Repeated diagnostic ultrasound exposure modifies the structural properties of CA1 dendrites and alters the hippocampal transcriptome.

The development of neurons is regulated by several spatiotemporally changing factors, which are crucial to give the ability of neurons to form functional networks. While external physical stimuli may impact the early developmental stages of neurons, the medium and long-term consequences of these influences have yet to be thoroughly examined. Using an animal model, this study focuses on the morphological and transcriptome changes of the hippocampus that may occur as a consequence of fetal ultrasound examination. We selectively labeled CA1 neurons of the hippocampus with in-utero electroporation to analyze their morphological features. Furthermore, certain samples also went through RNA sequencing after repetitive ultrasound exposure. US exposure significantly changed several morphological properties of the basal dendritic tree. A notable increase was also observed in the density of spines on the basal dendrites, accompanied by various alterations in individual spine morphology. Transcriptome analysis revealed several up or downregulated genes, which may explain the molecular background of these alterations. Our results suggest that US-derived changes in the dendritic trees of CA1 pyramidal cells might be connected to modification of the transcriptome of the hippocampus and may lead to an increased dendritic input.

Topological dissimilarities of hierarchical resting networks in type 2 diabetes mellitus and obesity.

Type 2 diabetes mellitus (T2DM) is reported to cause widespread changes in brain function, leading to cognitive impairments. Research using resting-state functional magnetic resonance imaging data already aims to understand functional changes in complex brain connectivity systems. However, no previous studies with dynamic causal modelling (DCM) tried to investigate large-scale effective connectivity in diabetes. We aimed to examine the differences in large-scale resting state networks in diabetic and obese patients using combined DCM and graph theory methodologies. With the participation of 70 subjects (43 diabetics, 27 obese), we used cross-spectra DCM to estimate connectivity between 36 regions, subdivided into seven resting networks (RSN) commonly recognized in the literature. We assessed group-wise connectivity of T2DM and obesity, as well as group differences, with parametric empirical Bayes and Bayesian model reduction techniques. We analyzed network connectivity globally, between RSNs, and regionally. We found that average connection strength was higher in T2DM globally and between RSNs, as well. On the network level, the salience network shows stronger total within-network connectivity in diabetes (8.07) than in the obese group (4.02). Regionally, we measured the most significant average decrease in the right middle temporal gyrus (-0.013 Hz) and the right inferior parietal lobule (-0.01 Hz) relative to the obese group. In comparison, connectivity increased most notably in the left anterior prefrontal cortex (0.01 Hz) and the medial dorsal thalamus (0.009 Hz). In conclusion, we find the usage of complex analysis of large-scale networks suitable for diabetes instead of focusing on specific changes in brain function.

CRISPR/Cas9-Based Mutagenesis of Histone H3.1 in Spinal Dynorphinergic Neurons Attenuates Thermal Sensitivity in Mice.

Burn injury is a trauma resulting in tissue degradation and severe pain, which is processed first by neuronal circuits in the spinal dorsal horn. We have recently shown that in mice, excitatory dynorphinergic (Pdyn) neurons play a pivotal role in the response to burn-injury-associated tissue damage via histone H3.1 phosphorylation-dependent signaling. As Pdyn neurons were mostly associated with mechanical allodynia, their involvement in thermonociception had to be further elucidated. Using a custom-made AAV9_mutH3.1 virus combined with the CRISPR/cas9 system, here we provide evidence that blocking histone H3.1 phosphorylation at position serine 10 (S10) in spinal Pdyn neurons significantly increases the thermal nociceptive threshold in mice. In contrast, neither mechanosensation nor acute chemonociception was affected by the transgenic manipulation of histone H3.1. These results suggest that blocking rapid epigenetic tagging of S10H3 in spinal Pdyn neurons alters acute thermosensation and thus explains the involvement of Pdyn cells in the immediate response to burn-injury-associated tissue damage.

An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset.

It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord). In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. Four research groups participated, submitting a total of 10 algorithms, demonstrating the benefits the dataset for the development of automatic algorithms.

Automated procedure assessing the accuracy of HRCT-PET registration applied in functional virtual bronchoscopy.

