Alzheimer’s disease initiates insidiously, with neuropathological changes developing years prior to overt cognitive decline or diagnosable dementia. Crucially, the earliest affected brain regions include the hippocampus and entorhinal cortex, which mediate spatial navigation and memory processing. This spatial navigation impairment often predates memory loss, making it a promising domain for early diagnostic indicators. Path integration, the brain’s intrinsic capacity to track one’s position and orientation by integrating self-motion cues, serves as a key navigational mechanism. Impairment in PI indicates early neural circuit dysfunction that could herald neurodegeneration.

Led by Senior Assistant Professor Kazuya Kawabata, the research team employed an innovative immersive VR paradigm to quantitatively assess PI in 71 cognitively healthy adults over an approximately one-year period. Participants donned head-mounted VR devices to navigate a circular virtual environment, visiting two designated checkpoints. Subsequently, without visual landmarks, they were tasked to return to the origin point relying solely on internal navigation cues, thereby isolating PI performance. The researchers extracted two primary metrics: PI error, which quantified the Euclidean distance deviation from the true start point, and angular error, measuring directional discrepancy.

The participants also underwent high-resolution magnetic resonance imaging (MRI) to capture detailed neuroanatomical metrics, including cortical thickness and volumetric measures of key brain regions. Concurrently, plasma samples were analyzed for established AD biomarkers such as phosphorylated tau at threonine 181 (p-tau181) and glial fibrillary acidic protein (GFAP), a marker reflecting astrocytic activation and neuroinflammation. Employing sophisticated linear mixed-effects models, the researchers interrogated the relationships between baseline VR-PI performance, longitudinal brain structural changes, and plasma biomarker trajectories.

Results were striking and coherent. Participants exhibiting greater PI error at baseline demonstrated significantly accelerated cortical thinning and volume loss over the follow-up interval. These neurodegenerative changes localized predominantly to brain regions known to be vulnerable in the early stages of Alzheimer’s pathology, notably the parahippocampal gyrus, middle temporal gyrus, posterior cingulate cortex, and caudal middle frontal gyrus. Angular error paralleled these findings, though it showed comparatively attenuated age-dependent variations, underscoring the robustness of VR-based navigation indices as sensitive markers of subtle cerebral decline.

Beyond structural associations, behavioral deficits in PI correlated strongly with molecular signatures of neurodegeneration. Elevated PI and angular errors were positively associated with increased plasma levels of p-tau181, confirming a link to pathological tau biomarker dynamics. Moreover, PI error also correlated significantly with GFAP concentrations, implicating astrocytic responses in the degenerative cascade. Notably, the extent of PI impairment at baseline accurately identified individuals destined for the most rapid decline, particularly in the parahippocampal region, suggesting potential utility in stratifying risk and prognosis.

Dr. Kawabata emphasized the translational relevance of these results, stating, “Our findings suggest that VR-PI performance captures both molecular (blood biomarker) and structural (MRI) signatures that emerge before overt clinical impairment.” This dual connection between behavior, brain atrophy, and plasma biomarkers highlights VR-based path integration as a uniquely integrative and early indicator of neurodegenerative vulnerability, possibly facilitating preemptive intervention strategies.

The technical innovation in this study lies not only in the use of immersive VR to isolate and quantify key navigational processes but also in the multi-modal approach that synergistically incorporates neural imaging and blood-based biomarkers. This enables a comprehensive framework connecting cognitive function, brain anatomy, and molecular pathology, which could revolutionize early detection approaches. By tracking subtle cognitive changes longitudinally in unimpaired individuals, researchers can elucidate the mechanistic progression toward symptomatic AD.

The implications extend beyond diagnostics. Early identification of at-risk individuals through VR navigation testing could permit timely lifestyle modifications and pharmacologic interventions, potentially delaying or modifying disease trajectory. This paradigm shift towards preclinical detection could transform clinical practice by moving from reactive to proactive models of dementia care, preserving cognitive function and enhancing quality of life.

Moreover, the study demonstrated excellent reliability of PI measures as predictors of cortical decline, independent of age effects, which often confound cognitive assessments in aging populations. This suggests that VR-PI could serve as a scalable, non-invasive screening tool accessible in both clinical and research settings, given the increasing availability of VR technologies.

The authors acknowledge some caveats, including the need for larger cohort validation and exploration of longer follow-up intervals to cement the prognostic power of VR-based metrics. Additionally, future research should investigate whether VR-PI assessments can differentiate between various forms of neurodegenerative dementia and delineate their specificity for AD pathology.

