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Alcohol acts as an addictive substance that may lead to alcohol use disorder. In humans, magnetic resonance imaging showed diverse structural and functional brain alterations associated with this complex pathology. Single magnetic resonance imaging modalities are used mostly but are insufficient to portray and understand the broad neuroadaptations to alcohol. Here, we combined structural and functional magnetic resonance imaging and connectome mapping in mice to establish brain-wide fingerprints of alcohol effects with translatable potential.Mice underwent a chronic intermittent alcohol drinking protocol for 6 weeks before being imaged under medetomidine anesthesia. We performed open-ended multivariate analysis of structural data and functional connectivity mapping on the same subjects.Structural analysis showed alcohol effects for the prefrontal cortex/anterior insula, hippocampus, and somatosensory cortex. Integration with microglia histology revealed distinct alcohol signatures, suggestive of advanced (prefrontal cortex/anterior insula, somatosensory cortex) and early (hippocampus) inflammation. Functional analysis showed major alterations of insula, ventral tegmental area, and retrosplenial cortex connectivity, impacting communication patterns for salience (insula), reward (ventral tegmental area), and default mode (retrosplenial cortex) networks. The insula appeared as a most sensitive brain center across structural and functional analyses.This study demonstrates alcohol effects in mice, which possibly underlie lower top-down control and impaired hedonic balance documented at the behavioral level, and aligns with neuroimaging findings in humans despite the potential limitation induced by medetomidine sedation. This study paves the way to identify further biomarkers and to probe neurobiological mechanisms of alcohol effects using genetic and pharmacological manipulations in mouse models of alcohol drinking and dependence.

Background Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients. Objective This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype’s utility in treatment recommendations. Methods We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital. Results We designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources–based format for the representation of clinical practice guidelines to provide an interoperable, standards-based guideline recommendation exchange format. Conclusions Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the system.

BackgroundClinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gap-less implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content, but also to recognize every situation in which they are applicable. To not miss situations in which guideline recommendations should be applied, computerized clinical decision support could be given through a system that allows an automated monitoring of adherence to clinical guideline recommendation in individual patients.Objectives(1) To derive the requirements for a system that allows to monitor the adherence to evidence-based clinical guideline recommendations in individual patients, and based on these requirements, (2) to implement a software prototype that integrates clinical guideline recommendations with individual patient data and (3) to demonstrate the prototype’s utility on a COVID-19 intensive care treatment recommendation.MethodsWe performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system for supporting recommendation adherence monitoring in a consensus-based requirements analysis within loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, software developers). Based on these requirements, we implemented a prototype and demonstrated its functionality by integrating clinical data with a treatment recommendation.ResultsBased on our conceptual flow chart model of recommendation adherence monitoring in clinical routine, we identified four main requirements of a software system for automated support of recommendation adherence monitoring of in-hospital patients: (i) Ability to interpret guideline recommendations’ semantics and logics, (ii) integration of clinical routine data from various underlying data structures, (iii) automatic adoption of new or updated guideline recommendations, and (iv) user interfaces optimized for distinct groups of users. Using a prototype implementation that fulfills these requirements, we demonstrate how such a system could be applied to monitor guideline recommendation adherence over time in clinical patients.ConclusionsThe four main requirements identified through our model-based analysis represent the most important aspects that need to be considered when developing a clinical decision support system for monitoring the adherence to evidence-based clinical guideline recommendations in individual patients. As each of the requirements corresponds to a different expertise (guideline development, health data engineering, software development, patient treatment), a modularized software architecture separated by area of required expertise seems favorable. Our prototype successfully demonstrates how such a modular architecture can be implemented to allow real-time monitoring of guideline recommendation adherence. This prototype, which we released as open source to invigorate collaboration, could serve as a basis for further development to integrate guideline recommendations with clinical information systems.

BACKGROUND Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients. OBJECTIVE This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype’s utility in treatment recommendations. METHODS We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital. RESULTS We designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources–based format for the representation of clinical practice guidelines to provide an interoperable, standards-based guideline recommendation exchange format. CONCLUSIONS Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the system.

