Abstract Background and aims Improved risk stratification for post-stroke atrial fibrillation (AF) is urgently needed. We previously developed a machine-learning (ML) model predicting AF using age and heart rate variability (HRV). Whether brain magnetic resonance imaging (MRI) provides additional predictive value remains controversial. MRI-derived features may differentially inform AF detected after stroke (AFDAS) versus previously known AF (KAF). Methods We extracted interpretable MRI-derived features including lesion volume, infarct distribution across vascular territories, insular involvement, and central autonomic network (CAN) involvement. Tree-based ML models were developed using MRI-derived features, clinical variables, and HRV metrics, with separate analyses for AFDAS and KAF. Incremental predictive value of MRI features was quantified, key predictors were identified using Shapley additive explanations (SHAP), and external validation was performed in an independent cohort. Results The primary cohort included 1,227 patients (69 AFDAS [5.6%], 287 KAF [23.4%]); the external cohort included 462 patients (17 AFDAS [3.7%]). Models based solely on MRI-derived features showed modest discrimination. SHAP analysis identified insular involvement, lesion volume, and CAN involvement as key predictors for AFDAS. Adding MRI-derived features significantly improved a clinical-feature model for AFDAS (ROC-AUC 0.67 vs. 0.75; p<0.01), but not for KAF (ROC-AUC 0.74 vs. 0.77). MRI-derived features did not improve performance of the previously published age+HRV model in internal and external validation (ROC-AUC 0.83 vs. 0.81). Conclusions MRI-derived features provide complementary, biologically meaningful information for AFDAS beyond clinical variables, supporting the concept of a neurogenic AF subgroup. However, for pragmatic post-stroke AF risk stratification in clinical practice, age and HRV alone may be sufficient. Conflict of interest Maximilian Schöls: nothing to disclose

Modern biomedical research requires the integration of heterogeneous clinical and research data across distributed systems. As part of Germany's Medical Informatics Initiative, Data Integration Centers have established FHIR repositories to support data-driven studies and federated research based on interoperability through the German National Core Dataset specification. We developed a FHIR-based dashboard to create a research patient record, enabling use cases, such as quality control and clinical trial pre-screening. The system addresses key challenges including FHIR implementation variability, differences in FHIR server capabilities and dynamic data de-pseudonymization. This is done by using a RESTful backend as an abstraction layer between the actual frontend and FHIR servers while supporting flexible, role-based access through OAuth 2.0 integration. Built as a multilayer web application that interfaces with an organization's infrastructure, the dashboard provides a wide range of customizable views for both aggregate and individual-level data visualization.

Background Electronic health records are a valuable asset for research, but their use is challenging due to inconsistencies of records, heterogeneous formats and the distribution over multiple, non-integrated information systems. Hence, specialized health data engineering and data science expertise are required to enable research. To facilitate secondary use of clinical routine data collected in our intensive care wards, we developed a scalable approach, consisting of cohort generation, variable filtering and data extraction steps. Objective With this report we share our workflow of data request, cohort identification and data extraction. We present an algorithm for automatic data extraction from our critical care information system (CCIS) that can be adapted to other object-oriented data bases. Methods We introduced a data request process with functionalities for automated identification of patient cohorts and a specialized hierarchical data structure that supports filtering relevant variables from the CCIS and further systems for the specified cohorts. The data extraction algorithm takes patient pseudonyms and variable lists as inputs. Algorithms are implemented in Python, leveraging the PySpark framework running on our data lake infrastructure. Results Our data request process is in operational use since June 2022. Since then we have served 121 projects with 148 service requests in total. We discuss the hierarchical structure and the frequently used data items of our CCIS in detail and present an application example, including cohort selection, data extraction and data transformation into an analyses-ready format. Conclusions Using clinical routine data for secondary research is challenging and requires an interdisciplinary team. We developed a scalable approach that automates steps for cohort identification, data extraction and common data pre-processing steps. Additionally, we facilitate data harmonization, integration and consult on typical data analysis scenarios, machine learning algorithms and visualizations in dashboards.

