Abnormalities in cardiac-induced brain tissue deformations are now detectable with MRI: A case-report of a patient who underwent craniotomy after trauma

Background: Heartbeat and respiration induce cyclic brain tissue deformations, which receive increasing attention as potential driving force for brain clearance. These deformations can now be assessed using a novel 3D strain tensor imaging (STI) method at 7 T MRI. Methods: An 18-year-old man had suffered a traumatic brain injury and was treated with a craniotomy with a maximal diameter of 12 cm. STI was employed to capture cardiac-induced brain tissue deformations and additional time-resolved 2D flow measurements were acquired to capture cerebrospinal fluid (CSF) flow towards the spinal canal. Results: The craniotomy caused major changes in all aspects of the brain's mechanical dynamics as compared to healthy volunteer references. Tissue strains increased, particularly around the craniotomy, and directionality of deformations showed large abnormalities, also in the contralateral hemisphere. As the brain tissue could pulsate outward from the skull, physiological pulsatile CSF flow at the foramen magnum was abolished. Conclusions: This work illustrates how STI can assess physiological patterns of brain tissue deformation and how craniotomy leads to widespread deformation abnormalities that can be detected at a single patient level. While this case is meant to provide proof of concept, application of STI in other conditions of abnormal brain mechanical dynamics warrants further study

When the central integrator disintegrates: A review of the role of the thalamus in cognition and dementia

The thalamus is a complex neural structure with numerous anatomical subdivisions and intricate connectivity patterns. In recent decades, the traditional view of the thalamus as a relay station and "gateway to the cortex" has expanded in recognition of its role as a central integrator of inputs from sensory systems, cortex, basal ganglia, limbic systems, brain stem nuclei, and cerebellum. As such, the thalamus is critical for numerous aspects of human cognition, mood, and behavior, as well as serving sensory processing and motor functions. Thalamus pathology is an important contributor to cognitive and functional decline, and it might be argued that the thalamus has been somewhat overlooked as an important player in dementia. In this review, we provide a comprehensive overview of thalamus anatomy and function, with an emphasis on human cognition and behavior, and discuss emerging insights on the role of thalamus pathology in dementia

When the central integrator disintegrates: A review of the role of the thalamus in cognition and dementia

The thalamus is a complex neural structure with numerous anatomical subdivisions and intricate connectivity patterns. In recent decades, the traditional view of the thalamus as a relay station and “gateway to the cortex” has expanded in recognition of its role as a central integrator of inputs from sensory systems, cortex, basal ganglia, limbic systems, brain stem nuclei, and cerebellum. As such, the thalamus is critical for numerous aspects of human cognition, mood, and behavior, as well as serving sensory processing and motor functions. Thalamus pathology is an important contributor to cognitive and functional decline, and it might be argued that the thalamus has been somewhat overlooked as an important player in dementia. In this review, we provide a comprehensive overview of thalamus anatomy and function, with an emphasis on human cognition and behavior, and discuss emerging insights on the role of thalamus pathology in dementia

Females with type 2 diabetes are at higher risk for accelerated cognitive decline than males: CAROLINA-COGNITION study

BACKGROUND AND AIM: Cognitive dysfunction is increasingly recognized as an important comorbidity of type 2 diabetes (T2D). We aimed to establish if the risk of accelerated cognitive decline (ACD) is higher in females with T2D than males. METHODS AND RESULTS: 3163 participants (38% female) with T2D from the cognition substudy of CAROLINA® (NCT01243424) were included (mean age 64.4 ± 9.2 years; T2D duration 7.6 ± 6.1 years). The cognitive outcome was occurrence of ACD at end of follow-up, defined as a regression based index score ≤16th percentile on either the Mini-Mental State Examination (MMSE) or a composite measure of attention and executive functioning (Trail Making and Verbal Fluency Test). Potential confounders, were taken into account at an individual patient level. Logistic regression analysis was used to investigate ACD risk by sex. We assessed potential mediators for sex differences in ACD using Causal Mediation Analysis (CMA). After a median follow-up duration of 6.1 ± 0.7 years, 361 (30.0%) females compared to 494 (25.2%) males exhibited ACD (OR 1.27 [95%CI 1.08-1.49], p = .003). Depressive symptoms, which were more common in females (24.3% vs 12.5%), mediated between sex and ACD (mediation effect 20.3%, p = 0.03). There were no other significant mediators. CONCLUSION: Females with T2D had a higher risk of ACD compared to males. This was partly explained by depressive symptoms. After evaluation of vascular and diabetes-related risk factors, complications and treatment, a major share of the higher risk of ACD in females remained unexplained. Our results highlight the need for further research on causes of sex-specific ACD in T2D

An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.Comment: 11 pages, 5 figure

Is it time for Heart-Brain clinics?: A clinical survey and proposition to improve current care for cognitive problems in heart failure

