Memory Research Articles

Frontal delta and theta power reflect strategy changes during human spatial memory retrieval in a virtual water maze task: an exploratory analysis

Brain oscillations in humans play a role in a wide range of cognitive processes, including navigation and memory. The oscillatory dynamics contributing to successful spatial memory recall in humans are not well-understood. To investigate specific oscillatory frequency bands during the recall process in human navigation, we recorded electroencephalographic (EEG) activity during a recall trial in healthy young adults (n = 15) following the learning of a goal location in a Virtual Water Maze task. We compared this to the activity during the same trial length, in a group of participants who did not learn a target location and navigated freely but were time-matched to the learning group (non-learning, n = 15). We compared relative power in Delta (2–4 Hz), Theta (5–7 Hz), Alpha (8–12 Hz), Beta (15–29 Hz), and Gamma (30–40 Hz) bands across the scalp. We found that delta and theta activity were greater during recall in our learning group, as opposed to our non-learning group. We also demonstrated clear suppression in the alpha band at posterior sites during memory-guided navigation compared to our non-learning group. Additionally, when goal-directed navigation switches to focused searching behavior, power becomes greater at the frontal region; with increases in the delta and theta bands reflecting this strategy change. There was also greater beta and gamma activity at posterior sites in our learning group. We discuss the results further in terms of the possible roles and functions of these oscillations during human navigation and hope this exploratory analysis can provide hypotheses for future spatial navigation and memory work.

Preparation of final qualifying work at a university: the view of modern students

The problem and the aim of the study. The need to study and use the capabilities of network technologies to improve general cultural and professional levels is becoming a condition for training highly qualified specialists. To effectively use computer devices and digital tools, computational thinking is required. The purpose of the study is to explore the possibilities of using a professionally oriented network course for the development of computational thinking of engineers. Research methods. Computational thinking is considered by the authors as a mental process consisting of the sequential execution of a set of actions: activation from human memory of a system of images of objects, and connections between them; formulation of the problem taking into account uncertainty factors; drawing up a solution algorithm and its effective implementation. 68 bachelors from the first year of the Russian State Agrarian University named after K. A. Timiryazev were involved in the study (Russian Federation). Training program 23.03.01 – “Technology of transport processes”, profile – “Digital transport and logistics systems of road transport”. The online training course “Computer Science and Networks in the Industry” was developed by E. V. Shchedrina, and presented on the Moodle platform. Registration certificate No. 24877 dated August 28, 2021. To diagnose and assess the maturity of computational thinking, the author’s testing materials are used. For statistical processing of data, the Pearson's chisquared test was used. Results. A characteristic of the network resource is the execution of all calculation tasks on a computer using application software. When working with course materials, engineers perform a sequence of actions characteristic of computational thinking: analyze the text of a professionally oriented task (formulates the task as a computational problem), decompose the problem; compose and implement an algorithm, perform its analysis and evaluation. Statistically significant differences were determined in the qualitative changes that occurred in the system of training specialists (χ2 = 6.842; p < 0.05). In conclusion, the positive aspects of using a professionally oriented network course for the development of computational thinking of engineers are highlighted (for example, to gain experience in formulating a problem taking into account the uncertainty of the future, students analyze a corporate network, determine a subnet mask for different conditions, etc.).

Professionally oriented network course as the basis for developing the computational thinking of future engineers

