- Email: [email protected]
P124. QEEG and CSD power analysis in depression
Society Proceedings / Clinical Neurophysiology 126 (2015) e63–e170
collected for 32 patients (MCI/non-MCI: 13/19) to test the stability over time. Results were compared between groups using permutation tests on t-statistics to correct for multiple comparisons. Effect sizes (ES) and intra-class correlation (ICC) were calculated. Results: An increase (p = 0.017; ES = 0.789) in the theta and a decrease (p = 0.042; ES = 0.782) in the alpha1 signal power were associated with MCI in PD patients. The ratio alpha1/theta showed a more robust negative association (p = 0.012; ES = 1.04) than those calculated for each variable separately. Moreover, the ratio was stable over time (4 W: p = 0.002; ES = 1.082 – 6 M: p = 0.002; ES = 1.084 – ICC = 0.76). Patients whose baseline positive MCI diagnosis did not change at 6 M exhibited a higher ratio than those with a negative MCI diagnosis at 6 M. However, the difference was not statistically significant (p = 0.1178). Conclusions: Reduction of the alpha1/theta ratio is reliably associated with MCI in PD patients. This finding might be used as a robust marker for screening PD patients for early cognitive deficits. References Bousleiman H, Zimmermann R, Ahmed S, Hardmeier M, Hatz F, Schindler C, et al. Power spectra for screening parkinsonian patients for mild cognitive impairment, Ann Clin Transl Neurol 2014 [Epub ahead of print]. Schmidt MT, Kanda PAM, Basile LFH, da Silva Lopes HF, et al. Index of alpha/theta ratio of the electroencephalogram: a new marker for Alzheimer’s disease. Front Aging Neurosci 2013;5:60. doi:10.1016/j.clinph.2015.04.249
P123. Correlation between EEG and clinical symptoms in depression—C. Berger a, P.C. Koo b, J. Bartz b, P.K. Wybitul b, J. Höppner b (a University Medicine Rostock, Department of Child and Adolescent Psychiatry, Neurology, Psychotherapy and Psychosomatics, Rostock, Germany, b University Medicine Rostock, Department of Psychiatry and Psychotherapy, Rostock, Germany) Introduction: The underlying neurophysiological characteristics in Major Depressive Disorders(MDD) have been examined intensively for the past decades. Some studies have indicated pattern of electroencephalography(EEG) activity that distinguishes MDD patients from healthy subjects. However, the correlation between either clinical symptoms such as psychometric scales or sleeping disturbances and EEG activity were seldom investigated to date. Therefore this study aims to examine the correlation between depressive and psychomotor symptoms as well as sleeping disturbances with EEG power values and with current source density (CSD) analysis. Methods: 20 patients with MDD (mean age ± SD: 47 ± 12.57; female: 13) were compared with 20 age matched healthy subjects (mean age ± SD: 51 ± 10.5; female: 12). A 10 minutes eyes-closed resting state EEG (31 channels, additional ECG channel, 10/20 system) was applied to all participants. Impedances were kept below 5kX. EEG data were pre-processed using Brain Vision Analyzer (Brain Products, Gilching). EEG raw data were manually cleaned from movement artefacts. ECG and ocular artefacts were excluded via independent component analysis. Artefact free EEG was filtered with 0.5Hz high pass and 50Hz low pass and average was set as reference. EEG power and asymmetry indices were calculated from 8 minutes of artefact free data for each frequency and pooled over 6 electrode sites. One minute of artefact free data in each participant were exported for CSD analysis. Voxelwise CSD power was calculated for each frequency with Low Resolution Electromagnetic Tomography (LORETA) and pooled for cortical brodmann areas with relation to executive, attentional, motor and emotional functions.
