Normative body mass-adjusted reference ranges of magnetic resonance imaging signs commonly used in diagnosing idiopathic intracranial hypertension in a healthy standard population.

Patients with chronic daily headaches (CDH) are often a diagnostic challenge and frequently undergo neuroimaging. One common underlying cause of CDH is idiopathic intracranial hypertension (IIH). However, certain neuroimaging abnormalities that suggest IIH, such as optic nerve sheath diameters (ONSD), pituitary gland height, and venous sinus diameter, require interpretation due to the absence of established normative values. Notably, intracranial pressure is known to varies with age, sex and weight, further complicating the determination of objectively abnormal findings within a specific patient group. This study aims to assist clinical neuroradiologists in differentiating neuroimaging results in CDH by providing weight-adjusted normative values for imaging characteristics of IIH. In addition to age and BMI we here assessed 1924 population-based T1-weighted MRI datasets of healthy participants for relevant MRI aspects of IIH. Association to BMI was analyzed using linear/logistic regression controlled for age and stratified for sex. ONSD was 4.3 mm [2.8; 5.9]/4.6 mm [3.6; 5.7] and diameter of transverse sinus was 4.67 mm [1.6; 6.5]/4.45 mm [3.0; 7.9]. Height of pituitary gland was 5.1 mm [2.2;8.1]/4.6 mm [1.9;7.1] for female and male respectively. Values generally varied with BMI with regression slopes spanning 0.0001 to 0.05 and were therefor presented as normative values stratified by BMI. Protrusion of ocular papilla, empty sella and transverse sinus occlusion were rare in total. Our data show an association between BMI and commonly used MRI features for diagnosing IIH. We provide categorized normative BMI values for ONSD, pituitary gland height, and transverse sinus diameter. This distinction helps objectively identify potential IIH indicators compared to regular population norms, enhancing diagnostic accuracy for suspected IIH patients. Notably, optic nerve head protrusion, empty sella, and transverse sinus occlusion are rare in healthy individuals, solidifying their importance as imaging markers regardless of BMI.

Development of a bioresorbable self-expanding microstent for interventional applications - an innovative approach for stent-assisted coiling.

OBJECTIVES: Stent-assisted coiling prevents coil migration in broad-based intracranial aneurysms. So far, only permanent metal stents are approved for intracranial use. Bioresorbable stents allow a new therapeutic approach that may prevent the need for lifelong anticoagulation. We developed a neurovascular bioresorbable microstent (NBRS) and compared it in vitro to the commercial Neuroform EZ stent. MATERIALS AND METHODS: The self-expanding NBRS design is oriented on the Neuroform EZ stent. Poly L-lactic acid (PLLA) was used to manufacture semi-finished products in a dipping process. For the compensation of the inferior material properties of PLLA, design adjustments were made. The NBRS were cut by means of femtosecond (fs) laser and were morphologically and mechanically compared in vitro to the Neuroform EZ stent. In vitro implantation of an NBRS was performed using a complex patient-specific 3D-printed aneurysm model. In addition, an in vitro coiling procedure to assess the stent's ability to support a coil package was conducted. RESULTS: The NBRS could be reproducibly manufactured and had high quality regarding surface morphology. The radial force at the indicated vessel diameter of 3.0 mm was slightly higher for the Neuroform EZ stent compared to the NBRS. The self-expansion ability of the NBRS could be proven. The kink behavior of the NBRS was comparable to that of the Neuroform EZ stent, so no vessel lumen size reduction is expected. The stents showed identical deformation under local compression of 25 % based on the initial diameter, resulting in maximum forces of 24 ±â€Š5 mN (Neuroform EZ) and 8 ±â€Š2 mN (NBRS). The implanted NBRS expanded uniformly, and proper vessel wall adaptation was observed. The NBRS has the ability to retain a coil package. CONCLUSION: This study reported a reproducible manufacturing process for the developed NBRS as well as mechanical and morphological in vitro tests. Furthermore, successful NBRS implantation into a complex patient-specific vessel model was presented as proof of concept. The promising results of this study, also considering the commercial Neuroform EZ stent, support the idea of fully biodegradable microstents for intracranial aneurysm treatment. KEY POINTS: · High-performance polymer-based self-expanding neurovascular microstents were manufactured with good reproducibility.. · The bioresorbable microstent meets the requirements to pass through narrow radii.. · Implantability in a patient-specific and close-to-physiology vascular in vitro model was proven..

