FLAIR Magnetic Resonance Imaging Research Articles

NIMG-52. ARTIFICIAL INTELLIGENCE-BASED VOLUMETRIC KINETICS FOR EVALUATING TREATMENT RESPONSE IN IDH-MUTANT GLIOMA TREATED WITH IMMUNOTHERAPY

Abstract BACKGROUND Interpretating radiographic findings of glioma patients receiving immunotherapy is challenging. Repeated imaging is often required for clinical decisions, potentially delaying next line of treatment. Here we assess a glioma segmentation AI model to evaluate treatment response in IDH-mutant glioma patients receiving nivolumab. METHODS Clinical magnetic resonance imaging (MRI)s from twenty-nine patients enrolled in a clinical trial (NCT03718767) were retrospectively analyzed. All patients had at least two sequential MRIs, including baseline study. T2, T1 (pre/post-contrast), and T2 FLAIR MRI sequences were pre-processed using publicly available AI-based whole-brain segmentation models. Lesion contours were derived from a validated 4-class brain tumor segmentation AI model, distinguishing healthy brain parenchyma, peri-lesional edema, non-contrast enhancing volume (NEV), and contrast-enhancing volume (CEV). Total lesion burden volume (TLBV=NEV + CEV + Edema) was also calculated. Growth rates between responders (R) and non-responders (NR), defined as progression-free survival ≥ or < 6months, respectively, were compared using a Wilcoxon Rank Sum Test. RESULTS The R group displayed significantly lower median rates of change for CEV (R -35% vs. NR +142% p = 4.31e-04) and TLBV (R +6% vs NR +43% p = 1.55e-03), compared to NR from baseline to post-cycle 1-2. Additionally, similar trends persisted at post-cycle 3-4 for CEV (R -41% vs. NR +367% p = 3.03e-03) and TLBV (R +12% vs. NR +40% p = 7.74e-03). Although NR showed a higher average baseline TLBV, there was no discernible correlation detected between baseline TLBV and the rate of change during treatment. CONCLUSIONS AI-based volumetric analysis offers an objective response assessment, showing promise in tracking disease progression in IDH-mutant glioma patients on nivolumab, potentially enabling earlier identification of progressive disease and allowing for timely treatment adjustments. A larger prospective study is warranted to refine volumetric pattern analysis for early clinical decisions in immunotherapy.

Identification of new instances of white matter hyperintensities in patients following TAVI (from the CAPITA study)

Abstract A third of patients diagnosed with symptomatic severe aortic valve stenosis and scheduled for transcatheter aortic valve implantation (TAVI) exhibit some level of cognitive impairment. The prospective CAPITA study aimed to systematically evaluate the impact of TAVI on cerebral and cognitive outcomes in patients undergoing the procedure. In this report, we focus on the identification of new incidence of white matter hyperintensities (WMH) and the correlation with changes in cognitive functioning. The CAPITA (CArdiac OutPut, Cerebral Blood Flow and Cognition In Patients With Severe Aortic Valve Stenosis Undergoing Transcatheter Aortic Valve Implantation) study is a prospective study of cerebral and cognitive outcomes following TAVI. Patients underwent magnetic resonance imaging (MRI) and extensive neuropsychological testing at baseline and three month follow-up. New cerebral infarcts were defined according to NeuroARC criteria. We examined the incidence of new WMH in terms of volume (Δ mL) and number on FLAIR MRI, and explored the correlation between new WMH and cognitive functioning. A total of 148 patients were included who underwent TAVI between August 2020 and October 2022. Mean age was 80.5±5.7 years, of whom 43% female with a median STS-PROM of 2.0% (IQR 1.4%-2.9%). Median baseline WMH comprised 127 (IQR 32-216) lesions, with a total volume of 13.1 (IQR 7.0-24.6) mL. At 3 months follow-up, the number of lesions increased to 164 (IQR 121-301) lesions, with a total volume of 13.3 (IQR 5.8-21.5) mL. Median WMH volume increase was 0.1 (IQR 0.0-0.3) ml (p=0.02). A total of 9 patients (9.5%) had an increase of 1 mL of WMH or more. At follow-up, 12% of patients exhibited cognitive decline of >0.5SD. These patient tended to have a greater increase in white matter hyperintensity volume (1.26±2.96 ml), compared to the 53% of patients with stable cognition (0.29±0.45 ml) and the 36% of patients with improved cognition (0.31±0.91 ml), (p=0.06). WMH lesions did not exhibit distribution patterns across various locations within the groups characterized by declined, stable, or improved cognition (Figure 1). WMH volumes were higher in patients who experienced postprocedural delirium than in patients without delirium (27.1±14.0 vs 15.0±12.5 mL, p<0.001). In this study, patients with severe symptomatic aortic valve stenosis undergoing TAVI, there was an overall increase of white matter hyperintensity volume between baseline and 3-month follow-up. Up to 12% of patients had cognitive decline, which was associated with new white matter hyperintensities on MRI.