This pioneering work from Fujita Health University represents a significant milestone in neurodegeneration research. By bridging sophisticated neurotechnology with classical neuropathological markers, it offers a promising avenue for early and accurate identification of individuals on the trajectory toward Alzheimer’s disease. Such insights pave the way for a new era of precision medicine in cognitive health.

Subject of Research: People

Article Title: VR-based path integration predicts individual risk of rapid cortical decline: a one-year longitudinal study in cognitively unimpaired adults

News Publication Date: 20-Apr-2026

References: DOI: 10.1186/s13195-026-02056-x

Image Credits: Dr. Hirohisa Watanabe, Fujita Health University, Japan

Keywords: Alzheimer’s disease, path integration, virtual reality, neurodegeneration, hippocampus, entorhinal cortex, plasma biomarkers, p-tau181, GFAP, cortical thinning, magnetic resonance imaging, cognitive decline

FINGERS-7B distinguishes itself through its integration of diverse biological data types—spanning lifestyle choices, clinical observations, genomic sequences, and proteomic profiles—into a unified analytical framework. It leverages data from tens of thousands of individuals identified as at-risk for Alzheimer’s, synthesizing multidimensional biological signals to elucidate novel multi-omic biomarkers capable of detecting preclinical Alzheimer’s disease with profound accuracy and sensitivity. This holistic approach transcends previous methodologies that primarily analyzed singular omics data streams, thereby enabling unprecedented early intervention possibilities.

Central to this innovative model is the application of multi-omic biomarker discovery, wherein genomic, proteomic, and clinical inputs are conjointly assessed through advanced computational simulations. By harnessing complex machine learning architectures, FINGERS-7B is able to discern intricate correlations and causal pathways underlying Alzheimer’s pathogenesis, far exceeding the diagnostic precision achieved by earlier standalone biomarker investigations. As a result, the model offers a fourfold improvement in preclinical diagnostic accuracy and enhances responder stratification by an impressive 130%, signaling a seminal advancement in precision medicine for neurodegenerative disorders.

The open-source nature of FINGERS-7B invites collaboration across the research community, enabling researchers globally to deploy the model within the Alzheimer’s Disease Data Initiative’s (ADDI) AD Workbench. This cloud-based secure environment facilitates seamless integration into ongoing clinical research without necessitating data relocation or new infrastructure setup, fostering a democratized scientific ecosystem. Research groups can apply the model to their cohorts and contribute to a growing repository of knowledge, catalyzing accelerated biomarker discovery and validation.

At the core of FINGERS-7B’s innovation lies the concept of an individual “biological fingerprint,” a unique composite of biological signals that encapsulate personalized disease risk profiles. By decoding this signature, the model not only predicts the likelihood of cognitive decline but also models the temporal trajectory and potential efficacy of preventive interventions—ranging from lifestyle modifications such as diet to pharmacological treatments. This degree of personalization provides a critical framework for tailored therapeutic strategies, moving beyond one-size-fits-all paradigms.

The foundation of this model is deeply rooted in the longstanding FINGER study led by Professor Miia Kivipelto, which elucidated the preventive potential of lifestyle interventions in cognitively unimpaired older adults at risk for Alzheimer’s. Informed by extensive data spanning over 40 countries and 30,000 participants via the World-Wide FINGERS network, FINGERS-7B synthesizes this rich phenomenological database with state-of-the-art omics research drawn from partner studies, further augmented by industrial collaborators.

Driving this endeavor is an interdisciplinary team led by Adrian Noriega and Arvid Gollwitzer, whose expertise in AI and computational biology catalyzed the architecture and training of FINGERS-7B. Their vision encapsulates FINGERPRINT as a comprehensive discovery platform—a confluence of AI agents and foundation models engineered to decode the complexity of Alzheimer’s risk biomarkers, accelerate novel intervention discovery, and streamline therapeutic development.

The rapid development timeline underscores the potency of targeted research funding and agile collaboration. Seeded in mid-2023 with support from MIT’s Aging Brain Initiative, the team succeeded in training FINGERS-7B and effectuating its deployment on the AD Workbench within just ten months. This rapid iteration exemplifies the potency of integrating AI methodologies with multi-omic data streams to combat complex, multifactorial diseases like Alzheimer’s swiftly.

