Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.

Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.

Graded functional organization in the left inferior frontal gyrus: evidence from task-free and task-based functional connectivity.

The left inferior frontal gyrus has been ascribed key roles in numerous cognitive domains, such as language and executive function. However, its functional organization is unclear. Possibilities include a singular domain-general function, or multiple functions that can be mapped onto distinct subregions. Furthermore, spatial transition in function may be either abrupt or graded. The present study explored the topographical organization of the left inferior frontal gyrus using a bimodal data-driven approach. We extracted functional connectivity gradients from (i) resting-state fMRI time-series and (ii) coactivation patterns derived meta-analytically from heterogenous sets of task data. We then sought to characterize the functional connectivity differences underpinning these gradients with seed-based resting-state functional connectivity, meta-analytic coactivation modeling and functional decoding analyses. Both analytic approaches converged on graded functional connectivity changes along 2 main organizational axes. An anterior-posterior gradient shifted from being preferentially associated with high-level control networks (anterior functional connectivity) to being more tightly coupled with perceptually driven networks (posterior). A second dorsal-ventral axis was characterized by higher connectivity with domain-general control networks on one hand (dorsal functional connectivity), and with the semantic network, on the other (ventral). These results provide novel insights into an overarching graded functional organization of the functional connectivity that explains its role in multiple cognitive domains.

A factor analytic comparison of three commonly used depression scales (HAMD, MADRS, BDI) in a large sample of depressed inpatients.

BACKGROUND: Quantifying depression mainly relies on the use of depression scales, and understanding their factor structure is crucial for evaluating their validity. METHODS: This post-hoc analysis utilized prospectively collected data from a naturalistic study of 1014 inpatients with major depression. Confirmatory and exploratory factor analyses were performed to test the psychometric abilities of the Hamilton Depression Rating Scale, the Montgomery Asberg Depression Rating Scale, and the self-rated Beck Depression Inventory. A combined factor analysis was also conducted including all items of all scales. RESULTS: All three scales showed good to very good internal consistency. The HAMD-17 had four factors: an "anxiety" factor, a "depression" factor, an "insomnia" factor, and a "somatic" factor. The MADRS also had four factors: a "sadness" factor, a neurovegetative factor, a "detachment" factor and a "negative thoughts" factor, while the BDI had three factors: a "negative attitude towards self" factor, a "performance impairment" factor, and a "somatic" factor. The combined factor analysis suggested that self-ratings might reflect a distinct illness dimension within major depression. CONCLUSIONS: The factors obtained in this study are comparable to those found in previous research. Self and clinician ratings are complementary and not redundant, highlighting the importance of using multiple measures to quantify depression.

Extended functional connectivity of convergent structural alterations among individuals with PTSD: a neuroimaging meta-analysis.

BACKGROUND: Post-traumatic stress disorder (PTSD) is a debilitating disorder defined by the onset of intrusive, avoidant, negative cognitive or affective, and/or hyperarousal symptoms after witnessing or experiencing a traumatic event. Previous voxel-based morphometry studies have provided insight into structural brain alterations associated with PTSD with notable heterogeneity across these studies. Furthermore, how structural alterations may be associated with brain function, as measured by task-free and task-based functional connectivity, remains to be elucidated. METHODS: Using emergent meta-analytic techniques, we sought to first identify a consensus of structural alterations in PTSD using the anatomical likelihood estimation (ALE) approach. Next, we generated functional profiles of identified convergent structural regions utilizing resting-state functional connectivity (rsFC) and meta-analytic co-activation modeling (MACM) methods. Finally, we performed functional decoding to examine mental functions associated with our ALE, rsFC, and MACM brain characterizations. RESULTS: We observed convergent structural alterations in a single region located in the medial prefrontal cortex. The resultant rsFC and MACM maps identified functional connectivity across a widespread, whole-brain network that included frontoparietal and limbic regions. Functional decoding revealed overlapping associations with attention, memory, and emotion processes. CONCLUSIONS: Consensus-based functional connectivity was observed in regions of the default mode, salience, and central executive networks, which play a role in the tripartite model of psychopathology. Taken together, these findings have important implications for understanding the neurobiological mechanisms associated with PTSD.

Combining machine learning algorithms for prediction of antidepressant treatment response.

OBJECTIVES: Predictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho-sociodemographic variables and their predictive impact on treatment outcome phenotypes. EXPERIMENTAL PROCEDURES: Treatment response was defined by a change of HAM-D 17-item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross-center validation design. In total, 88 predictors were implemented. RESULTS: Clustering revealed four distinct HAM-D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability. CONCLUSION: Our results support a decisive role for machine learning in the management of antidepressant treatment. Treatment- and symptom-specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.

