Citations

Hippocampal segmentation is essential in neuroimaging for evaluating conditions like Alzheimer’s dementia and mesial temporal sclerosis, where small volume changes can significantly impact normative percentiles. However, inaccurate segmentation is common due to the inclusion of non-hippocampal structures such as choroid plexus and cerebrospinal fluid (CSF), leading to volumetric overestimation and confounding of functional analyses. Current methods of assessment largely rely on virtual or manual ground truth labels, which can fail to capture these inaccuracies. To address this shortcoming, this study introduces a more direct voxel intensity-based method of segmentation assessment. Using paired precontrast and postcontrast T1-weighted MRIs, hippocampal segmentations were refined by adding marginal gray matter and removing marginal CSF and enhancement to determine a total required correction volume. Six segmentation algorithms—e2dhipseg, HippMapp3r, hippodeep, AssemblyNet, FastSurfer, and QuickNat—were implemented and compared. HippMapp3r and e2dhipseg, followed closely by hippodeep, exhibited the least total correction volumes, indicating superior accuracy. Dedicated hippocampal segmentation algorithms outperformed whole-brain methods.

Introduction: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an inherited cerebral small vessel disease, yet there is currently no biomarker for early detection. This study aims to identify retinal biomarkers of NOTCH3 carriers using optical coherence tomography (OCT) from the UK Biobank. Methods: We conducted a cross-sectional, matched case-control study of individuals with or without CADASIL from the UK Biobank between 2006 and 2010. All participants had retinal OCT scan and cognitive assessment. Cases were identified based on pathogenic NOTCH3 mutations and 1:1 matched with healthy controls based on age, sex, hypertension, diabetes mellitus, and smoking status. Baseline characteristics and cognitive test scores were compared using chi-square or Mann-Whitney-U test appropriately. Macular thickness (central, inner, and outer subfields) (Figure) and retinal nerve fiber layer (RNFL) thickness were compared using Wilcoxon signed-rank test. Results: Our analysis included 134 participants (67 CADASIL cases, 67 controls) with a mean age of 54±8 years. There were no differences in visual acuity between the two groups. CADASIL performed worse in prospective memory (0.043), verbal and numerical reasoning (p=0.178), visual memory (p=0.227), and processing speed (p=0.101) than matched controls. Participants with CADASIL had significantly thinner macular inner subfield at the superior (p=0.006), temporal (p=0.008), and nasal (p=0.034) quadrants, and significantly thinner RNFL (p=0.008) compared to controls (Table). Conclusion: Individuals with CADASIL have reduced thickness in the inner macular subfield and RFNL relative to matched controls. These retinal group differences were not associated with differences in visual acuity and may reflect early pericyte dysfunction and microvascular ischemia. Longitudinal studies are needed to assess the temporal relationship between these retinal changes and disease progression.

AbstractBackgroundCross‐modality translation between MRI and PET presents significant challenges due to the distinct mechanisms underlying those modalities. Currently, the leading method for this translation is CycleGAN. Previous studies have proved a strong correlation between BBBMs and brain amyloid status measured by PET. However, the impact of blood‐based biomarkers (BBBMs) on PET image synthesis has not yet been thoroughly evaluated. In this study, we propose Plasma CycleGAN, a generative model based on CycleGAN to synthesize PET images from MRI, while also incorporating BBBMs and other clinical covariates. We show that Plasma CycleGAN outperforms the state‐of‐the‐art CycleGAN for MRI‐to‐PET translation. Our method is the first to integrate BBBMs in a conditional cross modality translation from MRI to PET.MethodOur model was built on CycleGAN architecture, while the backbone was improved by integrating BBBMs as additional conditions in the input domain. To be specific, we introduce a masked perturbation in the MRI image, whose voxel‐level magnitudes are learnable parameters in the neural network. The dataset we used to evaluate our model is downloaded from the ADNI database. The cohort contains 160 subjects with matching BBBM, T1‐weighted MRI images, amyloid PET, and clinical features, including age, sex, weights, etc. The synthesized PET images are compared with the ground truth PET images for performance evaluation using traditional metrics like structural similarity index (SSIM), peak‐signal‐to‐noise ratio (PSNR) and mean square error (MSE). In addition, the Pearson correlation coefficients (PCCs) of the masked SUVR ratios were calculated.ResultWe evaluated the baseline CycleGAN model using MRI input, as well as combined inputs of MRI+BBBM and other clinical features. Our model achieved an SSIM of 0.72±0.16 and PSNR of 22.8±2.3 in PET image synthesis task using MRI+ Aβ42/40, both yielding the best results among all input combinations. MRI+sex achieved the best result using MSE measurement. PCCs of all experimental results ranged from 0.44 to 0.5.ConclusionWe introduce Plasma CycleGAN, the first generative model to integrate BBBMs and clinical features in PET image synthesis from MRI images. In PET image synthesis task, our proposed integrated model outperforms the baseline CycleGAN model.

Background: Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer’s disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques. Objective: To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio. Methods: We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer’s Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results: Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model’s superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors. CONCLUSIONS: Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer’s disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.