In Alzheimer’s disease, the tauopathy is known as a major mechanism responsible for the development of cognitive deficits. Early biomarkers of such affectations for diagnosis/stratification are crucial in Alzheimer’s disease research, and brain connectome studies increasingly show their potential establishing pathology fingerprints at the network level. In this context, we conducted an in vivo multimodal MRI study on young Thy-Tau22 transgenic mice expressing tauopathy, performing resting state functional MRI and structural brain imaging to identify early connectome signatures of the pathology, relating with histological and behavioural investigations. In the prodromal phase of tauopathy, before the emergence of cognitive impairments, Thy-Tau22 mice displayed selective modifications of brain functional connectivity involving three main centres: hippocampus (HIP), amygdala (AMG) and the isocortical areas, notably the somatosensory (SS) cortex. Each of these regions showed differential histopathological profiles. Disrupted ventral HIP-AMG functional pathway and altered dynamic functional connectivity were consistent with high pathological tau deposition and astrogliosis in both hippocampus and amygdala, and significant microglial reactivity in amygdalar nuclei. These patterns were concurrent with widespread functional hyperconnectivity of memory-related circuits of dorsal hippocampus—encompassing dorsal HIP-SS communication—in the absence of significant cortical histopathological markers. These findings suggest the coexistence of two intermingled mechanisms of response at the functional connectome level in the early phases of pathology: a maladaptive and a likely compensatory response. Captured in the connectivity patterns, such first responses to pathology could further be used in translational investigations as a lead towards an early biomarker of tauopathy as well as new targets for future treatments.

Contains fulltext : 219632.pdf (Publisher’s version ) (Open Access) Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories worldwide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. RSN spatial specificity was enhanced in datasets acquired at higher field strength, with cryoprobes, in ventilated animals, and under medetomidine-isoflurane combination sedation. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

Connectomics of brain disorders seeks to reveal how altered brain function emerges from the architecture of cerebral networks; however the causal impact of targeted cellular damage on the whole brain functional and structural connectivity remains unknown. In the central nervous system, demyelination is typically the consequence of an insult targeted at the oligodendrocytes, the cells forming and maintaining the myelin. This triggered perturbation generates cascades of pathological events that most likely alter the brain connectome. Here we induced oligodendrocyte death and subsequent demyelinating pathology via cuprizone treatment in mice and combining mouse brain resting state functional Magnetic Resonance Imaging and diffusion tractography we established functional and structural pathology-to-network signatures. We demonstrated that demyelinated brain fundamentally reorganizes its intrinsic functional connectivity paralleled by widespread damage of the structural scaffolding. We evidenced default mode-like network as core target of demyelination-induced connectivity modulations and hippocampus as the area with strongest connectional perturbations.

Introduction A noninvasive imaging strategy for diagnosis and localization of early myocarditis would be of great clinical interest. However, resolution of current imaging techniques is limited. Platelets play an important role in inflammatory processes but the role in myocarditis is unknown. Therefore, the aim of this project was to examine the role of platelets in myocarditis and establish a sensitive non-invasive molecular MRI in-vivo imaging strategy for diagnosis of myocarditis with a contrast agent against activated platelets in mice. Methods Myocarditis was induced by subcutaneous injection of an emulsion of porcine cardiac myosin and complete freud's adjuvant (CFA) in Balb/c mice. Inflammatory activity was targeted with a contrast agent against activated platelets consisting of microparticles of iron oxide (MPIO) conjugated to a single chain antibody directed against ligand-induced binding sites (LIBS) on activated glycoprotein IIb/IIIa (=LIBS-MPIO). In comparison, we applied an unspecific control antibody linked to microparticles of iron oxide (control-MPIO) and injected LIBS-MPIO to mice subjected to incomplete freud's adjuvant (iCFA). All imaging results were correlated to immunohistochemistry findings. Results Histological evaluation showed significantly higher binding of LIBS-MPIOs to platelet enriched, CD41-positive inflamed myocardium two days after induction of myocarditis in comparison to later time points (7d, 14d, 21d) (p As a perspective, we also found CD41 positive areas in histology of human myocarditis specimens. Conclusions Platelets are involved in the inflammation of myocarditis. Molecular MRI with LIBS-MPIO can image them at an early time point. This noninvasive imaging strategy is of clinical interest for both diagnostic and prognostic purposes, and highlights the potential of molecular MRI for characterization of cardiovascular pathologies such as myocardial inflammation.

