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Brain imaging in stroke: insight beyond diagnosis.

ABSTRACT: Stroke, whether hemorrhagic or ischemic in nature, has the ability to lead to devastating and debilitating patient outcomes, which not only has direct implications from a healthcare standpoint, but its effects are longstanding and they impact the community as a whole. For decades, the goal of advancement and refinement in imaging modalities has been to develop the most precise, convenient, widely available and reproducible interpretable modality for the detection of stroke, not only in its hyperacute phase, but a method to be able to predict its evolution through the natural course of disease. Diagnosis is one of the most important initial roles, which imaging fulfills after the identification of existent pathology. However, imaging fulfills an even more important goal by using a combination of imaging modalities and their precise interpretation, which lends itself to understanding the mechanisms and pathophysiology of underlying disease, and therefore guides therapeutic decision-making in a patient-tailored fashion. This review explores the most commonly used brain imaging modalities, computer tomography, and magnetic resonance imaging, with an aim to demonstrate their dynamic use in uncovering stroke mechanism, facilitating prognostication, and potentially guiding therapy.

SUBMITTER: Nour M

PROVIDER: S-EPMC3144982 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Pretargeting is a powerful nuclear imaging strategy to achieve enhanced imaging contrast for nanomedicines and reduce the radiation burden to healthy tissue. Pretargeting is based on bioorthogonal chemistry. The most attractive reaction for this purpose is currently the tetrazine ligation, which occurs between trans-cyclooctene (TCO) tags and tetrazines (Tzs). Pretargeted imaging beyond the blood-brain barrier (BBB) is challenging and has not been reported thus far. In this study, we developed Tz imaging agents that are capable of ligating in vivo to targets beyond the BBB. We chose to develop 18F-labeled Tzs as they can be applied to positron emission tomography (PET) - the most powerful molecular imaging technology. Fluorine-18 is an ideal radionuclide for PET due to its almost ideal decay properties. As a non-metal radionuclide, fluorine-18 also allows for development of Tzs with physicochemical properties enabling passive brain diffusion. To develop these imaging agents, we applied a rational drug design approach. This approach was based on estimated and experimentally determined parameters such as the BBB score, pretargeted autoradiography contrast, in vivo brain influx and washout as well as on peripheral metabolism profiles. From 18 initially developed structures, five Tzs were selected to be tested for their in vivo click performance. Whereas all selected structures clicked in vivo to TCO-polymer deposited into the brain, [18F]18 displayed the most favorable characteristics with respect to brain pretargeting. [18F]18 is our lead compound for future pretargeted neuroimaging studies based on BBB-penetrant monoclonal antibodies. Pretargeting beyond the BBB will allow us to image targets in the brain that are currently not imageable, such as soluble oligomers of neurodegeneration biomarker proteins. Imaging of such currently non-imageable targets will allow early diagnosis and personalized treatment monitoring. This in turn will accelerate drug development and greatly benefit patient care.

Project description:Pretargeting is a promising nuclear imaging technique that allows for the usage of antibodies (Abs) with enhanced imaging contrast and reduced patient radiation burden. It is based on bioorthogonal chemistry with the tetrazine ligation-a reaction between trans-cyclooctenes (TCOs) and tetrazines (Tzs)-currently being the most popular reaction due to its high selectivity and reactivity. As Abs can be designed to bind specifically to currently 'undruggable' targets such as protein isoforms or oligomers, which play a crucial role in neurodegenerative diseases, pretargeted imaging beyond the BBB is highly sought after, but has not been achieved yet. A challenge in this respect is that large molecules such as Abs show poor brain uptake. Uptake can be increased by receptor mediated transcytosis; however, it is largely unknown if the achieved brain concentrations are sufficient for pretargeted imaging. In this study, we investigated whether the required concentrations are feasible to reach. As a model Ab, we used the bispecific anti-amyloid beta (Aβ) anti-transferrin receptor (TfR) Ab 3D6scFv8D3 and conjugated it to a different amount of TCOs per Ab and tested different concentrations in vitro. With this model in hand, we estimated the minimum required TCO concentration to achieve a suitable contrast between the high and low binding regions. The estimation was carried out using pretargeted autoradiography on brain sections of an Alzheimer's disease mouse model. Biodistribution studies in wild-type (WT) mice were used to correlate how different TCO/Ab ratios alter the brain uptake. Pretargeted autoradiography showed that increasing the number of TCOs as well as increasing the TCO-Ab concentration increased the imaging contrast. A minimum brain concentration of TCOs for pretargeting purposes was determined to be 10.7 pmol/g in vitro. Biodistribution studies in WT mice showed a brain uptake of 1.1% ID/g using TCO-3D6scFv8D3 with 6.8 TCO/Ab. According to our estimations using the optimal parameters, pretargeted imaging beyond the BBB is not a utopia. Necessary brain TCO concentrations can be reached and are in the same order of magnitude as required to achieve sufficient contrast. This work gives a first estimate that pretargeted imaging is indeed possible with antibodies. This could allow the imaging of currently 'undruggable' targets and therefore be crucial to monitor (e.g., therapies for intractable neurodegenerative diseases).