What Executive Function Network is that? An Image-Based Meta-Analysis of Network Labels.

The current state of label conventions used to describe brain networks related to executive functions is highly inconsistent, leading to confusion among researchers regarding network labels. Visually similar networks are referred to by different labels, yet these same labels are used to distinguish networks within studies. We performed a literature review of fMRI studies and identified nine frequently-used labels that are used to describe topographically or functionally similar neural networks: central executive network (CEN), cognitive control network (CCN), dorsal attention network (DAN), executive control network (ECN), executive network (EN), frontoparietal network (FPN), working memory network (WMN), task positive network (TPN), and ventral attention network (VAN). Our aim was to meta-analytically determine consistency of network topography within and across these labels. We hypothesized finding considerable overlap in the spatial topography among the neural networks associated with these labels. An image-based meta-analysis was performed on 158 group-level statistical maps (SPMs) received from authors of 69 papers listed on PubMed. Our results indicated that there was very little consistency in the SPMs labeled with a given network name. We identified four clusters of SPMs representing four spatially distinct executive function networks. We provide recommendations regarding label nomenclature and propose that authors looking to assign labels to executive function networks adopt this template set for labeling networks.

Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms.

Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we used emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution that represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 & MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

What happens with schizophrenia patients after their discharge from hospital? Results on outcome and treatment from a "real-world" 2-year follow-up trial.

Aim of the study was to examine the course of schizophrenia patients within 2 years after discharge. Within a multicenter study of the German Competence Network on Schizophrenia, patients suffering from a schizophrenia spectrum disorder were examined regarding their psychopathological improvement, tolerability, and the treatment regime applied during hospitalization and a 2-year follow-up period. Response, remission, the level of everyday functioning, and relapse were furthermore evaluated during the follow-up period using established definitions for these outcome domains. The psychopharmacological treatment was specifically evaluated in terms of a potential association with relapse. 149 patients were available for analysis, with 65% of the patients being in response, 52% in symptomatic remission, and 64% having a satisfiable everyday functioning 2 years after their discharge from hospital. Despite these favorable outcome rates, 63% of the patients suffered from a relapse within the 2-year follow-up period with 86% of these patients being rehospitalized. Discharge non-responder and non-remitter were twice as likely to relapse during follow-up. A significant decrease of side-effects was observed with negligible rates of extrapyramidal side-effects, sedation, and weight gain during follow-up. Patients receiving treatment with atypical antipsychotics were found to have the lowest risk to relapse (p < 0.0001). The results highlight the natural and unsteady course of schizophrenia in most patients underlining the need to develop more specific treatment strategies ensuring ongoing stability and preventing relapse.

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Comparing Schizophrenia Patients With a Predicted High/Low Risk of Nonresponse Receiving Treatment with Ziprasidone and Haloperidol: A Randomized-Controlled Study.

INTRODUCTION: The aim of this double-blind randomized study was to evaluate the response to antipsychotic treatment in schizophrenia patients with predicted high/low risk of nonresponse identified by applying a set of well-established scales and predictors of outcome and to compare efficacy between ziprasidone and haloperidol. METHODS: One hundred twelve schizophrenia patients (ziprasidone: n=54; haloperidol: n=58) were rated weekly on the Positive and Negative Syndrome Scale for Schizophrenia (PANSS), the Global Assessment of Functioning Scale (GAF), the Social and Occupational Functioning Scale (SOFAS), the Simpson-Angus Scale (SAS), and Hillside Akathisia Scale (HAS). RESULTS: Ninety-two patients (82%) were predicted to have a high risk of nonresponse. No significant difference regarding PANSS improvement in this subsample was found comparing ziprasidone and haloperidol (p=0.563). Also, for the total patient sample, no significant difference was found regarding the course of the PANSS total score, GAF (p=0.921), and SOFAS (p=0.658) between ziprasidone and haloperidol. Haloperidol resulted in higher scores on the SAS (p=0.001) and HAS (p=0.011). DISCUSSION: An alarmingly high number of patients were at high risk of nonresponse to antipsychotic treatment.

Dissociable meta-analytic brain networks contribute to coordinated emotional processing.

Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks.

Critical Role for Telomerase in the Mechanism of Flow-Mediated Dilation in the Human Microcirculation.