Mapping the structural and functional brain connectivity fingerprints became an essential approach in neurology and experimental neuroscience because network properties can underlie behavioral phenotypes. In mouse models, revealing strain related patterns of brain wiring have a tremendous importance, since these animals are used to answer questions related to neurological or neuropsychiatric disorders. C57BL/6 and BALB/cJ inbred strains are primary “genetic backgrounds” for brain disease modelling and for testing therapeutic approaches. Nevertheless, extensive literature describes basal differences in the behavioral, neuroanatomical and neurochemical profiles of the two strains, which raises the question whether the observed effects are pathology specific or depend on the genetic background. Here we performed a systematic comparative exploration of brain structure and function of C57BL/6 and BALB/cJ mice via Magnetic Resonance Imaging (MRI). We combined voxel-based morphometry (VBM), diffusion MRI and high resolution fiber mapping (hrFM) and resting state functional MRI (rs-fMRI) and depicted brain-wide dissimilarities in the morphology and “connectome” features in the two strains. Particularly C57BL/6 animals show bigger and denser frontal cortical areas, cortico-striatal tracts and thalamic and midbrain pathways, and higher density of fibers in the genu and splenium of the corpus callosum. These features are fairly reflected in the functional connectograms that emphasize differences in “hubness”, frontal cortical and basal forbrain connectivity. We demonstrate strongly divergent reward-aversion circuitry patterns and some variations of the default mode network features. Inter-hemispherical functional connectivity showed flexibility and adjustment regarding the structural patterns in a strain specific manner. We further provide high-resolution tractograms illustrating also inter-individual variability across inter-hemispherical callosal pathways in the BALB/cJ strain.

In a longitudinal mouse study we evaluated whether diffusion tensor imaging (DTI) can monitor microstructural changes after administration of the neuromodulating drug EPO and whether erythropoietin (EPO) has an effect on cognitive performance. Twelve mice (2 groups with 6 mice each) were scanned in a 7T Bruker Biospin animal scanner with a highly resolved DTI sequence before and 16 days after intraperitoneal injections of EPO or saline. All mice underwent behavioral testing (Morris water maze) and histologic evaluation of hippocampal and corpus callosum cell proliferation and oligodendrogenesis. Whole brain DTI analysis showed significant Trace, RD and AD decrease within the dentate gyrus, subiculum, primary motor, somatosensory, and supplementary somatosensory areas and FA increase in the hippocampus, corpus callosum, and fimbria fornix in EPO treated mice only. ROI-based DTI analysis showed significant Trace and RD decrease and FA increase only in the corpus callosum of EPO treated mice, whereas in the dentate gyrus significant Trace, RD, and AD decrease occurred in both, EPO- and control-group. Behavioral tests showed that EPO treated mice performed better and learned faster than controls. Histologically, the number of BrdU-positive nuclei and optical density of DCX-labeled juvenile neurons significantly increased within the dentate gyrus, corpus callosum and fimbria fornix and the number of NG2-positive oligodendrocyte progenitors in corpus callosum and fimbria fornix, respectively. In conclusion we were able to monitor microstructural changes with DTI and showed EPO treatment-related alterations correlating with enhanced dentate gyrus and corpus callosum cell proliferation and better learning capabilities.

Recent studies have demonstrated that orchestrated gene activity and expression support synchronous activity of brain networks. However, there is a paucity of information on the consequences of single gene function on overall brain functional organization and connectivity and how this translates at the behavioral level. In this study, we combined mouse mutagenesis with functional and structural magnetic resonance imaging (MRI) to determine whether targeted inactivation of a single gene would modify whole-brain connectivity in live animals. The targeted gene encodes GPR88 (G protein-coupled receptor 88), an orphan G protein-coupled receptor enriched in the striatum and previously linked to behavioral traits relevant to neuropsychiatric disorders. Connectivity analysis of Gpr88-deficient mice revealed extensive remodeling of intracortical and cortico-subcortical networks. Most prominent modifications were observed at the level of retrosplenial cortex connectivity, central to the default mode network (DMN) whose alteration is considered a hallmark of many psychiatric conditions. Next, somatosensory and motor cortical networks were most affected. These modifications directly relate to sensorimotor gating deficiency reported in mutant animals and also likely underlie their hyperactivity phenotype. Finally, we identified alterations within hippocampal and dorsal striatum functional connectivity, most relevant to a specific learning deficit that we previously reported in Gpr88-/- animals. In addition, amygdala connectivity with cortex and striatum was weakened, perhaps underlying the risk-taking behavior of these animals. This is the first evidence demonstrating that GPR88 activity shapes the mouse brain functional and structural connectome. The concordance between connectivity alterations and behavior deficits observed in Gpr88-deficient mice suggests a role for GPR88 in brain communication.