Biomedical Engineering Reference
In-Depth Information
cebo responders could find utility in the drug development process where it is
important to be able to distinguish specific medication effects from nonspecific, that
is, “placebo” effects.
In yet another application involving placebo effects, qEEG has been used to
examine the interplay between placebo-related neurophysiological changes and sub-
sequent clinical response to antidepressant treatment. A typical design in clinical tri-
als for depression is the use of a 7- to 10-day placebo lead-in phase during which
time all subjects receive single-blind treatment with placebo prior to randomized
treatment with active medication or placebo for the duration of the trial. Examina-
tion of serial EEGs beginning at pretreatment baseline and spanning both the pla-
cebo lead-in period and the postrandomization phase may shed light on
relationships among brain functional changes, placebo effects, and medication
effects in the treatment of depression. To this point, a novel study examined regional
changes in qEEG cordance during the placebo lead-in phase in relation to final out-
comes for depressed subjects later randomized to antidepressant medication or pla-
cebo [135]. Results showed that prefrontal changes during placebo lead-in
explained 19% of the variance in final HamD
17
scores after 8 weeks of antidepres-
sant treatment. This suggests that nonpharmacological treatment factors (i.e., those
that are present during placebo lead-in) may act to prime the brain for better antide-
pressant response. Imaging with qEEG methods may help elucidate the role of
placebo mechanisms in determining antidepressant response [83].
11.2.3 Pitfalls
Prior biomarker work has encountered numerous pitfalls, and it is vital to learn
from past experiences. Perhaps most worrisome is the problem of premature clinical
application: Not only is there the risk of doing harm to patients (e.g., being misdi-
rected in treatment decisions), but there is also the risk associated with cynicism
about biomarkers in general that this can engender.
The usual vetting of new biomedical innovations—procedures, techniques,
medications, and devices—requires peer review of findings and independent replica-
tion: What applicability is there to a biomarker if it has only been shown to work in
a single laboratory and others researchers are unable to confirm the results? Further-
more, it must be clearly disclosed what patient group was used to develop the
biomarker, because this has great relevance to generalizability: In the universe of all
patients with any psychiatric disorder, only a minority will have a syndrome that is
refractory to multiple treatments, yet this is just the sort of patient who may seek out
expert care in desperation and consequently be enrolled in a biomarker discovery
research program. The generalizability may be quite limited for a biomarker devel-
oped with an idiosyncratic and nonrepresentative sample of patients, and without
clear disclosure of these details, it is difficult to evaluate these qualities of a
biomarker.
An additional caveat about biomarkers relates to the heterogeneity within a
given clinical diagnosis. With our clinically defined diagnostic categories, there is
variety both in the patients who seek care and in the individuals enrolled in research
projects. A telling example is shown in Table 11.1, in which two individuals who
both meet the formal diagnostic criteria for MDD have zero symptoms in common.
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