Functional neuroimaging of treatment effects in psychiatry: methodological challenges and recommendations

Abstract

Functional magnetic resonance imaging (fMRI) has helped to elucidate the neurobiological bases of psychiatric and neurodevelopmental disorders by localizing etiologically-relevant aberrations in brain function. Functional MRI also has shown great promise to help understand potential mechanisms of action of effective treatments for a range of psychiatric and neurodevelopmental disorders, including mood and anxiety disorders, schizophrenia, and autism. However, the use of fMRI to probe intervention effects in psychiatry is associated with unique methodological considerations, including the psychometric properties of repeated fMRI scans, how to assess potential relations between the effects of an intervention on symptoms and on specific brain activation patterns, and how to best make causal inferences about intervention effects on brain function. Additionally, the study of treatment effects in neurodevelopmental disorders presents additional unique challenges related to brain maturation, analysis methods, and the potential for motion artifacts. We review these methodological considerations and provide recommendations for best practices for each of these topics.

FIGURE 1

Functional MRI (fMRI) activation maps depicting the effects of regression filtering to remove respiratory noise. The fMRI task is a simple hand sensorimotor task. This patient had a large left frontal glioblastoma and irregularity in his respiration pattern during the scan (A) that caused significant artifacts in the motor activation map (B). These artifacts were filtered out to produce a more specific motor function map (C). The noise removal was done using regression filtering implemented in fScan [63] based on the RETROICOR method of Glover and colleagues [90].

FIGURE 2

Single-subject fMRI activation change maps in native subject space depicting areas of statistically increased activation after citalopram treatment for repetitive behaviors in two individuals with autism spectrum disorders. The fMRI task was an oddball target detection paradigm designed to recruit brain areas subserving cognitive control. Case 1 (left) was a treatment nonresponder and Case 2 (right) was a treatment-responder. The figure illustrates increased activation in relevant brain areas in the treatment responder but not in the treatment nonresponder. Contrasts are thresholded at corrected Z > 1.7. Reprinted from Dichter et al (2010) [7].

FIGURE 3

Relations between pre-treatment fMRI and response to psychotherapy treatment in unipolar depression. The top panel illustrates a cluster within the ventral paracingulate gyrus (ParaCG) that predicted change in Hamilton Rating Scale for Depression (HAM-D) [91] scores after psychotherapy in individuals with unipolar major depressive disorder. The fMRI task focused on decision making and reward processing. Contrasts are thresholded at corrected Z > 4.0. Adapted from Dichter et al (2009) [5].

FIGURE 4

The effects of psychotherapy on reward anticipation in unipolar depression were examined by comparing the change in brain activation in depression patients scanned before and after treatment with the change in brain activation observed in controls scanned twice. The red clusters reflect significant Group (depressed, non-depressed) X Time (Time 1, Time 2) interaction effects, and include the right caudate cluster highlighted by the cross hair. The bar graph illustrates that the depressed group showed significantly increased activation in this right caudate cluster after psychotherapy. Contrasts are thresholded at corrected Z > 4.0. Error bars represent standard errors of the mean. Adapted from Dichter et al (2009) [5].