A six-minute eye-tracking test may offer a new way to distinguish biological forms of Alzheimer’s-related pathology in older adults with mild cognitive impairment, according to a study published in GeroScience. The research found that people with two different cerebrospinal-fluid biomarker profiles did not primarily differ in how large their eye responses were. Instead, they differed in when their eyes and pupils reached their maximum response while they performed an attention task. The result suggests that the timing of subtle eye movements could provide a non-invasive functional complement to lumbar puncture, brain imaging and emerging blood tests used to characterize neurodegenerative disease.

The study focused on the AT(N) framework, which classifies Alzheimer’s biology according to amyloid-β accumulation, phosphorylated tau and neurodegeneration. The researchers compared 38 people with mild cognitive impairment: 26 had an A+T+ profile, meaning that both amyloid and tau biomarkers were abnormal, while 12 had an A−T+ profile, indicating tau abnormalities without detectable amyloid pathology. A+T+ is considered the biological profile of Alzheimer’s disease under current research criteria. A−T+, by contrast, may reflect primary age-related tauopathy or another non-Alzheimer’s tauopathy, although cerebrospinal-fluid testing alone cannot determine the precise underlying cause.

Participants were recruited from two hospitals in Barcelona and had already undergone lumbar puncture as part of their clinical evaluation. The researchers classified them using validated, hospital-specific cerebrospinal-fluid thresholds for amyloid-β42, phosphorylated tau and total tau. Two participants with isolated total-tau elevation but no amyloid or phosphorylated-tau abnormality were grouped with the A−T+ participants because total tau can indicate neurofibrillary degeneration across several tauopathies. A sensitivity analysis excluding those two people produced broadly similar findings, although one borderline vergence measure no longer reached statistical significance.

During the experiment, participants viewed strings of meaningless letters on a laptop screen while a remote binocular eye tracker recorded their gaze and pupil diameter. Most strings were blue distractors, appearing on 80 percent of trials, while 20 percent were red targets. Participants were instructed to press a button whenever they detected a red string. The visual oddball task is widely used to study attention because rare, salient stimuli recruit systems involved in arousal, target detection and decision-making. The test lasted about six minutes, and the tracker sampled eye position 33 times per second—sufficient for the relatively slow vergence and pupil responses that unfold over roughly half a second to two seconds after a stimulus appears.

The investigators calculated cognitive vergence, the small coordinated change in the angle between the two eyes that accompanies attention and visual processing, as well as changes in pupil diameter. They extracted several characteristics from each response, including initial, global and late slopes; cumulative response area; peak amplitude; and time to peak. These measurements allowed the team to distinguish the strength of a response from its temporal organization. Statistical models accounted for repeated observations within individuals, while penalized logistic regression was used to examine whether participant-level response patterns were associated with biomarker profile. The analysis was exploratory rather than a diagnostic-classifier study, and the sample was too small to establish sensitivity, specificity or clinical accuracy.

Across the full group, red target stimuli produced larger vergence and pupil responses than blue distractors. Yet average response magnitude did not distinguish the A+T+ and A−T+ groups. The important differences emerged in the interaction between biological profile and stimulus condition. During distractor trials, people in the A−T+ group generally reached their vergence and pupil peaks later than those in the A+T+ group. During target trials, the pattern reversed: A+T+ participants showed the most delayed peak responses, whereas A−T+ participants responded relatively earlier. Vergence global slope also differed between profiles during target trials, with the A−T+ group showing steeper dynamics. These effects indicate that the groups did not simply differ in overall slowing. Rather, their timing changed differently depending on whether attention was required.

Behavioral performance followed the same condition-dependent pattern. The two groups performed almost identically when they had to withhold responses to distractors, with accuracy close to 100 percent. On target trials, however, A−T+ participants detected 89.7 percent of the red strings, compared with 82.5 percent among A+T+ participants. That difference was statistically significant. At the individual level, the difference between target and distractor timing was associated with the likelihood of belonging to the A+T+ group for both vergence time to peak and pupillary time to peak. The pupil association was particularly stable: its direction remained unchanged when each participant was removed from the analysis, and statistical significance persisted in 37 of 38 leave-one-participant-out tests. The vergence association was less robust, remaining significant in only eight of those 38 refits.

The researchers interpret the findings through the biology of the locus coeruleus, a small noradrenaline-producing nucleus in the brainstem that helps regulate alertness, attention and responses to salient events. Post-mortem studies suggest that the locus coeruleus is among the earliest sites of tau accumulation in Alzheimer’s disease. Its connections influence pupil control through pathways linked to the Edinger–Westphal nucleus and may affect vergence indirectly through the superior colliculus, which participates in three-dimensional eye-movement control. Pupil diameter is therefore often used as an indirect index of locus-coeruleus activity, although it is also affected by light, medication, autonomic function and other physiological factors.

