Cortical thickness modeling and variability in Alzheimer’s disease and frontotemporal dementia

• Published in: Journal of neurology Vol. 271; no. 3; pp. 1428 - 1438
• Main Authors: Pérez-Millan, Agnès, Borrego-Écija, Sergi, Falgàs, Neus, Juncà-Parella, Jordi, Bosch, Beatriz, Tort-Merino, Adrià, Antonell, Anna, Bargalló, Nuria, Rami, Lorena, Balasa, Mircea, Lladó, Albert, Sala-Llonch, Roser, Sánchez-Valle, Raquel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2024; Springer Nature B.V
• Subjects: Alzheimer Disease - diagnosis; Alzheimer's disease; Biomarkers - cerebrospinal fluid; Cerebrospinal fluid; Cognitive ability; Dementia; Dementia disorders; Frontotemporal dementia; Frontotemporal Dementia - diagnostic imaging; Humans; Magnetic Resonance Imaging; Male; Medicine; Medicine & Public Health; Mental Status and Dementia Tests; Neurodegenerative diseases; Neurology; Neuroradiology; Neurosciences; Original Communication; Regression analysis; Predictive modeling; Cortical thickness; Alzheimer’s disease; Magnetic resonance imaging; Frontotemporal dementia
• ISSN: 0340-5354; 1432-1459; 1432-1459
• DOI: 10.1007/s00415-023-12087-1

Background and objective Alzheimer’s disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC. Methods We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14–3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity. Results We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability. Conclusion We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.