Project description:Clinical research based on neuroimaging data has benefited from machine learning methods, which have the ability to provide individualized predictions and to account for the interaction among units of information in the brain. Application of machine learning in structural imaging to investigate diseases that involve brain injury presents an additional challenge, especially in conditions like stroke, due to the high variability across patients regarding characteristics of the lesions. Extracting data from anatomical images in a way that translates brain damage information into features to be used as input to learning algorithms is still an open question. One of the most common approaches to capture regional information from brain injury is to obtain the lesion load per region (i.e. the proportion of voxels in anatomical structures that are considered to be damaged). However, no systematic evaluation has yet been performed to compare this approach with using patterns of voxels (i.e. considering each voxel as a single feature). In this paper we compared both approaches applying Gaussian Process Regression to decode motor scores in 50 chronic stroke patients based solely on data derived from structural MRI. For both approaches we compared different ways to delimit anatomical areas: regions of interest from an anatomical atlas, the corticospinal tract, a mask obtained from fMRI analysis with a motor task in healthy controls and regions selected using lesion-symptom mapping. Our analysis showed that extracting features through patterns of voxels that represent lesion probability produced better results than quantifying the lesion load per region. In particular, from the different ways to delimit anatomical areas compared, the best performance was obtained with a combination of a range of cortical and subcortical motor areas as well as the corticospinal tract. These results will inform the appropriate methodology for predicting long term motor outcomes from early post-stroke structural brain imaging.

Project description:Background: The clinical outcome of patients suffering from stroke is dependent on multiple factors. The features of the lesion itself play an important role but clinical recovery is remarkably influenced by the plasticity mechanisms triggered by the stroke and occurring at a distance from the lesion. The latter translate into functional and structural changes of which cortical thickness might be easy to quantify one of the main players. However, studies on the changes of cortical thickness in brain areas beyond stroke lesion and their relationship to sensory-motor recovery are sparse. Objectives: To evaluate the effects of cerebral stroke on cortical thickness (CT) beyond the stroke lesion and its association with sensory-motor recovery. Materials and Methods: Five electronic databases (PubMed, Embase, Web of Science, Scopus and the Cochrane Library) were searched. Methodological quality of the included studies was assessed with the Newcastle-Ottawa Scale for non-randomized controlled trials and the Risk of Bias Cochrane tool for randomized controlled trials. Results: The search strategy retrieved 821 records, 12 studies were included and risk of bias assessed. In most of the included studies, cortical thinning was seen at the ipsilesional motor area (M1). Cortical thinning can occur beyond the stroke lesion, typically in regions anatomically connected because of anterograde degeneration. Nonetheless, studies also reported cortical thickening of regions of the unaffected hemisphere, likely related to compensatory plasticity. Some studies revealed a significant correlation between changes in cortical thickness of M1 or somatosensory (S1) cortical areas and motor function recovery. Discussion and Conclusions: Following a stroke, changes in cortical thickness occur both in regions directly connected to the stroke lesion and in contralateral hemisphere areas as well as in the cerebellum. The underlying mechanisms leading to these changes in cortical thickness are still to be fully understood and further research in the field is needed. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020200539; PROSPERO 2020, identifier: CRD42020200539.

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Brain imaging in stroke: insight beyond diagnosis.

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