Biology subjects

Tasaki, S.

Publications and source records attributed to Tasaki, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Disease relevance and replicability of deep learning gene expression prediction

Recent deep learning (DL) models predict average gene expression levels from DNA sequences with high overall correlation to measured values. We examine these DL models through the lens of disease research. The seemingly high overall performance of DL models is largely due to capturing whether genes are "On" or "Off", and to a lesser extent, disease-relevant expression level changes for "On" genes. Indeed, the more bimodal the gene expression distribution, the better the reported performance. We track the extent of this issue across tissues, molecular systems, and cancers. Compounding the model evaluation issue, we find inconsistencies between the published code and reported performance, highlighting the importance of versioning and publishing performance evaluation code. These findings indicate that the high reported performance of popular DL models falls unexpectedly short in disease applications, and that the problem of personalized genomic prediction remains far from solved in a disease context.

genomics↗

bioRxiv2026

Altered microglial communication in brains with Alzheimers Disease pathology

Microglia are the resident immune cells of the central nervous system and are highly versatile, continuously monitoring the brain microenvironment with their motile processes and responding to perturbations in diverse ways. Recent studies have revealed substantial functional heterogeneity among microglial states, suggesting that analyzing individual subpopulations is essential to capture specialized signaling programs and disease-associated interactions that would otherwise be obscured in bulk analyses. To better understand how these cells interact, we performed a ligand-receptor communication analysis using single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex, comprising 16 microglial subpopulations together with other myeloid cells identified in brain tissue, including macrophages and monocytes. We characterized the signaling roles of these cell populations, compared individuals with and without neuropathologically defined Alzheimers disease, and associated cell-pair interactions with AD-related clinical and neuropathological traits. We found that AD was characterized by altered microglial composition and rewiring of intercellular communication, including disease-specific signaling pathways and distinct interaction hubs. Lipid-associated microglia were associated with tau pathology, whereas nuclear receptor signaling microglia were linked to hippocampal sclerosis. These findings were replicated in three independent datasets. Overall, this work provides an initial but comprehensive map of microglial communication in the aging human brain and identifies candidate signaling interactions for future functional validation.

neuroscience↗

bioRxiv2025

A hidden network of gene relationships unifies behavioral and molecular research

Behavioral and molecular approaches often share topics of inquiry across basic and applied research fields. However, for historical and practical reasons, these approaches conceptualize scientific questions differently. This segregation is particularly damaging to translational fields that are in need of the combined strength of these two major approaches. While studies are increasingly recognizing the value of integrated approaches through hybrid methods, these efforts require great resources. To potentially relieve this interdisciplinary detachment, we uncover similarities between behavioral and molecular work in the publication network to test if the two approaches are more aligned than they appear, through deeper common concepts. To do this, we first create a model to predict genes that are relevant to behavioral publications from their abstracts. These predictions are in turn used to define gene-based similarity across the entire medical literature network. This model is built on-top of a Large Language Model (LLM), used to find patterns in the semantic structure of abstracts. Our experiments demonstrated the models efficacy in predicting gene annotations in molecular abstracts and showed the practical value of predictions made on behavioral research. We found similar increased relative risks for differentially expressed genes in disease-specific publications compared to the global publication population in both the molecular and behavioral groups. Additionally, the model was able to correlate gene predictions between molecular publications and their behavioral references. We instantiated this model in a free and easy to use search engine that finds related publications via the latent representations weve established. Using this model, Abstract2Gene facilitates novel collaborations by uncovering shared gene signatures across medical literature, broadening the reach of research to distant fields and promoting collaboration between molecular and behavioral researchers.

systems biology↗

bioRxiv2025

Molecular and neuropathological determinants of neuronal dysfunction in Alzheimer's disease