RATIONALE: Telomerase is a nuclear regulator of telomere elongation with recent reports suggesting a role in regulation of mitochondrial reactive oxygen species. Flow-mediated dilation in patients with cardiovascular disease is dependent on the formation of reactive oxygen species. OBJECTIVE: We examined the hypothesis that telomerase activity modulates microvascular flow-mediated dilation, and loss of telomerase activity contributes to the change of mediator from nitric oxide to mitochondrial hydrogen peroxide in patients with coronary artery disease (CAD). METHODS AND RESULTS: Human coronary and adipose arterioles were isolated for videomicroscopy. Flow-mediated dilation was measured in vessels pretreated with the telomerase inhibitor BIBR-1532 or vehicle. Statistical differences between groups were determined using a 2-way analysis of variance repeated measure (n≥4; P<0.05). L-NAME (N(ω)-nitro-L-arginine methyl ester; nitric oxide synthase inhibitor) abolished flow-mediated dilation in arterioles from subjects without CAD, whereas polyethylene glycol-catalase (PEG-catalase; hydrogen peroxide scavenger) had no effect. After exposure to BIBR-1532, arterioles from non-CAD subjects maintained the magnitude of dilation but changed the mediator from nitric oxide to mitochondrial hydrogen peroxide (% max diameter at 100 cm H2O: vehicle 74.6±4.1, L-NAME 37.0±2.0*, PEG-catalase 82.1±2.8; BIBR-1532 69.9±4.0, L-NAME 84.7±2.2, PEG-catalase 36.5±6.9*). Conversely, treatment of microvessels from CAD patients with the telomerase activator AGS 499 converted the PEG-catalase-inhibitable dilation to one mediated by nitric oxide (% max diameter at 100 cm H2O: adipose, AGS 499 78.5±3.9; L-NAME 10.9±17.5*; PEG-catalase 79.2±4.9). Endothelial-independent dilation was not altered with either treatment. CONCLUSIONS: We have identified a novel role for telomerase in re-establishing a physiological mechanism of vasodilation in arterioles from subjects with CAD. These findings suggest a new target for reducing the oxidative milieu in the microvasculature of patients with CAD.

QTc prolongation in short-term treatment of schizophrenia patients: effects of different antipsychotics and genetic factors.

Antipsychotics are effective in treating schizophrenia but may lead to a higher cardiovascular risk due to QTc prolongation. Besides drugs, genetic and clinical factors may contribute to QTc prolongation. The aim of this study is to examine the effect of candidate genes known for QTc prolongation and their interaction with common antipsychotics. Thus, 199 patients were genotyped for nine polymorphisms in KCNQ1, KCNH2, SCN5A, LOC10537879, LOC101927066, NOS1AP and NUBPL. QTc interval duration was measured before treatment and weekly for 5 weeks while being treated with risperidone, quetiapine, olanzapine, amisulpride, aripiprazole and haloperidol in monotherapy. Antipsychotics used in this study showed a different potential to affect the QTc interval. We found no association between KCNH2, KCNQ1, LOC10537879, LOC101927066, NOS1AP and NUBPL polymorphisms and QTc duration at baseline and during antipsychotic treatment. Mixed general models showed a significant overall influence of SCN5A (H558R) on QTc duration but no significant interaction with antipsychotic treatment. Our results do not provide evidence for an involvement of candidate genes for QTc duration in the pathophysiology of QTc prolongation by antipsychotics during short-term treatment. Further association studies are needed to confirm our findings. With a better understanding of these interactions the cardiovascular risk of patients may be decreased.

The Human 343delT HSPB5 Chaperone Associated with Early-onset Skeletal Myopathy Causes Defects in Protein Solubility.

Mutations of HSPB5 (also known as CRYAB or αB-crystallin), a bona fide heat shock protein and molecular chaperone encoded by the HSPB5 (crystallin, alpha B) gene, are linked to multisystem disorders featuring variable combinations of cataracts, cardiomyopathy, and skeletal myopathy. This study aimed to investigate the pathological mechanisms involved in an early-onset myofibrillar myopathy manifesting in a child harboring a homozygous recessive mutation in HSPB5, 343delT. To study HSPB5 343delT protein dynamics, we utilize model cell culture systems including induced pluripotent stem cells derived from the 343delT patient (343delT/343delT) along with isogenic, heterozygous, gene-corrected control cells (WT KI/343delT) and BHK21 cells, a cell line lacking endogenous HSPB5 expression. 343delT/343delT and WT KI/343delT-induced pluripotent stem cell-derived skeletal myotubes and cardiomyocytes did not express detectable levels of 343delT protein, contributable to the extreme insolubility of the mutant protein. Overexpression of HSPB5 343delT resulted in insoluble mutant protein aggregates and induction of a cellular stress response. Co-expression of 343delT with WT prevented visible aggregation of 343delT and improved its solubility. Additionally, in vitro refolding of 343delT in the presence of WT rescued its solubility. We demonstrate an interaction between WT and 343delT both in vitro and within cells. These data support a loss-of-function model for the myopathy observed in the patient because the insoluble mutant would be unavailable to perform normal functions of HSPB5, although additional gain-of-function effects of the mutant protein cannot be excluded. Additionally, our data highlight the solubilization of 343delT by WT, concordant with the recessive inheritance of the disease and absence of symptoms in carrier individuals.