World-renowned neuroscientist Li-Huei Tsai highlighted the transformative potential of FINGERS-7B for integrating vast, heterogeneous biomolecular datasets into cohesive predictive frameworks. The model addresses one of the most formidable challenges facing neuroscience: synthesizing genetic, epigenetic, proteomic, and clinical datasets to achieve holistic individual risk profiling with prognostic foresight and therapeutic guidance.

FINGERS-7B’s release coincides with burgeoning efforts to globalize Alzheimer’s prevention research. Collaborations such as the partnership with the Davos Alzheimer’s Collaborative and the FINGERS Brain Health Institute exemplify ambitions to encompass globally diverse populations in their datasets, thereby enhancing the generalizability and inclusiveness of research outcomes in alignment with worldwide healthcare equity goals.

Before its public unveiling, FINGERPRINT already demonstrated international stature by placing as a finalist in the competitive AI Insights Data Prize, an accolade sponsored by the Alzheimer’s Disease Data Initiative and Gates Ventures. This recognition underscores the model’s impressively unique capability to elevate Alzheimer’s prevention research via cutting-edge AI and data science innovation.

In conclusion, the advent of FINGERS-7B heralds a new era where artificial intelligence and multi-omic data integration coalesce to redefine possible boundaries in Alzheimer’s disease prevention. By delivering earlier and more precise risk predictions coupled with personalized intervention analyses, FINGERS-7B equips the global research community with an unprecedented toolset, fulfilling a crucial need in the fight against one of humanity’s most devastating neurodegenerative afflictions.

Subject of Research: People

Article Title: FINGERS-7B: A Groundbreaking AI Foundation Model for Early Alzheimer’s Prediction and Prevention

Web References:

• https://fingerprint.bio
• https://picower.mit.edu/faculty/li-huei-tsai
• https://picower.mit.edu/research/aging-brain-initiative

Image Credits: The Fingerprint collaboration

Keywords

Alzheimer disease, Artificial intelligence, Genomic analysis, Omics, Neurodegenerative diseases

Alzheimer’s disease, a progressive neurodegenerative disorder affecting millions worldwide, has long eluded early and definitive diagnosis. Current diagnostic modalities often detect the disease only after significant cognitive decline has occurred, limiting the effectiveness of therapeutic interventions. However, the Duke research team has demonstrated that alterations in gene expression within nerve and immune cells accessible via the nasal cavity provide a sensitive biomarker for early-stage Alzheimer’s, thereby circumventing the traditional reliance on symptomatic presentation or post-mortem analysis.

The core of this pioneering method lies in the strategic sampling of cells using a fine brush inserted into the upper nasal cavity, an area populated by olfactory receptor neurons intimately connected to the brain’s neural networks. Following application of a topical anesthetic, this outpatient procedure collects living neural and immune cells, which are then subjected to robust single-cell RNA sequencing. This approach allows for the high-resolution profiling of gene activity, which reflects the dynamic molecular environment associated with Alzheimer’s disease progression.

Leveraging the power of single-cell transcriptomics, the study analyzed nasal tissue samples from 22 participants, representing healthy controls, individuals with early biomarker evidence of Alzheimer’s yet asymptomatic, and patients with established clinical diagnoses. The exhaustive examination encompassed thousands of genes across hundreds of thousands of cells, yielding millions of discrete data points. This comprehensive dataset unveiled distinct cellular signatures and gene expression profiles that delineate disease from health with remarkable precision.

One of the most striking findings was the ability to categorize individuals correctly as having early or clinical Alzheimer’s with approximately 81% accuracy based on a composite gene score derived from the nasal tissue samples. Such predictive capability underscores the potential utility of this approach not only as a diagnostic tool but also as a critical biomarker for monitoring disease progression and therapeutic response, which until now has been an elusive goal in Alzheimer’s research.

The impetus for this research was partially inspired by poignant personal narratives, such as that of Mary Umstead, who participated in the study to honor the memory of her sister Mariah, a young onset Alzheimer’s patient diagnosed at 57. Stories like hers not only underscore the devastating personal impact of the disease but also highlight the urgent need for early detection techniques that could provide families with hope and clinicians with actionable data before irreversible damage occurs.

Current Alzheimer’s blood tests and cerebrospinal fluid analyses identify markers that emerge relatively late in the disease course. In stark contrast, the nasal swab approach capitalizes on direct access to living neural and immune cells, capturing real-time biological changes that precede overt clinical symptoms. This breakthrough offers a window into the early pathophysiology of Alzheimer’s, opening avenues for transformative interventions during a critical therapeutic window.