Radiomic detection of abnormal brain regions in tuberous sclerosis complex.

Radiomics refers to the extraction of quantitative information from medical images and is most commonly utilized in oncology to provide ancillary information for solid tumor diagnosis, prognosis, and treatment response. The traditional radiomic pipeline involves segmentation of volumes of interest with comparison to normal brain. In other neurologic disorders, such as epilepsy, lesion delineation may be difficult or impossible due to poor anatomic definition, small size, and multifocal or diffuse distribution. Tuberous sclerosis complex (TSC) is a rare genetic disease in which brain magnetic resonance imaging (MRI) demonstrates multifocal abnormalities with variable imaging and epileptogenic features. The purpose of this study was to develop a radiomic workflow for identification of abnormal brain regions in TSC, using a whole-brain atlas-based approach with generation of heatmaps based on signal deviation from normal controls. This was a retrospective pilot study utilizing high-resolution whole-brain 3D FLAIR MRI datasets from retrospective enrollment of tuberous sclerosis complex (TSC) patients and normal controls. Subjects underwent MRI including high-resolution 3D FLAIR sequences. Preprocessing included skull stripping, coregistration, and intensity normalization. Using the Brainnetome and Harvard-Oxford atlases, brain regions were parcellated into 318 discrete regions. Expert neuroradiologists spatially labeled all tubers in TSC patients using ITK-SNAP. The pyradiomics toolbox was used to extract 88 radiomic features based on IBSI guidelines, comparing tuber-affected and non-tuber-affected parenchyma in TSC patients, as well as normal brain tissue in control patients. For model training and validation, regions with tubers from 20 TSC patients and 30 normal control subjects were randomly divided into two training sets (80%) and two validation sets (20%). Additional model testing was performed on a separate group of 20 healthy controls. LASSO (least absolute shrinkage and selection operator) was used to perform variable selection and regularization to identify regions containing tubers. Relevant radiomic features selected by LASSO were combined to produce a radiomic score ω, defined as the sum of squared differences from average control group values. Region-specific ω scores were converted to heat maps and spatially coregistered with brain MRI to reflect overall radiomic deviation from normal. The proposed radiomic workflow allows for quantification of deviation from normal in 318 regions of the brain with the use of a summative radiomic score ω. This score can be used to generate spatially registered heatmaps to identify brain regions with radiomic abnormalities. The pilot study of TSC showed radiomic scores ω that were statistically different in regions containing tubers from regions without tubers/normal brain (p<0.0001). Our model exhibits an AUC of 0.81 (95% confidence interval: 0.78-0.84) on the testing set, and the best threshold obtained on the training set, when applied to the testing set, allows us to identify regions with tubers with a specificity of 0.91 and a sensitivity of 0.60. We describe a whole-brain atlas-based radiomic approach to identify abnormal brain regions in TSC patients. This approach may be helpful for identifying specific regions of interest based on relatively greater signal deviation, particularly in clinical scenarios with numerous or poorly defined anatomic lesions.

Four-Hour-Delayed Gadolinium 3D REAL IR and SPACE FLAIR MRI Correlated to Meniere Disease Histology.

Objectives: This study aims to showcase the complementary nature of utilizing both histopathology and magnetic resonance imaging (MRI) in understanding the otologic pathophysiology of Meniere disease. In addition, it seeks to raise awareness of the value of preserving and curating historical temporal bone collections which continue to inform our understanding of otologic diseases. Methods: The essential anatomical feature of Meniere disease-the distended membranous labyrinth-is explored through a comparison of early temporal bone studies with contemporary MRI techniques. The histopathologic photomicrographs are of inner ear specimens from deceased patients with symptoms consistent with Meniere disease. The MRI sequences from living patients exhibiting classic Meniere disease symptoms during life are captured 4 hours post-administration of gadolinium. Results: Both histopathologic examination and MRI imaging reveal consistent distention of the saccule, utricle, and scala media in patients with Meniere disease. The study shows the histologic photomicrographs of actual Meniere patients compared to the MRIs and successfully demonstrates the correlation between postmortem histological findings and MRI evidence of distension in living patients. Conclusions: A corresponding distension of the membranous labyrinth is seen in both the histologic specimens and the Meniere MRIs. This correlation suggests the potential utility of utilizing MRI to aid in diagnosing atypical Meniere disease and distinguishing it from other disease processes, such as migraine equivalent vertigo. The integration of historical temporal bone studies with modern MRI techniques offers valuable insights into the pathophysiology of otologic diseases. In addition, it emphasizes the importance of preserving and curating historical temporal bone collections for continued research and medical education purposes. Previous studies of delayed MRIs did not use Meniere disease temporal bone histopathology images.

