Multimodal Imaging Data Research Articles

Spatial and temporal pattern of structure–function coupling of human brain connectome with development

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7–21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC–FC coupling. Our findings revealed that SC–FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC–FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC–FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC–FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC–FC coupling in typical development.

Left ventricular hypertrabeculation: clinical interpretation based on multimodality imaging data

Left ventricular hypertrabeculation: clinical interpretation based on multimodality imaging data

Deep Learning Model for Estimation of LV Ejection Fraction from Echocardiogram

Heart failure, a leading global cause of death, poses challenges for early prediction of cardiac dysfunction, especially ejection fraction (EF). This study employs Convolutional Neural Networks (CNNs), utilizing ResNet and MobileNet architectures, on the CAMUS dataset with 500 patient records (2CH and 4CH). The goal is to aid healthcare professionals in accurately measuring EF. The CAMUS dataset, comprising multi-modality cardiac imaging and segmentation data, serves as the foundation. The CNN, ResNet, and MobileNet models are fine-tuned through transfer learning and their performance is evaluated based on accuracy. This comparative analysis identifies the model with the best predictive capabilities for EF, showcasing their potential for earlier diagnosis and intervention. Deep learning techniques enhance cardiac healthcare by providing reliable, noninvasive means of predicting heart failure, reducing its impact on patients and healthcare systems.

A Multimodal Ensemble Deep Learning Model for Functional Outcome Prognosis of Stroke Patients.

The accurate prediction of functional outcomes in patients with acute ischemic stroke (AIS) is crucial for informed clinical decision-making and optimal resource utilization. As such, this study aimed to construct an ensemble deep learning model that integrates multimodal imaging and clinical data to predict the 90-day functional outcomes after AIS. We used data from the Korean Stroke Neuroimaging Initiative database, a prospective multicenter stroke registry to construct an ensemble model integrated individual 3D convolutional neural networks for diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR), along with a deep neural network for clinical data, to predict 90-day functional independence after AIS using a modified Rankin Scale (mRS) of 3-6. To evaluate the performance of the ensemble model, we compared the area under the curve (AUC) of the proposed method with that of individual models trained on each modality to identify patients with AIS with an mRS score of 3-6. Of the 2,606 patients with AIS, 993 (38.1%) achieved an mRS score of 3-6 at 90 days post-stroke. Our model achieved AUC values of 0.830 (standard cross-validation [CV]) and 0.779 (time-based CV), which significantly outperformed the other models relying on single modalities: b-value of 1,000 s/mm2 (P<0.001), apparent diffusion coefficient map (P<0.001), FLAIR (P<0.001), and clinical data (P=0.004). The integration of multimodal imaging and clinical data resulted in superior prediction of the 90-day functional outcomes in AIS patients compared to the use of a single data modality.

Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

Multi-teacher cross-modal distillation with cooperative deep supervision fusion learning for unimodal segmentation

Accurate brain tumor segmentation is a labor-intensive and time-consuming task that requires automation to enhance its efficacy. Recent advanced techniques have shown promising results in segmenting brain tumors; however, their dependency on extensive multimodal magnetic resonance imaging (MRI) data limits their practicality in clinical environments where such data may not be readily available. To address this, we propose a novel multi-teacher cross-modal knowledge distillation framework, which utilizes the privileged multimodal data during training while relying solely on unimodal data for inference. Our framework is tailored to the unimodal segmentation of the T1ce MRI sequence, which is prevalently available in clinical practice and structurally akin to the T1 modality, providing ample information for the segmentation task. Our framework introduces two learning strategies for knowledge distillation (KD): (1) performance-aware response-based KD and (2) cooperative deep supervision fusion learning (CDSFL). The first strategy involves dynamically assigning confidence weights to each teacher model based on its performance, ensuring that the KD is performance-driven, and the CDSFL module augments the learning capabilities of the multi-teacher models by fostering mutual learning. Moreover, the fused information is distilled into the student model to improve its deep supervision. Extensive experiments on BraTS datasets demonstrate that our framework achieves promising unimodal segmentation results on the T1ce and T1 modalities and outperforms previous state-of-the-art methods. Code is available at https://github.com/ami-lab-knut/mtcm_kd.

