Clinical assessments often use complex picture description tasks to elicit natural speech patterns and magnify changes occurring in brain regions implicated in Alzheimer's disease and dementia. As The Cookie Theft picture description task is used in the largest Alzheimer's disease and dementia cohort studies available, we aimed to create algorithms that could characterize the visual narrative path a participant takes in describing what is happening in this image. We proposed spatio-semantic graphs, models based on graph theory that transform the participants' narratives into graphs that retain semantic order and encode the visuospatial information between content units in the image. The resulting graphs differ between Cognitively Impaired and Unimpaired participants in several important ways. Cognitively Impaired participants consistently scored higher on features that are heavily associated with symptoms of cognitive decline, including repetition, evidence of short-term memory lapses, and generally disorganized narrative descriptions, while Cognitively Unimpaired participants produced more efficient narrative paths. These results provide evidence that spatio-semantic graph analysis of these tasks can generate important insights into a participant's cognitive performance that cannot be generated from semantic analysis alone.

Biomedical knowledge graphs, which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods, and non-uniform evaluation metrics.

In this work, we established a comprehensive knowledge graph (KG) system for the biomedical field in an attempt to bridge the gap. Here we introduced PharmKG, a multi-relational, attributed biomedical knowledge graph, composed of more than 500,000 individual interconnections between genes, drugs, and diseases, with 29 relation types over a vocabulary of ~8,000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure, and disease word embedding while preserving the semantic and biomedical features. For baselines, we offered 9 state-of-the-art knowledge graph embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a knowledge graph in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical knowledge graph construction, embedding, and application.

In the United States, around 39 percent of people with Alzheimer’s are 75 to 84 years old. Additionally, around 26 percent of those with Alzheimer’s are aged 65 to 74 years. Alzheimer’s disease is a form of dementia which impacts memory, behavior, and thinking and can lead to symptoms becoming so severe that those with the disease require support with basic daily tasks. Alzheimer’s remains a relevant problem around the world. Alzheimer’s disease deaths Alzheimer’s is currently the sixth leading cause of death in the United States, causing more deaths than diabetes and kidney disease. While advances in medicine and increased access to treatment and care have caused decreases in many major causes of death, deaths from Alzheimer’s have risen over the past couple of decades. For example, from 2000 to 2022, deaths from stroke in the U.S. declined by 1.4 percent, while deaths from Alzheimer’s increased 142 percent. Alzheimer’s disease worldwide Alzheimer’s is not only a problem in the United States but impacts every country around the globe. In 2018, there were an estimated 50 million people living with dementia worldwide. This figure is predicted to increase to some 152 million by the year 2050. Alzheimer’s does not only cause a significant amount of death but also has a significant economic impact. In 2018, cost estimates for Alzheimer’s care worldwide totaled around one trillion U.S. dollars, with this figure predicted to double by the year 2030.

Clinical assessments often use complex picture description tasks to elicit natural speech patterns and magnify changes occurring in brain regions implicated in Alzheimer's disease and dementia. As The Cookie Theft picture description task is used in the largest Alzheimer's disease and dementia cohort studies available, we aimed to create algorithms that could characterize the visual narrative path a participant takes in describing what is happening in this image. We proposed spatio-semantic graphs, models based on graph theory that transform the participants' narratives into graphs that retain semantic order and encode the visuospatial information between content units in the image. The resulting graphs differ between Cognitively Impaired and Unimpaired participants in several important ways. Cognitively Impaired participants consistently scored higher on features that are heavily associated with symptoms of cognitive decline, including repetition, evidence of short-term memory lapses, and generally disorganized narrative descriptions, while Cognitively Unimpaired participants produced more efficient narrative paths. These results provide evidence that spatio-semantic graph analysis of these tasks can generate important insights into a participant's cognitive performance that cannot be generated from semantic analysis alone.

Alzheimer's disease funding by the NIH was around 3.5 billion U.S. dollars in fiscal year 2023. This graph shows the actual Alzheimer's disease funding by the National Institutes for Health (NIH) from FY 2013 to FY 2023 and estimates for FY 2024 and FY 2025.

In 2024, Alzheimer's disease was estimated to cost Medicare and Medicaid around 231 billion dollars in care costs. This number is expected to grow to 637 billion dollars by 2050. This graph presents the costs of care for individuals with Alzheimer's disease to Medicare and Medicaid in the U.S. from 2024 to 2050.