FLAIR MRI sequence synthesis using squeeze attention generative model for reliable brain tumor segmentation

Manual segmentation of brain tumors using structural magnetic resonance imaging (MRI) is an arduous and time-consuming task. Therefore, automatic and robust segmentation will considerably influence neuro-oncological clinical trials by reducing excessive manual annotation time. Herein, we propose a deep learning model that automatically segments brain tumors even in cases of missing MRI sequences, which are common in practical clinical settings. To address this issue, we enhance a generative adversarial network (GAN) by incorporating a squeeze-and-excitation (SE) attention module into its generator and a PatchGAN into its discriminator. The SE module recalibrates channel responses by explicitly modeling interdependencies, enabling the network to focus on critical regions such as tumor areas. Our proposed generative model is optimized using a combination of adversarial, structural similarity, and mean absolute error losses to synthesize missing MRI sequences more effectively. This enhancement allows our model to synthesize the missing MRI sequence (fluid attenuated inversion recovery [FLAIR]) by leveraging information from other available sequences (T1-weighted, T2-weighted, or contrast-enhanced T1-weighted [T1ce]). For the segmentation task, we employ an optimized nnU-Net model, which is trained using existing sequences and evaluated using both available and synthesized sequences (including missing ones), mimicking real-world scenarios where often only limited MRI sequences are available or usable. Our findings reveal a notable enhancement in brain tumor segmentation, as indicated by a significant increase in overall the Dice similarity coefficient (DSC) from 0.688% (when FLAIR is missing) to 0.873% (when using synthesized FLAIR derived from T2). This improvement brings the segmentation performance closer to what was achieved when real FLAIR was available, where the DSC reaches 0.901%. Moreover, our synthesizing model was also tested on two additional datasets: the BraTS 2020 validation set and BraTS Africa 2023 training set, which produces results comparable to those of BraTS 2021, thereby proving its robustness and generalizability. In addition, the resulting tumor segmentations are subsequently employed to assess the response to treatment in cases where all sequences were available and when synthesis was employed, according to response assessment in neuro-oncology criteria.

Influence of Surgical Resection on Survival in Glioblastoma with Multiple Localizations—a Multi-Center Retrospective Analysis

INTRODUCTION: The presence of multiple localizations in glioblastoma is rare and associated with poor prognosis. The presence of a visible junction between enhancing lesions on magnetic resonance imaging (MRI) is defined as multifocal (MF), the absence of a dissemination pathway as multicentric (MC). METHODS: Data collection was performed as a multicenter retrospective analysis. From Jan 1, 2008, to Dec 31, 2020 patients presenting with IDH wild-type glioblastoma (CNS WHO grade 4) with multiple localizations were included. Demographic data, tumor size, Karnofsky Performance Score (KPS) before and after surgery were analyzed. EOR was measured by FLAIR and contrast-enhanced MRI. Gross total resection (GTR, residual tumor (RTM) &lt; 5%), subtotal resection (STR, RTM &lt; 30%), partial resection (PR, RTM &gt; 30%), and biopsy (RTM &gt; 75%). RESULTS: This study included 117 patients with a median age of 62 years. 45 (38.5%) patients had MC lesions, 72 (61.5%) MF. Median KPS on admission was similar in both subtypes. Median OS did not differ among MC and MF glioblastomas. In MF glioblastoma, GTR (p = 0.0001) and STR (p = 0.0001) significantly increased the median OS compared to PR and biopsy. Likewise, OS was increased after GTR (p = 0.0272) and STR (p = 0.0272) in MC glioblastoma, as well. Overall, resection significantly increased OS in patients with glioblastoma with multiple localizations compared to biopsy alone. In multivariate logistic regression analysis EOR and chemotherapy were significant and independent predictors of OS in MC and MF glioblastomas. CONCLUSIONS: These data indicate that GTR and STR positively affects OS in patients with MC and MF glioblastoma. These results argue for a more aggressive stance towards surgical treatment of these tumors with particularly poor prognosis, even when GTR is not within reach before resection.

Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan

BackgroundThe choroid plexus functions as the blood-cerebrospinal fluid (CSF) barrier, plays an important role in CSF production and circulation, and has gained increased attention in light of the recent elucidation of CSF circulation dysfunction in neurodegenerative conditions. However, methods for routinely quantifying choroid plexus volume are suboptimal and require technical improvements and validation. Here, we propose three deep learning models that can segment the choroid plexus from commonly-acquired anatomical MRI data and report performance metrics and changes across the adult lifespan.MethodsFully convolutional neural networks were trained from 3D T1-weighted, 3D T2-weighted, and 2D T2-weighted FLAIR MRI using gold-standard manual segmentations in control and neurodegenerative participants across the lifespan (n = 50; age = 21–85 years). Dice coefficients, 95% Hausdorff distances, and area-under-curve (AUCs) were calculated for each model and compared to segmentations from FreeSurfer using two-tailed Wilcoxon tests (significance criteria: p < 0.05 after false discovery rate multiple comparisons correction). Metrics were regressed against lateral ventricular volume using generalized linear models to assess model performance for varying levels of atrophy. Finally, models were applied to an expanded cohort of adult controls (n = 98; age = 21–89 years) to provide an exemplar of choroid plexus volumetry values across the lifespan.ResultsDeep learning results yielded Dice coefficient = 0.72, Hausdorff distance = 1.97 mm, AUC = 0.87 for T1-weighted MRI, Dice coefficient = 0.72, Hausdorff distance = 2.22 mm, AUC = 0.87 for T2-weighted MRI, and Dice coefficient = 0.74, Hausdorff distance = 1.69 mm, AUC = 0.87 for T2-weighted FLAIR MRI; values did not differ significantly between MRI sequences and were statistically improved compared to current commercially-available algorithms (p < 0.001). The intraclass coefficients were 0.95, 0.95, and 0.96 between T1-weighted and T2-weighted FLAIR, T1-weighted and T2-weighted, and T2-weighted and T2-weighted FLAIR models, respectively. Mean lateral ventricle choroid plexus volume across all participants was 3.20 ± 1.4 cm3; a significant, positive relationship (R2 = 0.54-0.60) was observed between participant age and choroid plexus volume for all MRI sequences (p < 0.001).ConclusionsFindings support comparable performance in choroid plexus delineation between standard, clinically available, non-contrasted anatomical MRI sequences. The software embedding the evaluated models is freely available online and should provide a useful tool for the growing number of studies that desire to quantitatively evaluate choroid plexus structure and function (https://github.com/hettk/chp_seg).

Fat droplets in the cerebrospinal fluid (CSF) spaces of the brain

&lt;p&gt;It is rare to find free floating fat droplets in the cerebral spinal fluid (CSF) spaces of the brain. When fat droplets are seen in the CSF spaces, the most common cause is the rupture of a dermoid cyst. Dermoid cysts are congenital inclusion cysts that form during the neural tube closure between the third and fifth weeks of embryogenesis. In this case report, we describe a case of a 74-year-old, right-handed female who presented with an acute onset of visual disturbances and left-hand numbness. Computed tomography (CT) and magnetic resonance imaging (MRI) of the head revealed hypodense “lesions” in the lateral ventricles and basal cisterns. The CT Hounsfield unit was between −41 to −83 Hounsfield Units, which is compatible with fat rather than air. The T1 weighted and FLAIR MRI showed hyperintense lesions “floating” on top of the CSF in the lateral ventricles, which is typical for fat droplets, presumably caused by a ruptured dermoid cyst. This case emphasizes the importance of analyzing Hounsfield Units to distinguish lesions by density, where fat ranges from −50 to −150 Hounsfield Units and air is −1000 Hounsfield Units. Pneumocephalus is the presence of air in the epidural, subdural, or subarachnoid space and can cause confusion, nausea, seizures and focal neurological symptoms. A careful analysis of the neuroimaging findings in the CT with or without MRI is important in making a correct diagnosis of a ruptured dermoid cyst versus pneumocephalus.&lt;/p&gt;

Regional White Matter Hyperintensities are Associated with Diminished Blood Flow and Microstructure in Middle Age