Clinical and imaging characteristics of patients with cardiac amyloidosis- a single center observational study

Amyloidosis is a disease characterized by the deposition of protein fibrils. Cardiac involvement is a significant factor in determining prognosis. This study aimed to examine the clinical profile, outcomes, and long-term mortality rates in patients with transthyretin (ATTR) and amyloid light-chain (AL) amyloidosis. The retrospective cohort study included 94 patients with amyloidosis (69 with AL and 25 with ATTR amyloidosis) diagnosed between 2010 and 2022. The study involved multimodality imaging (ECG, echocardiography and cardiac magnetic resonance (CMR) data and survival analyses. Patients with ATTR amyloidosis were older and had a higher proportion of males compared to those with AL amyloidosis. Cardiac involvement was more prevalent in the ATTR group, including atrial fibrillation (AF), while pleural and pericardial effusion were more frequent in the AL group. Biomarkers such as NT-proBNP and troponin T were significantly elevated in both groups and were associated with all-cause mortality only in univariate analyses. CMR data, especially typical late gadolinium enhancement (LGE) was not associated with increased mortality, while pleural effusion and left atrial dilatation on echocardiography were identified as powerful predictors of mortality. In conclusion, both AL and ATTR amyloidosis exhibited poor outcomes. Cardiac involvement, particularly dilated left atrium and pleural effusion on echocardiography were associated with an increased risk of mortality, while typical LGE on CMR was not.

Association of Lower Rostral Anterior Cingulate GABA+ and Dysregulated Cortisol Stress Response With Altered Functional Connectivity in Young Adults With Lifetime Depression: A Multimodal Imaging Investigation of Trait and State Effects.

Preclinical work suggests that excess glucocorticoids and reduced cortical γ-aminobutyric acid (GABA) may affect sex-dependent differences in brain regions implicated in stress regulation and depressive phenotypes. The authors sought to address a critical gap in knowledge, namely, how stress circuitry is functionally affected by glucocorticoids and GABA in current or remitted major depressive disorder (MDD). Multimodal imaging data were collected from 130 young adults (ages 18-25), of whom 44 had current MDD, 42 had remitted MDD, and 44 were healthy comparison subjects. GABA+ (γ-aminobutyric acid and macromolecules) was assessed using magnetic resonance spectroscopy, and task-related functional MRI data were collected under acute stress and analyzed using data-driven network modeling. Across modalities, trait-related abnormalities emerged. Relative to healthy comparison subjects, both clinical groups were characterized by lower rostral anterior cingulate cortex (rACC) GABA+ and frontoparietal network amplitude but higher amplitude in salience and stress-related networks. For the remitted MDD group, differences from the healthy comparison group emerged in the context of elevated cortisol levels, whereas the MDD group had lower cortisol levels than the healthy comparison group. In the comparison group, frontoparietal and stress-related network connectivity was positively associated with cortisol level (highlighting putative top-down regulation of stress), but the opposite relationship emerged in the MDD and remitted MDD groups. Finally, rACC GABA+ was associated with stress-induced changes in connectivity between overlapping default mode and salience networks. Lifetime MDD was characterized by reduced rACC GABA+ as well as dysregulated cortisol-related interactions between top-down control (frontoparietal) and threat (task-related) networks. These findings warrant further investigation of the role of GABA in the vulnerability to and treatment of MDD.

Spatially Resolved Association of Structural Biomarkers on Retinal Function in Non-Exudative Age-Related Macular Degeneration Over 4 Years.

To longitudinally assess the impact of high-risk structural biomarkers for natural disease progression in non-exudative age-related macular degeneration (AMD) on spatially resolved mesopic and scotopic fundus-controlled perimetry testing. Multimodal retinal imaging data and fundus-controlled perimetry stimuli points were semiautomatically registered according to landmark correspondences at each annual visit over a period of up to 4 years. The presence of sub-RPE drusen, subretinal drusenoid deposits, pigment epithelium detachments (PEDs), hyper-reflective foci (HRF), vitelliform lesions, refractile deposits, and incomplete RPE and outer retinal atrophy (iRORA) and complete RPE and outer retinal atrophy (cRORA) were graded at each stimulus position and visit. Localized retinal layer thicknesses were extracted. Mixed-effect models were used for structure-function correlation. Fifty-four eyes of 49 patients with non-exudative AMD (mean age, 70.7 ± 9.1years) and 27 eyes of 27 healthy controls (mean age, 63.4 ± 8.9years) were included. During study course, presence of PED had the highest functional impact with a mean estimated loss of -1.30 dB (P < 0.001) for mesopic and -1.23 dB (P < 0.001) for scotopic testing, followed by HRF with -0.89 dB (mesopic, P = 0.001) and -0.87 dB (scotopic, P = 0.005). Subretinal drusenoid deposits were associated with a stronger visual impairment (mesopic, -0.38 dB; P = 0.128; scotopic, -0.37 dB; P = 0.172) compared with sub-RPE drusen (-0.22 dB, P = 0.0004; -0.18 dB, P = 0.006). With development of c-RORA, scotopic retinal sensitivity further significantly decreased (-2.15 dB; P = 0.02). Thickening of the RPE-drusen-complex and thinning of the outer nuclear layer negatively impacted spatially resolved retinal sensitivity. The presence of PED and HRF had the greatest prognostic impact on progressive point-wise sensitivity losses. Higher predominant rod than cone-mediated localized retinal sensitivity losses with early signs of retinal atrophy development indicate photoreceptor preservation as a potential therapeutic target for future interventional AMD trials.

