Research Statement

Greg Siegle, Ph.D.


Affective psychopathologies, including depression and anxiety, are disabling disorders that affect a large portion of the population. Some of the most clinically salient aspects of these disorders involve disruptions in information processing, how people attend to, remember, and interpret information, e.g., the tendency for depressed individuals to ruminate on negative information. Moreover some of the most effective interventions for psychopathology (e.g., cognitive therapy) are based on changing aspects of how individuals process emotional information. Yet, the roles of individual differences in information processing biases and deficits in psychopathology are unclear. My research program is devoted to understanding how individual differences in disruptions of emotional information processing are related to affective psychopathologies, particularly unipolar depression. Ultimately, this research could lead to better understanding of the nature of affective psychopathologies as well as methods for improving the speed and effectiveness of interventions by tailoring them to account for individual differences in information processing styles.

Three constraints help to make this research rigorous and interpretable. First, the theoretical models I adopt must be physiologically motivated, so as to allow integration of psychological and physiological perspectives. Second, implementing analogs of the models as computational neural networks helps to specify theories in an internally consistent manner, i.e. so theoretical conclusions follow from theoretical assumptions and so variables relevant to a rigorous characterization of a theory have not been left out. Finally, model predictions, generated by computational analogs, must be empirically supported. This constraint leads to empirical advances in understanding psychopathology and the use of valid models in developing novel interventions. This process leads to a research cycle of model specification, hypothesis generation, empirical testing, and model refinement.

Recent Research

I have used computational neural network models of emotional information processing to understand how negative life events and disruptions in brain connectivity could interact to lead depressed individuals to focus on negative information 1-7. Model behaviors predicted that some depressed individuals would display sustained processing of negative personally relevant information long after they were exposed to it, as a function of increased feedback between limbic and cortical structures. Assessment of pupil dilation (a correlate of cognitive load) and functional magnetic resonance imaging (fMRI) suggested that depressed individuals demonstrated such sustained processing across a variety of emotional information processing tasks 8-13, and that this pattern was related to self-reported rumination 8, 9, 12. fMRI data, in particular, suggest that depressed individuals display sustained amygdala activity in response to negative information as well as decreased activity in prefrontal regions associated with emotion regulation 9, 12. Currently I am examining emotional, information processing in depressed and never-depressed individuals using computational modeling 1-7, 9, 14, 15, fMRI 9, 12, 16-19, pupil dilation 6, 10, 18, 20, 21, event related potentials (ERPs)22, and electromyography (EMGs) as well as self-report and behavioral measures 23.


Based on these data, it may be useful to provide interventions that target mechanisms underlying sustained affective information processing in depressed individuals who ruminate. Towards that my lab has recently begun to develop an adjunctive intervention for depression that attempts to increase prefrontal control, particularly involving inhibition of the amygdala system 24. Initial data suggest that this intervention is valuable in both decreasing depressive symptomatology and rumination.


Future Directions

In the coming years, my lab will continue to explore other aspects of information processing in both healthy individuals and individuals with affective disorders. We also plan to continue to examine the extent to which integrating cognitive theories with psychophysiological data, in concert with the creation and validation of computational models, can help to provide rigorous and well-specified theories of psychopathology. In particular, we plan to further examine relationships between physiological phenomena that occur within seconds of the presentation of emotional material and more long-lasting clinically relevant processes such as rumination and worry in individuals with and without psychopathology.


The ultimate goal of this research involves applying our results towards treatment design. This process will involve creating human analogs of interventions for depressive information processing that work to correct the computational model's biases. My lab is currently examining whether very short interventions based on this principle consistently alleviate depressive information processing biases in patients who display sustained pupil dilation or amygdala activity in response to emotional stimuli. Variations of cognitive therapy, coupled with attention control techniques may be especially useful in this regard. Similarly, it will be useful to examine whether individuals with sustained pupil dilation or amygdala activity in response to emotional stimuli (presumably indicating a cognitive vulnerability) respond more effectively to traditional cognitive therapies, and to medications that target brain areas implicated by the model, than other patients.


