| AUTORES | M. Belen Bachlia, Lucas Sedeño, Jeremi K. Ochab, Olivier Piguete, Fiona Kumfore, Pablo Reyesg, Teresa Torralva, María Roca, Juan Felipe Cardona, Cecilia Gonzalez Campo, Eduar Herrera, Andrea Slachevsky, Diana Matallana, Facundo Manes, Adolfo M. García, Agustín Ibáñez, and Dante R. Chialvoa. |
|---|---|
| 2019 | |
| JOURNAL | Neuroimage |
| VOLUMEN | 2019 Dec 10:116456. doi: 10.1016/j.neuroimage.2019. |
| ABSTRACT | 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 precision (> 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. |
| En este estudio utilizamos herramientas de marchine-learning para evaluar el poder predictivo de biomarcadores neurocognitivos (screenings de cognición general y funciones ejecutivas, junto con imágenes de resonancia magnética estructural) para clasificar dos enfermedades neurodegenerativas –la Enfermedad de Alzheimer y la variante conductual de la demencia Frontotemporal– en tres países diferentes (Argentina, Colombia y Australia; contando con más de 200 participantes). Nuestros resultados mostraron una precisión de más del 90% en la discriminación de los pacientes con enfermedades neurodegenerativas de los participantes sanos que fue consistente en los tres países que se analizaron. Nuestro hallazgo ofrece evidencia de la validez y robustez predictiva de combinar estrategias de machine-learning con información proveniente tanto de screenings cognitivos como de resonancia magnética estructural para identificar, con un gran nivel de confiabilidad, a personas con enfermedades neurodegenerativas. |