Effects of body mass index on the immune response within the first days after major stroke in humans.

INTRODUCTION: Immunological alterations associated with increased susceptibility to infection are an essential aspect of stroke pathophysiology. Several immunological functions of adipose tissue are altered by obesity and are accompanied by chronic immune activation. The purpose of this study was to examine immune function (monocytes, granulocytes, cytokines) as a function of body mass index (BMI: 1st group: 25; 2nd group: 25 BMI 30; 3rd group: 30) and changes in body weight post stroke. METHOD: Fat status was assessed using standardized weight measurements on days 1, 2, 3, 4, 5, and 7 after ischemic stroke in a cohort of 40 stroke patients and 16 control patients. Liver fat and visceral fat were assessed by MRI on day 1 or 2 [I] and on day 5 or 7 [II]. Leukocyte subpopulations in peripheral blood, cytokines, chemokines, and adipokine concentrations in sera were quantified. In a second cohort (stroke and control group, n = 17), multiple regression analysis was used to identify correlations between BMI and monocyte and granulocyte subpopulations. RESULTS: Weight and fat loss occurred from the day of admission to day 1 after stroke without further reduction in the postischemic course. No significant changes in liver or visceral fat were observed between MRI I and MRI II. BMI was inversely associated with IL-6 levels, while proinflammatory cytokines such as eotaxin, IFN-ß, IFN -γ and TNF-α were upregulated when BMI increased. The numbers of anti-inflammatory CD14+CD16+ monocytes and CD16+CD62L- granulocytes were reduced in patients with higher BMI values, while that of proinflammatory CD16dimCD62L+ granulocytes was increased. CONCLUSION: A small weight loss in stroke patients was detectable. The data demonstrate a positive correlation between BMI and a proinflammatory poststroke immune response. This provides a potential link to how obesity may affect the clinical outcome of stroke patients.

MR microscopy of the developing upper extremity of the chicken in ovo using 7 Tesla MRI.

MR microscopy (MRM) is known as ultra-high-field (UHF) magnetic resonance imaging with an in-plane spatial resolution of <100 µm, yields highly resolved non-invasive anatomical imaging and allows longitudinal assessment of embryonic avian development. The aim of the present study was to evaluate the feasibility of in vivo anatomical MRI assessment of the developing upper extremity of the chicken. Thirty-eight fertilized chicken eggs were examined at 7 Tesla acquiring high-resolution T2-weighted images with an in-plane resolution of 74 × 74 µm. To reduce motion artefacts, the eggs were moderately cooled before and during MRI. Development of the upper extremity was anatomically and quantitatively assessed. Chondrification and ossification on MRI were correlated with histological examination. MRM allowed the identification of the embryo from stage D5 onwards. First chondrification of the upper extremity was visible at stage D7, and the differentiation of the forearm was possible from stage D9 throughout the developmental period with excellent correlation to histology. MRM also allowed the differentiation between cortical and medullary bone as well as the detection of chondrified areas. UHF MRM allows the in vivo and in ovo evaluation of the upper limb during embryonic development and provides non-invasive longitudinal anatomical information. This technique allows longitudinal studies of the same embryo during the developmental period and may therefore provide further insights into the development of the upper extremity. With improved coil technique and increasing availability of UHF MR systems, there is great potential regarding several research topics in experimental musculoskeletal radiology.

Internal auditory canal volume in normal and malformed inner ears.