Personal Background

I became interested in computational models of cognitive processes when I was introduced to them by James Anderson and Tom Dean while pursuing my degree in Cognitive Science at Brown University. My undergraduate thesis involved an integration of neurological and cognitive processes in perception of visual deformations. After my undergraduate degree I spent two years creating computational models of human information processing with Dedre Gentner and Ken Forbus, and creating models of emotion in artificially intelligent agents with Clark Elliott at the Institute for the Learning Sciences at Northwestern University. My introduction to psychopathology research came while working with Martin Harrow at the University of Illinois at Chicago, creating connectionist models of thought disorder in schizophrenia in 1993. During that time I became especially interested in information processing in depression, due to the disorderís prevalence, extremely debilitating symptoms, and the growing literature on information processing in clinical depression.


My doctoral work was done at the San Diego State University/University of California, San Diego, Joint Doctoral Program in Clinical Psychology. My primary research involved creation and validation of neural network models of negative information processing biases. With Dr. Georg Matt, I also conducted research on fuzzy representations of memory and have assisted in a meta-analytic review of therapeutic outcome studies in clinically representative settings. With Dr. Rick Ingram, I conducted research into cognitive correlates of depression and anxiety, and the specificity of aspects of depressive information processing to depression. With Dr. Eric Granholm I investigated physiological correlates of information processing. I did a one year clinical internship at the Clarke Institute of Psychiatry in Toronto, supervised by Dr. Zindel Segal. In this position, I performed cognitive therapy for depression, interpersonal therapy for depression and personality disorders, and neuropsychological testing, in addition to my research.


I then completed an NIMH post-doctoral fellowship in the Department of Psychiatry, University of Pittsburgh School of Medicine in which I examined physiological correlates of information processing in individuals with psychopathology through post-doctoral research with Stuart Steinhauer, Ph.D., Cameron Carter, M.D., Michael Thase, M.D., Ruth Condray, Ph.D., and Patricia Moore, M.D. This work involved further refinement of computational neural network models of processes underlying depression, and empirical tests of model predictions by using pupil dilation, ERPs, EMG, and fMRI with depressed and never depressed individuals. I also used fMRI to examine convergence in cognitive aspects of anxiety and depression, and examined disturbances in language and attention in individuals with depression and schizophrenia by measuring ERPs in response to linguistic information processing tasks.

I am currently an Assistant Professor in the Department of Psychiatry, University of Pittsburgh School of Medicine. I direct the Program in Cognitive and Affective Neuroscience and also serve as the Director of Affective Neuroscience for the Clinical Cognitive Neuroscience Laboratory and Biometrics Research Program.



1.†††††††† Siegle GJ. A neural network model of attention biases in depression. In: Reggia JA, Ruppin E, Glanzman DL, eds. Disorders of brain behavior and cognition: The neurocomputational perspective†††††† NJ Elsevier; 1999:415-441.

2.†††††††† Siegle GJ, Hasselmo ME. Using connectionist models to guide assessment of psychological disorder Psychological Assessment. 2002;14 263-278.

3.†††††††† Siegle GJ. Connectionist models of psychopathology: Crossroads of the cognitive and affective neuroscience of disorder. Cognitive Processing. 2001;2:455-486.

4.†††††††† Siegle GJ, Aizenstein H. Computational approaches to understanding the neurobiology of major depression: Integration through simulation. In: Barch DM, ed. Cognitive and affective neuroscience of psychopathology. Oxford, England: Oxford University Press; In press.

5.†††††††† Thayer JF, Siegle GJ. Neurovisceral integration in cardiac and emotional regulation. IEEE Eng Med Biol Mag. Jul-Aug 2002;21(4):24-29.

6.†††††††† Siegle GJ, Steinhauer SR, Thase ME. Pupillary Assessment and Computational Modeling of the Stroop Task in Depression. International Journal of Psychophysiology. 2004;52(1):63-76.

