Gerard Marti-Juan
@upf.edu
Department of Information and Communication Technologies, BCN Medtech
Universitat Pompeu Fabra
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Radiology, Nuclear Medicine and imaging, Computer Vision and Pattern Recognition, General Neuroscience
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Gerard Martí-Juan, Jaume Sastre-Garriga, Eloy Martinez-Heras, Angela Vidal-Jordana, Sara Llufriu, Sergiu Groppa, Gabriel Gonzalez-Escamilla, Maria A Rocca, Massimo Filippi, Einar A Høgestøl,et al.
Oxford University Press (OUP)
Abstract The relationship between structural connectivity (SC) and functional connectivity (FC) captured from magnetic resonance imaging, as well as its interaction with disability and cognitive impairment, is not well understood in people with multiple sclerosis (pwMS). The Virtual Brain (TVB) is an open-source brain simulator for creating personalized brain models using SC and FC. The aim of this study was to explore SC–FC relationship in MS using TVB. Two different model regimes have been studied: stable and oscillatory, with the latter including conduction delays in the brain. The models were applied to 513 pwMS and 208 healthy controls (HC) from 7 different centers. Models were analyzed using structural damage, global diffusion properties, clinical disability, cognitive scores, and graph-derived metrics from both simulated and empirical FC. For the stable model, higher SC–FC coupling was associated with pwMS with low Single Digit Modalities Test (SDMT) score (F=3.48, P$\\lt$0.05), suggesting that cognitive impairment in pwMS is associated with a higher SC–FC coupling. Differences in entropy of the simulated FC between HC, high and low SDMT groups (F=31.57, P$\\lt$1e-5), show that the model captures subtle differences not detected in the empirical FC, suggesting the existence of compensatory and maladaptive mechanisms between SC and FC in MS.
Gerard Martí-Juan, Marco Lorenzi, and Gemma Piella
Elsevier BV
Gerard Martí-Juan, Marcos Frías, Aran Garcia-Vidal, Angela Vidal-Jordana, Manel Alberich, Willem Calderon, Gemma Piella, Oscar Camara, Xavier Montalban, Jaume Sastre-Garriga,et al.
Elsevier BV
Gerard Martí‐Juan, Gerard Sanroma‐Guell, Raffaele Cacciaglia, Carles Falcon, Grégory Operto, José Luis Molinuevo, Miguel Ángel González Ballester, Juan Domingo Gispert, Gemma Piella, ,et al.
Wiley
AbstractThe ε4 allele of the gene Apolipoprotein E is the major genetic risk factor for Alzheimer's Disease. APOE ε4 has been associated with changes in brain structure in cognitively impaired and unimpaired subjects, including atrophy of the hippocampus, which is one of the brain structures that is early affected by AD. In this work we analyzed the impact of APOE ε4 gene dose and its association with age, on hippocampal shape assessed with multivariate surface analysis, in a ε4‐enriched cohort of n = 479 cognitively healthy individuals. Furthermore, we sought to replicate our findings on an independent dataset of n = 969 individuals covering the entire AD spectrum. We segmented the hippocampus of the subjects with a multi‐atlas‐based approach, obtaining high‐dimensional meshes that can be analyzed in a multivariate way. We analyzed the effects of different factors including APOE, sex, and age (in both cohorts) as well as clinical diagnosis on the local 3D hippocampal surface changes. We found specific regions on the hippocampal surface where the effect is modulated by significant APOE ε4 linear and quadratic interactions with age. We compared between APOE and diagnosis effects from both cohorts, finding similarities between APOE ε4 and AD effects on specific regions, and suggesting that age may modulate the effect of APOE ε4 and AD in a similar way.
Gerard Martí-Juan, Gerard Sanroma-Guell, and Gemma Piella
Elsevier BV
Gerard Martí-Juan, Gerard Sanroma, Gemma Piella, and
Public Library of Science (PLoS)
Alzheimer’s disease (AD) affects millions of people and is a major rising problem in health care worldwide. Recent research suggests that AD could have different subtypes, presenting differences in how the disease develops. Characterizing those subtypes could be key to deepen the understanding of this complex disease. In this paper, we used a multivariate, non-supervised clustering method over blood-based markers to find subgroups of patients defined by distinctive blood profiles. Our analysis on ADNI database identified 4 possible subgroups, each with a different blood profile. More importantly, we show that subgroups with different profiles have a different relationship between brain phenotypes detected in magnetic resonance imaging and disease condition. Author summary Alzheimer’s disease (AD) degenerates the brain and causes cognitive deterioration and loss of memory, leading to death. It is one of the largest problems in public health in the world, and while many efforts have been inverted into studying it, many things about the disease are still unknown. One of the open questions is whether there are various subtypes of the disease. Does the disease behave differently between patients? If so, why? In this work we try to answer this question by using markers found in the blood, easily gathered with inexpensive, non-invasive methods, to identify different presentations of the disease where it interacts differently with the brain. We use a machine learning approach to process large amounts of data and detect possible hidden relationships between the markers. Our results show promising differences in interactions between the disease and brain degeneration depending on the found blood profiles.