IntroductionGraph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation method for individual patient classification.MethodsOur method involves analyzing the output variations resulting from decomposing input values, which allows us to determine the degree of impact on the prediction. Through this process, we gain insight into how each feature from various modalities, both at the individual and group levels, contributes to the diagnostic result. Given that graph data contains critical information in edges, we studied relational data by silencing all the edges of a particular class, thereby obtaining explanations at the neighborhood level.ResultsOur functional evaluation showed that the explanations remain stable with minor changes in input values, specifically for edge weights exceeding 0.80. Additionally, our comparative analysis against SHAP values yielded comparable results with significantly reduced computational time. To further validate the model's explanations, we conducted a survey study with 11 domain experts. The majority (71%) of the responses confirmed the correctness of the explanations, with a rating of above six on a 10-point scale for the understandability of the explanations.DiscussionStrategies to overcome perceived limitations, such as the GCN's overreliance on demographic information, were discussed to facilitate future adoption into clinical practice and gain clinicians' trust as a diagnostic decision support system.

Alzheimer’s disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition. With the assistance of multimodal brain networks and graph-based analysis approaches, AD-related network disruptions support the hypothesis that AD can be identified as a dysconnectivity syndrome. However, as the recent emerging of individual-based morphological network research of AD, the utilization of multiple morphometric features may provide a broader horizon for locating the lesions. Therefore, the present study applied the newly proposed individual morphological brain network with five commonly used morphometric features (cortical thickness, regional volume, surface area, mean curvature, and fold index) to explore the topological aberrations and their relationship with cognitive functioning alterations in the early stage of AD. A total of 40 right-handed participants were selected from Open Access Series of Imaging Studies Database with 20 AD patients (age ranged from 70 to 79, CDR = 0.5) and 20 age/gender-matched healthy controls. The significantly affected connections (p < 0.05 with FDR correction) were observed across multiple regions, both enhanced and attenuated correlations, primarily related to the left entorhinal cortex (ENT). In addition, profoundly changed Mini Mental State Examination (MMSE) score and global efficiency (p < 0.05) were noted in the AD patients, as well as the pronounced inter-group distinctions of betweenness centrality, global and local efficiency (p < 0.05) in the higher MMSE score zone (28–30), which indicating the potential role of graphic properties in determination of early-stage AD patients. Moreover, the reservations (regions in the occipital and frontal lobes) and alterations (regions in the right temporal lobe and cingulate cortex) of hubs were also detected in the AD patients. Overall, the findings further confirm the selective AD-related disruptions in morphological brain networks and also suggest the feasibility of applying the morphological graphic properties in the discrimination of early-stage AD patients.

Connectome, traits and behavior data for APOE234 mice.

• 1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.
• 2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.

columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance

• 3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.
• 4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.

The lifetime risk of developing Alzheimer's disease in the United States varies significantly by age and gender. Women face a slightly higher risk, with a 21.1 percent chance of developing Alzheimer's at age 65 compared to 19.5 percent for men. This gender disparity in Alzheimer's risk underscores the importance of targeted research and interventions to address the unique challenges faced by women in brain health and aging. Growing impact Alzheimer's disease is becoming an increasingly pressing health concern in the United States, with the number of Americans aged 65 and over with Alzheimer's projected to more than double by 2060, reaching 13.8 million. This dramatic increase will have far-reaching consequences for healthcare systems, families, and society as a whole. The disease's impact is already substantial, with Alzheimer's being the sixth leading cause of death in the country and causing more deaths than diabetes and kidney disease. Research funding The economic impact of Alzheimer's disease is staggering and in response to this growing crisis, the United States has increased research funding. The National Institutes of Health (NIH) allocated approximately 3.5 billion U.S. dollars to Alzheimer's disease research in fiscal year 2023. This investment reflects the urgency of finding effective treatments and potential cures for this devastating condition that affects millions of Americans and their families.

