All-night EEG spectral analysis as a tool for the prediction of clinical response to antidepressant treatment

Luthringer, R.; Minot, R.; Toussaint, Michel; Calvi-Gries, Francoise; Schaltenbrand, Nicolas; Mâcher, Jean-Paul

doi:10.1016/0006-3223(94)00246-y

Cited by 31 publications

(7 citation statements)

References 32 publications

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“…For instance, several of the agerelated changes in sleep-wake patterns and sleep EEG spectral power during the middle years of life are similar to those observed in depressed patients. For example, lower power spectral density in the delta range especially in the first N-REM period is characteristic of depressive state and recurrence of depression~Buysse, Frank, Lowe, Cherry, & Kupfer, Frank, & McEachran, 1990;Luthringer et al, 1995!. Therefore, as noted by Kupfer~1995!, age must rank as an important variable in understanding the differences between depressed and nondepressed individuals.…”

Section: Discussionmentioning

confidence: 99%

The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20–60 years old)

et al. 2001

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The effects of age and gender on sleep EEG power spectral density were assessed in a group of 100 subjects aged 20 to 60 years. We propose a new statistical strategy (mixed-model using fixed-knot regression splines) to analyze quantitative EEG measures. The effect of gender varied according to frequency, but no interactions emerged between age and gender, suggesting that the aging process does not differentially influence men and women. Women had higher power density than men in delta, theta, low alpha, and high spindle frequency range. The effect of age varied according to frequency and across the night. The decrease in power with age was not restricted to slow-wave activity, but also included theta and sigma activity. With increasing age, the attenuation over the night in power density between 1.25 and 8.00 Hz diminished, and the rise in power between 12.25 and 14.00 Hz across the night decreased. Increasing age was associated with higher power in the beta range. These results suggest that increasing age may be related to an attenuation of homeostatic sleep pressure and to an increase in cortical activation during sleep.

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Section: Discussionmentioning

confidence: 99%

The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20–60 years old)

et al. 2001

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“…Also the slow-wave activity itself seems to be important for treatment prediction. Luthringer et al [29] reported increased relative delta power in sleep EEG recordings in responders to antidepressant treatment, although others failed to replicate these findings [30,31]. Still, Nissen et al [31] reported decreased slow-wave activity in responders, expressed in a high delta sleep ratio, a finding that again could not be replicated by Argyropoulos et al [32].…”

Section: Biomarkersmentioning

confidence: 99%

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

2015

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Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography (EEG) during sleep or resting-state conditions and event-related potentials (ERPs) have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual. This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.

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“…Therefore, identification of reliable predictors of responsiveness to SRIs could potentially save patients lengthy trials of medications that are unlikely to be effective and steer treatment toward modalities that have higher probabilities of succeeding. Markers of treatment responsiveness might also provide further clues to the pathophysiology of OCD and major depressive disorder.Many methods have been employed to investigate neurobiological predictors of response to antidepressant medications in OCD and major depressive disorder, including neurotransmitter and receptor assays (3-8), neuroendocrine challenge tests (9-13), neurocognitive testing (14, 15), auditory evoked potentials (16), quantitative electroencephalography (17)(18)(19)(20), and pharmacogenetics (21-23). Unfortunately, few of these findings have been replicated, and none have localized specific neuroanatomical substrates for treatment response.…”

mentioning

confidence: 99%

“…Many methods have been employed to investigate neurobiological predictors of response to antidepressant medications in OCD and major depressive disorder, including neurotransmitter and receptor assays (3)(4)(5)(6)(7)(8), neuroendocrine challenge tests (9)(10)(11)(12)(13), neurocognitive testing (14,15), auditory evoked potentials (16), quantitative electroencephalography (17)(18)(19)(20), and pharmacogenetics (21)(22)(23). Unfortunately, few of these findings have been replicated, and none have localized specific neuroanatomical substrates for treatment response.…”

mentioning

confidence: 99%

Differential Brain Metabolic Predictors of Response to Paroxetine in Obsessive-Compulsive Disorder Versus Major Depression

Saxena¹,

Brody²,

Ho³

et al. 2003

AJP

193

139

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These findings suggest that, although both OCD and major depressive disorder respond to SRIs, the two syndromes have different neurobiological substrates for response. Elevated activity in the right caudate may be a marker of responsiveness to antiobsessional treatment, while lower right amygdala activity and higher midline prefrontal activity may be required for response of depressive symptoms to treatment.

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All-night EEG spectral analysis as a tool for the prediction of clinical response to antidepressant treatment

Cited by 31 publications

References 32 publications

The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20–60 years old)

The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20–60 years old)

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Differential Brain Metabolic Predictors of Response to Paroxetine in Obsessive-Compulsive Disorder Versus Major Depression

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