Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip

Einführung von Prozessmanagement und Personaleinsatzplanung für die Durchführung einer Wahl.

Die vorliegende Arbeit befasst sich mit der Einführung von Prozessmanagement für die Durchführung einer Wahl in der Landeshauptstadt Graz im Referat Wahlen des BürgerInnenamtes des Magistrates Graz. Dabei werden die Ist-Prozesse erhoben und in einem BPMN Modell in Adonis dargestellt. Gleichzeitig wird der Personaleinsatz für die projektmäßige Durchführung mitbetrachtet und fließt unter Zuhilfenahme eines Rollenmodells in das System mit ein. In weiterer Folge kann diese Arbeit herangezogen werden, weitere Prozesse der Abteilung 2, BürgerInnenamt abzubilden und dabei auch die Komponenten des internen Kontrollsystems mit Risiken und Kontrollen einfließen zu lassen

Molecular monitoring of minimal residual disease in two patients with MLL-rearranged acute myeloid leukemia and haploidentical transplantation after relapse

This report describes the clinical courses of two acute myeloid leukemia patients. Both had MLL translocations, the first a t(10;11)(p11.2;q23) with MLL-AF10 and the second a t(11;19)(q23;p13.1) with MLL-ELL fusion. They achieved a clinical remission under conventional chemotherapy but relapsed shortly after end of therapy. Both had a history of invasive mycoses (one had possible pulmonary mycosis, one systemic candidiasis). Because no HLA-identical donor was available, a haploidentical transplantation was performed in both cases. Using a specially designed PCR method for the assessment of minimal residual disease (MRD), based on the quantitative detection of the individual chromosomal breakpoint in the MLL gene, all patients achieved complete and persistent molecular remission after transplantation. The immune reconstitution after transplantation is described in terms of total CD3+/CD4+, CD3+/CD8+, CD19+, and CD16+/CD56+ cell numbers over time. The KIR and HLA genotypes of donors and recipients are reported and the possibility of a KIR-mediated alloreactivity is discussed. This report illustrates that haploidentical transplantation may offer a chance of cure without chronic graft-versus-host disease in situations where no suitable HLA-identical donor is available even in a high-risk setting and shows the value of MRD monitoring in the pre- and posttransplant setting

Embedding the Organizational Culture Profile into Schwartz’s Universal Value Theory using Multidimensional Scaling with Regional Restrictions

Person-organization fit is often measured by the congruence of a person’s values and the values that he or she ascribes to the organization. A popular instrument used in this context is the Organizational Culture Profile (O’Reilly, Chatman, & Caldwell, 1991). The OCP scales its 54 items on eight factors, derived by exploratory factor analysis. We investigate the extent to which the OCP can be embedded into Schwartz’s Theory of Universals in Values (TUV) that is formulated in terms of a circumplex in MDS space. To address this question, we develop a non-standard MDS method that enforces a TUV-based axial regionality onto the solution space together with a permutation test that assesses the consistency of the side constraints with the MDS representation. We find that the OCP can indeed be largely embedded into the TUV. The practical implication is that P-O fit can at least be approximated by the congruence of the person’s and the organization’s positions on two value dimensions, risk vs. rules and results vs. relations

Structural properties of InAlN single layers nearly latice-matched to GaN grown by plasma assisted molecular beal epitaxy

The high lattice mismatch between III-nitride binaries (InN, GaN and AlN) remains a key problem to grow high quality III-nitride heterostructures. Recent interest has been focused on the growth of high-quality InAlN layers, with approximately 18% of indium incorporation, in-plane lattice-matched (LM) to GaN. While a lot of work has been done by metal-organic vapour phase epitaxy (MOVPE) by Carlin and co-workers, its growth by molecular beam epitaxy (MBE) is still in infanc

Embedding the organizational culture profile into Schwartz’s theory of universals in values

Abstract Person-organization fit (P-O fit) is often measured by the congruence of a person’s values and the values that he or she ascribes to the organization. A popular instrument used in this context is the Organizational Culture Profile (O’Reilly, Chatman, & Caldwell, 1991). The OCP scales use 54 items that form eight factors in exploratory factor analysis. We investigate the extent to which the OCP can be embedded into Schwartz’s Theory of Universals in Values (TUV) that is formulated in terms of a circumplex in a 2-dimensional plane. To address this question, we develop a non-standard multidimensional scaling (MDS) method that enforces a TUV-based axial regionality onto the solution space together with a permutation test that assesses the consistency of the side constraints with the MDS representation. We find that the OCP can indeed be embedded into the TUV. The practical implication is that P-O fit can be assessed more simply by the congruence of the person’s and the organization’s positions on two value dimensions: risk vs. rules and results vs. relations

Validation and application of the Non-Verbal Behavior Analyzer: an automated tool to assess non-verbal emotional expressions in psychotherapy

Background Emotions play a key role in psychotherapy. However, a problem with examining emotional states via self-report questionnaires is that the assessment usually takes place after the actual emotion has been experienced which might lead to biases and continuous human ratings are time and cost intensive. Using the AI-based software package Non-Verbal Behavior Analyzer (NOVA), video-based emotion recognition of arousal and valence can be applied in naturalistic psychotherapeutic settings. In this study, four emotion recognition models (ERM) each based on specific feature sets (facial: OpenFace, OpenFace-Aureg; body: OpenPose-Activation, OpenPose-Energy) were developed and compared in their ability to predict arousal and valence scores correlated to PANAS emotion scores and processes of change (interpersonal experience, coping experience, affective experience) as well as symptoms (depression and anxiety in HSCL-11). Materials and methods A total of 183 patient therapy videos were divided into a training sample (55 patients), a test sample (50 patients), and a holdout sample (78 patients). The best ERM was selected for further analyses. Then, ERM based arousal and valence scores were correlated with patient and therapist estimates of emotions and processes of change. Furthermore, using regression models arousal and valence were examined as predictors of symptom severity in depression and anxiety. Results The ERM based on OpenFace produced the best agreement to the human coder rating. Arousal and valence correlated significantly with therapists’ ratings of sadness, shame, anxiety, and relaxation, but not with the patient ratings of their own emotions. Furthermore, a significant negative correlation indicates that negative valence was associated with higher affective experience. Negative valence was found to significantly predict higher anxiety but not depression scores. Conclusion This study shows that emotion recognition with NOVA can be used to generate ERMs associated with patient emotions, affective experiences and symptoms. Nevertheless, limitations were obvious. It seems necessary to improve the ERMs using larger databases of sessions and the validity of ERMs needs to be further investigated in different samples and different applications. Furthermore, future research should take ERMs to identify emotional synchrony between patient and therapists into account

Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning

Abstract The control of neuroprosthetic devices from the activity of motor cortex neurons benefits from learning effects where the function of these neurons is adapted to the control task. It was recently shown that tuning properties of neurons in monkey motor cortex are adapted selectively in order to compensate for an erroneous interpretation of their activity. In particular, it was shown that the tuning curves of those neurons whose preferred directions had been misinterpreted changed more than those of other neurons. In this article, we show that the experimentally observed self-tuning properties of the system can be explained on the basis of a simple learning rule. This learning rule utilizes neuronal noise for exploration and performs Hebbian weight updates that are modulated by a global reward signal. In contrast to most previously proposed reward-modulated Hebbian learning rules, this rule does not require extraneous knowledge about what is noise and what is signal. The learning rule is able to optimize the performance of the model system within biologically realistic periods of time and under high noise levels. When the neuronal noise is fitted to experimental data, the model produces learning effects similar to those found in monkey experiments