AbstractBackgroundWhite matter hyperintensities (WMH) are areas of increased signal visualized on T2‐weighted FLAIR magnetic resonance imaging (MRI), thought to reflect macrostructural damage due to small vessel cerebrovascular disease. Previous work established an association of WMH severity, particularly in posterior regions, with risk and progression of AD in late onset and genetic forms of AD, suggesting a vascular component to disease pathogenesis and course. Examination of the emergence and correlates of cerebrovascular changes in middle age, a critical time when vascular risk appears to be most penetrant, can give insight into mechanisms linking vascular brain injury and risk for AD. In the current study, we examined the association of cerebral blood flow (CBF) and white matter microstructure with total and regional WMH volume in middle‐aged, community dwelling adults.MethodFour hundred and ninety‐two middle‐aged participants (55+/‐11 age = 28 ‐85 years) from the community‐based Offspring Study of Mechanisms for Racial Disparities in Alzheimer’s Disease received multi‐modal MRI scanning. Regional WMH volumes were derived with in‐house software that labeled clusters of voxels within a hyperintense intensity distribution. Pulsed arterial spin labeling (PASL) sequences were processed to quantify CBF. Fractional anisotropy (FA) maps were obtained from diffusion weighted data. Total and regional WMH volumes were used as predictors for whole brain voxel‐wise correlation analyses in separate models for CBF and FA.ResultThere were negative associations between WMH volume and CBF in frontal, parietal, and occipital regions. Higher white matter hyperintensity volume was associated with lower FA, particularly in the periventricular region.ConclusionWe found associations of increased WMH volume with diminished blood flow and white matter microstructure. Future studies are needed to explain the relationship between these changes and the risk of cognitive impairment in middle aged adults and risk for AD.

Parietal resting-state EEG alpha source connectivity is associated with subcortical white matter lesions in HIV-positive people

ObjectiveParietal resting-state electroencephalographic (rsEEG) alpha (8–10 Hz) source connectivity is abnormal in HIV-positive persons. Here we tested whether this abnormality may be associated with subcortical white matter vascular lesions in the cerebral hemispheres. MethodsClinical, rsEEG, and magnetic resonance imaging (MRI) datasets in 38 HIV-positive persons and clinical and rsEEG datasets in 13 healthy controls were analyzed. Radiologists visually evaluated the subcortical white matter hyperintensities from T2-weighted FLAIR MRIs (i.e., Fazekas scale). In parallel, neurophysiologists estimated the eLORETA rsEEG source lagged linear connectivity from parietal cortical regions of interest. ResultsCompared to the HIV participants with no/negligible subcortical white matter hyperintensities, the HIV participants with mild/moderate subcortical white matter hyperintensities showed lower parietal interhemispheric rsEEG alpha lagged linear connectivity. This effect was also observed in HIV-positive persons with unimpaired cognition. This rsEEG marker allowed good discrimination (area under the receiver operating characteristic curve > 0.80) between the HIV-positive individuals with different amounts of subcortical white matter hyperintensities. ConclusionsThe parietal rsEEG alpha source connectivity is associated with subcortical white matter vascular lesions in HIV-positive persons, even without neurocognitive disorders. SignificanceThose MRI-rsEEG markers may be used to screen HIV-positive persons at risk of neurocognitive disorders.

Distinct characteristics and severity of brain magnetic resonance imaging lesions in women and men with multiple sclerosis assessed using verified texture analysis measures.

In vivo characterization of brain lesion types in multiple sclerosis (MS) has been an ongoing challenge. Based on verified texture analysis measures from clinical magnetic resonance imaging (MRI), this study aimed to develop a method to identify two extremes of brain MS lesions that were approximately severely demyelinated (sDEM) and highly remyelinated (hREM), and compare them in terms of common clinical variables. Texture analysis used an optimized gray-level co-occurrence matrix (GLCM) method based on FLAIR MRI from 200 relapsing-remitting MS participants. Two top-performing metrics were calculated: texture contrast and dissimilarity. Lesion identification applied a percentile approach according to texture values calculated: ≤ 25 percentile for hREM and ≥75 percentile for sDEM. The sDEM had a greater total normalized volume yet smaller average size, and worse MRI texture than hREM. In lesion distribution mapping, the two lesion types appeared to overlap largely in location and were present the most in the corpus callosum and periventricular regions. Further, in sDEM, the normalized volume was greater and in hREM, the average size was smaller in men than women. There were no other significant results in clinical variable-associated analyses. Percentile statistics of competitive MRI texture measures may be a promising method for probing select types of brain MS lesion pathology. Associated findings can provide another useful dimension for improved measurement and monitoring of disease activity in MS. The different characteristics of sDEM and hREM between men and women likely adds new information to the literature, deserving further confirmation.