In 2022, the mortality rate due to Alzheimer's disease was 36 deaths per 100,000 people. This statistic displays the annual Alzheimer's disease mortality rate in the United States from 2000 to 2022. Scientists believe that early detection of Alzheimer's can be the best way to prevent or slow the course of the disease. Alzheimer’s disease Alzheimer’s disease, a progressive and incurable brain disease, is among the top ten leading causes of death in the U.S. as well as worldwide. Furthermore, over the past two decades, the number of deaths due to Alzheimer’s and other dementias in the United States increased by over 140 percent. As with other dementias, Alzheimer’s commonly affects older individuals, although it can be diagnosed earlier on in life. In the United States, the majority of people with Alzheimer’s disease are over 75 years of age. Initial symptoms include difficulties in memory and mood changes, but the disease gradually progresses to impair communication and judgment, behavioral changes, and deficits in movement and motor skills, such as difficulties with swallowing, which often becomes a contributing cause of death. Care and treatment The cost of care for individuals with Alzheimer’s is expected to increase over the next couple of decades, with costs to Medicare and Medicaid expected to reach 637 billion U.S. dollars by 2050. Due to the increasing burden of Alzheimer’s and other dementias on healthcare and social systems, research into treatment and prevention is a major focus. Several major pharmaceutical companies currently have multiple drugs for Alzheimer’s treatment in various stages of development; other research is focused on identifying early brain changes associated with the disease in order to provide early diagnosis and intervention. Furthermore, personal health strategies include reducing modifiable risk factors commonly associated with cardiovascular health, such as quitting smoking, maintaining a healthy diet, and staying socially, mentally, and physically active.

The number of Americans aged 65 and over with Alzheimer's disease is projected to more than double by 2060, reaching **** million. This significant increase highlights the growing challenge of caring for an aging population, particularly those affected by dementia. As the prevalence of Alzheimer's rises, it will have far-reaching impacts on healthcare, families, and society as a whole. Aging population trends The surge in Alzheimer's cases is closely tied to broader demographic shifts in the United States. By 2050, it's estimated that 22 percent of the American population will be 65 years or older, up from 17.3 percent in 2022. This rapid aging of the population is expected to strain healthcare systems and change the nature of work and retirement. Challenges of aging in place As the number of older adults with Alzheimer's increases, there is a growing desire among seniors to age in their own homes. A 2024 survey found that ************** of adults aged 50 and older strongly or somewhat agreed they would like to remain in their current residence for as long as possible. This preference is even stronger among those 65 and older, with ** percent expressing this desire. However, the ability to age in place may be compromised by declining physical capabilities, as only about *** in **** adults aged 72 and older reported being fully able to perform self-care and mobility activities in 2021.

A survey from 2019 found that 49.2 percent of U.S. adults aged 45 years and above who belonged to a gender and sexual minority were suffering from high blood pressure, a modifiable risk factor for Alzheimer's disease and related dementias. This graph shows the percentage of U.S. adults aged 45 and above with selected modifiable risk factors for Alzheimer's disease and related dementias as of 2019, by sexual and gender minority status.

A survey from 2019 found that 52.5 percent of U.S. adult men aged 45 years and above were suffering from high blood pressure, a modifiable risk factor for Alzheimer's disease and related dementias. This graph shows the percentage of U.S. adults aged 45 and above with selected modifiable risk factors for Alzheimer's disease and related dementias as of 2019, by gender.

A survey from 2019 found that **** percent of U.S. adults aged 75 years and above were suffering from high blood pressure, a modifiable risk factor for Alzheimer's disease and related dementias. This graph shows the percentage of U.S. adults aged 45 and above with selected modifiable risk factors for Alzheimer's disease and related dementias as of 2019, by age group.

Alzheimer’s disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21–76 years) and the Open Access Series of Imaging Studies (OASIS, age 64–95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82–92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