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EEG power, asymmetry and CSD power were the subjects of Pearson’s correlation analyses to examine the interconnection with depressive symptoms (BDI, HDRS), psychomotor activity (Motor Agitation and Retardation Scale), psychomotor speed (TMT A), executive functions (TMT B, Stroop), Pittsburgh Sleep Quality Intervention (PSQI) and measurement of motor activity via actigraphy. Results: There were significant positive correlations between TMT A and B and EEG power in beta and gamma bands widespread from central to posterior regions (p). Conclusion: Our results suggested that EEG power in beta and gamma frequency bands over central to posterior regions as well as higher power in delta and beta CSD in frontal regions can possibly predict the decreased performances in neuropsychological tests related to psychomotor speed and divided attention in MDD group. Sleeping disturbances are associated with higher power in beta and gamma EEG frequencies over temporoparietal regions. These findings confirm that depressive symptoms in patients with MDD have neurofunctional expressions in the EEG domain and may suggest further that cognitive and sleep disturbances are associated with activity in distinct neurofunctional areas. doi:10.1016/j.clinph.2015.04.250
P124. QEEG and CSD power analysis in depression—P.C. Koo a, C. Berger b, J. Bartz a, P.K. Wybitul a, J. Höppner a (a University Medicine Rostock, Department of Psychiatry and Psychotherapy, Rostock, Germany, b University Medicine Rostock, Department of Child and Adolescent Psychiatry, Neurology, Psychotherapy and Psychosomatics, Rostock, Germany) Introduction: Major depressive disorder (MDD) is a chronic mood disorder with significant impairment of daily living. Neurophysiological studies have focused to identify endophenotypes of depression. Frontal asymmetry in EEG power within alpha band has been the most studied topic and a few results indicated the phenomenon of lower left and higher right frontal activity. However, some other studies failed to show homogeneous results. Our aims in this study are to extensively investigate the neurophysiogical pattern in depressive patients by using qEEG power value, transformed current source density (CSD) as well as the neural synchronization. Method: 20 patients with MDD (mean age ± SD: 47 ± 12.57 years old, female: 13) were compared with 20 age matched healthy subjects (mean age ± SD: 51 ± 10.5; female: 12). All participants underwent a 10 min resting state EEG recording with closed eyes. 31 electrodes elastic cap (10/20 system) with additional ECG channel were applied to all participants. Impedances were kept below 5 kX. EEG raw data were manually cleaned from movement artefacts. ECG and ocular artefacts were excluded via independent component analysis. Filter settings were 0.5 Hz for high pass and 30 Hz for low pass and all channels average was set as reference. EEG power and asymmetry indices were calculated from 8 min of artefact free data for each frequency and pooled over 6 electrode sites. One minute of artefact free data in each participant were exported for CSD analysis. Voxelwise CSD power was calculated for each frequency with Low Resolution Electromagnetic Tomography (LORETA). Lagged phase synchronisation between mean CSD power time series was calculated for frontal and cingular regions. ANOVA with the inner subject factors ‘‘pooled electrode locations’’ and ‘‘hemisphere’’ (6 2) and between subjects grouping factor (patients versus controls) were calculated to analyze EEG power and asymmetry for each frequency band. Repetitive measure post hoc t-test was further applied to investigate variables that caused the differences in ANOVA interactions. Group differences of CSD power and synchronisation were analyzed by t-tests.
