Technological advances in neuroimaging have enabled researchers to examine, in vivo, the relationship between psychotherapeutic interventions and markers of brain activity. This review focuses on two kinds of neuroimaging studies in psychotherapy: those that examine the patterns of brain activity associated with response to treatments and those that examine the changes that occur in brain activity during treatment. A general, hypothetical neural model of psychotherapy is presented, and support for the model is evaluated across anxiety disorders and major depression. Neuroimaging studies are broadly consistent in observing associations between response to psychotherapy and baseline activity in several key regions within the prefrontal cortex, basal ganglia, and limbic areas. These regions are involved in the generation and regulation of emotion, fear responding, and response to reward. Pre-post examinations of change following psychotherapy also typically observe that psychological treatments for anxiety and depression can affect neural activity in these regions. Despite general consensus that activity in these regions is associated with psychotherapy, substantial discrepancy persists regarding the precise direction of the observed relationships. Methodological challenges of the existing literature are considered, and future directions are discussed.
Psychotherapy’s efficacy is well established for a wide range of emotional disorders. Despite this, no psychotherapeutic intervention works equally well for all patients, and the mechanisms through which psychotherapy reduces symptoms and enhances functioning remain difficult to specify. With the advent of neuroimaging technologies, researchers have new tools with which to identify clinically meaningful markers of brain function that are associated with treatment response. Two kinds of associations have been examined. Treatment outcome prediction studies seek to identify those patterns of brain function that confer a higher likelihood that a treatment will work. Treatment mechanism studies examine changes in the brain as a result of the intervention in question, in order to help understand how treatments are exerting their effects. Both types of study hold promise for developing a more complete understanding of the neural mechanisms involved in successful therapy, and both may guide future treatment refinement, novel mechanistic treatment development, and personalized treatment prescriptions tailored to individual patients.
Below, a brief overview of the general neural architecture believed to be relevant for anxiety, depression, and psychotherapeutic interventions for these disorders, is provided. Anxiety and depression are the focus of this review because these are the two disorders for which the most evidence has accrued, and also because there is good reason to believe that anxiety and depression share at least some underlying neural mechanisms. Functional neuroimaging studies of psychotherapy are reviewed, and recent advances towards improving the methodology and clinical relevance of research in this area are highlighted. Ideally, work will continue to progress towards greater relevance and import for the practicing clinician.
Neural Circuitry of Anxiety and Depression
Contemporary neurobiological models of anxiety and depression include both distinct (anxiety- and depression-specific) and overlapping networks of brain regions. As displayed in Figure 1, the neural circuitry of general emotion dysregulation and high negative affect, which is implicated in both types of disorders, includes an interconnected set of brain regions involved in the generation and regulation of emotion (1-3). Limbic structures (such as the amygdala, hippocampus, and insula) react to emotional information. Activity from these regions feeds forward through the anterior cingulate cortex (ACC; involved in the appraisal and encoding of emotion), orbitofrontal cortex (OFC, involved in the integration of affective and sensory information and reward processing) and finally to the dorsomedial and ventromedial prefrontal cortices (DMPFC, VMPFC; involved in self-referential processing and in moderating emotional reactions). The initial activity in limbic regions can be regulated through top-down regions of the prefrontal cortex (PFC). Lateral prefrontal regions, including the dorsolateral and ventrolateral prefrontal cortex (DLPFC; VLPFC; both of which subserve higher-order cognitive functions), interact with the other frontal systems noted above, including the DMPFC, VMPFC, and ACC. These frontal systems are functionally interconnected with the amygdala and other limbic regions (3) and can modulate limbic activity during controlled processing of emotional stimuli (4).
The functioning of disorder specific networks is also key to understanding the relationship between psychotherapy and brain function. In addition to the general emotion processing and emotion regulation networks described above, there is a partially overlapping set of regions that shows increased activation to fear-related stimuli. This system forms a ‘fear network’ and is particularly relevant to the anxious arousal and exaggerated fear responses that characterize anxiety disorders. This fear-responsive circuitry includes limbic regions such as the amygdala, hippocampus, and parahippocampal gyrus, as well as the insula, periaqueductal gray, and medial portions of the PFC (mPFC) including VMPFC, OFC, and ACC (for a more detailed review, see (5)). Finally, the functioning of an additional network, the reward circuit, is particularly relevant in the treatment of major depression (2), as it may play a role in anhedonia.