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis

Background:Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation. Purpose:investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation. Methods:CT and corresponding T1-weighted MRI with/without contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation.A “Baseline” generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline. Results:The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE) = 106 ± 20.7 HU (mean ±σ). Performance on FLAIR significantly improved for the DR model with MAE = 99.0 ± 14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE = 72.6 ± 10.1 HU). Similarly, an improvement in γ-pass rate was obtained for DR vs Baseline. Conclusion:DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining.

Normative values of the brain health index in UK biobank

BackgroundThe Brain Health Index (BHI) is an automated approach to quantifying brain integrity, combining different types of structural magnetic resonance imaging (MRI). Normative values derived from generally healthy individuals provide a vital baseline for understanding neurodegenerative change. Although commonplace in other areas of medicine, these are not always established when proposing new analytical approaches using MRI. The scale and quality of the UK Biobank imaging cohort (approximately N = 50k, as of 2022) allows for derivation of such values, and the wealth of additional lifestyle, physiological and demographic data enables validation of BHI through comparison with more established variables which may affect brain health. AimThis study aimed to: 1) establish normative BHI values in a cohort of ‘healthy’ participants, and 2) explore associations between BHI and risk factors for brain health. MethodsThe BHI was computed using voxel-based Gaussian mixture model cluster analysis of T1 and T2 FLAIR MRI in a sub-cohort of UK Biobank participants. From these data, normative score curves – with bounds described as 1, 2 and 3 standard deviations from the mean – were produced for males and females, using regression analyses to measure the scale of the BHI values as a function of age. Additional Pearson’s correlation testing was used to examine known risk factors to brain health and their relationship to BHI scores, with t-tests and ANOVAs used to determine between-group differences in BHI scoring. ResultsData from 2,990 participants (50.07% male, 97.05% Caucasian, 43.6% with degree-level education) were used to derive normative BHI curves from 48 to 77 years old. BHI scores were higher in female than male participants (95% CI: 0.0103 to 0.0162, p <0.001, Cohen’s d = 0.0416), males with a degree (95% CI: 0.000 to 0.009; p < 0.05; Cohen’s d = 0.044), and lower in people with type 2 diabetes mellitus (95% CI: 0.018 to 0.033; p <0.001; Cohen’s d = 0.0417), hypertension (95% CI: 0.008 to 0.018; p <0.001; Cohen’s d = 0.0419), and regular smokers (95% CI: 0.009 to 0.017, p <0.001, Cohen’s d = 0.041). BHI scores were higher in those with lower waist-to-hip ratios (WHR; males: R2 = 0.02121, F(1, 1466) = 31.77, p <0.001; females: R2 = 0.02201, F(1, 1454) = 32.72, p <0.001), and lower pulse pressure (males: R2 = 0.06261, F(1, 1215) = 81.16, p <0.001; females: R2 = 0.07616, F(1, 1205) = 99.34, p <0.001). ConclusionsBHI score curves may provide useful reference values for future clinical research. More work is required to determine normative values in more diverse populations.

Progression of white matter signal abnormalities in predementia frontotemporal dementia mutation carriers

AbstractBackgroundWhite matter signal abnormalities (WMSA) are frequently observed on magnetic resonance imaging (MRI) scans of people with frontotemporal dementia (FTD). We hypothesized that carriers of mutations in progranulin (GRN+), chromosome 9 open reading frame 72 (C9orf72+) and microtubule associated protein tau (MAPT+) have increased rates of progression of WMSA compared to non‐carrier individuals prior to onset of dementia, and that different mutations may have different rates of WMSA accumulation.MethodParticipants were recruited through the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Study. We selected participants with Clinical Dementia Rating scale (CDR) scores of 0 and 0.5. If a participant developed FTD during the follow‐up, we only analyzed observations collected before the conversion. WMSA were defined as hyperintensities on FLAIR MRI. Total WMSA volumes were normalized to the respective intracranial volumes. We used a linear mixed effects model, with random intercepts and slopes, to compare the baseline burden and the longitudinal progression of total WMSA volumes between mutation carriers and controls. The model was adjusted for baseline age, sex, education, baseline Montreal Cognitive Assessment (MoCA) scores, and MRI acquisition sites.ResultWe analyzed MRI data from 199 participants who had at least two MRI visits (46 C9orf72+, 17 GRN+, 35 MAPT+, 101 non‐carrier controls; up to five years of follow‐up). We did not find significant group differences in baseline demographic features, except that MAPT+ was younger than non‐carrier controls (average age: C9orf72+ = 47, GRN+ = 56, MAPT+ = 40, controls = 47). Baseline WMSA volumes were not significantly different between mutation carriers and controls. Longitudinally, the rates of WMSA volume increase in C9orf72+ and MAPT+ were comparable to controls; whereas, GRN+ had higher rates of WMSA volume increases compared to controls (p = 0.01).ConclusionWMSA may accumulate at higher rates in GRN+ prior to the onset of FTD, which is consistent with the frequent presence of white matter diseases observed in symptomatic FTD patients with GRN mutations. Future work is warranted to investigate the association between white matter structural changes and cognitive/psychiatric changes in FTD mutation carriers.

