Published in:
Open Access
01-12-2012 | Research
Quantitative meta-analysis of neural activity in posttraumatic stress disorder
Authors:
Jasmeet P Hayes, Scott M Hayes, Amanda M Mikedis
Published in:
Biology of Mood & Anxiety Disorders
|
Issue 1/2012
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Abstract
Background
In recent years, neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have played a significant role in elucidating the neural underpinnings of posttraumatic stress disorder (PTSD). However, a detailed understanding of the neural regions implicated in the disorder remains incomplete because of considerable variability in findings across studies. The aim of this meta-analysis was to identify consistent patterns of neural activity across neuroimaging study designs in PTSD to improve understanding of the neurocircuitry of PTSD.
Methods
We conducted a literature search for PET and fMRI studies of PTSD that were published before February 2011. The article search resulted in 79 functional neuroimaging PTSD studies. Data from 26 PTSD peer-reviewed neuroimaging articles reporting results from 342 adult patients and 342 adult controls were included. Peak activation coordinates from selected articles were used to generate activation likelihood estimate maps separately for symptom provocation and cognitive-emotional studies of PTSD. A separate meta-analysis examined the coupling between ventromedial prefrontal cortex and amygdala activity in patients.
Results
Results demonstrated that the regions most consistently hyperactivated in PTSD patients included mid- and dorsal anterior cingulate cortex, and when ROI studies were included, bilateral amygdala. By contrast, widespread hypoactivity was observed in PTSD including the ventromedial prefrontal cortex and the inferior frontal gyrus. Furthermore, decreased ventromedial prefrontal cortex activity was associated with increased amygdala activity.
Conclusions
These results provide evidence for a neurocircuitry model of PTSD that emphasizes alteration in neural networks important for salience detection and emotion regulation.