Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms.

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.

Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion.

Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples prior to the neuropathological analysis. CF samples showed no significant spectral deviations, appearance, or disappearance of peaks, but an intensity reduction during freezing and subsequent recovery during the thawing process. In contrast, FF induces sustained spectral alterations depending on molecular composition, albeit with good signal-to-noise ratio preservation. These observations are also reflected in the varying dual-class classifier performance, initially trained on native, unfixed samples: The Matthews correlation coefficient is 81.0% for CF and 58.6% for FF meningioma and dura mater. Training on spectral differences between original FF and pure formalin spectra substantially improves FF samples' classifier performance (74.2%). CF is suitable for training global multiclass classifiers due to its consistent spectrum shape despite intensity reduction. FF introduces changes in peak relationships while preserving the signal-to-noise ratio, making it more suitable for dual-class classification, such as distinguishing between healthy and malignant tissues. Pure formalin spectrum subtraction represents a possible method for mathematical elimination of the FF influence. These findings enable retrospective analysis of processed samples, enhancing pathological work and expanding machine learning techniques.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization.

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.

Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden.

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social measures will shape infections and hospitalizations. Hence, we extend the Susceptible-Exposed-Infectious-Removed (SEIR) model including these elements. We calibrate it to data of Luxembourg, Austria and Sweden until 15 December 2020. Sweden results having the highest fraction of undetected, Luxembourg of infected and all three being far from herd immunity in December. We quantify the level of social interaction, showing that a level around 1/3 of before the pandemic was still required in December to keep the effective reproduction number Refft below 1, for all three countries. Aiming to vaccinate the whole population within 1 year at constant rate would require on average 1,700 fully vaccinated people/day in Luxembourg, 24,000 in Austria and 28,000 in Sweden, and could lead to herd immunity only by mid summer. Herd immunity might not be reached in 2021 if too slow vaccines rollout speeds are employed. The model thus estimates which vaccination rates are too low to allow reaching herd immunity in 2021, depending on social interactions. Vaccination will considerably, but not immediately, help to curb the infection; thus limiting social interactions remains crucial for the months to come.

FastField: An open-source toolbox for efficient approximation of deep brain stimulation electric fields.

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes of tissue activated are key for efficient parameter tuning and network analysis. Currently, we lack efficient and flexible software implementations supporting complex electrode geometries and stimulation settings. Available tools are either too slow (e.g. finite element method-FEM), or too simple, with limited applicability to basic use-cases. This paper introduces FastField, an efficient open-source toolbox for DBS electric field and VTA approximations. It computes scalable electric field approximations based on the principle of superposition, and VTA activation models from pulse width and axon diameter. In benchmarks and case studies, FastField is solved in about 0.2 s,  ~ 1000 times faster than using FEM. Moreover, it is almost as accurate as using FEM: average Dice overlap of 92%, which is around typical noise levels found in clinical data. Hence, FastField has the potential to foster efficient optimization studies and to support clinical applications.

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

Adult spinal V2a interneurons show increased excitability and serotonin-dependent bistability.

In mice, most studies of the organization of the spinal central pattern generator (CPG) for locomotion, and its component neuron classes, have been performed on neonatal [postnatal day (P)2-P4] animals. While the neonatal spinal cord can generate a basic locomotor pattern, it is often argued that the CPG network is in an immature form whose detailed properties mature with postnatal development. Here, we compare intrinsic properties and serotonergic modulation of the V2a class of excitatory spinal interneurons in behaviorally mature (older than P43) mice to those in neonatal mice. Using perforated patch recordings from genetically tagged V2a interneurons, we revealed an age-dependent increase in excitability. The input resistance increased, the rheobase values decreased, and the relation between injected current and firing frequency (F/I plot) showed higher excitability in the adult neurons, with almost all neurons firing tonically during a current step. The adult action potential (AP) properties became narrower and taller, and the AP threshold hyperpolarized. While in neonates the AP afterhyperpolarization was monophasic, most adult V2a interneurons showed a biphasic afterhyperpolarization. Serotonin increased excitability and depolarized most neonatal and adult V2a interneurons. However, in ∼30% of adult V2a interneurons, serotonin additionally elicited spontaneous intrinsic membrane potential bistability, resulting in alternations between hyperpolarized and depolarized states with a dramatically decreased membrane input resistance and facilitation of evoked plateau potentials. This was never seen in younger animals. Our findings indicate a significant postnatal development of the properties of locomotor-related V2a interneurons, which could alter their interpretation of synaptic inputs in the locomotor CPG.