Correlation between quantitative and qualitative assessments of white matter hyperintensities in patients from a multicentric memory clinic population

AbstractBackgroundWhite matter hyperintensities (WMH) can be visualized as abnormal T2 signal in the periventricular, deep and subcortical white matter on Magnetic Resonance Imaging (MRI). WMH increase with age and are associated with stroke, cognitive decline and dementia. The most common visual rating scale used to assess WMH in the clinical setting is the Fazekas scale (Fazekas et al., 1987). The current study investigated the relationship between WMH volume (expressed as a % of intra‐cranial volume (ICV)) calculated using an automated method included in the QyScore® medical device, and Fazekas scores in a multicentric memory clinic population at baseline.Method1959 participants from the MEMENTO cohort (Age 71.02 +/‐ 8.61, 1218 Female, MMSE 27.95 +/‐ 1.91) had 3DT1 and T2 FLAIR MRI scans analyzed with QyScore®, an FDA‐approved and CE‐marked image analysis software. 814 had amyloid status from CSF or PET, of which 216 were Amyloid+. WMH volumes are reported in mL, corrected for %ICV. Correlation between Fazekas deep white matter (DWM) scores and quantitative WMH measurements was ascertained by Spearman’s rank correlation.ResultThere was a significant positive correlation between Fazekas DWM scores and WMH %ICV (0.76 p&lt;0.001) for the total sample, amyloid positive (0.81 p&lt;0.001) and negative (0.72 p&lt;0.001) patients. The correlation was also strong when separated by clinical diagnosis: patients with Subjective Cognitive Complaint (SCC, N = 305, AGE: 69.87 +/‐ 8.03, 197 female, MMSE: 28.86 +/‐ 1.17) and MCI (N = 1638, Age: 71.2 +/‐ 8.69, 1014 Female, MMSE: 27.8 +/‐ 1.95) presented Fazekas DWM scores correlated with WMH %ICV (0.66 p&lt;0.001 for SCC; 0.77 p&lt;0.001 for MCI).ConclusionThe automated quantification of ICV‐corrected WMH were well‐correlated to Fazekas DWM scores. Considering that visual rating of WMH is time consuming and suffers from intra and inter‐operator variability, the quantitative assessment of WMH load provided by QyScore® may improve the assessment of patients with cognitive impairment.

Differentiation of MOGAD in ADEM-like presentation children based on FLAIR MRI features.

The differences in magnetic resonance imaging (MRI) between children with classic acute disseminated encephalomyelitis (ADEM) and myelinal oligodendrocyte glycoprotein antibody associated disease (MOGAD) with ADEM-like presentation are controversial. The purpose of this study was to investigate whether the radiological characteristics of the MRI-FLAIR sequence can predict MOGAD in children with ADEM-like presentation and to further explore its imaging differences. We extracted 1041 radiomics features from MRI-FLAIR lesions. Then we used the redundancy analysis (Spearman correlation coefficient), significance test (student test or Mann-Whitney U test), least absolute contraction and selection operator (LASSO) to select potential predictors from the feature groups. The selected potential predictors and MOG antibody test results were used to fit the machine learning model for classification. Combined with feature selection and machine learning classifiers, the optimal model for each subgroup was derived. The resulting models have been evaluated using the receiver operator characteristic curve (ROC) at the lesion level and the model performance was evaluated at the case level using decision curve analysis. We retrospectively reviewed and re-diagnosed 70 ADEM-like presentation cases in our center from April 2015 to January 2020. Including 49 cases with classic ADEM and 21 cases with MOGAD. 30(43%) were female, with a median age of 5.3 years. On the four subgroups by age and gender, the area under the curve (AUC) of the optimal models were 89%, 90%, 98%, and 99%, and the MOGAD detection rates (Specificity) were 83%, 83%, 92%, and 75%, respectively. The machine learning model trained on radiomics features of MR-FLAIR images can effectively predict patients' MOGAD. This study provides a fast, objective, and quantifiable method for MOGAD diagnosis.

