Postmortem Brain Imaging in Alzheimer's Disease and Related Dementias: The South Texas Alzheimer's Disease Research Center Repository.

BACKGROUND: Neuroimaging bears the promise of providing new biomarkers that could refine the diagnosis of dementia. Still, obtaining the pathology data required to validate the relationship between neuroimaging markers and neurological changes is challenging. Existing data repositories are focused on a single pathology, are too small, or do not precisely match neuroimaging and pathology findings. OBJECTIVE: The new data repository introduced in this work, the South Texas Alzheimer's Disease research center repository, was designed to address these limitations. Our repository covers a broad diversity of dementias, spans a wide age range, and was specifically designed to draw exact correspondences between neuroimaging and pathology data. METHODS: Using four different MRI sequences, we are reaching a sample size that allows for validating multimodal neuroimaging biomarkers and studying comorbid conditions. Our imaging protocol was designed to capture markers of cerebrovascular disease and related lesions. Quantification of these lesions is currently underway with MRI-guided histopathological examination. RESULTS: A total of 139 postmortem brains (70 females) with mean age of 77.9 years were collected, with 71 brains fully analyzed. Of these, only 3% showed evidence of AD-only pathology and 76% had high prevalence of multiple pathologies contributing to clinical diagnosis. CONCLUSION: This repository has a significant (and increasing) sample size consisting of a wide range of neurodegenerative disorders and employs advanced imaging protocols and MRI-guided histopathological analysis to help disentangle the effects of comorbid disorders to refine diagnosis, prognosis and better understand neurodegenerative disorders.

Cross-Vendor Test-Retest Validation of Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) for Evaluating Glymphatic System Function.

The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was proposed to evaluate glymphatic system (GS) function. However, few studies have validated its reliability and reproducibility. Fifty participants' DTI data from the MarkVCID consortium were included in this study. Two pipelines by using DSI studio and FSL software were developed for data processing and ALPS index calculation. The ALPS index was obtained by the average of bilateral ALPS index and was used for testing the cross-vendor, inter-rater and test-retest reliability by using R studio software. The ALPS index demonstrated favorable inter-scanner reproducibility (ICC=0.77 to 0.95, P < 0.001), inter-rater reliability (ICC=0.96 to 1, P< 0.001) and test-retest repeatability (ICC=0.89 to 0.95, P< 0.001), offering a potential biomarker for in vivo evaluation of GS function.

Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI.

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 deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. 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 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. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis.

Importance: Enlarged perivascular spaces (ePVSs) have been associated with cerebral small-vessel disease (cSVD). Although their etiology may differ based on brain location, study of ePVSs has been limited to specific brain regions; therefore, their risk factors and significance remain uncertain. Objective: Toperform a whole-brain investigation of ePVSs in a large community-based cohort. Design, Setting, and Participants: This cross-sectional study analyzed data from the Atrial Fibrillation substudy of the population-based Multi-Ethnic Study of Atherosclerosis. Demographic, vascular risk, and cardiovascular disease data were collected from September 2016 to May 2018. Brain magnetic resonance imaging was performed from March 2018 to July 2019. The reported analysis was conducted between August and October 2022. A total of 1026 participants with available brain magnetic resonance imaging data and complete information on demographic characteristics and vascular risk factors were included. Main Outcomes and Measures: Enlarged perivascular spaces were quantified using a fully automated deep learning algorithm. Quantified ePVS volumes were grouped into 6 anatomic locations: basal ganglia, thalamus, brainstem, frontoparietal, insular, and temporal regions, and were normalized for the respective regional volumes. The association of normalized regional ePVS volumes with demographic characteristics, vascular risk factors, neuroimaging indices, and prevalent cardiovascular disease was explored using generalized linear models. Results: In the 1026 participants, mean (SD) age was 72 (8) years; 541 (53%) of the participants were women. Basal ganglia ePVS volume was positively associated with age (ß = 3.59 × 10-3; 95% CI, 2.80 × 10-3 to 4.39 × 10-3), systolic blood pressure (ß = 8.35 × 10-4; 95% CI, 5.19 × 10-4 to 1.15 × 10-3), use of antihypertensives (ß = 3.29 × 10-2; 95% CI, 1.92 × 10-2 to 4.67 × 10-2), and negatively associated with Black race (ß = -3.34 × 10-2; 95% CI, -5.08 × 10-2 to -1.59 × 10-2). Thalamic ePVS volume was positively associated with age (ß = 5.57 × 10-4; 95% CI, 2.19 × 10-4 to 8.95 × 10-4) and use of antihypertensives (ß = 1.19 × 10-2; 95% CI, 6.02 × 10-3 to 1.77 × 10-2). Insular region ePVS volume was positively associated with age (ß = 1.18 × 10-3; 95% CI, 7.98 × 10-4 to 1.55 × 10-3). Brainstem ePVS volume was smaller in Black than in White participants (ß = -5.34 × 10-3; 95% CI, -8.26 × 10-3 to -2.41 × 10-3). Frontoparietal ePVS volume was positively associated with systolic blood pressure (ß = 1.14 × 10-4; 95% CI, 3.38 × 10-5 to 1.95 × 10-4) and negatively associated with age (ß = -3.38 × 10-4; 95% CI, -5.40 × 10-4 to -1.36 × 10-4). Temporal region ePVS volume was negatively associated with age (ß = -1.61 × 10-2; 95% CI, -2.14 × 10-2 to -1.09 × 10-2), as well as Chinese American (ß = -2.35 × 10-1; 95% CI, -3.83 × 10-1 to -8.74 × 10-2) and Hispanic ethnicities (ß = -1.73 × 10-1; 95% CI, -2.96 × 10-1 to -4.99 × 10-2). Conclusions and Relevance: In this cross-sectional study of ePVSs in the whole brain, increased ePVS burden in the basal ganglia and thalamus was a surrogate marker for underlying cSVD, highlighting the clinical importance of ePVSs in these locations.