The biological basis of neuronal excitatory/inhibitory (E/I) imbalance in Alzheimers disease (AD) remains unclear. Using a comprehensive cohort with ante-mortem functional neuroimaging and post-mortem molecular data from the same participants, we mapped individual, whole-brain E/I imbalances through biophysical modeling. E/I ratios in regions supporting higher-order cognitive functions were significantly associated with cognitive performance and decline, with mediation by global neuropathological burden. We also observed a significant inverted U-shaped relationship between E/I ratios and neurofibrillary tangle severity, peaking at the limbic stage (Braak III-IV) in 14 brain areas, including the bilateral hippocampus and superior frontal gyrus. In addition, we identified 89 genes and 101 proteins that predict regional E/I ratios, with pathways related to synaptic signaling and immune response overrepresented. The generalizability of these molecular predictors was confirmed in two independent cohorts, achieving good classification performance for neuropathology severity and AD dementia. Lastly, the estimated E/I imbalances in AD aligned with whole-brain distributions of microglia and oligodendrocyte precursor cells, suggesting that spatial cellular organization contributes to vulnerability to neuronal dysfunction. Overall, this study provides critical insights into the cellular, molecular, and neuropathological signatures of circuit-level dysfunction in AD.

neuroscience↗

bioRxiv2025

Integration of aged brain multi-omics reveals cross-system mechanisms underlying Alzheimer's disease heterogeneity

The molecular correlates of Alzheimers disease (AD) are increasingly being defined by omics. Yet, the findings from different data types or cohorts are often difficult to reconcile. Collecting multiple omics from the same individuals allows a comprehensive view of disease-related molecular mechanisms, while addressing conflicting findings derived from single omics. Such same-sample multi-omics can reveal, for instance, when changes observed in the transcriptome share distinct but coordinated signals in epigenetics and proteomics, relationships otherwise unclear. Here, we apply a data-driven multi-omic framework to integrate epigenomic, transcriptomic, proteomic, metabolomic, and cell-type-specific population data from up to 1,358 aged human brain samples from the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP). We demonstrate the existence of sprawling cross-omics cross-system biological factors that also relate to AD phenotypes. The strongest AD-associated factor (factor 8) involved elevated immune activity at the epigenetic level, decreased expression of heat shock genes in the transcriptome, and disrupted energy metabolism and cytoskeletal dynamics in the proteome. We also showed immune-related factors (factors 2 and 3) with discordant enrichments, reflecting reactive-like glial subpopulations and protective contributions from surveillance microglia. Both were negatively associated with AD pathology, suggesting potential immune resilience mechanisms. Finally, unsupervised clustering of participants revealed eleven molecular subtypes of the aging brain, including three clusters strongly associated with AD but displaying distinct molecular signatures and phenotypic characteristics. Our findings provide a comprehensive map of molecular mechanisms underlying AD heterogeneity, highlighting the complex role of neuroinflammatory processes, and yielding potential novel biomarkers and therapeutic targets for precision medicine approaches to AD treatment.

molecular biology↗

bioRxiv2025

Characterizing Post-Mortem Brain Molecular Taxonomy of Cognitive Resilience and Translating it to Living Humans

Here, we define cognitive resilience as slower or faster cognitive decline after we regress out the effects of common brain neuropathologies. Its understanding could provide important insights into the biology underlying cognitive health, enabling the development of more effective strategies to prevent cognitive decline and dementia. However, this requires the development of a practical method to quantify resilience and measure it in living individuals, as well as identifying heterogenous pathways associated with resilience in different individuals. Here, we approach this problem by using a data-driven framework to quantify and characterize molecular signatures underlying cognitive resilience. Using multimodal contrastive trajectory inference (mcTI) on bulk RNA sequencing and tandem mass tag (TMT) proteomic data from 898 post- mortem brain samples from the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP), we derived individual-level molecular pseudotime values reflecting the molecular path from high to low resilience across individuals. Additionally, we identified two distinct molecular subtypes of resilience, each characterized by unique transcriptomic and proteomic signatures, and differing associations with several phenotypes. To translate our brain-derived pseudotime and subtypes to living individuals, we developed prediction models with paired genetics, ante-mortem blood omics, clinical, psychosocial, imaging and device data from the same individuals, demonstrating the potential to predict brain molecular resilience profiles in living persons. Our findings establish a framework for quantifying resilience based on multi- level molecular signatures, identify molecularly distinct resilience subtypes, and demonstrate the feasibility of translating brain-derived molecular profiles to living individuals--laying the groundwork for the development of targeted resilience-promoting interventions in cognitive aging.

