Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach
Open accessAbstract
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions -Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)- across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.
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The literature examining binary comparisons of FTD variants is more extensive, individually assessing atrophy and functional and structural connectivity measures.
“Indeed, the literature examining binary comparisons of FTD variants is more extensive, individually assessing atrophy (Bachli et al., 2020; Bisenius et al., 2017; Meyer et al., 2017; Salvatore et al., 2014; Santillo et al., 2013) and functional (Moguilner et al., 2021) and structural (Mahoney et al., 2014; Santillo et al., 2013) connectivity measures.”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Bachli et al., 2020
The choice of parcellation was based on the reproducibility criteria.
“Thus, by considering the various studies that used the AAL atlas in dementia research, even with ML methods (Asim et al., 2018; Bachli et al., 2020; Castellazzi et al., 2020; Hao et al., 2020; McMillan et al., 2014; Park et al., 2022; Sedeño et al., 2017), our choice of parcellation was based on the reproducibility criteria.”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Bachli et al., 2020
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Custodio, N., Herrera‐Pérez, E., Lira, D., Roca, M., Manes, F. F., Báez, S., & Torralva, T. (2016) · eNeurologicalSci · doi:10.1016/j.ensci.2016.11.001 · Cited by 49 · see citing works
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Flagged first, then by citations
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Cited by
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Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
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Interoception primes emotional processing: multimodal evidence from neurodegeneration
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Self-citationDementia ConnEEGtome: towards multicentric harmonization of EEG connectivity in neurodegeneration
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Gonzalez-Gomez, R., Ibañez, A., & Moguilner, S. (2023) · Network Neuroscience · doi:10.1162/netn_a_00285 · Cited by 10 · see citing works
- Authors
- Bachli, M. B., Sedeño, L., Ochab, J. K., Piguet, O., Kumfor, F., Reyes, P., Torralva, T., Roca, M., Cardona, J. F., Campo, C. G., Herrera, E., Slachevsky, A., Matallana, D., Manes, F., García, A. M., Ibáñez, A., & Chialvo, D. R.
- Affiliations
- National University of General San Martín (AR)
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Jagiellonian University (PL)
- The University of Sydney (AU)
- Pontificia Universidad Javeriana (CO)
- Favaloro University (AR)
- Universidad del Valle (CO)
- Universidad Icesi (CO)
- Universidad del Desarrollo (CL)
- Corresponding author
- Sedeño, Lucas
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 208
- Pages
- 116456
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC7008715
- OpenAlex
- W2995123658
- Cited by
- 57
- References
- 98
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 5 · 2023: 13 · 2022: 11 · 2021: 14 · 2020: 4
- FWCI
- 5.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- Jagiellonian University Repository (Jagiellonian University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Universidad de Chile (submittedVersion, cc-by-nc-nd)
- Conicet (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health (GBHI ALZ UK-20-639295, R01AG057234)
- Universidad Nacional de San Martín
- National Eye Institute
- Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias (15150012)
- Inter-American Development Bank (2017-1818, 2017-1820)
- Departamento Administrativo de Ciencia, Tecnología e Innovación (110674455314, 697-2014)
- Consejo Nacional de Investigaciones Científicas y Técnicas
- Narodowe Centrum Nauki
- National Health and Medical Research Council (APP1037746)
- Comisión Nacional de Investigación Científica y Tecnológica
- Fondo Nacional de Desarrollo Científico y Tecnológico (1170010)
- Fundación INECO
- Australian Research Council
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- neurodegenerative diseases, Alzheimer's disease, behavioral variant frontotemporal dementia, brain atrophy, voxel-based morphometry, cognitive screening, executive function, cross-center validation, reproducibility, early diagnosis, decision support, multimodal biomarkers
- MeSH terms
- Machine Learning, Aged, Aged, 80 and over, Alzheimer Disease, Atrophy, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Reproducibility of Results, Biomarkers, Executive Function, Frontotemporal Dementia
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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