In accordance with the physiological networks that underlie it, human cognition is characterized by both the segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, can be conceptualized as a network of functions. A network approach to cognition has previously revealed topological differences in cognitive profiles between healthy and disease populations. The present study, therefore, used graph theory to determine variation in cognitive profiles across healthy aging and cognitive impairment. A comprehensive neuropsychological test battery was administered to 415 participants. This included three groups of healthy adults aged 18–39 (n = 75), 40–64 (n = 75), and 65 and over (n = 70) and three patient groups with either amnestic (n = 75) or non-amnestic (n = 60) mild cognitive impairment or Alzheimer’s type dementia (n = 60). For each group, cognitive networks were created reflective of test-to-test covariance, in which nodes represented cognitive tests and edges reflected statistical inter-nodal significance (p < 0.05). Network metrics were derived using the Brain Connectivity Toolbox. Network-wide clustering, local efficiency and global efficiency of nodes showed linear differences across the stages of aging, being significantly higher among older adults when compared with younger groups. Among patients, these metrics were significantly higher again when compared with healthy older controls. Conversely, average betweenness centralities were highest in middle-aged participants and lower among older adults and patients. In particular, compared with controls, patients demonstrated a distinct lack of centrality in the domains of semantic processing and abstract reasoning. Network composition in the amnestic mild cognitive impairment group was similar to the network of Alzheimer’s dementia patients. Using graph theoretical methods, this study demonstrates that the composition of cognitive networks may be measurably altered by the aging process and differentially impacted by pathological cognitive impairment. Network alterations characteristic of Alzheimer’s disease in particular may occur early and be distinct from alterations associated with differing types of cognitive impairment. A shift in centrality between domains may be particularly relevant in identifying cognitive profiles indicative of underlying disease. Such techniques may contribute to the future development of more sophisticated diagnostic tools for neurodegenerative disease.

HEterogeneous Network-based data set for Alzheimer's disease (HENA) brings together Alzheimer's disease related data from well known public collections, as well as novel data sets generated by the members of AgedBrainSYSBIO consortium. It comprises 64 datasets of 6 data types originating from 9 data sources.This file contains node attributes for HENA network. The downloads are available in txt and RData formats.

Alzheimer's disease (AD) is characterized by a sequence of pathological changes, which are commonly assessed in vivo using various brain imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Currently, the most approaches to analyze statistical associations between regions and imaging modalities rely on Pearson correlation or linear regression models. However, these models are prone to spurious correlations arising from uninformative shared variance and multicollinearity. Notably, there are no appropriate multivariate statistical models available that can easily integrate dozens of multicollinear variables derived from such data, being able to utilize the additional information provided from the combination of data sources. Gaussian graphical models (GGMs) can estimate the conditional dependency from given data, which is conceptually expected to closely reflect the underlying causal relationships between various variables. Hence, we applied GGMs to assess multimodal regional brain alterations in AD. We obtained data from N = 972 subjects from the Alzheimer's Disease Neuroimaging Initiative. The mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated for each of the 108 cortical and subcortical brain regions. GGMs were estimated using a Bayesian framework for the combined multimodal data and the resulted conditional dependency networks were compared to classical covariance networks based on Pearson correlation. Additionally, graph-theoretical network statistics were calculated to determine network alterations associated with disease status. The resulting conditional dependency matrices were much sparser (≈10% density) than Pearson correlation matrices (≈50% density). Within imaging modalities, conditional dependency networks yielded clusters connecting anatomically adjacent regions. For the associations between different modalities, only few region-specific connections were detected. Network measures such as small-world coefficient were significantly altered across diagnostic groups, with a biphasic u-shape trajectory, i.e., increased small-world coefficient in early mild cognitive impairment (MCI), similar values in late MCI, and decreased values in AD dementia patients compared to cognitively normal controls. In conclusion, GGMs removed commonly shared variance among multimodal measures of regional brain alterations in MCI and AD, and yielded sparser matrices compared to correlation networks based on the Pearson coefficient. Therefore, GGMs may be used as alternative to thresholding-approaches typically applied to correlation networks to obtain the most informative relations between variables.

In 2023, there were approximately 750.5 deaths by all causes per 100,000 inhabitants in the United States. This statistic shows the death rate for all causes in the United States between 1950 and 2023. Causes of death in the U.S. Over the past decades, chronic conditions and non-communicable diseases have come to the forefront of health concerns and have contributed to major causes of death all over the globe. In 2022, the leading cause of death in the U.S. was heart disease, followed by cancer. However, the death rates for both heart disease and cancer have decreased in the U.S. over the past two decades. On the other hand, the number of deaths due to Alzheimer’s disease – which is strongly linked to cardiovascular disease- has increased by almost 141 percent between 2000 and 2021. Risk and lifestyle factors Lifestyle factors play a major role in cardiovascular health and the development of various diseases and conditions. Modifiable lifestyle factors that are known to reduce risk of both cancer and cardiovascular disease among people of all ages include smoking cessation, maintaining a healthy diet, and exercising regularly. An estimated two million new cases of cancer in the U.S. are expected in 2025.