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Major neurocognitive disorders are one of the leading causes of disability and dependency among the elderly worldwide. Notably, their far-reaching impact extends beyond the estimated 50 million people currently living with a major neurocognitive disorder. As the conversion to Alzheimer's disease (AD) ...

Major neurocognitive disorders are one of the leading causes of disability and dependency among the elderly worldwide. Notably, their far-reaching impact extends beyond the estimated 50 million people currently living with a major neurocognitive disorder. As the conversion to Alzheimer's disease (AD) progresses, patients’ symptoms (e.g., memory loss, severe impairments in thinking and behavior) place a heavy toll on their caregivers, family, and friends, who face emotional frustration, coupled with great financial stress. Furthermore, in terms of global cost estimation, the World Health Organization predicted that by 2030, the treatment of patients with AD and other forms of acquired cognitive impairment will cost the healthcare system US$1.7 trillion (or US$2.8 trillion, if corrected for the increase in care costs).

Successful detection of early signs of major neurocognitive disorders is of great importance. Therefore, the presented Research Topic aims to obtain a more accurate picture of brain integrity in older adults and highlight biomarker studies that provide opportunities for early detection of cognitive impairments, in the predementia window, i.e., mild cognitive impairment (MCI) stage.

Until now, the first step toward diagnosing MCI and major neurocognitive disorders begins with performing standardized cognitive assessments, such as the Mini-Mental State Examination, Montreal Cognitive Assessment, or Alzheimer’s Disease Assessment Scale–Cognitive subscale. However, these tests require hand-scoring by trained administrators, which can lead to biased interpretation (e.g., omission of affected individuals by underscoring cognitive losses in higher functioning older adults).
Moreover, while administered alone, pen-and-paper tests are relatively insensitive to milder forms of cognitive impairment. Therefore, legitimate concerns have been raised against their use in MCI detection. To establish a reliable and sufficiently short routine screening for MCI, the research community needs a successful bridging between subjective questionnaires (e.g., self-and/or informant-reports) and objective physiological and behavioral measures.

In parallel, patients’ safety and well-being remain a priority. While older adults feel reluctant to undergo invasive examinations (e.g., lumbar puncture), methodologies such as electroencephalography (EEG), polysomnography (PSG, a procedure that combines electroencephalogram, electrooculogram, electrocardiogram, and pulse oximetry), optical coherence tomography (OCT), and eye-tracking (ET) open new opportunities in the MCI screening process. Therefore, our goal is to gather scientific contributions on non-invasive methodologies that will offer a significant improvement in the detection of early cognitive impairment and the ability to characterize individuals along the Alzheimer's disease trajectory.

All scientific contributions should follow a well-motivated goal to objectively identify the onset of atypical aging. We particularly welcome contributions that include, but are not limited to, the following topics:
• Eye-tracking tests detecting differences in gaze behavior between MCI subtypes, AD, and cognitively healthy populations.
• Worsening in visual function (e.g., changes in visual acuity, contrast sensitivity, or color perception) among cognitively healthy and cognitively impaired elderlies.
• Detection of preclinical stages of AD that occur both in the retinal neurons and in the retinal and choroidal vasculature, via non-invasive techniques, such as optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA).
• EEG correlation with behavioral and psychological symptoms of dementia (BPSD), commonly experienced by elderlies with MCI, AD, and other major neurocognitive disorders.
• Integrative biomarkers (M/EEG) that signalize deviation from the normal aging process and reflect the presence and severity of the cognitive decline.
• Biometric data obtained via wearable devices (health watches/bracelets) as future digital biomarkers for at-home monitoring.
• Machine learning (ML) algorithms and statistical approaches for dementia classification.
• Validity and cultural generalizability of biomarkers for dementia.
• Ethical constraints on data governance for dementia research.

Finally, addressing the major neurocognitive disorders challenge requires a coordinated effort of multiple scientific disciplines, including geriatrics, neuroscience, psychology, computer science, and gerontechnology. Therefore, high-quality and interdisciplinary contributions that translate results from experimental settings into real-life – and life-changing – applications will enrich this article collection.

Keywords: Atypical aging, dementia, Alzheimer’s disease, early detection, biomarker, cognitive impairment, screening, predementia, mild cognitive impairment, eye-tracking, ET, electroencephalography, EEG, magnetoencephalography, MEG, optical coherence tomography, OCT, polysomnography, PSG, machine learning

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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