A speech fluency brain network derived from gliomas.

The brain network of speech fluency has not yet been investigated via a study with a large and homogenous sample. This study analysed multimodal imaging data from 115 patients with low-grade glioma to explore the brain network of speech fluency. We applied voxel-based lesion-symptom mapping to identify domain-specific regions and white matter pathways associated with speech fluency. Direct cortical stimulation validated the domain-specific regions intra-operatively. We then performed connectivity-behaviour analysis with the aim of identifying connections that significantly correlated with speech fluency. Voxel-based lesion-symptom mapping analysis showed that damage to domain-specific regions (the middle frontal gyrus, the precentral gyrus, the orbital part of inferior frontal gyrus and the insula) and white matter pathways (corticospinal fasciculus, internal capsule, arcuate fasciculus, uncinate fasciculus, frontal aslant tract) are associated with reduced speech fluency. Furthermore, we identified connections emanating from these domain-specific regions that exhibited significant correlations with speech fluency. These findings illuminate the interaction between domain-specific regions and 17 domain-general regions-encompassing the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus and rolandic operculum, superior temporal gyrus, temporal pole, inferior temporal pole, middle cingulate gyrus, supramarginal gyrus, fusiform gyrus, inferior parietal lobe, as well as subcortical structures such as thalamus-implicating their collective role in supporting fluent speech. Our detailed mapping of the speech fluency network offers a strategic foundation for clinicians to safeguard language function during the surgical intervention for brain tumours.

Discrete Wavelet Transformation based Multimodal Medical Image Fusion for Disease Identification

In medical science, image processing techniques play a significant function. Computational automation of the treatment is the most authentic and prominent method. The disease of the brain is identified using Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Many more scan variations of MRI and PET have been executed for the medical diagnosis. The medical expert needs a solid strain of the computational scan and it’s related for diagnosis. The current era of computer research is turning towards clinical diagnosis and etiological analysis based on multimodal image processing. Based on individual medical modalities the accuracy of diseases diagnosis is decreases. Medical community requires a high accuracy in the disease’s identification based on multimodal scan image data. For the diagnosis and treatment of disorders requires precise information that is attained through various modalities of medical images such as Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI). In image processing the image fusion is the method of merging two images into a single picture. The obtained single fused image using various multi-modality medical images is enhanced anatomical, highly desirable spectral information compared to the raw single scanned image. This multi – modal fused image is useful for clinical diagnosis of medical experts. In this research work, the system is prepared for the preprocessing of the MRI and PET scan images. The pre-processing techniques enhance the quality of the input images which are degraded and non-readable. For the pre-processing approach we have applied the Gaussian filters of spatial filtering techniques. The enhanced images passed to the fusion of different region of brain images using Discrete Wavelet Transform (DWT). The system achieved around 90- 95% more accurate outcomes by diluting the color change. The outcome fused image is achieved without losing the spectral and anatomical data. The experiment has been tested on Alzheimer’s, normal axis, and normal coronal brain disease images dataset. The quantitative and graphical analysis indicates that’s the Discrete Wavelet Transform (DWT) significantly improves the quality of fused images.

Enhanced Diagnostics Based on Deep Learning for Varicose Vein Detection

Varicose veins, a prevalent vascular condition affecting a significant portion of the population, necessitate early detection for optimal intervention and improved patient outcomes. This study introduces a novel diagnostic approach for varicose vein detection, leveraging the capabilities of deep learning. Varicose veins represent a prevalent vascular condition that can lead to discomfort and complications if not identified and treated promptly. This research introduces an advanced diagnostic methodology for varicose vein detection, utilizing deep learning techniques. Our approach harnesses the capabilities of deep neural networks, specifically employing a convolution neural network (CNN) architecture. Trained on a diverse dataset of vascular images, the model demonstrates a high proficiency in identifying varicose veins by capturing nuanced patterns and subtle features. Fine-tuning through transfer learning ensures adaptability to variations in patient demographics and imaging conditions. To enhance diagnostic precision, our system incorporates advanced image processing techniques and feature extraction methods. The integration of image segmentation algorithms and anatomical feature analysis provides detailed insights into the extent and severity of varicose veins. Furthermore, the model is designed to accommodate multi-modal imaging data, incorporating information from various imaging modalities to enhance diagnostic accuracy. Validation of our approach is conducted on a comprehensive dataset encompassing a spectrum of varicose vein cases, including varying degrees of severity. The results underscore the superior performance of our deep learning-based diagnostic system, achieving notable sensitivity and specificity in varicose vein detection. This proposed enhanced diagnostic tool holds promise for healthcare professionals, streamlining the diagnostic process and facilitating early and accurate identification of varicose veins. The potential implications include improved patient outcomes through timely intervention and the development of personalized treatment strategies. As we continue refining and expanding our model, its integration into clinical practice could contribute to a more efficient and effective management of varicose vein disorders.

Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry.

Multimodal sensors capture and integrate diverse characteristics of a scene to maximize information gain. In optics, this may involve capturing intensity in specific spectra or polarization states to determine factors such as material properties or an individual's health conditions. Combining multimodal camera data with shape data from 3D sensors is a challenging issue. Multimodal cameras, e.g., hyperspectral cameras, or cameras outside the visible light spectrum, e.g., thermal cameras, lack strongly in terms of resolution and image quality compared with state-of-the-art photo cameras. In this article, a new method is demonstrated to superimpose multimodal image data onto a 3D model created by multi-view photogrammetry. While a high-resolution photo camera captures a set of images from varying view angles to reconstruct a detailed 3D model of the scene, low-resolution multimodal camera(s) simultaneously record the scene. All cameras are pre-calibrated and rigidly mounted on a rig, i.e., their imaging properties and relative positions are known. The method was realized in a laboratory setup consisting of a professional photo camera, a thermal camera, and a 12-channel multispectral camera. In our experiments, an accuracy better than one pixel was achieved for the data fusion using multimodal superimposition. Finally, application examples of multimodal 3D digitization are demonstrated, and further steps to system realization are discussed.

Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model.

The incidence of COVID-19, a virus that is responsible for infections in the upper respiratory tract and lungs, witnessed a daily rise in fatalities throughout the pandemic. The timely identification of COVID-19 can contribute to the formulation of strategies to control the disease and the selection of an appropriate treatment pathway. Given the necessity for broader COVID-19 diagnosis, researchers have developed more advanced, rapid, and efficient detection methods. By conducting an initial comparative analysis of various widely used convolutional neural network (CNN) models, we determine an appropriate CNN model. Subsequently, we enhance the chosen CNN model using the feature fusion strategy from multi-modal imaging datasets. Moreover, the Jaya optimization technique is employed to determine the optimal weighting for merging these dual features into a single feature vector. An SVM classifier is employed to categorize samples as either COVID-19 positive or negative. For the purpose of experimentation, a standard dataset consisting of 10,000 samples is used, divided equally between COVID-19 positive and negative classes. The experimental outcomes demonstrate that the proposed fine-tuned system, coupled with optimization techniques for multi-modal data, exhibits superior performance, achieving accuracy rates of 98.7% as compared to the existing state-of-the-art network models.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Utility of multimodal longitudinal imaging data for dynamic prediction of cardiovascular and renal disease: the CARDIA study.