The problem and the aim of the study. The need to study and use the capabilities of network technologies to improve general cultural and professional levels is becoming a condition for training highly qualified specialists. To effectively use computer devices and digital tools, computational thinking is required. The purpose of the study is to explore the possibilities of using a professionally oriented network course for the development of computational thinking of engineers. Research methods. Computational thinking is considered by the authors as a mental process consisting of the sequential execution of a set of actions: activation from human memory of a system of images of objects, and connections between them; formulation of the problem taking into account uncertainty factors; drawing up a solution algorithm and its effective implementation. 68 bachelors from the first year of the Russian State Agrarian University named after K. A. Timiryazev were involved in the study (Russian Federation). Training program 23.03.01 – “Technology of transport processes”, profile – “Digital transport and logistics systems of road transport”. The online training course “Computer Science and Networks in the Industry” was developed by E. V. Shchedrina, and presented on the Moodle platform. Registration certificate No. 24877 dated August 28, 2021. To diagnose and assess the maturity of computational thinking, the author’s testing materials are used. For statistical processing of data, the Pearson's chisquared test was used. Results. A characteristic of the network resource is the execution of all calculation tasks on a computer using application software. When working with course materials, engineers perform a sequence of actions characteristic of computational thinking: analyze the text of a professionally oriented task (formulates the task as a computational problem), decompose the problem; compose and implement an algorithm, perform its analysis and evaluation. Statistically significant differences were determined in the qualitative changes that occurred in the system of training specialists (χ2 = 6.842; p < 0.05). In conclusion, the positive aspects of using a professionally oriented network course for the development of computational thinking of engineers are highlighted (for example, to gain experience in formulating a problem taking into account the uncertainty of the future, students analyze a corporate network, determine a subnet mask for different conditions, etc.).

Human regulatory memory B cells defined by expression of TIM-1 and TIGIT are dysfunctional in multiple sclerosis.

Regulatory B cells (Bregs) play a pivotal role in suppressing immune responses, yet there is still a lack of cell surface markers that can rigorously identify them. In mouse models for multiple sclerosis (MS), TIM-1 or TIGIT expression on B cells is required for maintaining self-tolerance and regulating autoimmunity to the central nervous system. Here we investigated the activities of human memory B cells that differentially express TIM-1 and TIGIT to determine their potential regulatory function in healthy donors and patients with relapsing-remitting (RR) MS. FACS-sorted TIM-1+/-TIGIT+/- memory B (memB) cells co-cultured with allogenic CD4+ T cells were analyzed for proliferation and induction of inflammatory markers using flow cytometry and cytokine quantification, to determine Th1/Th17 cell differentiation. Transcriptional differences were assessed by SMARTSeq2 RNA sequencing analysis. TIM-1-TIGIT- double negative (DN) memB cells strongly induce T cell proliferation and pro-inflammatory cytokine expression. The TIM-1+ memB cells enabled low levels of CD4+ T cell activation and gave rise to T cells that co-express IL-10 with IFNγ and IL-17A or FoxP3. T cells cultured with the TIM-1+TIGIT+ double positive (DP) memB cells exhibited reduced proliferation and IFNγ, IL-17A, TNFα, and GM-CSF expression, and exhibited strong regulation in Breg suppression assays. The functional activity suggests the DP memB cells are a bonafide Breg population. However, MS DP memB cells were less inhibitory than HC DP memB cells. A retrospective longitudinal study of anti-CD20 treated patients found that post-treatment DP memB cell frequency and absolute number were associated with response to therapy. Transcriptomic analyses indicated that the dysfunctional MS-derived DP memB/Breg population exhibited increased expression of genes associated with T cell activation and survival (CD80, ZNF10, PIK3CA), and had distinct gene expression compared to the TIGIT+ or TIM-1+ memB cells. These findings demonstrate that TIM-1/TIGIT expressing memory B cell subsets have distinct functionalities. Co-expression of TIM-1 and TIGIT defines a regulatory memory B cell subset that is functionally impaired in MS.

Nanosurgical and Bioengineering Treatment of Human Anterior Cruciate Ligament Tears with Ultrasound-Guided Injection of Modified Platelet-Rich Plasma Using Human Cell Memory Based on Clinical, Ultrasound, MRI, and Nanoscope Analyses: A Double-Blind Randomized Trial.

