Browsing by Author "Ilankovic, Andrej (6504509995)"

Background: Burnout syndrome is under-researched within caregivers (CGs) of children with cerebral palsy. The primary aim was to determine the burnout level of formal CGs of children with cerebral palsy (G1) and to compare it with a control group (G2) of professional pediatric nurses, and second, to correlate the level of depression and anxiety with the burnout level. Method: In a total sample of 60 CGs, the Maslach Burnout Inventory Human Services Survey (MBI-HSS), consisting of three structural units - emotional exhaustion (MBIEE) subscale, depersonalization (MBI-DP) subscale and personal accomplishment (MBI-PA) subscale - was used to measure burnout. The Beck Anxiety Inventory (BAI) was used for the assessment of anxiety, and the Beck Depression Inventory (BDI) for depression. Results: A significant difference was shown on the MBI-EE subscale and on the BDI test (p-0.05), in both cases higher scores were obtained by G1. High burnout was observed in all subscales, on the MBI-EE subscale registered 50% of CGs in G1, and 17% in control G2. Correlation of the MBI-EE subscale with BDI and BAI tests was highly significant (p-0.01). Conclusions: These findings indicate the need for future research aimed at formulating preventive strategies for caregivers’ mental health. Better care for caregivers would provide them with better professional satisfaction, and consequently would lead to better care for patients. © 2016, Mediafarm Group. All right reserved.

Upon an exchange with an expert researcher in the field of dynamic time warping (DTW) we implemented an additional step (‘z-normalization’) in the preprocessing pipeline of our ecological momentary assessment (EMA) data. This step is required in DTW analysis to capture similarities between temporal dynamics, i.e. similarities in the shape, rather than similarities in the absolute (mean) rating in the EMA trajectories. Even small differences in the scale or offset will reduce any similarity information encoded in the dynamic or shape of this trajectory. As an example, when using DTW we want to be able to recognize both 7 and 7, despite their differences in size (scale), similarly, as we want to be able to recognize 7 and 7, despite different offsets. Z-normalization of the EMA trajectories before applying DTW is essential as it removes differences in mean ratings between EMA trajectories and in this way identifies differences in rating dynamics [1]. Furthermore, it was necessary to adapt the clustering algorithm in order to be able to use distance matrices for clustering. We adapted the following aspects of the original pipeline: We implemented the aforementioned z-normalization as part of the preprocessing prior to application of DTW and the clustering algorithm. We adjusted the selection of the clustering algorithms to better fit the data and investigated the results with a hierarchical as well as the PAM (partitioning around medoids) clustering algorithm. We compared several cluster indices to decide which number of clusters was the most optimal for the data set. In the new analysis the PAM clustering algorithm also reveals a two-cluster solution as most stable (Jaccard indices: cluster 1 = 0.77; cluster 2 = 0.88). However, in contrast to previous results the cluster indices regarding the cluster number were inconsistent and did not indicate a clear number of clusters. In line with our previous results, one cluster (cluster 2) shows higher mean EMA symptom ratings than the other (cluster 1). Cluster 2 had significantly higher ratings on cross-sectional Positive and Negative Syndrome Scale (PANSS) ratings of general symptoms than cluster 1. However, the new cluster solution shows no differences on other PANSS scales and the self-report Community Assessment of Psychic Experience (CAPE) questionnaire (Fig. 1; Table 1). This study included three study groups in the clustering, i.e. outpatients diagnosed with a psychotic disorder (PD), healthy individuals (HC), and healthy individuals with a first-degree relative with psychosis (RE). All three study groups were represented in both clusters while most of PD (73%, N = 40) and RE (70%, N = 14) were assigned to cluster 2. We find no clear differentiation of RE from HC or PD in clusters across the investigated 7-day EMA rating period. Cluster characteristics. (A) We obtained a