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Background The hippocampus and its subfields in the human brain play a pivotal role in forming new memories and spatial navigation. The automated assessment of the hippocampus and its subfields are useful tools for the early diagnosis of Alzheimer's disease and other neurodegenerative diseases such as primary age‐related tauopathy, Lewy body dementia, limbic‐predominant age‐related TDP‐43 encephalopathy (LATE), and frontotemporal lobar Dementia. Postmortem brain magnetic resonance imaging plays a crucial role in neuroscience and clinical research, providing valuable insights into the structural and pathological features of the brain after death. Postmortem MRI can be used to validate findings from in vivo imaging studies, link these findings to histopathological data. Deep learning methods such as convolutional neural networks are widely used in image segmentation. However, deep learning methods have not previously been applied to segmenting the hippocampus and its subfields (Head, Body, Tail, and Dentate Gyrus) from postmortem MRI. Method Double U‐Net is a convolutional neural network architecture that combines two U‐Net architectures. The U‐Net architecture is widely used for semantic segmentation tasks, particularly in medical image analysis, where it has shown success in functions such as tumor segmentation. We have trained a double U‐Net model on 15 annotated postmortem MRI scans include scans from nine females and six males, with an average age of 78 and a standard deviation of 7, which have been collected at our university brain bank (Figure 1). Before feeding the data to the model, all the data were registered, intensity normalized, and cropped to a size of 64 x 96 x 48. The model is trained with a learning rate of 0.0001s and dice loss as the objective function. Result The model's performance is evaluated quantitatively (Table 1) and qualitatively (Figure 2). The qualitative analysis is performed with metrics such as Dice, Precision, and Recall. In whole hippocampus segmentation, the model achieved an overall Dice score of 93.01%, and for subfield segmentation, it reached a Dice score of 87.30%. Conclusion Deep learning‐based segmentation of the hippocampus and its subfields can be performed on postmortem brain images and may be helpful in diagnosing Alzheimer's and other neurodegenerative diseases.

Background To validate the index of diffusivity along the perivascular space (ALPS index) as a biomarker for vascular cognitive impairment and dementia (VCID). Method The participants and MRI data used in this study were acquired as part of the MarkVCID consortium, which consisted of seven sites. A total of 578 participants (72.5±7.2 years old, 232 Male) who received baseline and follow‐up cognitive evaluations (Montreal Cognitive Assessment (MoCA), Principal Component Analysis derived General Cognitive Function (GCF_PCA), and composite score of Executive Function (EFC)) and MRI examinations were included in this study. The diffusion tensor imaging (DTI) data were processed by using an in‐house automatic processing pipeline with FMRIB Software Library 6.0.6. The mean free water (mFW) and peak width of skeletonized mean diffusivity (PSMD) were computed in the white matter (WM). The ALPS index was defined as the average of bilateral ALPS indices which were calculated by the ratio of mean of x‐axis diffusivity in the projection fibers (Dxxproj) and x‐axis diffusivity in the association fibers (Dxxassoc) to the mean of y‐axis diffusivity in the projection fibers (Dyyproj) and z‐axis diffusivity in the association fibers (Dzzassoc). The WM hyperintensity volumes (WMHV) were calculated on FLAIR images and normalized by intracranial volume (ICV). Univariate correlation (Pearson or Spearman) was used to examine the associations between imaging markers (ALPS index, mFW, PSMD, and WMHV). Linear regression models were used to evaluate the associations of baseline ALPS index with baseline and longitudinal changes of cognitive outcomes, regressing out three types of covariates: 1) age, sex, and education, 2) added vascular risk factors (VRFs), including diabetes, hypertension and smoking, 3) further added mFW, PSMD and WMHV. SAS 9.4 software was used for all statistical analyses, and P<0.05 was regarded as statistical significance. Result The baseline ALPS index was significantly correlated with existing biomarkers of cerebral small vessel disease (cSVD)‐related VCID, i.e., mFW and WMHV (P<0.01) (Figure 1), and baseline cognitive performances, i.e., MoCA total score, GCF_PCA score, and EFC score (P<0.05) after adjusting for the demographics, VRFs, and existing biomarkers (Figure 2). Conclusion the ALPS index is an independent contributor to the cognitive decline in cSVD.