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Society Proceedings / Clinical Neurophysiology 126 (2015) e63–e170
Result: We found a main effect of group in the alpha2 (p = .017), delta (p = .043), theta (p = .043) and beta band (p = .007), the MDD patients have higher overall power in these frequencies. For the beta frequency, the increased EEG power of the MDD patients was also significant when analysed separately in post hoc t-test for frontal, central and centroparietal regions (p.Post hoc t-test revealed that in particular the group differences of EEG power in the temporoparietal region (p = .05) was driving this interaction effect. CSD analysis showed no significant difference between groups. Conclusion: Depressive patients have shown a non-regional specific higher global EEG power in slow and high EEG frequencies compared to healthy controls. Especially increased overall power in the beta band in frontal, central and centroparietal regions can be seen in relation to difficulty falling asleep, and inner restlessness which is often found in MDD patients. Our result did not show frontal alpha asymmetry in depressive patients as well as in healthy control. doi:10.1016/j.clinph.2015.04.251
P125. mindBEAGLE: A BCI for communication and assessment of consciousness for patients with disorders of consciousness—A. Schnürer, A. Espinosa, C. Guger (Guger Technologies OG, Graz, Austria) Introduction: In this publication we present a tool for assessment and communication with patients suffering Disorder of Consciousness (DOC). Four different Brain–Computer Interface (BCI) approaches are used: An auditory P300 approach, a vibrotactile P300 approach with two or three tactile stimulators, and a Motor Imagery (MI) based approach. The vibrotactile P300 and the MI based approaches can be used for both, the assessment of conscious-
ness and communication with patients. The auditory P300 approach can be used only for assessment of consciousness. System overview: Fig. 1 shows an overview of the system. The user wears an EEG cap with 16 electrodes, distributed to fit for all three BCI approaches. The laptop controls the paradigms, performs the signal processing and displays the results to the user. Three vibrotactile stimulators are used for the tactile P300 approach, earphones are used for the auditory P300. The cues that are needed for the MI approach are also played via the earphones. System validation: Table 1 shows results of five healthy users, controlling mindBEAGLE. The vibrotactile P300 approach was already tested on a group of six Locked-In Patients showing a average accuracy of 80% for two tactors and 55.3% for three tactors (Lugo et al., 2014). The feasibility of auditory P300 for cognitive assessment was also proved in previous studies (Cipresso et al., 2012; Kübler et al., 2009). In future studies, all approaches of the device system will be validated on both: healthy users and patients.
Table 1 results of five healthy users controlling mindBEAGLE. User
Control level (%) Auditory stimuli
Two tactile stimuli
Three tactile stimuli
Motor imagery
1 2 3 4 5
100 100 100 100 100
100 100 100 90 100
40 100 100 100 100
77.7 91.6 58.6 80.4 64
Average
100
98
88
74.5
Fig. 1. Block diagram of the mindBEAGLE.
collected for 32 patients (MCI/non-MCI: 13/19) to test the stability over time. Results were compared between groups using permutation tests on t-statistics to correct for multiple comparisons. Effect sizes (ES) and intra-class correlation (ICC) were calculated. Results: An increase (p = 0.017; ES = 0.789) in the theta and a decrease (p = 0.042; ES = 0.782) in the alpha1 signal power were associated with MCI in PD patients. The ratio alpha1/theta showed a more robust negative association (p = 0.012; ES = 1.04) than those calculated for each variable separately. Moreover, the ratio was stable over time (4 W: p = 0.002; ES = 1.082 – 6 M: p = 0.002; ES = 1.084 – ICC = 0.76). Patients whose baseline positive MCI diagnosis did not change at 6 M exhibited a higher ratio than those with a negative MCI diagnosis at 6 M. However, the difference was not statistically significant (p = 0.1178). Conclusions: Reduction of the alpha1/theta ratio is reliably associated with MCI in PD patients. This finding might be used as a robust marker for screening PD patients for early cognitive deficits. References Bousleiman H, Zimmermann R, Ahmed S, Hardmeier M, Hatz F, Schindler C, et al. Power spectra for screening parkinsonian patients for mild cognitive impairment, Ann Clin Transl Neurol 2014 [Epub ahead of print]. Schmidt MT, Kanda PAM, Basile LFH, da Silva Lopes HF, et al. Index of alpha/theta ratio of the electroencephalogram: a new marker for Alzheimer’s disease. Front Aging Neurosci 2013;5:60. doi:10.1016/j.clinph.2015.04.249