PURPOSE: A narrow bony internal auditory canal (IAC) may be associated with a hypoplastic cochlear nerve and poorer hearing performances after cochlear implantation. However, definitions for a narrow IAC vary widely and commonly, qualitative grading or two-dimensional measures are used to characterize a narrow IAC. We aimed to refine the definition of a narrow IAC by determining IAC volume in both control patients and patients with inner ear malformations (IEMs). METHODS: In this multicentric study, we included high-resolution CT (HRCT) scans of 128 temporal bones (85 with IEMs: cochlear aplasia, n = 11; common cavity, n = 2; cochlear hypoplasia type, n = 19; incomplete partition type I/III, n = 8/8; Mondini malformation, n = 16; enlarged vestibular aqueduct syndrome, n = 19; 45 controls). The IAC diameter was measured in the axial plane and the IAC volume was measured by semi-automatic segmentation and three-dimensional reconstruction. RESULTS: In controls, the mean IAC diameter was 5.5 mm (SD 1.1 mm) and the mean IAC volume was 175.3 mm3 (SD 52.6 mm3). Statistically significant differences in IAC volumes were found in cochlear aplasia (68.3 mm3, p < 0.0001), IPI (107.4 mm3, p = 0.04), and IPIII (277.5 mm3, p = 0.0004 mm3). Inter-rater reliability was higher in IAC volume than in IAC diameter (intraclass correlation coefficient 0.92 vs. 0.77). CONCLUSIONS: Volumetric measurement of IAC in cases of IEMs reduces measurement variability and may add to classifying IEMs. Since a hypoplastic IAC can be associated with a hypoplastic cochlear nerve and sensorineural hearing loss, radiologic assessment of the IAC is crucial in patients with severe sensorineural hearing loss undergoing cochlear implantation.

Volumetry improves the assessment of the vestibular aqueduct size in inner ear malformation.

OBJECTIVES: Enlarged vestibular aqueduct (EVA) is a common finding associated with inner ear malformations (IEM). However, uniform radiologic definitions for EVA are missing and various 2D-measurement methods to define EVA have been reported. This study evaluates VA volume in different types of IEM and compares 3D-reconstructed VA volume to 2D-measurements. METHODS: A total of 98 high-resolution CT (HRCT) data sets from temporal bones were analyzed (56 with IEM; [cochlear hypoplasia (CH; n = 18), incomplete partition type I (IPI; n = 12) and type II (IPII; n = 11) and EVA (n = 15)]; 42 controls). VA diameter was measured in axial images. VA volume was analyzed by software-based, semi-automatic segmentation and 3D-reconstruction. Differences in VA volume between the groups and associations between VA volume and VA diameter were assessed. Inter-rater-reliability (IRR) was assessed using the intra-class-correlation-coefficient (ICC). RESULTS: Larger VA volumes were found in IEM compared to controls. Significant differences in VA volume between patients with EVA and controls (p < 0.001) as well as between IPII and controls (p < 0.001) were found. VA diameter at the midpoint (VA midpoint) and at the operculum (VA operculum) correlated to VA volume in IPI (VA midpoint: r = 0.78, VA operculum: r = 0.91), in CH (VA midpoint: r = 0.59, VA operculum: r = 0.61), in EVA (VA midpoint: r = 0.55, VA operculum: r = 0.66) and in controls (VA midpoint: r = 0.36, VA operculum: r = 0.42). The highest IRR was found for VA volume (ICC = 0.90). CONCLUSIONS: The VA diameter may be an insufficient estimate of VA volume, since (1) measurement of VA diameter does not reliably correlate with VA volume and (2) VA diameter shows a lower IRR than VA volume. 3D-reconstruction and VA volumetry may add information in diagnosing EVA in cases with or without additional IEM.

Computed tomography hypoperfusion-hypodensity mismatch vs. automated perfusion mismatch to identify stroke patients eligible for thrombolysis.

