@conference {cPina, title = {Structural Networks for Brain Age Prediction}, booktitle = {Medical Imaging with Deep Learning (MIDL 2022)}, year = {2022}, month = {08/2022}, abstract = {

Biological networks have gained considerable attention within the Deep Learning community because of the promising framework of Graph Neural Networks (GNN), neural models that operate in complex networks. In the context of neuroimaging, GNNs have successfully been employed for functional MRI processing but their application to ROI-level structural MRI (sMRI) remains mostly unexplored. In this work we analyze the implementation of these geometric models with sMRI by building graphs of ROIs (ROI graphs) using tools from Graph Signal Processing literature and evaluate their performance in a downstream supervised task, age prediction. We first make a qualitative and quantitative comparison of the resulting networks obtained with common graph topology learning strategies. In a second stage, we train GNN-based models for brain age prediction. Since the order of every ROI graph is exactly the same and each vertex is an entity by itself (a ROI), we evaluate whether including ROI information during message-passing or global pooling operations is beneficial and compare the performance of GNNs against a Fully-Connected Neural Network baseline. The results show that ROI-level information is needed during the global pooling operation in order to achieve competitive results. However, no relevant improvement has been detected when it is incorporated during the message passing. These models achieve a MAE of 4.27 in hold-out test data, which is a performance very similar to the baseline, suggesting that the inductive bias included with the obtained graph connectivity is relevant and useful to reduce the dimensionality of the problem

}, author = {Oscar Pina and Irene Cumplido-Mayoral and Raffaele Cacciaglia and Jos{\'e} Mar{\'\i}a Gonz{\'a}lez-de-Ech{\'a}varri and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @article {aCasamitjanaa, title = {Shared latent structures between imaging features and biomarkers in early stages of Alzheimer{\textquoteright}s disease: a predictive study}, journal = {IEEE Journal of Biomedical and Health Informatics}, year = {2019}, month = {08/2019}, abstract = {

Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e. the capacity of predicting biomarker values) is assessed in a cross-validation framework.\ 

}, keywords = {CSF biomarkers, Latent model, MRI, PLS, preclinical AD}, doi = {10.1109/JBHI.2019.2932565}, author = {Adri{\`a} Casamitjana and Paula Petrone and J.L. Molinuevo and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cCasamitjanac, title = {Shared latent structures between imaging features and biomarkers in early stages of Alzheimer{\textquoteright}s disease}, booktitle = {Workshop on Predictive Intelligence in Medicine (PRIME), MICCAI}, year = {2018}, month = {2018}, address = {Granada, Spain}, abstract = {

In this work, we identify meaningful latent patterns in MR images for patients across the Alzheimer{\textquoteright}s disease (AD) continuum. For this purpose, we apply Projection to Latent Structures (PLS) method using cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau, amyloid-beta) and age as response variables and imaging features as explanatory variables. Freesurfer pipeline is used to compute MRI surface and volumetric features\  \ resulting in 68 cortical ROIs and 84 cortical and subcortical ROIs, respectively. The main assumption of this work is that there are two main underlying processes governing brain morphology along the AD continuum: brain aging and dementia. We use two different and orthogonal PLS models to describe each process: PLS-aging and PLS-dementia. To define PLS-aging model we use normal aging subjects and age as predictor and response variables, respectively, while for PLS-dementia we only use demented subjects and biomarkers as response variables.\ 

}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert} } @inbook {bCasamitjana18a, title = {Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer{\textquoteright}s Disease}, booktitle = {PRedictive Intelligence in MEdicine}, volume = {11121}, year = {2018}, pages = {60-67}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

In this work, we identify meaningful latent patterns in MR images for patients across the Alzheimer{\textquoteright}s disease (AD) continuum. For this purpose, we apply Projection to Latent Structures (PLS) method using cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau, amyloid-beta) and age as response variables and imaging features as explanatory variables. Freesurfer pipeline is used to compute MRI surface and volumetric features resulting in 68 cortical ROIs and 84 cortical and subcortical ROIs, respectively. The main assumption of this work is that there are two main underlying processes governing brain morphology along the AD continuum: brain aging and dementia. We use two different and orthogonal PLS models to describe each process: PLS-aging and PLS-dementia. To define PLS-aging model we use normal aging subjects and age as predictor and response variables, respectively, while for PLS-dementia we only use demented subjects and biomarkers as response variables.

}, issn = {978-3-030-00320-3}, doi = {10.1007/978-3-030-00320-3}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert} }