neuroscience↗

bioRxiv2025

Cytokine expression profile in the human brain of older adults

Alzheimers disease (AD) is a complex neurodegenerative condition linked to chronic neuroinflammation. This study investigates the cytokine gene expression profile in cortical tissue samples from elderly individuals with and without AD to identify potential biomarkers and enhance our understanding of disease pathogenesis. Utilizing high-depth RNA sequencing data, we identified a set of cytokines whose expression significantly associated with different aspects of the AD phenotype, including measures of neurofibrillary tangles, amyloid-{beta} deposition, and a person-specific rate of cognitive decline. Single-nucleus transcriptomics data facilitated the identification of specific cell types, such as microglia and oligodendrocytes, that significantly contribute to the inflammatory response in AD. Additionally, we observed a strong correlation between the expression of certain cytokines and genetic risk for the disease. Our findings indicate that cytokine-mediated neuroinflammation plays a vital role in AD progression and that modulating the immune response may offer a promising strategy for developing new therapies.

molecular biology↗

bioRxiv2025

Brain Multi-Omic Subtypes of Neuroticism Reveal Molecular Signatures linked to Alzheimer's Disease

ImportanceMolecular mechanisms linking neuroticism with Alzheimers disease traits are unknown. ObjectiveTo identify molecular subtypes of neuroticism and examine their association with ADRD traits. DesignThree ongoing cohort studies were used; Religious Orders Study (ROS), Rush Memory and Aging Project (MAP) and Minority Aging Research Study (MARS), that began enrollment in 1994, 1997, and 2004, respectively. SettingOlder priests, nuns, and brothers from across the U.S. (ROS), older adults (MAP) and older African-American adults (MARS) from across the greater Chicago metropolitan area. Participants1,028 decedents with multi-omic data from the dorsolateral prefrontal cortex (DLPFC), the anterior cingulate cortex (AC), and the posterior cingulate gyrus (PCG). Exposure(s)Eight layers of omics (DNA methylation and histone acetylation from DLPFC; RNA seq from AC, DLPFC, and PCG, single-nucleus RNA, TMT proteomics and metabolomics from DLPFC) and Neuroticism using the 12-item version from the NEO Five-Factor Inventory. Main outcome(s) and measure(s)Person-specific multi-omic molecular pseudotime representing molecular progression from low to high phenotypic expression of neuroticism, and three multi-omic brain molecular subtypes of neuroticism which represent distinct omic pathways from no/low neuroticism to high neuroticism that differ by their omic constituents. Participants are exclusively assigned to the subtype which aligns mostly with their multi-omic profile. ResultsThe top drivers of subtype differentiation were transcriptomic alterations across three brain regions (DLPFC, AC, PCG) which extensively and differentially characterized the subtypes. The subtypes were also differentially associated with AD pathology, temporal lobe atrophy, and AD dementia, with subtype N1 showing the strongest associations. Conclusions and RelevanceNeuroticism may be driven by three distinct molecular subtypes, with subtype N1 driving ADRD-related associations, N2 showing some ADRD associations, and N3 being completely independent of these outcomes. Our data provide novel insights into the biology of individual differences in predispositions of neuroticism and its associations with ADRD traits. Key pointsO_ST_ABSQuestionC_ST_ABSWhat are the brain multi-omics molecular signatures linking neuroticism with Alzheimers diseases and related dementias (AD/ADRDs)? FindingsWe identified three distinct brain multi-omic molecular subtypes reflecting different molecular pathways underlying neuroticism. Top omic features of the subtypes were extensively and differentially characterized by transcriptomic alterations across three brain regions - dorsolateral prefrontal cortex, anterior cingulate cortex, and posterior cingulate gyrus. Subtype N1 was strongly associated with AD pathology, AD dementia, and temporal lobe atrophy. MeaningThe association we typically observe between phenotypic neuroticism and ADRD clinical traits might be largely driven by a molecular pathway underlying this trait.

neuroscience↗

bioRxiv2025

Translating the Post-Mortem Brain Multi-Omics Molecular Taxonomy of Alzheimer's Dementia to Living Humans

