John S. Barlow, M.D. and Robert M. Brown. / "July 14, 1955." / Bibliography: p. 40-41. / Army Signal Corps Contract DA36-039 sc-42607 Project 132B Dept. of the Army Project 3-99-12-022

Evolutionary accounts of empathy often focus on the ways in which empathy-motivated helping can give rise to indirect fitness benefits. These accounts posit that empathy is adaptive insofar as it motivates strategic helping behavior, but they neglect a key feature of the empathic process – it can prepare one to act effectively within a shared environment. In particular, adopting the affective and motivational states of others provides a rapid and automatic way to avoid danger and threat, which play a disproportionately large role in shaping behavior. Based on the idea that empathic processes facilitate adaptive reactions to threat, I conducted four experiments to test the hypothesis that empathic reactions reflect withdrawal motivation. In the first experiment I used electroencephalography (EEG) to measure baseline right-frontal cortical asymmetry, a reliable neural correlate of withdrawal motivation. I then assessed empathic reactions to images of children ostensibly taken from a charity campaign. Participants who showed greater right-frontal cortical asymmetry also showed stronger empathic reactions to the images. In the second study I used self-report measures fear and anger to assess dispositional withdrawal- and approach-motivation, respectively. This time, participants indicated their empathic reactions to targets experiencing happiness and targets experiencing sadness. Empathy for both types of targets was positively related to fear and negatively related to physical aggression, again supporting a link between empathy and withdrawal motivation. In the third study I measured state withdrawal motivation by using facial electromyography (EMG) to assess disgust expressions towards charity images. These expressions were positively correlated with empathic reactions, demonstrating that state withdrawal motivation is also positively related to empathy. In the final study I manipulated approach and withdrawal emotions by having participants make emotional facial expressions. Focusing on fear and anger, I found that participants were more empathic when making fearful faces than when making angry faces, although these results must be interpreted with caution, as the manipulation may not have had the intended effects on emotional state. Taken together, these four studies provide converging evidence of an association between withdrawal motivation and empathy, supporting the idea that empathy plays a role in the adaptive response to threat.

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (p. 89-102). Also available in print.