two-cluster solution with distinct characteristics in their EMA ratings. The Z-normalized EMA psychotic symptom scale data is shown in the left and the unscaled, i.e. raw data is shown in the right panel. Z-normalized psychotic symptom scores were used for clustering. (B) Clusters showed significantly different clinical scores on the PANSS general symptom scale (left), as well as on two of the individual PANSS items, suspiciousness (middle) and active social avoidance (right). (C) Additionally, the revised (z-normalized) clusters 1 and 2 showed significantly different mean EMA ratings of psychotic symptoms (left panel) but no significant difference with respect to within-subject EMA rating variance of these (right panel). Significances: * p < 0.05, ** p < 0.01, *** p < 0.001 Demographic and clinical cluster characteristics Cluster 1 (N = 34) Cluster 2 (N = 66) t value/chi2, F-value pfdr study groupa 15/6/13 40/14/12 4.861 0.704 age, mean (sd) 37.08 (9.31) 39.22 (11.58) -0.998 0.830 11 (39) 28 (74) 0.580 0.830 educational statusb 13/10/6/0/0/2 16/11/10/2/2/2 0.623 0.830 medication, n (%)c antipsychotic 14 (100) 35 (95) 0.788 0.830 antidepressant 3 (23) 12 (32) 0.401 0.838 benzodiazepine 0 (0) 5 (16) 1.806 0.830 mood stabilizers 0 (0) 1 (3) 0.331 0.999 positive symptoms, mean (sd) 11.73 (3.65) 15.13 (5.77) -2.570 0.061 negative symptoms, mean (sd) 12.40 (5.25) 16.36 (5.37) -2.467 0.076 general symptoms, mean (sd) 24.67 (4.37) 31.50 (7.28) -4.182 delusions (P1) 2.00 (1.25) 2.44 (1.37) -1.114 0.510 hallucinatory behavior (P3) 2.13 (1.60) 2.79 (1.70) -1.338 0.416 suspiciousness/persecution (P6) 2.20 (0.94) 3.12 (1.45) -2.758 emotional withdrawal (N2) 1.93 (1.22) 2.51 (1.35) -1.513 0.334 active social avoidance (A16) 1.53 (0.74) 2.64 (1.48) -3.636 CAPEd positive symptoms - freq, mean (sd) 1.47 (0.38) 1.65 (0.52) 1.543 0.435 positive symptoms - dis, mean (sd) 1.81 (0.61) 2.00 (0.71) 0.373 0.735 negative symptoms - freq, mean (sd) 1.72 (0.38) 1.97 (0.59) 2.395 0.326 negative symptoms - dis, mean (sd) 1.86 (0.61) 2.18 (0.61) 3.668 0.169 depressive symptoms - freq, mean (sd) 1.89 (0.55) 1.98 (0.62) 0.382 0.735 depressive symptoms - dis, mean (sd) 2.46 (0.69) 2.46 (0.66) 0.067 0.863 a: numbers correspond to PD/RE/HC b: numbers correspond to university/college/secondary school/primary school/other/none c = information on medication is based on PD individuals d = F- and p-values indicate the main effect for cluster; there were no significant interactions present Abbreviations: PANSS = Positive and Negative Syndrome Scale; CAPE = Community Assessment of Psychic Experience; freq = frequency; dis = distress; sd = standard deviation. Significant p values are in bold Due to the lack of z-normalization of our data in the original manuscript, DTW and therefore the clustering solution was skewed towards capturing differences between mean ratings of individuals and not as intended, capturing differences in the rating dynamics (‘shape’) of individuals. In the original results this was indicated by the finding that individuals assigned to cluster 1 show significantly higher mean average symptom ratings as compared to individuals assigned to cluster 2. In contrast, rating variance did not significantly differentiate the clusters. The high mean rating cluster (cluster 1) in return corresponded to the significantly higher cross-sectional ratings reported on the PANSS and CAPE questionnaire. After applying z-standardization, (1) cluster indices and cluster stability were inconsistent with respect to the optimal cluster solution for the current data and (2) the unsupervised machine learning did no longer identify individuals with distinct clinical characteristics in terms of positive and negative symptoms on the PANSS or CAPE. However, in contrast to our previous analysis we now found that individuals assigned to cluster 2 experienced higher general psychopathology. As for our previous analysis, differences on the single PANSS items of suspiciousness and active social avoidance emerged between the two clusters. Individuals in cluster 2 were further characterized by a specific pattern in their EMA ratings. However, as visible in Fig. 1C, it is likely that this effect was still driven by differences in the absolute mean EMA ratings and to a lesser extent by the rating dynamics. That is, in contrast to the original manuscript, we now need to conclude that