Background and purpose: Automated perfusion imaging can detect stroke patients with unknown time of symptom onset who are eligible for thrombolysis. However, the availability of this technique is limited. We, therefore, established the novel concept of computed tomography (CT) hypoperfusion-hypodensity mismatch, i.e., an ischemic core lesion visible on cerebral perfusion CT without visible hypodensity in the corresponding native cerebral CT. We compared both methods regarding their accuracy in identifying patients suitable for thrombolysis. Methods: In a retrospective analysis of the MissPerfeCT observational cohort study, patients were classified as suitable or not for thrombolysis based on established time window and imaging criteria. We calculated predictive values for hypoperfusion-hypodensity mismatch and automated perfusion imaging to compare accuracy in the identification of patients suitable for thrombolysis. Results: Of 247 patients, 219 (88.7%) were eligible for thrombolysis and 28 (11.3%) were not eligible for thrombolysis. Of 197 patients who were within 4.5 h of symptom onset, 190 (96.4%) were identified by hypoperfusion-hypodensity mismatch and 88 (44.7%) by automated perfusion mismatch (p < 0.001). Of 22 patients who were beyond 4.5 h of symptom onset but were eligible for thrombolysis, 5 patients (22.7%) were identified by hypoperfusion-hypodensity mismatch. Predictive values for the hypoperfusion-hypodensity mismatch vs. automated perfusion mismatch were as follows: sensitivity, 89.0% vs. 50.2%; specificity, 71.4% vs. 100.0%; positive predictive value, 96.1% vs. 100.0%; and negative predictive value, 45.5% vs. 20.4%. Conclusion: The novel method of hypoperfusion-hypodensity mismatch can identify patients suitable for thrombolysis with higher sensitivity and lower specificity than established techniques. Using this simple method might therefore increase the proportion of patients treated with thrombolysis without the use of special automated software.The MissPerfeCT study is a retrospective observational multicenter cohort study and is registered with clinicaltrials.gov (NCT04277728).

Performance in information processing speed is associated with parietal white matter tract integrity in multiple sclerosis.

Background: The Symbol Digit Modalities Test (SDMT) is most frequently used to test processing speed in patients with multiple sclerosis (MS). Functional imaging studies emphasize the importance of frontal and parietal areas for task performance, but the influence of frontoparietal tracts has not been thoroughly studied. We were interested in tract-specific characteristics and their association with processing speed in MS patients. Methods: Diffusion tensor imaging was obtained in 100 MS patients and 24 healthy matched controls to compare seed-based tract characteristics descending from the superior parietal lobule [Brodman area 7A (BA7A)], atlas-based tract characteristics from the superior longitudinal fasciculus (SLF), and control tract characteristics from the corticospinal tract (CST) and their respective association with ability on the SDMT. Results: Patients had decreased performance on the SDMT and decreased white matter volume (each p < 0.05). The mean fractional anisotropy (FA) for the BA7A tract and CST (p < 0.05), but not the SLF, differed between MS patients and controls. Furthermore, only the FA of the SLF was positively associated with SDMT performance even after exclusion of the lesions within the tract (r = 0.25, p < 0.05). However, only disease disability and total white matter volume were associated with information processing speed in a linear regression model. Conclusions: Processing speed in MS is associated with the structural integrity of frontoparietal white matter tracts.

Application of a Deep Learning Approach to Analyze Large-Scale MRI Data of the Spine.