MicroRNA and exosome: Key players in rheumatoid arthritis.

Rheumatoid arthritis (RA) is known as one of important autoimmune disorders which can lead to joint pain and damage throughout body. Given that internal (ie, genetic and epigenetic alterations) and external factors (ie, lifestyle changes, age, hormones, smoking, stress, and obesity) involved in RA pathogenesis. Increasing evidence indicated that cellular and molecular alterations play critical roles in the initiation and progression of RA. Among various targets and molecular signaling pathways, microRNAs (miRNAs) and their regulatory networks have key roles in the RA pathogenesis. It has been showed that deregulation of many miRNAs involved in different stages of RA. Hence, identification of miRNAs and their signaling pathways in RA, could contribute to new knowledge which help to better treatment of patients with RA. Besides miRNAs, exosomes have been emerged as key messengers in RA pathogenesis. Exsosomes are nanocarriers which could be released from various cells and lead to changing of behaviors recipient cells via targeting their cargos (eg, proteins, messenger RNAs, miRNAs, long noncoding RNAs, DNAs). Here, we summarized several miRNAs involved in RA pathogenesis. Moreover, we highlighted the roles of exosomes in RA pathogenesis.

Polymorphism in Leptin and Leptin Receptor Genes May Modify Leptin Levels and Represent Risk Factors for Multiple Sclerosis.

BACKGROUND: Leptin, the product of the ob gene, can modulate the immune responses and also seems to regulate Th1/Th2 balance by promoting a shift from the Th2 to the Th1 inflammatory cytokine pathway. Therefore, in this study, we aimed to investigate the association between polymorphisms of leptin gene (LEP) and leptin receptor gene (LEPR) and susceptibility to multiple sclerosis (MS). In addition, we investigated the influence of these two common polymorphisms on plasma levels of leptin. METHODS: This case-control study was conducted on 232 MS patients and 204 control subjects. Serum level measurement of leptin was performed using enzyme-linked immunosorbent assay (ELISA). G-2548-A LEP polymorphism and 223A/G polymorphism of the LEPR were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). RESULTS: There was a significant difference in allele/genotype frequencies of LEP gene among MS patients and control subjects (p<0.01). The genotype frequencies of LEPR polymorphism were also significantly different between control subjects and MS patients (p=0.02). The mean serum level of leptin was significantly higher in MS patients as compared with the controls (p<0.01). CONCLUSION: Our study implicates a significant role of LEP and LEPR polymorphisms and also leptin levels in the risk of MS and its severity. Furthermore, our findings suggest LEP and LEPR polymorphisms as important predictors for increased serum leptin in Iranian MS patients. Although this study provides new clinically relevant information regarding genetic determinants modulating risk of MS, further investigations are necessary to understand better the mechanistic implications of these observations in the development of MS.

A Novel Approach to Discriminate Subgroups in Multiple Sclerosis.

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.