P123. Correlation between EEG and clinical symptoms in depression—C. Berger a, P.C. Koo b, J. Bartz b, P.K. Wybitul b, J. Höppner b (a University Medicine Rostock, Department of Child and Adolescent Psychiatry, Neurology, Psychotherapy and Psychosomatics, Rostock, Germany, b University Medicine Rostock, Department of Psychiatry and Psychotherapy, Rostock, Germany) Introduction: The underlying neurophysiological characteristics in Major Depressive Disorders(MDD) have been examined intensively for the past decades. Some studies have indicated pattern of electroencephalography(EEG) activity that distinguishes MDD patients from healthy subjects. However, the correlation between either clinical symptoms such as psychometric scales or sleeping disturbances and EEG activity were seldom investigated to date. Therefore this study aims to examine the correlation between depressive and psychomotor symptoms as well as sleeping disturbances with EEG power values and with current source density (CSD) analysis. Methods: 20 patients with MDD (mean age ± SD: 47 ± 12.57; female: 13) were compared with 20 age matched healthy subjects (mean age ± SD: 51 ± 10.5; female: 12). A 10 minutes eyes-closed resting state EEG (31 channels, additional ECG channel, 10/20 system) was applied to all participants. Impedances were kept below 5kX. EEG data were pre-processed using Brain Vision Analyzer (Brain Products, Gilching). EEG raw data were manually cleaned from movement artefacts. ECG and ocular artefacts were excluded via independent component analysis. Artefact free EEG was filtered with 0.5Hz high pass and 50Hz low pass and average was set as reference. EEG power and asymmetry indices were calculated from 8 minutes of artefact free data for each frequency and pooled over 6 electrode sites. One minute of artefact free data in each participant were exported for CSD analysis. Voxelwise CSD power was calculated for each frequency with Low Resolution Electromagnetic Tomography (LORETA) and pooled for cortical brodmann areas with relation to executive, attentional, motor and emotional functions.
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EEG power, asymmetry and CSD power were the subjects of Pearson’s correlation analyses to examine the interconnection with depressive symptoms (BDI, HDRS), psychomotor activity (Motor Agitation and Retardation Scale), psychomotor speed (TMT A), executive functions (TMT B, Stroop), Pittsburgh Sleep Quality Intervention (PSQI) and measurement of motor activity via actigraphy. Results: There were significant positive correlations between TMT A and B and EEG power in beta and gamma bands widespread from central to posterior regions (p). Conclusion: Our results suggested that EEG power in beta and gamma frequency bands over central to posterior regions as well as higher power in delta and beta CSD in frontal regions can possibly predict the decreased performances in neuropsychological tests related to psychomotor speed and divided attention in MDD group. Sleeping disturbances are associated with higher power in beta and gamma EEG frequencies over temporoparietal regions. These findings confirm that depressive symptoms in patients with MDD have neurofunctional expressions in the EEG domain and may suggest further that cognitive and sleep disturbances are associated with activity in distinct neurofunctional areas. doi:10.1016/j.clinph.2015.04.250
P124. QEEG and CSD power analysis in depression—P.C. Koo a, C. Berger b, J. Bartz a, P.K. Wybitul a, J. Höppner a (a University Medicine Rostock, Department of Psychiatry and Psychotherapy, Rostock, Germany, b University Medicine Rostock, Department of Child and Adolescent Psychiatry, Neurology, Psychotherapy and Psychosomatics, Rostock, Germany) Introduction: Major depressive disorder (MDD) is a chronic mood disorder with significant impairment of daily living. Neurophysiological studies have focused to identify endophenotypes of depression. Frontal asymmetry in EEG power within alpha band has been the most studied topic and a few results indicated the phenomenon of lower left and higher right frontal activity. However, some other studies failed to show homogeneous results. Our aims in this study are to extensively investigate the neurophysiogical pattern in depressive patients by using qEEG power value, transformed current source density (CSD) as well as the neural synchronization. Method: 20 patients with MDD (mean age ± SD: 47 ± 12.57 years old, female: 13) were compared with 20 age matched healthy subjects (mean age ± SD: 51 ± 10.5; female: 12). All participants underwent a 10 min resting state EEG recording with closed eyes. 31 electrodes elastic cap (10/20 system) with additional ECG channel were applied to all participants. Impedances were kept below 5 kX. EEG raw data were manually cleaned from movement artefacts. ECG and ocular artefacts were excluded via independent component analysis. Filter settings were 0.5 Hz for high pass and 30 Hz for low pass and all channels average was set as reference. EEG power and asymmetry indices were calculated from 8 min of artefact free data for each frequency and pooled over 6 electrode sites. One minute of artefact free data in each participant were exported for CSD analysis. Voxelwise CSD power was calculated for each frequency with Low Resolution Electromagnetic Tomography (LORETA). Lagged phase synchronisation between mean CSD power time series was calculated for frontal and cingular regions. ANOVA with the inner subject factors ‘‘pooled electrode locations’’ and ‘‘hemisphere’’ (6 2) and between subjects grouping factor (patients versus controls) were calculated to analyze EEG power and asymmetry for each frequency band. Repetitive measure post hoc t-test was further applied to investigate variables that caused the differences in ANOVA interactions. Group differences of CSD power and synchronisation were analyzed by t-tests.