Background: Anterior cruciate ligament (ACL) tears account for 40% to 50% of all ligamentous knee injuries. Most patients with ACL ruptures undergo surgical treatment. There is currently no objective, well-documented, repeatable, and standardized nonsurgical method for ACL tear treatment. This study aimed to investigate ACL outcomes in patients who underwent a novel nanosurgery and bioengineering treatment (NSBT) for an ACL tear. Methods: This was a double-blind randomized trial including 44 patients with a history of traumatic knee injury and a confirmed ACL tear. The final sample comprised 40 patients who met all the eligibility criteria. The patients were divided into two groups: the treatment group (n = 30) and the control group (n = 10). The treatment group underwent nanosurgery with an ultrasound-guided injection of modified platelet-rich plasma (PRP) using human cell memory (RP-hCM). The control group was treated with an ultrasound-guided PRP injection into the joint capsule. At baseline and post-treatment, all patients underwent both ultrasonography and magnetic resonance imaging (MRI), and the following clinical variables were assessed: the WOMAC score, the Lysholm knee score, the visual analog scale score, and knee instability. In most patients, the clinical outcome was verified using nanoscopy. Results: The median WOMAC, VAS, and LKS scores, as well as knee instability, improved significantly 12 weeks after the procedure in the treatment group (p < 0.001). We found a significantly larger improvement in the assessed parameters in the treatment group compared to the control group (p < 0.001). In the treatment group, all the patients had good and very good clinical outcomes, while 90% of the patients had a normal ACL signal in a follow-up MRI scan. In the control group, a physical examination revealed no changes in knee stability after treatment. Conclusions: This study showed that there is a significant difference in patient experience and the duration of recovery for patients with ACL tears treated with NSBT. The novel nonsurgical method was shown to be repeatable, objective, well documented, standardized, and highly effective.

From Psychostasis to the Discovery of Cardiac Nerves: The Origins of the Modern Cardiac Neuromodulation Concept.

This review explores the historical development of cardiology knowledge, from ancient Egyptian psychostasis to the modern comprehension of cardiac neuromodulation. In ancient Egyptian religion, psychostasis was the ceremony in which the deceased was judged before gaining access to the afterlife. This ritual was also known as the "weighing of the heart" or "weighing of the soul". The Egyptians believed that the heart, not the brain, was the seat of human wisdom, emotions, and memory. They were the first to recognize the cardiocentric nature of the body, identifying the heart as the center of the circulatory system. Aristotle (fourth century BC) considered the importance of the heart in human physiology in his philosophical analyses. For Galen (third century AD), the heart muscle was the site of the vital spirit, which regulated body temperature. Cardiology knowledge advanced significantly in the 15th century, coinciding with Leonardo da Vinci and Vesalius's pioneering anatomical and physiological studies. It was William Harvey, in the 17th century, who introduced the concept of cardiac circulation. Servet's research and Marcello Malpighi's discovery of arterioles and capillaries provided a more detailed understanding of circulation. Richard Lower emerged as the foremost pioneer of experimental cardiology in the late 17th century. He demonstrated the heart's neural control by tying off the vagus nerve. In 1753, Albrecht von Haller, a professor at Göttingen, was the first to discover the heart's automaticity and the excitation of muscle fibers. Towards the end of the 18th century, Antonio Scarpa challenged the theories of Albrecht von Haller and Johann Bernhard Jacob Behrends, who maintained that the myocardium possessed its own "irritability", on which the heartbeat depended, and was independent of neuronal sensitivity. Instead, Scarpa argued that the heart required innervation to maintain life, refuting Galenic notions. In contemporary times, the study of cardiac innervation has regained prominence, particularly in understanding the post-acute sequelae of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection (PASC), which frequently involves cardiorespiratory symptoms and dysregulation of the intrinsic cardiac innervation. Recently, it has been recognized that post-acute sequelae of acute respiratory infections (ARIs) due to other pathogens can also be a cause of long-term vegetative and somatic symptoms. Understanding cardiac innervation and modulation can help to recognize and treat long COVID and long non-COVID-19 (coronavirus disease 2019) ARIs. This analysis explores the historical foundations of cardiac neuromodulation and its contemporary relevance. By focusing on this concept, we aim to bridge the gap between historical understanding and modern applications. This will illuminate the complex interplay between cardiac function, neural modulation, cardiovascular health, and disease management in the context of long-term cardiorespiratory symptoms and dysregulation of intrinsic cardiac innervations.