Heterogeneous fractionation profiles of meta-analytic coactivation networks.

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

The Nonspecific Lipid Transfer Protein AtLtpI-4 Is Involved in Suberin Formation of Arabidopsis thaliana Crown Galls.

Nonspecific lipid transfer proteins reversibly bind different types of lipid molecules in a hydrophobic cavity. They facilitate phospholipid transfer between membranes in vitro, play a role in cuticle and possibly in suberin formation, and might be involved in plant pathogen defense signaling. This study focuses on the role of the lipid transfer protein AtLTPI-4 in crown gall development. Arabidopsis (Arabidopsis thaliana) crown gall tumors, which develop upon infection with the virulent Agrobacterium tumefaciens strain C58, highly expressed AtLTPI-4 Crown galls of the atltpI-4 loss-of-function mutant were much smaller compared with those of wild-type plants. The gene expression pattern and localization of the protein to the plasma membrane pointed to a function of AtLTPI-4 in cell wall suberization. Since Arabidopsis crown galls are covered by a suberin-containing periderm instead of a cuticle, we analyzed the suberin composition of crown galls and found a reduction in the amounts of long-chain fatty acids (C18:0) in the atltpI-4 mutant. To demonstrate the impact of AtLtpI-4 on extracellular lipid composition, we expressed the protein in Arabidopsis epidermis cells. This led to a significant increase in the very-long-chain fatty acids C24 and C26 in the cuticular wax fraction. Homology modeling and lipid-protein-overlay assays showed that AtLtpI-4 protein can bind these very-long-chain fatty acids. Thus, AtLtpI-4 protein may facilitate the transfer of long-chain as well as very-long-chain fatty acids into the apoplast, depending on the cell type in which it is expressed. In crown galls, which endogenously express AtLtpI-4, it is involved in suberin formation.

Vascular Actions of Angiotensin 1-7 in the Human Microcirculation: Novel Role for Telomerase.

OBJECTIVE: This study examined vascular actions of angiotensin 1-7 (ANG 1-7) in human atrial and adipose arterioles. APPROACH AND RESULTS: The endothelium-derived hyperpolarizing factor of flow-mediated dilation (FMD) switches from antiproliferative nitric oxide (NO) to proatherosclerotic hydrogen peroxide in arterioles from humans with coronary artery disease (CAD). Given the known vasoprotective properties of ANG 1-7, we tested the hypothesis that overnight ANG 1-7 treatment restores the NO component of FMD in arterioles from patients with CAD. Endothelial telomerase activity is essential for preserving the NO component of vasodilation in the human microcirculation; thus, we also tested whether telomerase activity was necessary for ANG 1-7-mediated vasoprotection by treating separate arterioles with ANG 1-7±the telomerase inhibitor 2-[[(2E)-3-(2-naphthalenyl)-1-oxo-2-butenyl1-yl]amino]benzoic acid. ANG 1-7 dilated arterioles from patients without CAD, whereas dilation was significantly reduced in arterioles from patients with CAD. In atrial arterioles from patients with CAD incubated with ANG 1-7 overnight, the NO synthase inhibitor NG-nitro-l-arginine methyl ester abolished FMD, whereas the hydrogen peroxide scavenger polyethylene glycol catalase had no effect. Conversely, in vessels incubated with ANG 1-7+2-[[(2E)-3-(2-naphthalenyl)-1-oxo-2-butenyl1-yl]amino]benzoic acid, NG-nitro-l-arginine methyl ester had no effect on FMD, but polyethylene glycol catalase abolished dilation. In cultured human coronary artery endothelial cells, ANG 1-7 significantly increased telomerase activity. These results indicate that ANG 1-7 dilates human microvessels, and dilation is abrogated in the presence of CAD. Furthermore, ANG 1-7 treatment is sufficient to restore the NO component of FMD in arterioles from patients with CAD in a telomerase-dependent manner. CONCLUSIONS: ANG 1-7 exerts vasoprotection in the human microvasculature via modulation of telomerase activity.

