A multimodal digital NeuroMarker platform with composite and domain-level validity for identification of mild cognitive impairment: a cross-sectional study
Abstract
Background: Mild cognitive impairment (MCI) is a clinically heterogeneous syndrome that can represent an early manifestation of neurodegenerative disease. Diagnosis relies on assessments that are difficult to scale, leaving many individuals unidentified. Methods: In this cross-sectional study conducted in the United States, Australia, and Spain between May 2022 and October 2024, 638 adults aged ≥ 50 years were classified as cognitively unimpaired (CU; n = 441) or MCI (n = 197) using Petersen/NIA-AA criteria. Participants completed a self-administered, tablet-based assessment (Altoida NeuroMarker Platform) integrating motor, speech, and augmented reality tasks. An XGBoost classifier was trained to distinguish CU from MCI. Diagnostic performance, test-retest reliability, and convergent validity were evaluated. Findings: Median assessment time was 13.8 min. Participants reported comfort with digital technology (67%), willingness to engage in cognitive screening (90%), and positive usability. Altoida’s NeuroMarker differentiated MCI with ROC-AUC 0.90, 95% CI 0.88–0.92 and comparable performance in an independent cohort (ROC-AUC 0.87, 95% CI 0.80–0.93; n = 75). Cognitive composite and domain scores were lower in MCI than in CU. Classifier agreement across repeated assessments was κ = 0.60, and reliability of composite and domain scores was moderate-to-good (ICC 0.55–0.80). The composite score correlated with the Mini-Mental State Examination (MMSE) ( r s = 0.40, p < 0.001) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Total Index ( r s = 0.58, p < 0.001). The platform also showed convergent validity of cognitive domains (| r s | = 0.27–0.50, p < 0.001). Interpretation: A brief, self-administered multimodal assessment identified clinically adjudicated MCI and demonstrated convergent validity, test-retest reliability, and consistent performance in an independent cohort. Prospective studies should evaluate its implementation in real-world workflows. Funding: Alzheimer’s Drug Discovery Foundation ; Altoida Inc.
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BibTeX
@article{iulita2026multimodal,
title = {{A multimodal digital NeuroMarker platform with composite and domain-level validity for identification of mild cognitive impairment: a cross-sectional study}},
author = {M. Florencia Iulita and Vijaya B. Kolachalama and Victoria Brugada-Ramentol and John Harrison and Nicholas Griffin and Gonzalo Sánchez Benavides and Elena Vera-Campuzano and Carolina Minguillon and Daniel Alcolea and Lucy Mackintosh and Larry Ward and Adria Tort Merino and Mircea Balasa and Sahoko Ishida and Oriol Grau Rivera and Marc Suarez Calvet and Joanne Robertson and James Doecke and Claudia Porta Mas and Silvia Fallone and Mylea Charvat and Ioannis Tarnanas and Marc Jones and Emmanuel Streel},
journal = {eClinicalMedicine},
year = {2026},
month = oct,
doi = {10.1016/j.eclinm.2026.104266},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2589537026005195},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - A multimodal digital NeuroMarker platform with composite and domain-level validity for identification of mild cognitive impairment: a cross-sectional study
AU - M. Florencia Iulita
AU - Vijaya B. Kolachalama
AU - Victoria Brugada-Ramentol
AU - John Harrison
AU - Nicholas Griffin
AU - Gonzalo Sánchez Benavides
AU - Elena Vera-Campuzano
AU - Carolina Minguillon
AU - Daniel Alcolea
AU - Lucy Mackintosh
AU - Larry Ward
AU - Adria Tort Merino
AU - Mircea Balasa
AU - Sahoko Ishida
AU - Oriol Grau Rivera
AU - Marc Suarez Calvet
AU - Joanne Robertson
AU - James Doecke
AU - Claudia Porta Mas
AU - Silvia Fallone
AU - Mylea Charvat
AU - Ioannis Tarnanas
AU - Marc Jones
AU - Emmanuel Streel
PY - 2026
DA - 2026/10/23
JO - eClinicalMedicine
DO - 10.1016/j.eclinm.2026.104266
UR - https://linkinghub.elsevier.com/retrieve/pii/S2589537026005195
AB - Background: Mild cognitive impairment (MCI) is a clinically heterogeneous syndrome that can represent an early manifestation of neurodegenerative disease. Diagnosis relies on assessments that are difficult to scale, leaving many individuals unidentified. Methods: In this cross-sectional study conducted in the United States, Australia, and Spain between May 2022 and October 2024, 638 adults aged ≥ 50 years were classified as cognitively unimpaired (CU; n = 441) or MCI (n = 197) using Petersen/NIA-AA criteria. Participants completed a self-administered, tablet-based assessment (Altoida NeuroMarker Platform) integrating motor, speech, and augmented reality tasks. An XGBoost classifier was trained to distinguish CU from MCI. Diagnostic performance, test-retest reliability, and convergent validity were evaluated. Findings: Median assessment time was 13.8 min. Participants reported comfort with digital technology (67%), willingness to engage in cognitive screening (90%), and positive usability. Altoida’s NeuroMarker differentiated MCI with ROC-AUC 0.90, 95% CI 0.88–0.92 and comparable performance in an independent cohort (ROC-AUC 0.87, 95% CI 0.80–0.93; n = 75). Cognitive composite and domain scores were lower in MCI than in CU. Classifier agreement across repeated assessments was κ = 0.60, and reliability of composite and domain scores was moderate-to-good (ICC 0.55–0.80). The composite score correlated with the Mini-Mental State Examination (MMSE) ( r s = 0.40, p < 0.001) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Total Index ( r s = 0.58, p < 0.001). The platform also showed convergent validity of cognitive domains (| r s | = 0.27–0.50, p < 0.001). Interpretation: A brief, self-administered multimodal assessment identified clinically adjudicated MCI and demonstrated convergent validity, test-retest reliability, and consistent performance in an independent cohort. Prospective studies should evaluate its implementation in real-world workflows. Funding: Alzheimer’s Drug Discovery Foundation ; Altoida Inc.
SN - 2589-5370
ER -