Medical examinations contain repeatedly measured data from multiple visits, including imaging variables collected from different modalities. However, the utility of such data for the prediction of time-to-event is unknown, and only a fraction of the data is typically used for risk prediction. We hypothesized that multimodal longitudinal imaging data could improve dynamic disease prognosis of cardiovascular and renal disease (CVRD). In a multi-centered cohort of 5,114 CARDIA participants, we included 166 longitudinal imaging variables from five imaging modalities: Echocardiography (Echo), Cardiac and Abdominal Computed Tomography (CT), Dual-Energy x-ray Absorptiometry (DEXA), Brain Magnetic Resonance Imaging (MRI) collected from young adulthood to mid-life over 30 years (1985-2016) to perform dynamic survival analysis of CVRD events using machine learning dynamic survival analysis (Dynamic-DeepHit, LTRCforest, and Extended Cox for Time-varying Covariates). Risk probabilities were continuously updated as new data were collected. Model performance was assessed using integrated AUC and C-index and compared to traditional risk factors. Longitudinal imaging data, even when being irregularly collected with high missing rates, improved CVRD dynamic prediction (0.03 in integrated AUC, up to 0.05 in C-index compared to traditional risk factors; best model's C-index = 0.80-0.83 up to 20 years from baseline) from young adulthood followed up to midlife. Among imaging variables, Echo and CT variables contributed significantly to improved risk estimation. Echo measured in early adulthood predicted midlife CVRD risks almost as well as Echo measured 10-15 years later (0.01 C-index difference). The most recent CT exam provided the most accurate prediction for short-term risk estimation. Brain MRI markers provided additional information from cardiac Echo and CT variables that led to a slightly improved prediction. Longitudinal multimodal imaging data readily collected from follow-up exams can improve CVRD dynamic prediction. Echocardiography measured early can provide a good long-term risk estimation, while CT/calcium scoring variables carry atherosclerotic signatures that benefit more immediate risk assessment starting in middle-age.

Multi-Level fusion graph neural network: Application to PET and CT imaging for risk stratification of head and neck cancer

Background and objectiveRisk stratification of head and neck cancer (HNC) patients is important to settle individualized treatment strategies. This study proposed a multi-level fusion graph neural network (MLF-GNN) that fuses PET/CT image features and clinical data for improved prognosis prediction of HNC patients. MethodsThis study exploited 642 HNC patients from 7 different centers collected from The Cancer Imaging Archive (TCIA), which were arbitrarily separated into 507 patients from 4 centers for training and internal validation, and 135 patients from 3 centers for external testing. We constructed a population graph with edges representing the similarity between patients and the vertices representing radiomics features. Single-modality GNN models and MLF-GNN models by using feature-fusion and network-fusion strategies were constructed for progression-free survival (PFS) prediction, respectively. Model performance was evaluated by the concordance index (C-index), area under the receiver operator characteristic curve (ROC-AUC), and Kaplan–Meier curves. ResultsCompared with the single-modality GNN models and feature-fusion based MLF-GNN model, the network-fusion based MLF-GNN model achieved the highest C-index of 0.788 (95 % CI: 0.727–0.848) and AUC of 0.807 (95 % CI: 0.731–0.870) for PFS prediction in the external testing set. Besides, it also showed good performance on the secondary endpoints of overall survival (OS), recurrence-free survival (RFS), and metastasis-free survival (MFS) in the external testing set, with C-index of 0.800 (95 % CI: 0.729–0.871), 0.823 (95 % CI: 0.763–0.884) and 0.758 (95 % CI: 0.673–0.844), respectively. Subgroup analysis stratified with pathogenic site and treatment type showed that the proposed method has good prediction performance on subgroup risk stratification. ConclusionsThe proposed MLF-GNN model could capture the topological relationships among patients by taking full advantage of multi-modality imaging data and clinical data, which achieved improved prognostic performance and was beneficial to guide individual treatment.

Multi-modal ultrasound multistage classification of PTC cervical lymph node metastasis via DualSwinThyroid.

This study aims to predict cervical lymph node metastasis in papillary thyroid carcinoma (PTC) patients with high accuracy. To achieve this, we introduce a novel deep learning model, DualSwinThyroid, leveraging multi-modal ultrasound imaging data for prediction. We assembled a substantial dataset consisting of 3652 multi-modal ultrasound images from 299 PTC patients in this retrospective study. The newly developed DualSwinThyroid model integrates various ultrasound modalities and clinical data. Following its creation, we rigorously assessed the model's performance against a separate testing set, comparing it with established machine learning models and previous deep learning approaches. Demonstrating remarkable precision, DualSwinThyroid achieved an AUC of 0.924 and an 96.3% accuracy on the test set. The model efficiently processed multi-modal data, pinpointing features indicative of lymph node metastasis in thyroid nodule ultrasound images. It offers a three-tier classification that aligns each level with a specific surgical strategy for PTC treatment. DualSwinThyroid, a deep learning model designed with multi-modal ultrasound radiomics, effectively estimates the degree of cervical lymph node metastasis in PTC patients. In addition, it also provides early, precise identification and facilitation of interventions for high-risk groups, thereby enhancing the strategic selection of surgical approaches in managing PTC patients.