the correspondence between positive and negative symptom ratings on cross-sectional clinical interview and questionnaire measures and the EMA rating dynamics, i.e. the shape and pattern of individual EMA ratings over time, is relatively low. However, we need to acknowledge that even after removing mean differences we still find significant differences between clusters with respect to EMA mean symptom ratings. Thus, clustering of the standardized EMA rating dynamics, seems to be less informative with respect to the overall severity of symptoms, as indicated in clinical interviews or questionnaires before the EMA rating period. In sum, our findings with z-standardized data do not support many of our original conclusions, which suggested that dynamics in EMA ratings correspond well to ratings of positive and negative symptoms in clinical assessments and questionnaires. However, they do not exclude the potential informativeness and clinical usefulness of characterizing dynamic patterns of EMA ratings, e.g. in relationship to relapse or to differences in perceived functional impairment of an individual which we did not investigate in the current study. Acknowledgements: We would like to thank Prof. Eamonn Keogh (University of California – Riverside) who provided helpful comments and constructive feedback and who supported us in applying the corrections to our data analysis. © Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Upon an exchange with an expert researcher in the field of dynamic time warping (DTW) we implemented an additional step (‘z-normalization’) in the preprocessing pipeline of our ecological momentary assessment (EMA) data. This step is required in DTW analysis to capture similarities between temporal dynamics, i.e. similarities in the shape, rather than similarities in the absolute (mean) rating in the EMA trajectories. Even small differences in the scale or offset will reduce any similarity information encoded in the dynamic or shape of this trajectory. As an example, when using DTW we want to be able to recognize both 7 and 7, despite their differences in size (scale), similarly, as we want to be able to recognize 7 and 7, despite different offsets. Z-normalization of the EMA trajectories before applying DTW is essential as it removes differences in mean ratings between EMA trajectories and in this way identifies differences in rating dynamics [1]. Furthermore, it was necessary to adapt the clustering algorithm in order to be able to use distance matrices for clustering. We adapted the following aspects of the original pipeline: We implemented the aforementioned z-normalization as part of the preprocessing prior to application of DTW and the clustering algorithm. We adjusted the selection of the clustering algorithms to better fit the data and investigated the results with a hierarchical as well as the PAM (partitioning around medoids) clustering algorithm. We compared several cluster indices to decide which number of clusters was the most optimal for the data set. In the new analysis the PAM clustering algorithm also reveals a two-cluster solution as most stable (Jaccard indices: cluster 1 = 0.77; cluster 2 = 0.88). However, in contrast to previous results the cluster indices regarding the cluster number were inconsistent and did not indicate a clear number of clusters. In line with our previous results, one cluster (cluster 2) shows higher mean EMA symptom ratings than the other (cluster 1). Cluster 2 had significantly higher ratings on cross-sectional Positive and Negative Syndrome Scale (PANSS) ratings of general symptoms than cluster 1. However, the new cluster solution shows no differences on other PANSS scales and the self-report Community Assessment of Psychic Experience (CAPE) questionnaire (Fig. 1; Table 1). This study included three study groups in the clustering, i.e. outpatients diagnosed with a psychotic disorder (PD), healthy individuals (HC), and healthy individuals with a first-degree relative with psychosis (RE). All three study groups were represented in both clusters while most of PD (73%, N = 40) and RE (70%, N = 14) were assigned to cluster 2. We find no clear differentiation of RE from HC or PD in clusters across the investigated 7-day EMA rating period. Cluster characteristics. (A) We obtained a two-cluster solution with distinct characteristics in their EMA ratings. The Z-normalized