The GASROS study aims to discover genetic factors that contribute to stroke risk and outcome. Genetic variance in a population can be examined by looking at genetic polymorphisms. By collecting medical history and blood samples from AIS and TIA patients, this study examines the genetic polymorphisms in those who present with cerebrovascular disease and specific stroke-associated phenotypic traits. By additionally collecting follow up information at 3 and 6 months after stroke, this data can be assessed and analyzed to compare patient’s genetic markers with their outcome after stroke. With the aim to develop early preventative measures to treat and reduce or prevent stroke, this study strives to identify specific genetic clues as targets for developing new treatments and provide personalized medicine.
GASROS has a wealth of information, including manual identification of which areas in the brain are affected by white matter hyperintensities or stroke. This allowed us to investigate the spatial contribution of lesions in the brain, be that the acute lesion, i.e. the stroke, or the chronic leasion, i.e. the white matter hyperintensities.
With the outlines of the white matter hyperintensities, and the creation of a template to differentiate regions in the brain based on their blood supply by major cerebral arteries, we were able to identify risk factors which contribute to an increase of disease burden in these areas. In brief, older age, male sex, small-vessel stroke subtype, hypertension, hyperlipidemia, and smoking demonstrated effects that shifted the prevalence of the disease burden in these territories. A more complete summary can be found in our coverage by Advances in Motion or the corresponding paper in Frontiers in Neurology. Importantly, as part of our study, we created a template of these territories. We made this template publicly available (see our Data section) to help other investigators execute large scale studies and further disentangle the importance of location and contribution factors to spatially specific effects in stroke and beyond.
Additionally, we further investigated the importance of stroke location with respect to patients outcome adding to the growing opinion that the stroke volume alone, i.e. how big the by the stroke affected area in the brain is, does not fully capture this disease. Within GASROS, we investigated this approach in multiple ways. First, we independently identified regions of the brain that most significantly contributed to a worsening of the outcome by using voxel-based lesion symptom mapping. In this study, we, e.g., identified that injury to the white matter, post-central gyrus, putamen, and perculum were implicated with poor outcome, as measuerd by the modified Rankin Scale. The full investigation is openly available in our accompanying publication "The role of acute lesion topography in initial ischemic stroke severity and long-term functional outcomes".
Additionally, by incorporating structural connectivity information from healthy participants, we demonstrated that specific areas in the brain that are important for efficient information transport, have a much higher contribution to the patients outcome. In the field of connectomics (brain connecitivy analysis), these regions are referred to as the rich-club. With the help of the GASROS study, we were able to create an automated assessment if these regions were affected, to create a translational biomarker of stroke outcome, which can readily be assessed using standard clinical imaging, as it is often used in the emergency room. A full description of this work is openly available in the corresponding publication Rich-Club Organization: An Important Determinant of Functional Outcome After Acute Ischemic Stroke. Moreover, with the help of the SALVO study, we were able to expand this concept, thereby significantly improving our understanding of how structural and functional connectivity in the brain play a key role for stroke patients.
Besides the location of the acute and chronic lesions in acute stroke cohorts, there are other contributing biological factors that we can quantify based on the data from the acute imaging protocol in the emergency room. The ultimate goal is to understand and model stroke outcome at the time of admission, which can then further help stratify patient population, helping to optimize treatment options and developing new approach addressing the individual contributing factors. While we investigated many aspects of these contributing factors, we will outline two more recent areas of investigations in our lab, specifically, ventricles and effective reserve.
The ventricular system is one of the most promenent features visible on an MRI examination, due to their central location and there relatively large size. In general, ventricular volume increases with age, however, in certain diseases, pathological expansion has been observed. Nonetheless, in stroke patients, the ventricles as a potential biomarker, remain largely unexplored.
