Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia.

Introduction: We examined changes in large-scale functional connectivity and temporal dynamics and their underlying mechanisms in schizophrenia (ScZ) through measurements of resting-state functional magnetic resonance imaging (rs-fMRI) data and computational modelling. Methods: The rs-fMRI measurements from patients with chronic ScZ (n=38) and matched healthy controls (n=43), were obtained through the public schizConnect repository. Computational models were constructed based on diffusion-weighted MRI scans and fit to the experimental rs-fMRI data. Results: We found decreased large-scale functional connectivity across sensory and association areas and for all functional subnetworks for the ScZ group. Additionally global synchrony was reduced in patients while metastability was unaltered. Perturbations of the computational model revealed that decreased global coupling and increased background noise levels both explained the experimentally found deficits better than local changes to the GABAergic or glutamatergic system. Discussion: The current study suggests that large-scale alterations in ScZ are more likely the result of global rather than local network changes.

Attention Mechanisms in Clinical Text Classification: A Comparative Evaluation.

Attention mechanisms are now a mainstay architecture in neural networks and improve the performance of biomedical text classification tasks. In particular, models that perform automated medical encoding of clinical documents make extensive use of the label-wise attention mechanism. A label-wise attention mechanism increases a model's discriminatory ability by using label-specific reference information. This information can either be implicitly learned during training or explicitly provided through embedded textual code descriptions or information on the code hierarchy; however, contemporary studies arbitrarily select the type of label-specific reference information. To address this shortcoming, we evaluated label-wise attention initialized with either implicit or explicit label-specific reference information against two common baseline methods-target-attention and text-encoder architecture-specific methods-to generate document embeddings across four text-encoder architectures-a convolutional neural network, two recurrent neural networks, and a transformer. We also present an extension of label-wise attention that can embed the information on the code hierarchy. We performed our experiments on the MIMIC III dataset, which is a standard dataset in the clinical text classification domain. Our experiments showed that using pretrained reference information and the hierarchical design helped improve classification performance. These performance improvements had less impact on larger datasets and label spaces across all text-encoder architectures. In our analysis, we used an attention mechanism's energy scores to explain the perceived differences in performance and interpretability between the text-encoder architectures and types of label-attention.

Extracellular Vesicles and Their Membranes: Exosomes vs. Virus-Related Particles.

Cells produce nanosized lipid membrane-enclosed vesicles which play important roles in intercellular communication. Interestingly, a certain type of extracellular vesicle, termed exosomes, share physical, chemical, and biological properties with enveloped virus particles. To date, most similarities have been discovered with lentiviral particles, however, other virus species also frequently interact with exosomes. In this review, we will take a closer look at the similarities and differences between exosomes and enveloped viral particles, with a focus on events taking place at the vesicle or virus membrane. Since these structures present an area with an opportunity for interaction with target cells, this is relevant for basic biology as well as any potential research or medical applications.

40-Hz Auditory Steady-State Responses in Schizophrenia: Toward a Mechanistic Biomarker for Circuit Dysfunctions and Early Detection and Diagnosis.

There is converging evidence that 40-Hz auditory steady-state responses (ASSRs) are robustly impaired in schizophrenia and could constitute a potential biomarker for characterizing circuit dysfunctions as well as enable early detection and diagnosis. Here, we provide an overview of the mechanisms involved in 40-Hz ASSRs, drawing on computational, physiological, and pharmacological data with a focus on parameters modulating the balance between excitation and inhibition. We will then summarize findings from electro- and magnetoencephalographic studies in participants at clinical high risk for psychosis, patients with first-episode psychosis, and patients with schizophrenia to identify the pattern of deficits across illness stages, the relationship with clinical variables, and the prognostic potential. Finally, data on genetics and developmental modifications will be reviewed, highlighting the importance of late modifications of 40-Hz ASSRs during adolescence, which are closely related to the underlying changes in GABA (gamma-aminobutyric acid) interneurons. Together, our review suggests that 40-Hz ASSRs may constitute an informative electrophysiological approach to characterize circuit dysfunctions in psychosis that could be relevant for the development of mechanistic biomarkers.

The effect of alterations of schizophrenia-associated genes on gamma band oscillations.

