Biology subjects

Damborska, A.

Publications and source records attributed to Damborska, A..

3 recordsLinked to original sources

bioRxiv2019

Deviant EEG resting-state large-scale brain network dynamics in euthymic bipolar disorder patients

BackgroundNeuroimaging studies provided evidence for disrupted resting-state functional brain network activity in bipolar disorder (BD). Electroencephalographic (EEG) studies found altered temporal characteristics of functional EEG microstates during depressive episode within different affective disorders. Here we investigated whether euthymic patients with BD show deviant resting-state large-scale brain network dynamics as reflected by altered temporal characteristics of EEG microstates.\n\nMethodsWe used high-density EEG to explore between-group differences in duration, coverage and occurrence of the resting-state functional EEG microstates in 17 euthymic adults with BD in on-medication state and 17 age- and gender-matched healthy controls. Two types of anxiety, state and trait, were assessed separately with scores ranging from 20 to 80.\n\nResultsMicrostate analysis revealed five microstates (A-E) in global clustering across all subjects. In patients compared to controls, we found increased occurrence and coverage of microstate A that did not significantly correlate with anxiety scores.\n\nConclusionOur results provide neurophysiological evidence for altered large-scale brain network dynamics in BD patients and suggest the increased presence of A microstate to be an electrophysiological trait characteristic of BD.

neuroscience↗

bioRxiv2019

EEG resting-state large-scale brain network dynamics are related to depressive symptoms

BackgroundThe few previous studies on resting-state EEG microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology.\n\nMethodsTo evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in patients with moderate to severe depression within bipolar affective disorder, depressive episode, and periodic depressive disorder, and in healthy controls.\n\nResultsMicrostate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher symptomatology on the Montgomery-[A]sberg Depression Rating Scale, a questionnaire validated as measuring severity of depressive episodes in patients with mood disorders, correlated with higher occurrence of microstate A (Spearmans rank correlation, r = 0.70, p < 0.01).\n\nConclusionOur results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. These findings suggest the utility of the microstate analysis approach in an objective depression assessment.

neuroscience↗

bioRxiv2019

Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p<0.001) in patients than in controls. The global efficiency was significantly higher (p<0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake (R2 = 0.59 and p = 1.52e - 05). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

neuroscience↗