One possible explanation is that isolated tau pathology and combined amyloid-tau pathology interfere with attention through partly different routes. Under low-demand distractor conditions, the A−T+ pattern of delayed responses could reflect altered tonic regulation of arousal by the locus coeruleus. Under target conditions, successful detection requires a rapid, coordinated response involving the dorsal and ventral attention networks, frontoparietal control systems, hippocampus and phasic locus-coeruleus signaling. Amyloid pathology is known to affect hubs of the default mode network, including the posterior cingulate cortex and precuneus, and may impair the suppression of that internally oriented network when goal-directed attention is needed. The additional cortical network disruption in A+T+ participants could help explain their delayed target-related eye and pupil responses and lower detection accuracy. The authors emphasize, however, that the study did not directly measure locus-coeruleus integrity, default-mode connectivity or compensatory brain activity, so this mechanism remains a hypothesis rather than a demonstrated cause.

The potential clinical appeal lies in the simplicity of the measurement. The protocol required a calibrated remote eye tracker and an ordinary computer, with no consumables, radiation or invasive procedure. A portable test of this kind could eventually help identify people who need more definitive biomarker assessment, particularly in settings where cerebrospinal-fluid analysis or positron-emission tomography is expensive or difficult to access. It could also add a functional dimension to blood biomarkers such as plasma phosphorylated tau, which estimate molecular pathology but do not show how the brain responds to cognitive demand. The eye signal might therefore be useful not because it replaces molecular tests, but because it captures the performance of attention and arousal networks in real time.

The evidence is not yet ready for clinical deployment. The study was cross-sectional, involved only 38 participants, lacked a cognitively unimpaired control group and included an imbalanced number of people in the two biomarker categories. The A−T+ group itself is biologically heterogeneous, potentially containing people with primary age-related tauopathy and other tauopathies. Medication use and autonomic dysfunction—both of which can influence pupil responses—were not fully assessed. Larger, balanced and independently replicated studies will need to test whether the timing signatures generalize across devices, languages, clinical populations and stages of disease. Longitudinal research will also be necessary to determine whether these eye-movement patterns track progression or treatment response, or appear before measurable cognitive decline. For now, the study offers a striking possibility: in the earliest stages of cognitive impairment, the brain’s molecular history may be reflected not in how dramatically the eyes react, but in the precise moment at which they do so.

A groundbreaking observational study conducted by the Sant Pau Research Institute (IR Sant Pau) and published in the journal Neurology delves deeply into the role of Alzheimer’s disease biomarkers in very old adults, specifically those aged 80 and above diagnosed with mild cognitive impairment (MCI). Utilizing sophisticated biomarker analysis from cerebrospinal fluid and blood, researchers have established that Alzheimer’s disease biology is not only prevalent in this age group but also correlates strongly with accelerated cognitive decline and increased likelihood of progression to dementia.

This research stands firmly against the widespread skepticism that biomarkers lose diagnostic value in the oldest old due to the confounding effects of multiple coexisting pathologies. Contrary to this belief, the study reveals that, even among highly complex clinical presentations typical of advanced age, identifying Alzheimer’s pathology through biomarkers offers robust, actionable insights into disease progression and patient outcomes, paving the way for improved clinical decision-making.

One of the most pertinent challenges in managing cognitive decline in the elderly is the inherent difficulty of accurately diagnosing Alzheimer’s disease based solely on clinical symptoms. Older adults are frequently assessed without biomarker support, resulting in diagnostic uncertainty. This is in part due to the frequent coexistence of various neurodegenerative and vascular conditions that create a heterogeneous clinical picture, making differential diagnosis challenging and often leading to therapeutic nihilism—a reluctance to pursue aggressive treatment due to diagnostic ambiguity.

The study highlights that as patients age, clinical assessments lose sensitivity; cognitive test performances—especially memory tests—show overlapping results between patients with and without Alzheimer’s disease biology. This overlap diminishes the power of traditional clinical evaluation to distinguish Alzheimer’s disease from other causes of cognitive impairment, underscoring the need for biomarker-supported diagnostics to enhance accuracy, particularly in the mild stages of impairment.

Dr. Chiara Ceriello, a geriatrician and lead author of the study, underlines the complexity of dementia diagnoses in the very old. Her findings indicate that about half of the memory impairment cases lack pure Alzheimer’s pathology, underscoring the challenge clinicians face in disentangling multifactorial contributors to cognitive decline without biomarker data. This diagnostic ambiguity complicates prognosis and management, reinforcing the importance of precision medicine approaches adapted to this age segment.