EMA psychotic symptom scale data is shown in the left and the unscaled, i.e. raw data is shown in the right panel. Z-normalized psychotic symptom scores were used for clustering. (B) Clusters showed significantly different clinical scores on the PANSS general symptom scale (left), as well as on two of the individual PANSS items, suspiciousness (middle) and active social avoidance (right). (C) Additionally, the revised (z-normalized) clusters 1 and 2 showed significantly different mean EMA ratings of psychotic symptoms (left panel) but no significant difference with respect to within-subject EMA rating variance of these (right panel). Significances: * p < 0.05, ** p < 0.01, *** p < 0.001 Demographic and clinical cluster characteristics Cluster 1 (N = 34) Cluster 2 (N = 66) t value/chi2, F-value pfdr study groupa 15/6/13 40/14/12 4.861 0.704 age, mean (sd) 37.08 (9.31) 39.22 (11.58) -0.998 0.830 11 (39) 28 (74) 0.580 0.830 educational statusb 13/10/6/0/0/2 16/11/10/2/2/2 0.623 0.830 medication, n (%)c antipsychotic 14 (100) 35 (95) 0.788 0.830 antidepressant 3 (23) 12 (32) 0.401 0.838 benzodiazepine 0 (0) 5 (16) 1.806 0.830 mood stabilizers 0 (0) 1 (3) 0.331 0.999 positive symptoms, mean (sd) 11.73 (3.65) 15.13 (5.77) -2.570 0.061 negative symptoms, mean (sd) 12.40 (5.25) 16.36 (5.37) -2.467 0.076 general symptoms, mean (sd) 24.67 (4.37) 31.50 (7.28) -4.182 delusions (P1) 2.00 (1.25) 2.44 (1.37) -1.114 0.510 hallucinatory behavior (P3) 2.13 (1.60) 2.79 (1.70) -1.338 0.416 suspiciousness/persecution (P6) 2.20 (0.94) 3.12 (1.45) -2.758 emotional withdrawal (N2) 1.93 (1.22) 2.51 (1.35) -1.513 0.334 active social avoidance (A16) 1.53 (0.74) 2.64 (1.48) -3.636 CAPEd positive symptoms - freq, mean (sd) 1.47 (0.38) 1.65 (0.52) 1.543 0.435 positive symptoms - dis, mean (sd) 1.81 (0.61) 2.00 (0.71) 0.373 0.735 negative symptoms - freq, mean (sd) 1.72 (0.38) 1.97 (0.59) 2.395 0.326 negative symptoms - dis, mean (sd) 1.86 (0.61) 2.18 (0.61) 3.668 0.169 depressive symptoms - freq, mean (sd) 1.89 (0.55) 1.98 (0.62) 0.382 0.735 depressive symptoms - dis, mean (sd) 2.46 (0.69) 2.46 (0.66) 0.067 0.863 a: numbers correspond to PD/RE/HC b: numbers correspond to university/college/secondary school/primary school/other/none c = information on medication is based on PD individuals d = F- and p-values indicate the main effect for cluster; there were no significant interactions present Abbreviations: PANSS = Positive and Negative Syndrome Scale; CAPE = Community Assessment of Psychic Experience; freq = frequency; dis = distress; sd = standard deviation. Significant p values are in bold Due to the lack of z-normalization of our data in the original manuscript, DTW and therefore the clustering solution was skewed towards capturing differences between mean ratings of individuals and not as intended, capturing differences in the rating dynamics (‘shape’) of individuals. In the original results this was indicated by the finding that individuals assigned to cluster 1 show significantly higher mean average symptom ratings as compared to individuals assigned to cluster 2. In contrast, rating variance did not significantly differentiate the clusters. The high mean rating cluster (cluster 1) in return corresponded to the significantly higher cross-sectional ratings reported on the PANSS and CAPE questionnaire. After applying z-standardization, (1) cluster indices and cluster stability were inconsistent with respect to the optimal cluster solution for the current data and (2) the unsupervised machine learning did no longer identify individuals with distinct clinical characteristics in terms of positive and negative symptoms on the PANSS or CAPE. However, in contrast to our previous analysis we now found that individuals assigned to cluster 2 experienced higher general psychopathology. As for our previous analysis, differences on the single PANSS items of suspiciousness and active social avoidance emerged between the two clusters. Individuals in cluster 2 were further characterized by a specific pattern in their EMA ratings. However, as visible in Fig. 1C, it is likely that this effect was still driven by differences in the absolute mean EMA ratings and to a lesser extent by the rating dynamics. That is, in contrast to the original manuscript, we now need to conclude that the correspondence between positive and negative symptom ratings on cross-sectional