Abstract 97: Acute Ischemic Stroke Despite Anticoagulant Use: The Neuro-AFib Study

Background: Acute ischemic stroke (AIS) admissions in patients with atrial fibrillation (AF) using anticoagulants (AIS-despite-AC) are commonly seen in stroke units but data on their frequency, causes, and outcomes are scarce. Methods: The Neuro-AFib study is a multicenter effort geared toward elucidating the causes and consequences of strokes in a contemporary AF cohort. Detailed clinical, laboratory and multimodal imaging data from known AF patients consecutively admitted to 20 stroke centers with an IS between 1/2018-12/2019 were used to define characteristics of AIS-despite-AC and compared to AIS-off-AC. Results: Out of 4456 patients with known AF prior to the AIS, 2051 (46%) were using anticoagulants. Patients who had AIS-despite-AC were younger (76.8 + 11 vs 77.8 + 12, p=0.007), had higher mean CHA 2 DS 2 -VASc scores (4.57 + 1.7 vs 4.21 + 1.7, p&lt;0.001), and they were more likely to have permanent AF (23.5% vs 19.6%, p=0.002) and a past-history of stroke/TIA (36% vs 25%, p&lt;0.001) when compared to non-AC group. Chronic embolic (non-lacunar) infarcts on imaging were more common among AIS-despite-AC (34.5% vs 26.7%, p&lt;0.001). Acute large vessel occlusion (LVO) was common among AIS-despite-AC (48%). The pattern of the acute infarcts (numbers, embolic features) was not different. The calculated acute infarct volume was large (34ml) and mean NIHSS was 10.4 for AIS-despite-AC. Intravenous thrombolysis was used much less commonly for AIS-despite-AC (10.5% vs 32.1%, p&lt;0.001). Symptomatic carotid disease was suspected in 2.4%, a hypercoagulable condition in 5.7%, whereas acute lacunar infarct was found in 6.5% of all patients. Death or significant disability (mRS 3-5) at hospital discharge were common in AIS on vs off AC (70.6% vs 75.2%, p&lt;0.001). Conclusions: Based on a large multicenter cohort, AF patients who have AIS-despite-AC have higher AF load and risk scores, most don't have a concurrent etiology and they are more likely to have recurrent embolic events. The stroke severity, common presence of LVO, infarct size, and poor outcomes are alarming features, and most patients are unable to receive thrombolysis because of AC use. Overall, detection of AF patients at risk of AIS-despite-AC and development of specific preventive modalities should be priorities.

Serous maculopathy with absence of retinal pigment epithelium (SMARPE) associated with large drusen

PurposeTo describe the association of serous maculopathy with absence of retinal pigment epithelium (SMARPE) and large drusen in patients with non-neovascular age-related macular degeneration (AMD).MethodsA retrospective study of ophthalmic examination and multimodal imaging data of individuals with SMARPE and large drusen observed over a period of 12-month was accomplished. SMARPE was defined as subretinal accumulation of fluid within the macular area due to retinal pigment epithelium (RPE) aperture. Large drusen were identified by the presence of sub-RPE deposits using multimodal imaging analysis (color fundus photography, fundus autofluorescence, and spectral-domain optical coherence tomography).ResultsTwelve eyes of 7 white patients with a mean age of 77 years were observed to have SMARPE associated with large drusen. The median visual acuity was 20/100. Bilateral SMARPE lesions were observed in 71% of study patients. All SMARPE lesions were hypoautofluorescent, located in the subretinal space between the RPE and the ellipsoid zone, and presented as complete or incomplete RPE apertures associated with subretinal fluid. The SMARPE in this study had coincident multimodal imaging features as the SMARPE described in other reports in the literature.ConclusionsBilateral SMARPE can occur in association with typical AMD large drusen. Anomalisms resulting in drusen biogenesis or mechanisms that act alongside to these may be related to SMARPE development.