Abstract BackgroundPseudonymization has become a best practice to securely manage the identities of patients and study participants in medical research projects and data sharing initiatives. This method offers the advantage of not requiring the direct identification of data to support various research processes while still allowing for advanced processing activities, such as data linkage. Often, pseudonymization and related functionalities are bundled in specific technical and organization units known as trusted third parties (TTPs). However, pseudonymization can significantly increase the complexity of data management and research workflows, necessitating adequate tool support. Common tasks of TTPs include supporting the secure registration and pseudonymization of patient and sample identities as well as managing consent. ObjectiveDespite the challenges involved, little has been published about successful architectures and functional tools for implementing TTPs in large university hospitals. The aim of this paper is to fill this research gap by describing the software architecture and tool set developed and deployed as part of a TTP established at Charité – Universitätsmedizin Berlin. MethodsThe infrastructure for the TTP was designed to provide a modular structure while keeping maintenance requirements low. Basic functionalities were realized with the free MOSAIC tools. However, supporting common study processes requires implementing workflows that span different basic services, such as patient registration, followed by pseudonym generation and concluded by consent collection. To achieve this, an integration layer was developed to provide a unified Representational state transfer (REST) application programming interface (API) as a basis for more complex workflows. Based on this API, a unified graphical user interface was also implemented, providing an integrated view of information objects and workflows supported by the TTP. The API was implemented using Java and Spring Boot, while the graphical user interface was implemented in PHP and Laravel. Both services use a shared Keycloak instance as a unified management system for roles and rights. ResultsBy the end of 2022, the TTP has already supported more than 10 research projects since its launch in December 2019. Within these projects, more than 3000 identities were stored, more than 30,000 pseudonyms were generated, and more than 1500 consent forms were submitted. In total, more than 150 people regularly work with the software platform. By implementing the integration layer and the unified user interface, together with comprehensive roles and rights management, the effort for operating the TTP could be significantly reduced, as personnel of the supported research projects can use many functionalities independently. ConclusionsWith the architecture and components described, we created a user-friendly and compliant environment for supporting research projects. We believe that the insights into the design and implementation of our TTP can help other institutions to efficiently and effectively set up corresponding structures.

Pseudonymisation has become a best practice to securely manage the identities of patients and study participants in medical research projects and data sharing initiatives. This method offers the advantage of not requiring directly identifying data to support various research processes, while still allowing for advanced processing activities, such as data linkage. Often, pseudonymization and related functionalities are bundled in specific technical and organization units, the so-called Trusted Third Parties (TTPs). However, pseudonymization can significantly increase the complexity of data management and research workflows, necessitating the need for adequate tool support. Common tasks for TTPs include supporting the secure registration and pseudonymization of patient and sample identities as well as consent management. Despite the challenges involved, little has been published about successful architectures and functional tools for implementing TTPs in large-scale university hospitals. The aim of this manuscript is to bridge this gap by describing the software architecture and tool set developed and deployed as part of a TTP established at Charité – Universitätsmedizin Berlin. The infrastructure for the TTP was designed to provide a modular structure while keeping maintenance requirements low. Basic functionalities were realized with the free MOSAIC tools. However, supporting common study processes required to implement workflows that span different basic services, e.g., patient registration, followed by pseudonym generation and concluded by consent collection. To achieve this, an integration layer was developed that provides a unified RESTful Application Programming Interface (API) as a basis for more complex workflows. Based on this API, a unified Graphical User Interface (GUI) was also implemented, providing an integrated view on information objects and workflows supported by the TTP. The API was implemented using Java and Spring Boot, while the GUI was implemented in PHP and Laravel. Both services use a shared Keycloak instance as a unified management system for roles and rights. By the end of 2022, the TTP has already supported more than 10 research projects since it took up operation in December 2019. Within these projects, more than 3,000 identities were stored, more than 30,000 pseudonyms were generated and more than 1,500 consent forms were submitted. In total, more than 150 people regularly work with the software platform. By implementing the integration layer and the unified user interface together with comprehensive roles and rights management, the effort for operating the TTP could be significantly reduced, since personnel of the supported research projects can use many functionalities independently. With the architecture and components described, a user-friendly and compliant environment for supporting research projects has been created. We believe that the insights into the design and implementation of our TTP can help other institutions to efficiently and effectively set up corresponding structures.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.

Abstract Objective Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models such as GermanBERT and GottBERT have been published, adaptations for biomedical data are unavailable. This study evaluated the suitability of existing and novel transformer-based models for the German biomedical and clinical domain. Materials and Methods We used 8 transformer-based models and pre-trained 3 new models on a newly generated biomedical corpus, and systematically compared them with each other. We annotated a new dataset of clinical notes and used it with 4 other corpora (BRONCO150, CLEF eHealth 2019 Task 1, GGPONC, and JSynCC) to perform named entity recognition (NER) and document classification tasks. Results General-purpose language models can be used effectively for biomedical and clinical natural language processing (NLP) tasks, still, our newly trained BioGottBERT model outperformed GottBERT on both clinical NER tasks. However, training new biomedical models from scratch proved ineffective. Discussion The domain-adaptation strategy’s potential is currently limited due to a lack of pre-training data. Since general-purpose language models are only marginally inferior to domain-specific models, both options are suitable for developing German-language biomedical applications. Conclusion General-purpose language models perform remarkably well on biomedical and clinical NLP tasks. If larger corpora become available in the future, domain-adapting these models may improve performances.