Alzheimers disease (AD) dementia is characterized by significant molecular and phenotypic heterogeneity, which confounds its mechanistic understanding, diagnosis, and effective treatment. In this study, we harness the most comprehensive dataset of paired ante-mortem blood omics, clinical, psychological, and post-mortem brain multi-omics data and neuroimaging to extensively characterize and translate the molecular taxonomy of AD dementia to living individuals. First, utilizing a comprehensive integration of eight complementary molecular layers from brain multi-omics data (N = 1,189), we identified three distinct molecular AD dementia subtypes exhibiting strong associations with cognitive decline, sex, psychological traits, brain morphology, and characterized by specific cellular and molecular drivers involving immune, vascular, and oligodendrocyte precursor cells. Next, in a significant translational effort, we developed predictive models to convert these advanced brain-derived molecular profiles (AD dementia pseudotimes and subtypes) into blood-, MRI- and psychological traits-based markers. The translation results underscore both the promise of these models and the opportunities for further enhancement. Our findings enhance the understanding of AD heterogeneity, underscore the value of multi-scale molecular approaches for elucidating causal mechanisms, and lay the groundwork for the development of novel therapies in living persons that target multi-level brain molecular subtypes of AD dementia.

neuroscience↗

bioRxiv2025

Gene module-trait network analysis uncovers cell type specific systems and genes relevant to Alzheimers Disease

Alzheimers Disease (AD) is marked by the accumulation of pathology, neuronal loss, and gliosis and frequently accompanied by cognitive decline. Understanding brain cell interactions is key to identifying new therapeutic targets to slow its progression. Here, we used systems biology methods to analyze single-nucleus RNA sequencing (snRNASeq) data generated from dorsolateral prefrontal cortex (DLPFC) tissues of 424 participants in the Religious Orders Study or the Rush Memory and Aging Project (ROSMAP). We identified modules of co-regulated genes in seven major cell types, assigned them to coherent cellular processes, and assessed which modules were associated with AD traits such as cognitive decline, tangle density, and amyloid-{beta} deposition. Coexpression network structure was conserved in the majority of modules across cell types, but we also found distinct communities with altered connectivity, especially when compared to bulk RNASeq, suggesting cell-specific gene co-regulation. These coexpression modules can also capture signatures of cell subpopulations and be influenced by cell proportions. Using a Bayesian network framework, we modeled the direction of relationships between the modules and AD progression. We highlight two key modules, a microglia module (mic_M46), associated with tangles; and an astrocyte module (ast_M19), associated with cognitive decline. Our work provides cell-specific molecular networks modeling the molecular events leading to AD.

systems biology↗

bioRxiv2024

Spatial Expression of Long Non-Coding RNAs in Human Brains of Alzheimer's Disease

BackgroundLong non-coding RNAs (lncRNAs) are critical regulators of physiological and pathological processes, with their dysregulation increasingly implicated in aging and Alzheimers disease (AD). To investigate the spatial and cellular distribution of lncRNAs in the aging brain, we leveraged published spatial transcriptomics (ST), single-nucleus RNA sequencing (snRNA-seq), and bulk RNA-seq datasets from the dorsolateral prefrontal cortex (DLPFC) of ROSMAP participants with and without pathological AD. ResultsLncRNAs exhibited greater subregion-specific expression than mRNAs, with enrichment in antisense and lincRNA biotypes. Subregion-enriched lncRNAs were generally not cell-type specific, and vice versa. Differential expression analysis of ST data identified AD-associated lncRNAs with distinct spatial patterns and moderate overlap with differentially expressed (DE) lncRNAs from bulk RNA-seq. Gene set enrichment revealed their involvement in chromatin remodeling, epigenetic regulation, and RNA metabolism. We also identified AD DE lncRNAs across major brain cell types using snRNA-seq but overlap with ST DE lncRNAs was limited. Among previously reported lncRNAs, OIP5-AS1 was consistently upregulated in AD in all cortical subregions. Antisense oligonucleotide (ASO) knockdown of OIP5-AS1 in iPSC-derived microglia led to upregulation of pro-inflammatory genes and downregulation of DNA replication and repair pathways. Immunoassays confirmed increased secretion of pro-inflammatory cytokines. The knockdown expression pattern was enriched for microglia-specific AD DE genes and microglia states. ConclusionsThis study provides a spatial and cellular map of lncRNAs in the aging human cortex and identifies subregion-and cell-type-enriched DE lncRNAs in AD. Our findings implicate OIP5-AS1 in microglial activation, suggesting its potential contribution to AD pathogenesis.