The Sant Pau study incorporated 167 participants aged 80 years and above with MCI, systematically investigating them through the SPIN cohort—one of the foremost clinical research cohorts targeting neurodegenerative diseases. Remarkably, nearly 70% exhibited biomarkers indicative of Alzheimer’s disease pathology, as evidenced by elevated cerebrospinal fluid p-Tau181 to β-amyloid ratios and corresponding elevations in the blood biomarker p-Tau217. While initial cognitive differences were subtle, their trajectories diverged significantly over time, with biomarker-positive individuals experiencing substantially faster cognitive deterioration.

Quantitatively, those with Alzheimer’s disease pathology demonstrated an average annual decline of 0.47 points on the Mini-Mental State Examination (MMSE) compared to 0.18 points per year among those without such biomarkers. Although seemingly modest annually, this disparity compounds, culminating in markedly accelerated cognitive impairment and diminishing functional independence. Importantly, biomarker positivity also conferred a significantly heightened risk of conversion to overt dementia during the follow-up period.

Of particular note is the prognostic utility of blood-based biomarkers such as p-Tau217. This biomarker not only reflects the presence of Alzheimer’s pathology but provides critical information on disease progression dynamics. Elevated p-Tau217 levels were linked to nearly a 50% increase in risk for dementia development, emphasizing the future potential of blood assays as both diagnostic and prognostic tools in routine clinical practice.

Dr. Ignacio Illán-Gala, neurologist and co-author, emphasizes the clinical relevance of these findings: although early cognitive symptoms overlap across groups, the crucial differentiation emerges in disease trajectory—the Alzheimer’s biology-positive patients exhibit a more aggressive decline, underscoring the importance of identifying the underlying pathology early to optimize patient management and improve quality of life.

Clinicians, particularly those working with geriatric populations, stand to benefit from integrating biomarker data into their assessment protocols. Knowledge of Alzheimer’s disease biology enables healthcare providers to refine diagnoses, anticipate progression trajectories more accurately, and customize care plans to better meet patient needs. This includes more informed monitoring strategies and proactive family counseling, which are especially crucial as older adults face increasing vulnerability to cognitive and functional deterioration.

Perhaps the most transformative advance emerging from this work is the validation of blood-based biomarkers like p-Tau217 for clinical application. Unlike invasive or costly procedures such as lumbar puncture or amyloid PET scanning, blood tests present a simpler, scalable, and more patient-friendly diagnostic option. This has significant implications for expanding biomarker use in routinely strained geriatric services, enabling broader, systematic approaches to Alzheimer’s disease diagnosis among the oldest cohorts.

Dr. Ceriello underscores that while biomarkers are invaluable, their interpretation must be contextualized within the broader clinical picture—including assessments of frailty, comorbidities, and baseline functional status—to ensure accurate, individualized care planning. Precision medicine, she argues, transcends age boundaries; even the very old benefit profoundly from targeted diagnostics that can influence therapeutic choices and optimize quality of life.

Furthermore, given the advent of emerging disease-modifying therapies capable of halting or slowing Alzheimer’s disease progression, early and accurate identification of patients with Alzheimer’s biology has never been more critical. Timely diagnosis in the very old could open avenues for these new interventions, which hold promise for altering the course of cognitive decline and extending autonomy in this vulnerable population.

Addressing cognitive concerns openly and proactively resonates deeply within the clinical community and among older adults themselves. As Dr. Illán-Gala notes, there is a growing number of octogenarians and nonagenarians seeking clarity on memory changes and prognosis. The inclusion of biomarker-informed diagnostics meets this demand, enabling clinicians to provide evidence-based answers and compassionate care tailored to the unique challenges of aging.

In sum, this study firmly establishes that Alzheimer’s disease biomarkers retain significant diagnostic and prognostic value well into advanced age. By leveraging these tools, the medical community can finally move past outdated assumptions, ushering in an era of precision geriatric neurology that acknowledges the complexity of cognitive decline but also empowers clinicians and patients with clearer understanding and hope.

Subject of Research: People

Article Title: Clinical Impact and Prognostic Value of Alzheimer Disease Biomarkers in the Very Old

News Publication Date: 17-Jun-2026

Web References: http://dx.doi.org/10.1212/WNL.0000000000218180

Image Credits: IR Sant Pau

Keywords: Alzheimer disease, Biomarkers, Neurodegenerative diseases, Cognitive decline, Mild cognitive impairment, Blood-based biomarkers, p-Tau217, Neurobiology of Dementias, Geriatric neurology, Precision medicine

Mild Cognitive Impairment represents a gray area between normal cognitive function and more severe dementia. Patients suffering from MCI often face subtle but noticeable declines in memory and thinking skills, making early detection imperative for potential intervention strategies. The University of Missouri’s portable device represents a move toward revolutionizing how healthcare professionals can identify and evaluate cognitive impairment, particularly in rural communities where access to specialists may be limited.