Dr. Bradley J. Goldstein, the study’s senior author and a professor across multiple disciplines at Duke University School of Medicine, emphasizes the ambition behind this research: “Our goal is to detect Alzheimer’s disease as early as possible, before irreversible brain damage occurs. By recognizing the disease at its biological inception, we can aim to deploy therapies that halt or prevent clinical decline.” This paradigm shift moves the field from reactive diagnosis toward proactive management.

Vincent M. D’Anniballe, lead author and medical scientist trainee, elaborates on the novelty of studying living neural tissue within human subjects: “Traditionally, much of our understanding of Alzheimer’s has come from autopsy samples, which only tell part of the story. The ability to examine living neural and immune cells from the nasal cavity allows us to uncover dynamic molecular processes and cellular interactions, offering fresh insights into disease mechanisms and treatment opportunities.”

Collaboration with the Duke & UNC Alzheimer’s Disease Research Center has facilitated expansion efforts to validate these findings across larger populations and to evaluate the nasal swab’s utility in longitudinal tracking of therapeutic efficacy. This work is supported by several National Institutes of Health grants, attesting to the broad recognition of its scientific and clinical importance. In parallel, Duke University has pursued intellectual property protection through a U.S. patent filing related to this innovative diagnostic approach.

Beyond its diagnostic promise, the nasal swab method represents a uniquely patient-friendly alternative to invasive procedures like lumbar punctures or expensive neuroimaging. Its rapid administration and minimal discomfort make it ideally suited for widespread screening initiatives, especially in primary care or outpatient settings. Such accessibility could revolutionize public health strategies by identifying at-risk individuals far earlier than current paradigms allow.

The implications for the broader neurodegenerative disease community are profound. By providing a scalable platform for high-dimensional molecular phenotyping of neural tissue in living patients, this methodology may extend beyond Alzheimer’s to other disorders where early pathobiological changes precede clinical impairment. The nexus of nasal cellular biology and neurodegeneration is an emergent frontier poised to reshape our understanding and management of brain diseases.

In sum, this novel nasal swab technique ushers in a new era for Alzheimer’s research and clinical practice. By detecting subtle, disease-related shifts at the molecular level well before memory declines become evident, it offers the tantalizing prospect of preemptive therapy and improved outcomes. As this technology matures and integrates into wider clinical use, it promises to transform the landscape of neurodegenerative disease diagnosis and patient care worldwide.

Subject of Research: Human tissue samples
Article Title: (Not explicitly provided in the source material)
News Publication Date: 18-Mar-2026
Web References: https://www.nature.com/articles/s41467-026-70099-7
References: DOI: 10.1038/s41467-026-70099-7
Image Credits: Duke Health/ Shawn Rocco
Keywords: Alzheimer disease, neurodegenerative diseases, neurological disorders, nasal swab diagnostics, early detection, single-cell transcriptomics, neural tissue, gene expression profiling

The research team, led by Chen, Y., Zeng, X., Olvera-Rojas, M., and their colleagues, has introduced an innovative mass spectrometry assay that significantly optimizes the measurement of amyloid-beta peptides in plasma, a blood component easily accessible compared to cerebrospinal fluid. This development addresses a critical bottleneck in Alzheimer’s disease diagnostics: the invasive nature and high cost of traditional biomarker assessments, along with their limited scalability for widespread screening.

Key to their success is the meticulous refinement of the assay workflow, which minimizes reagent usage and sample volume without compromising sensitivity. By leveraging state-of-the-art mass spectrometry techniques, the team improved signal detection while reducing background noise, enabling the precise quantification of amyloid-beta isoforms. These amyloid-beta peptides, particularly Aβ42 and Aβ40, are pivotal biomarkers reflecting brain amyloid pathology, with their plasma ratios correlating strongly with cerebral amyloid deposition.

The assay’s resource efficiency does not merely reduce cost; it enhances throughput and accessibility, making it a pragmatic choice for large-scale population screenings. Such screenings are crucial in identifying individuals in the preclinical stage of Alzheimer’s, where therapeutic interventions have the greatest potential to modify disease trajectory before significant cognitive decline ensues.

Notably, the team validated their assay in a cohort of asymptomatic individuals at risk of developing Alzheimer’s disease. The results demonstrated superior biomarker performance compared to existing plasma-based methods, underscoring its value in detecting subtle pathological changes well before clinical symptoms manifest. This heralds a transformational shift from reactive to proactive approaches in Alzheimer’s management.