Abnormalities in the synchronized oscillatory activity of neurons in general and, specifically in the gamma band, might play a crucial role in the pathophysiology of schizophrenia. While these changes in oscillatory activity have traditionally been linked to alterations at the synaptic level, we demonstrate here, using computational modeling, that common genetic variants of ion channels can contribute strongly to this effect. Our model of primary auditory cortex highlights multiple schizophrenia-associated genetic variants that reduce gamma power in an auditory steady-state response task. Furthermore, we show that combinations of several of these schizophrenia-associated variants can produce similar effects as the more traditionally considered synaptic changes. Overall, our study provides a mechanistic link between schizophrenia-associated common genetic variants, as identified by genome-wide association studies, and one of the most robust neurophysiological endophenotypes of schizophrenia.

Synchronization Through Uncorrelated Noise in Excitatory-Inhibitory Networks.

Gamma rhythms play a major role in many different processes in the brain, such as attention, working memory, and sensory processing. While typically considered detrimental, counterintuitively noise can sometimes have beneficial effects on communication and information transfer. Recently, Meng and Riecke showed that synchronization of interacting networks of inhibitory neurons in the gamma band (i.e., gamma generated through an ING mechanism) increases while synchronization within these networks decreases when neurons are subject to uncorrelated noise. However, experimental and modeling studies point towardz an important role of the pyramidal-interneuronal network gamma (PING) mechanism in the cortex. Therefore, we investigated the effect of uncorrelated noise on the communication between excitatory-inhibitory networks producing gamma oscillations via a PING mechanism. Our results suggest that, at least in a certain range of noise strengths and natural frequency differences between the regions, synaptic noise can have a supporting role in facilitating inter-regional communication, similar to the ING case for a slightly larger parameter range. Furthermore, the noise-induced synchronization between networks is generated via a different mechanism than when synchronization is mediated by strong synaptic coupling. Noise-induced synchronization is achieved by lowering synchronization within networks which allows the respective other network to impose its own gamma rhythm resulting in synchronization between networks.

The beta component of gamma-band auditory steady-state responses in patients with schizophrenia.

The mechanisms underlying circuit dysfunctions in schizophrenia (SCZ) remain poorly understood. Auditory steady-state responses (ASSRs), especially in the gamma and beta band, have been suggested as a potential biomarker for SCZ. While the reduction of 40 Hz power for 40 Hz drive has been well established and replicated in SCZ patients, studies are inconclusive when it comes to an increase in 20 Hz power during 40 Hz drive. There might be several factors explaining the inconsistencies, including differences in the sensitivity of the recording modality (EEG vs MEG), differences in stimuli (click-trains vs amplitude-modulated tones) and large differences in the amplitude of the stimuli. Here, we used a computational model of ASSR deficits in SCZ and explored the effect of three SCZ-associated microcircuit alterations: reduced GABA activity, increased GABA decay times and NMDA receptor hypofunction. We investigated the effect of input strength on gamma (40 Hz) and beta (20 Hz) band power during gamma ASSR stimulation and saw that the pronounced increase in beta power during gamma stimulation seen experimentally could only be reproduced in the model when GABA decay times were increased and only for a specific range of input strengths. More specifically, when the input was in this specific range, the rhythmic drive at 40 Hz produced a strong 40 Hz rhythm in the control network; however, in the 'SCZ-like' network, the prolonged inhibition led to a so-called 'beat-skipping', where the network would only strongly respond to every other input. This mechanism was responsible for the emergence of the pronounced 20 Hz beta peak in the power spectrum. The other two microcircuit alterations were not able to produce a substantial 20 Hz component but they further narrowed the input strength range for which the network produced a beta component when combined with increased GABAergic decay times. Our finding that the beta component only existed for a specific range of input strengths might explain the seemingly inconsistent reporting in experimental studies and suggests that future ASSR studies should systematically explore different amplitudes of their stimuli. Furthermore, we provide a mechanistic link between a microcircuit alteration and an electrophysiological marker in schizophrenia and argue that more complex ASSR stimuli are needed to disentangle the nonlinear interactions of microcircuit alterations. The computational modelling approach put forward here is ideally suited to facilitate the development of such stimuli in a theory-based fashion.

On the Relationship of Viral Particles and Extracellular Vesicles: Implications for Viral Vector Technology.

