Conventional treatments for Major Depression, although reasonably effective, leave many without lasting relief. Alternative approaches would therefore be welcome for both short- and long-term treatment of depression. Thirty-eight women were randomized to one of three treatment conditions in a double-blind randomized controlled trial of acupuncture in depression. All participants eventually received eight weeks of acupuncture treatment specifically for depression. From among the 33 women who completed treatment, 26 (79%) were interviewed at six-month follow-up. Relapse rates were comparable to those of established treatments, with four of the 17 women (24%) who achieved full remission at the conclusion of treatment experiencing a relapse six months later. Compared to other empirically validated treatments, acupuncture designed specifically to treat major depression produces results that are comparable in terms of rates of response and of relapse or recurrence. These results suggest a larger trial of acupuncture in the acute- and maintenance-phase treatment of depression is warranted. © 2002 Elsevier Science Ltd. All rights reserved.
Psychophysiological measures hold great potential for informing clinical assessments. The challenge, before such measures can be widely used, is to develop test procedures and analysis strategies that allow for statistically reliable and valid decisions to be made for any particular examinee, despite large individual differences in psychophysiological responding. Focusing on the evaluation of memory in clinical, criminal, and experimental contexts, this paper reviews the rationale for and development of ERP-based memory assessment procedures, with a focus on methods that allow for statistically supported decisions to be made in the case of a single examinee. The application of one such procedure to the study of amnesia in Dissociative Identity Disorder is highlighted. To facilitate the development of other psychophysiological assessment tools, psychophysiological researchers are encouraged to report the sensitivity and specificity of their measures where possible.
Perception of illness has been described as an important predictor in the medical health psychology literature, but has been given little attention in the domain of mental disorders. The patient's Perception of Depression Questionnaire (PDIQ) is a newly developed measure whose factor structure and psychometric properties were evaluated on a sample of 174 outpatients meeting criteria for major depressive disorder. The clinical utility of the questionnaire was assessed on a sub-sample of 121 participants in a study of acupuncture treatment for depression. The questionnaire has four subscales, each with high internal consistency and high test-retest reliability. These four subscales are: Self-Efficacy, which reflects perceived controllability of the illness, Externalizing, which reflects attributing the illness to external causes, Hopeless/Flawed, which reflect a belief that depression is a personal trait and therefore there is little hope for cure, and Holistic, which reflects a belief in alternative therapies. Although the PDIQ did not predict outcome, its subscales were related to adherence to treatment, treatment preference, expectations, and therapeutic alliance. The subscales have adequate convergent/discriminant validity and are clinically relevant to aspects of treatment provision. © 2003 Elsevier Science Ltd. All rights reserved.
Emotion is an important aspect in the interaction between humans. It is fundamental to human experience and rational decision-making. There is a great interest for detecting emotions automatically. A number of techniques have been employed for this purpose using channels such as voice and facial expressions. However, these channels are not very accurate because they can be affected by users' intentions. Other techniques use physiological signals along with electroencephalography (EEG) for emotion detection. However, these approaches are not very practical for real time applications because they either ask the participants to reduce any motion and facial muscle movement or reject EEG data contaminated with artifacts. In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature selection mechanism to extract features that are relevant to the emotion detection task based on neuroscience findings. We reached an average accuracy of 51% for joy emotion, 53% for anger, 58% for fear and 61% for sadness. © 2010 ACM.