Periodic Reporting for period 2 - ARTEMIS (Assessment of Reserve: Translational Evaluation of Medical Images and Statistics-Prediction models for outcomes of brain health)
Okres sprawozdawczy: 2019-04-01 do 2020-03-31
Stroke and cognitive decline are among the leading contributors to disease burden and long-term disability worldwide. However, despite their prevalence, the contributing disease processes are not fully understood. This is in part due to the lack of (early) prediction models and ways to characterize protective mechanisms, which can help to distinguish between patients and healthy controls before symptoms show. Such prediction models can facilitate prevention strategies for adverse cognitive and functional outcomes, thereby enriching patients’ life quality and reducing the economic burden on society. Advanced neuroimaging techniques such as MRI have provided additional insight into the underlying disease biology. One major challenge when using neuroimaging techniques lies in the fact that large amounts of data are required to account for variations in clinical presentation and assessment, necessitating the use of dedicated pipelines for extracting phenotypes. However, most pipelines are developed in research settings and tend to fail when applied to clinical cohorts, leading to a subpar use of rich, available datasets.
Here, a fully-automated, translational pipeline for extracting imaging phenotypes from data acquired in clinical and research settings is developed with a particular focus on outlining white matter hyperintensities (WMH). WMH are a common phenotype in aging and across diseases, however, group differences are poorly understood. This makes WMH a prime candidate for extracting additional information, which can be used for outcome prediction. The proposed prediction models in this project utilize newly extracted characteristics, clinical/demographic information and a latent variable construct to predict general cognitive decline and outcome after stroke. In particular, the proposed latent variable has shown promise in acting as a surrogate measure for protective mechanisms in stroke patients, where its biological meaning is assessed as part of this project.
At its conclusion this project has delivered on the fully-automated, translational pipeline with which we have quantified the WMH burden in over 6000 stroke patients from data in clinical settings. The results of this pipeline, in addition to other investigations, have significantly refined outcome models of stroke patients and led to the demonstration of the protective mechanism in stroke patients that is likely a surrogate measure of vascular health in the human brain. While additional investigations are necessary, this project succeeded in bridging the wide gap between the development of advanced methodological image analysis approaches and the clinic, paving the way for future investigations to utilize the largely untapped potential that is clinical data.
Here, a fully-automated, translational pipeline for extracting imaging phenotypes from data acquired in clinical and research settings is developed with a particular focus on outlining white matter hyperintensities (WMH). WMH are a common phenotype in aging and across diseases, however, group differences are poorly understood. This makes WMH a prime candidate for extracting additional information, which can be used for outcome prediction. The proposed prediction models in this project utilize newly extracted characteristics, clinical/demographic information and a latent variable construct to predict general cognitive decline and outcome after stroke. In particular, the proposed latent variable has shown promise in acting as a surrogate measure for protective mechanisms in stroke patients, where its biological meaning is assessed as part of this project.
At its conclusion this project has delivered on the fully-automated, translational pipeline with which we have quantified the WMH burden in over 6000 stroke patients from data in clinical settings. The results of this pipeline, in addition to other investigations, have significantly refined outcome models of stroke patients and led to the demonstration of the protective mechanism in stroke patients that is likely a surrogate measure of vascular health in the human brain. While additional investigations are necessary, this project succeeded in bridging the wide gap between the development of advanced methodological image analysis approaches and the clinic, paving the way for future investigations to utilize the largely untapped potential that is clinical data.
"The automated WMH segmentation pipeline was developed and applied to over 6000 acute ischemic stroke patients. We created a new, for clinical images viable, deep-learning based brain extraction methodology in clinical magnetic resonance imaging, specifically FLuid Attenuated Inversion Recovery (FLAIR) images. This allowed the estimation of each patient's brain volume, which was utilized in an automated quality control step and, independently, to investigate the effect of brain volume on long-term stroke outcome. In addition, the processing pipeline only requires a rough registration to a template, which increases its utility by reducing the computational cost to under 3.5 minutes per patient (initially 2.5 hours), while increasing its reliability. Outlines of the WMH have been improved using a deep-learning based framework for segmentation, showing good agreement with manual segmentations.