With its standardized MRI datasets of the entire spine, the German National Cohort (GNC) has the potential to deliver standardized biometric reference values for intervertebral discs (VD), vertebral bodies (VB) and spinal canal (SC). To handle such large-scale big data, artificial intelligence (AI) tools are needed. In this manuscript, we will present an AI software tool to analyze spine MRI and generate normative standard values. 330 representative GNC MRI datasets were randomly selected in equal distribution regarding parameters of age, sex and height. By using a 3D U-Net, an AI algorithm was trained, validated and tested. Finally, the machine learning algorithm explored the full dataset (n = 10,215). VB, VD and SC were successfully segmented and analyzed by using an AI-based algorithm. A software tool was developed to analyze spine-MRI and provide age, sex, and height-matched comparative biometric data. Using an AI algorithm, the reliable segmentation of MRI datasets of the entire spine from the GNC was possible and achieved an excellent agreement with manually segmented datasets. With the analysis of the total GNC MRI dataset with almost 30,000 subjects, it will be possible to generate real normative standard values in the future.

Simplified Assessment of Lesion Water Uptake for Identification of Patients within 4.5 Hours of Stroke Onset: An Analysis of the MissPerfeCT Study.

BACKGROUND AND PURPOSE: Many patients with stroke cannot receive intravenous thrombolysis because the time of symptom onset is unknown. We tested whether a simple method of computed tomography (CT)-based quantification of water uptake in the ischemic tissue can identify patients with stroke onset within 4.5 hours. METHODS: This retrospective analysis of the MissPerfeCT study (August 2009 to November 2017) includes consecutive patients with known onset of symptoms from seven tertiary stroke centers. We developed a simplified algorithm based on region of interest (ROI) measurements to quantify water uptake of the ischemic lesion and thereby quantify time of symptom onset within and beyond 4.5 hours. Perfusion CT was used to identify ischemic brain tissue, and its density was measured in non-contrast CT and related to the density of the corresponding area of the contralateral hemisphere to quantify lesion water uptake. RESULTS: Of 263 patients, 204 (77.6%) had CT within 4.5 hours. Water uptake was significantly lower in patients with stroke onset within (6.7%; 95% confidence interval [CI], 6.0% to 7.4%) compared to beyond 4.5 hours (12.7%; 95% CI, 10.7% to 14.7%). The area under the curve for distinguishing these patient groups according to percentage water uptake was 0.744 with an optimal cut-off value of 9.5%. According to this cut-off the positive predictive value was 88.8%, sensitivity was 73.5%, specificity 67.8%, negative predictive value was 42.6%. CONCLUSIONS: Ischemic stroke patients with unknown time of symptom onset can be identified as being within a timeframe of 4.5 hours using a ROI-based method to assess water uptake on admission non-contrast head CT.

An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction.

OBJECTIVE: Distributed learning avoids problems associated with central data collection by training models locally at each site. This can be achieved by federated learning (FL) aggregating multiple models that were trained in parallel or training a single model visiting sites sequentially, the traveling model (TM). While both approaches have been applied to medical imaging tasks, their performance in limited local data scenarios remains unknown. In this study, we specifically analyze FL and TM performances when very small sample sizes are available per site. MATERIALS AND METHODS: 2025 T1-weighted magnetic resonance imaging scans were used to investigate the effect of sample sizes on FL and TM for brain age prediction. We evaluated models across 18 scenarios varying the number of samples per site (1, 2, 5, 10, and 20) and the number of training rounds (20, 40, and 200). RESULTS: Our results demonstrate that the TM outperforms FL, for every sample size examined. In the extreme case when each site provided only one sample, FL achieved a mean absolute error (MAE) of 18.9 ± 0.13 years, while the TM achieved a MAE of 6.21 ± 0.50 years, comparable to central learning (MAE = 5.99 years). DISCUSSION: Although FL is more commonly used, our study demonstrates that TM is the best implementation for small sample sizes. CONCLUSION: The TM offers new opportunities to apply machine learning models in rare diseases and pediatric research but also allows even small hospitals to contribute small datasets.

An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors.