7.†††††††† Siegle GJ, Ingram RE. Modeling individual differences in negative information processing biases. In: Matthews G, ed. Cognitive Science Perspectives on Personality and Emotion. New York NY: Elsevier; 1997:301-353.

8.†††††††† Siegle GJ, Steinhauer SR, Carter CS, Ramel W, Thase ME. Do the seconds turn into hours? Relationships between sustained pupil dilation in response to emotional information and self reported rumination. Cognitive Therapy and Research. 2003;27(3):365-383.

9.†††††††† Siegle GJ, Steinhauer SR, Thase ME, Stenger VA, Carter CS. Canít shake that feeling: fMRI assessment of sustained amygdala activity in response to emotional information in depressed individuals. Biological Psychiatry. 2002;51:693-707.

10.†††††† Siegle GJ, Granholm E, Ingram RE, Matt GE. Pupillary response and reaction time measures of sustained processing of negative information in depression. Biological Psychiatry. 2001;49:624-636.

11.†††††† Siegle GJ, Steinhauer SR, Carter CS, Thase ME. Is Sustained Processing Specific to Emotional Information in Depression? Evidence from Pupil Dilation. submitted.

12.†††††† Siegle GJ, Thase ME, Steinhauer SR, Stenger VA, Carter CS. Increased Amygdala and Decreased Prefrontal BOLD Responses in Depression. submitted.

13.†††††† Siegle GJ, Konecky RO, Thase ME, Carter CS. Relationships between amygdala volume and activity during emotional information processing tasks in depressed and never-depressed individuals: an fMRI investigation. Annals of the New York Academy of Sciences. 2003;985:481-484.

14.†††††† Siegle GJ. Why I make models or what I learned in graduate school about validating clinical causal theories with computational models. The Behavior Therapist. 1997;20:179-184.

15.†††††† Elliot C, Siegle GJ. Emotion intensity factors in simulated believable agents WAUME 93 Workshop on Architectures Underlying Motivation and Emotion Birmingham UK The University of Birmingham 1993.

16.†††††† Brown G, Kinderman S, Siegle GJ, Granholm E, Wong EC, Buxton RB. Brain activation and pupil response during covert performance of the Stroop color word task. Journal of the International Neuropsychological Society. 1999;5(4):308-319.

17.†††††† Forman SD, Dougherty GG, Casey BJ, et al. Opiate addicts lack error-dependent activation of anterior cingulate. Biological Psychiatry. in press.

18.†††††† Siegle GJ, Steinhauer SR, Konecky R, Thase ME, Carter CS. Use of concurrent pupil dilation and fMRI assessment to understand working memory impairment in depression. Paper presented at: Organization for Human Brain Mapping; June, 2003; New York.

19.†††††† Gianaros P, May JC, Jennings JR, Siegle GJ. Is there a functional neural correlate of individual differences in cardiovascular reactivity? Psychosomatic Medicine. in press.

20.†††††† Siegle GJ, Steinhauer SR, Carter CS, Ramel W, Thase ME. Do the Seconds Turn Into Hours? Relationships between Sustained Pupil Dilation in Response to Emotional Information and Self-Reported Rumination. Cognitive Therapy & Research. Jun 2003;27(3):365-382.

21.†††††† Steinhauer SR, Siegle GJ, Condray R, Pless M. Sympathetic and Parasympathetic Innervation of Pupillary Dilation During Sustained Processing. International Journal of Psychophysiology. in press.

22.†††††† Condray R, Siegle GJ, Cohen JD, van Kammen DP, Steinhauer SR. Automatic activation of the semantic network in schizophrenia: evidence from event-related brain potentials. Biological Psychiatry. 2003;54(11):1134-1148.

23.†††††† Siegle GJ, Moore P, Thase ME. Rumination: One construct, many features in healthy individuals, depressed individuals, and individuals with Lupus. Cognitive Therapy & Research. 2004;28:645-668.

24.†††††† Siegle GJ. From brain activity to intervention. Paper presented at: Advances in the Treatment of Mood Disorders, 2004; Pittsburgh, PA.