neuroscience↗

bioRxiv2024

The YTHDF Proteins Shape the Brain Gene Signatures of Alzheimer's Disease

The gene signatures of Alzheimers Disease (AD) brains reflect an output of a complex interplay of genetic, epigenetic, epi-transcriptomic, and post-transcriptional regulation., yet the dominant factor shaping these signatures remains unclear. To identify the most significant factor that shapes the AD brain signatures, we integrated cellular and molecular features with differential gene expression in in an explainable machine learning framework. Our result indicates that YTHDF proteins, the canonical readers of N6-methyladenosine RNA modification (m6A), are the most influential predictors of the AD brain signatures. We then show that protein modules containing YTHDFs are downregulated in human AD brains, and knocking down and pharmacologically inhibiting YTHDFs in iPSC-derived 2D and 3D neuronal models recapitulate the AD-associated transcriptional signatures. Furthermore, eCLIP-seq analysis revealed that YTHDF proteins influence AD signatures through both m6A-dependent and independent pathways. These results highlight the central role of YTHDF proteins in shaping the gene signatures of AD brains.

neuroscience↗

bioRxiv2024

Uncovering Plaque-Glia Niches in Human Alzheimer's Disease Brains Using Spatial Transcriptomics

Amyloid-beta (A{beta}) plaques and surrounding glial activation are prominent histopathological hallmarks of Alzheimers Disease (AD). However, it is unclear how A{beta} plaques interact with surrounding glial cells in the human brain. Here, we applied spatial transcriptomics (ST) and immunohistochemistry (IHC) for A{beta}, GFAP, and IBA1 to acquire data from 258,987 ST spots within 78 postmortem brain sections of 21 individuals. By coupling ST and adjacent-section IHC, we showed that low A{beta} spots exhibit transcriptomic profiles indicative of greater neuronal loss than high A{beta} spots, and high-glia spots present transcriptomic changes indicative of more significant inflammation and neurodegeneration. Furthermore, we observed that this ST glial response bears signatures of reported mouse gene modules of plaque-induced genes (PIG), oligodendrocyte (OLIG) response, disease-associated microglia (DAM), and disease-associated astrocytes (DAA), as well as different microglia (MG) states identified in human AD brains, indicating that multiple glial cell states arise around plaques and contribute to local immune response. We then validated the observed effects of A{beta} on cell apoptosis and plaque-surrounding glia on inflammation and synaptic loss using IHC. In addition, transcriptomic changes of iPSC-derived microglia-like cells upon short-interval A{beta} treatment mimic the ST glial response and mirror the reported activated MG states. Our results demonstrate an exacerbation of synaptic and neuronal loss in low-A{beta} or high-glia areas, indicating that microglia response to A{beta}-oligomers likely initiates glial activation in plaque-glia niches. Our study lays the groundwork for future pathology genomics studies, opening the door for investigating pathological heterogeneity and causal effects in neurodegenerative diseases.

genomics↗

bioRxiv2023

scEGOT: Single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport

Time-series scRNA-seq data have opened a door to elucidate cell differentiation, and in this context, the optimal transport theory has been attracting much attention. However, there remain critical issues in interpretability and computational cost. We present scEGOT, a comprehensive framework for single-cell trajectory inference, as a generative model with high interpretability and low computational cost. Applied to the human primordial germ cell-like cell (PGCLC) induction system, scEGOT identified the PGCLC progenitor population and bifurcation time of segregation. Our analysis shows TFAP2A is insufficient for identifying PGCLC progenitors, requiring NKX1-2. Additionally, MESP1 and GATA6 are also crucial for PGCLC/somatic cell segregation. These findings shed light on the mechanism that segregates PGCLC from somatic lineages. Notably, not limited to scRNA-seq, scEGOTs versatility can extend to general single-cell data like scATAC-seq, and hence has the potential to revolutionize our understanding of such datasets and, thereby also, developmental biology.