Transient ischemic attack (TIA) is defined as focal neurological deficit caused by ischemia resolving within 24 hours. In a secondary analysis of a large monocentric cohort of 446 TIA patients, we explored the frequency and determinants of diffusion‐weighted imaging (DWI) lesions on high‐resolution magnetic resonance imaging. Overall, 240 (54%) of all TIA patients presented with DWI lesions. These patients had higher National Institute of Health Stroke Scale and ABCD2 scores and presented more frequently with vessel occlusion and perfusion deficits, but had similar functional outcome at 3 months. Taken together, high‐resolution DWI provides evidence of ischemic brain injury in the majority of TIA patients. ANN NEUROL 2019;86:452–457

BACKGROUND AND PURPOSE: In acute stroke, arterial-input-function (AIF) determination is essential for obtaining perfusion estimates with dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging (DSC-MRI). Standard DSC-MRI postprocessing applies single AIF selection, ie, global AIF. Physiological considerations, however, suggest that a multiple AIFs selection method would improve perfusion estimates to detect penumbral flow. In this study, we developed a framework based on comparable DSC-MRI and positron emission tomography (PET) images to compare the two AIF selection approaches and assess their performance in penumbral flow detection in acute stroke. METHODS: In a retrospective analysis of 17 sub(acute) stroke patients with consecutive MRI and PET scans, voxel-wise optimized AIFs were calculated based on the kinetic model as derived from both imaging modalities. Perfusion maps were calculated based on the optimized-AIF using two methodologies: (1) Global AIF and (2) multiple AIFs as identified by cluster analysis. Performance of penumbral-flow detection was tested by receiver-operating characteristics (ROC) curve analysis, ie, the area under the curve (AUC). RESULTS: Large variation of optimized AIFs across brain voxels demonstrated that there is no optimal single AIF. Subsequently, the multiple-AIF method (AUC range over all maps:.82-. 90) outperformed the global AIF methodology (AUC.72-. 85) significantly. CONCLUSIONS: We provide PET imaging-based evidence that a multiple AIF methodology is beneficial for penumbral flow detection in comparison with the standard global AIF methodology in acute stroke.

Objective: To quantitatively evaluate blood–brain barrier changes in ischemic stroke patients using dynamic contrast-enhanced (DCE) MRI. Methods: We examined 54 stroke patients (clinicaltrials.gov NCT00715533, NCT02077582) in a 3T MRI scanner within 48 hours after symptom onset. Twenty-eight patients had a follow-up examination on day 5–7. DCE T1 mapping and Patlak analysis were employed to assess BBB permeability changes. Results: Median stroke Ktrans values (0.7 × 10−3 min−1 [interquartile range (IQR) 0.4–1.8] × 10−3 min−1) were more than 3-fold higher compared to median mirror Ktrans values (0.2 × 10−3 min−1, IQR 0.1–0.7 × 10−3 min−1, p < 0.001) and further increased at follow-up (n = 28, 2.3 × 10−3 min−1, IQR 0.8–4.6 × 10−3 min−1, p < 0.001). By contrast, mirror Ktrans values decreased over time with a clear interaction of timepoint and stroke/mirror side (p < 0.001). Median stroke Ktrans values were 2.5 times lower than in hemorrhagic transformed regions (0.7 vs 1.8 × 10−3 min−1; p = 0.055). There was no association between stroke Ktrans values and the delay from symptom onset to baseline examination, age, and presence of hyperintense acute reperfusion marker. Conclusion: BBB in acute stroke patients can be successfully assessed quantitatively. The decrease of BBB permeability in unaffected regions at follow-up may be an indicator of global BBB leakage even in vessel territories remote from the index infarct.

