Article

The Promise of the Quantitative Electroencephalogram as a Predictor of Antidepressant Treatment Outcomes in Major Depressive Disorder

Laboratory of Brain, Behavior, and Pharmacology, Semel Institute for Neuroscience and Human Behavior at UCLA, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024-1759, USA.
Psychiatric Clinics of North America (Impact Factor: 2.13). 04/2007; 30(1):105-24. DOI: 10.1016/j.psc.2006.12.002
Source: PubMed

ABSTRACT

Recent studies have shown overall accuracy rates of 72% and 88% using baseline and/or 1-week change in QEEG biomarkers to predict clinical outcome to treatment with various antidepressant medications. In some cases, findings have been replicated across academic institutions and have been studied in the context of randomized, placebo-controlled trials. Recent EEG findings are corroborated by studies that use techniques with greater spatial resolution (eg, PET, MEG) in localizing brain regions pertinent to clinical response. As such, EEG measurements increasingly are validated by other physiologic measurements that have the ability to assess deeper brain structures. Continued progress along these lines may lead to the realized promise of QEEG biomarkers as predictors of antidepressant treatment outcome in routine clinical practice. In the larger context, use of QEEG technology to predict antidepressant response in major depression may mean that more patients will achieve response and remission with less of the trial-and-error approach that currently accompanies antidepressant treatment.

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Available from: Andrew F Leuchter, Jun 18, 2015
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    • "In our case, the statistical quantities we select as candidate features are a subset of the complete set of moments, and therefore offer a partial description of the process generating the EEG observations. Such quantities have been used in previous related studies; e.g., PSD values are used in (Cook et al., 2002; Hunter et al., 2007; Hinrikus et al., 2009; Knott et al., 2001; Kwon et al., 1996; Bruder et al., 2001); left-to-right hemisphere powers are used in Hinrikus et al. (2009), Knott et al. (2001); and anterior/posterior power ratios are used in Knott et al. (2001). The work of Knott et al. (2001), Hinrikus et al. (2009), Knott et al. (2002) used coherence between electrode pairs to assess the effect of the anti-psychotic drug clozapine and characterize depression, respectively. "
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    ABSTRACT: OBJECTIVE: The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). METHODS: A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure. RESULTS: A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%]. CONCLUSIONS: These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG. SIGNIFICANCE: The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.
    Full-text · Article · May 2013 · Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology
    • "The authors appear not to be familiar with validation studies performed using qEEG, O 15 positron emission tomography (PET), and HMPAO single photon emission computed tomography (SPECT) scanning (Leuchter et al., 1994a, 1994b, 1999). These studies, reviewed by Hunter et al. (2007), demonstrate that cordance has a stronger association with cerebral perfusion than do commonly used qEEG measures such as absolute and relative power. A common failing in the qEEG literature is that some measures have not been rigorously subjected to validation with regard to other brain imaging or physiologic methods; the meaning of cordance, however, is well established in this regard. "

    No preview · Article · Aug 2012 · Journal of Psychopharmacology
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    • "Quantitative electroencephalographic (QEEG) biomarkers of brain functional changes within the first week of antidepressant treatment have shown strong promise as predictors of outcome(Cook et al., 2002; Hunter et al., 2007). The most clinically refined of these is the Antidepressant Treatment Response (ATR) Index, version 4.1 (Aspect Medical Systems; Norwood, MA)(Leuchter et al., 2009a, 2009b). "
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    ABSTRACT: Recent research aims at developing a biomarker to predict antidepressant treatment outcomes in major depressive disorder. The Antidepressant Treatment Response (ATR) index has been correlated with response to antidepressant medication (, ) but has not been assessed in a placebo-controlled trial. EEGs recorded at pretreatment baseline and after 1 week of randomized treatment were used to calculate ATR index for 23 subjects with major depressive disorder who were treated for 8 weeks with fluoxetine (FLX) 20 mg (n = 12) or placebo (n = 11). The 17-item Hamilton Depression Rating Scale (HamD17) assessed symptom severity; ATR index was assessed as a predictor of percent change in HamD17 score, endpoint response (≥ 50% improvement) and remission (HamD17 score ≤ 7). The ATR index was significantly associated with improvement on FLX (r = 0.64, P = 0.01), with a higher mean ATR index for FLX responders than for nonresponders (t(10) = -2.07, P = 0.03). Receiver operating characteristic analysis found a 0.83 area under the curve (P = 0.03), for ATR index as a predictor for FLX, while an optimized ATR index cutoff of 47.3 yielded 100% sensitivity, 66.7% specificity, 75% positive predictive value, and 100% negative predictive value. Importantly, ATR index did not correlate significantly with placebo outcomes. Results extend ATR index findings to include predictive validity with fluoxetine, suggesting that this biomarker has specificity for drug effects.
    Full-text · Article · Sep 2011 · Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society
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