P.1.e.010 Understanding postpartum blues: a time course of MAO-A levels in the living human brain during the postpartum period

2009 ◽  
Vol 19 ◽  
pp. S305-S306
Author(s):  
J. Sacher ◽  
A.A. Wilson ◽  
P. Rusjan ◽  
S. Hassan ◽  
L. Jacobs ◽  
...  
2020 ◽  
Vol 1 (1) ◽  
pp. 61-70
Author(s):  
Agnes Mahayanti ◽  
Intansari Nurjannah ◽  
Widyawati

Background: The postpartum period was a physical and psychological adaptation process. Psychological disturbances could present in form of postpartum blues, postpartum depression, and postpartum psychosis. Objective: The objective of this research was to determine the incidence of postpartum depression, identify predictors of postpartum depression and identify the dominant predictors of postpartum depression. Methods: this research used a cross sectional design. Sample were pregnant women which were chosen by random sampling technique. Data was collected with questionnaire to measure the predictors of postpartum depression was carried out with the Revision Postpartum Depression Predictors Inventory (PDPI) questionnaire and to measure depression scores used with the Edinburgh Postnatal Depression Scale (EPDS). Data analysis was done by univariate and bivariate analysis with with chi-square statistical tests and multivariate with logistic regression tests. Results: The results showed that the significant predictors were economic status, self-esteem, marital satisfaction, postpartum blues, and prenatal depression. The dominant predictor factor is satisfaction in marriage with a t value of 2.778 times. Conclusion: The results of the study show that marital satisfaction is a dominant predictor of postpartum depression, indicating that marital satisfaction or relationship quality is one of the important support systems because of the need for partner involvement in care actions during the pregnancy period until with the puerperium as efforts to prevent postpartum depression.     Keywords: postpartum depression, postpartum period, postpartum bues


1995 ◽  
Vol 202 (1-2) ◽  
pp. 117-120 ◽  
Author(s):  
Erich Schröger ◽  
Mari Tervaniemi ◽  
Risto Näätänen
Keyword(s):  

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
Ambrose Jong ◽  
Chun-Hua Wu ◽  
Wensheng Zhou ◽  
Han-Min Chen ◽  
Sheng-He Huang

In order to dissect the pathogenesis ofCryptococcus neoformansmeningoencephalitis, a genomic survey of the changes in gene expression of human brain microvascular endothelial cells infected byC.neoformanswas carried out in a time-course study. Principal component analysis (PCA) revealed sigificant fluctuations in the expression levels of different groups of genes during the pathogen-host interaction. Self-organizing map (SOM) analysis revealed that most genes were up- or downregulated 2 folds or more at least at one time point during the pathogen-host engagement. The microarray data were validated by Western blot analysis of a group of genes, includingβ-actin, Bcl-x, CD47, Bax, Bad, and Bcl-2. Hierarchical cluster profile showed that 61 out of 66 listed interferon genes were changed at least at one time point. Similarly, the active responses in expression of MHC genes were detected at all stages of the interaction. Taken together, our infectomic approaches suggest that the host cells significantly change the gene profiles and also actively participate in immunoregulations of the central nervous system (CNS) duringC.neoformansinfection.


PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e63293 ◽  
Author(s):  
Milan Brázdil ◽  
Jiří Janeček ◽  
Petr Klimeš ◽  
Radek Mareček ◽  
Robert Roman ◽  
...  

2017 ◽  
Vol 29 (12) ◽  
pp. 1995-2010 ◽  
Author(s):  
Tijl Grootswagers ◽  
J. Brendan Ritchie ◽  
Susan G. Wardle ◽  
Andrew Heathcote ◽  
Thomas A. Carlson

Animacy is a robust organizing principle among object category representations in the human brain. Using multivariate pattern analysis methods, it has been shown that distance to the decision boundary of a classifier trained to discriminate neural activation patterns for animate and inanimate objects correlates with observer RTs for the same animacy categorization task [Ritchie, J. B., Tovar, D. A., & Carlson, T. A. Emerging object representations in the visual system predict reaction times for categorization. PLoS Computational Biology, 11, e1004316, 2015; Carlson, T. A., Ritchie, J. B., Kriegeskorte, N., Durvasula, S., & Ma, J. Reaction time for object categorization is predicted by representational distance. Journal of Cognitive Neuroscience, 26, 132–142, 2014]. Using MEG decoding, we tested if the same relationship holds when a stimulus manipulation (degradation) increases task difficulty, which we predicted would systematically decrease the distance of activation patterns from the decision boundary and increase RTs. In addition, we tested whether distance to the classifier boundary correlates with drift rates in the linear ballistic accumulator [Brown, S. D., & Heathcote, A. The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57, 153–178, 2008]. We found that distance to the classifier boundary correlated with RT, accuracy, and drift rates in an animacy categorization task. Split by animacy, the correlations between brain and behavior were sustained longer over the time course for animate than for inanimate stimuli. Interestingly, when examining the distance to the classifier boundary during the peak correlation between brain and behavior, we found that only degraded versions of animate, but not inanimate, objects had systematically shifted toward the classifier decision boundary as predicted. Our results support an asymmetry in the representation of animate and inanimate object categories in the human brain.


2008 ◽  
Vol 79 (5) ◽  
pp. 1039-1046 ◽  
Author(s):  
J. S. Fowler ◽  
J. Logan ◽  
Y.-S. Ding ◽  
D. Franceschi ◽  
G.-J. Wang ◽  
...  
Keyword(s):  

NeuroImage ◽  
2013 ◽  
Vol 67 ◽  
pp. 77-88 ◽  
Author(s):  
Justin M. Ales ◽  
L. Gregory Appelbaum ◽  
Benoit R. Cottereau ◽  
Anthony M. Norcia

2015 ◽  
Author(s):  
Radoslaw Martin Cichy ◽  
Aditya Khosla ◽  
Dimitrios Pantazis ◽  
Aude Oliva

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative explanation that captures the complexity of scene recognition, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and a novel quantitative model of how spatial layout representations may emerge in the human brain.


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