BACKGROUND: Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining anatomical and functional imaging. This communication describes an advanced functional virtual bronchoscopic (fVB) method based on the registration of PET images to high-resolution diagnostic CT images instead of low-dose CT images of lower resolution obtained from PET/CT scans. PET/CT and diagnostic CT data were collected from 22 oncological patients to develop a computer-aided high-precision fVB. Registration of segmented images was performed using elastix. RESULTS: For virtual bronchoscopy, we used an in-house developed segmentation method. The quality of low- and high-dose CT image registrations was characterised by expert's scoring the spatial distance of manually paired corresponding points and by eight voxel intensity-based (dis)similarity parameters. The distribution of (dis)similarity parameter correlating best with anatomic scoring was bootstrapped, and 95% confidence intervals were calculated separately for acceptable and insufficient registrations. We showed that mutual information (MI) of the eight investigated (dis)similarity parameters displayed the closest correlation with the anatomy-based distance metrics used to characterise the quality of image registrations. The 95% confidence intervals of the bootstrapped MI distribution were [0.15, 0.22] and [0.28, 0.37] for insufficient and acceptable registrations, respectively. In case of any new patient, a calculated MI value of registered low- and high-dose CT image pair within the [0.28, 0.37] or the [0.15, 0.22] interval would suggest acceptance or rejection, respectively, serving as an aid for the radiologist. CONCLUSION: A computer-aided solution was proposed in order to reduce reliance on radiologist's contribution for the approval of acceptable image registrations.

Characterizing Network Search Algorithms Developed for Dynamic Causal Modeling.

Dynamic causal modeling (DCM) is a widely used tool to estimate the effective connectivity of specified models of a brain network. Finding the model explaining measured data is one of the most important outstanding problems in Bayesian modeling. Using heuristic model search algorithms enables us to find an optimal model without having to define a model set a priori. However, the development of such methods is cumbersome in the case of large model-spaces. We aimed to utilize commonly used graph theoretical search algorithms for DCM to create a framework for characterizing them, and to investigate relevance of such methods for single-subject and group-level studies. Because of the enormous computational demand of DCM calculations, we separated the model estimation procedure from the search algorithm by providing a database containing the parameters of all models in a full model-space. For test data a publicly available fMRI dataset of 60 subjects was used. First, we reimplemented the deterministic bilinear DCM algorithm in the ReDCM R package, increasing computational speed during model estimation. Then, three network search algorithms have been adapted for DCM, and we demonstrated how modifications to these methods, based on DCM posterior parameter estimates, can enhance search performance. Comparison of the results are based on model evidence, structural similarities and the number of model estimations needed during search. An analytical approach using Bayesian model reduction (BMR) for efficient network discovery is already available for DCM. Comparing model search methods we found that topological algorithms often outperform analytical methods for single-subject analysis and achieve similar results for recovering common network properties of the winning model family, or set of models, obtained by multi-subject family-wise analysis. However, network search methods show their limitations in higher level statistical analysis of parametric empirical Bayes. Optimizing such linear modeling schemes the BMR methods are still considered the recommended approach. We envision the freely available database of estimated model-spaces to help further studies of the DCM model-space, and the ReDCM package to be a useful contribution for Bayesian inference within and beyond the field of neuroscience.

Effective connectivity differences in motor network during passive movement of paretic and non-paretic ankles in subacute stroke patients.

BACKGROUND: A better understanding of the neural changes associated with paresis in stroke patients could have important implications for therapeutic approaches. Dynamic Causal Modeling (DCM) for functional magnetic resonance imaging (fMRI) is commonly used for analyzing effective connectivity patterns of brain networks due to its significant property of modeling neural states behind fMRI signals. We applied this technique to analyze the differences between motor networks (MNW) activated by continuous passive movement (CPM) of paretic and non-paretic ankles in subacute stroke patients. This study aimed to identify CPM induced connectivity characteristics of the primary sensory area (S1) and the differences in extrinsic directed connections of the MNW and to explain the hemodynamic differences of brain regions of MNW. METHODS: For the network analysis, we used ten stroke patients' task fMRI data collected under CPMs of both ankles. Regions for the MNW, the primary motor cortex (M1), the premotor cortex (PM), the supplementary motor area (SMA) and the S1 were defined in a data-driven way, by independent component analysis. For the network analysis of both CPMs, we compared twelve models organized into two model-families, depending on the S1 connections and input stimulus modeling. Using DCM, we evaluated the extrinsic connectivity strengths and hemodynamic parameters of both stimulations of all patients. RESULTS: After a statistical comparison of the extrinsic connections and their modulations of the "best model", we concluded that three contralateral self-inhibitions (cM1, cS1 and cSMA), one contralateral inter-regional connection (cSMA→cM1), and one interhemispheric connection (cM1→iM1) were significantly different. Our research shows that hemodynamic parameters can be estimated with the Balloon model using DCM but the parameters do not change with stroke. CONCLUSIONS: Our results confirm that the DCM-based connectivity analyses combined with Bayesian model selection may be a useful technique for quantifying the alteration or differences in the characteristics of the motor network in subacute stage stroke patients and in determining the degree of MNW changes.