The technical sophistication of the assay lies in its streamlined sample preparation process. Traditional amyloid-beta assays often require labor-intensive and time-consuming steps, including immunoprecipitation and extensive chromatographic separations. By contrast, the new protocol employs targeted proteomics combined with advanced mass spectrometric technology to reduce these complexities, thereby accelerating sample turnaround time without loss of analytical fidelity.

Furthermore, the study highlights the assay’s robustness across various clinical settings, showcasing its adaptability to different laboratory environments, an essential attribute for broad clinical adoption. The reproducibility of results across independent centers affirms the assay’s validity as a reliable diagnostic tool.

Beyond diagnostic applications, the assay holds promise for monitoring disease progression and therapeutic responses. Quantitative tracking of plasma amyloid-beta levels could provide valuable insights into treatment efficacy in clinical trials, potentially expediting the development of novel therapeutics.

Importantly, this advancement aligns with the ongoing global efforts to develop minimally invasive, cost-effective diagnostic tools for neurodegenerative diseases. Given the projected demographic shifts and the ensuing rise in Alzheimer’s cases worldwide, scalable solutions like this assay are vital for sustainable healthcare strategies.

From a molecular perspective, accurately quantifying amyloid-beta peptides in plasma has been challenging due to their low concentration and the complex biological matrix interfering with detection. By refining mass spectrometry parameters and incorporating novel calibration protocols, the team overcame previous technical hurdles, setting new standards in sensitivity and specificity.

The study also delves into the biochemical nuances underpinning plasma amyloid-beta dynamics. It discusses how peripheral clearance mechanisms and blood-brain barrier interactions influence plasma biomarker levels, offering a comprehensive understanding that complements diagnostic findings.

Notably, the assay’s implementation could bridge current gaps between experimental research and clinical practice, facilitating a seamless translation from biomarker discovery to patient care. This has profound implications for personalized medicine approaches, where early and accurate diagnosis is a cornerstone.

In summary, Chen and colleagues’ streamlined resource-efficient plasma amyloid-beta mass spectrometry assay represents a pivotal advancement in Alzheimer’s disease biomarker research. Its enhanced performance in detecting preclinical disease stages amplifies the prospects for early intervention, ultimately aiming to alter the course of this debilitating condition and alleviate its global burden.

As the scientific community embraces this innovation, further longitudinal studies and integration with other biomarkers and neuroimaging modalities will likely enhance diagnostic algorithms, fostering a multi-faceted approach to tackling Alzheimer’s disease.

This work, published in Nature Communications in 2026, stands as a testament to the power of interdisciplinary collaboration, marrying cutting-edge analytical chemistry techniques with clinical neuroscience to advance human health.

Subject of Research: Streamlined mass spectrometry assay development for plasma amyloid-beta detection to improve biomarker performance in preclinical Alzheimer’s disease.

Article Title: Streamlined resource-efficient plasma amyloid-beta mass spectrometry assay has improved biomarker performance in preclinical Alzheimer’s disease.

Article References:
Chen, Y., Zeng, X., Olvera-Rojas, M. et al. Streamlined resource-efficient plasma amyloid-beta mass spectrometry assay has improved biomarker performance in preclinical Alzheimer’s disease. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68372-w

Image Credits: AI Generated

Traditional methods of diagnosing Alzheimer’s rely heavily on subjective cognitive assessments and symptomatic evaluation, which frequently miss the early, preclinical stages of the disease. Fastball EEG, by contrast, operates on a principle of passivity: participants are only required to view a rapid sequence of images while their brain’s electrical responses are recorded. This approach bypasses the need for active memory recall or instruction following, thereby delivering an unbiased and sensitive measure of recognition memory function that is both scalable and accessible.

The research team published their findings in the respected journal Brain Communications, detailing the performance of this technique in various settings, including real-world environments such as participants’ own homes. This is a crucial advance, as most neurological diagnostics necessitate specialized clinical facilities and trained personnel, factors which limit widespread, early screening efforts. The ability to administer Fastball outside of hospital or laboratory settings heralds a democratization of dementia diagnosis, enabling earlier interventions and monitoring.