The findings reviewed above are broadly consistent with predictions regarding the neural substrates of psychotherapy. Activity in regions associated with negative emotion, emotion regulation, fear, and reward are associated with response to psychotherapy, and psychotherapy appears to alter the functioning of these regions. Beyond understanding which regions are involved, however, the state of the field has not yet evolved sufficiently to make many specific conclusions regarding the direction of these effects. Conflicting directional observations may, for example, be due to differences in task states (imaging during a resting-state vs. symptom provocation vs. application of a specific therapy skill). Increased PFC function at rest may in fact contribute to decreased capacity for activation of the PFC in response to symptom provocation or skill application, leading to findings in opposing directions depending on the task state that is examined (7). Furthermore, regional increases and decreases observed in neuroimaging are currently subject to multiple interpretations. Increased activation in a given region might be interpreted as reflecting an improvement in the strength of the region’s function, or as an impairment in the region’s efficiency, reflecting a need for greater activity in order to accomplish the same effect. One goal of future research will be to further clarify the precise nature of associations and to resolve the inconsistencies that have been observed.
The fact that brain function measured pretreatment is associated with the likelihood of response to psychotherapies is important. It suggests that future refinements regarding the precise direction of these effects across multiple kinds of tasks may enable treatments to be selected or individually tailored to the unique needs of the individual. Currently, several studies of psychotherapeutic treatment for anxiety have consistently implicated increased hyperresponsivity of regions reacting to threatening stimuli (e.g., limbic and visual processing areas) as a marker of better response to therapy. Such hyperreactivity may represent a clinically useful biomarker conferring a higher chance of a positive outcome from therapy, perhaps due to increased engagement with anxiety-provoking stimuli at baseline.
The pattern of predictive findings from studies of depression suggests that the functional state of the emotion regulation system, and potentially the reward system, prior to treatment has important consequences for the efficacy of cognitive behavioral therapy. This may not be surprising. Cognitive behavioral therapy is believed to engage and strengthen the patient’s ability to regulate and alter their emotional states. Little doubt remains that the ventral portions of the ACC play a key role in determining the likelihood of treatment response for depression, however, more work is needed to specify which specific sub-regions are critical, and what patterns of activity (to which tasks) are predictive of good or bad outcomes.
In order for brain-based predictive findings to become applicable in the clinic, greater clarity is needed regarding the precise task and imaging parameters that will lead to reproducible results at the single-patient level. Future work should aim to build on the strengths of recent studies, which used larger samples (11, 15), randomization of participants to different treatments (26), and more sophisticated analytic approaches that allow researchers to estimate the added benefit of neuroimaging data (11), to examine patterns of communication between brain regions (25), and to draw inferences that are valid at the individual patient level (15). These advances will help to address questions such as which treatment option is best for which patient. Additionally, when multiple brain regions are observed to predict response, as in (26), effort needs to be made to combine these multiple predictors in order to make a single treatment
4 Multiple Choice CME questions
Which of the following statements best characterizes neuroimaging findings on the prediction of therapy outcome in anxiety disorders?
Increased activity in limbic and visual regions processing threat stimuli predicts better therapy outcome across anxiety disorders, while the direction of PFC effects is less consistent.
Decreased activity in PFC regions predicts better therapy outcome across anxiety disorders, while the direction of limbic region findings is less consistent.
Increased activity in limbic and visual regions processing threat stimuli predicts better therapy outcome for specific disorders including PTSD, while decreased activity in these regions predicts better therapy outcome for other disorders including OCD.
Decreased activity in limbic and visual regions processing threat, and increased activity in PFC regions, predicts better therapy outcome for social anxiety.
Activity in which region is most consistently implicated in the prediction of response to psychotherapeutic treatments for depression?
Dorsolateral prefrontal cortex (DLPFC)
Ventrolateral prefrontal cortex (VLPFC)
Ventral anterior cingulate cortex (ACC)
Which of the following statements best characterizes pre-post therapy neuroimaging findings in anxiety disorders?
Activity in PFC regions increases while activity in limbic regions decreases across anxiety disorders.
Activity in both PFC and limbic regions decreases across anxiety disorders.
Activity in PFC regions tends to increase for specific disorders including PTSD, while activity in PFC regions tends to decrease for other disorders including OCD.
There are no replicated findings indicating a consistent direction of change in PFC or limbic regions for any specific anxiety disorder.
Which of the following statements best reflects changes in prefrontal cortical activation/metabolism following different psychotherapies for depression?
Decreases in activity/metabolism have been observed following interpersonal and psychodynamic psychotherapy, whereas increases have been observed following cognitive behavioral therapy and behavioral activation.
Increases in activity/metabolism have been observed following interpersonal and psychodynamic psychotherapy, whereas decreases have been observed following cognitive behavioral therapy and behavioral activation.
All four major models of psychotherapy for depression have been associated with increases in prefrontal cortical activity/metabolism.
All four major models of psychotherapy for depression have been associated with decreases in prefrontal cortical activity/metabolism.
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