Susceptibility-Weighted MRI for Deep Brain Stimulation: Potentials in Trajectory Planning.

BACKGROUND: Deep brain stimulation (DBS) trajectory planning is mostly based on standard 3-D T1-weighted gadolinium-enhanced MRI sequences (T1-Gd). Susceptibility-weighted MRI sequences (SWI) show neurovascular structures without the use of contrast agents. The aim of this study was to investigate whether SWI might be useful in DBS trajectory planning. METHODS: We performed bilateral DBS planning using conventional T1-Gd images of 10 patients with different kinds of movement disorders. Afterwards, we matched SWI sequences and compared the visibility of vascular structures in both imaging modalities. RESULTS: By analyzing 100 possible trajectories, we found a potential vascular conflict in 13 trajectories based on T1-Gd in contrast to 53 in SWI. Remarkably, all vessels visible in T1-Gd were also depicted in SWI, whereas SWI showed many additional vascular structures which could not be identified in T1-Gd. CONCLUSION/DISCUSSION: The sensitivity for detecting neurovascular structures for DBS planning seems to be significantly higher in SWI. As SWI does not require a contrast agent, we suggest that SWI may be a valuable alternative to T1-Gd MRI for DBS trajectory planning. Furthermore, the data analysis suggests that vascular interactions of DBS trajectories might be more frequent than expected from the very low incidence of symptomatic bleedings. The explanation for this is currently the subject of debate and merits further studies.

Towards automatic home-based sleep apnea estimation using deep learning.

Apnea and hypopnea are common sleep disorders characterized by the obstruction of the airways. Polysomnography (PSG) is a sleep study typically used to compute the Apnea-Hypopnea Index (AHI), the number of times a person has apnea or certain types of hypopnea per hour of sleep, and diagnose the severity of the sleep disorder. Early detection and treatment of apnea can significantly reduce morbidity and mortality. However, long-term PSG monitoring is unfeasible as it is costly and uncomfortable for patients. To address these issues, we propose a method, named DRIVEN, to estimate AHI at home from wearable devices and detect when apnea, hypopnea, and periods of wakefulness occur throughout the night. The method can therefore assist physicians in diagnosing the severity of apneas. Patients can wear a single sensor or a combination of sensors that can be easily measured at home: abdominal movement, thoracic movement, or pulse oximetry. For example, using only two sensors, DRIVEN correctly classifies 72.4% of all test patients into one of the four AHI classes, with 99.3% either correctly classified or placed one class away from the true one. This is a reasonable trade-off between the model's performance and the patient's comfort. We use publicly available data from three large sleep studies with a total of 14,370 recordings. DRIVEN consists of a combination of deep convolutional neural networks and a light-gradient-boost machine for classification. It can be implemented for automatic estimation of AHI in unsupervised long-term home monitoring systems, reducing costs to healthcare systems and improving patient care.

Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy.

Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue.

Spinal cord injury induces serotonin supersensitivity without increasing intrinsic excitability of mouse V2a interneurons.

Denervation-induced plastic changes impair locomotor recovery after spinal cord injury (SCI). Spinal motoneurons become hyperexcitable after SCI, but the plastic responses of locomotor network interneurons (INs) after SCI have not been studied. Using an adult mouse SCI model, we analyzed the effects of complete spinal cord lesions on the intrinsic electrophysiological properties, excitability, and neuromodulatory responses to serotonin (5-HT) in mouse lumbar V2a spinal INs, which help regulate left-right alternation during locomotion. Four weeks after SCI, V2a INs showed almost no changes in baseline excitability or action potential properties; the only parameter that changed was a reduced input resistance. However, V2a INs became 100- to 1000-fold more sensitive to 5-HT. Immunocytochemical analysis showed that SCI caused a coordinated loss of serotonergic fibers and the 5-HT transporter (SERT). Blocking the SERT with citalopram in intact mice did not increase 5-HT sensitivity to the level seen after SCI. SCI also evoked an increase in 5-HT(2C) receptor cluster number and intensity, suggesting that several plastic changes cooperate in increasing 5-HT sensitivity. Our results suggest that different components of the spinal neuronal network responsible for coordinating locomotion are differentially affected by SCI, and highlight the importance of understanding these changes when considering therapies targeted at functional recovery.

PDK1 deficiency in POMC-expressing cells reveals FOXO1-dependent and -independent pathways in control of energy homeostasis and stress response.