Σκοπός της παρούσας εργασίας είναι η μελέτη μεθόδoυ υπολογισμού της συνδεσιμότητας περιοχών του εγκεφάλου κατά την διάρκεια μιας πραγματικής και μιας φανταστικής κίνησης του δακτύλου. Η μελέτη βασίζεται σε μετρήσεις (καταγραφές) ηλεκτροεγκεφαλογραφημάτων (ΗΕΓ). Η συνδεσιμότητα εκφράζεται ποσοτικά με το μέγεθος της συνάφειας (coherence) και μπορεί να δώσει απάντηση στο κατά πόσο τα σήματα του ηλεκτροεγκεφαλογραφήματος που παίρνουμε από τα δυο κανάλια σχετίζονται μεταξύ τους. Για να το πετύχουμε αυτό υλοποιήσαμε μια μη γραμμική υπολογιστική μέθοδο συγχρονισμού. Η μη γραμμική μέθοδος ανάλυσης και υπολογισμού της συνδεσιμότητας των εγκεφαλικών περιοχών που υλοποιήθηκε έγινε στο μαθηματικό χώρο της φάσης. Σε αυτό το χώρο το σήμα μου είναι ένα δυναμικό σύστημα. Η πληροφορία που περιέχεται σε αυτό ποσοτικοποιείται με μια μέθοδο που βασίζεται στην ανάλυση του συγχρονισμού φάσης (Phase Dynamics Modelling, PDM). Η ιδέα του συγχρονισμού φάσεων προσφέρει μία νέα προοπτική στην κατανόηση και ποσοτικοποίηση των δυναμικών αλληλεπιδράσεων μεταξύ συστημάτων συζευγμένων ταλαντωτών. Λαμβάνοντας υπόψη μας την μη γραμμική φύση των νευρικών βιοσημάτων, παρουσιάζουμε μια νέα προσέγγιση στην αναγνώριση της εγκεφαλικής δραστηριότητας και συσχέτισης κατά την διάρκεια πραγματικής και φανταστικής κίνησης του δείκτη των δακτύλων του χεριού. Χρησιμοποιούμε την τεχνική Empirical Mode Decomposition. Η τεχνική αυτή, η οποία είναι το βασικό αντικείμενο μελέτης της εργασίας ονομάζεται εμπειρικός τρόπος αποδόμησης του σήματος (empirical-mode-decomposition EMD) και χρησιμοποιείται για να κάνουμε αποσύνθεση των δεδομένων του ηλεκτροεγκεφαλογραφήματος (EEG) σε έναν αριθμό στοιχειωδών 3 ορθογώνιων συναρτήσεων (elementary – orthogonal modes) με καλά καθορισμένη χρονομεταβλητη συμπεριφορά.(IMFs). Πιθανό πλεονέκτημα αυτής της μεθόδου είναι οτι με τις παραδοσιακές η μη γραμμικότητα του συστήματος ίσως οδηγήσει σε απώλεια της ενέργειας σε υψηλή συχνότητα με αποτέλεσμα να έχουμε ψεύτικο συγχρονισμό. Είναι μια νέα τεχνική μέθοδος ανάλυσης σημάτων η οποία βασίζεται στον μετασχηματισμό Hilbert – Huang. Στην παρούσα εργασία δίνεται έμφαση στις ομοιότητες δομικής και λειτουργικής οργάνωσης των εγκεφαλικών διασυνδέσεων που πραγματοποιούνται κατά την διάρκεια πραγματικής και φανταστικής κίνησης Στο πείραμα που μελετάμε η ανάλυση συσχέτισης φάσης πραγματοποιείται στο κομμάτι (imf) που αντιστοιχεί στον βητα ρυθμό ( 4th component). H αναπαραγωγή των αποτελεσμάτων με την μέθοδο EMD στον βητα ρυθμό και στην συνέχεια η ανάλυση συγχρονισμόυ φάσης προάγει αυτή την μέθοδο ως: προσαρμοστική (adaptive)-εναλλακτική (alternative) μέθοδο αναγνώρισης πραγματικών συναρτήσεων που προέρχονται από ολόκληρο το σήμα , παρέχοντας έναν νέο τρόπο καθορισμού συγκεκριμένου εύρους ζώνης συχνοτήτων μέσα στην οποία έχουμε φαινομενα συσχέτισης[13]. Η συνέπεια των αποτελεσμάτων τα οποία προέρχονται από ποικίλες καταγραφές αποτελούν σαφή ένδειξη ότι η ανάλυση του συγχρονισμού φάσης μπορεί να χρησιμοποιηθεί σαν ένα επιπλέον κριτήριο για ένα σύστημα (E-HY) προκειμένου να κωδικοποιήσει την πρόθεση της κίνησης. / -

Direct and indirect stimulation of the brain have produced a range of perceptual, motor, and cognitive experiences, including experiences historically ascribed to religious or spiritual domains. Weak intensity, extremely low frequency electromagnetic fields patterned after physiological processes have been the subject of much research and controversy. The current study examined the Shiva technology, a unique method of field production that utilizes the same fields used in previous research. Quantitative changes in brain activity were measured using quantitative electroencephalograph and subjective reports of experiences were examined. The investigation included two different configurations of the Shiva technology. Results indicated that individuals exposed to specific patterned fields exhibited different patterns of neural activity and greater reports of unusual experiences compared to a sham condition. The importance of particular enhancement of power in regions of the brain due to the sequence of different patterns of magnetic fields was a key discovery. Personality characteristics, particularly those involved with the Default Mode Network, and their relation to baseline electroencephalographic data were also examined.

No description available.

No description available.