BACKGROUND: Cognitive impairment is highly prevalent among patients with heart failure (HF). International guidelines on the management of HF recommend screening for cognitive impairment and tailored care for patients with cognitive impairment. However, practical guidance is lacking. In this study, we explore cardiologists' perspective on screening and care for cognitive impairment in patients with HF. We give an example of a multidisciplinary Heart-Brain care pathway that facilitates screening for cognitive impairment in patients with HF. METHODS: We distributed an online survey to cardiologists from the Dutch working groups on Geriatric Cardiology and Heart Failure. It covered questions about current clinical practice, impact of cognitive impairment on clinical decision-making, and their knowledge and skills to recognize cognitive impairment. RESULTS: Thirty-six out of 55 invited cardiologists responded. Only 3% performed structured cognitive screening, while 83% stated that not enough attention is paid to cognitive impairment. More than half of the cardiologists desired more training in recognizing cognitive impairment and three-quarters indicated that knowing about cognitive impairment would change their treatment plan. Eighty percent agreed that systematic cognitive screening would benefit their patients and 74% wished to implement a Heart-Brain clinic. Time and expertise were addressed as the major barriers to screening for cognitive impairment. CONCLUSION: Although cardiologists are aware of the clinical relevance of screening for cognitive impairment in cardiology patients, such clinical conduct is not yet commonly practiced due to lack of time and expertise. The Heart-Brain care pathway could facilitate this screening, thus improving personalized care in cardiology

Cortical microinfarcts in memory clinic patients are associated with reduced cerebral perfusion

Cerebral cortical microinfarcts (CMIs) are small ischemic lesions associated with cognitive impairment and dementia. CMIs are frequently observed in cortical watershed areas suggesting that hypoperfusion contributes to their development. We investigated if presence of CMIs was related to a decrease in cerebral perfusion, globally or specifically in cortex surrounding CMIs. In 181 memory clinic patients (mean age 72 ± 9 years, 51% male), CMI presence was rated on 3-T magnetic resonance imaging (MRI). Cerebral perfusion was assessed from cortical gray matter of the anterior circulation using pseudo-continuous arterial spin labeling parameters cerebral blood flow (CBF) (perfusion in mL blood/100 g tissue/min) and spatial coefficient of variation (CoV) (reflecting arterial transit time (ATT)). Patients with CMIs had a 12% lower CBF (beta = −.20) and 22% higher spatial CoV (beta =.20) (both p <.05) without a specific regional pattern on voxel-based CBF analysis. CBF in a 2 cm region-of-interest around the CMIs did not differ from CBF in a reference zone in the contralateral hemisphere. These findings show that CMIs in memory clinic patients are primarily related to global reductions in cerebral perfusion, thus shedding new light on the etiology of vascular brain injury in dementia

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease

This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01).Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice

Sexual dimorphism in peripheral blood cell characteristics linked to recanalization success of endovascular thrombectomy in acute ischemic stroke

Endovascular thrombectomy (EVT) success to treat acute ischemic stroke varies with factors like stroke etiology and clot composition, which can differ between sexes. We studied if sex-specific blood cell characteristics (BCCs) are related to recanalization success. We analyzed electronic health records of 333 EVT patients from a single intervention center, and extracted 71 BCCs from the Sapphire flow cytometry analyzer. Through Sparse Partial Least Squares Discriminant Analysis, incorporating cross-validation and stability selection, we identified BCCs associated with successful recanalization (TICI 3) in both sexes. Stroke etiology was considered, while controlling for cardiovascular risk factors. Of the patients, successful recanalization was achieved in 51% of women and 49% of men. 21 of the 71 BCCs showed significant differences between sexes (pFDR-corrected < 0.05). The female-focused recanalization model had lower error rates than both combined [t(192.4) = 5.9, p < 0.001] and male-only models [t(182.6) = - 15.6, p < 0.001]. In women, successful recanalization and cardioembolism were associated with a higher number of reticulocytes, while unsuccessful recanalization and large artery atherosclerosis (LAA) as cause of stroke were associated with a higher mean corpuscular hemoglobin concentration. In men, unsuccessful recanalization and LAA as cause of stroke were associated with a higher coefficient of variance of lymphocyte complexity of the intracellular structure. Sex-specific BCCs related to recanalization success varied and were linked to stroke etiology. This enhanced understanding may facilitate personalized treatment for acute ischemic stroke

Prediction of outcome in patients with suspected acute ischaemic stroke with CT perfusion and CT angiography: The Dutch acute stroke trial (DUST) study protocol

Background: Prediction of clinical outcome in the acute stage of ischaemic stroke can be difficult when based on patient characteristics, clinical findings and on non-contrast CT. CT perfusion and CT angiography may provide additional prognostic information and guide treatment in the early stage. We present the study protocol of the Dutch acute Stroke Trial (DUST). The DUST aims to assess the prognostic value of CT perfusion and CT angiography in predicting stroke outcome, in addition to patient characteristics and non-contrast CT. For this purpose, individualised prediction models for clinical outcome after stroke based on the best predictors from patient characteristics and CT imaging will be developed and validated.Methods/design: The DUST is a prospective multi-centre cohort study in 1500 patients with suspected acute ischaemic stroke. All patients undergo non-contrast CT, CT perfusion and CT angiography within 9 hours after onset of the neurological deficits, and, if possible, follow-up imaging after 3 days. The primary outcome is a dichotomised score on the modified Rankin Scale, assessed at 90 days. A score of 0-2 represents good outcome, and a score of 3-6 represents poor outcome. Three logistic regression models will be developed, including patient characteristics and non-contrast CT (model A), with addition of CT angiography (model B), and CT perfusion parameters (model C). Model derivation will be performed in 60% of the study population, and model validation in the remaining 40% of the patients. Additional prognostic value of the models will be determined with the area under the curve (AUC) from the receiver operating characteristic (ROC) curve, calibration plots, assessment of goodness-of-fit, and likelihood ratio tests.Discussion: This study will provide insight in the added prognosti