Stress and Dyadic Coping in Personal Projects of Couples - A Pattern-Oriented Analysis.

Relational accounts of goal striving have barely considered dyadic coping as an element of the process, nor has dyadic coping research utilized the unique advantages of the goal construct (e.g., in form of personal project assessment) so far. Therefore, the primary aim of the present study was to explore stress and dyadic coping experiences associated with the personal projects of partners in a close relationship. Moreover, we approached data analysis in a pattern-oriented way, instead of using variable-centered linear models. We used cross-sectional data from 270 married and cohabiting Hungarian heterosexual couples (mean age 40.1 ± 11.2 and 37.8 ± 10.9 years for male and female partners, respectively). Partners individually completed an adapted version of the Personal Project Assessment procedure. First, they named an important but stressful personal project. Respondents appraised their experiences with the chosen personal project along several predefined aspects. These included: (1) stress experiences; (2) dyadic coping, using the adapted Dyadic Coping Inventory; (3) positive emotions; and (4) sense of community. The Relationship Assessment Scale was also assessed. Cluster analysis of both partners' stress experiences, positive and negative dyadic coping strategies in their own personal projects revealed six relationship-level clusters. Cluster solutions represented typical variations of the stress and dyadic coping patterns of the couples, and could be arranged in a three- (lower, medium, and higher stress) by-two (positively vs. negatively balanced dyadic coping pattern) array. Further analyses indicated the general trend that couples with lower (vs. higher) stress together with more positively (vs. negatively) balanced dyadic coping may have experienced better functioning in projects (more positive emotions and higher sense of community) and higher relationship satisfaction. Results confirm that the partners' pursuit of their personal projects is embedded in their relationship, and their functioning in these projects may partly depend on dyadic coping with the stress that arises during the accomplishment of the project. By using a pattern-oriented approach to dyadic data, we were able to distill stress and coping patterns that capture the specific types of couples' relationships and indicate the non-linear and multidimensional nature of stress and dyadic coping processes.

Population-Level Correction of Systematic Motion Artifacts in fMRI in Patients with Ischemic Stroke.

BACKGROUND: The aim of this study was to reveal potential sources of systematic motion artifacts in stroke functional magnetic resonance imaging (fMRI) focusing on those causing stimulus-correlated motion on the individual-level and separate the motion effect on the fMRI signal changing from the activation-induced alteration at population level. METHODS: Eleven ischemic stroke patients were examined by fMRI. The fMRI paradigm was based on passive ankle movement on both the healthy and the paretic leg's side. Three individual-level motion correction strategies were compared and we introduced five measures to characterize each subjects' in-scanner relative head movement. After analyzing the correlation of motion parameters and the subjects' physiological scale scores, we selected a parameter to model the motion-related artifacts in the second-level analysis. RESULTS: At first (individual) level analysis, the noise-component correction-based CompCor method provided the highest -log10(p) value of cluster-level occurrence probability at 12.4/13.6 for healthy and paretic side stimulus, respectively, with a maximal z-value of 15/16.3. Including the motion parameter at second (group) level resulted in lower cluster occurrence values at 10.9/5.55 while retaining the maximal z-value. CONCLUSIONS: We proposed a postprocessing pipeline for ischemic stroke fMRI data that combine the CompCor correction at first level with the modeling of motion effect at second-level analysis by a parameter obtained from fMRI data. Our solution is applicable for any fMRI-based stroke rehabilitation study since it does not require any MRI-compatible motion capture system and is based on commonly used methods.

[The role of biological agents in the treatment of psoriatic arthritis, literature review]. / A biológiai terápia szerepe az arthritis psoriatica gyógykezelésében (irodalmi attekintés).

