For post-menopausal women, stroke is complicated by the variable effects of estrogen therapy and the age-related therapeutic consequences involved. Estrogen therapy has been shown to have an age-dimorphic effect, which is neuroprotective in young females, but non-neuroprotective, even neurotoxic in acyclic females. We hypothesized that arterial baroreflex (ABR) and its downstream acetylcholine-α7 nicotinic acetylcholine receptor (α7nAChR) anti-inflammatory pathways are involved in estrogen efficacy toward cerebral ischemic damage. Our data showed that estrogen supplements contributed to ABR improvement and neuroprotection in adult, not aged, ovariectomized (OVX) rats. In adult rats, OVX-induced estrogen deficiency aggravated middle cerebral artery occlusion (MCAO), which induced brain infarction and reduced ABR function, with decreased α7nAChR expression of the brain and exaggerated inflammation following MCAO; these effects were significantly prevented by supplementation with estrogen. ABR impairment by sinoaortic denervation partly attenuated the estrogen effect on baroreflex sensitivity (BRS) and ischemic damage in adult rats, as well as α7nAChR expression and inflammatory response. These data suggested that ABR and acetylcholine-α7nAChR anti-inflammatory pathways are involved in the neuroprotection of estrogen in adult OVX rats. In contrast, aged rats exhibited more severe ischemic damage and inflammatory response than adult rats, as well as poorer baroreflex function and lower α7nAChR expression. Estrogen supplements did not improve BRS or confer neuroprotection in aged rats without affecting brain α7nAChR and post-ischemic inflammation. Most importantly, ketanserin restored ABR function and significantly postponed the onset of stroke in aged female strokeprone spontaneously hypertensive rats, whereas estrogen treatment failed to delay the development of stroke. Our findings reveal that estrogen is protective against ischemic stroke (IS) in adult female rats and that ABR played a role in this beneficial action. Dysfunction of ABR and unresponsiveness to estrogen in aged female rats may contribute to a reduced estrogen efficacy against cerebral ischemia.

PurposeBrain glymphatic dysfunction is involved in the pathologic process of acute ischemic stroke (IS). The relationship between brain glymphatic activity and dysfunction in subacute IS has not been fully elucidated. Diffusion tensor image analysis along the perivascular space (DTI-ALPS) index was used in this study to explore whether glymphatic activity was related to motor dysfunction in subacute IS patients.MethodsTwenty-six subacute IS patients with a single lesion in the left subcortical region and 32 healthy controls (HCs) were recruited in this study. The DTI-ALPS index and DTI metrics (fractional anisotropy, FA, and mean diffusivity, MD) were compared within and between groups. Spearman's and Pearson's partial correlation analyses were performed to analyze the relationships of the DTI-ALPS index with Fugl-Meyer assessment (FMA) scores and with corticospinal tract (CST) integrity in the IS group, respectively.ResultsSix IS patients and two HCs were excluded. The left DTI-ALPS index of the IS group was significantly lower than that of the HC group (t = −3.02, p = 0.004). In the IS group, a positive correlation between the left DTI-ALPS index and the simple Fugl-Meyer motor function score (ρ = 0.52, p = 0.019) and a significant negative correlation between the left DTI-ALPS index and the FA (R = −0.55, p = 0.023) and MD (R = −0.48, p = 0.032) values of the right CST were found.ConclusionsGlymphatic dysfunction is involved in subacute IS. DTI-ALPS could be a potential magnetic resonance (MR) biomarker of motor dysfunction in subacute IS patients. These findings contribute to a better understanding of the pathophysiological mechanisms of IS and provide a new target for alternative treatments for IS.

