Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach

Publication date: Available online 10 December 2019Source: NeuroImageAuthor(s): M. Belen Bachli, Lucas Sedeño, Jeremi K. Ochab, Olivier Piguet, Fiona Kumfor, Pablo Reyes, Teresa Torralva, María Roca, Juan Felipe Cardona, Cecilia Gonzalez Campo, Eduar Herrera, Andrea Slachevsky, Diana Matallana, Facundo Manes, Adolfo M. García, Agustín Ibáñez, Dante R. ChialvoAbstractAccurate 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...
Source: NeuroImage - Category: Neuroscience Source Type: research