Fastball works by detecting characteristic neural responses known as event-related potentials (ERPs), which are elicited when the brain recognizes previously seen images within a rapid visual stream. The technique quantifies these electrical markers using electroencephalography (EEG), a non-invasive and cost-effective brain imaging modality with millisecond temporal resolution. The researchers demonstrated that diminished ERP signatures correspond strongly with early cognitive decline, even identifying subtle impairments in individuals who later progressed towards dementia.

This technological breakthrough arrives at a critical juncture in Alzheimer’s treatment landscape. Recently approved disease-modifying therapies such as donanemab and lecanemab have shown exceptional promise in slowing progression when administered during the early symptomatic phases of Alzheimer’s. However, these treatments’ maximal efficacy hinges on timely diagnosis— a challenge given that an estimated one in three people with dementia in England remain undiagnosed. Fastball EEG’s ability to facilitate early, objective detection could bridge this diagnostic gap, improving patient outcomes through prompt therapeutic intervention.

The study’s lead investigator, Dr. George Stothart, a cognitive neuroscientist specializing in memory neuroscience, highlighted the urgency of uncovering Alzheimer’s disease in its nascent stages. Conventional cognitive tests tend to detect memory decline only after substantial neurodegeneration has occurred. Fastball’s passive design, requiring minimal participant engagement, offers a radically new avenue for screening large populations efficiently and objectively, mitigating biases and variability inherent in subjective assessments.

Crucially, this research validates the reliability and robustness of the Fastball EEG protocol in diverse environments, showing consistent detection of memory impairment across both controlled laboratory conditions and everyday settings. This paves the way for its practical implementation in primary care facilities, memory clinics, and home-based health monitoring. The portable and user-friendly nature of the technology further facilitates large-scale deployment, potentially revolutionizing population screening for cognitive decline.

From a neuroscientific perspective, the Fastball test encapsulates cutting-edge application of cognitive electrophysiology in clinical diagnostics. By precisely capturing early-stage aberrations in recognition memory circuitry, it provides a window into the neural substrates affected by Alzheimer’s pathology. This objective probe into brain function stands in contrast to the limitations of neuroimaging techniques which, though informative, are costly and less scalable for widespread early detection.

The implications of this study extend beyond diagnosis; continuous and accessible monitoring of memory performance could shape the future landscape of personalized medicine for neurodegenerative disorders. Patients at risk could be tracked longitudinally with repeated Fastball assessments, enabling dynamic adjustment of therapeutic strategies and early detection of cognitive decline progression. Additionally, such tools may enhance recruitment and stratification in clinical trials aiming to test novel Alzheimer’s therapies.

Financially supported by the Academy of Medical Sciences and dementia charity BRACE, this research exemplifies successful collaboration between academia and charitable organizations dedicated to conquering dementia. BRACE’s ongoing investment underscores the transformative potential of Fastball EEG in expanding diagnostic capabilities and delivering equitable access to cognitive health assessments.

Leading voices in dementia research have praised this work as a crucial stepping stone toward overcoming the daunting challenge of underdiagnosis. By offering a low-cost, portable, and accurate diagnostic tool, Fastball EEG could catalyze a global shift in dementia care, reducing the burden on healthcare systems by enabling preemptive measures, early treatment, and more targeted support for affected individuals and their families.

Looking forward, the research team aims to refine the Fastball protocol further and expand studies to larger, more diverse populations. Integration with wearable EEG devices and machine learning algorithms for automated data interpretation could further enhance the scalability and precision of this early detection method. This innovation not only holds promise for Alzheimer’s but could be adapted for monitoring other neurodegenerative and cognitive disorders, broadening its impact on neurological health worldwide.

In sum, the University of Bath’s development of the Fastball test represents a transformative fusion of cognitive neuroscience, clinical research, and technological innovation. It addresses a critical unmet need for early, objective, and accessible detection of memory impairment associated with Alzheimer’s disease, with the potential to alter clinical practices and improve countless lives globally.

Subject of Research: People

Article Title: A passive and objective measure of recognition memory in mild cognitive impairment using Fastball memory assessment

News Publication Date: 1-Sep-2025

References:

1. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial, [DOI/link]
2. Lecanemab in Early Alzheimer’s Disease, [DOI/link]
3. Primary Care Dementia Data, NHS England [DOI/link]

Image Credits: Credit BRACE Dementia Research

Keywords: Alzheimer disease; Neurodegenerative diseases; Diseases and disorders; Neurological disorders; Health and medicine; Human health; Psychological science; Cognitive psychology; Cognition; Cognitive function; Mental images