This state-of-the-art device integrates several sophisticated components, including a depth camera, a force plate, and a user-friendly interface board. By employing multiple modalities, it captures an array of motor performance metrics that can be critically analyzed in real-time. This capability is essential for detecting nuances in motor function that traditional observation methods may overlook, thereby enhancing the accuracy of cognitive assessment.

The research team, comprising Trent Guess from the College of Health Sciences, Jamie Hall from the College of Health Sciences, and Praveen Rao from the College of Engineering, conducted a study that involved older adults, some diagnosed with MCI. Participants were asked to perform three specific tasks: standing still, walking, and standing up from a bench, all while simultaneously counting backward by sevens. This dual-task approach mirrors real-life scenarios where cognitive load can influence motor function.

The data collected during these activities were processed by a machine learning model, a sophisticated form of artificial intelligence. The model demonstrated a remarkable accuracy rate of 83% in identifying individuals with MCI, illustrating the potential of utilizing advanced technologies in clinical settings to improve diagnostic efficacy. This result underpins the hypothesis that cognitive impairment and motor function are closely intertwined.

In an interview, Trent Guess emphasized the overlap between the regions of the brain responsible for motor skills and cognitive function. "The areas related to motor function and cognitive impairment have intricate interconnections," he noted. Subtle differences in motor control related to balance and gait can serve as critical indicators of cognitive decline. The device they developed could effectively reveal these differences, facilitating earlier and more accurate diagnosis.

Statistics from the Centers for Disease Control and Prevention indicate that the aging population in the United States is projected to see a dramatic increase in Alzheimer’s disease cases by 2060. This trend highlights the urgent need for efficient screening tools like the portable system created by the University of Missouri. With only a paltry 8% of individuals believed to have MCI receiving clinical diagnoses, the need for widespread deployment of such diagnostic tools is clear.

Jamie Hall added that an essential aspect of their long-term objective is to extend the reach of this technology into community health settings. Potential applications include county health departments, senior centers, assisted living facilities, and physical therapy clinics. By integrating the portable assessment system into these environments, the research team hopes to facilitate more frequent and widespread screenings for MCI and other cognitive disorders.

The implications of this research extend beyond merely diagnosing cognitive impairment; they also address the pressing need for early intervention strategies. Hall pointed out that emerging pharmacological treatments targeting MCI require formal diagnoses for eligibility. “Many patients who display cognitive issues could benefit substantially from interventions if we can identify them in the early stages,” stated Hall. The research and resulting technology have the power to impact healthcare delivery significantly.

Additionally, the versatility of the portable assessment system opens avenues for further research into detecting fall risks and frailty among older adults, areas that are crucial for elderly patient care. Recognizing subtle kinematic changes in gait and stability could also have implications for other conditions, including concussions, sports rehabilitation, and neurodegenerative diseases like ALS and Parkinson’s. As Guess remarked, “Movement is intrinsic to our existence, and identifying its patterns can yield insights into various health conditions.”

As the study progresses, the University of Missouri team remains committed to refining the device based on feedback and data from ongoing assessments. They acknowledge the enthusiasm and investment from participants, many of whom have personal experiences with MCI or Alzheimer’s disease in their families, fostering a shared commitment to advancing this essential research.

The paper titled “Feasibility of Using a Novel, Multimodal Motor Function Assessment Platform With Machine Learning to Identify Individuals With Mild Cognitive Impairment,” published in Alzheimer’s Disease and Associated Disorders, showcases the promising potential of this technology to shift the paradigm in cognitive assessment. Funded by the University of Missouri Coulter Biomedical Accelerator, which champions interdisciplinary collaborations aimed at societal improvement, this initiative exemplifies the vitality of research that bridges engineering and clinical practice.

Ultimately, the development of this portable assessment system embodies a significant leap toward democratizing access to cognitive health assessments. By enabling earlier identification of MCI through comprehensive motor function evaluations, the University of Missouri researchers are not just advancing science; they are paving the way for improved quality of life for millions facing the daunting prospects of cognitive decline.

In conclusion, as the demand for effective cognitive health assessment tools increases, innovations like those being developed at the University of Missouri are critical for meeting this challenge head-on, effectively preparing healthcare systems for the inevitable growth in patients requiring attention and treatment for cognitive impairment and dementia.

Subject of Research: Portable System to Measure Motor Function and Identify Mild Cognitive Impairment
Article Title: Feasibility of Using a Novel, Multimodal Motor Function Assessment Platform With Machine Learning to Identify Individuals With Mild Cognitive Impairment
News Publication Date: 31-Dec-2024
Web References: http://dx.doi.org/10.1097/WAD.0000000000000646
References: Alzheimer’s Disease and Associated Disorders
Image Credits: University of Missouri

Keywords: MCI, Alzheimer’s disease, cognitive impairment, portable assessment, motor function, machine learning, neuropsychology, early diagnosis, health intervention, aging population.