Objectives To assess the sensitivity of stimulated echo acquisition mode diffusion weighted imaging (STEAM-DWI) to ischemic stroke in comparison to echo-planar imaging diffusion weighted imaging (EPI-DWI) in the infratentorial compartment. Methods Fifty-seven patients presenting with clinical features of infratentorial stroke underwent STEAM-DWI, high-resolution EPI-DWI (HR-DWI, 2.5 mm slice thickness) and low-resolution EPI-DWI (LR-DWI, 5 mm slice thickness). Four readers assessed the presence of ischemic lesions and artifacts. Agreement between sequences and interobserver agreement on the presence of ischemia were calculated. The sensitivities of the DWI sequences were calculated in 45 patients with a confirmed diagnosis of infratentorial stroke. Results Median time from symptom onset to imaging was 24 hours. STEAM-DWI agreed with LR-DWI in 89.5% of cases (kappa = 0.72, p<0.0001) and with HR-DWI in 89.5% of cases (kappa = 0.68, p<0.0001). STEAM-DWI showed fewer intraparenchymal artifacts (1/57) than HR-DWI (44/57) and LR-DWI (41/57). Ischemia was visible in 87% of cases for LR-DWI, 93% of cases for HR-DWI, and 89% of cases for STEAM-DWI. Interobserver agreement was good for STEAM-DWI (kappa = 0.62, p<0.0001). Conclusions Compared to the best currently available MR sequence for detecting ischemia (HR-DWI), STEAM-DWI shows fewer artifacts and a similar sensitivity to infratentorial stroke.

Introduction / Hypothesis: Diffusion/perfusion mismatch (MM) is commonly used to assess tissue at risk in stroke patients. In small subcortical infarcts (SSI) the reverse pattern i.e. “inverse mismatch” (IM) has been shown [1]. We aimed to evaluate the frequency and characteristics of IM in a larger cohort using two different perfusion diffusion analysis approaches. Methods: We enrolled 129 patients with SSI within 24 hours of symptom onset (1000+ study, NCT00715533) comprising 44 infarcts of the thalamus, 9 located juxtacortically, 58 in the anterior choroidal artery, 18 in the lenticulostriate territory. Lesion volumes were assessed on DWI images using AnToNIa software. To evaluate perfusion maps a time-shift insensitive technique using a block-circulant matrix for deconvolution (oSVD) [2] was employed. Perfusion lesions were evaluated on MTT and Tmax (delay >2s and >6s) maps. A perfusion/diffusion ratio of 0.7 was used to define IM and 1.3 to define mismatch (MM). Results: Results are summarized in table1. For all types of data analysis and all time points, IM was the by far most frequent pathophysiological pattern. Obviously, IM was seen most often at Tmax>6s, but it was still clearly observed with looser thresholds (Tmax>2s, MTT). At earlier time of onset IM occurred less often, but was still the most frequent pattern (p=0.0026). No significant association between infarct location and perfusion pattern was observed Conclusion: IM is the most common pathophysiological constellation in SSI. We hypothesize that this is due to a lack of collateral circulation in these “endstream infarcts” and that the diffusion changes in areas without perfusion deficit reflect cytotoxic mechanisms. 1. Doege CA et al. AJNR 2003;24:1355-63 2. Wu O et al. Magn Reson Med 2003;50:164-174

Collateral blood flow is accepted as a predictive factor of tissue fate in ischemic stroke. Thus, we aimed to evaluate a new method derived from MR perfusion source images to assess collateral flow in patients with ICA/MCA occlusions.

Reductions of cerebral blood flow and gray matter structure have been implicated in early pathogenesis of Alzheimer's disease, potentially providing complementary information. The present study evaluated regional patterns of cerebral hypoperfusion and atrophy in patients with mild cognitive impairment and healthy older adults. In each participant, cerebral perfusion and gray matter structure were extracted within selected brain regions vulnerable to Alzheimer's disease using magnetic resonance imaging. Measures were compared between diagnostic groups with/without adjustment for covariates. In mild cognitive impairment patients, cerebral blood flow was significantly reduced in comparison with healthy controls in temporo-parietal regions and the basal ganglia in the absence of local gray matter atrophy. By contrast, gray matter structure was significantly reduced in the hippocampus in the absence of local hypoperfusion. Both, cerebral perfusion and gray matter structure were significantly reduced in the entorhinal and isthmus cingulate cortex in mild cognitive impairment patients compared with healthy older adults. Our results demonstrated partly divergent patterns of temporo-parietal hypoperfusion and medial-temporal atrophy in mild cognitive impairment patients, potentially indicating biomarker sensitivity to dissociable pathological mechanisms. The findings support applicability of cerebral perfusion and gray matter structure as complementary magnetic resonance imaging-based biomarkers in early Alzheimer's disease detection, a hypothesis to be further evaluated in longitudinal studies.