Cross-Study of English Education and Cognitive Science: Language Processing and Learning Mechanisms

With the acceleration of globalization and the rapid development of information technology, English, as the main language of international communication, has increasingly prominent importance in education and learning. English education, as a discipline that studies the theory and practice of English education, has been exploring more effective teaching methods and learning strategies. At the same time, cognitive science, as a discipline that studies cognitive processes such as human thinking, learning, and memory, provides a profound theoretical foundation for understanding language processing and learning mechanisms. The cross-study of English education and cognitive science helps to build a more complete theoretical framework for language processing and learning mechanisms. By integrating the theoretical achievements of the two disciplines, we can more fully understand the cognitive processes and laws of English learning, revealing the intrinsic connection and essential characteristics of language processing and learning mechanisms. This can not only enrich and develop the theoretical systems of English education and cognitive science but also provide new ideas and perspectives for research in other related fields. In addition, with the continuous development of artificial intelligence and big data technology, research on language processing and learning mechanisms can also provide technical support and innovative ideas for fields such as natural language processing and intelligent education. Therefore, the cross-study of English education and cognitive science not only has far-reaching academic value but also has broad application prospects and social significance.

Hepatitis B virus-specific human stem cell memory T cells differentiate into cytotoxic T cells and eradicate HBV-infected hepatocytes in mice.

Chronic infection with the hepatitis B virus (HBV) induces progressive hepatic impairment. Achieving complete eradication of the virus remains a formidable challenge. Cytotoxic T lymphocytes, specific to viral antigens, either exhibit a numerical deficiency or succumb to an exhausted state in individuals chronically afflicted with HBV. The comprehension of the genesis and dissemination of stem cell memory T cells (TSCMs) targeting HBV remains inadequately elucidated. We identified TSCMs in subjects with chronic HBV infection and scrutinized their efficacy in a murine model with human hepatocyte transplants, specifically the TK-NOG mice. TSCMs were discerned in all subjects under examination. Introduction of TSCMs into the HBV mouse model precipitated a severe necro-inflammatory response, resulting in the elimination of human hepatocytes. TSCMs may constitute a valuable tool in the pursuit of a remedial therapy for HBV infection.

NeuroGPT-X: toward a clinic-ready large language model.

The objective was to assess the performance of a context-enriched large language model (LLM) compared with international neurosurgical experts on questions related to the management of vestibular schwannoma. Furthermore, another objective was to develop a chat-based platform incorporating in-text citations, references, and memory to enable accurate, relevant, and reliable information in real time. The analysis involved 1) creating a data set through web scraping, 2) developing a chat-based platform called neuroGPT-X, 3) enlisting 8 expert neurosurgeons across international centers to independently create questions (n = 1) and to answer (n = 4) and evaluate responses (n = 3) while blinded, and 4) analyzing the evaluation results on the management of vestibular schwannoma. In the blinded phase, all answers were assessed for accuracy, coherence, relevance, thoroughness, speed, and overall rating. All experts were unblinded and provided their thoughts on the utility and limitations of the tool. In the unblinded phase, all neurosurgeons provided answers to a Likert scale survey and long-answer questions regarding the clinical utility, likelihood of use, and limitations of the tool. The tool was then evaluated on the basis of a set of 103 consensus statements on vestibular schwannoma care from the 8th Quadrennial International Conference on Vestibular Schwannoma. Responses from the naive and context-enriched Generative Pretrained Transformer (GPT) models were consistently rated not significantly different in terms of accuracy, coherence, relevance, thoroughness, and overall performance, and they were often rated significantly higher than expert responses. Both the naive and content-enriched GPT models provided faster responses to the standardized question set than expert neurosurgeon respondents (p < 0.01). The context-enriched GPT model agreed with 98 of the 103 (95%) consensus statements. Of interest, all expert surgeons expressed concerns about the reliability of GPT in accurately addressing the nuances and controversies surrounding the management of vestibular schwannoma. Furthermore, the authors developed neuroGPT-X, a chat-based platform designed to provide point-of-care clinical support and mitigate the limitations of human memory. neuroGPT-X incorporates features such as in-text citations and references to enable accurate, relevant, and reliable information in real time. The present study, with its subspecialist-level performance in generating written responses to complex neurosurgical problems for which evidence-based consensus for management is lacking, suggests that context-enriched LLMs show promise as a point-of-care medical resource. The authors anticipate that this work will be a springboard for expansion into more medical specialties, incorporating evidence-based clinical information and developing expert-level dialogue surrounding LLMs in healthcare.

