ArticleGeroScience2026
Machine learning assessment of cognitive reserve using functional near-infrared spectroscopy in older adults with cognitive frailty.
Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Cognitive reserve mitigates aging-related cognitive decline and frailty, yet current assessments lack neurobiological specificity. We aimed to develop a noninvasive, functional near infrared spectroscopy (fNIRS)-based machine learning model to classify cognitive reserve levels in older adults with cognitive frailty. Seventy-one community-dwelling adults underwent resting-state and task-based (Stroop, n-back) fNIRS scans. Graph theory metrics and task-related β-values were extracted. Support vector machine classifiers were trained on 70% of the dataset and tested on 30%. Models incorporating β-values from significantly activated channels during the Stroop, 0-back, and 1-back tasks achieved the best performance (accuracy = 0.727, recall = 0.857, area under the curve [AUC] = 0.829). Resting-state features alone yielded lower performance (AUC = 0.714), while combining both resting-state and task-based features improved it moderately (AUC = 0.790). fNIRS-based modeling enables objective classification of cognitive reserve levels in older adults with cognitive frailty. This approach offers a portable, scalable, real-time strategy for early risk stratification and may support precision interventions in both clinical and community settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.