e152
Society Proceedings / Clinical Neurophysiology 126 (2015) e63–e170
Result: We found a main effect of group in the alpha2 (p = .017), delta (p = .043), theta (p = .043) and beta band (p = .007), the MDD patients have higher overall power in these frequencies. For the beta frequency, the increased EEG power of the MDD patients was also significant when analysed separately in post hoc t-test for frontal, central and centroparietal regions (p.Post hoc t-test revealed that in particular the group differences of EEG power in the temporoparietal region (p = .05) was driving this interaction effect. CSD analysis showed no significant difference between groups. Conclusion: Depressive patients have shown a non-regional specific higher global EEG power in slow and high EEG frequencies compared to healthy controls. Especially increased overall power in the beta band in frontal, central and centroparietal regions can be seen in relation to difficulty falling asleep, and inner restlessness which is often found in MDD patients. Our result did not show frontal alpha asymmetry in depressive patients as well as in healthy control. doi:10.1016/j.clinph.2015.04.251
P125. mindBEAGLE: A BCI for communication and assessment of consciousness for patients with disorders of consciousness—A. Schnürer, A. Espinosa, C. Guger (Guger Technologies OG, Graz, Austria) Introduction: In this publication we present a tool for assessment and communication with patients suffering Disorder of Consciousness (DOC). Four different Brain–Computer Interface (BCI) approaches are used: An auditory P300 approach, a vibrotactile P300 approach with two or three tactile stimulators, and a Motor Imagery (MI) based approach. The vibrotactile P300 and the MI based approaches can be used for both, the assessment of conscious-
ness and communication with patients. The auditory P300 approach can be used only for assessment of consciousness. System overview: Fig. 1 shows an overview of the system. The user wears an EEG cap with 16 electrodes, distributed to fit for all three BCI approaches. The laptop controls the paradigms, performs the signal processing and displays the results to the user. Three vibrotactile stimulators are used for the tactile P300 approach, earphones are used for the auditory P300. The cues that are needed for the MI approach are also played via the earphones. System validation: Table 1 shows results of five healthy users, controlling mindBEAGLE. The vibrotactile P300 approach was already tested on a group of six Locked-In Patients showing a average accuracy of 80% for two tactors and 55.3% for three tactors (Lugo et al., 2014). The feasibility of auditory P300 for cognitive assessment was also proved in previous studies (Cipresso et al., 2012; Kübler et al., 2009). In future studies, all approaches of the device system will be validated on both: healthy users and patients.
Table 1 results of five healthy users controlling mindBEAGLE. User
Control level (%) Auditory stimuli
Two tactile stimuli
Three tactile stimuli
Motor imagery
1 2 3 4 5
100 100 100 100 100
100 100 100 90 100
40 100 100 100 100
77.7 91.6 58.6 80.4 64
Average
100
98
88
74.5
Fig. 1. Block diagram of the mindBEAGLE.