Memristive Bionic Memory Circuit Implementation and Its Application in Multisensory Mutual Associative Learning Networks.

Memory is vital and indispensable for organisms and brain-inspired intelligence to gain complete sensation and cognition of the environment. In this work, a memristive bionic memory circuit inspired by human memory model is proposed, which includes 1) receptor and sensory neuron (SN), 2) short-term memory (STM) module, and 3) long-term memory (LTM) module. By leveraging the in-memory computing characteristic of memristors, various functions such as sensation, learning, forgetting, recall, consolidation, reconsolidation, retrieval, and reset are realized. Besides, a multisensory mutual associative learning network is constructed with several bionic memory units to memorize and associate sensory information of different modalities bidirectionally. Except for association establishment, enhancement, and extinction, we also mimicked multisensory integration to manifest the synthetic process of information from different sensory channels. According to the simulation results in PSPICE, the proposed circuit performs high robustness, low area overhead, and low power consumption. Combining associative memory with human memory model, this work provides a possible idea for further research in associative learning networks.

Percentage of using formulaic language in media discourseA comparison between the visual media and the written press

the language we use daily; A product of society, but each of us embodies it in his way. Therefore, the speaker produces the language, but there is another aspect of the language, which is ready-made linguistic pieces, used directly by the individual owned by the linguistic group. Formulaic language is a general term for ready-made phrases in the language that have previously been produced and stored in human memory. This language usually has a fixed grammatical formula and a special meaning according to the situation and context in which it is presented. The use of this language leads to the coordination of sentences in the process of writing and communication, as well as adopting the eloquence of the sender and can influence the recipient more and convince him of his ideas, as well as help in logical intentions, automatic translation, and smart answers. This research, titled "Percentage of using formulaic language in media discourse: A comparison between the visual media and the written press", sheds light on the intensity of the formal language in the visual media and the written press. In the beginning, the formulaic language was explained, and then its use in the Kurdish media was explained. The importance of research in linking the media discourse with formal language, the purpose of which is to know the percentage of using this language on the one hand, and on the other hand, to know the percentage of its use between the two types. To conduct the research, (18) interviews and articles were taken from the visual media and the written press. After that, six components of the grammatical language were extracted, which are (linguistic accompaniment, coins (expressions), metaphor, proverbs, phrasal verbs, and lexical expressions) according to (Wadi), and the use of each one of them was indicated in a table.

Методы сетевого моделирования структуры семантической памяти: теоретический обзор

The article is aimed at considering the network modeling methods for human semantic memory structure. The work provides an analytical review of existing approaches to modeling the structure of human semantic memory, as a result of which the most methodologically optimal way of modeling semantic memory is estab-lished – based on the construction and analysis of semantic networks. The authors study various methods of network modeling of semantic memory structure: verbal fluency problem, snowball problem, semantic connec-tivity problem. In addition, examples of using these methods in psychological research are given, and their methodological advantages and disadvantages are highlighted. The results of the present work can be useful in the formation of new methods for network modeling of the structure of human semantic memory, as well as in the creation of intelligent systems that process graph data.

A Neural Decision Signal during Internal Sampling from Working Memory in Humans.

How humans transform sensory information into decisions that steer purposeful behavior is a central question in psychology and neuroscience that is traditionally investigated during the sampling of external environmental signals. The decision-making framework of gradual information sampling toward a decision has also been proposed to apply when sampling internal sensory evidence from working memory. However, neural evidence for this proposal remains scarce. Here we show (using scalp EEG in male and female human volunteers) that sampling internal visual representations from working memory elicits a scalp EEG potential associated with gradual evidence accumulation-the central parietal positivity. Consistent with an evolving decision process, we show how this signal (1) scales with the time participants require to reach a decision about the cued memory content and (2) is amplified when having to decide among multiple contents in working memory. These results bring the electrophysiology of decision-making into the domain of working memory and suggest that variability in memory-guided behavior may be driven (at least in part) by variations in the sampling of our inner mental contents.