clinical interview and questionnaire measures and the EMA rating dynamics, i.e. the shape and pattern of individual EMA ratings over time, is relatively low. However, we need to acknowledge that even after removing mean differences we still find significant differences between clusters with respect to EMA mean symptom ratings. Thus, clustering of the standardized EMA rating dynamics, seems to be less informative with respect to the overall severity of symptoms, as indicated in clinical interviews or questionnaires before the EMA rating period. In sum, our findings with z-standardized data do not support many of our original conclusions, which suggested that dynamics in EMA ratings correspond well to ratings of positive and negative symptoms in clinical assessments and questionnaires. However, they do not exclude the potential informativeness and clinical usefulness of characterizing dynamic patterns of EMA ratings, e.g. in relationship to relapse or to differences in perceived functional impairment of an individual which we did not investigate in the current study. Acknowledgements: We would like to thank Prof. Eamonn Keogh (University of California – Riverside) who provided helpful comments and constructive feedback and who supported us in applying the corrections to our data analysis. © Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can distinguish groups on the continuum of genetic risk toward psychotic illness and identify individuals with need for extended healthcare. Individuals with psychotic disorder (PD, N = 55), healthy individuals (HC, N = 25) and HC with first-degree relatives with psychosis (RE, N = 20) were assessed at two sites over 7 days using EMA. Cluster analysis determined subgroups based on similarities in longitudinal trajectories of psychotic symptom ratings in EMA, agnostic of study group assignment. Psychotic symptom ratings were calculated as average of items related to hallucinations and paranoid ideas. Prior to EMA we assessed symptoms using the Positive and Negative Syndrome Scale (PANSS) and the Community Assessment of Psychic Experience (CAPE) to characterize the EMA subgroups. We identified two clusters with distinct longitudinal EMA characteristics. Cluster 1 (NPD = 12, NRE = 1, NHC = 2) showed higher mean EMA symptom ratings as compared to cluster 2 (NPD = 43, NRE = 19, NHC = 23) (p < 0.001). Cluster 1 showed a higher burden on negative (p < 0.05) and positive (p < 0.05) psychotic symptoms in cross-sectional PANSS and CAPE ratings than cluster 2. Findings indicate a separation of PD with high symptom burden (cluster 1) from PD with healthy-like rating patterns grouping together with HC and RE (cluster 2). Individuals in cluster 1 might particularly profit from exchange with a clinician underlining the idea of EMA as clinical monitoring tool. © The Author(s) 2023.

Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can distinguish groups on the continuum of genetic risk toward psychotic illness and identify individuals with need for extended healthcare. Individuals with psychotic disorder (PD, N = 55), healthy individuals (HC, N = 25) and HC with first-degree relatives with psychosis (RE, N = 20) were assessed at two sites over 7 days using EMA. Cluster analysis determined subgroups based on similarities in longitudinal trajectories of psychotic symptom ratings in EMA, agnostic of study group assignment. Psychotic symptom ratings were calculated as average of items related to hallucinations and paranoid ideas. Prior to EMA we assessed symptoms using the Positive and Negative Syndrome Scale (PANSS) and the Community Assessment of Psychic Experience (CAPE) to characterize the EMA subgroups. We identified two clusters with distinct longitudinal EMA characteristics. Cluster 1 (NPD = 12, NRE = 1, NHC = 2) showed higher mean EMA symptom ratings as compared to cluster 2 (NPD = 43, NRE = 19, NHC = 23) (p < 0.001). Cluster 1 showed a higher burden on negative (p < 0.05) and positive (p < 0.05) psychotic symptoms in cross-sectional PANSS and CAPE ratings than cluster 2. Findings indicate a separation of PD with high symptom burden (cluster 1) from PD with healthy-like rating patterns grouping together with HC and RE (cluster 2). Individuals in cluster 1 might particularly profit from exchange with a clinician underlining the idea of EMA as clinical monitoring tool. © The Author(s) 2023.