To further improve registration quality with clinical image data, which can further help to reduce the complexity of the WMH segmentation task, we created a new multi-template-based registration framework, where each patient's FLAIR image is first registered to a set of age-specific templates before they are then transferred into a common space. After developing an automated ventricle segmentation algorithm on clinical FLAIR sequences, we are able to assess the quality of registration automatically, based on the overlap of the ventricles of the template and the subject.
Additionally, we explored the spatial disease patterns in the brain, utilizing information of vascular territories. This resulted in the identification of spatial variations, which are affected/modified by different risk factors (such as hypertension).
With a latent variable model, we quantified the often-observed protective mechanism in the brain leading to better post-stroke functional outcome. We extended the idea of ""brain reserve"", which is widely studied in populations with cognitive decline, by including pre-existing disease burden. The resulting concept characterizes the remaining reserve, ""effective reserve"", and was estimated in a set of stroke patients where we showed its relation with long-term stroke outcome. In these patients a higher reserve was associated with better outcome.
An extension of the outcome models by including information from the field of brain connectivity, we elucidated the contribution of the structural connectivity backbone, as well as the contributing factors of functional connectivity after stroke. This led to a significant improvement in the 90-day outcome models estimated within the first few days after admission.
All together, this work has already led to 11 scientific journal publications, three of them in collaborative efforts with international researchers. Part of the work was highlighted in the Advances in Motion online portal, and all of it has been communicated to scientific and non-scientific audiences throughout the duration of the action at conferences, as well as small group meetings with former stroke patients."
To further improve registration quality with clinical image data, which can further help to reduce the complexity of the WMH segmentation task, we created a new multi-template-based registration framework, where each patient's FLAIR image is first registered to a set of age-specific templates before they are then transferred into a common space. After developing an automated ventricle segmentation algorithm on clinical FLAIR sequences, we are able to assess the quality of registration automatically, based on the overlap of the ventricles of the template and the subject.
Additionally, we explored the spatial disease patterns in the brain, utilizing information of vascular territories. This resulted in the identification of spatial variations, which are affected/modified by different risk factors (such as hypertension).
With a latent variable model, we quantified the often-observed protective mechanism in the brain leading to better post-stroke functional outcome. We extended the idea of ""brain reserve"", which is widely studied in populations with cognitive decline, by including pre-existing disease burden. The resulting concept characterizes the remaining reserve, ""effective reserve"", and was estimated in a set of stroke patients where we showed its relation with long-term stroke outcome. In these patients a higher reserve was associated with better outcome.
An extension of the outcome models by including information from the field of brain connectivity, we elucidated the contribution of the structural connectivity backbone, as well as the contributing factors of functional connectivity after stroke. This led to a significant improvement in the 90-day outcome models estimated within the first few days after admission.
All together, this work has already led to 11 scientific journal publications, three of them in collaborative efforts with international researchers. Part of the work was highlighted in the Advances in Motion online portal, and all of it has been communicated to scientific and non-scientific audiences throughout the duration of the action at conferences, as well as small group meetings with former stroke patients."
So far, this project has made significant progress beyond the state of the art. We created an effective, high-throughput automated pipeline for clinically relevant MRI phenotype analyses, which has the potential to accelerate the pace of scientific and medical discoveries and to advance the development of clinical applications in risk and outcome modeling in stroke. Importantly, by utilizing clinical data as it becomes available in the emergency room, we ensured translatability of the investigated approaches and started to close an important gap that currently exists in the translational application of advanced MRI analyses. Additionally, we demonstrated the first conceptualization of the brain's capacity to compensate for the negative effects of stroke and identified multiple contributing factors modifying stroke outcome, which helped us to create a more complete picture of the adverse effects of stroke.
Based on our progress so far, we believe that our research will lead to a better understanding of disease processes and patient specific differences in outcomes. It paved the way to create a model for general brain health, which may subsequently lead to effective disease models, help in developing prevention strategies for adverse cognitive and functional outcomes, and ultimately guide us to enriching patients' quality of life and reducing the economic burden on society.
Based on our progress so far, we believe that our research will lead to a better understanding of disease processes and patient specific differences in outcomes. It paved the way to create a model for general brain health, which may subsequently lead to effective disease models, help in developing prevention strategies for adverse cognitive and functional outcomes, and ultimately guide us to enriching patients' quality of life and reducing the economic burden on society.