Replication study on the role of dopamine-dependent prefrontal reactivations in human extinction memory retrieval

Even after successful extinction, conditioned fear can return. Strengthening the consolidation of the fear-inhibitory safety memory formed during extinction is one way to counteract return of fear. In a previous study, we found that post-extinction L-DOPA administration improved extinction memory retrieval 24 h later. Furthermore, spontaneous post-extinction reactivations of a neural activation pattern evoked in the ventromedial prefrontal cortex (vmPFC) during extinction predicted extinction memory retrieval, L-DOPA increased the number of these reactivations, and this mediated the effect of L-DOPA on extinction memory retrieval. Here, we conducted a preregistered replication study of this work in healthy male participants. We confirm that spontaneous post-extinction vmPFC reactivations predict extinction memory retrieval. This predictive effect, however, was only observed 90 min after extinction, and was not statistically significant at 45 min as in the discovery study. In contrast to our previous study, we find no evidence that L-DOPA administration significantly enhances retrieval and that this is mediated by enhancement of the number of vmPFC reactivations. However, additional non-preregistered analyses reveal a beneficial effect of L-DOPA on extinction retrieval when controlling for the trait-like stable baseline levels of salivary alpha-amylase enzymatic activity. Further, trait salivary alpha-amylase negatively predicts retrieval, and this effect is reduced by L-DOPA treatment. Importantly, the latter findings result from non-preregistered analyses and thus further investigation is needed.

Causal functional maps of brain rhythms in working memory

Human working memory is a key cognitive process that engages multiple functional anatomical nodes across the brain. Despite a plethora of correlative neuroimaging evidence regarding the working memory architecture, our understanding of critical hubs causally controlling overall performance is incomplete. Causal interpretation requires cognitive testing following safe, temporal, and controllable neuromodulation of specific functional anatomical nodes. Such experiments became available in healthy humans with the advance of transcranial alternating current stimulation (tACS). Here, we synthesize findings of 28 placebo-controlled studies (in total, 1,057 participants) that applied frequency-specific noninvasive stimulation of neural oscillations and examined working memory performance in neurotypical adults. We use a computational meta-modeling method to simulate each intervention in realistic virtual brains and test reported behavioral outcomes against the stimulation-induced electric fields in different brain nodes. Our results show that stimulating anterior frontal and medial temporal theta oscillations and occipitoparietal gamma rhythms leads to significant dose-dependent improvement in working memory task performance. Conversely, prefrontal gamma modulation is detrimental to performance. Moreover, we found distinct spatial expression of theta subbands, where working memory changes followed orbitofrontal high-theta modulation and medial temporal low-theta modulation. Finally, all these results are driven by changes in working memory accuracy rather than processing time measures. These findings provide a fresh view of the working memory mechanisms, complementary to neuroimaging research, and propose hypothesis-driven targets for the clinical treatment of working memory deficits.

IL23R G149R Promotes IL-23 Unresponsiveness in Human Memory Th17 Cells

IL23R G149R Promotes IL-23 Unresponsiveness in Human Memory Th17 Cells

DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning

Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources. On the other hand, the direct use of raw data often leads to overfitting towards frequently occurring class information. To address class imbalances cost-efficiently, we propose an active data filtering process during self-supervised pre-training in our novel framework, Duplicate Elimination (DUEL). This framework integrates an active memory inspired by human working memory and introduces distinctiveness information, which measures the diversity of the data in the memory, to optimize both the feature extractor and the memory. The DUEL policy, which replaces the most duplicated data with new samples, aims to enhance the distinctiveness information in the memory and thereby mitigate class imbalances. We validate the effectiveness of the DUEL framework in class-imbalanced environments, demonstrating its robustness and providing reliable results in downstream tasks. We also analyze the role of the DUEL policy in the training process through various metrics and visualizations.

Vector Symbolic Finite State Machines in Attractor Neural Networks.