BackgroundDespite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not findable or accessible. Users face significant challenges in reusing neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for interoperability. To implement the FAIR guidelines for neuroimaging data, we have developed an iterative ontology engineering process and used it to create the NeuroBridge ontology. The NeuroBridge ontology is a computable model of provenance terms to implement FAIR principles and together with an international effort to annotate full text articles with ontology terms, the ontology enables users to locate relevant neuroimaging datasets.MethodsBuilding on our previous work in metadata modeling, and in concert with an initial annotation of a representative corpus, we modeled diagnosis terms (e.g., schizophrenia, alcohol usage disorder), magnetic resonance imaging (MRI) scan types (T1-weighted, task-based, etc.), clinical symptom assessments (PANSS, AUDIT), and a variety of other assessments. We used the feedback of the annotation team to identify missing metadata terms, which were added to the NeuroBridge ontology, and we restructured the ontology to support both the final annotation of the corpus of neuroimaging articles by a second, independent set of annotators, as well as the functionalities of the NeuroBridge search portal for neuroimaging datasets.ResultsThe NeuroBridge ontology consists of 660 classes with 49 properties with 3,200 axioms. The ontology includes mappings to existing ontologies, enabling the NeuroBridge ontology to be interoperable with other domain specific terminological systems. Using the ontology, we annotated 186 neuroimaging full-text articles describing the participant types, scanning, clinical and cognitive assessments.ConclusionThe NeuroBridge ontology is the first computable metadata model that represents the types of data available in recent neuroimaging studies in schizophrenia and substance use disorders research; it can be extended to include more granular terms as needed. This metadata ontology is expected to form the computational foundation to help both investigators to make their data FAIR compliant and support users to conduct reproducible neuroimaging research.

IntroductionOpen science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies.MethodsThe NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles.ResultsThe NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/ provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section “Methods” of the article. Articles returned from NeuroQuery based on the same search are also presented.ConclusionThe NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user’s research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in full-text papers. Toward the overall goal of discovering “enough data of the right kind,” ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility.

ObjectivesThis study aimed to investigate local and remote functional connectivity in mild Alzheimer’s disease patients with sleep disturbances (ADSD) and those without sleep disturbances (ADNSD).MethodsThirty eight mild AD patients with sleep disturbances and 21 mild AD patients without sleep disturbances participated in this study. All subjects underwent neuropsychological assessments and 3.0 Tesla magnetic resonance scanning. Static and dynamic regional homogeneity (ReHo) were used to represent the local functional connectivity. Seed-based whole-brain functional connectivity was used to represent the remote functional connectivity. The seed was chosen based on the results of ReHo.ResultsCompared to ADNSD, ADSD showed decreased static ReHo in the left posterior central gyrus and the right cuneus and increased dynamic ReHo in the left posterior central gyrus. As for the remote functional connectivity, comparing ADSD to ADNSD, it was found that there was a decreased functional connection between the left posterior central gyrus and the left cuneus as well as the left calcarine.ConclusionThe current study demonstrated that, compared with ADNSD, ADSD is impaired in both local and remote functional connectivity, manifested as reduced functional connectivity involving the primary sensory network and the primary visual network. The abnormality of the above functional connectivity is one of the reasons why sleep disorders promote cognitive impairment in AD. Moreover, sleep disorders change the temporal sequence of AD pathological damage to brain functional networks, but more evidence is needed to support this conclusion.

IntroductionSemantic segmentation is a crucial visual representation learning task for autonomous driving systems, as it enables the perception of surrounding objects and road conditions to ensure safe and efficient navigation.MethodsIn this paper, we present a novel semantic segmentation approach for autonomous driving scenes using a Multi-Scale Adaptive Mechanism (MSAAM). The proposed method addresses the challenges associated with complex driving environments, including large-scale variations, occlusions, and diverse object appearances. Our MSAAM integrates multiple scale features and adaptively selects the most relevant features for precise segmentation. We introduce a novel attention module that incorporates spatial, channel-wise and scale-wise attention mechanisms to effectively enhance the discriminative power of features.ResultsThe experimental results of the model on key objectives in the Cityscapes dataset are: ClassAvg:81.13, mIoU:71.46. The experimental results on comprehensive evaluation metrics are: AUROC:98.79, AP:68.46, FPR95:5.72. The experimental results in terms of computational cost are: GFLOPs:2117.01, Infer. Time (ms):61.06. All experimental results data are superior to the comparative method model.DiscussionThe proposed method achieves superior performance compared to state-of-the-art techniques on several benchmark datasets demonstrating its efficacy in addressing the challenges of autonomous driving scene understanding.