Background and Purpose Dynamic susceptibility-weighted contrast-enhanced (DSC) magnetic resonance imaging (MRI) is used to identify the tissue-at-risk in acute stroke, but the choice of optimal DSC postprocessing in the clinical setting remains a matter of debate. Using 15O-water positron emission tomography (PET), we validated the performance of 2 common deconvolution methods for DSC-MRI.Methods In (sub)acute stroke patients with consecutive MRI and PET imaging, DSC maps were calculated applying 2 deconvolution methods, standard and block-circulant single value decomposition. We used 2 standardized analysis methods, a region of interest-based and a voxel-based analysis, where PET cerebral blood flow masks of <20 mL/100 g per minute (penumbral flow) and gray matter masks were overlaid on DSC parameter maps. For both methods, receiver operating characteristic curve analysis was performed to identify the accuracy of each DSC-MR map for the detection of PET penumbral flow.Results In 18 data sets (median time after stroke onset: 18 hours; median time PET to MRI: 101 minutes), block-circulant single value decomposition showed significantly better performance to detect PET penumbral flow only for mean transit time maps. Time-to-maximum (Tmax) had the highest performance independent of the deconvolution method.Conclusions Block-circulant single value decomposition seems only significantly beneficial for mean transit time maps in (sub)acute stroke. Tmax is likely the most stable deconvolved parameter for the detection of tissue-at-risk using DSC-MRI.

Introduction: The association between insular infarction and mortality has often been described. However, whether this is simply due to higher lesion volumes is still controversial. Hypothesis: We hypothesized that there is an association between insular infarction and mortality independent of lesion volume. Methods: We included consecutive stroke patients between 01.09.2008 and 11.11.2012 from the 1000Plus data base with an acute ischemic lesion on diffusion-weighted imaging on day one and a completed 90 days follow-up. Insular location of the infarction was determined using the Stroke Lesion Atlas (SLA). The SLA is an in-house developed spatial database for analyses of imaging studies (Figure). In multivariate regression analyses of mortality and insular infarcts we adjusted for age, lesion volume, atrial fibrillation, NIHSS, capsular infarcts, and left hemispheric infarcts. Results: We included 718 patients. Out of 165 patients with insular infarcts 20 patients died; among the 553 patients with lesions outside the Insula 16 patients died (mortality 12% vs. 3%; p<.001). In analyses adjusting for all mentioned confounders insular infarction was not an independent predictor of mortality. After exclusion of NIHSS insular infarcts were independently associated with mortality (OR=3.003, CI 1.41 - 6.38, p=.004). Right insular infarction was an independent mortality predictor adjusted for all confounders including the NIHSS (OR=2.793, CI 1.27 - 6.15, p=.011). Conclusion: Right insular involvement is an independent predictor of mortality in ischemic stroke and may be used to improve identification of patients at risk. Figure. Lesion overlap of patients who survived (A) or died (B). Red indicates voxels involved in at least 15% of patients. Deceased patients show a maximum lesion overlap in the right Insula.

Focal ischemia may induce pathological alterations in brain areas distant from the primary lesion. In animal models, exofocal neuron death in the ipsilateral midbrain has been described after occlusion of the middle cerebral artery (MCA). Using sequential magnetic resonance imaging (T2- and diffusion-weighted) at 3 Tesla, we investigated acute ischemic stroke patients on days 1, 2, 6, 8, and 10 after stroke onset. Sixteen consecutive patients who had suffered a stroke involving the caudate nucleus and/or putamen of either hemisphere were recruited into the study. Four additional patients with strokes sparing the caudate nucleus and putamen but encompassing at least one-third of the MCA territory served as controls. Ischemic lesions involving striatal structures resulted in hyperintense lesions in ipsilateral midbrain that emerged between days 6 and 10 after stroke and were not present on the initial scans. In contrast, none of the control stroke patients developed secondary midbrain lesions. Hyperintense lesions in the pyramidal tract or the brain stem caused by degeneration of the corticospinal tract could be clearly distinguished from these secondary midbrain gray matter lesions and were detectable from day 2 after ischemia. Co-registration of high-resolution images with a digitized anatomic atlas revealed localization of secondary lesions primarily in the substantia nigra pars compacta. Apparent diffusion coefficient (ADC) values in the secondary lesions showed a delayed sharp decline through day 10. Normalization of ADC values was observed at late measurements. Taken together, our study demonstrates that striatal infarction elicits delayed degenerative changes in ipsilateral substantia nigra pars compacta.