bioinformatics↗

bioRxiv2023

A Molecular Basis of Human Brain Connectivity

Neuroimaging is commonly used to infer human brain connectivity, but those measurements are far-removed from the molecular underpinnings at synapses. To uncover the molecular basis of human brain connectivity, we analyzed a unique cohort of 98 individuals who provided neuroimaging and genetic data contemporaneous with dendritic spine morphometric, proteomic, and gene expression data from the superior frontal and inferior temporal gyri. Through cellular contextualization of the molecular data with dendritic spine morphology, we identified hundreds of proteins related to synapses, energy metabolism, and RNA processing that explain between-individual differences in functional connectivity and structural covariation. By integrating data at the genetic, molecular, subcellular, and tissue levels, we bridged the divergent fields of molecular biology and neuroimaging to identify a molecular basis of brain connectivity. One-Sentence SummaryDendritic spine morphometry and synaptic proteins unite the divergent fields of molecular biology and neuroimaging.

neuroscience↗

bioRxiv2023

Polygenic Risk Associated with Alzheimer's Disease and Other Traits Influences Genes Involved in T Cell Signaling and Activation

T cells, members of the adaptive immune system known for their ability to respond to an enormous variety of pathogens and other insults, are increasingly recognized as important mediators of pathology in neurodegeneration and other diseases. Previously, we and others have shown that T cell gene expression phenotypes are regulated by genetic variants associated with autoimmune disease, neurodegenerative disease, and inflammatory processes. However, many complex diseases have polygenic risk with thousands of common variants contributing a small amount to disease heritability. Here, we compute the polygenic risk score (PRS) of several autoimmune, neurological, and psychiatric disorders and present the first correlation of these PRSs with T cell gene expression, using transcriptomic and genomic sequencing data from a cohort of Alzheimers disease (AD) patients and age-matched controls. We validate our AD PRS against clinical metrics in our cohort and then compare PRS-associated genes across traits and four T cell subtypes. Several genes and biological pathways associated with the PRS for these traits relate to functions such as T cell chemotaxis, differentiation, response to and production of cytokines, and regulation of T cell receptor signaling. We also found that the trait-associated gene expression signature for certain traits was polarized towards a particular T cell subset, such as CD4+ for autoimmune disease traits or CD8+ for some psychiatric disease traits. Our findings may help guide efforts in precision medicine to target specific T cell functions in individuals with high polygenic risk for various complex diseases.

bioinformatics↗

bioRxiv2023

Nutrient-driven dedifferentiation of enteroendocrine cells promotes adaptive intestinal growth

Post-developmental organ resizing improves organismal fitness under constantly changing nutrient environments. Although stem cell abundance is a fundamental determinant of adaptive resizing, our understanding of its underlying mechanisms remains primarily limited to the regulation of stem cell division. Here we demonstrate that nutrient fluctuation induces dedifferentiation in the Drosophila adult midgut to drive adaptive intestinal growth. From lineage tracing and single-cell RNA-sequencing, we identify a subpopulation of enteroendocrine cells (EEs) that convert into functional intestinal stem cells (ISCs) in response to dietary glucose and amino acids by activating the JAK-STAT pathway. Genetic ablation of EE-derived ISCs severely impairs ISC expansion and midgut growth despite the retention of resident ISCs, and in silico modeling further indicates that EE dedifferentiation enables efficient increase in the midgut cell number while maintaining epithelial cell composition. Our findings uncover a physiologically-induced dedifferentiation that ensures ISC expansion during adaptive organ growth in concert with nutrient conditions.

developmental biology↗

bioRxiv2023

Interpretable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons in older adults

Hand drawing involves multiple neural systems for planning and precise control of sequential movements, making it a valuable cognitive test for older adults. However, conventional visual assessment of drawings may not capture intricate nuances that could help track cognitive states. To address this issue, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3,111 participants in three aging cohorts, explained 23.3% of the variance in global cognitive scores, a comprehensive hour-long cognitive battery. The models performance, which was 1.92 times more accurate than conventional visual assessment, significantly improved the detection of cognitive decline. The improvement in accuracy was due to capturing additional drawing features that we found to be associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that hand-drawn images can provide rich cognitive information, enabling rapid assessment of cognitive decline and suggesting potential clinical implications in dementia.

neuroscience↗