Biomedical Engineering Reference
In-Depth Information
utility in studying circuit function in aging and in dementia; this is discussed at
greater length later in this chapter.
Another approach, the quantification of cerebral microstates through adaptive
segmentation [36], may help in differentiating healthy brain aging from processes of
cognitive decline. Although a reduction in the duration of microstate periods can be
interpreted as evidence of a fragmentation of quasistationary EEG periodic activity
and, thus, some sort of problem in sustaining coordinated activity among brain
regions, it is not clear whether this may arise from disrupted corticocortical connec-
tions, from dysfunction of cortical neurons themselves, or from disturbances in
neuromodulatory activity. Prichep [37] has reported a novel approach examining
both Neurometrics [26] and source localization to differentiate elders with healthy
aging from those with cognitive decline; this approach was interpreted in the
context of levels of regional brain activity.
Yet other methods can be interpreted within their own particular context. Sleep
polysomnography has been used to assess stage of sleep (e.g. the classic manual by
Rechtschafen and Kales [38] and Chapter 10 in this topic), and abnormalities in
sleep architecture have long been reported in many but not all patients with depres-
sion [39-42]. Sleep deprivation has been reported to have a transient mood-restor-
ing effect in some individuals with depression [43, 44]. The use of nonlinear
methods [45] has also been explored to characterize the sleeping brain in depres-
sion, yet physiological monitoring of sleep abnormalities has not become a part of
routine clinical care for depression. Additional work may help demonstrate how
chronobiological
perspectives
can
contribute
to
improved
patient
care
for
depression [46].
11.2
qEEG Measures as Clinical Biomarkers in Psychiatry
11.2.1 Biomarkers in Clinical Medicine
The use of
biomarkers
is commonplace in most branches of medicine: Specific bio-
logical features of an individual patient provide critical information about that per-
son's diagnosis, prognosis, or predicted response to treatment. Examples include
tumor markers in oncology [47-50], alpha-feto-protein in obstetrics [51], troponin
and other serum factors in cardiology [52-54], and inflammatory markers and spe-
cific serum antibody levels in rheumatology [55]. Additionally, the use of
biomarkers may be useful in drug discovery and development, by monitoring
response to a test exposure of an experimental medication [56]. Nonetheless, in the
field of psychiatry, the biological features of a patient's illness generally continue to
be eclipsed by the central role played by clinical signs and symptoms [57].
Although a number of recent research reports suggest that biomarkers may
soon be suitable for clinical use in the care of psychiatric patients, the quest for
biomarkers to improve the care of mental illnesses is not new in the twenty-first cen-
tury. For several decades, measurements of specific molecules in cerebrospinal fluid,
such as homovanillic acid (HVA) and 5-hydroxy-indoleacetic acid (5-HIAA) [58];
metabolites of neurotransmitters in urine, such as 3-methoxy-
4-hydroxyphenylglycol (MHPG) [59]; and serum markers of neuroendocrine
dysregulation [60], that is, dexamethasone suppression test (DST) [61]; have been
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