Regional variation in the degree of functional (i.e., hypoperfusion and hypometabolism) and structural brain injury is seen in Alzheimer's disease (AD) patients, often accompanied by cerebrovascular burden. These AD pathological hallmarks need further characterization at prodromal disease stages (Mattsson et al., 2014) to develop sensitive biomarkers for early AD detection (Wierenga et al., 2014). We evaluated cerebral hypoperfusion, gray-matter atrophy, and white-matter lesions (WMLs) in incipient AD using magnetic resonance imaging (MRI). Cognitively-normal older adults (n=107, 63±7 years) and mild cognitive impairment (MCI) patients (n=52, 67±8 years) were examined. Cerebral perfusion (blood volume and blood flow, measured using dynamic-susceptibility contrast imaging), gray-matter atrophy (measured by volume and, if available, cortical thickness using structural MRI) were extracted within AD-sensitive regions of interest. Regional biomarker variation was compared between diagnostic groups (adjusting for age and sex) and relationships with WML volumes were assessed. In MCI patients, region-specific cerebral hypoperfusion (mainly blood flow reduction, Figure 1) was detected in temporo-parietal regions and the basal ganglia compared to cognitively-normal older adults; region-specific atrophy occurred in the hippocampus (Figure 1). Both, hypoperfusion and atrophy (i.e., cortical thinning) were present in entorhinal cortex and isthmus cingulate. WMLs were correlated with hypoperfusion in temporo-partial regions and the basal ganglia and with atrophy in hippocampal and entorhinal regions across diagnostic groups. Results of multiple regression models with cerebral blood volume, cerebral blood flow (both normalized to gray- matter cerebellum), gray-matter volume (normalized for intracranial volume), or cortical thickness as dependent variable; diagnostic group (cognitively-normal older adults, MCI patients) as independent variable, adjusted for age and sex. Standardized regression coefficients for effects of diagnostic group are provided with significance indicated (*p<0.05, uncorrected). Negative values reflect hypoperfusion or gray-matter atrophy in MCI patients. Our data indicate extensive cerebral blood flow alterations within AD-sensitive regions in incipient AD. We also document divergent patterns in the degree of regional hypoperfusion and atrophy, pointing toward applicability as sensitive and independent MRI-based biomarkers in early AD detection. Hypoperfusion and atrophy were both affected by white-matter pathology, suggesting additive effects of regional pathological mechanisms and cerebrovascular burden at prodromal stages of AD.

Objective: M1 occlusions with concomitant large cortical infarcts can result in life long severe disabilities. Therapeutic decisions for iv thrombolysis only or bridging to endovascular treatment require sufficient information of collateral flow. Hence, we tried to evaluate the potential of dynamic perfusion in the assessment of collateral flow with respect to tissue fate and outcome. Method: Between 1/2009 until 1/2012 ninety patients with MCA infarcts due to M1 occlusions were examined in a 3 T MR scanner. Selection criteria were DWI, PI, MR angiography, NIHSS on admission; FLAIR, MR angiography day 6 and mRS day 90, which left 30 patients for further evaluation. For evaluating dynamic perfusion the second previous image was subtracted from each frame of the raw perfusion data and the final images were assessed according to Higashida′s scale (Stroke 2003;34: e109-37). Rapid collateral flow was defined as arrival of signal drop in the ischemic side before the last arterial phase signal in the unaffected side. The ischemic region was monitored for complete or incomplete filling and flow direction. Results: Collateral flow was inversely correlated with infarct size on day 1 (p=0.005) and day 6 (p=0.006) as well as infarct growth (p=0.025) indicating collateral flow can influence infarct development. Collateral grade was significantly better in proximal M1 occlusions than in distal ones (p<0.001), however the capillary filling was significantly delayed (p<0.001). Mismatch volume was found to directly correlate with the delay of capillary filling (p=0.015) but not with infarct growth (p=0.064) or infarct size on day 6 (p=0.15). Higher NIHSS was directly correlated with capillary delay (p=0.042) and inversely correlated with collateral grade (p=0.027). mRS at day 90 correlated inversely with collateral grade (p=0.027), directly with infarct size on day 1 (p=0.006) and day 6 (p=0.011) demonstrating good collateral flow beneficial for tissue survival. Conclusion: Dynamic perfusion can provide additional information of collateral flow and capillary delay, which showed strong associations with infarct size and infarct growth, as well as clinical scores, such as NIHSS and mRS at follow-up. It can add to the assessment of tissue fate before therapy decision.