Gene therapy vectors derived from different viral species have become a fixture in biomedicine, both for direct therapeutic intervention and as tools to facilitate cell-based therapies, such as chimeric antigen receptor-based immunotherapies. On the contrary, extracellular vesicles have only recently gained a massive increase in interest and, concomitantly, knowledge in the field has drastically risen. Viral infections and extracellular vesicle biology overlap in many ways, both with pro- and antiviral outcomes. In this review, we take a closer look at these interactions for the most prominent groups of viral vectors (Adenoviral, Adeno-associated and Retro/Lentiviral vectors) and the possible implications of these overlaps for viral vector technology and its biomedical applications.

Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective.

The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are "easily" reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

Surface Modification of E. coli Outer Membrane Vesicles with Glycosylphosphatidylinositol-Anchored Proteins: Generating Pro/Eukaryote Chimera Constructs.

Extracellular vesicles produced by different types of cells have recently attracted great attention, not only for their role in physiology and pathology, but also because of the emerging applications in gene therapy, vaccine production and diagnostics. Less well known than their eukaryotic counterpart, also bacteria produce extracellular vesicles, in the case of the Gram-negative E. coli the main species is termed outer membrane vesicles (OMVs). In this study, we show for the first time the functional surface modification of E. coli OMVs with glycosylphosphatidylinositol (GPI)-anchored protein, exploiting a process variably described as molecular painting or protein engineering in eukaryotic membranes, whereby the lipid part of the GPI anchor inserts in cell membranes. By transferring the process to bacterial vesicles, we can generate a hybrid of perfectly eukaryotic proteins (in terms of folding and post-translational modifications) on a prokaryotic platform. We could demonstrate that two different GPI proteins can be displayed on the same OMV. In addition to fluorescent marker proteins, cytokines, growth factors and antigens canb be potentially transferred, generating a versatile modular platform for a novel vaccine strategy.

Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions.

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.

The Role of Parvalbumin-positive Interneurons in Auditory Steady-State Response Deficits in Schizophrenia.

Despite an increasing body of evidence demonstrating subcellular alterations in parvalbumin-positive (PV+) interneurons in schizophrenia, their functional consequences remain elusive. Since PV+ interneurons are involved in the generation of fast cortical rhythms, these changes have been hypothesized to contribute to well-established alterations of beta and gamma range oscillations in patients suffering from schizophrenia. However, the precise role of these alterations and the role of different subtypes of PV+ interneurons is still unclear. Here we used a computational model of auditory steady-state response (ASSR) deficits in schizophrenia. We investigated the differential effects of decelerated synaptic dynamics, caused by subcellular alterations at two subtypes of PV+ interneurons: basket cells and chandelier cells. Our simulations suggest that subcellular alterations at basket cell synapses rather than chandelier cell synapses are the main contributor to these deficits. Particularly, basket cells might serve as target for innovative therapeutic interventions aiming at reversing the oscillatory deficits.

Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders.

The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the discovery of disease mechanisms that would link genetic findings to clinical symptoms and behavior. This applies also to schizophrenia, for which genome-wide association studies have identified a large number of genetic risk loci, spanning hundreds of genes with diverse functionalities. Importantly, the multitude of the associated variants and their prevalence in the healthy population limit the potential of a reductionist functional genetics approach as a stand-alone solution to discover the disease pathology. In this review, we outline the key concepts of a "biophysical psychiatry," an approach that employs large-scale mechanistic, biophysics-founded computational modelling to increase transdisciplinary understanding of the pathophysiology and strive toward robust predictions. We discuss recent scientific advances that allow a synthesis of previously disparate fields of psychiatry, neurophysiology, functional genomics, and computational modelling to tackle open questions regarding the pathophysiology of heritable mental disorders. We argue that the complexity of the increasing amount of genetic data exceeds the capabilities of classical experimental assays and requires computational approaches. Biophysical psychiatry, based on modelling diseased brain networks using existing and future knowledge of basic genetic, biochemical, and functional properties on a single neuron to a microcircuit level, may allow a leap forward in deriving interpretable biomarkers and move the field toward novel treatment options.

Alterations in Schizophrenia-Associated Genes Can Lead to Increased Power in Delta Oscillations.