The brain age gap (BAG) has been shown to capture accelerated brain aging patterns and might serve as a biomarker for several neurological diseases. Moreover, it was also shown that it captures other biological information related to modifiable cardiovascular risk factors. Previous studies have explored statistical relationships between the BAG and cardiovascular risk factors. However, none of those studies explored causal relationships between the BAG and cardiovascular risk factors. In this work, we employ causal structure discovery techniques and define a Bayesian network to model the assumed causal relationships between the BAG, estimated using morphometric T1-weighted magnetic resonance imaging brain features from 2025 adults, and several cardiovascular risk factors. This setup allows us to not only assess observed conditional probability distributions of the BAG given cardiovascular risk factors, but also to isolate the causal effect of each cardiovascular risk factor on BAG using causal inference. Results demonstrate the feasibility of the proposed causal analysis approach by illustrating intuitive causal relationships between variables. For example, body-mass-index, waist-to-hip ratio, smoking, and alcohol consumption were found to impact the BAG, with the greatest impact for obesity markers resulting in higher chances of developing accelerated brain aging. Moreover, the findings show that causal effects differ from correlational effects, demonstrating the importance of accounting for variable relationships and confounders when evaluating the information captured by a biomarker. Our work demonstrates the feasibility and advantages of using causal analyses instead of purely correlation-based and univariate statistical analyses in the context of brain aging and related problems.

Functional MRI in Radiology-A Personal Review.

We, here, provide a personal review article on the development of a functional MRI in the radiology departments of two German university medicine units. Although the international community for human brain mapping has met since 1995, the researchers fascinated by human brain function are still young and innovative. However, the impact of functional magnetic resonance imaging (fMRI) on prognosis and treatment decisions is restricted, even though standardized methods have been developed. The tradeoff between the groundbreaking studies on brain function and the attempt to provide reliable biomarkers for clinical decisions is large. By describing some historical developments in the field of fMRI, from a personal view, the rise of this method in clinical neuroscience during the last 25 years might be understandable. We aim to provide some background for (a) the historical developments of fMRI, (b) the establishment of two research units for fMRI in the departments of radiology in Germany, and (c) a description of some contributions within the selected fields of systems neuroscience, clinical neurology, and behavioral psychology.

Cochlear and Vestibular Volumes in Inner Ear Malformations.

A "gold standard" for quantitatively diagnosing inner ear malformations (IEMs) and a consensus on normative measurements are lacking. Reference ranges and cutoff values of inner ear dimensions may add in distinguishing IEM types. This study evaluates the volumes of the cochlea and vestibular system in different types of IEM. STUDY DESIGN: Retrospective cohort. SETTING: Tertiary academic center. PATIENTS: High-resolution CT scans of 115 temporal bones (70 with IEM; cochlear hypoplasia [CH]; n = 19), incomplete partition (IP) Types I and III (n = 16), IP Type II with an enlarged vestibular aqueduct (Mondini malformation; n = 16), enlarged vestibular aqueduct syndrome (n = 19), and 45 controls. INTERVENTIONS: Volumetry by software-based, semiautomatic segmentation, and 3D reconstruction. MAIN OUTCOME MEASURES: Differences in volumes among IEM and between IEM types and controls; interrater reliability. RESULTS: Compared with controls (mean volume, 78.0 mm3), only CH showed a significantly different cochlear volume (mean volume, 30.2 mm3; p < 0.0001) among all types of IEM. A cutoff value of 60 mm3 separated 100% of CH cases from controls. Compared with controls, significantly larger vestibular system volumes were found in Mondini malformation (mean difference, 22.9 mm3; p = 0.009) and IP (mean difference, 24.1 mm3; p = 0.005). In contrast, CH showed a significantly smaller vestibular system volume (mean difference, 41.1 mm3; p < 0.0001). A good interrater reliability was found for all three-dimensional measurements (ICC = 0.86-0.91). CONCLUSION: Quantitative reference values for IEM obtained in this study were in line with existing qualitative diagnostic characteristics. A cutoff value less than 60 mm3 may indicate an abnormally small cochlea. Normal reference values for volumes of the cochlea and vestibular system may aid in diagnosing IEM.

