John JB Allen
Distinguished Professor
Professor, BIO5 Institute
Professor, Cognitive Science - GIDP
Professor, Psychology
Professor, Neuroscience - GIDP
Primary Department
Department Affiliations
(520) 621-7448
Work Summary
Depression is a major health problem that is often chronic or recurrent. Existing treatments have limited effectiveness, and are provided wihtout a clear indication that they will match a particular patient's needs. In this era of precision medicine, we strive to develop neurally-informed treatments for depression and related disorders.
Research Interest
Dr. Allen is Distinguished Professor of Psychology, Cognitive Science, and Neuroscience at the University of Arizona in Tucson Arizona. After receiving his graduate training at the University of Minnesota, and completing an internship at the VA medical center in Minneapolis, he assumed his current position in Arizona in 1992. Dr. Allen has published over 150 peer-reviewed scientific papers, and been the recipient of grants from the National Institutes of Health and from the National Alliance for Research in Schizophrenia and Depression to fund his research. Dr. Allen has received numerous awards for his research, including the Distinguished Early Career Award from the Society for Psychophysiological Research. He is also the recipient in 2004 of the Leicester & Kathryn Sherrill Creative Teaching Award, in 2005 of the Graduate College and Professional Education Teaching and Mentoring Award, and was named Distinguished Professor in 2006. He is a Fellow of the Association for Psychological Science, and a past-president of the Society for Psychophysiological Research, and received the Alexander von Humboldt Foundation Research Prize in 2016. Dr. Allen’s research spans several areas, but the main focus is the etiology and treatment of mood and anxiety disorders. His work focuses on identifying risk factors for depression using electroencephalographic and autonomic psychophysiological measures, especially EEG asymmetry, resting state fMRI connectivity, and cardiac vagal control. Based on these findings, he is developing novel and neurally-informed treatments for mood and anxiety disorders, including Transcranial Ultrasound, EEG biofeedback, and Transcranial Direct Current and Transcranial Alternating Current. Other work includes understanding how emotion and emotional disorders influence the way we make decisions and monitor our actions. Keywords: Depression, Neuromodulation, EEG, Resting-state fMRI

Publications

Gallagher, S. M., Allen, J. J., Hitt, S. K., Schnyer, R. N., & Manber, R. (2001). Six-month depression relapse rates among women treated with acupuncture. Complementary Therapies in Medicine, 9(4), 216-218.

PMID: 12184348;Abstract:

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.

Allen, J. J. (2002). The role of psychophysiology in clinical assessment: ERPs in the evaluation of memory. Psychophysiology, 39(3), 261-280.

PMID: 12212646;Abstract:

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.

Smith, E. E., Reznik, S. J., Stewart, J. L., & Allen, J. J. (2017). Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry. International Journal of Psychophysiology, 111, 98--114.
Manber, R., Chambers, A. S., Hitt, S. K., McGahuey, C., Delgado, P., & Allen, J. J. (2003). Patients' perception of their depressive illness. Journal of Psychiatric Research, 37(4), 335-343.

PMID: 12765856;Abstract:

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.

Mikhail, M., El-Ayat, K., Kaliouby, R. E., Coan, J., & J., J. (2010). Emotion detection using noisy EEG data. ACM International Conference Proceeding Series.

Abstract:

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.