Utilizing the GASROS study, we first developed an automated segmentation tool for the ventricular system in clincal scans. This helped us in two independent manners. First, we analyzed the shape of the ventricles to see if clinical aspects of stroke patients, such as hypertension, white matter hyperintensity volume, and outcome, are associated with specific shape differences. Using advanced image analysis methodology (conditional joint models of shape, image features, and clinical indicators), we were able to create a model of the ventricular shape and explore the individual contributions. This was first published as part of the Medical Image Computation and Computer Assisted Intervention (MICCAI) international conference (Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators), and is illustrated in the video below.
This ventricle segmentation also gave us an important advantage for evaluating automated processing pipelines. As described in the section above, location plays an important role in functional stroke outcome. However, to evaluate the location in large data sets, an image registration step is required, which aligns the patient's MR scan to a template. This is a particularly difficult step, when working with clinical data that, due to medical time constraints in treatment, are acquired at a lower resolution. While the registration can still be performed, it is important to assess if and how much the computer struggled with the alignment. Utilizing the ventricle segmentation, however, we were able to automatically assess the accuracy of this alignment, in addition to employ age-specific templates as an intermediate step, which can significantly reduced the error rate in the registration. Importantly, through this methodology, we were able to move from the typical qualitative to a quantiative assessment strategy of automated processing pipelines. The figure below demonstrates the improvement after identifying errors in the alignment using our quantiative, automated approach, when creating white matter hyperintensity distributions on a template. For more details, please see the corresponding publication on "Automated Image Registration Quality Assessment Utilizing Deep-learning based Ventricle Extraction in Clinical Data".
Despite all the efforts in determining the contributing factors, a key challenge remains. Assume we look at two patients from the same country, same age, same sex, and similar lifestyles, who also have a stroke in roughly the same area of the brain. In clinical practice, their outcome can still be vastly different. This suggests that an underlying mechanism exists, which influences patient recovery, but which we are unable to identify and measure at this point: effective reserve. Effective reserve is an extension of the principle of brain reserve, which aims to describe the brain's capacity to compensate for negative impacts of pathology. A simple metaphor for reserve represents a bucket that holds back the effects of pathological processes (water). At some point, this bucket is filled to it's maximum and cannot compensate for negative effects anymore; when the bucket overflows, symptoms manifest.
In general, scientists differentiate between two types of reserve. Brain reserve considers the structural or biological compensation mechanism, where as cognitive reserve describes the mechanism originating from functional and/or cognitive activity. However, these principles do not take existing pathology into account. If a brain's reserve is already compensating for existing pathology, a new event, such as a stroke, cannot utilize this 'occupied' reserve. The remaining, available reserve is then called effective reserve.
With the help of the GASROS study, we were able to investigate this concept in a stroke population. The full study is described in our journal publication "Effective reserve: a latent variable to improve outcome prediction in stroke", or in a high-level summary of our results here. In brief, we were able to model effective reserve and demonstrate that an increase in this capacity to compensate for negative effects is directly related with a better post-stroke outcome. A possible biological explanation of what effective reserve in stroke patients may represent, is vascular health. With the available additional information on hypertension in our patients, which can be seen as a proxy of vascular health, we demonstrated that non-hypertensive patients exhibit a higher effective reserve, i.e. a higher capacity to compensate for negative effects. While further studies are necessary to fully elucidate the building blocks of this concept, our study indicates that we are starting to uncover a crucial element of functional post-stroke outcome.
Integrity of normal-appearing white matter and functional outcomes after acute ischemic stroke.
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Determinants of white matter hyperintensity burden differ at the extremes of ages of ischemic stroke onset.
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17q25 Locus is associated with white matter hyperintensity volume in ischemic stroke, but not with lacunar stroke status.
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Failure to validate association between 12p13 variants and ischemic stroke.
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Erratum in: Stroke. 2017 Aug;48(8):e241
Remote supervision of IV-tPA for acute ischemic stroke by telemedicine or telephone before transfer to a regional stroke center is feasible and safe.
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Severity of leukoaraiosis correlates with clinical outcome after ischemic stroke.
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