Electroencephalogram alpha asymmetry in geriatric depression

Kaiser, Andreas; Doppelmayr, Michael; Iglseder, Bernhard

doi:10.1007/s00391-016-1108-z

Cited by 47 publications

(25 citation statements)

References 38 publications

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“… 8 , 79 Studies that addressed age had difficulties in validating previous findings on EEG AA. 16 , 17 , 80 Especially in young people and the oldest olds, previous EEG AA findings were not able to be replicated. 16 , 17 Other factors such as cortical thickness as a mediator of AA could be ruled out.…”

Section: Resultsmentioning

confidence: 86%

“… 16 , 17 , 80 Especially in young people and the oldest olds, previous EEG AA findings were not able to be replicated. 16 , 17 Other factors such as cortical thickness as a mediator of AA could be ruled out. 81 Cognition was discussed as a possible moderator of EEG AA.…”

Section: Resultsmentioning

confidence: 86%

“… 81 Cognition was discussed as a possible moderator of EEG AA. 15 – 17 , 82 , 83 Hereditary effects might play a role, 84 but it was found that less left frontal activity at lateral sites was only associated with lifetime major depressive disorder (MDD) in offspring and not in parental MDD. 47 The issue of drug effects on EEG AA was discussed.…”

Section: Resultsmentioning

confidence: 99%

“…Another issue worth considering is the fact that most studies include only young patients, 14 and studies including older individuals were not able to replicate the diagnostic validity of EEG AA. 15 – 17 One major problem in this context might be publication bias, which makes it hard to publish negative results on EEG AA and leads to overinterpretation of results. Another interesting aspect is the fact that most studies deal with female individuals and not with males.…”

Section: Introductionmentioning

confidence: 99%

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Electroencephalogram alpha asymmetry in patients with depressive disorders: current perspectives

Kaiser¹,

Gnjezda²,

Knasmüller³

et al. 2018

NDT

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PurposeElectroencephalogram (EEG) alpha asymmetry (AA) in depressive disorders has been of interest over the last few decades, but it continues to remain unclear whether EEG AA can discriminate between healthy and depressive individuals.Materials and methodsA systematic literature search for papers addressing EEG AA using the keywords alpha asymmetry, depression, and EEG was performed in PubMed. All studies were checked for sample size, gender, handedness, reference, recording protocol, EEG band range, impedance, type of analysis, drugs, and comorbidity.ResultsA total of 61 articles were found, of which 44 met our inclusion criteria. They have been consecutively analyzed in respect of methodology and results. Approximately 25% (11/44) of the studies did not mention or ignored handedness, 41% (18/44) of the studies used data with only self-reported handedness, and only 34.1% (15/44) of all studies tested handedness. Only 35% (15/44) of the studies reported pharmacological treatment, and only 35% (15/44) of the studies controlled for medication. A total of 52% (23/44) of the studies reported comorbidity, and only 30% (13/44) of the studies controlled for comorbidity. Only 29.6% (13/44) of the studies reported education. In all, 30.5% (13/44) of the studies analyzed group differences and correlations, while 15.9 (7/44) of the studies used only correlational analyses. A total of 52.3% (23/44) of the studies analyzed only group differences. Alpha range was fixed (8–13 Hz) in 59.1% (26/44) of all studies. Reference to common average was used in seven of 44 studies (15.9%). In all, nine of 44 (20.5%) studies used the midline central position as reference, 22 of 44 (50%) studies used the ear or the mastoid as reference, and four of 44 (9.1%) studies used the nose as reference.ConclusionDiscriminative power of EEG AA for depressed and healthy controls remains unclear. A systematic analysis of 44 studies revealed that differences in methodology and disregarding proper sampling are problematic. Ignoring handedness, gender, age, medication, and comorbidity could explain inconsistent findings. Hence, we formulated a guideline for requirements for future studies on EEG AA in order to allow for better comparisons.

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Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Electroencephalogram alpha asymmetry in patients with depressive disorders: current perspectives

Kaiser¹,

Gnjezda²,

Knasmüller³

et al. 2018

NDT

View full text Add to dashboard Cite

show abstract

“…Samples of the salivary cortisol and resting state EEG based alpha asymmetry were assessed before and after performing TSST. The frontal and parietal alpha asymmetry was used to classify depression in elderly people [30]. The correlation between frontal and parietal alpha asymmetry, the geriatric depression scale, and the mini mental state examination were analyzed.…”

Section: Related Workmentioning

confidence: 99%

EEG Based Classification of Long-Term Stress Using Psychological Labeling

Saeed

Anwar

Khalid

et al. 2020

Sensors

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Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

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Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

Vidaurre

Nikulin²,

Ruiz³

2022

Neural Comput & Applic

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Anxiety affects approximately 5–10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4–7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8–12 Hz) or beta (13–30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

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Electroencephalogram alpha asymmetry in geriatric depression

Abstract: The alpha asymmetry in EEG seemed to disappear with age. Correlations between PAA and anxiety and depression were found. The results are in line with the right (hemisphere) hemi-aging hypothesis.

Cited by 47 publications

References 38 publications

Electroencephalogram alpha asymmetry in patients with depressive disorders: current perspectives

Electroencephalogram alpha asymmetry in patients with depressive disorders: current perspectives

EEG Based Classification of Long-Term Stress Using Psychological Labeling

Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

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