops an image processing framework with unique feature extraction and similarity measurements for human face recognition in the thermal mid-wave infrared portion of the electromagnetic spectrum. The goals of this research is to design specialized algorithms that would extract facial vasculature information, create a thermal facial signature and identify the individual. The objective is to use such findings in support of a biometrics system for human identification with a high degree of accuracy and a high degree of reliability. This last assertion is due to the minimal to no risk for potential alteration of the intrinsic physiological characteristics seen through thermal infrared imaging. The proposed thermal facial signature recognition is fully integrated and consolidates the main and critical steps of feature extraction, registration, matching through similarity measures, and validation through testing our algorithm on a database, referred to as C-X1, provided by the Computer Vision Research Laboratory at the University of Notre Dame. Feature extraction was accomplished by first registering the infrared images to a reference image using the functional MRI of the Brain’s (FMRIB’s) Linear Image Registration Tool (FLIRT) modified to suit thermal infrared images. This was followed by segmentation of the facial region using an advanced localized contouring algorithm applied on anisotropically diffused thermal images. Thermal feature extraction from facial images was attained by performing morphological operations such as opening and top-hat segmentation to yield thermal signatures for each subject. Four thermal images taken over a period of six months were used to generate thermal signatures and a thermal template for each subject, the thermal template contains only the most prevalent and consistent features. Finally a similarity measure technique was used to match signatures to templates and the Principal Component Analysis (PCA) was used to validate the results of the matching process. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using an Euclidean-based similarity measure showed 88% accuracy in the case of skeletonized signatures and templates, we obtained 90% accuracy for anisotropically diffused signatures and templates. We also employed the Manhattan-based similarity measure and obtained an accuracy of 90.39% for skeletonized and diffused templates and signatures. It was found that an average 18.9% improvement in the similarity measure was obtained when using diffused templates. The Euclidean- and Manhattan-based similarity measure was also applied to skeletonized signatures and templates of 25 subjects in the C-X1 database. The highly accurate results obtained in the matching process along with the generalized design process clearly demonstrate the ability of the thermal infrared system to be used on other thermal imaging based systems and related databases. A novel user-initialization registration of thermal facial images has been successfully implemented. Furthermore, the novel approach at developing a thermal signature template using four images taken at various times ensured that unforeseen changes in the vasculature did not affect the biometric matching process as it relied on consistent thermal features.

Development of an instrumented customizable total knee prosthesis for experimental tests.

Total knee arthroplasty (TKA) has revolutionized the life of millions of patients and it is the most efficient treatment in cases of osteoarthritis. The increase in life expectancy has lowered the average age of the patient, which requires a more enduring and performing prosthesis. To improve the design of implants and satisfying the patient's needs, a deep understanding of the knee Biomechanics is needed. To overcome the uncertainties of numerical models, recently instrumented knee prostheses are spreading. The aim of the thesis was to design and manifacture a new prototype of instrumented implant, able to measure kinetics and kinematics (in terms of medial and lateral forces and patellofemoral forces) of different interchangeable designs of prosthesis during experiments tests within a research laboratory, on robotic knee simulator. Unlike previous prototypes it was not aimed for industrial applications, but purely focusing on research. After a careful study of the literature, and a preliminary analytic study, the device was created modifying the structure of a commercial prosthesis and transforming it in a load cell. For monitoring the kinematics of the femoral component a three-layers, piezoelettric position sensor was manifactured using a Velostat foil. This sensor has responded well to pilot test. Once completed, such device can be used to validate existing numerical models of the knee and of TKA and create new ones, more accurate.It can lead to refinement of surgical techniques, to enhancement of prosthetic designs and, once validated, and if properly modified, it can be used also intraoperatively.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Facial affect processing inbipolar disorder:a magnetoencephalography study

Background: Identifying biological markers to aid diagnosis of bipolar disorder (BD) is critically important. To be considered a possible biological marker, neural patterns in BD should be discriminant from those in healthy individuals (HI). We examined patterns of neuromagnetic responses revealed by magnetoencephalography (MEG) during implicit emotion-processing using emotional (happy, fearful, sad) and neutral facial expressions, in sixteen BD and sixteen age- and gender-matched healthy individuals. Methods: Neuromagnetic data were recorded using a 306-channel whole-head MEG ELEKTA Neuromag System, and preprocessed using Signal Space Separation as implemented in MaxFilter (ELEKTA). Custom Matlab programs removed EOG and ECG signals from filtered MEG data, and computed means of epoched data (0-250ms, 250-500ms, 500-750ms). A generalized linear model with three factors (individual, emotion intensity and time) compared BD and HI. A principal component analysis of normalized mean channel data in selected brain regions identified principal components that explained 95% of data variation. These components were used in a quadratic support vector machine (SVM) pattern classifier. SVM classifier performance was assessed using the leave-one-out approach. Results: BD and HI showed significantly different patterns of activation for 0-250ms within both left occipital and temporal regions, specifically for neutral facial expressions. PCA analysis revealed significant differences between BD and HI for mild fearful, happy, and sad facial expressions within 250-500ms. SVM quadratic classifier showed greatest accuracy (84%) and sensitivity (92%) for neutral faces, in left occipital regions within 500-750ms. Conclusions: MEG responses may be used in the search for disease specific neural markers.

An analysis of facial description in static images and video streams

[ES]This paper describes an analysis performed for facial description in static images and video streams. The still image context is first analyzed in order to decide the optimal classifier configuration for each problem: gender recognition, race classification, and glasses and moustache presence. These results are later applied to significant samples which are automatically extracted in real-time from video streams achieving promising results in the facial description of 70 individuals by means of gender, race and the presence of glasses and moustache.