Use of alcohol, cannabis and opioids is highly prevalent and is associated with global disease burden and high economic costs. The exact pathophysiology of abuse or addiction associated with these sedative substances is not completely understood, but previous research implicates the important role of the striatal dopamine system in the addiction process. Multiple studies investigated changes in the striatal dopamine systems of users of sedative substances, but currently these results are very heterogeneous. Therefore, we conducted a meta-analysis of in vivo neuroimaging studies investigating dopaminergic alterations in the striatum of users of alcohol, opioids or cannabis. Analyses for each substance were conducted separately for the availability of D2/D3 dopamine receptors, dopamine transporters and dopamine synthesis capacity. In total, 723 substance users and 752 healthy controls were included. The results indicated a significant lower striatal D2/D3 receptor availability in alcohol users compared to controls (g = 0.46) but no difference in dopamine transporter availability or dopamine synthesis capacity. Our analysis indicated that changes of dopamine receptors and transporters are moderated by the duration of abstinence. Comparing opioid users with controls revealed a significant lower D2/D3 receptor availability (g = 1.17) and a significantly lower transporter availability (g = 1.55) in opioid users. For cannabis users, there was no significant difference in receptor availability compared to controls and too few studies provided information on dopamine transporter availability or synthesis capacity. Our analysis provides strong evidence for a central role of the striatal dopamine system in use of alcohol or opioids. Further studies are needed to clarify the impact of the dopamine system in cannabis users. © 2018, American College of Neuropsychopharmacology.

Use of alcohol, cannabis and opioids is highly prevalent and is associated with global disease burden and high economic costs. The exact pathophysiology of abuse or addiction associated with these sedative substances is not completely understood, but previous research implicates the important role of the striatal dopamine system in the addiction process. Multiple studies investigated changes in the striatal dopamine systems of users of sedative substances, but currently these results are very heterogeneous. Therefore, we conducted a meta-analysis of in vivo neuroimaging studies investigating dopaminergic alterations in the striatum of users of alcohol, opioids or cannabis. Analyses for each substance were conducted separately for the availability of D2/D3 dopamine receptors, dopamine transporters and dopamine synthesis capacity. In total, 723 substance users and 752 healthy controls were included. The results indicated a significant lower striatal D2/D3 receptor availability in alcohol users compared to controls (g = 0.46) but no difference in dopamine transporter availability or dopamine synthesis capacity. Our analysis indicated that changes of dopamine receptors and transporters are moderated by the duration of abstinence. Comparing opioid users with controls revealed a significant lower D2/D3 receptor availability (g = 1.17) and a significantly lower transporter availability (g = 1.55) in opioid users. For cannabis users, there was no significant difference in receptor availability compared to controls and too few studies provided information on dopamine transporter availability or synthesis capacity. Our analysis provides strong evidence for a central role of the striatal dopamine system in use of alcohol or opioids. Further studies are needed to clarify the impact of the dopamine system in cannabis users. © 2018, American College of Neuropsychopharmacology.

This study was conducted to explore the effects of specific psychosocial paradigm on predator animal posttraumatic stress model and to test the hypothesis that psychosocially stressed rats would exibit abnormal levels of cortisol and a larger suppression of cortisol levels after the application of dexamethasone. Animals were divided in two groups: experimental and control groups. The experimental group was exposed to two types of stressors: Acute immobilization stress, and combined predator stress and daily social stress with application of dexamethasone. Blood sampling was performed at three different times. We found statistically significant results after analyzing the differences between cortisol levels in different times of blood sampling in the group of animals exposed to stress with dexamethasone application. Statistical significance was found when we compared the experimental group with the control group in terms of elevated cortisol levels during blood sampling after stress paradigm exposition. Many significant disruptions in the functioning of the hypothalamic-pituitary-Adrenal axis were observed, such as decrease in basal cortisol levels and enhanced dexamethasone-induced inhibition of cortisol levels. These findings are important because their impact can translate to human individuals with posttraumatic stress disorder, which is the most important role of every animal model in research. © Copyright 2016, Kaohsiung Medical University.