Validity of remission and recovery criteria for schizophrenia and major depression: comparison of the results of two one-year follow-up naturalistic studies.

The objective of the present study was the application and comparison of common remission and recovery criteria between patients with the diagnosis of schizophrenia and major depressive disorder (MDD) under inclusion of other outcome parameters. Patients with schizophrenia and MDD who were treated as inpatients at the beginning of the study were examined within two naturalistic follow-up trials from admission to discharge of an inpatient treatment period and the one-year follow-up assessment. PANSS criteria of the Remission in Schizophrenia Working Group (RSWG) for schizophrenia and HAMD criteria of the ACNP Task Force in MDD for depressive patients as well as the Clinical Global Impression-Severity Scale (CGI-S) were applied as symptomatic outcome measures additionally to functional outcome parameters. Data of 153 schizophrenia patients and 231 patients with a MDD episode have been included in the analysis. More depressive than schizophrenia patients reached a threshold score of ≤3 on the CGI-S, indicating symptomatic remission at discharge and at the one-year follow-up. In contrast similar proportions of patients reaching symptomatic remission at discharge from inpatient treatment and at the one-year follow-up in the schizophrenia and in the MDD group were found when disease-related consensus criteria (RSWG vs. ACNP Task Force) were used. Functional remission and recovery rates were significantly lower in schizophrenia than in depressive patients at the one-year follow-up visit. Common outcome criteria for remission and recovery in schizophrenia and major depression were not directly comparable. However, our results indicated a significantly poorer outcome in schizophrenia than in depressive patients according to terms of remission and recovery.

Add-on Antidepressants in the Naturalistic Treatment of Schizophrenia Spectrum Disorder - When, Who, and How?

The aim of this study was to evaluate antidepressant add-on treatment within the acute treatment of schizophrenia spectrum disorder patients. Antidepressant add-on was evaluated in 365 patients within a naturalistic multicenter study. Patients with/without antidepressant add-on were compared regarding clinical and treatment-related variables, response and remission, and remission of depressive and negative symptoms. The efficacy of antidepressant add-on treatment was furthermore analyzed applying marginal structure models. Twenty-three percent of the patients received antidepressant add-on for a mean duration of 50.28 (33.42) days. Patients with the diagnosis of a schizoaffective disorder, multiple illness episodes, and a longer duration of their illness as well as those with significantly fewer baseline positive symptoms, more negative and depressive symptoms, more side effects, and less subjective well-being were augmented with antidepressants. At discharge no significant effect of antidepressant add-on treatment was observed in terms of a 25% improvement (p=0.2623), a 50% improvement (p=0.3946), remission (p=0.0552), or remission of depressive (p=0.6336) and negative symptoms (p=0.8756). Also, when analyzing marginal structure models considering the diagnostic subgroups, no significant effect was found. Add-on with antidepressants is common. A final recommendation in terms of this strategy's efficacy cannot be given.

Associations of NEUROD2 polymorphisms and change of cognitive dysfunctions in schizophrenia and schizoaffective disorder after eight weeks of antipsychotic treatment.

INTRODUCTION: NEUROD2 is a neurospecific helix-loop-helix transcription factor which has an impact on the regulation of glutamatergic and GABAergic genes. We investigated an association of NEUROD2 with neurocognitive dysfunctions in schizophrenia and schizoaffective disorder patients before and during treatment with different second-generation antipsychotics. METHODS: Patients were genotyped for four different polymorphisms of the NEUROD2 gene ((rs9889354(A/G), rs1877032(C/T), rs12453682(C/T) and rs11078918(C/G)). Cognitive function was assessed at baseline and week 8. Results of individual neuropsychological tests were assigned to six cognitive domains (reaction time and quality; executive function; working, verbal and visual memory) and a general cognitive index. RESULTS: 167 patients were included in the study. The NEUROD2 exonic polymorphism rs11078918 showed significant associations with verbal memory and executive functions, whereas the NEUROD2 polymorphism rs12453682 was significantly associated with working and verbal memory, executive functions and with a cognitive index. Significant associations were found at baseline and after eight weeks. Moreover, significant associations between the change in neuropsychological test results during antipsychotic treatment and the NEUROD2 polymorphisms rs11078918 and rs12453682 were observed. CONCLUSIONS: Our findings suggest that the NEUROD2 gene could play a role in the pathophysiology of neurocognitive dysfunctions as well as in the change of cognitive symptoms under antipsychotic treatment in schizophrenia and schizoaffective disorder.