Evolutionary accounts of empathy often focus on the ways in which empathy-motivated helping can give rise to indirect fitness benefits. These accounts posit that empathy is adaptive insofar as it motivates strategic helping behavior, but they neglect a key feature of the empathic process – it can prepare one to act effectively within a shared environment. In particular, adopting the affective and motivational states of others provides a rapid and automatic way to avoid danger and threat, which play a disproportionately large role in shaping behavior. Based on the idea that empathic processes facilitate adaptive reactions to threat, I conducted four experiments to test the hypothesis that empathic reactions reflect withdrawal motivation. In the first experiment I used electroencephalography (EEG) to measure baseline right-frontal cortical asymmetry, a reliable neural correlate of withdrawal motivation. I then assessed empathic reactions to images of children ostensibly taken from a charity campaign. Participants who showed greater right-frontal cortical asymmetry also showed stronger empathic reactions to the images. In the second study I used self-report measures fear and anger to assess dispositional withdrawal- and approach-motivation, respectively. This time, participants indicated their empathic reactions to targets experiencing happiness and targets experiencing sadness. Empathy for both types of targets was positively related to fear and negatively related to physical aggression, again supporting a link between empathy and withdrawal motivation. In the third study I measured state withdrawal motivation by using facial electromyography (EMG) to assess disgust expressions towards charity images. These expressions were positively correlated with empathic reactions, demonstrating that state withdrawal motivation is also positively related to empathy. In the final study I manipulated approach and withdrawal emotions by having participants make emotional facial expressions. Focusing on fear and anger, I found that participants were more empathic when making fearful faces than when making angry faces, although these results must be interpreted with caution, as the manipulation may not have had the intended effects on emotional state. Taken together, these four studies provide converging evidence of an association between withdrawal motivation and empathy, supporting the idea that empathy plays a role in the adaptive response to threat.

Modeling of brain activity is often seen as requiring great computing power. However in the special case of modeling scalp EEG it is possible to adopt a continuum approximation for the cortex, and then to use the techniques of wave physics to describe its consequent large-scale dynamics. The model incorporates the following critical components: two classes of neurons (excitatory and inhibitory), the typical number and strength of connections between these two classes, the corresponding connections within the thalamus and between the thalamus and cortex, the time constants and basic physiology of neurons, and the propagation of activity between neurons. Representing the immense intricacy of brain anatomy and physiology with suitable summary equations and average parameter values has meant that the model is able to capture the essential characteristics of EEG and ERPs, and to do so in a computationally manageable way.

Doctor of Philosophy (Medicine) / Sleepiness is an important source of morbidity in the community, with potentially catastrophic consequences of occupational or driving injuries or accidents. Although many measures of sleepiness exist, there is no gold standard. The electroencephalograph (EEG) has been studied as an indicator of sleep pressure in the waking organism, or sleep depth. A mathematical model has been developed, relating the observed EEG to interactions between groups of neurons in the cortex and thalamus (Robinson, Rennie, Rowe, O'Connor, & Gordon, 2005; Robinson, Rennie, & Wright, 1997). These interactions are thought to be important in the transition from wake to sleep. Sleepiness is common in obstructive sleep apnea (OSA). The measurement of sleepiness would have great utility in quantifying the disease burden, measuring treatment response, or determining fitness for work or driving. This study will evaluate parameters derived from the EEG mathematical model as a measure of sleepiness. It is divided into the following four parts: 1. Subjects with likely OSA based on symptoms and demographics from an international database were compared with matched non-OSA controls. The OSA group showed deficits in executive function and abnormalities on evoked response potential testing. 2. Outcomes from a cross-sectional study in a sleep-clinic OSA population were aggregated by factor analysis into a five summary variables relevant to sleepiness: subjective sleepiness, mood & anxiety, memory & learning, driving, and executive functioning. 3. EEG mathematical model parameters from wake EEG recordings were related to the five summary outcomes. Executive function correlated with a parameter Z, representing the negative feedback loop between the thalamic reticular nucleus and the thalamocortical relay nuclei. 4. EEG model parameters during first NREM sleep cycle of 8 subjects with regular sleep architecture were studied. Net cortical excitation (parameter X) is predicted to increase across the cycle, while there was, as predicted, a greater inhibitory effect of the thalamic reticular nucleus upon thalamocortical relay cells (parameter Z). In this preliminary assessment, EEG model parameters reflecting thalamocortical interactions are sensitive to prefrontal lobe tasks such as executive function, which are known to be vulnerable to sleep loss and sleepiness, and these parameters also show variation with increasing sleep depth.