Hopfield attractor networks are robust distributed models of human memory, but they lack a general mechanism for effecting state-dependent attractor transitions in response to input. We propose construction rules such that an attractor network may implement an arbitrary finite state machine (FSM), where states and stimuli are represented by high-dimensional random vectors and all state transitions are enacted by the attractor network's dynamics. Numerical simulations show the capacity of the model, in terms of the maximum size of implementable FSM, to be linear in the size of the attractor network for dense bipolar state vectors and approximately quadratic for sparse binary state vectors. We show that the model is robust to imprecise and noisy weights, and so a prime candidate for implementation with high-density but unreliable devices. By endowing attractor networks with the ability to emulate arbitrary FSMs, we propose a plausible path by which FSMs could exist as a distributed computational primitive in biological neural networks.

Abstract P250: Vascular Contributions to Alzheimer's Disease

Introduction: Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that affects over 50 million people worldwide. The role of vascular risk factors have been described as a contributor to and a major comorbidity of AD pathogenesis; yet there is discordant evidence of this complex relationship between cardiovascular diseases (CVD) and its risk factors (like hypertension, hyperlipidemia, obesity, etc.) with AD pathophysiology and cognitive decline. AD associated Genome-wide association studies (GWAS) and derived polygenic risk scores (AD PRS) have uncovered some significant common genetic variants relating to CVD risk factors; however, we are still yet to unravel the comprehensive impact of these vascular genetic contributions to AD pathogenesis and cognitive decline. Method: In this study, we used 1800 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort spanning the full spectrum of clinical and pathological diagnoses of the AD spectrum, as our target dataset used for polygenic risk score calculation. Thereafter, we obtained GWAS summary statistics for cumulative cardiovascular risk by searching the Polygenic Score (PGS) GWAS Catalog using CVD as the ‘mapped trait” and then calculated cardiovascular-based PRS using HRS-based imputed GWAS data of the ADNI cohort. We conducted association analysis using a multivariate linear regression model with appropriate covariates (age, sex, APOE, education, etc.) between CVD-related polygenic risk score (CVD-PRS) with the different clinical AD diagnoses groups (Cognitively Normal/CN, Significant Memory Concerns/SMC, Mild Cognitive Impairment/MCI, Alzheimer's Disease/AD). We also performed association analysis of this CVD-PRS with white matter hyperintensity (WMHI) values, a vascular biomarker, at baseline using a linear regression model. Furthermore, we extended our analyses of CVD-related PRS with cognitive performance at baseline using composite scores for memory (MEM) and executive functioning (EF). Results and Conclusion: We found significantly strong correlation of CVD-related PRS with only certain AD Diagnoses groups (Significant Memory Concerns: SMC and Alzheimer's: AD) in the AD continuum. Additionally, we found strong associations (p &lt; 0.05) of this cardiovascular based- polygenic risk score with baseline memory as well as White Matter Hyperintensity (WMH) in our sample, specifically the group with a clinical diagnosis of AD. Furthermore, we are performing longitudinal association analysis of CVD-related PRS with longitudinal cognitive decline using a linear mixed effects model. Our findings will help us understand 1) how a cumulative CVD-related PRS associates with different AD diagnoses subgroups as well as vascular biomarkers for AD and 2) how CVD-related PRS associates with cognitive performance and longitudinal cognitive decline.

Isotype switching in human memory B cells sets intrinsic antigen-affinity thresholds that dictate antigen-driven fates

Memory B cells (MBCs) play a critical role in protection against homologous and variant pathogen challenge by either differentiating to plasma cells (PCs) or to germinal center (GC) B cells. The human MBC compartment contains both switched IgG+ and unswitched IgM+ MBCs; however, whether these MBC subpopulations are equivalent in their response to B cell receptor cross-linking and their resulting fates is incompletely understood. Here, we show that IgG+ and IgM+ MBCs can be distinguished based on their response to κ-specific monoclonal antibodies of differing affinities. IgG+ MBCs responded only to high-affinity anti-κ and differentiated almost exclusively toward PC fates. In contrast, IgM+ MBCs were eliminated by apoptosis by high-affinity anti-κ but responded to low-affinity anti-κ by differentiating toward GC B cell fates. These results suggest that IgG+ and IgM+ MBCs may play distinct yet complementary roles in response to pathogen challenge ensuring the immediate production of high-affinity antibodies to homologous and closely related challenges and the generation of variant-specific MBCs through GC reactions.