Genome-wide association studies have implicated many ion channels in schizophrenia pathophysiology. Although the functions of these channels are relatively well characterized by single-cell studies, the contributions of common variation in these channels to neurophysiological biomarkers and symptoms of schizophrenia remain elusive. Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta-frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a deficit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia.

Real-time analysis of protein and protein mixture interaction with lipid bilayers.

Artificial lipid bilayers in the form of planar supported or vesicular bilayers are commonly used as models for studying interaction of biological membranes with different substances such as proteins and small molecule pharmaceutical compounds. Lipid membranes are typically regarded as inert and passive scaffolds for membrane proteins, but both non-specific and specific interactions between biomolecules and lipid membranes are indeed ubiquitous; dynamic exchange of proteins from the environment at the membrane interface can strongly influence the function of biological membranes. Such exchanges would either be of a superficial (peripheral) or integrative (penetrating) nature. In the context of viral membranes (termed envelopes), this could contribute to the emergence of zoonotic infections as well as change the virulence and/or pathogenicity of viral diseases. In this study, we analyze adsorption/desorption patterns upon challenging tethered liposomes and enveloped virus particles with proteins - or protein mixtures - such as bovine serum albumin, glycosylphosphatidylinositol anchored proteins and serum, chosen for their different lipid-interaction capabilities. We employed quartz crystal microbalance and dual polarization interferometry measurements to measure protein/membrane interaction in real time. We identified differences in mass uptake between the challenges, as well as differences between variants of lipid bilayers. Tethered viral particles showed a similar adsorption/desorption behavior to liposomes, underlining their value as model system. We believe that this methodology may be developed into a new approach in virology and membrane research by enabling the combination of biophysical and biochemical information.

A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells.

BACKGROUND: Recent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level. NEW METHOD: In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. RESULT: We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. COMPARISON WITH EXISTING METHODS: Our approach combines features from several previously applied model-fitting strategies. The reduced-morphology neuron model obtained using our approach reliably reproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphology model. CONCLUSIONS: The network models produced using our method are cost-efficient and predict that interconnected L5PCs are able to amplify delta-range oscillatory inputs across a large range of network sizes and topologies, largely due to the medium after hyperpolarization mediated by the Ca2+-activated SK current.

Integrated Method for Purification and Single-Particle Characterization of Lentiviral Vector Systems by Size Exclusion Chromatography and Tunable Resistive Pulse Sensing.

Elements derived from lentiviral particles such as viral vectors or virus-like particles are commonly used for biotechnological and biomedical applications, for example in mammalian protein expression, gene delivery or therapy, and vaccine development. Preparations of high purity are necessary in most cases, especially for clinical applications. For purification, a wide range of methods are available, from density gradient centrifugation to affinity chromatography. In this study we have employed size exclusion columns specifically designed for the easy purification of extracellular vesicles including exosomes. In addition to viral marker protein and total protein analysis, a well-established single-particle characterization technology, termed tunable resistive pulse sensing, was employed to analyze fractions of highest particle load and purity and characterize the preparations by size and surface charge/electrophoretic mobility. With this study, we propose an integrated platform combining size exclusion chromatography and tunable resistive pulse sensing for monitoring production and purification of viral particles.

Sustainable computational science: the ReScience initiative.

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

Multifactorial Modeling of Impairment of Evoked Gamma Range Oscillations in Schizophrenia.

Despite a significant increase in efforts to identify biomarkers and endophenotypic measures of psychiatric illnesses, only a very limited amount of computational models of these markers and measures has been implemented so far. Moreover, existing computational models dealing with biomarkers typically only examine one possible mechanism in isolation, disregarding the possibility that other combinations of model parameters might produce the same network behavior (what has been termed "multifactoriality"). In this study we describe a step toward a computational instantiation of an endophenotypic finding for schizophrenia, namely the impairment of evoked auditory gamma and beta oscillations in schizophrenia. We explore the multifactorial nature of this impairment using an established model of primary auditory cortex, by performing an extensive search of the parameter space. We find that single network parameters contain only little information about whether the network will show impaired gamma entrainment and that different regions in the parameter space yield similar network level oscillation abnormalities. These regions in the parameter space, however, show strong differences in the underlying network dynamics. To sum up, we present a first step toward an in silico instantiation of an important biomarker of schizophrenia, which has great potential for the identification and study of disease mechanisms and for understanding of existing treatments and development of novel ones.