Insulin- and leptin-stimulated phosphatidylinositol-3 kinase (PI3K) activation has been demonstrated to play a critical role in central control of energy homeostasis. To delineate the importance of pathways downstream of PI3K specifically in pro-opiomelanocortin (POMC) cell regulation, we have generated mice with selective inactivation of 3-phosphoinositide-dependent protein kinase 1 (PDK1) in POMC-expressing cells (PDK1(DeltaPOMC) mice). PDK1(DeltaPOMC) mice initially display hyperphagia, increased body weight, and impaired glucose metabolism caused by reduced hypothalamic POMC expression. On the other hand, PDK1(DeltaPOMC) mice exhibit progressive, severe hypocortisolism caused by loss of POMC-expressing corticotrophs in the pituitary. Expression of a dominant-negative mutant of FOXO1 specifically in POMC cells is sufficient to ameliorate positive energy balance in PDK1(DeltaPOMC) mice but cannot restore regular pituitary function. These results reveal important but differential roles for PDK1 signaling in hypothalamic and pituitary POMC cells in the control of energy homeostasis and stress response.

Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions.

Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.

Generalising from conventional pipelines using deep learning in high-throughput screening workflows.

The study of complex diseases relies on large amounts of data to build models toward precision medicine. Such data acquisition is feasible in the context of high-throughput screening, in which the quality of the results relies on the accuracy of the image analysis. Although state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manually generating ground truth labels for model training hampers the day-to-day application in experimental laboratories. Alternatively, traditional computer vision-based solutions do not need expensive labels for their implementation. Our work combines both approaches by training a deep learning network using weak training labels automatically generated with conventional computer vision methods. Our network surpasses the conventional segmentation quality by generalising beyond noisy labels, providing a 25% increase of mean intersection over union, and simultaneously reducing the development and inference times. Our solution was embedded into an easy-to-use graphical user interface that allows researchers to assess the predictions and correct potential inaccuracies with minimal human input. To demonstrate the feasibility of training a deep learning solution on a large dataset of noisy labels automatically generated by a conventional pipeline, we compared our solution against the common approach of training a model from a small manually curated dataset by several experts. Our work suggests that humans perform better in context interpretation, such as error assessment, while computers outperform in pixel-by-pixel fine segmentation. Such pipelines are illustrated with a case study on image segmentation for autophagy events. This work aims for better translation of new technologies to real-world settings in microscopy-image analysis.

An active vesicle priming machinery suppresses axon regeneration upon adult CNS injury.

Axons in the adult mammalian central nervous system fail to regenerate after spinal cord injury. Neurons lose their capacity to regenerate during development, but the intracellular processes underlying this loss are unclear. We found that critical components of the presynaptic active zone prevent axon regeneration in adult mice. Transcriptomic analysis combined with live-cell imaging revealed that adult primary sensory neurons downregulate molecular constituents of the synapse as they acquire the ability to rapidly grow their axons. Pharmacogenetic reduction of neuronal excitability stimulated axon regeneration after adult spinal cord injury. Genetic gain- and loss-of-function experiments uncovered that essential synaptic vesicle priming proteins of the presynaptic active zone, but not clostridial-toxin-sensitive VAMP-family SNARE proteins, inhibit axon regeneration. Systemic administration of Baclofen reduced voltage-dependent Ca2+ influx in primary sensory neurons and promoted their regeneration after spinal cord injury. These findings indicate that functional presynaptic active zones constitute a major barrier to axon regeneration.

Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Long-duration perforated patch recordings from spinal interneurons of adult mice.

It has been very difficult to record from interneurons in acute slices of the lumbar spinal cord from mice >3 wk of age. The low success rate and short recording times limit in vitro experimentation on mouse spinal networks to neonatal and early postnatal periods when locomotor networks are still developmentally immature. To overcome this limitation and enable investigation of mature locomotor network neurons, we have established a reliable procedure to record from spinal cord neurons in slices from adult, behaviorally mature mice of any age. Two key changes to the established neonate procedure were implemented. First, we remove the cord by a dorsal laminectomy from a deeply anesthetized animal. This enables respiration and other vital functions to continue up to the moment the maximally oxygenated lumbar spinal cord is removed, improving the health of the slices. Second, since adult spinal cord interneurons appear more sensitive to the intracellular dialysis that occurs during whole cell recordings, we introduced perforated patch recordings to the procedure. Stable recordings up to 12 h in duration were obtained with our new method. This will allow investigation of changes in mature neuronal properties in disease states or after spinal cord injury and allow prolonged recordings of responses to drug application that were previously impossible.

Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.

Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks.

Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model.