Aspiration Versus Stent Retriever Thrombectomy for Distal, Medium Vessel Occlusion Stroke in the Posterior Circulation: A Subanalysis of the TOPMOST Study.

BACKGROUND: The optimal endovascular strategy for reperfusing distal medium-vessel occlusions (DMVO) remains unknown. This study evaluates angiographic and clinical outcomes of thrombectomy strategies in DMVO stroke of the posterior circulation. METHODS: TOPMOST (Treatment for Primary Medium Vessel Occlusion Stroke) is an international, retrospective, multicenter, observational registry of patients treated for DMVO between January 2014 and June 2020. This study analyzed endovascularly treated isolated primary DMVO of the posterior cerebral artery in the P2 and P3 segment. Technical feasibility was evaluated with the first-pass effect defined as a modified Thrombolysis in Cerebral Infarction Scale score of 3. Rates of early neurological improvement and functional modified Rankin Scale scores at 90 days were compared. Safety was assessed by the occurrence of symptomatic intracranial hemorrhage and intervention-related serious adverse events. RESULTS: A total of 141 patients met the inclusion criteria and were treated endovascularly for primary isolated DMVO in the P2 (84.4%, 119) or P3 segment (15.6%, 22) of the posterior cerebral artery. The median age was 75 (IQR, 62-81), and 45.4% (64) were female. The initial reperfusion strategy was aspiration only in 29% (41) and stent retriever in 71% (100), both achieving similar first-pass effect rates of 53.7% (22) and 44% (44; P=0.297), respectively. There were no significant differences in early neurological improvement (aspiration: 64.7% versus stent retriever: 52.2%; P=0.933) and modified Rankin Scale rates (modified Rankin Scale score 0-1, aspiration: 60.5% versus stent retriever 68.6%; P=0.4). In multivariable logistic regression analysis, the time from groin puncture to recanalization was associated with the first-pass effect (adjusted odds ratio, 0.97 [95% CI, 0.95-0.99]; P<0.001) that in turn was associated with early neurological improvement (aOR, 3.27 [95% CI, 1.16-9.21]; P<0.025). Symptomatic intracranial hemorrhage occurred in 2.8% (4) of all cases. CONCLUSIONS: Both first-pass aspiration and stent retriever thrombectomy for primary isolated posterior circulation DMVO seem to be safe and technically feasible leading to similar favorable rates of angiographic and clinical outcome.

Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging.

Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.

Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions.

Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.

Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.

Introduction: The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG. Methods: T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction. Results: The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers. Discussion: In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.

Ultrahigh field MRI determination of water diffusion rates in ex vivo human lenses of different age.

BACKGROUND: The development of presbyopia is correlated with increased lens stiffness. To reveal structural changes with age, ultrahigh field magnetic resonance imaging (UHF-MRI) was used to analyze water diffusion in differently aged human lenses ex vivo. METHODS: After enucleation lens extractions were performed. Lenses were photographed, weighed, and embedded in 0.5% agarose dissolved in culture medium. UHF-MRI was conducted to analyze anatomical characteristics of the lens using T2-weighted Turbo-RARE imaging and to obtain apparent diffusion coefficients (ADC) measurements. A Gaussian fit routine was used to examine the ADC histograms. RESULTS: An age-dependent increase in lens wet weight, lens thickness, and lens diameter was found (P<0.001). T2-weighted images revealed a hyperintense lens cortex and a gradually negative gradient in signal intensity towards the nucleus. ADC histograms of the lens showed bimodal distributions (lower ADC values mainly located in the nucleus and higher ADC values mainly located in the cortex), which did not change significantly with age [ßPeak1=1.96E-7 (-20E-7, 10E-7), P=0.804 or ßPeak2=15.4E-7 (-10E-7, 40E-7), P=0.276; respectively]. CONCLUSIONS: Clinically relevant age dependent lens hardening is probably not correlated with ADC changes within the nucleus, which could be confirmed by further measurements.