BACKGROUND Peak‐width of skeletonized mean diffusivity (PSMD), a neuroimaging marker of cerebral small vessel disease (SVD), has shown excellent instrumental properties. Here, we extend our work to perform a biological validation of PSMD. METHODS We included 396 participants from the Biomarkers for Vascular Contributions to Cognitive Impairment and Dementia (MarkVCID‐1) Consortium and three replication samples (Cohorts for Heart and Aging Research in Genomic Epidemiology = 6172, Rush University Medical Center = 287, University of California Davis Alzheimer's Disease Research Center = 567). PSMD was derived from diffusion tensor imaging using an automated algorithm. We related PSMD to a composite measure of general cognitive function using linear regression models adjusting for confounders. RESULTS Higher PSMD was associated with lower general cognition in MarkVCID‐1 independent of age, sex, education, and intracranial volume (Beta [95% confidence interval], −0.8 [−1.2, −0.4], P < 0.001). These findings were replicated in independent samples. Furthermore, PSMD explained cognitive status above and beyond white matter hyperintensities. DISCUSSION Our biological validation work supports the pursuit of larger clinical validation studies evaluating PSMD as a susceptibility/risk biomarker of small vessel disease contributing to cognitive impairment and dementia. Highlights Peak‐width of skeletonized mean diffusivity (PSMD) is a novel small vessel disease neuroimaging biomarker. A prior instrumental validation study demonstrated that PSMD is a robust biomarker. This biological validation study shows that high PSMD relates to worse cognition. PSMD explains cognitive function above and beyond white matter hyperintensities. Future clinical validation will assess PSMD as a vascular contribution to cognitive impairment and dementia biomarker in clinical trials.

Multiple sclerosis (MS) is an autoimmune disease of central nervous system. Since different types of immune cells are involved in MS pathogenesis, in this study we aimed to evaluate serum levels of several immunological components including soluble CD4 (sCD4), sCD8, sCD163, and immunoglobulins as markers of activity of T-cells, macrophages, and B-cells in different types of MS. Serum levels of sCD4, sCD8, and sCD163 of patients with relapsing-remitting MS (RRMS, n=61), primary progressive MS (PRMS, n=31), secondary progressive MS (SPMS, n=31), clinical isolated syndrome (CIS, n=31) and neuromyelitis optica (NMO, n=31), and healthy controls (n=49) were measured using enzyme-linked immunosorbent assay (ELISA). Serum levels of Ig-G, Ig-M, and Ig-A were determined using nephelometric technique. Serum levels of sCD4, sCD8, sCD163, Ig-G, Ig-M, and Ig-A were significantly different in five groups of cases (p<0.05). Furthermore, application of stepwise method of discriminant analysis yielded 4 significant discriminant functions of classification due to the presence of six levels of categorical variables in the analysis. The most important function explained 85.5% of the total variance with the correlation value of 0.79. Taken together, our preliminary analysis suggests that although we found some functions to discriminate most of the patients, further studies will be required to individuate immunological markers characterizing the different type of MS including RRMS, PPMS, SPMS, CIS and NMO as proved by the data on sCD4, sCD163, Ig-M, and Ig-G in blood.

Multiple sclerosis (MS) is an autoimmune disease of central nervous system. Since different types of immune cells are involved in MS pathogenesis, in this study we aimed to evaluate serum levels of several immunological components including soluble CD4 (sCD4), sCD8, sCD163, and immunoglobulins as markers of activity of T-cells, macrophages, and B-cells in different types of MS. Serum levels of sCD4, sCD8, and sCD163 of patients with relapsing-remitting MS (RRMS, n=61), primary progressive MS (PRMS, n=31), secondary progressive MS (SPMS, n=31), clinical isolated syndrome (CIS, n=31) and neuromyelitis optica (NMO, n=31), and healthy controls (n=49) were measured using enzyme-linked immunosorbent assay (ELISA). Serum levels of Ig-G, Ig-M, and Ig-A were determined using nephelometric technique. Serum levels of sCD4, sCD8, sCD163, Ig-G, Ig-M, and Ig-A were significantly different in five groups of cases (p<0.05). Furthermore, application of stepwise method of discriminant analysis yielded 4 significant discriminant functions of classification due to the presence of six levels of categorical variables in the analysis. The most important function explained 85.5% of the total variance with the correlation value of 0.79. Taken together, our preliminary analysis suggests that although we found some functions to discriminate most of the patients, further studies will be required to individuate immunological markers characterizing the different type of MS including RRMS, PPMS, SPMS, CIS and NMO as proved by the data on sCD4, sCD163, Ig-M, and Ig-G in blood.

BACKGROUND AND PURPOSE: Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a novel deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. MATERIALS AND METHODS: We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The training data included 21 participants, which were randomly selected from the MESA cohort. Participants had ePVS 683 lesions on average. For T1w, T2w, and FLAIR images, the MESA study collected 3D isotropic MRI scans at six different sites with Siemens scanners. Our training data included participants from all these sites and all the scanner models, and the proposed model was applied to the whole brain instead of selective regions. RESULTS: The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading.