SynthesisFrontiers in neurology2025
Functional near-infrared spectroscopy for identifying mild cognitive impairment and Alzheimer's disease: a systematic review.
Synthesis in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Advances in fully integrated wearable near-infrared spectroscopy: hardware innovations and biomedical applications.JPhys photonics · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Functional Near-Infrared Spectroscopy (fNIRS) has been used to detect changes in haemodynamic response in patients with neurodegenerative diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). We aimed to evaluate the efficacy of fNIRS in identifying early dementia-related changes and distinguishing between MCI and AD. Methods: A comprehensive literature search was conducted using PubMed and Web of Science, focusing on studies that employed fNIRS to measure cerebral hemodynamics in MCI and AD patients. The search included articles published up to February 2024. Studies were selected based on predefined criteria, including the use of fNIRS, inclusion of MCI or AD patients, and publication in English. Data extraction focused on study design, fNIRS device specifications, experimental paradigms, and diagnostic criteria. Results: A total of 58 studies were included in the review. Of these, 4 studies employed both resting-state and task-based paradigms, 11 studies focused on resting-state paradigms, and 43 studies utilized task-based paradigms. Resting-state studies revealed reduced brain activation in the frontal, temporal, and parietal lobes in AD and MCI patients, along with significant reductions in tissue oxygenation index (TOI) and functional connectivity (FC). Task-based studies demonstrated diminished activation across multiple brain regions during cognitive tasks, with reduced FC intensity and signal complexity in AD and MCI patients. Machine learning models applied to fNIRS data showed high accuracy in classifying MCI and AD, with some models achieving accuracy rates of up to 90%. Conclusion: fNIRS is a promising tool for the diagnosis and monitoring of MCI and AD, and further research is needed to establish its full potential.
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Registered trials
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