INTRODUCTION: The recognition of the key pathogenetic role of TNF-alpha in psoriatic arthritis has made it possible to introduce new drugs in the treatment. TNF-alpha inhibitors available in Hungary (infliximab, etanercept, adalimumab) are potential therapies for patients who have not adequately responded to traditional disease-modifying antirheumatic drugs. OBJECTIVE: The aim of the study was to present the epidemiology and progression of psoriatic arthritis in Hungary based on national and international data, to assess the target population for biological therapy and to analyze their effectiveness, reviewing the available literature of randomized controlled trials. METHODS: The prevalence of psoriatic arthritis in Hungary was estimated using international data. Characteristics of psoriatic arthritis population were studied using the database of a rheumatology ward. A systematic literature search was performed to identify each relevant trial. A synthesis and comparison of the results from the 5 identified trials was performed and the average effect of biological agents was calculated. Both the fixed and the random effect model were used for the data synthesis; the results were probed with Mantel-Haenzel test. RESULTS: The prevalence of psoriatic arthritis is about 10.000-20.000 in Hungary. Average disease-duration was 10 years in the sample (n = 189), the most frequent was polyarticular form (51%). Regarding functional status the mean HAQ score was 1.46, with an average progression of 0.05 points/year. The trial data confirmed that biological agents are superior to placebo in improving symptoms (achieving ACR20); risk difference between biological treatments and placebo is 47% (RD = 0.47, 95% CI: 0.42-0.53). The biological treatment of 2 patients improves the status of 1 patient (NNT = 2.1 95% CI 1.9-2.4). There is no significant difference in efficacy between the three biological agents. CONCLUSIONS: TNF-alpha inhibitors are effective treatments of psoriatic arthritis and are safe under strict medical control. The principles of indications, contraindications, administration and control have been worked out by the Rheumatology and Physical Medicine Board.

[The role of biologic agents in the therapy of ankylosing spondylitis]. / A biológiai szerek szerepe a spondylitis ankylopoetica gyógykezelésében.

Ankylosing spondylitis is a chronic, inflammatory rheumatic disease which etiology and pathogenesis are not yet fully understood. The inflammation involves the spine and also the peripheral joints might be affected in some cases resulting in a progressive ankylosis, restricted mobility, significant disability, loss of productivity and decreased quality of life. Gene technology based new drugs of the past decade, the biologic agents, offer an alternative opportunity for the treatment of ankylosing spondylitis in comparison with the previous drugs with doubtful efficiency. In Hungary infliximab and etanercept has been registered for ankylosing spondylitis. The aim of this study was to evaluate the efficacy of infliximab and etanercept by the available randomised controlled trials. A systematic search of the literature was performed from 01. 01. 2000 to 08. 31. 2005. and the relevant publications were analysed following the concepts of evidence based medicine. 7 double blind, randomised, placebo controlled trials were identified, three for infliximab (n = 389) and four for etanercept (n = 431). Although the inclusion criteria, the duration of the trials and the primary endpoints were different, the results confirm that both drugs significantly decrease symptoms and disease activity, and this effect is sustained during the therapy, nevertheless half of the patients did not achieve the standardised criteria of 50% decrease in disease activity. Both agents are well tolerated by patients. The outcomes of long-term therapy are reassuring by open extension studies of three years. Guidelines for biologic therapy has been developed in Hungary determining the target patient group, the conditions of the therapy and also an arthritis centre network has been established. Though individual admission is feasible, biologic drugs are not under reimbursement in Hungary. High drug costs makes the implementation of this new therapeutic opportunity difficult in the daily medical practice.

Mathematical generation of normal data for evaluating myocardial perfusion studies.

In this paper, we present a new mathematical method that synthesizes normal data sets for quantification of regional myocardium perfusion. In clinical practice, regional myocardial perfusion is often measured with a gamma camera and quantified via circumferential profile analysis. Normal reference profile data is used to increase the accuracy of the clinical interpretations. Our goal is to create reference data from an existing set of archived studies. An iterative mathematical method, based on two statistical hypotheses, was used to generate the study set instead of collecting normal examinations from a healthy population. Clinical validation is based on interpretations by six independent observers. Results of evaluation with synthesized normal data and its validation are presented.