Using a semi-automated approach to quantify Unidentified Bright Objects in Neurofibromatosis type 1 and linkages to cognitive and academic outcomes

Neurofibromatosis type 1 (NF1) is an autosomal dominant neurocutaneous syndrome that affects multiple organ systems resulting in widespread symptoms, including cognitive deficits. In addition to the criteria required for an NF1 diagnosis, approximately 70% of children with NF1 present with Unidentified Bright Objects (UBOs) or Focal Areas of Signal Intensity, which are hyperintense bright spots seen on T2-weighted magnetic resonance images and seen more prominently on FLAIR magnetic resonance images (Sabol et al., 2011). Current findings relating the presence/absence, quantities, sizes, and locations of these bright spots to cognitive abilities are mixed. To contribute to and hopefully disentangle some of these mixed findings, we explored potential relationships between metrics related to UBOs and cognitive abilities in a sample of 28 children and adolescents with NF1 (M=12.52 years; SD=3.18 years; 16 male). We used the Lesion Segmentation Tool (LST) to automatically detect and segment the UBOs. The LST was able to qualitatively and quantitatively reliably detect UBOs in images of children with NF1. Using these automatically detected and segmented lesions, we found that while controlling for age, biological sex, perceptual IQ, study, and scanner, “total UBO volume”, defined as the sum of all the voxels representing all of the UBOs for each participant, helped explain differences in word reading, phonological awareness, and visuospatial skills. These findings contribute to the emerging NF1 literature and help parse the specific deficits that children with NF1 have, to then help improve the efficacy of reading interventions for children with NF1.

Unraveling the peripheral and local role of inflammatory cytokines in glioblastoma survival.

Glioblastoma (GBM) is a life-threatening disease that presents high morbidity and mortality. The standardized treatment protocol results in a global survival of less than three years in the majority of cases. Immunotherapies have gained wide recognition in cancer treatment; however, GBM has an immunosuppressive microenvironment diminishing the possible effectiveness of this therapy. In this sense, investigating the inflammatory settings and the tumoral nature of GBM patients are an important goal to create an individual plan of treatment to improve overall survival rate and quality of life of these patients. Thirty-two patients who underwent surgical resection of GBM were included in this study. Tumor samples and 10mL of peripheral blood were collected and immediately frozen. TNF-a, IL-1a and IL-4 were evaluated in the tumor and TNF-a, IL-1a and TGF-b in the plasma by Luminex assay. Immunohistochemistry analysis to determine immune celular profile was done, including immunohistochemistry for CD20, CD68 and CD3. Three cases were excluded. Tumor topography, tumor nature, and tumor volume reconstructions were accurately analyzed by T1-weighted, T2-weighted, and FLAIR magnetic resonance imaging. We found that GBM patients with below median peripheral levels of TNF-a and IL-1a had a decreased survival rate when compared to above median patients. On the other hand, patients with below median peripheral levels of TGF-b increased overall survival rate. Intratumoral IL-1a above median was associated with higher number of macrophages and fewer with B cells. Furthermore, plasmatic TNF-a levels were correlated with intratumoral TNF-a levels, suggesting that peripheral cytokines are related to the tumoral microenvironment. Even though tumor size has no difference regarding survival rate, we found a negative correlation between intratumoral IL-4 and tumor size, where larger tumors have less IL-4 expression. Nevertheless, the tumoral nature had a significant effect in overall survival rate, considering that infiltrative tumors showed decreased survival rate and intratumoral TNF-a. Moreover, expansive tumors revealed fewer macrophages and higher T cells. In multiple variation analyzes, we demonstrated that infiltrative tumors and below median peripheral IL-1a expression represent 3 times and 5 times hazard ratio, respectively, demonstrating a poor prognosis. Here we found that peripheral cytokines had a critical role as prognostic tools in a small cohort of GBM patients.

Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling.

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains.

Symptoms: Purple Ear Drum and Hearing Loss

Symptoms: Purple Ear Drum and Hearing Loss